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The "assert" statement
**********************
Assert statements are a convenient way to insert debugging assertions
into a program:
assert_stmt ::= "assert" expression ["," expression]
The simple form, "assert expression", is equivalent to
if __debug__:
if not expression: raise AssertionError
The extended form, "assert expression1, expression2", is equivalent to
if __debug__:
if not expression1: raise AssertionError(expression2)
These equivalences assume that "__debug__" and "AssertionError" refer
to the built-in variables with those names. In the current
implementation, the built-in variable "__debug__" is "True" under
normal circumstances, "False" when optimization is requested (command
line option -O). The current code generator emits no code for an
assert statement when optimization is requested at compile time. Note
that it is unnecessary to include the source code for the expression
that failed in the error message; it will be displayed as part of the
stack trace.
Assignments to "__debug__" are illegal. The value for the built-in
variable is determined when the interpreter starts.
t asserts
Assignment statements
*********************
Assignment statements are used to (re)bind names to values and to
modify attributes or items of mutable objects:
assignment_stmt ::= (target_list "=")+ (expression_list | yield_expression)
target_list ::= target ("," target)* [","]
target ::= identifier
| "(" target_list ")"
| "[" [target_list] "]"
| attributeref
| subscription
| slicing
(See section Primaries for the syntax definitions for the last three
symbols.)
An assignment statement evaluates the expression list (remember that
this can be a single expression or a comma-separated list, the latter
yielding a tuple) and assigns the single resulting object to each of
the target lists, from left to right.
Assignment is defined recursively depending on the form of the target
(list). When a target is part of a mutable object (an attribute
reference, subscription or slicing), the mutable object must
ultimately perform the assignment and decide about its validity, and
may raise an exception if the assignment is unacceptable. The rules
observed by various types and the exceptions raised are given with the
definition of the object types (see section The standard type
hierarchy).
Assignment of an object to a target list is recursively defined as
follows.
* If the target list is a single target: The object is assigned to
that target.
* If the target list is a comma-separated list of targets: The
object must be an iterable with the same number of items as there
are targets in the target list, and the items are assigned, from
left to right, to the corresponding targets.
Assignment of an object to a single target is recursively defined as
follows.
* If the target is an identifier (name):
* If the name does not occur in a "global" statement in the
current code block: the name is bound to the object in the current
local namespace.
* Otherwise: the name is bound to the object in the current global
namespace.
The name is rebound if it was already bound. This may cause the
reference count for the object previously bound to the name to reach
zero, causing the object to be deallocated and its destructor (if it
has one) to be called.
* If the target is a target list enclosed in parentheses or in
square brackets: The object must be an iterable with the same number
of items as there are targets in the target list, and its items are
assigned, from left to right, to the corresponding targets.
* If the target is an attribute reference: The primary expression in
the reference is evaluated. It should yield an object with
assignable attributes; if this is not the case, "TypeError" is
raised. That object is then asked to assign the assigned object to
the given attribute; if it cannot perform the assignment, it raises
an exception (usually but not necessarily "AttributeError").
Note: If the object is a class instance and the attribute reference
occurs on both sides of the assignment operator, the RHS expression,
"a.x" can access either an instance attribute or (if no instance
attribute exists) a class attribute. The LHS target "a.x" is always
set as an instance attribute, creating it if necessary. Thus, the
two occurrences of "a.x" do not necessarily refer to the same
attribute: if the RHS expression refers to a class attribute, the
LHS creates a new instance attribute as the target of the
assignment:
class Cls:
x = 3 # class variable
inst = Cls()
inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3
This description does not necessarily apply to descriptor
attributes, such as properties created with "property()".
* If the target is a subscription: The primary expression in the
reference is evaluated. It should yield either a mutable sequence
object (such as a list) or a mapping object (such as a dictionary).
Next, the subscript expression is evaluated.
If the primary is a mutable sequence object (such as a list), the
subscript must yield a plain integer. If it is negative, the
sequence's length is added to it. The resulting value must be a
nonnegative integer less than the sequence's length, and the
sequence is asked to assign the assigned object to its item with
that index. If the index is out of range, "IndexError" is raised
(assignment to a subscripted sequence cannot add new items to a
list).
If the primary is a mapping object (such as a dictionary), the
subscript must have a type compatible with the mapping's key type,
and the mapping is then asked to create a key/datum pair which maps
the subscript to the assigned object. This can either replace an
existing key/value pair with the same key value, or insert a new
key/value pair (if no key with the same value existed).
* If the target is a slicing: The primary expression in the
reference is evaluated. It should yield a mutable sequence object
(such as a list). The assigned object should be a sequence object
of the same type. Next, the lower and upper bound expressions are
evaluated, insofar they are present; defaults are zero and the
sequence's length. The bounds should evaluate to (small) integers.
If either bound is negative, the sequence's length is added to it.
The resulting bounds are clipped to lie between zero and the
sequence's length, inclusive. Finally, the sequence object is asked
to replace the slice with the items of the assigned sequence. The
length of the slice may be different from the length of the assigned
sequence, thus changing the length of the target sequence, if the
object allows it.
**CPython implementation detail:** In the current implementation, the
syntax for targets is taken to be the same as for expressions, and
invalid syntax is rejected during the code generation phase, causing
less detailed error messages.
WARNING: Although the definition of assignment implies that overlaps
between the left-hand side and the right-hand side are 'safe' (for
example "a, b = b, a" swaps two variables), overlaps *within* the
collection of assigned-to variables are not safe! For instance, the
following program prints "[0, 2]":
x = [0, 1]
i = 0
i, x[i] = 1, 2
print x
Augmented assignment statements
===============================
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions for the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
t
assignments�
Identifiers (Names)
*******************
An identifier occurring as an atom is a name. See section Identifiers
and keywords for lexical definition and section Naming and binding for
documentation of naming and binding.
When the name is bound to an object, evaluation of the atom yields
that object. When a name is not bound, an attempt to evaluate it
raises a "NameError" exception.
**Private name mangling:** When an identifier that textually occurs in
a class definition begins with two or more underscore characters and
does not end in two or more underscores, it is considered a *private
name* of that class. Private names are transformed to a longer form
before code is generated for them. The transformation inserts the
class name, with leading underscores removed and a single underscore
inserted, in front of the name. For example, the identifier "__spam"
occurring in a class named "Ham" will be transformed to "_Ham__spam".
This transformation is independent of the syntactical context in which
the identifier is used. If the transformed name is extremely long
(longer than 255 characters), implementation defined truncation may
happen. If the class name consists only of underscores, no
transformation is done.
s atom-identifierss
Literals
********
Python supports string literals and various numeric literals:
literal ::= stringliteral | integer | longinteger
| floatnumber | imagnumber
Evaluation of a literal yields an object of the given type (string,
integer, long integer, floating point number, complex number) with the
given value. The value may be approximated in the case of floating
point and imaginary (complex) literals. See section Literals for
details.
All literals correspond to immutable data types, and hence the
object's identity is less important than its value. Multiple
evaluations of literals with the same value (either the same
occurrence in the program text or a different occurrence) may obtain
the same object or a different object with the same value.
s
atom-literalssU*
Customizing attribute access
****************************
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when an attribute lookup has not found the attribute in the
usual places (i.e. it is not an instance attribute nor is it found
in the class tree for "self"). "name" is the attribute name. This
method should return the (computed) attribute value or raise an
"AttributeError" exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control in new-style classes.
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should not simply execute "self.name = value" --- this would cause
a recursive call to itself. Instead, it should insert the value in
the dictionary of instance attributes, e.g., "self.__dict__[name] =
value". For new-style classes, rather than accessing the instance
dictionary, it should call the base class method with the same
name, for example, "object.__setattr__(self, name, value)".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
More attribute access for new-style classes
===========================================
The following methods only apply to new-style classes.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note: This method may still be bypassed when looking up special
methods as the result of implicit invocation via language syntax
or built-in functions. See Special method lookup for new-style
classes.
Implementing Descriptors
========================
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner's class
dictionary or in the class dictionary for one of its parents). In the
examples below, "the attribute" refers to the attribute whose name is
the key of the property in the owner class' "__dict__".
object.__get__(self, instance, owner)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). *owner* is always the owner class, while *instance* is the
instance that the attribute was accessed through, or "None" when
the attribute is accessed through the *owner*. This method should
return the (computed) attribute value or raise an "AttributeError"
exception.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
Invoking Descriptors
====================
In general, a descriptor is an object attribute with "binding
behavior", one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object's dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called. Note that descriptors are only invoked for new
style objects or classes (ones that subclass "object()" or "type()").
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to a new-style object instance, "a.x" is transformed
into the call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a new-style class, "A.x" is transformed into the
call: "A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined. A descriptor can define
any combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object's
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
=========
By default, instances of both old and new-style classes have a
dictionary for attribute storage. This wastes space for objects
having very few instance variables. The space consumption can become
acute when creating large numbers of instances.
The default can be overridden by defining *__slots__* in a new-style
class definition. The *__slots__* declaration takes a sequence of
instance variables and reserves just enough space in each instance to
hold a value for each variable. Space is saved because *__dict__* is
not created for each instance.
__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. If defined in a
new-style class, *__slots__* reserves space for the declared
variables and prevents the automatic creation of *__dict__* and
*__weakref__* for each instance.
New in version 2.2.
Notes on using *__slots__*
* When inheriting from a class without *__slots__*, the *__dict__*
attribute of that class will always be accessible, so a *__slots__*
definition in the subclass is meaningless.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
Changed in version 2.3: Previously, adding "'__dict__'" to the
*__slots__* declaration would not enable the assignment of new
attributes not specifically listed in the sequence of instance
variable names.
* Without a *__weakref__* variable for each instance, classes
defining *__slots__* do not support weak references to its
instances. If weak reference support is needed, then add
"'__weakref__'" to the sequence of strings in the *__slots__*
declaration.
Changed in version 2.3: Previously, adding "'__weakref__'" to the
*__slots__* declaration would not enable support for weak
references.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is limited to the class
where it is defined. As a result, subclasses will have a *__dict__*
unless they also define *__slots__* (which must only contain names
of any *additional* slots).
* If a class defines a slot also defined in a base class, the
instance variable defined by the base class slot is inaccessible
(except by retrieving its descriptor directly from the base class).
This renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
"variable-length" built-in types such as "long", "str" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings
may also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
Changed in version 2.6: Previously, *__class__* assignment raised an
error if either new or old class had *__slots__*.
s attribute-accesss_
Attribute references
********************
An attribute reference is a primary followed by a period and a name:
attributeref ::= primary "." identifier
The primary must evaluate to an object of a type that supports
attribute references, e.g., a module, list, or an instance. This
object is then asked to produce the attribute whose name is the
identifier. If this attribute is not available, the exception
"AttributeError" is raised. Otherwise, the type and value of the
object produced is determined by the object. Multiple evaluations of
the same attribute reference may yield different objects.
s attribute-referencess�
Augmented assignment statements
*******************************
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions for the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
t augassignsn
Binary arithmetic operations
****************************
The binary arithmetic operations have the conventional priority
levels. Note that some of these operations also apply to certain non-
numeric types. Apart from the power operator, there are only two
levels, one for multiplicative operators and one for additive
operators:
m_expr ::= u_expr | m_expr "*" u_expr | m_expr "//" u_expr | m_expr "/" u_expr
| m_expr "%" u_expr
a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr
The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer (plain or long) and the other must be a sequence. In the
former case, the numbers are converted to a common type and then
multiplied together. In the latter case, sequence repetition is
performed; a negative repetition factor yields an empty sequence.
The "/" (division) and "//" (floor division) operators yield the
quotient of their arguments. The numeric arguments are first
converted to a common type. Plain or long integer division yields an
integer of the same type; the result is that of mathematical division
with the 'floor' function applied to the result. Division by zero
raises the "ZeroDivisionError" exception.
The "%" (modulo) operator yields the remainder from the division of
the first argument by the second. The numeric arguments are first
converted to a common type. A zero right argument raises the
"ZeroDivisionError" exception. The arguments may be floating point
numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 +
0.34".) The modulo operator always yields a result with the same sign
as its second operand (or zero); the absolute value of the result is
strictly smaller than the absolute value of the second operand [2].
The integer division and modulo operators are connected by the
following identity: "x == (x/y)*y + (x%y)". Integer division and
modulo are also connected with the built-in function "divmod()":
"divmod(x, y) == (x/y, x%y)". These identities don't hold for
floating point numbers; there similar identities hold approximately
where "x/y" is replaced by "floor(x/y)" or "floor(x/y) - 1" [3].
In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string and unicode objects to perform
string formatting (also known as interpolation). The syntax for string
formatting is described in the Python Library Reference, section
String Formatting Operations.
Deprecated since version 2.3: The floor division operator, the modulo
operator, and the "divmod()" function are no longer defined for
complex numbers. Instead, convert to a floating point number using
the "abs()" function if appropriate.
The "+" (addition) operator yields the sum of its arguments. The
arguments must either both be numbers or both sequences of the same
type. In the former case, the numbers are converted to a common type
and then added together. In the latter case, the sequences are
concatenated.
The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.
t binarys�
Binary bitwise operations
*************************
Each of the three bitwise operations has a different priority level:
and_expr ::= shift_expr | and_expr "&" shift_expr
xor_expr ::= and_expr | xor_expr "^" and_expr
or_expr ::= xor_expr | or_expr "|" xor_expr
The "&" operator yields the bitwise AND of its arguments, which must
be plain or long integers. The arguments are converted to a common
type.
The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be plain or long integers. The arguments are
converted to a common type.
The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be plain or long integers. The arguments are converted to
a common type.
t bitwises~
Code Objects
************
Code objects are used by the implementation to represent "pseudo-
compiled" executable Python code such as a function body. They differ
from function objects because they don't contain a reference to their
global execution environment. Code objects are returned by the built-
in "compile()" function and can be extracted from function objects
through their "func_code" attribute. See also the "code" module.
A code object can be executed or evaluated by passing it (instead of a
source string) to the "exec" statement or the built-in "eval()"
function.
See The standard type hierarchy for more information.
s bltin-code-objectssE
The Ellipsis Object
*******************
This object is used by extended slice notation (see Slicings). It
supports no special operations. There is exactly one ellipsis object,
named "Ellipsis" (a built-in name).
It is written as "Ellipsis". When in a subscript, it can also be
written as "...", for example "seq[...]".
s bltin-ellipsis-objects�+
File Objects
************
File objects are implemented using C's "stdio" package and can be
created with the built-in "open()" function. File objects are also
returned by some other built-in functions and methods, such as
"os.popen()" and "os.fdopen()" and the "makefile()" method of socket
objects. Temporary files can be created using the "tempfile" module,
and high-level file operations such as copying, moving, and deleting
files and directories can be achieved with the "shutil" module.
When a file operation fails for an I/O-related reason, the exception
"IOError" is raised. This includes situations where the operation is
not defined for some reason, like "seek()" on a tty device or writing
a file opened for reading.
Files have the following methods:
file.close()
Close the file. A closed file cannot be read or written any more.
Any operation which requires that the file be open will raise a
"ValueError" after the file has been closed. Calling "close()"
more than once is allowed.
As of Python 2.5, you can avoid having to call this method
explicitly if you use the "with" statement. For example, the
following code will automatically close *f* when the "with" block
is exited:
from __future__ import with_statement # This isn't required in Python 2.6
with open("hello.txt") as f:
for line in f:
print line,
In older versions of Python, you would have needed to do this to
get the same effect:
f = open("hello.txt")
try:
for line in f:
print line,
finally:
f.close()
Note: Not all "file-like" types in Python support use as a
context manager for the "with" statement. If your code is
intended to work with any file-like object, you can use the
function "contextlib.closing()" instead of using the object
directly.
file.flush()
Flush the internal buffer, like "stdio"'s "fflush()". This may be
a no-op on some file-like objects.
Note: "flush()" does not necessarily write the file's data to
disk. Use "flush()" followed by "os.fsync()" to ensure this
behavior.
file.fileno()
Return the integer "file descriptor" that is used by the underlying
implementation to request I/O operations from the operating system.
This can be useful for other, lower level interfaces that use file
descriptors, such as the "fcntl" module or "os.read()" and friends.
Note: File-like objects which do not have a real file descriptor
should *not* provide this method!
file.isatty()
Return "True" if the file is connected to a tty(-like) device, else
"False".
Note: If a file-like object is not associated with a real file,
this method should *not* be implemented.
file.next()
A file object is its own iterator, for example "iter(f)" returns
*f* (unless *f* is closed). When a file is used as an iterator,
typically in a "for" loop (for example, "for line in f: print
line.strip()"), the "next()" method is called repeatedly. This
method returns the next input line, or raises "StopIteration" when
EOF is hit when the file is open for reading (behavior is undefined
when the file is open for writing). In order to make a "for" loop
the most efficient way of looping over the lines of a file (a very
common operation), the "next()" method uses a hidden read-ahead
buffer. As a consequence of using a read-ahead buffer, combining
"next()" with other file methods (like "readline()") does not work
right. However, using "seek()" to reposition the file to an
absolute position will flush the read-ahead buffer.
New in version 2.3.
file.read([size])
Read at most *size* bytes from the file (less if the read hits EOF
before obtaining *size* bytes). If the *size* argument is negative
or omitted, read all data until EOF is reached. The bytes are
returned as a string object. An empty string is returned when EOF
is encountered immediately. (For certain files, like ttys, it
makes sense to continue reading after an EOF is hit.) Note that
this method may call the underlying C function "fread()" more than
once in an effort to acquire as close to *size* bytes as possible.
Also note that when in non-blocking mode, less data than was
requested may be returned, even if no *size* parameter was given.
Note: This function is simply a wrapper for the underlying
"fread()" C function, and will behave the same in corner cases,
such as whether the EOF value is cached.
file.readline([size])
Read one entire line from the file. A trailing newline character
is kept in the string (but may be absent when a file ends with an
incomplete line). [6] If the *size* argument is present and non-
negative, it is a maximum byte count (including the trailing
newline) and an incomplete line may be returned. When *size* is not
0, an empty string is returned *only* when EOF is encountered
immediately.
Note: Unlike "stdio"'s "fgets()", the returned string contains
null characters ("'\0'") if they occurred in the input.
file.readlines([sizehint])
Read until EOF using "readline()" and return a list containing the
lines thus read. If the optional *sizehint* argument is present,
instead of reading up to EOF, whole lines totalling approximately
*sizehint* bytes (possibly after rounding up to an internal buffer
size) are read. Objects implementing a file-like interface may
choose to ignore *sizehint* if it cannot be implemented, or cannot
be implemented efficiently.
file.xreadlines()
This method returns the same thing as "iter(f)".
New in version 2.1.
Deprecated since version 2.3: Use "for line in file" instead.
file.seek(offset[, whence])
Set the file's current position, like "stdio"'s "fseek()". The
*whence* argument is optional and defaults to "os.SEEK_SET" or "0"
(absolute file positioning); other values are "os.SEEK_CUR" or "1"
(seek relative to the current position) and "os.SEEK_END" or "2"
(seek relative to the file's end). There is no return value.
For example, "f.seek(2, os.SEEK_CUR)" advances the position by two
and "f.seek(-3, os.SEEK_END)" sets the position to the third to
last.
Note that if the file is opened for appending (mode "'a'" or
"'a+'"), any "seek()" operations will be undone at the next write.
If the file is only opened for writing in append mode (mode "'a'"),
this method is essentially a no-op, but it remains useful for files
opened in append mode with reading enabled (mode "'a+'"). If the
file is opened in text mode (without "'b'"), only offsets returned
by "tell()" are legal. Use of other offsets causes undefined
behavior.
Note that not all file objects are seekable.
Changed in version 2.6: Passing float values as offset has been
deprecated.
file.tell()
Return the file's current position, like "stdio"'s "ftell()".
Note: On Windows, "tell()" can return illegal values (after an
"fgets()") when reading files with Unix-style line-endings. Use
binary mode ("'rb'") to circumvent this problem.
file.truncate([size])
Truncate the file's size. If the optional *size* argument is
present, the file is truncated to (at most) that size. The size
defaults to the current position. The current file position is not
changed. Note that if a specified size exceeds the file's current
size, the result is platform-dependent: possibilities include that
the file may remain unchanged, increase to the specified size as if
zero-filled, or increase to the specified size with undefined new
content. Availability: Windows, many Unix variants.
file.write(str)
Write a string to the file. There is no return value. Due to
buffering, the string may not actually show up in the file until
the "flush()" or "close()" method is called.
file.writelines(sequence)
Write a sequence of strings to the file. The sequence can be any
iterable object producing strings, typically a list of strings.
There is no return value. (The name is intended to match
"readlines()"; "writelines()" does not add line separators.)
Files support the iterator protocol. Each iteration returns the same
result as "readline()", and iteration ends when the "readline()"
method returns an empty string.
File objects also offer a number of other interesting attributes.
These are not required for file-like objects, but should be
implemented if they make sense for the particular object.
file.closed
bool indicating the current state of the file object. This is a
read-only attribute; the "close()" method changes the value. It may
not be available on all file-like objects.
file.encoding
The encoding that this file uses. When Unicode strings are written
to a file, they will be converted to byte strings using this
encoding. In addition, when the file is connected to a terminal,
the attribute gives the encoding that the terminal is likely to use
(that information might be incorrect if the user has misconfigured
the terminal). The attribute is read-only and may not be present
on all file-like objects. It may also be "None", in which case the
file uses the system default encoding for converting Unicode
strings.
New in version 2.3.
file.errors
The Unicode error handler used along with the encoding.
New in version 2.6.
file.mode
The I/O mode for the file. If the file was created using the
"open()" built-in function, this will be the value of the *mode*
parameter. This is a read-only attribute and may not be present on
all file-like objects.
file.name
If the file object was created using "open()", the name of the
file. Otherwise, some string that indicates the source of the file
object, of the form "<...>". This is a read-only attribute and may
not be present on all file-like objects.
file.newlines
If Python was built with *universal newlines* enabled (the default)
this read-only attribute exists, and for files opened in universal
newline read mode it keeps track of the types of newlines
encountered while reading the file. The values it can take are
"'\r'", "'\n'", "'\r\n'", "None" (unknown, no newlines read yet) or
a tuple containing all the newline types seen, to indicate that
multiple newline conventions were encountered. For files not opened
in universal newlines read mode the value of this attribute will be
"None".
file.softspace
Boolean that indicates whether a space character needs to be
printed before another value when using the "print" statement.
Classes that are trying to simulate a file object should also have
a writable "softspace" attribute, which should be initialized to
zero. This will be automatic for most classes implemented in
Python (care may be needed for objects that override attribute
access); types implemented in C will have to provide a writable
"softspace" attribute.
Note: This attribute is not used to control the "print"
statement, but to allow the implementation of "print" to keep
track of its internal state.
s bltin-file-objectss�
The Null Object
***************
This object is returned by functions that don't explicitly return a
value. It supports no special operations. There is exactly one null
object, named "None" (a built-in name).
It is written as "None".
s bltin-null-objects3
Type Objects
************
Type objects represent the various object types. An object's type is
accessed by the built-in function "type()". There are no special
operations on types. The standard module "types" defines names for
all standard built-in types.
Types are written like this: "<type 'int'>".
s bltin-type-objectss�
Boolean operations
******************
or_test ::= and_test | or_test "or" and_test
and_test ::= not_test | and_test "and" not_test
not_test ::= comparison | "not" not_test
In the context of Boolean operations, and also when expressions are
used by control flow statements, the following values are interpreted
as false: "False", "None", numeric zero of all types, and empty
strings and containers (including strings, tuples, lists,
dictionaries, sets and frozensets). All other values are interpreted
as true. (See the "__nonzero__()" special method for a way to change
this.)
The operator "not" yields "True" if its argument is false, "False"
otherwise.
The expression "x and y" first evaluates *x*; if *x* is false, its
value is returned; otherwise, *y* is evaluated and the resulting value
is returned.
The expression "x or y" first evaluates *x*; if *x* is true, its value
is returned; otherwise, *y* is evaluated and the resulting value is
returned.
(Note that neither "and" nor "or" restrict the value and type they
return to "False" and "True", but rather return the last evaluated
argument. This is sometimes useful, e.g., if "s" is a string that
should be replaced by a default value if it is empty, the expression
"s or 'foo'" yields the desired value. Because "not" has to invent a
value anyway, it does not bother to return a value of the same type as
its argument, so e.g., "not 'foo'" yields "False", not "''".)
t booleanss%
The "break" statement
*********************
break_stmt ::= "break"
"break" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop.
It terminates the nearest enclosing loop, skipping the optional "else"
clause if the loop has one.
If a "for" loop is terminated by "break", the loop control target
keeps its current value.
When "break" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
loop.
t breaks�
Emulating callable objects
**************************
object.__call__(self[, args...])
Called when the instance is "called" as a function; if this method
is defined, "x(arg1, arg2, ...)" is a shorthand for
"x.__call__(arg1, arg2, ...)".
s callable-typess�
Calls
*****
A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:
call ::= primary "(" [argument_list [","]
| expression genexpr_for] ")"
argument_list ::= positional_arguments ["," keyword_arguments]
["," "*" expression] ["," keyword_arguments]
["," "**" expression]
| keyword_arguments ["," "*" expression]
["," "**" expression]
| "*" expression ["," keyword_arguments] ["," "**" expression]
| "**" expression
positional_arguments ::= expression ("," expression)*
keyword_arguments ::= keyword_item ("," keyword_item)*
keyword_item ::= identifier "=" expression
A trailing comma may be present after the positional and keyword
arguments but does not affect the semantics.
The primary must evaluate to a callable object (user-defined
functions, built-in functions, methods of built-in objects, class
objects, methods of class instances, and certain class instances
themselves are callable; extensions may define additional callable
object types). All argument expressions are evaluated before the call
is attempted. Please refer to section Function definitions for the
syntax of formal *parameter* lists.
If keyword arguments are present, they are first converted to
positional arguments, as follows. First, a list of unfilled slots is
created for the formal parameters. If there are N positional
arguments, they are placed in the first N slots. Next, for each
keyword argument, the identifier is used to determine the
corresponding slot (if the identifier is the same as the first formal
parameter name, the first slot is used, and so on). If the slot is
already filled, a "TypeError" exception is raised. Otherwise, the
value of the argument is placed in the slot, filling it (even if the
expression is "None", it fills the slot). When all arguments have
been processed, the slots that are still unfilled are filled with the
corresponding default value from the function definition. (Default
values are calculated, once, when the function is defined; thus, a
mutable object such as a list or dictionary used as default value will
be shared by all calls that don't specify an argument value for the
corresponding slot; this should usually be avoided.) If there are any
unfilled slots for which no default value is specified, a "TypeError"
exception is raised. Otherwise, the list of filled slots is used as
the argument list for the call.
**CPython implementation detail:** An implementation may provide
built-in functions whose positional parameters do not have names, even
if they are 'named' for the purpose of documentation, and which
therefore cannot be supplied by keyword. In CPython, this is the case
for functions implemented in C that use "PyArg_ParseTuple()" to parse
their arguments.
If there are more positional arguments than there are formal parameter
slots, a "TypeError" exception is raised, unless a formal parameter
using the syntax "*identifier" is present; in this case, that formal
parameter receives a tuple containing the excess positional arguments
(or an empty tuple if there were no excess positional arguments).
If any keyword argument does not correspond to a formal parameter
name, a "TypeError" exception is raised, unless a formal parameter
using the syntax "**identifier" is present; in this case, that formal
parameter receives a dictionary containing the excess keyword
arguments (using the keywords as keys and the argument values as
corresponding values), or a (new) empty dictionary if there were no
excess keyword arguments.
If the syntax "*expression" appears in the function call, "expression"
must evaluate to an iterable. Elements from this iterable are treated
as if they were additional positional arguments; if there are
positional arguments *x1*, ..., *xN*, and "expression" evaluates to a
sequence *y1*, ..., *yM*, this is equivalent to a call with M+N
positional arguments *x1*, ..., *xN*, *y1*, ..., *yM*.
A consequence of this is that although the "*expression" syntax may
appear *after* some keyword arguments, it is processed *before* the
keyword arguments (and the "**expression" argument, if any -- see
below). So:
>>> def f(a, b):
... print a, b
...
>>> f(b=1, *(2,))
2 1
>>> f(a=1, *(2,))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: f() got multiple values for keyword argument 'a'
>>> f(1, *(2,))
1 2
It is unusual for both keyword arguments and the "*expression" syntax
to be used in the same call, so in practice this confusion does not
arise.
If the syntax "**expression" appears in the function call,
"expression" must evaluate to a mapping, the contents of which are
treated as additional keyword arguments. In the case of a keyword
appearing in both "expression" and as an explicit keyword argument, a
"TypeError" exception is raised.
Formal parameters using the syntax "*identifier" or "**identifier"
cannot be used as positional argument slots or as keyword argument
names. Formal parameters using the syntax "(sublist)" cannot be used
as keyword argument names; the outermost sublist corresponds to a
single unnamed argument slot, and the argument value is assigned to
the sublist using the usual tuple assignment rules after all other
parameter processing is done.
A call always returns some value, possibly "None", unless it raises an
exception. How this value is computed depends on the type of the
callable object.
If it is---
a user-defined function:
The code block for the function is executed, passing it the
argument list. The first thing the code block will do is bind the
formal parameters to the arguments; this is described in section
Function definitions. When the code block executes a "return"
statement, this specifies the return value of the function call.
a built-in function or method:
The result is up to the interpreter; see Built-in Functions for the
descriptions of built-in functions and methods.
a class object:
A new instance of that class is returned.
a class instance method:
The corresponding user-defined function is called, with an argument
list that is one longer than the argument list of the call: the
instance becomes the first argument.
a class instance:
The class must define a "__call__()" method; the effect is then the
same as if that method was called.
t callssJ
Class definitions
*****************
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= "class" classname [inheritance] ":" suite
inheritance ::= "(" [expression_list] ")"
classname ::= identifier
A class definition is an executable statement. It first evaluates the
inheritance list, if present. Each item in the inheritance list
should evaluate to a class object or class type which allows
subclassing. The class's suite is then executed in a new execution
frame (see section Naming and binding), using a newly created local
namespace and the original global namespace. (Usually, the suite
contains only function definitions.) When the class's suite finishes
execution, its execution frame is discarded but its local namespace is
saved. [4] A class object is then created using the inheritance list
for the base classes and the saved local namespace for the attribute
dictionary. The class name is bound to this class object in the
original local namespace.
**Programmer's note:** Variables defined in the class definition are
class variables; they are shared by all instances. To create instance
variables, they can be set in a method with "self.name = value". Both
class and instance variables are accessible through the notation
""self.name"", and an instance variable hides a class variable with
the same name when accessed in this way. Class variables can be used
as defaults for instance variables, but using mutable values there can
lead to unexpected results. For *new-style class*es, descriptors can
be used to create instance variables with different implementation
details.
Class definitions, like function definitions, may be wrapped by one or
more *decorator* expressions. The evaluation rules for the decorator
expressions are the same as for functions. The result must be a class
object, which is then bound to the class name.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless
there is a "finally" clause which happens to raise another
exception. That new exception causes the old one to be lost.
[2] Currently, control "flows off the end" except in the case of
an exception or the execution of a "return", "continue", or
"break" statement.
[3] A string literal appearing as the first statement in the
function body is transformed into the function's "__doc__"
attribute and therefore the function's *docstring*.
[4] A string literal appearing as the first statement in the class
body is transformed into the namespace's "__doc__" item and
therefore the class's *docstring*.
t classs$
Comparisons
***********
Unlike C, all comparison operations in Python have the same priority,
which is lower than that of any arithmetic, shifting or bitwise
operation. Also unlike C, expressions like "a < b < c" have the
interpretation that is conventional in mathematics:
comparison ::= or_expr ( comp_operator or_expr )*
comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "<>" | "!="
| "is" ["not"] | ["not"] "in"
Comparisons yield boolean values: "True" or "False".
Comparisons can be chained arbitrarily, e.g., "x < y <= z" is
equivalent to "x < y and y <= z", except that "y" is evaluated only
once (but in both cases "z" is not evaluated at all when "x < y" is
found to be false).
Formally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,
*op2*, ..., *opN* are comparison operators, then "a op1 b op2 c ... y
opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except
that each expression is evaluated at most once.
Note that "a op1 b op2 c" doesn't imply any kind of comparison between
*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though
perhaps not pretty).
The forms "<>" and "!=" are equivalent; for consistency with C, "!="
is preferred; where "!=" is mentioned below "<>" is also accepted.
The "<>" spelling is considered obsolescent.
Value comparisons
=================
The operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects. The objects do not need to have the same type.
Chapter Objects, values and types states that objects have a value (in
addition to type and identity). The value of an object is a rather
abstract notion in Python: For example, there is no canonical access
method for an object's value. Also, there is no requirement that the
value of an object should be constructed in a particular way, e.g.
comprised of all its data attributes. Comparison operators implement a
particular notion of what the value of an object is. One can think of
them as defining the value of an object indirectly, by means of their
comparison implementation.
Types can customize their comparison behavior by implementing a
"__cmp__()" method or *rich comparison methods* like "__lt__()",
described in Basic customization.
The default behavior for equality comparison ("==" and "!=") is based
on the identity of the objects. Hence, equality comparison of
instances with the same identity results in equality, and equality
comparison of instances with different identities results in
inequality. A motivation for this default behavior is the desire that
all objects should be reflexive (i.e. "x is y" implies "x == y").
The default order comparison ("<", ">", "<=", and ">=") gives a
consistent but arbitrary order.
(This unusual definition of comparison was used to simplify the
definition of operations like sorting and the "in" and "not in"
operators. In the future, the comparison rules for objects of
different types are likely to change.)
The behavior of the default equality comparison, that instances with
different identities are always unequal, may be in contrast to what
types will need that have a sensible definition of object value and
value-based equality. Such types will need to customize their
comparison behavior, and in fact, a number of built-in types have done
that.
The following list describes the comparison behavior of the most
important built-in types.
* Numbers of built-in numeric types (Numeric Types --- int, float,
long, complex) and of the standard library types
"fractions.Fraction" and "decimal.Decimal" can be compared within
and across their types, with the restriction that complex numbers do
not support order comparison. Within the limits of the types
involved, they compare mathematically (algorithmically) correct
without loss of precision.
* Strings (instances of "str" or "unicode") compare
lexicographically using the numeric equivalents (the result of the
built-in function "ord()") of their characters. [4] When comparing
an 8-bit string and a Unicode string, the 8-bit string is converted
to Unicode. If the conversion fails, the strings are considered
unequal.
* Instances of "tuple" or "list" can be compared only within each of
their types. Equality comparison across these types results in
unequality, and ordering comparison across these types gives an
arbitrary order.
These sequences compare lexicographically using comparison of
corresponding elements, whereby reflexivity of the elements is
enforced.
In enforcing reflexivity of elements, the comparison of collections
assumes that for a collection element "x", "x == x" is always true.
Based on that assumption, element identity is compared first, and
element comparison is performed only for distinct elements. This
approach yields the same result as a strict element comparison
would, if the compared elements are reflexive. For non-reflexive
elements, the result is different than for strict element
comparison.
Lexicographical comparison between built-in collections works as
follows:
* For two collections to compare equal, they must be of the same
type, have the same length, and each pair of corresponding
elements must compare equal (for example, "[1,2] == (1,2)" is
false because the type is not the same).
* Collections are ordered the same as their first unequal elements
(for example, "cmp([1,2,x], [1,2,y])" returns the same as
"cmp(x,y)"). If a corresponding element does not exist, the
shorter collection is ordered first (for example, "[1,2] <
[1,2,3]" is true).
* Mappings (instances of "dict") compare equal if and only if they
have equal *(key, value)* pairs. Equality comparison of the keys and
values enforces reflexivity.
Outcomes other than equality are resolved consistently, but are not
otherwise defined. [5]
* Most other objects of built-in types compare unequal unless they
are the same object; the choice whether one object is considered
smaller or larger than another one is made arbitrarily but
consistently within one execution of a program.
User-defined classes that customize their comparison behavior should
follow some consistency rules, if possible:
* Equality comparison should be reflexive. In other words, identical
objects should compare equal:
"x is y" implies "x == y"
* Comparison should be symmetric. In other words, the following
expressions should have the same result:
"x == y" and "y == x"
"x != y" and "y != x"
"x < y" and "y > x"
"x <= y" and "y >= x"
* Comparison should be transitive. The following (non-exhaustive)
examples illustrate that:
"x > y and y > z" implies "x > z"
"x < y and y <= z" implies "x < z"
* Inverse comparison should result in the boolean negation. In other
words, the following expressions should have the same result:
"x == y" and "not x != y"
"x < y" and "not x >= y" (for total ordering)
"x > y" and "not x <= y" (for total ordering)
The last two expressions apply to totally ordered collections (e.g.
to sequences, but not to sets or mappings). See also the
"total_ordering()" decorator.
* The "hash()" result should be consistent with equality. Objects
that are equal should either have the same hash value, or be marked
as unhashable.
Python does not enforce these consistency rules.
Membership test operations
==========================
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y". If an exception is raised
during the iteration, it is as if "in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse true value of
"in".
Identity comparisons
====================
The operators "is" and "is not" test for object identity: "x is y" is
true if and only if *x* and *y* are the same object. "x is not y"
yields the inverse truth value. [6]
t comparisonsspP
Compound statements
*******************
Compound statements contain (groups of) other statements; they affect
or control the execution of those other statements in some way. In
general, compound statements span multiple lines, although in simple
incarnations a whole compound statement may be contained in one line.
The "if", "while" and "for" statements implement traditional control
flow constructs. "try" specifies exception handlers and/or cleanup
code for a group of statements. Function and class definitions are
also syntactically compound statements.
Compound statements consist of one or more 'clauses.' A clause
consists of a header and a 'suite.' The clause headers of a
particular compound statement are all at the same indentation level.
Each clause header begins with a uniquely identifying keyword and ends
with a colon. A suite is a group of statements controlled by a
clause. A suite can be one or more semicolon-separated simple
statements on the same line as the header, following the header's
colon, or it can be one or more indented statements on subsequent
lines. Only the latter form of suite can contain nested compound
statements; the following is illegal, mostly because it wouldn't be
clear to which "if" clause a following "else" clause would belong:
if test1: if test2: print x
Also note that the semicolon binds tighter than the colon in this
context, so that in the following example, either all or none of the
"print" statements are executed:
if x < y < z: print x; print y; print z
Summarizing:
compound_stmt ::= if_stmt
| while_stmt
| for_stmt
| try_stmt
| with_stmt
| funcdef
| classdef
| decorated
suite ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT
statement ::= stmt_list NEWLINE | compound_stmt
stmt_list ::= simple_stmt (";" simple_stmt)* [";"]
Note that statements always end in a "NEWLINE" possibly followed by a
"DEDENT". Also note that optional continuation clauses always begin
with a keyword that cannot start a statement, thus there are no
ambiguities (the 'dangling "else"' problem is solved in Python by
requiring nested "if" statements to be indented).
The formatting of the grammar rules in the following sections places
each clause on a separate line for clarity.
The "if" statement
==================
The "if" statement is used for conditional execution:
if_stmt ::= "if" expression ":" suite
( "elif" expression ":" suite )*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
The "while" statement
=====================
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause's suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
The "for" statement
===================
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order of ascending indices. Each
item in turn is assigned to the target list using the standard rules
for assignments, and then the suite is executed. When the items are
exhausted (which is immediately when the sequence is empty), the suite
in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause's suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there was no next
item.
The suite may assign to the variable(s) in the target list; this does
not affect the next item assigned to it.
The target list is not deleted when the loop is finished, but if the
sequence is empty, it will not have been assigned to at all by the
loop. Hint: the built-in function "range()" returns a sequence of
integers suitable to emulate the effect of Pascal's "for i := a to b
do"; e.g., "range(3)" returns the list "[0, 1, 2]".
Note: There is a subtlety when the sequence is being modified by the
loop (this can only occur for mutable sequences, i.e. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
The "try" statement
===================
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression [("as" | ",") identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
Changed in version 2.5: In previous versions of Python,
"try"..."except"..."finally" did not work. "try"..."except" had to be
nested in "try"..."finally".
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is "compatible" with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object, or a tuple containing an item compatible with the exception.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified in that except clause, if present, and the except
clause's suite is executed. All except clauses must have an
executable block. When the end of this block is reached, execution
continues normally after the entire try statement. (This means that
if two nested handlers exist for the same exception, and the exception
occurs in the try clause of the inner handler, the outer handler will
not handle the exception.)
Before an except clause's suite is executed, details about the
exception are assigned to three variables in the "sys" module:
"sys.exc_type" receives the object identifying the exception;
"sys.exc_value" receives the exception's parameter;
"sys.exc_traceback" receives a traceback object (see section The
standard type hierarchy) identifying the point in the program where
the exception occurred. These details are also available through the
"sys.exc_info()" function, which returns a tuple "(exc_type,
exc_value, exc_traceback)". Use of the corresponding variables is
deprecated in favor of this function, since their use is unsafe in a
threaded program. As of Python 1.5, the variables are restored to
their previous values (before the call) when returning from a function
that handled an exception.
The optional "else" clause is executed if and when control flows off
the end of the "try" clause. [2] Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a 'cleanup' handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception, it is re-raised at the end of the
"finally" clause. If the "finally" clause raises another exception or
executes a "return" or "break" statement, the saved exception is
discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"..."finally" statement, the "finally" clause is
also executed 'on the way out.' A "continue" statement is illegal in
the "finally" clause. (The reason is a problem with the current
implementation --- this restriction may be lifted in the future).
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
The "with" statement
====================
New in version 2.5.
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"..."except"..."finally"
usage patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one "item" proceeds as
follows:
1. The context expression (the expression given in the "with_item")
is evaluated to obtain a context manager.
2. The context manager's "__exit__()" is loaded for later use.
3. The context manager's "__enter__()" method is invoked.
4. If a target was included in the "with" statement, the return
value from "__enter__()" is assigned to it.
Note: The "with" statement guarantees that if the "__enter__()"
method returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
5. The suite is executed.
6. The context manager's "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
suite
is equivalent to
with A() as a:
with B() as b:
suite
Note: In Python 2.5, the "with" statement is only allowed when the
"with_statement" feature has been enabled. It is always enabled in
Python 2.6.
Changed in version 2.7: Support for multiple context expressions.
See also:
**PEP 343** - The "with" statement
The specification, background, and examples for the Python "with"
statement.
Function definitions
====================
A function definition defines a user-defined function object (see
section The standard type hierarchy):
decorated ::= decorators (classdef | funcdef)
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
funcdef ::= "def" funcname "(" [parameter_list] ")" ":" suite
dotted_name ::= identifier ("." identifier)*
parameter_list ::= (defparameter ",")*
( "*" identifier ["," "**" identifier]
| "**" identifier
| defparameter [","] )
defparameter ::= parameter ["=" expression]
sublist ::= parameter ("," parameter)* [","]
parameter ::= identifier | "(" sublist ")"
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [3]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code:
@f1(arg)
@f2
def func(): pass
is equivalent to:
def func(): pass
func = f1(arg)(f2(func))
When one or more top-level *parameters* have the form *parameter* "="
*expression*, the function is said to have "default parameter values."
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter's default value is
substituted. If a parameter has a default value, all following
parameters must also have a default value --- this is a syntactic
restriction that is not expressed by the grammar.
**Default parameter values are evaluated when the function definition
is executed.** This means that the expression is evaluated once, when
the function is defined, and that the same "pre-computed" value is
used for each call. This is especially important to understand when a
default parameter is a mutable object, such as a list or a dictionary:
if the function modifies the object (e.g. by appending an item to a
list), the default value is in effect modified. This is generally not
what was intended. A way around this is to use "None" as the
default, and explicitly test for it in the body of the function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from position arguments, from keyword
arguments, or from default values. If the form ""*identifier"" is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
""**identifier"" is present, it is initialized to a new dictionary
receiving any excess keyword arguments, defaulting to a new empty
dictionary.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a ""def"" statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The ""def"" form is actually more powerful since it
allows the execution of multiple statements.
**Programmer's note:** Functions are first-class objects. A ""def""
form executed inside a function definition defines a local function
that can be returned or passed around. Free variables used in the
nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
Class definitions
=================
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= "class" classname [inheritance] ":" suite
inheritance ::= "(" [expression_list] ")"
classname ::= identifier
A class definition is an executable statement. It first evaluates the
inheritance list, if present. Each item in the inheritance list
should evaluate to a class object or class type which allows
subclassing. The class's suite is then executed in a new execution
frame (see section Naming and binding), using a newly created local
namespace and the original global namespace. (Usually, the suite
contains only function definitions.) When the class's suite finishes
execution, its execution frame is discarded but its local namespace is
saved. [4] A class object is then created using the inheritance list
for the base classes and the saved local namespace for the attribute
dictionary. The class name is bound to this class object in the
original local namespace.
**Programmer's note:** Variables defined in the class definition are
class variables; they are shared by all instances. To create instance
variables, they can be set in a method with "self.name = value". Both
class and instance variables are accessible through the notation
""self.name"", and an instance variable hides a class variable with
the same name when accessed in this way. Class variables can be used
as defaults for instance variables, but using mutable values there can
lead to unexpected results. For *new-style class*es, descriptors can
be used to create instance variables with different implementation
details.
Class definitions, like function definitions, may be wrapped by one or
more *decorator* expressions. The evaluation rules for the decorator
expressions are the same as for functions. The result must be a class
object, which is then bound to the class name.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless
there is a "finally" clause which happens to raise another
exception. That new exception causes the old one to be lost.
[2] Currently, control "flows off the end" except in the case of
an exception or the execution of a "return", "continue", or
"break" statement.
[3] A string literal appearing as the first statement in the
function body is transformed into the function's "__doc__"
attribute and therefore the function's *docstring*.
[4] A string literal appearing as the first statement in the class
body is transformed into the namespace's "__doc__" item and
therefore the class's *docstring*.
t compounds�
With Statement Context Managers
*******************************
New in version 2.5.
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method's return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller's responsibility.
See also:
**PEP 343** - The "with" statement
The specification, background, and examples for the Python "with"
statement.
s context-managerss�
The "continue" statement
************************
continue_stmt ::= "continue"
"continue" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition or "finally"
clause within that loop. It continues with the next cycle of the
nearest enclosing loop.
When "continue" passes control out of a "try" statement with a
"finally" clause, that "finally" clause is executed before really
starting the next loop cycle.
t continuesB
Arithmetic conversions
**********************
When a description of an arithmetic operator below uses the phrase
"the numeric arguments are converted to a common type," the arguments
are coerced using the coercion rules listed at Coercion rules. If
both arguments are standard numeric types, the following coercions are
applied:
* If either argument is a complex number, the other is converted to
complex;
* otherwise, if either argument is a floating point number, the
other is converted to floating point;
* otherwise, if either argument is a long integer, the other is
converted to long integer;
* otherwise, both must be plain integers and no conversion is
necessary.
Some additional rules apply for certain operators (e.g., a string left
argument to the '%' operator). Extensions can define their own
coercions.
t conversionss�/
Basic customization
*******************
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass's "__new__()" method using
"super(currentclass, cls).__new__(cls[, ...])" with appropriate
arguments and then modifying the newly-created instance as
necessary before returning it.
If "__new__()" returns an instance of *cls*, then the new
instance's "__init__()" method will be invoked like
"__init__(self[, ...])", where *self* is the new instance and the
remaining arguments are the same as were passed to "__new__()".
If "__new__()" does not return an instance of *cls*, then the new
instance's "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class's "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"BaseClass.__init__(self, [args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customise
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a destructor. If a base class has a "__del__()" method, the
derived class's "__del__()" method, if any, must explicitly call it
to ensure proper deletion of the base class part of the instance.
Note that it is possible (though not recommended!) for the
"__del__()" method to postpone destruction of the instance by
creating a new reference to it. It may then be called at a later
time when this new reference is deleted. It is not guaranteed that
"__del__()" methods are called for objects that still exist when
the interpreter exits.
Note: "del x" doesn't directly call "x.__del__()" --- the former
decrements the reference count for "x" by one, and the latter is
only called when "x"'s reference count reaches zero. Some common
situations that may prevent the reference count of an object from
going to zero include: circular references between objects (e.g.,
a doubly-linked list or a tree data structure with parent and
child pointers); a reference to the object on the stack frame of
a function that caught an exception (the traceback stored in
"sys.exc_traceback" keeps the stack frame alive); or a reference
to the object on the stack frame that raised an unhandled
exception in interactive mode (the traceback stored in
"sys.last_traceback" keeps the stack frame alive). The first
situation can only be remedied by explicitly breaking the cycles;
the latter two situations can be resolved by storing "None" in
"sys.exc_traceback" or "sys.last_traceback". Circular references
which are garbage are detected when the option cycle detector is
enabled (it's on by default), but can only be cleaned up if there
are no Python-level "__del__()" methods involved. Refer to the
documentation for the "gc" module for more information about how
"__del__()" methods are handled by the cycle detector,
particularly the description of the "garbage" value.
Warning: Due to the precarious circumstances under which
"__del__()" methods are invoked, exceptions that occur during
their execution are ignored, and a warning is printed to
"sys.stderr" instead. Also, when "__del__()" is invoked in
response to a module being deleted (e.g., when execution of the
program is done), other globals referenced by the "__del__()"
method may already have been deleted or in the process of being
torn down (e.g. the import machinery shutting down). For this
reason, "__del__()" methods should do the absolute minimum needed
to maintain external invariants. Starting with version 1.5,
Python guarantees that globals whose name begins with a single
underscore are deleted from their module before other globals are
deleted; if no other references to such globals exist, this may
help in assuring that imported modules are still available at the
time when the "__del__()" method is called.
See also the "-R" command-line option.
object.__repr__(self)
Called by the "repr()" built-in function and by string conversions
(reverse quotes) to compute the "official" string representation of
an object. If at all possible, this should look like a valid
Python expression that could be used to recreate an object with the
same value (given an appropriate environment). If this is not
possible, a string of the form "<...some useful description...>"
should be returned. The return value must be a string object. If a
class defines "__repr__()" but not "__str__()", then "__repr__()"
is also used when an "informal" string representation of instances
of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by the "str()" built-in function and by the "print"
statement to compute the "informal" string representation of an
object. This differs from "__repr__()" in that it does not have to
be a valid Python expression: a more convenient or concise
representation may be used instead. The return value must be a
string object.
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
New in version 2.1.
These are the so-called "rich comparison" methods, and are called
for comparison operators in preference to "__cmp__()" below. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" and "x<>y" call "x.__ne__(y)",
"x>y" calls "x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
There are no implied relationships among the comparison operators.
The truth of "x==y" does not imply that "x!=y" is false.
Accordingly, when defining "__eq__()", one should also define
"__ne__()" so that the operators will behave as expected. See the
paragraph on "__hash__()" for some important notes on creating
*hashable* objects which support custom comparison operations and
are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other's
reflection, "__le__()" and "__ge__()" are each other's reflection,
and "__eq__()" and "__ne__()" are their own reflection.
Arguments to rich comparison methods are never coerced.
To automatically generate ordering operations from a single root
operation, see "functools.total_ordering()".
object.__cmp__(self, other)
Called by comparison operations if rich comparison (see above) is
not defined. Should return a negative integer if "self < other",
zero if "self == other", a positive integer if "self > other". If
no "__cmp__()", "__eq__()" or "__ne__()" operation is defined,
class instances are compared by object identity ("address"). See
also the description of "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys. (Note: the
restriction that exceptions are not propagated by "__cmp__()" has
been removed since Python 1.5.)
object.__rcmp__(self, other)
Changed in version 2.1: No longer supported.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
If a class does not define a "__cmp__()" or "__eq__()" method it
should not define a "__hash__()" operation either; if it defines
"__cmp__()" or "__eq__()" but not "__hash__()", its instances will
not be usable in hashed collections. If a class defines mutable
objects and implements a "__cmp__()" or "__eq__()" method, it
should not implement "__hash__()", since hashable collection
implementations require that an object's hash value is immutable
(if the object's hash value changes, it will be in the wrong hash
bucket).
User-defined classes have "__cmp__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns a result derived from
"id(x)".
Classes which inherit a "__hash__()" method from a parent class but
change the meaning of "__cmp__()" or "__eq__()" such that the hash
value returned is no longer appropriate (e.g. by switching to a
value-based concept of equality instead of the default identity
based equality) can explicitly flag themselves as being unhashable
by setting "__hash__ = None" in the class definition. Doing so
means that not only will instances of the class raise an
appropriate "TypeError" when a program attempts to retrieve their
hash value, but they will also be correctly identified as
unhashable when checking "isinstance(obj, collections.Hashable)"
(unlike classes which define their own "__hash__()" to explicitly
raise "TypeError").
Changed in version 2.5: "__hash__()" may now also return a long
integer object; the 32-bit integer is then derived from the hash of
that object.
Changed in version 2.6: "__hash__" may now be set to "None" to
explicitly flag instances of a class as unhashable.
object.__nonzero__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True", or their integer
equivalents "0" or "1". When this method is not defined,
"__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__nonzero__()", all its instances are
considered true.
object.__unicode__(self)
Called to implement "unicode()" built-in; should return a Unicode
object. When this method is not defined, string conversion is
attempted, and the result of string conversion is converted to
Unicode using the system default encoding.
t
customizations�
"pdb" --- The Python Debugger
*****************************
**Source code:** Lib/pdb.py
======================================================================
The module "pdb" defines an interactive source code debugger for
Python programs. It supports setting (conditional) breakpoints and
single stepping at the source line level, inspection of stack frames,
source code listing, and evaluation of arbitrary Python code in the
context of any stack frame. It also supports post-mortem debugging
and can be called under program control.
The debugger is extensible --- it is actually defined as the class
"Pdb". This is currently undocumented but easily understood by reading
the source. The extension interface uses the modules "bdb" and "cmd".
The debugger's prompt is "(Pdb)". Typical usage to run a program under
control of the debugger is:
>>> import pdb
>>> import mymodule
>>> pdb.run('mymodule.test()')
> <string>(0)?()
(Pdb) continue
> <string>(1)?()
(Pdb) continue
NameError: 'spam'
> <string>(1)?()
(Pdb)
"pdb.py" can also be invoked as a script to debug other scripts. For
example:
python -m pdb myscript.py
When invoked as a script, pdb will automatically enter post-mortem
debugging if the program being debugged exits abnormally. After post-
mortem debugging (or after normal exit of the program), pdb will
restart the program. Automatic restarting preserves pdb's state (such
as breakpoints) and in most cases is more useful than quitting the
debugger upon program's exit.
New in version 2.4: Restarting post-mortem behavior added.
The typical usage to break into the debugger from a running program is
to insert
import pdb; pdb.set_trace()
at the location you want to break into the debugger. You can then
step through the code following this statement, and continue running
without the debugger using the "c" command.
The typical usage to inspect a crashed program is:
>>> import pdb
>>> import mymodule
>>> mymodule.test()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "./mymodule.py", line 4, in test
test2()
File "./mymodule.py", line 3, in test2
print spam
NameError: spam
>>> pdb.pm()
> ./mymodule.py(3)test2()
-> print spam
(Pdb)
The module defines the following functions; each enters the debugger
in a slightly different way:
pdb.run(statement[, globals[, locals]])
Execute the *statement* (given as a string) under debugger control.
The debugger prompt appears before any code is executed; you can
set breakpoints and type "continue", or you can step through the
statement using "step" or "next" (all these commands are explained
below). The optional *globals* and *locals* arguments specify the
environment in which the code is executed; by default the
dictionary of the module "__main__" is used. (See the explanation
of the "exec" statement or the "eval()" built-in function.)
pdb.runeval(expression[, globals[, locals]])
Evaluate the *expression* (given as a string) under debugger
control. When "runeval()" returns, it returns the value of the
expression. Otherwise this function is similar to "run()".
pdb.runcall(function[, argument, ...])
Call the *function* (a function or method object, not a string)
with the given arguments. When "runcall()" returns, it returns
whatever the function call returned. The debugger prompt appears
as soon as the function is entered.
pdb.set_trace()
Enter the debugger at the calling stack frame. This is useful to
hard-code a breakpoint at a given point in a program, even if the
code is not otherwise being debugged (e.g. when an assertion
fails).
pdb.post_mortem([traceback])
Enter post-mortem debugging of the given *traceback* object. If no
*traceback* is given, it uses the one of the exception that is
currently being handled (an exception must be being handled if the
default is to be used).
pdb.pm()
Enter post-mortem debugging of the traceback found in
"sys.last_traceback".
The "run*" functions and "set_trace()" are aliases for instantiating
the "Pdb" class and calling the method of the same name. If you want
to access further features, you have to do this yourself:
class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None)
"Pdb" is the debugger class.
The *completekey*, *stdin* and *stdout* arguments are passed to the
underlying "cmd.Cmd" class; see the description there.
The *skip* argument, if given, must be an iterable of glob-style
module name patterns. The debugger will not step into frames that
originate in a module that matches one of these patterns. [1]
Example call to enable tracing with *skip*:
import pdb; pdb.Pdb(skip=['django.*']).set_trace()
New in version 2.7: The *skip* argument.
run(statement[, globals[, locals]])
runeval(expression[, globals[, locals]])
runcall(function[, argument, ...])
set_trace()
See the documentation for the functions explained above.
t debuggers�
The "del" statement
*******************
del_stmt ::= "del" target_list
Deletion is recursively defined very similar to the way assignment is
defined. Rather than spelling it out in full details, here are some
hints.
Deletion of a target list recursively deletes each target, from left
to right.
Deletion of a name removes the binding of that name from the local or
global namespace, depending on whether the name occurs in a "global"
statement in the same code block. If the name is unbound, a
"NameError" exception will be raised.
It is illegal to delete a name from the local namespace if it occurs
as a free variable in a nested block.
Deletion of attribute references, subscriptions and slicings is passed
to the primary object involved; deletion of a slicing is in general
equivalent to assignment of an empty slice of the right type (but even
this is determined by the sliced object).
t dels�
Dictionary displays
*******************
A dictionary display is a possibly empty series of key/datum pairs
enclosed in curly braces:
dict_display ::= "{" [key_datum_list | dict_comprehension] "}"
key_datum_list ::= key_datum ("," key_datum)* [","]
key_datum ::= expression ":" expression
dict_comprehension ::= expression ":" expression comp_for
A dictionary display yields a new dictionary object.
If a comma-separated sequence of key/datum pairs is given, they are
evaluated from left to right to define the entries of the dictionary:
each key object is used as a key into the dictionary to store the
corresponding datum. This means that you can specify the same key
multiple times in the key/datum list, and the final dictionary's value
for that key will be the last one given.
A dict comprehension, in contrast to list and set comprehensions,
needs two expressions separated with a colon followed by the usual
"for" and "if" clauses. When the comprehension is run, the resulting
key and value elements are inserted in the new dictionary in the order
they are produced.
Restrictions on the types of the key values are listed earlier in
section The standard type hierarchy. (To summarize, the key type
should be *hashable*, which excludes all mutable objects.) Clashes
between duplicate keys are not detected; the last datum (textually
rightmost in the display) stored for a given key value prevails.
t dicts+
Interaction with dynamic features
*********************************
There are several cases where Python statements are illegal when used
in conjunction with nested scopes that contain free variables.
If a variable is referenced in an enclosing scope, it is illegal to
delete the name. An error will be reported at compile time.
If the wild card form of import --- "import *" --- is used in a
function and the function contains or is a nested block with free
variables, the compiler will raise a "SyntaxError".
If "exec" is used in a function and the function contains or is a
nested block with free variables, the compiler will raise a
"SyntaxError" unless the exec explicitly specifies the local namespace
for the "exec". (In other words, "exec obj" would be illegal, but
"exec obj in ns" would be legal.)
The "eval()", "execfile()", and "input()" functions and the "exec"
statement do not have access to the full environment for resolving
names. Names may be resolved in the local and global namespaces of
the caller. Free variables are not resolved in the nearest enclosing
namespace, but in the global namespace. [1] The "exec" statement and
the "eval()" and "execfile()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
s dynamic-featuressE
The "if" statement
******************
The "if" statement is used for conditional execution:
if_stmt ::= "if" expression ":" suite
( "elif" expression ":" suite )*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
t elsesh
Exceptions
**********
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" ... "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the "termination" model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Exceptions can also be identified by strings, in which case the
"except" clause is selected by object identity. An arbitrary value
can be raised along with the identifying string which can be passed to
the handler.
Note: Messages to exceptions are not part of the Python API. Their
contents may change from one version of Python to the next without
warning and should not be relied on by code which will run under
multiple versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by
these operations is not available at the time the module is
compiled.
t
exceptionss�
The "exec" statement
********************
exec_stmt ::= "exec" or_expr ["in" expression ["," expression]]
This statement supports dynamic execution of Python code. The first
expression should evaluate to either a Unicode string, a *Latin-1*
encoded string, an open file object, a code object, or a tuple. If it
is a string, the string is parsed as a suite of Python statements
which is then executed (unless a syntax error occurs). [1] If it is an
open file, the file is parsed until EOF and executed. If it is a code
object, it is simply executed. For the interpretation of a tuple, see
below. In all cases, the code that's executed is expected to be valid
as file input (see section File input). Be aware that the "return"
and "yield" statements may not be used outside of function definitions
even within the context of code passed to the "exec" statement.
In all cases, if the optional parts are omitted, the code is executed
in the current scope. If only the first expression after "in" is
specified, it should be a dictionary, which will be used for both the
global and the local variables. If two expressions are given, they
are used for the global and local variables, respectively. If
provided, *locals* can be any mapping object. Remember that at module
level, globals and locals are the same dictionary. If two separate
objects are given as *globals* and *locals*, the code will be executed
as if it were embedded in a class definition.
The first expression may also be a tuple of length 2 or 3. In this
case, the optional parts must be omitted. The form "exec(expr,
globals)" is equivalent to "exec expr in globals", while the form
"exec(expr, globals, locals)" is equivalent to "exec expr in globals,
locals". The tuple form of "exec" provides compatibility with Python
3, where "exec" is a function rather than a statement.
Changed in version 2.4: Formerly, *locals* was required to be a
dictionary.
As a side effect, an implementation may insert additional keys into
the dictionaries given besides those corresponding to variable names
set by the executed code. For example, the current implementation may
add a reference to the dictionary of the built-in module "__builtin__"
under the key "__builtins__" (!).
**Programmer's hints:** dynamic evaluation of expressions is supported
by the built-in function "eval()". The built-in functions "globals()"
and "locals()" return the current global and local dictionary,
respectively, which may be useful to pass around for use by "exec".
-[ Footnotes ]-
[1] Note that the parser only accepts the Unix-style end of line
convention. If you are reading the code from a file, make sure to
use *universal newlines* mode to convert Windows or Mac-style
newlines.
t execs &
Execution model
***************
Naming and binding
==================
*Names* refer to objects. Names are introduced by name binding
operations. Each occurrence of a name in the program text refers to
the *binding* of that name established in the innermost function block
containing the use.
A *block* is a piece of Python program text that is executed as a
unit. The following are blocks: a module, a function body, and a class
definition. Each command typed interactively is a block. A script
file (a file given as standard input to the interpreter or specified
on the interpreter command line the first argument) is a code block.
A script command (a command specified on the interpreter command line
with the '**-c**' option) is a code block. The file read by the
built-in function "execfile()" is a code block. The string argument
passed to the built-in function "eval()" and to the "exec" statement
is a code block. The expression read and evaluated by the built-in
function "input()" is a code block.
A code block is executed in an *execution frame*. A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block's execution has
completed.
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name. The scope of names defined in a
class block is limited to the class block; it does not extend to the
code blocks of methods -- this includes generator expressions since
they are implemented using a function scope. This means that the
following will fail:
class A:
a = 42
b = list(a + i for i in range(10))
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block's *environment*.
If a name is bound in a block, it is a local variable of that block.
If a name is bound at the module level, it is a global variable. (The
variables of the module code block are local and global.) If a
variable is used in a code block but not defined there, it is a *free
variable*.
When a name is not found at all, a "NameError" exception is raised.
If the name refers to a local variable that has not been bound, a
"UnboundLocalError" exception is raised. "UnboundLocalError" is a
subclass of "NameError".
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, in the
second position of an "except" clause header or after "as" in a "with"
statement. The "import" statement of the form "from ... import *"
binds all names defined in the imported module, except those beginning
with an underscore. This form may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name). It
is illegal to unbind a name that is referenced by an enclosing scope;
the compiler will report a "SyntaxError".
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the global statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "__builtin__". The global namespace is searched first.
If the name is not found there, the builtins namespace is searched.
The global statement must precede all uses of the name.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module's dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "__builtin__" (note: no
's'); when in any other module, "__builtins__" is an alias for the
dictionary of the "__builtin__" module itself. "__builtins__" can be
set to a user-created dictionary to create a weak form of restricted
execution.
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "__builtin__" (no 's') module and modify its attributes
appropriately.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
A class definition is an executable statement that may use and define
names. These references follow the normal rules for name resolution.
The namespace of the class definition becomes the attribute dictionary
of the class. Names defined at the class scope are not visible in
methods.
Interaction with dynamic features
---------------------------------
There are several cases where Python statements are illegal when used
in conjunction with nested scopes that contain free variables.
If a variable is referenced in an enclosing scope, it is illegal to
delete the name. An error will be reported at compile time.
If the wild card form of import --- "import *" --- is used in a
function and the function contains or is a nested block with free
variables, the compiler will raise a "SyntaxError".
If "exec" is used in a function and the function contains or is a
nested block with free variables, the compiler will raise a
"SyntaxError" unless the exec explicitly specifies the local namespace
for the "exec". (In other words, "exec obj" would be illegal, but
"exec obj in ns" would be legal.)
The "eval()", "execfile()", and "input()" functions and the "exec"
statement do not have access to the full environment for resolving
names. Names may be resolved in the local and global namespaces of
the caller. Free variables are not resolved in the nearest enclosing
namespace, but in the global namespace. [1] The "exec" statement and
the "eval()" and "execfile()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
Exceptions
==========
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" ... "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the "termination" model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Exceptions can also be identified by strings, in which case the
"except" clause is selected by object identity. An arbitrary value
can be raised along with the identifying string which can be passed to
the handler.
Note: Messages to exceptions are not part of the Python API. Their
contents may change from one version of Python to the next without
warning and should not be relied on by code which will run under
multiple versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by
these operations is not available at the time the module is
compiled.
t execmodelsK
Expression lists
****************
expression_list ::= expression ( "," expression )* [","]
An expression list containing at least one comma yields a tuple. The
length of the tuple is the number of expressions in the list. The
expressions are evaluated from left to right.
The trailing comma is required only to create a single tuple (a.k.a. a
*singleton*); it is optional in all other cases. A single expression
without a trailing comma doesn't create a tuple, but rather yields the
value of that expression. (To create an empty tuple, use an empty pair
of parentheses: "()".)
t exprlistss�
Floating point literals
***********************
Floating point literals are described by the following lexical
definitions:
floatnumber ::= pointfloat | exponentfloat
pointfloat ::= [intpart] fraction | intpart "."
exponentfloat ::= (intpart | pointfloat) exponent
intpart ::= digit+
fraction ::= "." digit+
exponent ::= ("e" | "E") ["+" | "-"] digit+
Note that the integer and exponent parts of floating point numbers can
look like octal integers, but are interpreted using radix 10. For
example, "077e010" is legal, and denotes the same number as "77e10".
The allowed range of floating point literals is implementation-
dependent. Some examples of floating point literals:
3.14 10. .001 1e100 3.14e-10 0e0
Note that numeric literals do not include a sign; a phrase like "-1"
is actually an expression composed of the unary operator "-" and the
literal "1".
t floatingsZ
The "for" statement
*******************
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order of ascending indices. Each
item in turn is assigned to the target list using the standard rules
for assignments, and then the suite is executed. When the items are
exhausted (which is immediately when the sequence is empty), the suite
in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause's suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there was no next
item.
The suite may assign to the variable(s) in the target list; this does
not affect the next item assigned to it.
The target list is not deleted when the loop is finished, but if the
sequence is empty, it will not have been assigned to at all by the
loop. Hint: the built-in function "range()" returns a sequence of
integers suitable to emulate the effect of Pascal's "for i := a to b
do"; e.g., "range(3)" returns the list "[0, 1, 2]".
Note: There is a subtlety when the sequence is being modified by the
loop (this can only occur for mutable sequences, i.e. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
t fors�Q
Format String Syntax
********************
The "str.format()" method and the "Formatter" class share the same
syntax for format strings (although in the case of "Formatter",
subclasses can define their own format string syntax).
Format strings contain "replacement fields" surrounded by curly braces
"{}". Anything that is not contained in braces is considered literal
text, which is copied unchanged to the output. If you need to include
a brace character in the literal text, it can be escaped by doubling:
"{{" and "}}".
The grammar for a replacement field is as follows:
replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}"
field_name ::= arg_name ("." attribute_name | "[" element_index "]")*
arg_name ::= [identifier | integer]
attribute_name ::= identifier
element_index ::= integer | index_string
index_string ::= <any source character except "]"> +
conversion ::= "r" | "s"
format_spec ::= <described in the next section>
In less formal terms, the replacement field can start with a
*field_name* that specifies the object whose value is to be formatted
and inserted into the output instead of the replacement field. The
*field_name* is optionally followed by a *conversion* field, which is
preceded by an exclamation point "'!'", and a *format_spec*, which is
preceded by a colon "':'". These specify a non-default format for the
replacement value.
See also the Format Specification Mini-Language section.
The *field_name* itself begins with an *arg_name* that is either a
number or a keyword. If it's a number, it refers to a positional
argument, and if it's a keyword, it refers to a named keyword
argument. If the numerical arg_names in a format string are 0, 1, 2,
... in sequence, they can all be omitted (not just some) and the
numbers 0, 1, 2, ... will be automatically inserted in that order.
Because *arg_name* is not quote-delimited, it is not possible to
specify arbitrary dictionary keys (e.g., the strings "'10'" or
"':-]'") within a format string. The *arg_name* can be followed by any
number of index or attribute expressions. An expression of the form
"'.name'" selects the named attribute using "getattr()", while an
expression of the form "'[index]'" does an index lookup using
"__getitem__()".
Changed in version 2.7: The positional argument specifiers can be
omitted, so "'{} {}'" is equivalent to "'{0} {1}'".
Some simple format string examples:
"First, thou shalt count to {0}" # References first positional argument
"Bring me a {}" # Implicitly references the first positional argument
"From {} to {}" # Same as "From {0} to {1}"
"My quest is {name}" # References keyword argument 'name'
"Weight in tons {0.weight}" # 'weight' attribute of first positional arg
"Units destroyed: {players[0]}" # First element of keyword argument 'players'.
The *conversion* field causes a type coercion before formatting.
Normally, the job of formatting a value is done by the "__format__()"
method of the value itself. However, in some cases it is desirable to
force a type to be formatted as a string, overriding its own
definition of formatting. By converting the value to a string before
calling "__format__()", the normal formatting logic is bypassed.
Two conversion flags are currently supported: "'!s'" which calls
"str()" on the value, and "'!r'" which calls "repr()".
Some examples:
"Harold's a clever {0!s}" # Calls str() on the argument first
"Bring out the holy {name!r}" # Calls repr() on the argument first
The *format_spec* field contains a specification of how the value
should be presented, including such details as field width, alignment,
padding, decimal precision and so on. Each value type can define its
own "formatting mini-language" or interpretation of the *format_spec*.
Most built-in types support a common formatting mini-language, which
is described in the next section.
A *format_spec* field can also include nested replacement fields
within it. These nested replacement fields may contain a field name,
conversion flag and format specification, but deeper nesting is not
allowed. The replacement fields within the format_spec are
substituted before the *format_spec* string is interpreted. This
allows the formatting of a value to be dynamically specified.
See the Format examples section for some examples.
Format Specification Mini-Language
==================================
"Format specifications" are used within replacement fields contained
within a format string to define how individual values are presented
(see Format String Syntax). They can also be passed directly to the
built-in "format()" function. Each formattable type may define how
the format specification is to be interpreted.
Most built-in types implement the following options for format
specifications, although some of the formatting options are only
supported by the numeric types.
A general convention is that an empty format string ("""") produces
the same result as if you had called "str()" on the value. A non-empty
format string typically modifies the result.
The general form of a *standard format specifier* is:
format_spec ::= [[fill]align][sign][#][0][width][,][.precision][type]
fill ::= <any character>
align ::= "<" | ">" | "=" | "^"
sign ::= "+" | "-" | " "
width ::= integer
precision ::= integer
type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"
If a valid *align* value is specified, it can be preceded by a *fill*
character that can be any character and defaults to a space if
omitted. It is not possible to use a literal curly brace (""{"" or
""}"") as the *fill* character when using the "str.format()" method.
However, it is possible to insert a curly brace with a nested
replacement field. This limitation doesn't affect the "format()"
function.
The meaning of the various alignment options is as follows:
+-----------+------------------------------------------------------------+
| Option | Meaning |
+===========+============================================================+
| "'<'" | Forces the field to be left-aligned within the available |
| | space (this is the default for most objects). |
+-----------+------------------------------------------------------------+
| "'>'" | Forces the field to be right-aligned within the available |
| | space (this is the default for numbers). |
+-----------+------------------------------------------------------------+
| "'='" | Forces the padding to be placed after the sign (if any) |
| | but before the digits. This is used for printing fields |
| | in the form '+000000120'. This alignment option is only |
| | valid for numeric types. It becomes the default when '0' |
| | immediately precedes the field width. |
+-----------+------------------------------------------------------------+
| "'^'" | Forces the field to be centered within the available |
| | space. |
+-----------+------------------------------------------------------------+
Note that unless a minimum field width is defined, the field width
will always be the same size as the data to fill it, so that the
alignment option has no meaning in this case.
The *sign* option is only valid for number types, and can be one of
the following:
+-----------+------------------------------------------------------------+
| Option | Meaning |
+===========+============================================================+
| "'+'" | indicates that a sign should be used for both positive as |
| | well as negative numbers. |
+-----------+------------------------------------------------------------+
| "'-'" | indicates that a sign should be used only for negative |
| | numbers (this is the default behavior). |
+-----------+------------------------------------------------------------+
| space | indicates that a leading space should be used on positive |
| | numbers, and a minus sign on negative numbers. |
+-----------+------------------------------------------------------------+
The "'#'" option is only valid for integers, and only for binary,
octal, or hexadecimal output. If present, it specifies that the
output will be prefixed by "'0b'", "'0o'", or "'0x'", respectively.
The "','" option signals the use of a comma for a thousands separator.
For a locale aware separator, use the "'n'" integer presentation type
instead.
Changed in version 2.7: Added the "','" option (see also **PEP 378**).
*width* is a decimal integer defining the minimum field width. If not
specified, then the field width will be determined by the content.
When no explicit alignment is given, preceding the *width* field by a
zero ("'0'") character enables sign-aware zero-padding for numeric
types. This is equivalent to a *fill* character of "'0'" with an
*alignment* type of "'='".
The *precision* is a decimal number indicating how many digits should
be displayed after the decimal point for a floating point value
formatted with "'f'" and "'F'", or before and after the decimal point
for a floating point value formatted with "'g'" or "'G'". For non-
number types the field indicates the maximum field size - in other
words, how many characters will be used from the field content. The
*precision* is not allowed for integer values.
Finally, the *type* determines how the data should be presented.
The available string presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'s'" | String format. This is the default type for strings and |
| | may be omitted. |
+-----------+------------------------------------------------------------+
| None | The same as "'s'". |
+-----------+------------------------------------------------------------+
The available integer presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'b'" | Binary format. Outputs the number in base 2. |
+-----------+------------------------------------------------------------+
| "'c'" | Character. Converts the integer to the corresponding |
| | unicode character before printing. |
+-----------+------------------------------------------------------------+
| "'d'" | Decimal Integer. Outputs the number in base 10. |
+-----------+------------------------------------------------------------+
| "'o'" | Octal format. Outputs the number in base 8. |
+-----------+------------------------------------------------------------+
| "'x'" | Hex format. Outputs the number in base 16, using lower- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'X'" | Hex format. Outputs the number in base 16, using upper- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'d'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| None | The same as "'d'". |
+-----------+------------------------------------------------------------+
In addition to the above presentation types, integers can be formatted
with the floating point presentation types listed below (except "'n'"
and "None"). When doing so, "float()" is used to convert the integer
to a floating point number before formatting.
The available presentation types for floating point and decimal values
are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'e'" | Exponent notation. Prints the number in scientific |
| | notation using the letter 'e' to indicate the exponent. |
| | The default precision is "6". |
+-----------+------------------------------------------------------------+
| "'E'" | Exponent notation. Same as "'e'" except it uses an upper |
| | case 'E' as the separator character. |
+-----------+------------------------------------------------------------+
| "'f'" | Fixed point. Displays the number as a fixed-point number. |
| | The default precision is "6". |
+-----------+------------------------------------------------------------+
| "'F'" | Fixed point. Same as "'f'". |
+-----------+------------------------------------------------------------+
| "'g'" | General format. For a given precision "p >= 1", this |
| | rounds the number to "p" significant digits and then |
| | formats the result in either fixed-point format or in |
| | scientific notation, depending on its magnitude. The |
| | precise rules are as follows: suppose that the result |
| | formatted with presentation type "'e'" and precision "p-1" |
| | would have exponent "exp". Then if "-4 <= exp < p", the |
| | number is formatted with presentation type "'f'" and |
| | precision "p-1-exp". Otherwise, the number is formatted |
| | with presentation type "'e'" and precision "p-1". In both |
| | cases insignificant trailing zeros are removed from the |
| | significand, and the decimal point is also removed if |
| | there are no remaining digits following it. Positive and |
| | negative infinity, positive and negative zero, and nans, |
| | are formatted as "inf", "-inf", "0", "-0" and "nan" |
| | respectively, regardless of the precision. A precision of |
| | "0" is treated as equivalent to a precision of "1". The |
| | default precision is "6". |
+-----------+------------------------------------------------------------+
| "'G'" | General format. Same as "'g'" except switches to "'E'" if |
| | the number gets too large. The representations of infinity |
| | and NaN are uppercased, too. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'g'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| "'%'" | Percentage. Multiplies the number by 100 and displays in |
| | fixed ("'f'") format, followed by a percent sign. |
+-----------+------------------------------------------------------------+
| None | The same as "'g'". |
+-----------+------------------------------------------------------------+
Format examples
===============
This section contains examples of the "str.format()" syntax and
comparison with the old "%"-formatting.
In most of the cases the syntax is similar to the old "%"-formatting,
with the addition of the "{}" and with ":" used instead of "%". For
example, "'%03.2f'" can be translated to "'{:03.2f}'".
The new format syntax also supports new and different options, shown
in the follow examples.
Accessing arguments by position:
>>> '{0}, {1}, {2}'.format('a', 'b', 'c')
'a, b, c'
>>> '{}, {}, {}'.format('a', 'b', 'c') # 2.7+ only
'a, b, c'
>>> '{2}, {1}, {0}'.format('a', 'b', 'c')
'c, b, a'
>>> '{2}, {1}, {0}'.format(*'abc') # unpacking argument sequence
'c, b, a'
>>> '{0}{1}{0}'.format('abra', 'cad') # arguments' indices can be repeated
'abracadabra'
Accessing arguments by name:
>>> 'Coordinates: {latitude}, {longitude}'.format(latitude='37.24N', longitude='-115.81W')
'Coordinates: 37.24N, -115.81W'
>>> coord = {'latitude': '37.24N', 'longitude': '-115.81W'}
>>> 'Coordinates: {latitude}, {longitude}'.format(**coord)
'Coordinates: 37.24N, -115.81W'
Accessing arguments' attributes:
>>> c = 3-5j
>>> ('The complex number {0} is formed from the real part {0.real} '
... 'and the imaginary part {0.imag}.').format(c)
'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.'
>>> class Point(object):
... def __init__(self, x, y):
... self.x, self.y = x, y
... def __str__(self):
... return 'Point({self.x}, {self.y})'.format(self=self)
...
>>> str(Point(4, 2))
'Point(4, 2)'
Accessing arguments' items:
>>> coord = (3, 5)
>>> 'X: {0[0]}; Y: {0[1]}'.format(coord)
'X: 3; Y: 5'
Replacing "%s" and "%r":
>>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2')
"repr() shows quotes: 'test1'; str() doesn't: test2"
Aligning the text and specifying a width:
>>> '{:<30}'.format('left aligned')
'left aligned '
>>> '{:>30}'.format('right aligned')
' right aligned'
>>> '{:^30}'.format('centered')
' centered '
>>> '{:*^30}'.format('centered') # use '*' as a fill char
'***********centered***********'
Replacing "%+f", "%-f", and "% f" and specifying a sign:
>>> '{:+f}; {:+f}'.format(3.14, -3.14) # show it always
'+3.140000; -3.140000'
>>> '{: f}; {: f}'.format(3.14, -3.14) # show a space for positive numbers
' 3.140000; -3.140000'
>>> '{:-f}; {:-f}'.format(3.14, -3.14) # show only the minus -- same as '{:f}; {:f}'
'3.140000; -3.140000'
Replacing "%x" and "%o" and converting the value to different bases:
>>> # format also supports binary numbers
>>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: {0:b}".format(42)
'int: 42; hex: 2a; oct: 52; bin: 101010'
>>> # with 0x, 0o, or 0b as prefix:
>>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: {0:#b}".format(42)
'int: 42; hex: 0x2a; oct: 0o52; bin: 0b101010'
Using the comma as a thousands separator:
>>> '{:,}'.format(1234567890)
'1,234,567,890'
Expressing a percentage:
>>> points = 19.5
>>> total = 22
>>> 'Correct answers: {:.2%}'.format(points/total)
'Correct answers: 88.64%'
Using type-specific formatting:
>>> import datetime
>>> d = datetime.datetime(2010, 7, 4, 12, 15, 58)
>>> '{:%Y-%m-%d %H:%M:%S}'.format(d)
'2010-07-04 12:15:58'
Nesting arguments and more complex examples:
>>> for align, text in zip('<^>', ['left', 'center', 'right']):
... '{0:{fill}{align}16}'.format(text, fill=align, align=align)
...
'left<<<<<<<<<<<<'
'^^^^^center^^^^^'
'>>>>>>>>>>>right'
>>>
>>> octets = [192, 168, 0, 1]
>>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets)
'C0A80001'
>>> int(_, 16)
3232235521
>>>
>>> width = 5
>>> for num in range(5,12):
... for base in 'dXob':
... print '{0:{width}{base}}'.format(num, base=base, width=width),
... print
...
5 5 5 101
6 6 6 110
7 7 7 111
8 8 10 1000
9 9 11 1001
10 A 12 1010
11 B 13 1011
t
formatstringssz
Function definitions
********************
A function definition defines a user-defined function object (see
section The standard type hierarchy):
decorated ::= decorators (classdef | funcdef)
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
funcdef ::= "def" funcname "(" [parameter_list] ")" ":" suite
dotted_name ::= identifier ("." identifier)*
parameter_list ::= (defparameter ",")*
( "*" identifier ["," "**" identifier]
| "**" identifier
| defparameter [","] )
defparameter ::= parameter ["=" expression]
sublist ::= parameter ("," parameter)* [","]
parameter ::= identifier | "(" sublist ")"
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [3]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code:
@f1(arg)
@f2
def func(): pass
is equivalent to:
def func(): pass
func = f1(arg)(f2(func))
When one or more top-level *parameters* have the form *parameter* "="
*expression*, the function is said to have "default parameter values."
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter's default value is
substituted. If a parameter has a default value, all following
parameters must also have a default value --- this is a syntactic
restriction that is not expressed by the grammar.
**Default parameter values are evaluated when the function definition
is executed.** This means that the expression is evaluated once, when
the function is defined, and that the same "pre-computed" value is
used for each call. This is especially important to understand when a
default parameter is a mutable object, such as a list or a dictionary:
if the function modifies the object (e.g. by appending an item to a
list), the default value is in effect modified. This is generally not
what was intended. A way around this is to use "None" as the
default, and explicitly test for it in the body of the function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from position arguments, from keyword
arguments, or from default values. If the form ""*identifier"" is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
""**identifier"" is present, it is initialized to a new dictionary
receiving any excess keyword arguments, defaulting to a new empty
dictionary.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a ""def"" statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The ""def"" form is actually more powerful since it
allows the execution of multiple statements.
**Programmer's note:** Functions are first-class objects. A ""def""
form executed inside a function definition defines a local function
that can be returned or passed around. Free variables used in the
nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
t functions�
The "global" statement
**********************
global_stmt ::= "global" identifier ("," identifier)*
The "global" statement is a declaration which holds for the entire
current code block. It means that the listed identifiers are to be
interpreted as globals. It would be impossible to assign to a global
variable without "global", although free variables may refer to
globals without being declared global.
Names listed in a "global" statement must not be used in the same code
block textually preceding that "global" statement.
Names listed in a "global" statement must not be defined as formal
parameters or in a "for" loop control target, "class" definition,
function definition, or "import" statement.
**CPython implementation detail:** The current implementation does not
enforce the latter two restrictions, but programs should not abuse
this freedom, as future implementations may enforce them or silently
change the meaning of the program.
**Programmer's note:** "global" is a directive to the parser. It
applies only to code parsed at the same time as the "global"
statement. In particular, a "global" statement contained in an "exec"
statement does not affect the code block *containing* the "exec"
statement, and code contained in an "exec" statement is unaffected by
"global" statements in the code containing the "exec" statement. The
same applies to the "eval()", "execfile()" and "compile()" functions.
t globals�
Reserved classes of identifiers
*******************************
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "__builtin__" module. When
not in interactive mode, "_" has no special meaning and is not
defined. See section The import statement.
Note: The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names. These names are defined by the interpreter
and its implementation (including the standard library). Current
system names are discussed in the Special method names section and
elsewhere. More will likely be defined in future versions of
Python. *Any* use of "__*__" names, in any context, that does not
follow explicitly documented use, is subject to breakage without
warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between "private" attributes of base and
derived classes. See section Identifiers (Names).
s
id-classess�
Identifiers and keywords
************************
Identifiers (also referred to as *names*) are described by the
following lexical definitions:
identifier ::= (letter|"_") (letter | digit | "_")*
letter ::= lowercase | uppercase
lowercase ::= "a"..."z"
uppercase ::= "A"..."Z"
digit ::= "0"..."9"
Identifiers are unlimited in length. Case is significant.
Keywords
========
The following identifiers are used as reserved words, or *keywords* of
the language, and cannot be used as ordinary identifiers. They must
be spelled exactly as written here:
and del from not while
as elif global or with
assert else if pass yield
break except import print
class exec in raise
continue finally is return
def for lambda try
Changed in version 2.4: "None" became a constant and is now recognized
by the compiler as a name for the built-in object "None". Although it
is not a keyword, you cannot assign a different object to it.
Changed in version 2.5: Using "as" and "with" as identifiers triggers
a warning. To use them as keywords, enable the "with_statement"
future feature .
Changed in version 2.6: "as" and "with" are full keywords.
Reserved classes of identifiers
===============================
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "__builtin__" module. When
not in interactive mode, "_" has no special meaning and is not
defined. See section The import statement.
Note: The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names. These names are defined by the interpreter
and its implementation (including the standard library). Current
system names are discussed in the Special method names section and
elsewhere. More will likely be defined in future versions of
Python. *Any* use of "__*__" names, in any context, that does not
follow explicitly documented use, is subject to breakage without
warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between "private" attributes of base and
derived classes. See section Identifiers (Names).
t identifierst ifs%
Imaginary literals
******************
Imaginary literals are described by the following lexical definitions:
imagnumber ::= (floatnumber | intpart) ("j" | "J")
An imaginary literal yields a complex number with a real part of 0.0.
Complex numbers are represented as a pair of floating point numbers
and have the same restrictions on their range. To create a complex
number with a nonzero real part, add a floating point number to it,
e.g., "(3+4j)". Some examples of imaginary literals:
3.14j 10.j 10j .001j 1e100j 3.14e-10j
t imaginarysK.
The "import" statement
**********************
import_stmt ::= "import" module ["as" name] ( "," module ["as" name] )*
| "from" relative_module "import" identifier ["as" name]
( "," identifier ["as" name] )*
| "from" relative_module "import" "(" identifier ["as" name]
( "," identifier ["as" name] )* [","] ")"
| "from" module "import" "*"
module ::= (identifier ".")* identifier
relative_module ::= "."* module | "."+
name ::= identifier
Import statements are executed in two steps: (1) find a module, and
initialize it if necessary; (2) define a name or names in the local
namespace (of the scope where the "import" statement occurs). The
statement comes in two forms differing on whether it uses the "from"
keyword. The first form (without "from") repeats these steps for each
identifier in the list. The form with "from" performs step (1) once,
and then performs step (2) repeatedly.
To understand how step (1) occurs, one must first understand how
Python handles hierarchical naming of modules. To help organize
modules and provide a hierarchy in naming, Python has a concept of
packages. A package can contain other packages and modules while
modules cannot contain other modules or packages. From a file system
perspective, packages are directories and modules are files.
Once the name of the module is known (unless otherwise specified, the
term "module" will refer to both packages and modules), searching for
the module or package can begin. The first place checked is
"sys.modules", the cache of all modules that have been imported
previously. If the module is found there then it is used in step (2)
of import.
If the module is not found in the cache, then "sys.meta_path" is
searched (the specification for "sys.meta_path" can be found in **PEP
302**). The object is a list of *finder* objects which are queried in
order as to whether they know how to load the module by calling their
"find_module()" method with the name of the module. If the module
happens to be contained within a package (as denoted by the existence
of a dot in the name), then a second argument to "find_module()" is
given as the value of the "__path__" attribute from the parent package
(everything up to the last dot in the name of the module being
imported). If a finder can find the module it returns a *loader*
(discussed later) or returns "None".
If none of the finders on "sys.meta_path" are able to find the module
then some implicitly defined finders are queried. Implementations of
Python vary in what implicit meta path finders are defined. The one
they all do define, though, is one that handles "sys.path_hooks",
"sys.path_importer_cache", and "sys.path".
The implicit finder searches for the requested module in the "paths"
specified in one of two places ("paths" do not have to be file system
paths). If the module being imported is supposed to be contained
within a package then the second argument passed to "find_module()",
"__path__" on the parent package, is used as the source of paths. If
the module is not contained in a package then "sys.path" is used as
the source of paths.
Once the source of paths is chosen it is iterated over to find a
finder that can handle that path. The dict at
"sys.path_importer_cache" caches finders for paths and is checked for
a finder. If the path does not have a finder cached then
"sys.path_hooks" is searched by calling each object in the list with a
single argument of the path, returning a finder or raises
"ImportError". If a finder is returned then it is cached in
"sys.path_importer_cache" and then used for that path entry. If no
finder can be found but the path exists then a value of "None" is
stored in "sys.path_importer_cache" to signify that an implicit, file-
based finder that handles modules stored as individual files should be
used for that path. If the path does not exist then a finder which
always returns "None" is placed in the cache for the path.
If no finder can find the module then "ImportError" is raised.
Otherwise some finder returned a loader whose "load_module()" method
is called with the name of the module to load (see **PEP 302** for the
original definition of loaders). A loader has several responsibilities
to perform on a module it loads. First, if the module already exists
in "sys.modules" (a possibility if the loader is called outside of the
import machinery) then it is to use that module for initialization and
not a new module. But if the module does not exist in "sys.modules"
then it is to be added to that dict before initialization begins. If
an error occurs during loading of the module and it was added to
"sys.modules" it is to be removed from the dict. If an error occurs
but the module was already in "sys.modules" it is left in the dict.
The loader must set several attributes on the module. "__name__" is to
be set to the name of the module. "__file__" is to be the "path" to
the file unless the module is built-in (and thus listed in
"sys.builtin_module_names") in which case the attribute is not set. If
what is being imported is a package then "__path__" is to be set to a
list of paths to be searched when looking for modules and packages
contained within the package being imported. "__package__" is optional
but should be set to the name of package that contains the module or
package (the empty string is used for module not contained in a
package). "__loader__" is also optional but should be set to the
loader object that is loading the module.
If an error occurs during loading then the loader raises "ImportError"
if some other exception is not already being propagated. Otherwise the
loader returns the module that was loaded and initialized.
When step (1) finishes without raising an exception, step (2) can
begin.
The first form of "import" statement binds the module name in the
local namespace to the module object, and then goes on to import the
next identifier, if any. If the module name is followed by "as", the
name following "as" is used as the local name for the module.
The "from" form does not bind the module name: it goes through the
list of identifiers, looks each one of them up in the module found in
step (1), and binds the name in the local namespace to the object thus
found. As with the first form of "import", an alternate local name
can be supplied by specifying ""as" localname". If a name is not
found, "ImportError" is raised. If the list of identifiers is
replaced by a star ("'*'"), all public names defined in the module are
bound in the local namespace of the "import" statement..
The *public names* defined by a module are determined by checking the
module's namespace for a variable named "__all__"; if defined, it must
be a sequence of strings which are names defined or imported by that
module. The names given in "__all__" are all considered public and
are required to exist. If "__all__" is not defined, the set of public
names includes all names found in the module's namespace which do not
begin with an underscore character ("'_'"). "__all__" should contain
the entire public API. It is intended to avoid accidentally exporting
items that are not part of the API (such as library modules which were
imported and used within the module).
The "from" form with "*" may only occur in a module scope. If the
wild card form of import --- "import *" --- is used in a function and
the function contains or is a nested block with free variables, the
compiler will raise a "SyntaxError".
When specifying what module to import you do not have to specify the
absolute name of the module. When a module or package is contained
within another package it is possible to make a relative import within
the same top package without having to mention the package name. By
using leading dots in the specified module or package after "from" you
can specify how high to traverse up the current package hierarchy
without specifying exact names. One leading dot means the current
package where the module making the import exists. Two dots means up
one package level. Three dots is up two levels, etc. So if you execute
"from . import mod" from a module in the "pkg" package then you will
end up importing "pkg.mod". If you execute "from ..subpkg2 import mod"
from within "pkg.subpkg1" you will import "pkg.subpkg2.mod". The
specification for relative imports is contained within **PEP 328**.
"importlib.import_module()" is provided to support applications that
determine which modules need to be loaded dynamically.
Future statements
=================
A *future statement* is a directive to the compiler that a particular
module should be compiled using syntax or semantics that will be
available in a specified future release of Python. The future
statement is intended to ease migration to future versions of Python
that introduce incompatible changes to the language. It allows use of
the new features on a per-module basis before the release in which the
feature becomes standard.
future_statement ::= "from" "__future__" "import" feature ["as" name]
("," feature ["as" name])*
| "from" "__future__" "import" "(" feature ["as" name]
("," feature ["as" name])* [","] ")"
feature ::= identifier
name ::= identifier
A future statement must appear near the top of the module. The only
lines that can appear before a future statement are:
* the module docstring (if any),
* comments,
* blank lines, and
* other future statements.
The features recognized by Python 2.6 are "unicode_literals",
"print_function", "absolute_import", "division", "generators",
"nested_scopes" and "with_statement". "generators", "with_statement",
"nested_scopes" are redundant in Python version 2.6 and above because
they are always enabled.
A future statement is recognized and treated specially at compile
time: Changes to the semantics of core constructs are often
implemented by generating different code. It may even be the case
that a new feature introduces new incompatible syntax (such as a new
reserved word), in which case the compiler may need to parse the
module differently. Such decisions cannot be pushed off until
runtime.
For any given release, the compiler knows which feature names have
been defined, and raises a compile-time error if a future statement
contains a feature not known to it.
The direct runtime semantics are the same as for any import statement:
there is a standard module "__future__", described later, and it will
be imported in the usual way at the time the future statement is
executed.
The interesting runtime semantics depend on the specific feature
enabled by the future statement.
Note that there is nothing special about the statement:
import __future__ [as name]
That is not a future statement; it's an ordinary import statement with
no special semantics or syntax restrictions.
Code compiled by an "exec" statement or calls to the built-in
functions "compile()" and "execfile()" that occur in a module "M"
containing a future statement will, by default, use the new syntax or
semantics associated with the future statement. This can, starting
with Python 2.2 be controlled by optional arguments to "compile()" ---
see the documentation of that function for details.
A future statement typed at an interactive interpreter prompt will
take effect for the rest of the interpreter session. If an
interpreter is started with the "-i" option, is passed a script name
to execute, and the script includes a future statement, it will be in
effect in the interactive session started after the script is
executed.
See also:
**PEP 236** - Back to the __future__
The original proposal for the __future__ mechanism.
t importsO
Membership test operations
**************************
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y". If an exception is raised
during the iteration, it is as if "in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse true value of
"in".
t inso
Integer and long integer literals
*********************************
Integer and long integer literals are described by the following
lexical definitions:
longinteger ::= integer ("l" | "L")
integer ::= decimalinteger | octinteger | hexinteger | bininteger
decimalinteger ::= nonzerodigit digit* | "0"
octinteger ::= "0" ("o" | "O") octdigit+ | "0" octdigit+
hexinteger ::= "0" ("x" | "X") hexdigit+
bininteger ::= "0" ("b" | "B") bindigit+
nonzerodigit ::= "1"..."9"
octdigit ::= "0"..."7"
bindigit ::= "0" | "1"
hexdigit ::= digit | "a"..."f" | "A"..."F"
Although both lower case "'l'" and upper case "'L'" are allowed as
suffix for long integers, it is strongly recommended to always use
"'L'", since the letter "'l'" looks too much like the digit "'1'".
Plain integer literals that are above the largest representable plain
integer (e.g., 2147483647 when using 32-bit arithmetic) are accepted
as if they were long integers instead. [1] There is no limit for long
integer literals apart from what can be stored in available memory.
Some examples of plain integer literals (first row) and long integer
literals (second and third rows):
7 2147483647 0177
3L 79228162514264337593543950336L 0377L 0x100000000L
79228162514264337593543950336 0xdeadbeef
t integerssx
Lambdas
*******
lambda_expr ::= "lambda" [parameter_list]: expression
old_lambda_expr ::= "lambda" [parameter_list]: old_expression
Lambda expressions (sometimes called lambda forms) have the same
syntactic position as expressions. They are a shorthand to create
anonymous functions; the expression "lambda arguments: expression"
yields a function object. The unnamed object behaves like a function
object defined with
def name(arguments):
return expression
See section Function definitions for the syntax of parameter lists.
Note that functions created with lambda expressions cannot contain
statements.
t lambdas�
List displays
*************
A list display is a possibly empty series of expressions enclosed in
square brackets:
list_display ::= "[" [expression_list | list_comprehension] "]"
list_comprehension ::= expression list_for
list_for ::= "for" target_list "in" old_expression_list [list_iter]
old_expression_list ::= old_expression [("," old_expression)+ [","]]
old_expression ::= or_test | old_lambda_expr
list_iter ::= list_for | list_if
list_if ::= "if" old_expression [list_iter]
A list display yields a new list object. Its contents are specified
by providing either a list of expressions or a list comprehension.
When a comma-separated list of expressions is supplied, its elements
are evaluated from left to right and placed into the list object in
that order. When a list comprehension is supplied, it consists of a
single expression followed by at least one "for" clause and zero or
more "for" or "if" clauses. In this case, the elements of the new
list are those that would be produced by considering each of the "for"
or "if" clauses a block, nesting from left to right, and evaluating
the expression to produce a list element each time the innermost block
is reached [1].
t listss�
Naming and binding
******************
*Names* refer to objects. Names are introduced by name binding
operations. Each occurrence of a name in the program text refers to
the *binding* of that name established in the innermost function block
containing the use.
A *block* is a piece of Python program text that is executed as a
unit. The following are blocks: a module, a function body, and a class
definition. Each command typed interactively is a block. A script
file (a file given as standard input to the interpreter or specified
on the interpreter command line the first argument) is a code block.
A script command (a command specified on the interpreter command line
with the '**-c**' option) is a code block. The file read by the
built-in function "execfile()" is a code block. The string argument
passed to the built-in function "eval()" and to the "exec" statement
is a code block. The expression read and evaluated by the built-in
function "input()" is a code block.
A code block is executed in an *execution frame*. A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block's execution has
completed.
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name. The scope of names defined in a
class block is limited to the class block; it does not extend to the
code blocks of methods -- this includes generator expressions since
they are implemented using a function scope. This means that the
following will fail:
class A:
a = 42
b = list(a + i for i in range(10))
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block's *environment*.
If a name is bound in a block, it is a local variable of that block.
If a name is bound at the module level, it is a global variable. (The
variables of the module code block are local and global.) If a
variable is used in a code block but not defined there, it is a *free
variable*.
When a name is not found at all, a "NameError" exception is raised.
If the name refers to a local variable that has not been bound, a
"UnboundLocalError" exception is raised. "UnboundLocalError" is a
subclass of "NameError".
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, in the
second position of an "except" clause header or after "as" in a "with"
statement. The "import" statement of the form "from ... import *"
binds all names defined in the imported module, except those beginning
with an underscore. This form may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name). It
is illegal to unbind a name that is referenced by an enclosing scope;
the compiler will report a "SyntaxError".
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the global statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "__builtin__". The global namespace is searched first.
If the name is not found there, the builtins namespace is searched.
The global statement must precede all uses of the name.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module's dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "__builtin__" (note: no
's'); when in any other module, "__builtins__" is an alias for the
dictionary of the "__builtin__" module itself. "__builtins__" can be
set to a user-created dictionary to create a weak form of restricted
execution.
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "__builtin__" (no 's') module and modify its attributes
appropriately.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
A class definition is an executable statement that may use and define
names. These references follow the normal rules for name resolution.
The namespace of the class definition becomes the attribute dictionary
of the class. Names defined at the class scope are not visible in
methods.
Interaction with dynamic features
=================================
There are several cases where Python statements are illegal when used
in conjunction with nested scopes that contain free variables.
If a variable is referenced in an enclosing scope, it is illegal to
delete the name. An error will be reported at compile time.
If the wild card form of import --- "import *" --- is used in a
function and the function contains or is a nested block with free
variables, the compiler will raise a "SyntaxError".
If "exec" is used in a function and the function contains or is a
nested block with free variables, the compiler will raise a
"SyntaxError" unless the exec explicitly specifies the local namespace
for the "exec". (In other words, "exec obj" would be illegal, but
"exec obj in ns" would be legal.)
The "eval()", "execfile()", and "input()" functions and the "exec"
statement do not have access to the full environment for resolving
names. Names may be resolved in the local and global namespaces of
the caller. Free variables are not resolved in the nearest enclosing
namespace, but in the global namespace. [1] The "exec" statement and
the "eval()" and "execfile()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
t namings�
Numeric literals
****************
There are four types of numeric literals: plain integers, long
integers, floating point numbers, and imaginary numbers. There are no
complex literals (complex numbers can be formed by adding a real
number and an imaginary number).
Note that numeric literals do not include a sign; a phrase like "-1"
is actually an expression composed of the unary operator '"-"' and the
literal "1".
t numberssy
Emulating numeric types
***********************
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "//", "%", "divmod()", "pow()", "**",
"<<", ">>", "&", "^", "|"). For instance, to evaluate the
expression "x + y", where *x* is an instance of a class that has an
"__add__()" method, "x.__add__(y)" is called. The "__divmod__()"
method should be the equivalent to using "__floordiv__()" and
"__mod__()"; it should not be related to "__truediv__()" (described
below). Note that "__pow__()" should be defined to accept an
optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__div__(self, other)
object.__truediv__(self, other)
The division operator ("/") is implemented by these methods. The
"__truediv__()" method is used when "__future__.division" is in
effect, otherwise "__div__()" is used. If only one of these two
methods is defined, the object will not support division in the
alternate context; "TypeError" will be raised instead.
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rdiv__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other)
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "/", "%", "divmod()", "pow()", "**",
"<<", ">>", "&", "^", "|") with reflected (swapped) operands.
These functions are only called if the left operand does not
support the corresponding operation and the operands are of
different types. [2] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note: If the right operand's type is a subclass of the left
operand's type and that subclass provides the reflected method
for the operation, this method will be called before the left
operand's non-reflected method. This behavior allows subclasses
to override their ancestors' operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__idiv__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "/=", "//=", "%=", "**=", "<<=",
">>=", "&=", "^=", "|="). These methods should attempt to do the
operation in-place (modifying *self*) and return the result (which
could be, but does not have to be, *self*). If a specific method
is not defined, the augmented assignment falls back to the normal
methods. For instance, to execute the statement "x += y", where
*x* is an instance of a class that has an "__iadd__()" method,
"x.__iadd__(y)" is called. If *x* is an instance of a class that
does not define a "__iadd__()" method, "x.__add__(y)" and
"y.__radd__(x)" are considered, as with the evaluation of "x + y".
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__long__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()",
"long()", and "float()". Should return a value of the appropriate
type.
object.__oct__(self)
object.__hex__(self)
Called to implement the built-in functions "oct()" and "hex()".
Should return a string value.
object.__index__(self)
Called to implement "operator.index()". Also called whenever
Python needs an integer object (such as in slicing). Must return
an integer (int or long).
New in version 2.5.
object.__coerce__(self, other)
Called to implement "mixed-mode" numeric arithmetic. Should either
return a 2-tuple containing *self* and *other* converted to a
common numeric type, or "None" if conversion is impossible. When
the common type would be the type of "other", it is sufficient to
return "None", since the interpreter will also ask the other object
to attempt a coercion (but sometimes, if the implementation of the
other type cannot be changed, it is useful to do the conversion to
the other type here). A return value of "NotImplemented" is
equivalent to returning "None".
s
numeric-typessZ
Objects, values and types
*************************
*Objects* are Python's abstraction for data. All data in a Python
program is represented by objects or by relations between objects. (In
a sense, and in conformance to Von Neumann's model of a "stored
program computer," code is also represented by objects.)
Every object has an identity, a type and a value. An object's
*identity* never changes once it has been created; you may think of it
as the object's address in memory. The '"is"' operator compares the
identity of two objects; the "id()" function returns an integer
representing its identity (currently implemented as its address). An
object's *type* is also unchangeable. [1] An object's type determines
the operations that the object supports (e.g., "does it have a
length?") and also defines the possible values for objects of that
type. The "type()" function returns an object's type (which is an
object itself). The *value* of some objects can change. Objects
whose value can change are said to be *mutable*; objects whose value
is unchangeable once they are created are called *immutable*. (The
value of an immutable container object that contains a reference to a
mutable object can change when the latter's value is changed; however
the container is still considered immutable, because the collection of
objects it contains cannot be changed. So, immutability is not
strictly the same as having an unchangeable value, it is more subtle.)
An object's mutability is determined by its type; for instance,
numbers, strings and tuples are immutable, while dictionaries and
lists are mutable.
Objects are never explicitly destroyed; however, when they become
unreachable they may be garbage-collected. An implementation is
allowed to postpone garbage collection or omit it altogether --- it is
a matter of implementation quality how garbage collection is
implemented, as long as no objects are collected that are still
reachable.
**CPython implementation detail:** CPython currently uses a reference-
counting scheme with (optional) delayed detection of cyclically linked
garbage, which collects most objects as soon as they become
unreachable, but is not guaranteed to collect garbage containing
circular references. See the documentation of the "gc" module for
information on controlling the collection of cyclic garbage. Other
implementations act differently and CPython may change. Do not depend
on immediate finalization of objects when they become unreachable (ex:
always close files).
Note that the use of the implementation's tracing or debugging
facilities may keep objects alive that would normally be collectable.
Also note that catching an exception with a '"try"..."except"'
statement may keep objects alive.
Some objects contain references to "external" resources such as open
files or windows. It is understood that these resources are freed
when the object is garbage-collected, but since garbage collection is
not guaranteed to happen, such objects also provide an explicit way to
release the external resource, usually a "close()" method. Programs
are strongly recommended to explicitly close such objects. The
'"try"..."finally"' statement provides a convenient way to do this.
Some objects contain references to other objects; these are called
*containers*. Examples of containers are tuples, lists and
dictionaries. The references are part of a container's value. In
most cases, when we talk about the value of a container, we imply the
values, not the identities of the contained objects; however, when we
talk about the mutability of a container, only the identities of the
immediately contained objects are implied. So, if an immutable
container (like a tuple) contains a reference to a mutable object, its
value changes if that mutable object is changed.
Types affect almost all aspects of object behavior. Even the
importance of object identity is affected in some sense: for immutable
types, operations that compute new values may actually return a
reference to any existing object with the same type and value, while
for mutable objects this is not allowed. E.g., after "a = 1; b = 1",
"a" and "b" may or may not refer to the same object with the value
one, depending on the implementation, but after "c = []; d = []", "c"
and "d" are guaranteed to refer to two different, unique, newly
created empty lists. (Note that "c = d = []" assigns the same object
to both "c" and "d".)
t objectss
Operator precedence
*******************
The following table summarizes the operator precedences in Python,
from lowest precedence (least binding) to highest precedence (most
binding). Operators in the same box have the same precedence. Unless
the syntax is explicitly given, operators are binary. Operators in
the same box group left to right (except for comparisons, including
tests, which all have the same precedence and chain from left to right
--- see section Comparisons --- and exponentiation, which groups from
right to left).
+-------------------------------------------------+---------------------------------------+
| Operator | Description |
+=================================================+=======================================+
| "lambda" | Lambda expression |
+-------------------------------------------------+---------------------------------------+
| "if" -- "else" | Conditional expression |
+-------------------------------------------------+---------------------------------------+
| "or" | Boolean OR |
+-------------------------------------------------+---------------------------------------+
| "and" | Boolean AND |
+-------------------------------------------------+---------------------------------------+
| "not" "x" | Boolean NOT |
+-------------------------------------------------+---------------------------------------+
| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership |
| ">=", "<>", "!=", "==" | tests and identity tests |
+-------------------------------------------------+---------------------------------------+
| "|" | Bitwise OR |
+-------------------------------------------------+---------------------------------------+
| "^" | Bitwise XOR |
+-------------------------------------------------+---------------------------------------+
| "&" | Bitwise AND |
+-------------------------------------------------+---------------------------------------+
| "<<", ">>" | Shifts |
+-------------------------------------------------+---------------------------------------+
| "+", "-" | Addition and subtraction |
+-------------------------------------------------+---------------------------------------+
| "*", "/", "//", "%" | Multiplication, division, remainder |
| | [7] |
+-------------------------------------------------+---------------------------------------+
| "+x", "-x", "~x" | Positive, negative, bitwise NOT |
+-------------------------------------------------+---------------------------------------+
| "**" | Exponentiation [8] |
+-------------------------------------------------+---------------------------------------+
| "x[index]", "x[index:index]", | Subscription, slicing, call, |
| "x(arguments...)", "x.attribute" | attribute reference |
+-------------------------------------------------+---------------------------------------+
| "(expressions...)", "[expressions...]", "{key: | Binding or tuple display, list |
| value...}", "`expressions...`" | display, dictionary display, string |
| | conversion |
+-------------------------------------------------+---------------------------------------+
-[ Footnotes ]-
[1] In Python 2.3 and later releases, a list comprehension "leaks"
the control variables of each "for" it contains into the
containing scope. However, this behavior is deprecated, and
relying on it will not work in Python 3.
[2] While "abs(x%y) < abs(y)" is true mathematically, for floats
it may not be true numerically due to roundoff. For example, and
assuming a platform on which a Python float is an IEEE 754 double-
precision number, in order that "-1e-100 % 1e100" have the same
sign as "1e100", the computed result is "-1e-100 + 1e100", which
is numerically exactly equal to "1e100". The function
"math.fmod()" returns a result whose sign matches the sign of the
first argument instead, and so returns "-1e-100" in this case.
Which approach is more appropriate depends on the application.
[3] If x is very close to an exact integer multiple of y, it's
possible for "floor(x/y)" to be one larger than "(x-x%y)/y" due to
rounding. In such cases, Python returns the latter result, in
order to preserve that "divmod(x,y)[0] * y + x % y" be very close
to "x".
[4] The Unicode standard distinguishes between *code points* (e.g.
U+0041) and *abstract characters* (e.g. "LATIN CAPITAL LETTER A").
While most abstract characters in Unicode are only represented
using one code point, there is a number of abstract characters
that can in addition be represented using a sequence of more than
one code point. For example, the abstract character "LATIN
CAPITAL LETTER C WITH CEDILLA" can be represented as a single
*precomposed character* at code position U+00C7, or as a sequence
of a *base character* at code position U+0043 (LATIN CAPITAL
LETTER C), followed by a *combining character* at code position
U+0327 (COMBINING CEDILLA).
The comparison operators on unicode strings compare at the level
of Unicode code points. This may be counter-intuitive to humans.
For example, "u"\u00C7" == u"\u0043\u0327"" is "False", even
though both strings represent the same abstract character "LATIN
CAPITAL LETTER C WITH CEDILLA".
To compare strings at the level of abstract characters (that is,
in a way intuitive to humans), use "unicodedata.normalize()".
[5] Earlier versions of Python used lexicographic comparison of
the sorted (key, value) lists, but this was very expensive for the
common case of comparing for equality. An even earlier version of
Python compared dictionaries by identity only, but this caused
surprises because people expected to be able to test a dictionary
for emptiness by comparing it to "{}".
[6] Due to automatic garbage-collection, free lists, and the
dynamic nature of descriptors, you may notice seemingly unusual
behaviour in certain uses of the "is" operator, like those
involving comparisons between instance methods, or constants.
Check their documentation for more info.
[7] The "%" operator is also used for string formatting; the same
precedence applies.
[8] The power operator "**" binds less tightly than an arithmetic
or bitwise unary operator on its right, that is, "2**-1" is "0.5".
s operator-summarysx
The "pass" statement
********************
pass_stmt ::= "pass"
"pass" is a null operation --- when it is executed, nothing happens.
It is useful as a placeholder when a statement is required
syntactically, but no code needs to be executed, for example:
def f(arg): pass # a function that does nothing (yet)
class C: pass # a class with no methods (yet)
t passs�
The power operator
******************
The power operator binds more tightly than unary operators on its
left; it binds less tightly than unary operators on its right. The
syntax is:
power ::= primary ["**" u_expr]
Thus, in an unparenthesized sequence of power and unary operators, the
operators are evaluated from right to left (this does not constrain
the evaluation order for the operands): "-1**2" results in "-1".
The power operator has the same semantics as the built-in "pow()"
function, when called with two arguments: it yields its left argument
raised to the power of its right argument. The numeric arguments are
first converted to a common type. The result type is that of the
arguments after coercion.
With mixed operand types, the coercion rules for binary arithmetic
operators apply. For int and long int operands, the result has the
same type as the operands (after coercion) unless the second argument
is negative; in that case, all arguments are converted to float and a
float result is delivered. For example, "10**2" returns "100", but
"10**-2" returns "0.01". (This last feature was added in Python 2.2.
In Python 2.1 and before, if both arguments were of integer types and
the second argument was negative, an exception was raised).
Raising "0.0" to a negative power results in a "ZeroDivisionError".
Raising a negative number to a fractional power results in a
"ValueError".
t powers�
The "print" statement
*********************
print_stmt ::= "print" ([expression ("," expression)* [","]]
| ">>" expression [("," expression)+ [","]])
"print" evaluates each expression in turn and writes the resulting
object to standard output (see below). If an object is not a string,
it is first converted to a string using the rules for string
conversions. The (resulting or original) string is then written. A
space is written before each object is (converted and) written, unless
the output system believes it is positioned at the beginning of a
line. This is the case (1) when no characters have yet been written
to standard output, (2) when the last character written to standard
output is a whitespace character except "' '", or (3) when the last
write operation on standard output was not a "print" statement. (In
some cases it may be functional to write an empty string to standard
output for this reason.)
Note: Objects which act like file objects but which are not the
built-in file objects often do not properly emulate this aspect of
the file object's behavior, so it is best not to rely on this.
A "'\n'" character is written at the end, unless the "print" statement
ends with a comma. This is the only action if the statement contains
just the keyword "print".
Standard output is defined as the file object named "stdout" in the
built-in module "sys". If no such object exists, or if it does not
have a "write()" method, a "RuntimeError" exception is raised.
"print" also has an extended form, defined by the second portion of
the syntax described above. This form is sometimes referred to as
""print" chevron." In this form, the first expression after the ">>"
must evaluate to a "file-like" object, specifically an object that has
a "write()" method as described above. With this extended form, the
subsequent expressions are printed to this file object. If the first
expression evaluates to "None", then "sys.stdout" is used as the file
for output.
t prints�
The "raise" statement
*********************
raise_stmt ::= "raise" [expression ["," expression ["," expression]]]
If no expressions are present, "raise" re-raises the last exception
that was active in the current scope. If no exception is active in
the current scope, a "TypeError" exception is raised indicating that
this is an error (if running under IDLE, a "Queue.Empty" exception is
raised instead).
Otherwise, "raise" evaluates the expressions to get three objects,
using "None" as the value of omitted expressions. The first two
objects are used to determine the *type* and *value* of the exception.
If the first object is an instance, the type of the exception is the
class of the instance, the instance itself is the value, and the
second object must be "None".
If the first object is a class, it becomes the type of the exception.
The second object is used to determine the exception value: If it is
an instance of the class, the instance becomes the exception value. If
the second object is a tuple, it is used as the argument list for the
class constructor; if it is "None", an empty argument list is used,
and any other object is treated as a single argument to the
constructor. The instance so created by calling the constructor is
used as the exception value.
If a third object is present and not "None", it must be a traceback
object (see section The standard type hierarchy), and it is
substituted instead of the current location as the place where the
exception occurred. If the third object is present and not a
traceback object or "None", a "TypeError" exception is raised. The
three-expression form of "raise" is useful to re-raise an exception
transparently in an except clause, but "raise" with no expressions
should be preferred if the exception to be re-raised was the most
recently active exception in the current scope.
Additional information on exceptions can be found in section
Exceptions, and information about handling exceptions is in section
The try statement.
t raises�
The "return" statement
**********************
return_stmt ::= "return" [expression_list]
"return" may only occur syntactically nested in a function definition,
not within a nested class definition.
If an expression list is present, it is evaluated, else "None" is
substituted.
"return" leaves the current function call with the expression list (or
"None") as return value.
When "return" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
function.
In a generator function, the "return" statement is not allowed to
include an "expression_list". In that context, a bare "return"
indicates that the generator is done and will cause "StopIteration" to
be raised.
t returns�
Emulating container types
*************************
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. (For backwards compatibility, the method
"__getslice__()" (see below) can also be defined to handle simple, but
not extended slices.) It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "has_key()", "get()",
"clear()", "setdefault()", "iterkeys()", "itervalues()",
"iteritems()", "pop()", "popitem()", "copy()", and "update()" behaving
similar to those for Python's standard dictionary objects. The
"UserDict" module provides a "DictMixin" class to help create those
methods from a base set of "__getitem__()", "__setitem__()",
"__delitem__()", and "keys()". Mutable sequences should provide
methods "append()", "count()", "index()", "extend()", "insert()",
"pop()", "remove()", "reverse()" and "sort()", like Python standard
list objects. Finally, sequence types should implement addition
(meaning concatenation) and multiplication (meaning repetition) by
defining the methods "__add__()", "__radd__()", "__iadd__()",
"__mul__()", "__rmul__()" and "__imul__()" described below; they
should not define "__coerce__()" or other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should be equivalent of "has_key()"; for sequences,
it should search through the values. It is further recommended that
both mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should be the same as "iterkeys()"; for sequences, it should iterate
through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn't define a "__nonzero__()" method and whose "__len__()"
method returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__nonzero__()" method.
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note: "for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container, and should also be made
available as the method "iterkeys()".
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
New in version 2.6.
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a sequence. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don't define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
s sequence-typess
Shifting operations
*******************
The shifting operations have lower priority than the arithmetic
operations:
shift_expr ::= a_expr | shift_expr ( "<<" | ">>" ) a_expr
These operators accept plain or long integers as arguments. The
arguments are converted to a common type. They shift the first
argument to the left or right by the number of bits given by the
second argument.
A right shift by *n* bits is defined as division by "pow(2, n)". A
left shift by *n* bits is defined as multiplication with "pow(2, n)".
Negative shift counts raise a "ValueError" exception.
Note: In the current implementation, the right-hand operand is
required to be at most "sys.maxsize". If the right-hand operand is
larger than "sys.maxsize" an "OverflowError" exception is raised.
t shiftings�
Slicings
********
A slicing selects a range of items in a sequence object (e.g., a
string, tuple or list). Slicings may be used as expressions or as
targets in assignment or "del" statements. The syntax for a slicing:
slicing ::= simple_slicing | extended_slicing
simple_slicing ::= primary "[" short_slice "]"
extended_slicing ::= primary "[" slice_list "]"
slice_list ::= slice_item ("," slice_item)* [","]
slice_item ::= expression | proper_slice | ellipsis
proper_slice ::= short_slice | long_slice
short_slice ::= [lower_bound] ":" [upper_bound]
long_slice ::= short_slice ":" [stride]
lower_bound ::= expression
upper_bound ::= expression
stride ::= expression
ellipsis ::= "..."
There is ambiguity in the formal syntax here: anything that looks like
an expression list also looks like a slice list, so any subscription
can be interpreted as a slicing. Rather than further complicating the
syntax, this is disambiguated by defining that in this case the
interpretation as a subscription takes priority over the
interpretation as a slicing (this is the case if the slice list
contains no proper slice nor ellipses). Similarly, when the slice
list has exactly one short slice and no trailing comma, the
interpretation as a simple slicing takes priority over that as an
extended slicing.
The semantics for a simple slicing are as follows. The primary must
evaluate to a sequence object. The lower and upper bound expressions,
if present, must evaluate to plain integers; defaults are zero and the
"sys.maxint", respectively. If either bound is negative, the
sequence's length is added to it. The slicing now selects all items
with index *k* such that "i <= k < j" where *i* and *j* are the
specified lower and upper bounds. This may be an empty sequence. It
is not an error if *i* or *j* lie outside the range of valid indexes
(such items don't exist so they aren't selected).
The semantics for an extended slicing are as follows. The primary
must evaluate to a mapping object, and it is indexed with a key that
is constructed from the slice list, as follows. If the slice list
contains at least one comma, the key is a tuple containing the
conversion of the slice items; otherwise, the conversion of the lone
slice item is the key. The conversion of a slice item that is an
expression is that expression. The conversion of an ellipsis slice
item is the built-in "Ellipsis" object. The conversion of a proper
slice is a slice object (see section The standard type hierarchy)
whose "start", "stop" and "step" attributes are the values of the
expressions given as lower bound, upper bound and stride,
respectively, substituting "None" for missing expressions.
t slicingss�
Special Attributes
******************
The implementation adds a few special read-only attributes to several
object types, where they are relevant. Some of these are not reported
by the "dir()" built-in function.
object.__dict__
A dictionary or other mapping object used to store an object's
(writable) attributes.
object.__methods__
Deprecated since version 2.2: Use the built-in function "dir()" to
get a list of an object's attributes. This attribute is no longer
available.
object.__members__
Deprecated since version 2.2: Use the built-in function "dir()" to
get a list of an object's attributes. This attribute is no longer
available.
instance.__class__
The class to which a class instance belongs.
class.__bases__
The tuple of base classes of a class object.
definition.__name__
The name of the class, type, function, method, descriptor, or
generator instance.
The following attributes are only supported by *new-style class*es.
class.__mro__
This attribute is a tuple of classes that are considered when
looking for base classes during method resolution.
class.mro()
This method can be overridden by a metaclass to customize the
method resolution order for its instances. It is called at class
instantiation, and its result is stored in "__mro__".
class.__subclasses__()
Each new-style class keeps a list of weak references to its
immediate subclasses. This method returns a list of all those
references still alive. Example:
>>> int.__subclasses__()
[<type 'bool'>]
-[ Footnotes ]-
[1] Additional information on these special methods may be found
in the Python Reference Manual (Basic customization).
[2] As a consequence, the list "[1, 2]" is considered equal to
"[1.0, 2.0]", and similarly for tuples.
[3] They must have since the parser can't tell the type of the
operands.
[4] Cased characters are those with general category property
being one of "Lu" (Letter, uppercase), "Ll" (Letter, lowercase),
or "Lt" (Letter, titlecase).
[5] To format only a tuple you should therefore provide a
singleton tuple whose only element is the tuple to be formatted.
[6] The advantage of leaving the newline on is that returning an
empty string is then an unambiguous EOF indication. It is also
possible (in cases where it might matter, for example, if you want
to make an exact copy of a file while scanning its lines) to tell
whether the last line of a file ended in a newline or not (yes
this happens!).
t specialattrssa�
Special method names
********************
A class can implement certain operations that are invoked by special
syntax (such as arithmetic operations or subscripting and slicing) by
defining methods with special names. This is Python's approach to
*operator overloading*, allowing classes to define their own behavior
with respect to language operators. For instance, if a class defines
a method named "__getitem__()", and "x" is an instance of this class,
then "x[i]" is roughly equivalent to "x.__getitem__(i)" for old-style
classes and "type(x).__getitem__(x, i)" for new-style classes. Except
where mentioned, attempts to execute an operation raise an exception
when no appropriate method is defined (typically "AttributeError" or
"TypeError").
When implementing a class that emulates any built-in type, it is
important that the emulation only be implemented to the degree that it
makes sense for the object being modelled. For example, some
sequences may work well with retrieval of individual elements, but
extracting a slice may not make sense. (One example of this is the
"NodeList" interface in the W3C's Document Object Model.)
Basic customization
===================
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass's "__new__()" method using
"super(currentclass, cls).__new__(cls[, ...])" with appropriate
arguments and then modifying the newly-created instance as
necessary before returning it.
If "__new__()" returns an instance of *cls*, then the new
instance's "__init__()" method will be invoked like
"__init__(self[, ...])", where *self* is the new instance and the
remaining arguments are the same as were passed to "__new__()".
If "__new__()" does not return an instance of *cls*, then the new
instance's "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class's "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"BaseClass.__init__(self, [args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customise
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a destructor. If a base class has a "__del__()" method, the
derived class's "__del__()" method, if any, must explicitly call it
to ensure proper deletion of the base class part of the instance.
Note that it is possible (though not recommended!) for the
"__del__()" method to postpone destruction of the instance by
creating a new reference to it. It may then be called at a later
time when this new reference is deleted. It is not guaranteed that
"__del__()" methods are called for objects that still exist when
the interpreter exits.
Note: "del x" doesn't directly call "x.__del__()" --- the former
decrements the reference count for "x" by one, and the latter is
only called when "x"'s reference count reaches zero. Some common
situations that may prevent the reference count of an object from
going to zero include: circular references between objects (e.g.,
a doubly-linked list or a tree data structure with parent and
child pointers); a reference to the object on the stack frame of
a function that caught an exception (the traceback stored in
"sys.exc_traceback" keeps the stack frame alive); or a reference
to the object on the stack frame that raised an unhandled
exception in interactive mode (the traceback stored in
"sys.last_traceback" keeps the stack frame alive). The first
situation can only be remedied by explicitly breaking the cycles;
the latter two situations can be resolved by storing "None" in
"sys.exc_traceback" or "sys.last_traceback". Circular references
which are garbage are detected when the option cycle detector is
enabled (it's on by default), but can only be cleaned up if there
are no Python-level "__del__()" methods involved. Refer to the
documentation for the "gc" module for more information about how
"__del__()" methods are handled by the cycle detector,
particularly the description of the "garbage" value.
Warning: Due to the precarious circumstances under which
"__del__()" methods are invoked, exceptions that occur during
their execution are ignored, and a warning is printed to
"sys.stderr" instead. Also, when "__del__()" is invoked in
response to a module being deleted (e.g., when execution of the
program is done), other globals referenced by the "__del__()"
method may already have been deleted or in the process of being
torn down (e.g. the import machinery shutting down). For this
reason, "__del__()" methods should do the absolute minimum needed
to maintain external invariants. Starting with version 1.5,
Python guarantees that globals whose name begins with a single
underscore are deleted from their module before other globals are
deleted; if no other references to such globals exist, this may
help in assuring that imported modules are still available at the
time when the "__del__()" method is called.
See also the "-R" command-line option.
object.__repr__(self)
Called by the "repr()" built-in function and by string conversions
(reverse quotes) to compute the "official" string representation of
an object. If at all possible, this should look like a valid
Python expression that could be used to recreate an object with the
same value (given an appropriate environment). If this is not
possible, a string of the form "<...some useful description...>"
should be returned. The return value must be a string object. If a
class defines "__repr__()" but not "__str__()", then "__repr__()"
is also used when an "informal" string representation of instances
of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by the "str()" built-in function and by the "print"
statement to compute the "informal" string representation of an
object. This differs from "__repr__()" in that it does not have to
be a valid Python expression: a more convenient or concise
representation may be used instead. The return value must be a
string object.
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
New in version 2.1.
These are the so-called "rich comparison" methods, and are called
for comparison operators in preference to "__cmp__()" below. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" and "x<>y" call "x.__ne__(y)",
"x>y" calls "x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
There are no implied relationships among the comparison operators.
The truth of "x==y" does not imply that "x!=y" is false.
Accordingly, when defining "__eq__()", one should also define
"__ne__()" so that the operators will behave as expected. See the
paragraph on "__hash__()" for some important notes on creating
*hashable* objects which support custom comparison operations and
are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other's
reflection, "__le__()" and "__ge__()" are each other's reflection,
and "__eq__()" and "__ne__()" are their own reflection.
Arguments to rich comparison methods are never coerced.
To automatically generate ordering operations from a single root
operation, see "functools.total_ordering()".
object.__cmp__(self, other)
Called by comparison operations if rich comparison (see above) is
not defined. Should return a negative integer if "self < other",
zero if "self == other", a positive integer if "self > other". If
no "__cmp__()", "__eq__()" or "__ne__()" operation is defined,
class instances are compared by object identity ("address"). See
also the description of "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys. (Note: the
restriction that exceptions are not propagated by "__cmp__()" has
been removed since Python 1.5.)
object.__rcmp__(self, other)
Changed in version 2.1: No longer supported.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
If a class does not define a "__cmp__()" or "__eq__()" method it
should not define a "__hash__()" operation either; if it defines
"__cmp__()" or "__eq__()" but not "__hash__()", its instances will
not be usable in hashed collections. If a class defines mutable
objects and implements a "__cmp__()" or "__eq__()" method, it
should not implement "__hash__()", since hashable collection
implementations require that an object's hash value is immutable
(if the object's hash value changes, it will be in the wrong hash
bucket).
User-defined classes have "__cmp__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns a result derived from
"id(x)".
Classes which inherit a "__hash__()" method from a parent class but
change the meaning of "__cmp__()" or "__eq__()" such that the hash
value returned is no longer appropriate (e.g. by switching to a
value-based concept of equality instead of the default identity
based equality) can explicitly flag themselves as being unhashable
by setting "__hash__ = None" in the class definition. Doing so
means that not only will instances of the class raise an
appropriate "TypeError" when a program attempts to retrieve their
hash value, but they will also be correctly identified as
unhashable when checking "isinstance(obj, collections.Hashable)"
(unlike classes which define their own "__hash__()" to explicitly
raise "TypeError").
Changed in version 2.5: "__hash__()" may now also return a long
integer object; the 32-bit integer is then derived from the hash of
that object.
Changed in version 2.6: "__hash__" may now be set to "None" to
explicitly flag instances of a class as unhashable.
object.__nonzero__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True", or their integer
equivalents "0" or "1". When this method is not defined,
"__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__nonzero__()", all its instances are
considered true.
object.__unicode__(self)
Called to implement "unicode()" built-in; should return a Unicode
object. When this method is not defined, string conversion is
attempted, and the result of string conversion is converted to
Unicode using the system default encoding.
Customizing attribute access
============================
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when an attribute lookup has not found the attribute in the
usual places (i.e. it is not an instance attribute nor is it found
in the class tree for "self"). "name" is the attribute name. This
method should return the (computed) attribute value or raise an
"AttributeError" exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control in new-style classes.
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should not simply execute "self.name = value" --- this would cause
a recursive call to itself. Instead, it should insert the value in
the dictionary of instance attributes, e.g., "self.__dict__[name] =
value". For new-style classes, rather than accessing the instance
dictionary, it should call the base class method with the same
name, for example, "object.__setattr__(self, name, value)".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
More attribute access for new-style classes
-------------------------------------------
The following methods only apply to new-style classes.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note: This method may still be bypassed when looking up special
methods as the result of implicit invocation via language syntax
or built-in functions. See Special method lookup for new-style
classes.
Implementing Descriptors
------------------------
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner's class
dictionary or in the class dictionary for one of its parents). In the
examples below, "the attribute" refers to the attribute whose name is
the key of the property in the owner class' "__dict__".
object.__get__(self, instance, owner)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). *owner* is always the owner class, while *instance* is the
instance that the attribute was accessed through, or "None" when
the attribute is accessed through the *owner*. This method should
return the (computed) attribute value or raise an "AttributeError"
exception.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
Invoking Descriptors
--------------------
In general, a descriptor is an object attribute with "binding
behavior", one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object's dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called. Note that descriptors are only invoked for new
style objects or classes (ones that subclass "object()" or "type()").
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to a new-style object instance, "a.x" is transformed
into the call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a new-style class, "A.x" is transformed into the
call: "A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined. A descriptor can define
any combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object's
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
---------
By default, instances of both old and new-style classes have a
dictionary for attribute storage. This wastes space for objects
having very few instance variables. The space consumption can become
acute when creating large numbers of instances.
The default can be overridden by defining *__slots__* in a new-style
class definition. The *__slots__* declaration takes a sequence of
instance variables and reserves just enough space in each instance to
hold a value for each variable. Space is saved because *__dict__* is
not created for each instance.
__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. If defined in a
new-style class, *__slots__* reserves space for the declared
variables and prevents the automatic creation of *__dict__* and
*__weakref__* for each instance.
New in version 2.2.
Notes on using *__slots__*
* When inheriting from a class without *__slots__*, the *__dict__*
attribute of that class will always be accessible, so a *__slots__*
definition in the subclass is meaningless.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
Changed in version 2.3: Previously, adding "'__dict__'" to the
*__slots__* declaration would not enable the assignment of new
attributes not specifically listed in the sequence of instance
variable names.
* Without a *__weakref__* variable for each instance, classes
defining *__slots__* do not support weak references to its
instances. If weak reference support is needed, then add
"'__weakref__'" to the sequence of strings in the *__slots__*
declaration.
Changed in version 2.3: Previously, adding "'__weakref__'" to the
*__slots__* declaration would not enable support for weak
references.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is limited to the class
where it is defined. As a result, subclasses will have a *__dict__*
unless they also define *__slots__* (which must only contain names
of any *additional* slots).
* If a class defines a slot also defined in a base class, the
instance variable defined by the base class slot is inaccessible
(except by retrieving its descriptor directly from the base class).
This renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
"variable-length" built-in types such as "long", "str" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings
may also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
Changed in version 2.6: Previously, *__class__* assignment raised an
error if either new or old class had *__slots__*.
Customizing class creation
==========================
By default, new-style classes are constructed using "type()". A class
definition is read into a separate namespace and the value of class
name is bound to the result of "type(name, bases, dict)".
When the class definition is read, if *__metaclass__* is defined then
the callable assigned to it will be called instead of "type()". This
allows classes or functions to be written which monitor or alter the
class creation process:
* Modifying the class dictionary prior to the class being created.
* Returning an instance of another class -- essentially performing
the role of a factory function.
These steps will have to be performed in the metaclass's "__new__()"
method -- "type.__new__()" can then be called from this method to
create a class with different properties. This example adds a new
element to the class dictionary before creating the class:
class metacls(type):
def __new__(mcs, name, bases, dict):
dict['foo'] = 'metacls was here'
return type.__new__(mcs, name, bases, dict)
You can of course also override other class methods (or add new
methods); for example defining a custom "__call__()" method in the
metaclass allows custom behavior when the class is called, e.g. not
always creating a new instance.
__metaclass__
This variable can be any callable accepting arguments for "name",
"bases", and "dict". Upon class creation, the callable is used
instead of the built-in "type()".
New in version 2.2.
The appropriate metaclass is determined by the following precedence
rules:
* If "dict['__metaclass__']" exists, it is used.
* Otherwise, if there is at least one base class, its metaclass is
used (this looks for a *__class__* attribute first and if not found,
uses its type).
* Otherwise, if a global variable named __metaclass__ exists, it is
used.
* Otherwise, the old-style, classic metaclass (types.ClassType) is
used.
The potential uses for metaclasses are boundless. Some ideas that have
been explored including logging, interface checking, automatic
delegation, automatic property creation, proxies, frameworks, and
automatic resource locking/synchronization.
Customizing instance and subclass checks
========================================
New in version 2.6.
The following methods are used to override the default behavior of the
"isinstance()" and "issubclass()" built-in functions.
In particular, the metaclass "abc.ABCMeta" implements these methods in
order to allow the addition of Abstract Base Classes (ABCs) as
"virtual base classes" to any class or type (including built-in
types), including other ABCs.
class.__instancecheck__(self, instance)
Return true if *instance* should be considered a (direct or
indirect) instance of *class*. If defined, called to implement
"isinstance(instance, class)".
class.__subclasscheck__(self, subclass)
Return true if *subclass* should be considered a (direct or
indirect) subclass of *class*. If defined, called to implement
"issubclass(subclass, class)".
Note that these methods are looked up on the type (metaclass) of a
class. They cannot be defined as class methods in the actual class.
This is consistent with the lookup of special methods that are called
on instances, only in this case the instance is itself a class.
See also:
**PEP 3119** - Introducing Abstract Base Classes
Includes the specification for customizing "isinstance()" and
"issubclass()" behavior through "__instancecheck__()" and
"__subclasscheck__()", with motivation for this functionality in
the context of adding Abstract Base Classes (see the "abc"
module) to the language.
Emulating callable objects
==========================
object.__call__(self[, args...])
Called when the instance is "called" as a function; if this method
is defined, "x(arg1, arg2, ...)" is a shorthand for
"x.__call__(arg1, arg2, ...)".
Emulating container types
=========================
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. (For backwards compatibility, the method
"__getslice__()" (see below) can also be defined to handle simple, but
not extended slices.) It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "has_key()", "get()",
"clear()", "setdefault()", "iterkeys()", "itervalues()",
"iteritems()", "pop()", "popitem()", "copy()", and "update()" behaving
similar to those for Python's standard dictionary objects. The
"UserDict" module provides a "DictMixin" class to help create those
methods from a base set of "__getitem__()", "__setitem__()",
"__delitem__()", and "keys()". Mutable sequences should provide
methods "append()", "count()", "index()", "extend()", "insert()",
"pop()", "remove()", "reverse()" and "sort()", like Python standard
list objects. Finally, sequence types should implement addition
(meaning concatenation) and multiplication (meaning repetition) by
defining the methods "__add__()", "__radd__()", "__iadd__()",
"__mul__()", "__rmul__()" and "__imul__()" described below; they
should not define "__coerce__()" or other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should be equivalent of "has_key()"; for sequences,
it should search through the values. It is further recommended that
both mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should be the same as "iterkeys()"; for sequences, it should iterate
through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn't define a "__nonzero__()" method and whose "__len__()"
method returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__nonzero__()" method.
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note: "for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container, and should also be made
available as the method "iterkeys()".
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
New in version 2.6.
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a sequence. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don't define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
Additional methods for emulation of sequence types
==================================================
The following optional methods can be defined to further emulate
sequence objects. Immutable sequences methods should at most only
define "__getslice__()"; mutable sequences might define all three
methods.
object.__getslice__(self, i, j)
Deprecated since version 2.0: Support slice objects as parameters
to the "__getitem__()" method. (However, built-in types in CPython
currently still implement "__getslice__()". Therefore, you have to
override it in derived classes when implementing slicing.)
Called to implement evaluation of "self[i:j]". The returned object
should be of the same type as *self*. Note that missing *i* or *j*
in the slice expression are replaced by zero or "sys.maxsize",
respectively. If negative indexes are used in the slice, the
length of the sequence is added to that index. If the instance does
not implement the "__len__()" method, an "AttributeError" is
raised. No guarantee is made that indexes adjusted this way are not
still negative. Indexes which are greater than the length of the
sequence are not modified. If no "__getslice__()" is found, a slice
object is created instead, and passed to "__getitem__()" instead.
object.__setslice__(self, i, j, sequence)
Called to implement assignment to "self[i:j]". Same notes for *i*
and *j* as for "__getslice__()".
This method is deprecated. If no "__setslice__()" is found, or for
extended slicing of the form "self[i:j:k]", a slice object is
created, and passed to "__setitem__()", instead of "__setslice__()"
being called.
object.__delslice__(self, i, j)
Called to implement deletion of "self[i:j]". Same notes for *i* and
*j* as for "__getslice__()". This method is deprecated. If no
"__delslice__()" is found, or for extended slicing of the form
"self[i:j:k]", a slice object is created, and passed to
"__delitem__()", instead of "__delslice__()" being called.
Notice that these methods are only invoked when a single slice with a
single colon is used, and the slice method is available. For slice
operations involving extended slice notation, or in absence of the
slice methods, "__getitem__()", "__setitem__()" or "__delitem__()" is
called with a slice object as argument.
The following example demonstrate how to make your program or module
compatible with earlier versions of Python (assuming that methods
"__getitem__()", "__setitem__()" and "__delitem__()" support slice
objects as arguments):
class MyClass:
...
def __getitem__(self, index):
...
def __setitem__(self, index, value):
...
def __delitem__(self, index):
...
if sys.version_info < (2, 0):
# They won't be defined if version is at least 2.0 final
def __getslice__(self, i, j):
return self[max(0, i):max(0, j):]
def __setslice__(self, i, j, seq):
self[max(0, i):max(0, j):] = seq
def __delslice__(self, i, j):
del self[max(0, i):max(0, j):]
...
Note the calls to "max()"; these are necessary because of the handling
of negative indices before the "__*slice__()" methods are called.
When negative indexes are used, the "__*item__()" methods receive them
as provided, but the "__*slice__()" methods get a "cooked" form of the
index values. For each negative index value, the length of the
sequence is added to the index before calling the method (which may
still result in a negative index); this is the customary handling of
negative indexes by the built-in sequence types, and the "__*item__()"
methods are expected to do this as well. However, since they should
already be doing that, negative indexes cannot be passed in; they must
be constrained to the bounds of the sequence before being passed to
the "__*item__()" methods. Calling "max(0, i)" conveniently returns
the proper value.
Emulating numeric types
=======================
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "//", "%", "divmod()", "pow()", "**",
"<<", ">>", "&", "^", "|"). For instance, to evaluate the
expression "x + y", where *x* is an instance of a class that has an
"__add__()" method, "x.__add__(y)" is called. The "__divmod__()"
method should be the equivalent to using "__floordiv__()" and
"__mod__()"; it should not be related to "__truediv__()" (described
below). Note that "__pow__()" should be defined to accept an
optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__div__(self, other)
object.__truediv__(self, other)
The division operator ("/") is implemented by these methods. The
"__truediv__()" method is used when "__future__.division" is in
effect, otherwise "__div__()" is used. If only one of these two
methods is defined, the object will not support division in the
alternate context; "TypeError" will be raised instead.
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rdiv__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other)
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "/", "%", "divmod()", "pow()", "**",
"<<", ">>", "&", "^", "|") with reflected (swapped) operands.
These functions are only called if the left operand does not
support the corresponding operation and the operands are of
different types. [2] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note: If the right operand's type is a subclass of the left
operand's type and that subclass provides the reflected method
for the operation, this method will be called before the left
operand's non-reflected method. This behavior allows subclasses
to override their ancestors' operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__idiv__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "/=", "//=", "%=", "**=", "<<=",
">>=", "&=", "^=", "|="). These methods should attempt to do the
operation in-place (modifying *self*) and return the result (which
could be, but does not have to be, *self*). If a specific method
is not defined, the augmented assignment falls back to the normal
methods. For instance, to execute the statement "x += y", where
*x* is an instance of a class that has an "__iadd__()" method,
"x.__iadd__(y)" is called. If *x* is an instance of a class that
does not define a "__iadd__()" method, "x.__add__(y)" and
"y.__radd__(x)" are considered, as with the evaluation of "x + y".
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__long__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()",
"long()", and "float()". Should return a value of the appropriate
type.
object.__oct__(self)
object.__hex__(self)
Called to implement the built-in functions "oct()" and "hex()".
Should return a string value.
object.__index__(self)
Called to implement "operator.index()". Also called whenever
Python needs an integer object (such as in slicing). Must return
an integer (int or long).
New in version 2.5.
object.__coerce__(self, other)
Called to implement "mixed-mode" numeric arithmetic. Should either
return a 2-tuple containing *self* and *other* converted to a
common numeric type, or "None" if conversion is impossible. When
the common type would be the type of "other", it is sufficient to
return "None", since the interpreter will also ask the other object
to attempt a coercion (but sometimes, if the implementation of the
other type cannot be changed, it is useful to do the conversion to
the other type here). A return value of "NotImplemented" is
equivalent to returning "None".
Coercion rules
==============
This section used to document the rules for coercion. As the language
has evolved, the coercion rules have become hard to document
precisely; documenting what one version of one particular
implementation does is undesirable. Instead, here are some informal
guidelines regarding coercion. In Python 3, coercion will not be
supported.
* If the left operand of a % operator is a string or Unicode object,
no coercion takes place and the string formatting operation is
invoked instead.
* It is no longer recommended to define a coercion operation. Mixed-
mode operations on types that don't define coercion pass the
original arguments to the operation.
* New-style classes (those derived from "object") never invoke the
"__coerce__()" method in response to a binary operator; the only
time "__coerce__()" is invoked is when the built-in function
"coerce()" is called.
* For most intents and purposes, an operator that returns
"NotImplemented" is treated the same as one that is not implemented
at all.
* Below, "__op__()" and "__rop__()" are used to signify the generic
method names corresponding to an operator; "__iop__()" is used for
the corresponding in-place operator. For example, for the operator
'"+"', "__add__()" and "__radd__()" are used for the left and right
variant of the binary operator, and "__iadd__()" for the in-place
variant.
* For objects *x* and *y*, first "x.__op__(y)" is tried. If this is
not implemented or returns "NotImplemented", "y.__rop__(x)" is
tried. If this is also not implemented or returns "NotImplemented",
a "TypeError" exception is raised. But see the following exception:
* Exception to the previous item: if the left operand is an instance
of a built-in type or a new-style class, and the right operand is an
instance of a proper subclass of that type or class and overrides
the base's "__rop__()" method, the right operand's "__rop__()"
method is tried *before* the left operand's "__op__()" method.
This is done so that a subclass can completely override binary
operators. Otherwise, the left operand's "__op__()" method would
always accept the right operand: when an instance of a given class
is expected, an instance of a subclass of that class is always
acceptable.
* When either operand type defines a coercion, this coercion is
called before that type's "__op__()" or "__rop__()" method is
called, but no sooner. If the coercion returns an object of a
different type for the operand whose coercion is invoked, part of
the process is redone using the new object.
* When an in-place operator (like '"+="') is used, if the left
operand implements "__iop__()", it is invoked without any coercion.
When the operation falls back to "__op__()" and/or "__rop__()", the
normal coercion rules apply.
* In "x + y", if *x* is a sequence that implements sequence
concatenation, sequence concatenation is invoked.
* In "x * y", if one operand is a sequence that implements sequence
repetition, and the other is an integer ("int" or "long"), sequence
repetition is invoked.
* Rich comparisons (implemented by methods "__eq__()" and so on)
never use coercion. Three-way comparison (implemented by
"__cmp__()") does use coercion under the same conditions as other
binary operations use it.
* In the current implementation, the built-in numeric types "int",
"long", "float", and "complex" do not use coercion. All these types
implement a "__coerce__()" method, for use by the built-in
"coerce()" function.
Changed in version 2.7: The complex type no longer makes implicit
calls to the "__coerce__()" method for mixed-type binary arithmetic
operations.
With Statement Context Managers
===============================
New in version 2.5.
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method's return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller's responsibility.
See also:
**PEP 343** - The "with" statement
The specification, background, and examples for the Python "with"
statement.
Special method lookup for old-style classes
===========================================
For old-style classes, special methods are always looked up in exactly
the same way as any other method or attribute. This is the case
regardless of whether the method is being looked up explicitly as in
"x.__getitem__(i)" or implicitly as in "x[i]".
This behaviour means that special methods may exhibit different
behaviour for different instances of a single old-style class if the
appropriate special attributes are set differently:
>>> class C:
... pass
...
>>> c1 = C()
>>> c2 = C()
>>> c1.__len__ = lambda: 5
>>> c2.__len__ = lambda: 9
>>> len(c1)
5
>>> len(c2)
9
Special method lookup for new-style classes
===========================================
For new-style classes, implicit invocations of special methods are
only guaranteed to work correctly if defined on an object's type, not
in the object's instance dictionary. That behaviour is the reason why
the following code raises an exception (unlike the equivalent example
with old-style classes):
>>> class C(object):
... pass
...
>>> c = C()
>>> c.__len__ = lambda: 5
>>> len(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: object of type 'C' has no len()
The rationale behind this behaviour lies with a number of special
methods such as "__hash__()" and "__repr__()" that are implemented by
all objects, including type objects. If the implicit lookup of these
methods used the conventional lookup process, they would fail when
invoked on the type object itself:
>>> 1 .__hash__() == hash(1)
True
>>> int.__hash__() == hash(int)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: descriptor '__hash__' of 'int' object needs an argument
Incorrectly attempting to invoke an unbound method of a class in this
way is sometimes referred to as 'metaclass confusion', and is avoided
by bypassing the instance when looking up special methods:
>>> type(1).__hash__(1) == hash(1)
True
>>> type(int).__hash__(int) == hash(int)
True
In addition to bypassing any instance attributes in the interest of
correctness, implicit special method lookup generally also bypasses
the "__getattribute__()" method even of the object's metaclass:
>>> class Meta(type):
... def __getattribute__(*args):
... print "Metaclass getattribute invoked"
... return type.__getattribute__(*args)
...
>>> class C(object):
... __metaclass__ = Meta
... def __len__(self):
... return 10
... def __getattribute__(*args):
... print "Class getattribute invoked"
... return object.__getattribute__(*args)
...
>>> c = C()
>>> c.__len__() # Explicit lookup via instance
Class getattribute invoked
10
>>> type(c).__len__(c) # Explicit lookup via type
Metaclass getattribute invoked
10
>>> len(c) # Implicit lookup
10
Bypassing the "__getattribute__()" machinery in this fashion provides
significant scope for speed optimisations within the interpreter, at
the cost of some flexibility in the handling of special methods (the
special method *must* be set on the class object itself in order to be
consistently invoked by the interpreter).
-[ Footnotes ]-
[1] It *is* possible in some cases to change an object's type,
under certain controlled conditions. It generally isn't a good
idea though, since it can lead to some very strange behaviour if
it is handled incorrectly.
[2] For operands of the same type, it is assumed that if the non-
reflected method (such as "__add__()") fails the operation is not
supported, which is why the reflected method is not called.
t specialnamess�K
String Methods
**************
Below are listed the string methods which both 8-bit strings and
Unicode objects support. Some of them are also available on
"bytearray" objects.
In addition, Python's strings support the sequence type methods
described in the Sequence Types --- str, unicode, list, tuple,
bytearray, buffer, xrange section. To output formatted strings use
template strings or the "%" operator described in the String
Formatting Operations section. Also, see the "re" module for string
functions based on regular expressions.
str.capitalize()
Return a copy of the string with its first character capitalized
and the rest lowercased.
For 8-bit strings, this method is locale-dependent.
str.center(width[, fillchar])
Return centered in a string of length *width*. Padding is done
using the specified *fillchar* (default is a space).
Changed in version 2.4: Support for the *fillchar* argument.
str.count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring *sub*
in the range [*start*, *end*]. Optional arguments *start* and
*end* are interpreted as in slice notation.
str.decode([encoding[, errors]])
Decodes the string using the codec registered for *encoding*.
*encoding* defaults to the default string encoding. *errors* may
be given to set a different error handling scheme. The default is
"'strict'", meaning that encoding errors raise "UnicodeError".
Other possible values are "'ignore'", "'replace'" and any other
name registered via "codecs.register_error()", see section Codec
Base Classes.
New in version 2.2.
Changed in version 2.3: Support for other error handling schemes
added.
Changed in version 2.7: Support for keyword arguments added.
str.encode([encoding[, errors]])
Return an encoded version of the string. Default encoding is the
current default string encoding. *errors* may be given to set a
different error handling scheme. The default for *errors* is
"'strict'", meaning that encoding errors raise a "UnicodeError".
Other possible values are "'ignore'", "'replace'",
"'xmlcharrefreplace'", "'backslashreplace'" and any other name
registered via "codecs.register_error()", see section Codec Base
Classes. For a list of possible encodings, see section Standard
Encodings.
New in version 2.0.
Changed in version 2.3: Support for "'xmlcharrefreplace'" and
"'backslashreplace'" and other error handling schemes added.
Changed in version 2.7: Support for keyword arguments added.
str.endswith(suffix[, start[, end]])
Return "True" if the string ends with the specified *suffix*,
otherwise return "False". *suffix* can also be a tuple of suffixes
to look for. With optional *start*, test beginning at that
position. With optional *end*, stop comparing at that position.
Changed in version 2.5: Accept tuples as *suffix*.
str.expandtabs([tabsize])
Return a copy of the string where all tab characters are replaced
by one or more spaces, depending on the current column and the
given tab size. Tab positions occur every *tabsize* characters
(default is 8, giving tab positions at columns 0, 8, 16 and so on).
To expand the string, the current column is set to zero and the
string is examined character by character. If the character is a
tab ("\t"), one or more space characters are inserted in the result
until the current column is equal to the next tab position. (The
tab character itself is not copied.) If the character is a newline
("\n") or return ("\r"), it is copied and the current column is
reset to zero. Any other character is copied unchanged and the
current column is incremented by one regardless of how the
character is represented when printed.
>>> '01\t012\t0123\t01234'.expandtabs()
'01 012 0123 01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01 012 0123 01234'
str.find(sub[, start[, end]])
Return the lowest index in the string where substring *sub* is
found within the slice "s[start:end]". Optional arguments *start*
and *end* are interpreted as in slice notation. Return "-1" if
*sub* is not found.
Note: The "find()" method should be used only if you need to know
the position of *sub*. To check if *sub* is a substring or not,
use the "in" operator:
>>> 'Py' in 'Python'
True
str.format(*args, **kwargs)
Perform a string formatting operation. The string on which this
method is called can contain literal text or replacement fields
delimited by braces "{}". Each replacement field contains either
the numeric index of a positional argument, or the name of a
keyword argument. Returns a copy of the string where each
replacement field is replaced with the string value of the
corresponding argument.
>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'
See Format String Syntax for a description of the various
formatting options that can be specified in format strings.
This method of string formatting is the new standard in Python 3,
and should be preferred to the "%" formatting described in String
Formatting Operations in new code.
New in version 2.6.
str.index(sub[, start[, end]])
Like "find()", but raise "ValueError" when the substring is not
found.
str.isalnum()
Return true if all characters in the string are alphanumeric and
there is at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isalpha()
Return true if all characters in the string are alphabetic and
there is at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isdigit()
Return true if all characters in the string are digits and there is
at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.islower()
Return true if all cased characters [4] in the string are lowercase
and there is at least one cased character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isspace()
Return true if there are only whitespace characters in the string
and there is at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.istitle()
Return true if the string is a titlecased string and there is at
least one character, for example uppercase characters may only
follow uncased characters and lowercase characters only cased ones.
Return false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isupper()
Return true if all cased characters [4] in the string are uppercase
and there is at least one cased character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.join(iterable)
Return a string which is the concatenation of the strings in
*iterable*. A "TypeError" will be raised if there are any non-
string values in *iterable*, including "bytes" objects. The
separator between elements is the string providing this method.
str.ljust(width[, fillchar])
Return the string left justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is a
space). The original string is returned if *width* is less than or
equal to "len(s)".
Changed in version 2.4: Support for the *fillchar* argument.
str.lower()
Return a copy of the string with all the cased characters [4]
converted to lowercase.
For 8-bit strings, this method is locale-dependent.
str.lstrip([chars])
Return a copy of the string with leading characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a prefix; rather,
all combinations of its values are stripped:
>>> ' spacious '.lstrip()
'spacious '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'
Changed in version 2.2.2: Support for the *chars* argument.
str.partition(sep)
Split the string at the first occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing the string itself, followed by
two empty strings.
New in version 2.5.
str.replace(old, new[, count])
Return a copy of the string with all occurrences of substring *old*
replaced by *new*. If the optional argument *count* is given, only
the first *count* occurrences are replaced.
str.rfind(sub[, start[, end]])
Return the highest index in the string where substring *sub* is
found, such that *sub* is contained within "s[start:end]".
Optional arguments *start* and *end* are interpreted as in slice
notation. Return "-1" on failure.
str.rindex(sub[, start[, end]])
Like "rfind()" but raises "ValueError" when the substring *sub* is
not found.
str.rjust(width[, fillchar])
Return the string right justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is a
space). The original string is returned if *width* is less than or
equal to "len(s)".
Changed in version 2.4: Support for the *fillchar* argument.
str.rpartition(sep)
Split the string at the last occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing two empty strings, followed by
the string itself.
New in version 2.5.
str.rsplit([sep[, maxsplit]])
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit* splits
are done, the *rightmost* ones. If *sep* is not specified or
"None", any whitespace string is a separator. Except for splitting
from the right, "rsplit()" behaves like "split()" which is
described in detail below.
New in version 2.4.
str.rstrip([chars])
Return a copy of the string with trailing characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a suffix; rather,
all combinations of its values are stripped:
>>> ' spacious '.rstrip()
' spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'
Changed in version 2.2.2: Support for the *chars* argument.
str.split([sep[, maxsplit]])
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit*
splits are done (thus, the list will have at most "maxsplit+1"
elements). If *maxsplit* is not specified or "-1", then there is
no limit on the number of splits (all possible splits are made).
If *sep* is given, consecutive delimiters are not grouped together
and are deemed to delimit empty strings (for example,
"'1,,2'.split(',')" returns "['1', '', '2']"). The *sep* argument
may consist of multiple characters (for example,
"'1<>2<>3'.split('<>')" returns "['1', '2', '3']"). Splitting an
empty string with a specified separator returns "['']".
If *sep* is not specified or is "None", a different splitting
algorithm is applied: runs of consecutive whitespace are regarded
as a single separator, and the result will contain no empty strings
at the start or end if the string has leading or trailing
whitespace. Consequently, splitting an empty string or a string
consisting of just whitespace with a "None" separator returns "[]".
For example, "' 1 2 3 '.split()" returns "['1', '2', '3']", and
"' 1 2 3 '.split(None, 1)" returns "['1', '2 3 ']".
str.splitlines([keepends])
Return a list of the lines in the string, breaking at line
boundaries. This method uses the *universal newlines* approach to
splitting lines. Line breaks are not included in the resulting list
unless *keepends* is given and true.
Python recognizes ""\r"", ""\n"", and ""\r\n"" as line boundaries
for 8-bit strings.
For example:
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
['ab c', '', 'de fg', 'kl']
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(True)
['ab c\n', '\n', 'de fg\r', 'kl\r\n']
Unlike "split()" when a delimiter string *sep* is given, this
method returns an empty list for the empty string, and a terminal
line break does not result in an extra line:
>>> "".splitlines()
[]
>>> "One line\n".splitlines()
['One line']
For comparison, "split('\n')" gives:
>>> ''.split('\n')
['']
>>> 'Two lines\n'.split('\n')
['Two lines', '']
unicode.splitlines([keepends])
Return a list of the lines in the string, like "str.splitlines()".
However, the Unicode method splits on the following line
boundaries, which are a superset of the *universal newlines*
recognized for 8-bit strings.
+-------------------------+-------------------------------+
| Representation | Description |
+=========================+===============================+
| "\n" | Line Feed |
+-------------------------+-------------------------------+
| "\r" | Carriage Return |
+-------------------------+-------------------------------+
| "\r\n" | Carriage Return + Line Feed |
+-------------------------+-------------------------------+
| "\v" or "\x0b" | Line Tabulation |
+-------------------------+-------------------------------+
| "\f" or "\x0c" | Form Feed |
+-------------------------+-------------------------------+
| "\x1c" | File Separator |
+-------------------------+-------------------------------+
| "\x1d" | Group Separator |
+-------------------------+-------------------------------+
| "\x1e" | Record Separator |
+-------------------------+-------------------------------+
| "\x85" | Next Line (C1 Control Code) |
+-------------------------+-------------------------------+
| "\u2028" | Line Separator |
+-------------------------+-------------------------------+
| "\u2029" | Paragraph Separator |
+-------------------------+-------------------------------+
Changed in version 2.7: "\v" and "\f" added to list of line
boundaries.
str.startswith(prefix[, start[, end]])
Return "True" if string starts with the *prefix*, otherwise return
"False". *prefix* can also be a tuple of prefixes to look for.
With optional *start*, test string beginning at that position.
With optional *end*, stop comparing string at that position.
Changed in version 2.5: Accept tuples as *prefix*.
str.strip([chars])
Return a copy of the string with the leading and trailing
characters removed. The *chars* argument is a string specifying the
set of characters to be removed. If omitted or "None", the *chars*
argument defaults to removing whitespace. The *chars* argument is
not a prefix or suffix; rather, all combinations of its values are
stripped:
>>> ' spacious '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'
Changed in version 2.2.2: Support for the *chars* argument.
str.swapcase()
Return a copy of the string with uppercase characters converted to
lowercase and vice versa.
For 8-bit strings, this method is locale-dependent.
str.title()
Return a titlecased version of the string where words start with an
uppercase character and the remaining characters are lowercase.
The algorithm uses a simple language-independent definition of a
word as groups of consecutive letters. The definition works in
many contexts but it means that apostrophes in contractions and
possessives form word boundaries, which may not be the desired
result:
>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"
A workaround for apostrophes can be constructed using regular
expressions:
>>> import re
>>> def titlecase(s):
... return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
... lambda mo: mo.group(0)[0].upper() +
... mo.group(0)[1:].lower(),
... s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."
For 8-bit strings, this method is locale-dependent.
str.translate(table[, deletechars])
Return a copy of the string where all characters occurring in the
optional argument *deletechars* are removed, and the remaining
characters have been mapped through the given translation table,
which must be a string of length 256.
You can use the "maketrans()" helper function in the "string"
module to create a translation table. For string objects, set the
*table* argument to "None" for translations that only delete
characters:
>>> 'read this short text'.translate(None, 'aeiou')
'rd ths shrt txt'
New in version 2.6: Support for a "None" *table* argument.
For Unicode objects, the "translate()" method does not accept the
optional *deletechars* argument. Instead, it returns a copy of the
*s* where all characters have been mapped through the given
translation table which must be a mapping of Unicode ordinals to
Unicode ordinals, Unicode strings or "None". Unmapped characters
are left untouched. Characters mapped to "None" are deleted. Note,
a more flexible approach is to create a custom character mapping
codec using the "codecs" module (see "encodings.cp1251" for an
example).
str.upper()
Return a copy of the string with all the cased characters [4]
converted to uppercase. Note that "str.upper().isupper()" might be
"False" if "s" contains uncased characters or if the Unicode
category of the resulting character(s) is not "Lu" (Letter,
uppercase), but e.g. "Lt" (Letter, titlecase).
For 8-bit strings, this method is locale-dependent.
str.zfill(width)
Return the numeric string left filled with zeros in a string of
length *width*. A sign prefix is handled correctly. The original
string is returned if *width* is less than or equal to "len(s)".
New in version 2.2.2.
The following methods are present only on unicode objects:
unicode.isnumeric()
Return "True" if there are only numeric characters in S, "False"
otherwise. Numeric characters include digit characters, and all
characters that have the Unicode numeric value property, e.g.
U+2155, VULGAR FRACTION ONE FIFTH.
unicode.isdecimal()
Return "True" if there are only decimal characters in S, "False"
otherwise. Decimal characters include digit characters, and all
characters that can be used to form decimal-radix numbers, e.g.
U+0660, ARABIC-INDIC DIGIT ZERO.
s string-methodssF
String literals
***************
String literals are described by the following lexical definitions:
stringliteral ::= [stringprefix](shortstring | longstring)
stringprefix ::= "r" | "u" | "ur" | "R" | "U" | "UR" | "Ur" | "uR"
| "b" | "B" | "br" | "Br" | "bR" | "BR"
shortstring ::= "'" shortstringitem* "'" | '"' shortstringitem* '"'
longstring ::= "'''" longstringitem* "'''"
| '"""' longstringitem* '"""'
shortstringitem ::= shortstringchar | escapeseq
longstringitem ::= longstringchar | escapeseq
shortstringchar ::= <any source character except "\" or newline or the quote>
longstringchar ::= <any source character except "\">
escapeseq ::= "\" <any ASCII character>
One syntactic restriction not indicated by these productions is that
whitespace is not allowed between the "stringprefix" and the rest of
the string literal. The source character set is defined by the
encoding declaration; it is ASCII if no encoding declaration is given
in the source file; see section Encoding declarations.
In plain English: String literals can be enclosed in matching single
quotes ("'") or double quotes ("""). They can also be enclosed in
matching groups of three single or double quotes (these are generally
referred to as *triple-quoted strings*). The backslash ("\")
character is used to escape characters that otherwise have a special
meaning, such as newline, backslash itself, or the quote character.
String literals may optionally be prefixed with a letter "'r'" or
"'R'"; such strings are called *raw strings* and use different rules
for interpreting backslash escape sequences. A prefix of "'u'" or
"'U'" makes the string a Unicode string. Unicode strings use the
Unicode character set as defined by the Unicode Consortium and ISO
10646. Some additional escape sequences, described below, are
available in Unicode strings. A prefix of "'b'" or "'B'" is ignored in
Python 2; it indicates that the literal should become a bytes literal
in Python 3 (e.g. when code is automatically converted with 2to3). A
"'u'" or "'b'" prefix may be followed by an "'r'" prefix.
In triple-quoted strings, unescaped newlines and quotes are allowed
(and are retained), except that three unescaped quotes in a row
terminate the string. (A "quote" is the character used to open the
string, i.e. either "'" or """.)
Unless an "'r'" or "'R'" prefix is present, escape sequences in
strings are interpreted according to rules similar to those used by
Standard C. The recognized escape sequences are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
+===================+===================================+=========+
| "\newline" | Ignored | |
+-------------------+-----------------------------------+---------+
| "\\" | Backslash ("\") | |
+-------------------+-----------------------------------+---------+
| "\'" | Single quote ("'") | |
+-------------------+-----------------------------------+---------+
| "\"" | Double quote (""") | |
+-------------------+-----------------------------------+---------+
| "\a" | ASCII Bell (BEL) | |
+-------------------+-----------------------------------+---------+
| "\b" | ASCII Backspace (BS) | |
+-------------------+-----------------------------------+---------+
| "\f" | ASCII Formfeed (FF) | |
+-------------------+-----------------------------------+---------+
| "\n" | ASCII Linefeed (LF) | |
+-------------------+-----------------------------------+---------+
| "\N{name}" | Character named *name* in the | |
| | Unicode database (Unicode only) | |
+-------------------+-----------------------------------+---------+
| "\r" | ASCII Carriage Return (CR) | |
+-------------------+-----------------------------------+---------+
| "\t" | ASCII Horizontal Tab (TAB) | |
+-------------------+-----------------------------------+---------+
| "\uxxxx" | Character with 16-bit hex value | (1) |
| | *xxxx* (Unicode only) | |
+-------------------+-----------------------------------+---------+
| "\Uxxxxxxxx" | Character with 32-bit hex value | (2) |
| | *xxxxxxxx* (Unicode only) | |
+-------------------+-----------------------------------+---------+
| "\v" | ASCII Vertical Tab (VT) | |
+-------------------+-----------------------------------+---------+
| "\ooo" | Character with octal value *ooo* | (3,5) |
+-------------------+-----------------------------------+---------+
| "\xhh" | Character with hex value *hh* | (4,5) |
+-------------------+-----------------------------------+---------+
Notes:
1. Individual code units which form parts of a surrogate pair can
be encoded using this escape sequence.
2. Any Unicode character can be encoded this way, but characters
outside the Basic Multilingual Plane (BMP) will be encoded using a
surrogate pair if Python is compiled to use 16-bit code units (the
default).
3. As in Standard C, up to three octal digits are accepted.
4. Unlike in Standard C, exactly two hex digits are required.
5. In a string literal, hexadecimal and octal escapes denote the
byte with the given value; it is not necessary that the byte
encodes a character in the source character set. In a Unicode
literal, these escapes denote a Unicode character with the given
value.
Unlike Standard C, all unrecognized escape sequences are left in the
string unchanged, i.e., *the backslash is left in the string*. (This
behavior is useful when debugging: if an escape sequence is mistyped,
the resulting output is more easily recognized as broken.) It is also
important to note that the escape sequences marked as "(Unicode only)"
in the table above fall into the category of unrecognized escapes for
non-Unicode string literals.
When an "'r'" or "'R'" prefix is present, a character following a
backslash is included in the string without change, and *all
backslashes are left in the string*. For example, the string literal
"r"\n"" consists of two characters: a backslash and a lowercase "'n'".
String quotes can be escaped with a backslash, but the backslash
remains in the string; for example, "r"\""" is a valid string literal
consisting of two characters: a backslash and a double quote; "r"\""
is not a valid string literal (even a raw string cannot end in an odd
number of backslashes). Specifically, *a raw string cannot end in a
single backslash* (since the backslash would escape the following
quote character). Note also that a single backslash followed by a
newline is interpreted as those two characters as part of the string,
*not* as a line continuation.
When an "'r'" or "'R'" prefix is used in conjunction with a "'u'" or
"'U'" prefix, then the "\uXXXX" and "\UXXXXXXXX" escape sequences are
processed while *all other backslashes are left in the string*. For
example, the string literal "ur"\u0062\n"" consists of three Unicode
characters: 'LATIN SMALL LETTER B', 'REVERSE SOLIDUS', and 'LATIN
SMALL LETTER N'. Backslashes can be escaped with a preceding
backslash; however, both remain in the string. As a result, "\uXXXX"
escape sequences are only recognized when there are an odd number of
backslashes.
t stringss
Subscriptions
*************
A subscription selects an item of a sequence (string, tuple or list)
or mapping (dictionary) object:
subscription ::= primary "[" expression_list "]"
The primary must evaluate to an object of a sequence or mapping type.
If the primary is a mapping, the expression list must evaluate to an
object whose value is one of the keys of the mapping, and the
subscription selects the value in the mapping that corresponds to that
key. (The expression list is a tuple except if it has exactly one
item.)
If the primary is a sequence, the expression (list) must evaluate to a
plain integer. If this value is negative, the length of the sequence
is added to it (so that, e.g., "x[-1]" selects the last item of "x".)
The resulting value must be a nonnegative integer less than the number
of items in the sequence, and the subscription selects the item whose
index is that value (counting from zero).
A string's items are characters. A character is not a separate data
type but a string of exactly one character.
t
subscriptionss�
Truth Value Testing
*******************
Any object can be tested for truth value, for use in an "if" or
"while" condition or as operand of the Boolean operations below. The
following values are considered false:
* "None"
* "False"
* zero of any numeric type, for example, "0", "0L", "0.0", "0j".
* any empty sequence, for example, "''", "()", "[]".
* any empty mapping, for example, "{}".
* instances of user-defined classes, if the class defines a
"__nonzero__()" or "__len__()" method, when that method returns the
integer zero or "bool" value "False". [1]
All other values are considered true --- so objects of many types are
always true.
Operations and built-in functions that have a Boolean result always
return "0" or "False" for false and "1" or "True" for true, unless
otherwise stated. (Important exception: the Boolean operations "or"
and "and" always return one of their operands.)
t truths
The "try" statement
*******************
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression [("as" | ",") identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
Changed in version 2.5: In previous versions of Python,
"try"..."except"..."finally" did not work. "try"..."except" had to be
nested in "try"..."finally".
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is "compatible" with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object, or a tuple containing an item compatible with the exception.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified in that except clause, if present, and the except
clause's suite is executed. All except clauses must have an
executable block. When the end of this block is reached, execution
continues normally after the entire try statement. (This means that
if two nested handlers exist for the same exception, and the exception
occurs in the try clause of the inner handler, the outer handler will
not handle the exception.)
Before an except clause's suite is executed, details about the
exception are assigned to three variables in the "sys" module:
"sys.exc_type" receives the object identifying the exception;
"sys.exc_value" receives the exception's parameter;
"sys.exc_traceback" receives a traceback object (see section The
standard type hierarchy) identifying the point in the program where
the exception occurred. These details are also available through the
"sys.exc_info()" function, which returns a tuple "(exc_type,
exc_value, exc_traceback)". Use of the corresponding variables is
deprecated in favor of this function, since their use is unsafe in a
threaded program. As of Python 1.5, the variables are restored to
their previous values (before the call) when returning from a function
that handled an exception.
The optional "else" clause is executed if and when control flows off
the end of the "try" clause. [2] Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a 'cleanup' handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception, it is re-raised at the end of the
"finally" clause. If the "finally" clause raises another exception or
executes a "return" or "break" statement, the saved exception is
discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"..."finally" statement, the "finally" clause is
also executed 'on the way out.' A "continue" statement is illegal in
the "finally" clause. (The reason is a problem with the current
implementation --- this restriction may be lifted in the future).
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
t trys��
The standard type hierarchy
***************************
Below is a list of the types that are built into Python. Extension
modules (written in C, Java, or other languages, depending on the
implementation) can define additional types. Future versions of
Python may add types to the type hierarchy (e.g., rational numbers,
efficiently stored arrays of integers, etc.).
Some of the type descriptions below contain a paragraph listing
'special attributes.' These are attributes that provide access to the
implementation and are not intended for general use. Their definition
may change in the future.
None
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name "None". It
is used to signify the absence of a value in many situations, e.g.,
it is returned from functions that don't explicitly return
anything. Its truth value is false.
NotImplemented
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name
"NotImplemented". Numeric methods and rich comparison methods may
return this value if they do not implement the operation for the
operands provided. (The interpreter will then try the reflected
operation, or some other fallback, depending on the operator.) Its
truth value is true.
Ellipsis
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name
"Ellipsis". It is used to indicate the presence of the "..." syntax
in a slice. Its truth value is true.
"numbers.Number"
These are created by numeric literals and returned as results by
arithmetic operators and arithmetic built-in functions. Numeric
objects are immutable; once created their value never changes.
Python numbers are of course strongly related to mathematical
numbers, but subject to the limitations of numerical representation
in computers.
Python distinguishes between integers, floating point numbers, and
complex numbers:
"numbers.Integral"
These represent elements from the mathematical set of integers
(positive and negative).
There are three types of integers:
Plain integers
These represent numbers in the range -2147483648 through
2147483647. (The range may be larger on machines with a
larger natural word size, but not smaller.) When the result
of an operation would fall outside this range, the result is
normally returned as a long integer (in some cases, the
exception "OverflowError" is raised instead). For the
purpose of shift and mask operations, integers are assumed to
have a binary, 2's complement notation using 32 or more bits,
and hiding no bits from the user (i.e., all 4294967296
different bit patterns correspond to different values).
Long integers
These represent numbers in an unlimited range, subject to
available (virtual) memory only. For the purpose of shift
and mask operations, a binary representation is assumed, and
negative numbers are represented in a variant of 2's
complement which gives the illusion of an infinite string of
sign bits extending to the left.
Booleans
These represent the truth values False and True. The two
objects representing the values "False" and "True" are the
only Boolean objects. The Boolean type is a subtype of plain
integers, and Boolean values behave like the values 0 and 1,
respectively, in almost all contexts, the exception being
that when converted to a string, the strings ""False"" or
""True"" are returned, respectively.
The rules for integer representation are intended to give the
most meaningful interpretation of shift and mask operations
involving negative integers and the least surprises when
switching between the plain and long integer domains. Any
operation, if it yields a result in the plain integer domain,
will yield the same result in the long integer domain or when
using mixed operands. The switch between domains is transparent
to the programmer.
"numbers.Real" ("float")
These represent machine-level double precision floating point
numbers. You are at the mercy of the underlying machine
architecture (and C or Java implementation) for the accepted
range and handling of overflow. Python does not support single-
precision floating point numbers; the savings in processor and
memory usage that are usually the reason for using these are
dwarfed by the overhead of using objects in Python, so there is
no reason to complicate the language with two kinds of floating
point numbers.
"numbers.Complex"
These represent complex numbers as a pair of machine-level
double precision floating point numbers. The same caveats apply
as for floating point numbers. The real and imaginary parts of a
complex number "z" can be retrieved through the read-only
attributes "z.real" and "z.imag".
Sequences
These represent finite ordered sets indexed by non-negative
numbers. The built-in function "len()" returns the number of items
of a sequence. When the length of a sequence is *n*, the index set
contains the numbers 0, 1, ..., *n*-1. Item *i* of sequence *a* is
selected by "a[i]".
Sequences also support slicing: "a[i:j]" selects all items with
index *k* such that *i* "<=" *k* "<" *j*. When used as an
expression, a slice is a sequence of the same type. This implies
that the index set is renumbered so that it starts at 0.
Some sequences also support "extended slicing" with a third "step"
parameter: "a[i:j:k]" selects all items of *a* with index *x* where
"x = i + n*k", *n* ">=" "0" and *i* "<=" *x* "<" *j*.
Sequences are distinguished according to their mutability:
Immutable sequences
An object of an immutable sequence type cannot change once it is
created. (If the object contains references to other objects,
these other objects may be mutable and may be changed; however,
the collection of objects directly referenced by an immutable
object cannot change.)
The following types are immutable sequences:
Strings
The items of a string are characters. There is no separate
character type; a character is represented by a string of one
item. Characters represent (at least) 8-bit bytes. The
built-in functions "chr()" and "ord()" convert between
characters and nonnegative integers representing the byte
values. Bytes with the values 0--127 usually represent the
corresponding ASCII values, but the interpretation of values
is up to the program. The string data type is also used to
represent arrays of bytes, e.g., to hold data read from a
file.
(On systems whose native character set is not ASCII, strings
may use EBCDIC in their internal representation, provided the
functions "chr()" and "ord()" implement a mapping between
ASCII and EBCDIC, and string comparison preserves the ASCII
order. Or perhaps someone can propose a better rule?)
Unicode
The items of a Unicode object are Unicode code units. A
Unicode code unit is represented by a Unicode object of one
item and can hold either a 16-bit or 32-bit value
representing a Unicode ordinal (the maximum value for the
ordinal is given in "sys.maxunicode", and depends on how
Python is configured at compile time). Surrogate pairs may
be present in the Unicode object, and will be reported as two
separate items. The built-in functions "unichr()" and
"ord()" convert between code units and nonnegative integers
representing the Unicode ordinals as defined in the Unicode
Standard 3.0. Conversion from and to other encodings are
possible through the Unicode method "encode()" and the built-
in function "unicode()".
Tuples
The items of a tuple are arbitrary Python objects. Tuples of
two or more items are formed by comma-separated lists of
expressions. A tuple of one item (a 'singleton') can be
formed by affixing a comma to an expression (an expression by
itself does not create a tuple, since parentheses must be
usable for grouping of expressions). An empty tuple can be
formed by an empty pair of parentheses.
Mutable sequences
Mutable sequences can be changed after they are created. The
subscription and slicing notations can be used as the target of
assignment and "del" (delete) statements.
There are currently two intrinsic mutable sequence types:
Lists
The items of a list are arbitrary Python objects. Lists are
formed by placing a comma-separated list of expressions in
square brackets. (Note that there are no special cases needed
to form lists of length 0 or 1.)
Byte Arrays
A bytearray object is a mutable array. They are created by
the built-in "bytearray()" constructor. Aside from being
mutable (and hence unhashable), byte arrays otherwise provide
the same interface and functionality as immutable bytes
objects.
The extension module "array" provides an additional example of a
mutable sequence type.
Set types
These represent unordered, finite sets of unique, immutable
objects. As such, they cannot be indexed by any subscript. However,
they can be iterated over, and the built-in function "len()"
returns the number of items in a set. Common uses for sets are fast
membership testing, removing duplicates from a sequence, and
computing mathematical operations such as intersection, union,
difference, and symmetric difference.
For set elements, the same immutability rules apply as for
dictionary keys. Note that numeric types obey the normal rules for
numeric comparison: if two numbers compare equal (e.g., "1" and
"1.0"), only one of them can be contained in a set.
There are currently two intrinsic set types:
Sets
These represent a mutable set. They are created by the built-in
"set()" constructor and can be modified afterwards by several
methods, such as "add()".
Frozen sets
These represent an immutable set. They are created by the
built-in "frozenset()" constructor. As a frozenset is immutable
and *hashable*, it can be used again as an element of another
set, or as a dictionary key.
Mappings
These represent finite sets of objects indexed by arbitrary index
sets. The subscript notation "a[k]" selects the item indexed by "k"
from the mapping "a"; this can be used in expressions and as the
target of assignments or "del" statements. The built-in function
"len()" returns the number of items in a mapping.
There is currently a single intrinsic mapping type:
Dictionaries
These represent finite sets of objects indexed by nearly
arbitrary values. The only types of values not acceptable as
keys are values containing lists or dictionaries or other
mutable types that are compared by value rather than by object
identity, the reason being that the efficient implementation of
dictionaries requires a key's hash value to remain constant.
Numeric types used for keys obey the normal rules for numeric
comparison: if two numbers compare equal (e.g., "1" and "1.0")
then they can be used interchangeably to index the same
dictionary entry.
Dictionaries are mutable; they can be created by the "{...}"
notation (see section Dictionary displays).
The extension modules "dbm", "gdbm", and "bsddb" provide
additional examples of mapping types.
Callable types
These are the types to which the function call operation (see
section Calls) can be applied:
User-defined functions
A user-defined function object is created by a function
definition (see section Function definitions). It should be
called with an argument list containing the same number of items
as the function's formal parameter list.
Special attributes:
+-------------------------+---------------------------------+-------------+
| Attribute | Meaning | |
+=========================+=================================+=============+
| "__doc__" "func_doc" | The function's documentation | Writable |
| | string, or "None" if | |
| | unavailable. | |
+-------------------------+---------------------------------+-------------+
| "__name__" "func_name" | The function's name | Writable |
+-------------------------+---------------------------------+-------------+
| "__module__" | The name of the module the | Writable |
| | function was defined in, or | |
| | "None" if unavailable. | |
+-------------------------+---------------------------------+-------------+
| "__defaults__" | A tuple containing default | Writable |
| "func_defaults" | argument values for those | |
| | arguments that have defaults, | |
| | or "None" if no arguments have | |
| | a default value. | |
+-------------------------+---------------------------------+-------------+
| "__code__" "func_code" | The code object representing | Writable |
| | the compiled function body. | |
+-------------------------+---------------------------------+-------------+
| "__globals__" | A reference to the dictionary | Read-only |
| "func_globals" | that holds the function's | |
| | global variables --- the global | |
| | namespace of the module in | |
| | which the function was defined. | |
+-------------------------+---------------------------------+-------------+
| "__dict__" "func_dict" | The namespace supporting | Writable |
| | arbitrary function attributes. | |
+-------------------------+---------------------------------+-------------+
| "__closure__" | "None" or a tuple of cells that | Read-only |
| "func_closure" | contain bindings for the | |
| | function's free variables. | |
+-------------------------+---------------------------------+-------------+
Most of the attributes labelled "Writable" check the type of the
assigned value.
Changed in version 2.4: "func_name" is now writable.
Changed in version 2.6: The double-underscore attributes
"__closure__", "__code__", "__defaults__", and "__globals__"
were introduced as aliases for the corresponding "func_*"
attributes for forwards compatibility with Python 3.
Function objects also support getting and setting arbitrary
attributes, which can be used, for example, to attach metadata
to functions. Regular attribute dot-notation is used to get and
set such attributes. *Note that the current implementation only
supports function attributes on user-defined functions. Function
attributes on built-in functions may be supported in the
future.*
Additional information about a function's definition can be
retrieved from its code object; see the description of internal
types below.
User-defined methods
A user-defined method object combines a class, a class instance
(or "None") and any callable object (normally a user-defined
function).
Special read-only attributes: "im_self" is the class instance
object, "im_func" is the function object; "im_class" is the
class of "im_self" for bound methods or the class that asked for
the method for unbound methods; "__doc__" is the method's
documentation (same as "im_func.__doc__"); "__name__" is the
method name (same as "im_func.__name__"); "__module__" is the
name of the module the method was defined in, or "None" if
unavailable.
Changed in version 2.2: "im_self" used to refer to the class
that defined the method.
Changed in version 2.6: For Python 3 forward-compatibility,
"im_func" is also available as "__func__", and "im_self" as
"__self__".
Methods also support accessing (but not setting) the arbitrary
function attributes on the underlying function object.
User-defined method objects may be created when getting an
attribute of a class (perhaps via an instance of that class), if
that attribute is a user-defined function object, an unbound
user-defined method object, or a class method object. When the
attribute is a user-defined method object, a new method object
is only created if the class from which it is being retrieved is
the same as, or a derived class of, the class stored in the
original method object; otherwise, the original method object is
used as it is.
When a user-defined method object is created by retrieving a
user-defined function object from a class, its "im_self"
attribute is "None" and the method object is said to be unbound.
When one is created by retrieving a user-defined function object
from a class via one of its instances, its "im_self" attribute
is the instance, and the method object is said to be bound. In
either case, the new method's "im_class" attribute is the class
from which the retrieval takes place, and its "im_func"
attribute is the original function object.
When a user-defined method object is created by retrieving
another method object from a class or instance, the behaviour is
the same as for a function object, except that the "im_func"
attribute of the new instance is not the original method object
but its "im_func" attribute.
When a user-defined method object is created by retrieving a
class method object from a class or instance, its "im_self"
attribute is the class itself, and its "im_func" attribute is
the function object underlying the class method.
When an unbound user-defined method object is called, the
underlying function ("im_func") is called, with the restriction
that the first argument must be an instance of the proper class
("im_class") or of a derived class thereof.
When a bound user-defined method object is called, the
underlying function ("im_func") is called, inserting the class
instance ("im_self") in front of the argument list. For
instance, when "C" is a class which contains a definition for a
function "f()", and "x" is an instance of "C", calling "x.f(1)"
is equivalent to calling "C.f(x, 1)".
When a user-defined method object is derived from a class method
object, the "class instance" stored in "im_self" will actually
be the class itself, so that calling either "x.f(1)" or "C.f(1)"
is equivalent to calling "f(C,1)" where "f" is the underlying
function.
Note that the transformation from function object to (unbound or
bound) method object happens each time the attribute is
retrieved from the class or instance. In some cases, a fruitful
optimization is to assign the attribute to a local variable and
call that local variable. Also notice that this transformation
only happens for user-defined functions; other callable objects
(and all non-callable objects) are retrieved without
transformation. It is also important to note that user-defined
functions which are attributes of a class instance are not
converted to bound methods; this *only* happens when the
function is an attribute of the class.
Generator functions
A function or method which uses the "yield" statement (see
section The yield statement) is called a *generator function*.
Such a function, when called, always returns an iterator object
which can be used to execute the body of the function: calling
the iterator's "next()" method will cause the function to
execute until it provides a value using the "yield" statement.
When the function executes a "return" statement or falls off the
end, a "StopIteration" exception is raised and the iterator will
have reached the end of the set of values to be returned.
Built-in functions
A built-in function object is a wrapper around a C function.
Examples of built-in functions are "len()" and "math.sin()"
("math" is a standard built-in module). The number and type of
the arguments are determined by the C function. Special read-
only attributes: "__doc__" is the function's documentation
string, or "None" if unavailable; "__name__" is the function's
name; "__self__" is set to "None" (but see the next item);
"__module__" is the name of the module the function was defined
in or "None" if unavailable.
Built-in methods
This is really a different disguise of a built-in function, this
time containing an object passed to the C function as an
implicit extra argument. An example of a built-in method is
"alist.append()", assuming *alist* is a list object. In this
case, the special read-only attribute "__self__" is set to the
object denoted by *alist*.
Class Types
Class types, or "new-style classes," are callable. These
objects normally act as factories for new instances of
themselves, but variations are possible for class types that
override "__new__()". The arguments of the call are passed to
"__new__()" and, in the typical case, to "__init__()" to
initialize the new instance.
Classic Classes
Class objects are described below. When a class object is
called, a new class instance (also described below) is created
and returned. This implies a call to the class's "__init__()"
method if it has one. Any arguments are passed on to the
"__init__()" method. If there is no "__init__()" method, the
class must be called without arguments.
Class instances
Class instances are described below. Class instances are
callable only when the class has a "__call__()" method;
"x(arguments)" is a shorthand for "x.__call__(arguments)".
Modules
Modules are imported by the "import" statement (see section The
import statement). A module object has a namespace implemented by a
dictionary object (this is the dictionary referenced by the
func_globals attribute of functions defined in the module).
Attribute references are translated to lookups in this dictionary,
e.g., "m.x" is equivalent to "m.__dict__["x"]". A module object
does not contain the code object used to initialize the module
(since it isn't needed once the initialization is done).
Attribute assignment updates the module's namespace dictionary,
e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".
Special read-only attribute: "__dict__" is the module's namespace
as a dictionary object.
**CPython implementation detail:** Because of the way CPython
clears module dictionaries, the module dictionary will be cleared
when the module falls out of scope even if the dictionary still has
live references. To avoid this, copy the dictionary or keep the
module around while using its dictionary directly.
Predefined (writable) attributes: "__name__" is the module's name;
"__doc__" is the module's documentation string, or "None" if
unavailable; "__file__" is the pathname of the file from which the
module was loaded, if it was loaded from a file. The "__file__"
attribute is not present for C modules that are statically linked
into the interpreter; for extension modules loaded dynamically from
a shared library, it is the pathname of the shared library file.
Classes
Both class types (new-style classes) and class objects (old-
style/classic classes) are typically created by class definitions
(see section Class definitions). A class has a namespace
implemented by a dictionary object. Class attribute references are
translated to lookups in this dictionary, e.g., "C.x" is translated
to "C.__dict__["x"]" (although for new-style classes in particular
there are a number of hooks which allow for other means of locating
attributes). When the attribute name is not found there, the
attribute search continues in the base classes. For old-style
classes, the search is depth-first, left-to-right in the order of
occurrence in the base class list. New-style classes use the more
complex C3 method resolution order which behaves correctly even in
the presence of 'diamond' inheritance structures where there are
multiple inheritance paths leading back to a common ancestor.
Additional details on the C3 MRO used by new-style classes can be
found in the documentation accompanying the 2.3 release at
https://www.python.org/download/releases/2.3/mro/.
When a class attribute reference (for class "C", say) would yield a
user-defined function object or an unbound user-defined method
object whose associated class is either "C" or one of its base
classes, it is transformed into an unbound user-defined method
object whose "im_class" attribute is "C". When it would yield a
class method object, it is transformed into a bound user-defined
method object whose "im_self" attribute is "C". When it would
yield a static method object, it is transformed into the object
wrapped by the static method object. See section Implementing
Descriptors for another way in which attributes retrieved from a
class may differ from those actually contained in its "__dict__"
(note that only new-style classes support descriptors).
Class attribute assignments update the class's dictionary, never
the dictionary of a base class.
A class object can be called (see above) to yield a class instance
(see below).
Special attributes: "__name__" is the class name; "__module__" is
the module name in which the class was defined; "__dict__" is the
dictionary containing the class's namespace; "__bases__" is a tuple
(possibly empty or a singleton) containing the base classes, in the
order of their occurrence in the base class list; "__doc__" is the
class's documentation string, or "None" if undefined.
Class instances
A class instance is created by calling a class object (see above).
A class instance has a namespace implemented as a dictionary which
is the first place in which attribute references are searched.
When an attribute is not found there, and the instance's class has
an attribute by that name, the search continues with the class
attributes. If a class attribute is found that is a user-defined
function object or an unbound user-defined method object whose
associated class is the class (call it "C") of the instance for
which the attribute reference was initiated or one of its bases, it
is transformed into a bound user-defined method object whose
"im_class" attribute is "C" and whose "im_self" attribute is the
instance. Static method and class method objects are also
transformed, as if they had been retrieved from class "C"; see
above under "Classes". See section Implementing Descriptors for
another way in which attributes of a class retrieved via its
instances may differ from the objects actually stored in the
class's "__dict__". If no class attribute is found, and the
object's class has a "__getattr__()" method, that is called to
satisfy the lookup.
Attribute assignments and deletions update the instance's
dictionary, never a class's dictionary. If the class has a
"__setattr__()" or "__delattr__()" method, this is called instead
of updating the instance dictionary directly.
Class instances can pretend to be numbers, sequences, or mappings
if they have methods with certain special names. See section
Special method names.
Special attributes: "__dict__" is the attribute dictionary;
"__class__" is the instance's class.
Files
A file object represents an open file. File objects are created by
the "open()" built-in function, and also by "os.popen()",
"os.fdopen()", and the "makefile()" method of socket objects (and
perhaps by other functions or methods provided by extension
modules). The objects "sys.stdin", "sys.stdout" and "sys.stderr"
are initialized to file objects corresponding to the interpreter's
standard input, output and error streams. See File Objects for
complete documentation of file objects.
Internal types
A few types used internally by the interpreter are exposed to the
user. Their definitions may change with future versions of the
interpreter, but they are mentioned here for completeness.
Code objects
Code objects represent *byte-compiled* executable Python code,
or *bytecode*. The difference between a code object and a
function object is that the function object contains an explicit
reference to the function's globals (the module in which it was
defined), while a code object contains no context; also the
default argument values are stored in the function object, not
in the code object (because they represent values calculated at
run-time). Unlike function objects, code objects are immutable
and contain no references (directly or indirectly) to mutable
objects.
Special read-only attributes: "co_name" gives the function name;
"co_argcount" is the number of positional arguments (including
arguments with default values); "co_nlocals" is the number of
local variables used by the function (including arguments);
"co_varnames" is a tuple containing the names of the local
variables (starting with the argument names); "co_cellvars" is a
tuple containing the names of local variables that are
referenced by nested functions; "co_freevars" is a tuple
containing the names of free variables; "co_code" is a string
representing the sequence of bytecode instructions; "co_consts"
is a tuple containing the literals used by the bytecode;
"co_names" is a tuple containing the names used by the bytecode;
"co_filename" is the filename from which the code was compiled;
"co_firstlineno" is the first line number of the function;
"co_lnotab" is a string encoding the mapping from bytecode
offsets to line numbers (for details see the source code of the
interpreter); "co_stacksize" is the required stack size
(including local variables); "co_flags" is an integer encoding a
number of flags for the interpreter.
The following flag bits are defined for "co_flags": bit "0x04"
is set if the function uses the "*arguments" syntax to accept an
arbitrary number of positional arguments; bit "0x08" is set if
the function uses the "**keywords" syntax to accept arbitrary
keyword arguments; bit "0x20" is set if the function is a
generator.
Future feature declarations ("from __future__ import division")
also use bits in "co_flags" to indicate whether a code object
was compiled with a particular feature enabled: bit "0x2000" is
set if the function was compiled with future division enabled;
bits "0x10" and "0x1000" were used in earlier versions of
Python.
Other bits in "co_flags" are reserved for internal use.
If a code object represents a function, the first item in
"co_consts" is the documentation string of the function, or
"None" if undefined.
Frame objects
Frame objects represent execution frames. They may occur in
traceback objects (see below).
Special read-only attributes: "f_back" is to the previous stack
frame (towards the caller), or "None" if this is the bottom
stack frame; "f_code" is the code object being executed in this
frame; "f_locals" is the dictionary used to look up local
variables; "f_globals" is used for global variables;
"f_builtins" is used for built-in (intrinsic) names;
"f_restricted" is a flag indicating whether the function is
executing in restricted execution mode; "f_lasti" gives the
precise instruction (this is an index into the bytecode string
of the code object).
Special writable attributes: "f_trace", if not "None", is a
function called at the start of each source code line (this is
used by the debugger); "f_exc_type", "f_exc_value",
"f_exc_traceback" represent the last exception raised in the
parent frame provided another exception was ever raised in the
current frame (in all other cases they are "None"); "f_lineno"
is the current line number of the frame --- writing to this from
within a trace function jumps to the given line (only for the
bottom-most frame). A debugger can implement a Jump command
(aka Set Next Statement) by writing to f_lineno.
Traceback objects
Traceback objects represent a stack trace of an exception. A
traceback object is created when an exception occurs. When the
search for an exception handler unwinds the execution stack, at
each unwound level a traceback object is inserted in front of
the current traceback. When an exception handler is entered,
the stack trace is made available to the program. (See section
The try statement.) It is accessible as "sys.exc_traceback", and
also as the third item of the tuple returned by
"sys.exc_info()". The latter is the preferred interface, since
it works correctly when the program is using multiple threads.
When the program contains no suitable handler, the stack trace
is written (nicely formatted) to the standard error stream; if
the interpreter is interactive, it is also made available to the
user as "sys.last_traceback".
Special read-only attributes: "tb_next" is the next level in the
stack trace (towards the frame where the exception occurred), or
"None" if there is no next level; "tb_frame" points to the
execution frame of the current level; "tb_lineno" gives the line
number where the exception occurred; "tb_lasti" indicates the
precise instruction. The line number and last instruction in
the traceback may differ from the line number of its frame
object if the exception occurred in a "try" statement with no
matching except clause or with a finally clause.
Slice objects
Slice objects are used to represent slices when *extended slice
syntax* is used. This is a slice using two colons, or multiple
slices or ellipses separated by commas, e.g., "a[i:j:step]",
"a[i:j, k:l]", or "a[..., i:j]". They are also created by the
built-in "slice()" function.
Special read-only attributes: "start" is the lower bound; "stop"
is the upper bound; "step" is the step value; each is "None" if
omitted. These attributes can have any type.
Slice objects support one method:
slice.indices(self, length)
This method takes a single integer argument *length* and
computes information about the extended slice that the slice
object would describe if applied to a sequence of *length*
items. It returns a tuple of three integers; respectively
these are the *start* and *stop* indices and the *step* or
stride length of the slice. Missing or out-of-bounds indices
are handled in a manner consistent with regular slices.
New in version 2.3.
Static method objects
Static method objects provide a way of defeating the
transformation of function objects to method objects described
above. A static method object is a wrapper around any other
object, usually a user-defined method object. When a static
method object is retrieved from a class or a class instance, the
object actually returned is the wrapped object, which is not
subject to any further transformation. Static method objects are
not themselves callable, although the objects they wrap usually
are. Static method objects are created by the built-in
"staticmethod()" constructor.
Class method objects
A class method object, like a static method object, is a wrapper
around another object that alters the way in which that object
is retrieved from classes and class instances. The behaviour of
class method objects upon such retrieval is described above,
under "User-defined methods". Class method objects are created
by the built-in "classmethod()" constructor.
t typess�
Functions
*********
Function objects are created by function definitions. The only
operation on a function object is to call it: "func(argument-list)".
There are really two flavors of function objects: built-in functions
and user-defined functions. Both support the same operation (to call
the function), but the implementation is different, hence the
different object types.
See Function definitions for more information.
t typesfunctionss�/
Mapping Types --- "dict"
************************
A *mapping* object maps *hashable* values to arbitrary objects.
Mappings are mutable objects. There is currently only one standard
mapping type, the *dictionary*. (For other containers see the built
in "list", "set", and "tuple" classes, and the "collections" module.)
A dictionary's keys are *almost* arbitrary values. Values that are
not *hashable*, that is, values containing lists, dictionaries or
other mutable types (that are compared by value rather than by object
identity) may not be used as keys. Numeric types used for keys obey
the normal rules for numeric comparison: if two numbers compare equal
(such as "1" and "1.0") then they can be used interchangeably to index
the same dictionary entry. (Note however, that since computers store
floating-point numbers as approximations it is usually unwise to use
them as dictionary keys.)
Dictionaries can be created by placing a comma-separated list of "key:
value" pairs within braces, for example: "{'jack': 4098, 'sjoerd':
4127}" or "{4098: 'jack', 4127: 'sjoerd'}", or by the "dict"
constructor.
class dict(**kwarg)
class dict(mapping, **kwarg)
class dict(iterable, **kwarg)
Return a new dictionary initialized from an optional positional
argument and a possibly empty set of keyword arguments.
If no positional argument is given, an empty dictionary is created.
If a positional argument is given and it is a mapping object, a
dictionary is created with the same key-value pairs as the mapping
object. Otherwise, the positional argument must be an *iterable*
object. Each item in the iterable must itself be an iterable with
exactly two objects. The first object of each item becomes a key
in the new dictionary, and the second object the corresponding
value. If a key occurs more than once, the last value for that key
becomes the corresponding value in the new dictionary.
If keyword arguments are given, the keyword arguments and their
values are added to the dictionary created from the positional
argument. If a key being added is already present, the value from
the keyword argument replaces the value from the positional
argument.
To illustrate, the following examples all return a dictionary equal
to "{"one": 1, "two": 2, "three": 3}":
>>> a = dict(one=1, two=2, three=3)
>>> b = {'one': 1, 'two': 2, 'three': 3}
>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
>>> e = dict({'three': 3, 'one': 1, 'two': 2})
>>> a == b == c == d == e
True
Providing keyword arguments as in the first example only works for
keys that are valid Python identifiers. Otherwise, any valid keys
can be used.
New in version 2.2.
Changed in version 2.3: Support for building a dictionary from
keyword arguments added.
These are the operations that dictionaries support (and therefore,
custom mapping types should support too):
len(d)
Return the number of items in the dictionary *d*.
d[key]
Return the item of *d* with key *key*. Raises a "KeyError" if
*key* is not in the map.
If a subclass of dict defines a method "__missing__()" and *key*
is not present, the "d[key]" operation calls that method with
the key *key* as argument. The "d[key]" operation then returns
or raises whatever is returned or raised by the
"__missing__(key)" call. No other operations or methods invoke
"__missing__()". If "__missing__()" is not defined, "KeyError"
is raised. "__missing__()" must be a method; it cannot be an
instance variable:
>>> class Counter(dict):
... def __missing__(self, key):
... return 0
>>> c = Counter()
>>> c['red']
0
>>> c['red'] += 1
>>> c['red']
1
The example above shows part of the implementation of
"collections.Counter". A different "__missing__" method is used
by "collections.defaultdict".
New in version 2.5: Recognition of __missing__ methods of dict
subclasses.
d[key] = value
Set "d[key]" to *value*.
del d[key]
Remove "d[key]" from *d*. Raises a "KeyError" if *key* is not
in the map.
key in d
Return "True" if *d* has a key *key*, else "False".
New in version 2.2.
key not in d
Equivalent to "not key in d".
New in version 2.2.
iter(d)
Return an iterator over the keys of the dictionary. This is a
shortcut for "iterkeys()".
clear()
Remove all items from the dictionary.
copy()
Return a shallow copy of the dictionary.
fromkeys(seq[, value])
Create a new dictionary with keys from *seq* and values set to
*value*.
"fromkeys()" is a class method that returns a new dictionary.
*value* defaults to "None".
New in version 2.3.
get(key[, default])
Return the value for *key* if *key* is in the dictionary, else
*default*. If *default* is not given, it defaults to "None", so
that this method never raises a "KeyError".
has_key(key)
Test for the presence of *key* in the dictionary. "has_key()"
is deprecated in favor of "key in d".
items()
Return a copy of the dictionary's list of "(key, value)" pairs.
**CPython implementation detail:** Keys and values are listed in
an arbitrary order which is non-random, varies across Python
implementations, and depends on the dictionary's history of
insertions and deletions.
If "items()", "keys()", "values()", "iteritems()", "iterkeys()",
and "itervalues()" are called with no intervening modifications
to the dictionary, the lists will directly correspond. This
allows the creation of "(value, key)" pairs using "zip()":
"pairs = zip(d.values(), d.keys())". The same relationship
holds for the "iterkeys()" and "itervalues()" methods: "pairs =
zip(d.itervalues(), d.iterkeys())" provides the same value for
"pairs". Another way to create the same list is "pairs = [(v, k)
for (k, v) in d.iteritems()]".
iteritems()
Return an iterator over the dictionary's "(key, value)" pairs.
See the note for "dict.items()".
Using "iteritems()" while adding or deleting entries in the
dictionary may raise a "RuntimeError" or fail to iterate over
all entries.
New in version 2.2.
iterkeys()
Return an iterator over the dictionary's keys. See the note for
"dict.items()".
Using "iterkeys()" while adding or deleting entries in the
dictionary may raise a "RuntimeError" or fail to iterate over
all entries.
New in version 2.2.
itervalues()
Return an iterator over the dictionary's values. See the note
for "dict.items()".
Using "itervalues()" while adding or deleting entries in the
dictionary may raise a "RuntimeError" or fail to iterate over
all entries.
New in version 2.2.
keys()
Return a copy of the dictionary's list of keys. See the note
for "dict.items()".
pop(key[, default])
If *key* is in the dictionary, remove it and return its value,
else return *default*. If *default* is not given and *key* is
not in the dictionary, a "KeyError" is raised.
New in version 2.3.
popitem()
Remove and return an arbitrary "(key, value)" pair from the
dictionary.
"popitem()" is useful to destructively iterate over a
dictionary, as often used in set algorithms. If the dictionary
is empty, calling "popitem()" raises a "KeyError".
setdefault(key[, default])
If *key* is in the dictionary, return its value. If not, insert
*key* with a value of *default* and return *default*. *default*
defaults to "None".
update([other])
Update the dictionary with the key/value pairs from *other*,
overwriting existing keys. Return "None".
"update()" accepts either another dictionary object or an
iterable of key/value pairs (as tuples or other iterables of
length two). If keyword arguments are specified, the dictionary
is then updated with those key/value pairs: "d.update(red=1,
blue=2)".
Changed in version 2.4: Allowed the argument to be an iterable
of key/value pairs and allowed keyword arguments.
values()
Return a copy of the dictionary's list of values. See the note
for "dict.items()".
viewitems()
Return a new view of the dictionary's items ("(key, value)"
pairs). See below for documentation of view objects.
New in version 2.7.
viewkeys()
Return a new view of the dictionary's keys. See below for
documentation of view objects.
New in version 2.7.
viewvalues()
Return a new view of the dictionary's values. See below for
documentation of view objects.
New in version 2.7.
Dictionaries compare equal if and only if they have the same "(key,
value)" pairs.
Dictionary view objects
=======================
The objects returned by "dict.viewkeys()", "dict.viewvalues()" and
"dict.viewitems()" are *view objects*. They provide a dynamic view on
the dictionary's entries, which means that when the dictionary
changes, the view reflects these changes.
Dictionary views can be iterated over to yield their respective data,
and support membership tests:
len(dictview)
Return the number of entries in the dictionary.
iter(dictview)
Return an iterator over the keys, values or items (represented as
tuples of "(key, value)") in the dictionary.
Keys and values are iterated over in an arbitrary order which is
non-random, varies across Python implementations, and depends on
the dictionary's history of insertions and deletions. If keys,
values and items views are iterated over with no intervening
modifications to the dictionary, the order of items will directly
correspond. This allows the creation of "(value, key)" pairs using
"zip()": "pairs = zip(d.values(), d.keys())". Another way to
create the same list is "pairs = [(v, k) for (k, v) in d.items()]".
Iterating views while adding or deleting entries in the dictionary
may raise a "RuntimeError" or fail to iterate over all entries.
x in dictview
Return "True" if *x* is in the underlying dictionary's keys, values
or items (in the latter case, *x* should be a "(key, value)"
tuple).
Keys views are set-like since their entries are unique and hashable.
If all values are hashable, so that (key, value) pairs are unique and
hashable, then the items view is also set-like. (Values views are not
treated as set-like since the entries are generally not unique.) Then
these set operations are available ("other" refers either to another
view or a set):
dictview & other
Return the intersection of the dictview and the other object as a
new set.
dictview | other
Return the union of the dictview and the other object as a new set.
dictview - other
Return the difference between the dictview and the other object
(all elements in *dictview* that aren't in *other*) as a new set.
dictview ^ other
Return the symmetric difference (all elements either in *dictview*
or *other*, but not in both) of the dictview and the other object
as a new set.
An example of dictionary view usage:
>>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500}
>>> keys = dishes.viewkeys()
>>> values = dishes.viewvalues()
>>> # iteration
>>> n = 0
>>> for val in values:
... n += val
>>> print(n)
504
>>> # keys and values are iterated over in the same order
>>> list(keys)
['eggs', 'bacon', 'sausage', 'spam']
>>> list(values)
[2, 1, 1, 500]
>>> # view objects are dynamic and reflect dict changes
>>> del dishes['eggs']
>>> del dishes['sausage']
>>> list(keys)
['spam', 'bacon']
>>> # set operations
>>> keys & {'eggs', 'bacon', 'salad'}
{'bacon'}
t typesmappingsz
Methods
*******
Methods are functions that are called using the attribute notation.
There are two flavors: built-in methods (such as "append()" on lists)
and class instance methods. Built-in methods are described with the
types that support them.
The implementation adds two special read-only attributes to class
instance methods: "m.im_self" is the object on which the method
operates, and "m.im_func" is the function implementing the method.
Calling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to
calling "m.im_func(m.im_self, arg-1, arg-2, ..., arg-n)".
Class instance methods are either *bound* or *unbound*, referring to
whether the method was accessed through an instance or a class,
respectively. When a method is unbound, its "im_self" attribute will
be "None" and if called, an explicit "self" object must be passed as
the first argument. In this case, "self" must be an instance of the
unbound method's class (or a subclass of that class), otherwise a
"TypeError" is raised.
Like function objects, methods objects support getting arbitrary
attributes. However, since method attributes are actually stored on
the underlying function object ("meth.im_func"), setting method
attributes on either bound or unbound methods is disallowed.
Attempting to set an attribute on a method results in an
"AttributeError" being raised. In order to set a method attribute,
you need to explicitly set it on the underlying function object:
>>> class C:
... def method(self):
... pass
...
>>> c = C()
>>> c.method.whoami = 'my name is method' # can't set on the method
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'instancemethod' object has no attribute 'whoami'
>>> c.method.im_func.whoami = 'my name is method'
>>> c.method.whoami
'my name is method'
See The standard type hierarchy for more information.
t typesmethodss
Modules
*******
The only special operation on a module is attribute access: "m.name",
where *m* is a module and *name* accesses a name defined in *m*'s
symbol table. Module attributes can be assigned to. (Note that the
"import" statement is not, strictly speaking, an operation on a module
object; "import foo" does not require a module object named *foo* to
exist, rather it requires an (external) *definition* for a module
named *foo* somewhere.)
A special attribute of every module is "__dict__". This is the
dictionary containing the module's symbol table. Modifying this
dictionary will actually change the module's symbol table, but direct
assignment to the "__dict__" attribute is not possible (you can write
"m.__dict__['a'] = 1", which defines "m.a" to be "1", but you can't
write "m.__dict__ = {}"). Modifying "__dict__" directly is not
recommended.
Modules built into the interpreter are written like this: "<module
'sys' (built-in)>". If loaded from a file, they are written as
"<module 'os' from '/usr/local/lib/pythonX.Y/os.pyc'>".
t typesmodulessy�
Sequence Types --- "str", "unicode", "list", "tuple", "bytearray", "buffer", "xrange"
*************************************************************************************
There are seven sequence types: strings, Unicode strings, lists,
tuples, bytearrays, buffers, and xrange objects.
For other containers see the built in "dict" and "set" classes, and
the "collections" module.
String literals are written in single or double quotes: "'xyzzy'",
""frobozz"". See String literals for more about string literals.
Unicode strings are much like strings, but are specified in the syntax
using a preceding "'u'" character: "u'abc'", "u"def"". In addition to
the functionality described here, there are also string-specific
methods described in the String Methods section. Lists are constructed
with square brackets, separating items with commas: "[a, b, c]".
Tuples are constructed by the comma operator (not within square
brackets), with or without enclosing parentheses, but an empty tuple
must have the enclosing parentheses, such as "a, b, c" or "()". A
single item tuple must have a trailing comma, such as "(d,)".
Bytearray objects are created with the built-in function
"bytearray()".
Buffer objects are not directly supported by Python syntax, but can be
created by calling the built-in function "buffer()". They don't
support concatenation or repetition.
Objects of type xrange are similar to buffers in that there is no
specific syntax to create them, but they are created using the
"xrange()" function. They don't support slicing, concatenation or
repetition, and using "in", "not in", "min()" or "max()" on them is
inefficient.
Most sequence types support the following operations. The "in" and
"not in" operations have the same priorities as the comparison
operations. The "+" and "*" operations have the same priority as the
corresponding numeric operations. [3] Additional methods are provided
for Mutable Sequence Types.
This table lists the sequence operations sorted in ascending priority.
In the table, *s* and *t* are sequences of the same type; *n*, *i* and
*j* are integers:
+--------------------+----------------------------------+------------+
| Operation | Result | Notes |
+====================+==================================+============+
| "x in s" | "True" if an item of *s* is | (1) |
| | equal to *x*, else "False" | |
+--------------------+----------------------------------+------------+
| "x not in s" | "False" if an item of *s* is | (1) |
| | equal to *x*, else "True" | |
+--------------------+----------------------------------+------------+
| "s + t" | the concatenation of *s* and *t* | (6) |
+--------------------+----------------------------------+------------+
| "s * n, n * s" | equivalent to adding *s* to | (2) |
| | itself *n* times | |
+--------------------+----------------------------------+------------+
| "s[i]" | *i*th item of *s*, origin 0 | (3) |
+--------------------+----------------------------------+------------+
| "s[i:j]" | slice of *s* from *i* to *j* | (3)(4) |
+--------------------+----------------------------------+------------+
| "s[i:j:k]" | slice of *s* from *i* to *j* | (3)(5) |
| | with step *k* | |
+--------------------+----------------------------------+------------+
| "len(s)" | length of *s* | |
+--------------------+----------------------------------+------------+
| "min(s)" | smallest item of *s* | |
+--------------------+----------------------------------+------------+
| "max(s)" | largest item of *s* | |
+--------------------+----------------------------------+------------+
| "s.index(x)" | index of the first occurrence of | |
| | *x* in *s* | |
+--------------------+----------------------------------+------------+
| "s.count(x)" | total number of occurrences of | |
| | *x* in *s* | |
+--------------------+----------------------------------+------------+
Sequence types also support comparisons. In particular, tuples and
lists are compared lexicographically by comparing corresponding
elements. This means that to compare equal, every element must compare
equal and the two sequences must be of the same type and have the same
length. (For full details see Comparisons in the language reference.)
Notes:
1. When *s* is a string or Unicode string object the "in" and "not
in" operations act like a substring test. In Python versions
before 2.3, *x* had to be a string of length 1. In Python 2.3 and
beyond, *x* may be a string of any length.
2. Values of *n* less than "0" are treated as "0" (which yields an
empty sequence of the same type as *s*). Note that items in the
sequence *s* are not copied; they are referenced multiple times.
This often haunts new Python programmers; consider:
>>> lists = [[]] * 3
>>> lists
[[], [], []]
>>> lists[0].append(3)
>>> lists
[[3], [3], [3]]
What has happened is that "[[]]" is a one-element list containing
an empty list, so all three elements of "[[]] * 3" are references
to this single empty list. Modifying any of the elements of
"lists" modifies this single list. You can create a list of
different lists this way:
>>> lists = [[] for i in range(3)]
>>> lists[0].append(3)
>>> lists[1].append(5)
>>> lists[2].append(7)
>>> lists
[[3], [5], [7]]
Further explanation is available in the FAQ entry How do I create a
multidimensional list?.
3. If *i* or *j* is negative, the index is relative to the end of
sequence *s*: "len(s) + i" or "len(s) + j" is substituted. But
note that "-0" is still "0".
4. The slice of *s* from *i* to *j* is defined as the sequence of
items with index *k* such that "i <= k < j". If *i* or *j* is
greater than "len(s)", use "len(s)". If *i* is omitted or "None",
use "0". If *j* is omitted or "None", use "len(s)". If *i* is
greater than or equal to *j*, the slice is empty.
5. The slice of *s* from *i* to *j* with step *k* is defined as the
sequence of items with index "x = i + n*k" such that "0 <= n <
(j-i)/k". In other words, the indices are "i", "i+k", "i+2*k",
"i+3*k" and so on, stopping when *j* is reached (but never
including *j*). When *k* is positive, *i* and *j* are reduced to
"len(s)" if they are greater. When *k* is negative, *i* and *j* are
reduced to "len(s) - 1" if they are greater. If *i* or *j* are
omitted or "None", they become "end" values (which end depends on
the sign of *k*). Note, *k* cannot be zero. If *k* is "None", it
is treated like "1".
6. **CPython implementation detail:** If *s* and *t* are both
strings, some Python implementations such as CPython can usually
perform an in-place optimization for assignments of the form "s = s
+ t" or "s += t". When applicable, this optimization makes
quadratic run-time much less likely. This optimization is both
version and implementation dependent. For performance sensitive
code, it is preferable to use the "str.join()" method which assures
consistent linear concatenation performance across versions and
implementations.
Changed in version 2.4: Formerly, string concatenation never
occurred in-place.
String Methods
==============
Below are listed the string methods which both 8-bit strings and
Unicode objects support. Some of them are also available on
"bytearray" objects.
In addition, Python's strings support the sequence type methods
described in the Sequence Types --- str, unicode, list, tuple,
bytearray, buffer, xrange section. To output formatted strings use
template strings or the "%" operator described in the String
Formatting Operations section. Also, see the "re" module for string
functions based on regular expressions.
str.capitalize()
Return a copy of the string with its first character capitalized
and the rest lowercased.
For 8-bit strings, this method is locale-dependent.
str.center(width[, fillchar])
Return centered in a string of length *width*. Padding is done
using the specified *fillchar* (default is a space).
Changed in version 2.4: Support for the *fillchar* argument.
str.count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring *sub*
in the range [*start*, *end*]. Optional arguments *start* and
*end* are interpreted as in slice notation.
str.decode([encoding[, errors]])
Decodes the string using the codec registered for *encoding*.
*encoding* defaults to the default string encoding. *errors* may
be given to set a different error handling scheme. The default is
"'strict'", meaning that encoding errors raise "UnicodeError".
Other possible values are "'ignore'", "'replace'" and any other
name registered via "codecs.register_error()", see section Codec
Base Classes.
New in version 2.2.
Changed in version 2.3: Support for other error handling schemes
added.
Changed in version 2.7: Support for keyword arguments added.
str.encode([encoding[, errors]])
Return an encoded version of the string. Default encoding is the
current default string encoding. *errors* may be given to set a
different error handling scheme. The default for *errors* is
"'strict'", meaning that encoding errors raise a "UnicodeError".
Other possible values are "'ignore'", "'replace'",
"'xmlcharrefreplace'", "'backslashreplace'" and any other name
registered via "codecs.register_error()", see section Codec Base
Classes. For a list of possible encodings, see section Standard
Encodings.
New in version 2.0.
Changed in version 2.3: Support for "'xmlcharrefreplace'" and
"'backslashreplace'" and other error handling schemes added.
Changed in version 2.7: Support for keyword arguments added.
str.endswith(suffix[, start[, end]])
Return "True" if the string ends with the specified *suffix*,
otherwise return "False". *suffix* can also be a tuple of suffixes
to look for. With optional *start*, test beginning at that
position. With optional *end*, stop comparing at that position.
Changed in version 2.5: Accept tuples as *suffix*.
str.expandtabs([tabsize])
Return a copy of the string where all tab characters are replaced
by one or more spaces, depending on the current column and the
given tab size. Tab positions occur every *tabsize* characters
(default is 8, giving tab positions at columns 0, 8, 16 and so on).
To expand the string, the current column is set to zero and the
string is examined character by character. If the character is a
tab ("\t"), one or more space characters are inserted in the result
until the current column is equal to the next tab position. (The
tab character itself is not copied.) If the character is a newline
("\n") or return ("\r"), it is copied and the current column is
reset to zero. Any other character is copied unchanged and the
current column is incremented by one regardless of how the
character is represented when printed.
>>> '01\t012\t0123\t01234'.expandtabs()
'01 012 0123 01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01 012 0123 01234'
str.find(sub[, start[, end]])
Return the lowest index in the string where substring *sub* is
found within the slice "s[start:end]". Optional arguments *start*
and *end* are interpreted as in slice notation. Return "-1" if
*sub* is not found.
Note: The "find()" method should be used only if you need to know
the position of *sub*. To check if *sub* is a substring or not,
use the "in" operator:
>>> 'Py' in 'Python'
True
str.format(*args, **kwargs)
Perform a string formatting operation. The string on which this
method is called can contain literal text or replacement fields
delimited by braces "{}". Each replacement field contains either
the numeric index of a positional argument, or the name of a
keyword argument. Returns a copy of the string where each
replacement field is replaced with the string value of the
corresponding argument.
>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'
See Format String Syntax for a description of the various
formatting options that can be specified in format strings.
This method of string formatting is the new standard in Python 3,
and should be preferred to the "%" formatting described in String
Formatting Operations in new code.
New in version 2.6.
str.index(sub[, start[, end]])
Like "find()", but raise "ValueError" when the substring is not
found.
str.isalnum()
Return true if all characters in the string are alphanumeric and
there is at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isalpha()
Return true if all characters in the string are alphabetic and
there is at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isdigit()
Return true if all characters in the string are digits and there is
at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.islower()
Return true if all cased characters [4] in the string are lowercase
and there is at least one cased character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isspace()
Return true if there are only whitespace characters in the string
and there is at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.istitle()
Return true if the string is a titlecased string and there is at
least one character, for example uppercase characters may only
follow uncased characters and lowercase characters only cased ones.
Return false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isupper()
Return true if all cased characters [4] in the string are uppercase
and there is at least one cased character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.join(iterable)
Return a string which is the concatenation of the strings in
*iterable*. A "TypeError" will be raised if there are any non-
string values in *iterable*, including "bytes" objects. The
separator between elements is the string providing this method.
str.ljust(width[, fillchar])
Return the string left justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is a
space). The original string is returned if *width* is less than or
equal to "len(s)".
Changed in version 2.4: Support for the *fillchar* argument.
str.lower()
Return a copy of the string with all the cased characters [4]
converted to lowercase.
For 8-bit strings, this method is locale-dependent.
str.lstrip([chars])
Return a copy of the string with leading characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a prefix; rather,
all combinations of its values are stripped:
>>> ' spacious '.lstrip()
'spacious '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'
Changed in version 2.2.2: Support for the *chars* argument.
str.partition(sep)
Split the string at the first occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing the string itself, followed by
two empty strings.
New in version 2.5.
str.replace(old, new[, count])
Return a copy of the string with all occurrences of substring *old*
replaced by *new*. If the optional argument *count* is given, only
the first *count* occurrences are replaced.
str.rfind(sub[, start[, end]])
Return the highest index in the string where substring *sub* is
found, such that *sub* is contained within "s[start:end]".
Optional arguments *start* and *end* are interpreted as in slice
notation. Return "-1" on failure.
str.rindex(sub[, start[, end]])
Like "rfind()" but raises "ValueError" when the substring *sub* is
not found.
str.rjust(width[, fillchar])
Return the string right justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is a
space). The original string is returned if *width* is less than or
equal to "len(s)".
Changed in version 2.4: Support for the *fillchar* argument.
str.rpartition(sep)
Split the string at the last occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing two empty strings, followed by
the string itself.
New in version 2.5.
str.rsplit([sep[, maxsplit]])
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit* splits
are done, the *rightmost* ones. If *sep* is not specified or
"None", any whitespace string is a separator. Except for splitting
from the right, "rsplit()" behaves like "split()" which is
described in detail below.
New in version 2.4.
str.rstrip([chars])
Return a copy of the string with trailing characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a suffix; rather,
all combinations of its values are stripped:
>>> ' spacious '.rstrip()
' spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'
Changed in version 2.2.2: Support for the *chars* argument.
str.split([sep[, maxsplit]])
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit*
splits are done (thus, the list will have at most "maxsplit+1"
elements). If *maxsplit* is not specified or "-1", then there is
no limit on the number of splits (all possible splits are made).
If *sep* is given, consecutive delimiters are not grouped together
and are deemed to delimit empty strings (for example,
"'1,,2'.split(',')" returns "['1', '', '2']"). The *sep* argument
may consist of multiple characters (for example,
"'1<>2<>3'.split('<>')" returns "['1', '2', '3']"). Splitting an
empty string with a specified separator returns "['']".
If *sep* is not specified or is "None", a different splitting
algorithm is applied: runs of consecutive whitespace are regarded
as a single separator, and the result will contain no empty strings
at the start or end if the string has leading or trailing
whitespace. Consequently, splitting an empty string or a string
consisting of just whitespace with a "None" separator returns "[]".
For example, "' 1 2 3 '.split()" returns "['1', '2', '3']", and
"' 1 2 3 '.split(None, 1)" returns "['1', '2 3 ']".
str.splitlines([keepends])
Return a list of the lines in the string, breaking at line
boundaries. This method uses the *universal newlines* approach to
splitting lines. Line breaks are not included in the resulting list
unless *keepends* is given and true.
Python recognizes ""\r"", ""\n"", and ""\r\n"" as line boundaries
for 8-bit strings.
For example:
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
['ab c', '', 'de fg', 'kl']
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(True)
['ab c\n', '\n', 'de fg\r', 'kl\r\n']
Unlike "split()" when a delimiter string *sep* is given, this
method returns an empty list for the empty string, and a terminal
line break does not result in an extra line:
>>> "".splitlines()
[]
>>> "One line\n".splitlines()
['One line']
For comparison, "split('\n')" gives:
>>> ''.split('\n')
['']
>>> 'Two lines\n'.split('\n')
['Two lines', '']
unicode.splitlines([keepends])
Return a list of the lines in the string, like "str.splitlines()".
However, the Unicode method splits on the following line
boundaries, which are a superset of the *universal newlines*
recognized for 8-bit strings.
+-------------------------+-------------------------------+
| Representation | Description |
+=========================+===============================+
| "\n" | Line Feed |
+-------------------------+-------------------------------+
| "\r" | Carriage Return |
+-------------------------+-------------------------------+
| "\r\n" | Carriage Return + Line Feed |
+-------------------------+-------------------------------+
| "\v" or "\x0b" | Line Tabulation |
+-------------------------+-------------------------------+
| "\f" or "\x0c" | Form Feed |
+-------------------------+-------------------------------+
| "\x1c" | File Separator |
+-------------------------+-------------------------------+
| "\x1d" | Group Separator |
+-------------------------+-------------------------------+
| "\x1e" | Record Separator |
+-------------------------+-------------------------------+
| "\x85" | Next Line (C1 Control Code) |
+-------------------------+-------------------------------+
| "\u2028" | Line Separator |
+-------------------------+-------------------------------+
| "\u2029" | Paragraph Separator |
+-------------------------+-------------------------------+
Changed in version 2.7: "\v" and "\f" added to list of line
boundaries.
str.startswith(prefix[, start[, end]])
Return "True" if string starts with the *prefix*, otherwise return
"False". *prefix* can also be a tuple of prefixes to look for.
With optional *start*, test string beginning at that position.
With optional *end*, stop comparing string at that position.
Changed in version 2.5: Accept tuples as *prefix*.
str.strip([chars])
Return a copy of the string with the leading and trailing
characters removed. The *chars* argument is a string specifying the
set of characters to be removed. If omitted or "None", the *chars*
argument defaults to removing whitespace. The *chars* argument is
not a prefix or suffix; rather, all combinations of its values are
stripped:
>>> ' spacious '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'
Changed in version 2.2.2: Support for the *chars* argument.
str.swapcase()
Return a copy of the string with uppercase characters converted to
lowercase and vice versa.
For 8-bit strings, this method is locale-dependent.
str.title()
Return a titlecased version of the string where words start with an
uppercase character and the remaining characters are lowercase.
The algorithm uses a simple language-independent definition of a
word as groups of consecutive letters. The definition works in
many contexts but it means that apostrophes in contractions and
possessives form word boundaries, which may not be the desired
result:
>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"
A workaround for apostrophes can be constructed using regular
expressions:
>>> import re
>>> def titlecase(s):
... return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
... lambda mo: mo.group(0)[0].upper() +
... mo.group(0)[1:].lower(),
... s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."
For 8-bit strings, this method is locale-dependent.
str.translate(table[, deletechars])
Return a copy of the string where all characters occurring in the
optional argument *deletechars* are removed, and the remaining
characters have been mapped through the given translation table,
which must be a string of length 256.
You can use the "maketrans()" helper function in the "string"
module to create a translation table. For string objects, set the
*table* argument to "None" for translations that only delete
characters:
>>> 'read this short text'.translate(None, 'aeiou')
'rd ths shrt txt'
New in version 2.6: Support for a "None" *table* argument.
For Unicode objects, the "translate()" method does not accept the
optional *deletechars* argument. Instead, it returns a copy of the
*s* where all characters have been mapped through the given
translation table which must be a mapping of Unicode ordinals to
Unicode ordinals, Unicode strings or "None". Unmapped characters
are left untouched. Characters mapped to "None" are deleted. Note,
a more flexible approach is to create a custom character mapping
codec using the "codecs" module (see "encodings.cp1251" for an
example).
str.upper()
Return a copy of the string with all the cased characters [4]
converted to uppercase. Note that "str.upper().isupper()" might be
"False" if "s" contains uncased characters or if the Unicode
category of the resulting character(s) is not "Lu" (Letter,
uppercase), but e.g. "Lt" (Letter, titlecase).
For 8-bit strings, this method is locale-dependent.
str.zfill(width)
Return the numeric string left filled with zeros in a string of
length *width*. A sign prefix is handled correctly. The original
string is returned if *width* is less than or equal to "len(s)".
New in version 2.2.2.
The following methods are present only on unicode objects:
unicode.isnumeric()
Return "True" if there are only numeric characters in S, "False"
otherwise. Numeric characters include digit characters, and all
characters that have the Unicode numeric value property, e.g.
U+2155, VULGAR FRACTION ONE FIFTH.
unicode.isdecimal()
Return "True" if there are only decimal characters in S, "False"
otherwise. Decimal characters include digit characters, and all
characters that can be used to form decimal-radix numbers, e.g.
U+0660, ARABIC-INDIC DIGIT ZERO.
String Formatting Operations
============================
String and Unicode objects have one unique built-in operation: the "%"
operator (modulo). This is also known as the string *formatting* or
*interpolation* operator. Given "format % values" (where *format* is
a string or Unicode object), "%" conversion specifications in *format*
are replaced with zero or more elements of *values*. The effect is
similar to the using "sprintf()" in the C language. If *format* is a
Unicode object, or if any of the objects being converted using the
"%s" conversion are Unicode objects, the result will also be a Unicode
object.
If *format* requires a single argument, *values* may be a single non-
tuple object. [5] Otherwise, *values* must be a tuple with exactly
the number of items specified by the format string, or a single
mapping object (for example, a dictionary).
A conversion specifier contains two or more characters and has the
following components, which must occur in this order:
1. The "'%'" character, which marks the start of the specifier.
2. Mapping key (optional), consisting of a parenthesised sequence
of characters (for example, "(somename)").
3. Conversion flags (optional), which affect the result of some
conversion types.
4. Minimum field width (optional). If specified as an "'*'"
(asterisk), the actual width is read from the next element of the
tuple in *values*, and the object to convert comes after the
minimum field width and optional precision.
5. Precision (optional), given as a "'.'" (dot) followed by the
precision. If specified as "'*'" (an asterisk), the actual width
is read from the next element of the tuple in *values*, and the
value to convert comes after the precision.
6. Length modifier (optional).
7. Conversion type.
When the right argument is a dictionary (or other mapping type), then
the formats in the string *must* include a parenthesised mapping key
into that dictionary inserted immediately after the "'%'" character.
The mapping key selects the value to be formatted from the mapping.
For example:
>>> print '%(language)s has %(number)03d quote types.' % \
... {"language": "Python", "number": 2}
Python has 002 quote types.
In this case no "*" specifiers may occur in a format (since they
require a sequential parameter list).
The conversion flag characters are:
+-----------+-----------------------------------------------------------------------+
| Flag | Meaning |
+===========+=======================================================================+
| "'#'" | The value conversion will use the "alternate form" (where defined |
| | below). |
+-----------+-----------------------------------------------------------------------+
| "'0'" | The conversion will be zero padded for numeric values. |
+-----------+-----------------------------------------------------------------------+
| "'-'" | The converted value is left adjusted (overrides the "'0'" conversion |
| | if both are given). |
+-----------+-----------------------------------------------------------------------+
| "' '" | (a space) A blank should be left before a positive number (or empty |
| | string) produced by a signed conversion. |
+-----------+-----------------------------------------------------------------------+
| "'+'" | A sign character ("'+'" or "'-'") will precede the conversion |
| | (overrides a "space" flag). |
+-----------+-----------------------------------------------------------------------+
A length modifier ("h", "l", or "L") may be present, but is ignored as
it is not necessary for Python -- so e.g. "%ld" is identical to "%d".
The conversion types are:
+--------------+-------------------------------------------------------+---------+
| Conversion | Meaning | Notes |
+==============+=======================================================+=========+
| "'d'" | Signed integer decimal. | |
+--------------+-------------------------------------------------------+---------+
| "'i'" | Signed integer decimal. | |
+--------------+-------------------------------------------------------+---------+
| "'o'" | Signed octal value. | (1) |
+--------------+-------------------------------------------------------+---------+
| "'u'" | Obsolete type -- it is identical to "'d'". | (7) |
+--------------+-------------------------------------------------------+---------+
| "'x'" | Signed hexadecimal (lowercase). | (2) |
+--------------+-------------------------------------------------------+---------+
| "'X'" | Signed hexadecimal (uppercase). | (2) |
+--------------+-------------------------------------------------------+---------+
| "'e'" | Floating point exponential format (lowercase). | (3) |
+--------------+-------------------------------------------------------+---------+
| "'E'" | Floating point exponential format (uppercase). | (3) |
+--------------+-------------------------------------------------------+---------+
| "'f'" | Floating point decimal format. | (3) |
+--------------+-------------------------------------------------------+---------+
| "'F'" | Floating point decimal format. | (3) |
+--------------+-------------------------------------------------------+---------+
| "'g'" | Floating point format. Uses lowercase exponential | (4) |
| | format if exponent is less than -4 or not less than | |
| | precision, decimal format otherwise. | |
+--------------+-------------------------------------------------------+---------+
| "'G'" | Floating point format. Uses uppercase exponential | (4) |
| | format if exponent is less than -4 or not less than | |
| | precision, decimal format otherwise. | |
+--------------+-------------------------------------------------------+---------+
| "'c'" | Single character (accepts integer or single character | |
| | string). | |
+--------------+-------------------------------------------------------+---------+
| "'r'" | String (converts any Python object using repr()). | (5) |
+--------------+-------------------------------------------------------+---------+
| "'s'" | String (converts any Python object using "str()"). | (6) |
+--------------+-------------------------------------------------------+---------+
| "'%'" | No argument is converted, results in a "'%'" | |
| | character in the result. | |
+--------------+-------------------------------------------------------+---------+
Notes:
1. The alternate form causes a leading zero ("'0'") to be inserted
between left-hand padding and the formatting of the number if the
leading character of the result is not already a zero.
2. The alternate form causes a leading "'0x'" or "'0X'" (depending
on whether the "'x'" or "'X'" format was used) to be inserted
before the first digit.
3. The alternate form causes the result to always contain a decimal
point, even if no digits follow it.
The precision determines the number of digits after the decimal
point and defaults to 6.
4. The alternate form causes the result to always contain a decimal
point, and trailing zeroes are not removed as they would otherwise
be.
The precision determines the number of significant digits before
and after the decimal point and defaults to 6.
5. The "%r" conversion was added in Python 2.0.
The precision determines the maximal number of characters used.
6. If the object or format provided is a "unicode" string, the
resulting string will also be "unicode".
The precision determines the maximal number of characters used.
7. See **PEP 237**.
Since Python strings have an explicit length, "%s" conversions do not
assume that "'\0'" is the end of the string.
Changed in version 2.7: "%f" conversions for numbers whose absolute
value is over 1e50 are no longer replaced by "%g" conversions.
Additional string operations are defined in standard modules "string"
and "re".
XRange Type
===========
The "xrange" type is an immutable sequence which is commonly used for
looping. The advantage of the "xrange" type is that an "xrange"
object will always take the same amount of memory, no matter the size
of the range it represents. There are no consistent performance
advantages.
XRange objects have very little behavior: they only support indexing,
iteration, and the "len()" function.
Mutable Sequence Types
======================
List and "bytearray" objects support additional operations that allow
in-place modification of the object. Other mutable sequence types
(when added to the language) should also support these operations.
Strings and tuples are immutable sequence types: such objects cannot
be modified once created. The following operations are defined on
mutable sequence types (where *x* is an arbitrary object):
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
+================================+==================================+=======================+
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | same as "s[len(s):len(s)] = [x]" | (2) |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | for the most part the same as | (3) |
| | "s[len(s):len(s)] = t" | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (11) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.count(x)" | return number of *i*'s for which | |
| | "s[i] == x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.index(x[, i[, j]])" | return smallest *k* such that | (4) |
| | "s[k] == x" and "i <= k < j" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | same as "s[i:i] = [x]" | (5) |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop([i])" | same as "x = s[i]; del s[i]; | (6) |
| | return x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | same as "del s[s.index(x)]" | (4) |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (7) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
| "s.sort([cmp[, key[, | sort the items of *s* in place | (7)(8)(9)(10) |
| reverse]]])" | | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The C implementation of Python has historically accepted
multiple parameters and implicitly joined them into a tuple; this
no longer works in Python 2.0. Use of this misfeature has been
deprecated since Python 1.4.
3. *t* can be any iterable object.
4. Raises "ValueError" when *x* is not found in *s*. When a
negative index is passed as the second or third parameter to the
"index()" method, the list length is added, as for slice indices.
If it is still negative, it is truncated to zero, as for slice
indices.
Changed in version 2.3: Previously, "index()" didn't have arguments
for specifying start and stop positions.
5. When a negative index is passed as the first parameter to the
"insert()" method, the list length is added, as for slice indices.
If it is still negative, it is truncated to zero, as for slice
indices.
Changed in version 2.3: Previously, all negative indices were
truncated to zero.
6. The "pop()" method's optional argument *i* defaults to "-1", so
that by default the last item is removed and returned.
7. The "sort()" and "reverse()" methods modify the list in place
for economy of space when sorting or reversing a large list. To
remind you that they operate by side effect, they don't return the
sorted or reversed list.
8. The "sort()" method takes optional arguments for controlling the
comparisons.
*cmp* specifies a custom comparison function of two arguments (list
items) which should return a negative, zero or positive number
depending on whether the first argument is considered smaller than,
equal to, or larger than the second argument: "cmp=lambda x,y:
cmp(x.lower(), y.lower())". The default value is "None".
*key* specifies a function of one argument that is used to extract
a comparison key from each list element: "key=str.lower". The
default value is "None".
*reverse* is a boolean value. If set to "True", then the list
elements are sorted as if each comparison were reversed.
In general, the *key* and *reverse* conversion processes are much
faster than specifying an equivalent *cmp* function. This is
because *cmp* is called multiple times for each list element while
*key* and *reverse* touch each element only once. Use
"functools.cmp_to_key()" to convert an old-style *cmp* function to
a *key* function.
Changed in version 2.3: Support for "None" as an equivalent to
omitting *cmp* was added.
Changed in version 2.4: Support for *key* and *reverse* was added.
9. Starting with Python 2.3, the "sort()" method is guaranteed to
be stable. A sort is stable if it guarantees not to change the
relative order of elements that compare equal --- this is helpful
for sorting in multiple passes (for example, sort by department,
then by salary grade).
10. **CPython implementation detail:** While a list is being
sorted, the effect of attempting to mutate, or even inspect, the
list is undefined. The C implementation of Python 2.3 and newer
makes the list appear empty for the duration, and raises
"ValueError" if it can detect that the list has been mutated
during a sort.
11. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the
sequence. Items in the sequence are not copied; they are
referenced multiple times, as explained for "s * n" under Sequence
Types --- str, unicode, list, tuple, bytearray, buffer, xrange.
t typesseqsI
Mutable Sequence Types
**********************
List and "bytearray" objects support additional operations that allow
in-place modification of the object. Other mutable sequence types
(when added to the language) should also support these operations.
Strings and tuples are immutable sequence types: such objects cannot
be modified once created. The following operations are defined on
mutable sequence types (where *x* is an arbitrary object):
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
+================================+==================================+=======================+
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | same as "s[len(s):len(s)] = [x]" | (2) |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | for the most part the same as | (3) |
| | "s[len(s):len(s)] = t" | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (11) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.count(x)" | return number of *i*'s for which | |
| | "s[i] == x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.index(x[, i[, j]])" | return smallest *k* such that | (4) |
| | "s[k] == x" and "i <= k < j" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | same as "s[i:i] = [x]" | (5) |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop([i])" | same as "x = s[i]; del s[i]; | (6) |
| | return x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | same as "del s[s.index(x)]" | (4) |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (7) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
| "s.sort([cmp[, key[, | sort the items of *s* in place | (7)(8)(9)(10) |
| reverse]]])" | | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The C implementation of Python has historically accepted
multiple parameters and implicitly joined them into a tuple; this
no longer works in Python 2.0. Use of this misfeature has been
deprecated since Python 1.4.
3. *t* can be any iterable object.
4. Raises "ValueError" when *x* is not found in *s*. When a
negative index is passed as the second or third parameter to the
"index()" method, the list length is added, as for slice indices.
If it is still negative, it is truncated to zero, as for slice
indices.
Changed in version 2.3: Previously, "index()" didn't have arguments
for specifying start and stop positions.
5. When a negative index is passed as the first parameter to the
"insert()" method, the list length is added, as for slice indices.
If it is still negative, it is truncated to zero, as for slice
indices.
Changed in version 2.3: Previously, all negative indices were
truncated to zero.
6. The "pop()" method's optional argument *i* defaults to "-1", so
that by default the last item is removed and returned.
7. The "sort()" and "reverse()" methods modify the list in place
for economy of space when sorting or reversing a large list. To
remind you that they operate by side effect, they don't return the
sorted or reversed list.
8. The "sort()" method takes optional arguments for controlling the
comparisons.
*cmp* specifies a custom comparison function of two arguments (list
items) which should return a negative, zero or positive number
depending on whether the first argument is considered smaller than,
equal to, or larger than the second argument: "cmp=lambda x,y:
cmp(x.lower(), y.lower())". The default value is "None".
*key* specifies a function of one argument that is used to extract
a comparison key from each list element: "key=str.lower". The
default value is "None".
*reverse* is a boolean value. If set to "True", then the list
elements are sorted as if each comparison were reversed.
In general, the *key* and *reverse* conversion processes are much
faster than specifying an equivalent *cmp* function. This is
because *cmp* is called multiple times for each list element while
*key* and *reverse* touch each element only once. Use
"functools.cmp_to_key()" to convert an old-style *cmp* function to
a *key* function.
Changed in version 2.3: Support for "None" as an equivalent to
omitting *cmp* was added.
Changed in version 2.4: Support for *key* and *reverse* was added.
9. Starting with Python 2.3, the "sort()" method is guaranteed to
be stable. A sort is stable if it guarantees not to change the
relative order of elements that compare equal --- this is helpful
for sorting in multiple passes (for example, sort by department,
then by salary grade).
10. **CPython implementation detail:** While a list is being
sorted, the effect of attempting to mutate, or even inspect, the
list is undefined. The C implementation of Python 2.3 and newer
makes the list appear empty for the duration, and raises
"ValueError" if it can detect that the list has been mutated
during a sort.
11. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the
sequence. Items in the sequence are not copied; they are
referenced multiple times, as explained for "s * n" under Sequence
Types --- str, unicode, list, tuple, bytearray, buffer, xrange.
s typesseq-mutables�
Unary arithmetic and bitwise operations
***************************************
All unary arithmetic and bitwise operations have the same priority:
u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr
The unary "-" (minus) operator yields the negation of its numeric
argument.
The unary "+" (plus) operator yields its numeric argument unchanged.
The unary "~" (invert) operator yields the bitwise inversion of its
plain or long integer argument. The bitwise inversion of "x" is
defined as "-(x+1)". It only applies to integral numbers.
In all three cases, if the argument does not have the proper type, a
"TypeError" exception is raised.
t unarys�
The "while" statement
*********************
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause's suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
t whiles�
The "with" statement
********************
New in version 2.5.
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"..."except"..."finally"
usage patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one "item" proceeds as
follows:
1. The context expression (the expression given in the "with_item")
is evaluated to obtain a context manager.
2. The context manager's "__exit__()" is loaded for later use.
3. The context manager's "__enter__()" method is invoked.
4. If a target was included in the "with" statement, the return
value from "__enter__()" is assigned to it.
Note: The "with" statement guarantees that if the "__enter__()"
method returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
5. The suite is executed.
6. The context manager's "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
suite
is equivalent to
with A() as a:
with B() as b:
suite
Note: In Python 2.5, the "with" statement is only allowed when the
"with_statement" feature has been enabled. It is always enabled in
Python 2.6.
Changed in version 2.7: Support for multiple context expressions.
See also:
**PEP 343** - The "with" statement
The specification, background, and examples for the Python "with"
statement.
t withs
The "yield" statement
*********************
yield_stmt ::= yield_expression
The "yield" statement is only used when defining a generator function,
and is only used in the body of the generator function. Using a
"yield" statement in a function definition is sufficient to cause that
definition to create a generator function instead of a normal
function.
When a generator function is called, it returns an iterator known as a
generator iterator, or more commonly, a generator. The body of the
generator function is executed by calling the generator's "next()"
method repeatedly until it raises an exception.
When a "yield" statement is executed, the state of the generator is
frozen and the value of "expression_list" is returned to "next()"'s
caller. By "frozen" we mean that all local state is retained,
including the current bindings of local variables, the instruction
pointer, and the internal evaluation stack: enough information is
saved so that the next time "next()" is invoked, the function can
proceed exactly as if the "yield" statement were just another external
call.
As of Python version 2.5, the "yield" statement is now allowed in the
"try" clause of a "try" ... "finally" construct. If the generator is
not resumed before it is finalized (by reaching a zero reference count
or by being garbage collected), the generator-iterator's "close()"
method will be called, allowing any pending "finally" clauses to
execute.
For full details of "yield" semantics, refer to the Yield expressions
section.
Note: In Python 2.2, the "yield" statement was only allowed when the
"generators" feature has been enabled. This "__future__" import
statement was used to enable the feature:
from __future__ import generators
See also:
**PEP 255** - Simple Generators
The proposal for adding generators and the "yield" statement to
Python.
**PEP 342** - Coroutines via Enhanced Generators
The proposal that, among other generator enhancements, proposed
allowing "yield" to appear inside a "try" ... "finally" block.
t yieldN( t topics( ( ( s) /usr/lib64/python2.7/pydoc_data/topics.pyt <module> s�
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# Autogenerated by Sphinx on Sun Dec 23 16:24:21 2018
topics = {'assert': 'The "assert" statement\n'
'**********************\n'
'\n'
'Assert statements are a convenient way to insert debugging '
'assertions\n'
'into a program:\n'
'\n'
' assert_stmt ::= "assert" expression ["," expression]\n'
'\n'
'The simple form, "assert expression", is equivalent to\n'
'\n'
' if __debug__:\n'
' if not expression: raise AssertionError\n'
'\n'
'The extended form, "assert expression1, expression2", is '
'equivalent to\n'
'\n'
' if __debug__:\n'
' if not expression1: raise AssertionError(expression2)\n'
'\n'
'These equivalences assume that "__debug__" and "AssertionError" '
'refer\n'
'to the built-in variables with those names. In the current\n'
'implementation, the built-in variable "__debug__" is "True" under\n'
'normal circumstances, "False" when optimization is requested '
'(command\n'
'line option "-O"). The current code generator emits no code for '
'an\n'
'assert statement when optimization is requested at compile time. '
'Note\n'
'that it is unnecessary to include the source code for the '
'expression\n'
'that failed in the error message; it will be displayed as part of '
'the\n'
'stack trace.\n'
'\n'
'Assignments to "__debug__" are illegal. The value for the '
'built-in\n'
'variable is determined when the interpreter starts.\n',
'assignment': 'Assignment statements\n'
'*********************\n'
'\n'
'Assignment statements are used to (re)bind names to values and '
'to\n'
'modify attributes or items of mutable objects:\n'
'\n'
' assignment_stmt ::= (target_list "=")+ (starred_expression '
'| yield_expression)\n'
' target_list ::= target ("," target)* [","]\n'
' target ::= identifier\n'
' | "(" [target_list] ")"\n'
' | "[" [target_list] "]"\n'
' | attributeref\n'
' | subscription\n'
' | slicing\n'
' | "*" target\n'
'\n'
'(See section Primaries for the syntax definitions for '
'*attributeref*,\n'
'*subscription*, and *slicing*.)\n'
'\n'
'An assignment statement evaluates the expression list '
'(remember that\n'
'this can be a single expression or a comma-separated list, the '
'latter\n'
'yielding a tuple) and assigns the single resulting object to '
'each of\n'
'the target lists, from left to right.\n'
'\n'
'Assignment is defined recursively depending on the form of the '
'target\n'
'(list). When a target is part of a mutable object (an '
'attribute\n'
'reference, subscription or slicing), the mutable object must\n'
'ultimately perform the assignment and decide about its '
'validity, and\n'
'may raise an exception if the assignment is unacceptable. The '
'rules\n'
'observed by various types and the exceptions raised are given '
'with the\n'
'definition of the object types (see section The standard type\n'
'hierarchy).\n'
'\n'
'Assignment of an object to a target list, optionally enclosed '
'in\n'
'parentheses or square brackets, is recursively defined as '
'follows.\n'
'\n'
'* If the target list is a single target with no trailing '
'comma,\n'
' optionally in parentheses, the object is assigned to that '
'target.\n'
'\n'
'* Else: The object must be an iterable with the same number of '
'items\n'
' as there are targets in the target list, and the items are '
'assigned,\n'
' from left to right, to the corresponding targets.\n'
'\n'
' * If the target list contains one target prefixed with an\n'
' asterisk, called a “starred” target: The object must be '
'an\n'
' iterable with at least as many items as there are targets '
'in the\n'
' target list, minus one. The first items of the iterable '
'are\n'
' assigned, from left to right, to the targets before the '
'starred\n'
' target. The final items of the iterable are assigned to '
'the\n'
' targets after the starred target. A list of the remaining '
'items\n'
' in the iterable is then assigned to the starred target '
'(the list\n'
' can be empty).\n'
'\n'
' * Else: The object must be an iterable with the same number '
'of\n'
' items as there are targets in the target list, and the '
'items are\n'
' assigned, from left to right, to the corresponding '
'targets.\n'
'\n'
'Assignment of an object to a single target is recursively '
'defined as\n'
'follows.\n'
'\n'
'* If the target is an identifier (name):\n'
'\n'
' * If the name does not occur in a "global" or "nonlocal" '
'statement\n'
' in the current code block: the name is bound to the object '
'in the\n'
' current local namespace.\n'
'\n'
' * Otherwise: the name is bound to the object in the global\n'
' namespace or the outer namespace determined by '
'"nonlocal",\n'
' respectively.\n'
'\n'
' The name is rebound if it was already bound. This may cause '
'the\n'
' reference count for the object previously bound to the name '
'to reach\n'
' zero, causing the object to be deallocated and its '
'destructor (if it\n'
' has one) to be called.\n'
'\n'
'* If the target is an attribute reference: The primary '
'expression in\n'
' the reference is evaluated. It should yield an object with\n'
' assignable attributes; if this is not the case, "TypeError" '
'is\n'
' raised. That object is then asked to assign the assigned '
'object to\n'
' the given attribute; if it cannot perform the assignment, it '
'raises\n'
' an exception (usually but not necessarily '
'"AttributeError").\n'
'\n'
' Note: If the object is a class instance and the attribute '
'reference\n'
' occurs on both sides of the assignment operator, the RHS '
'expression,\n'
' "a.x" can access either an instance attribute or (if no '
'instance\n'
' attribute exists) a class attribute. The LHS target "a.x" '
'is always\n'
' set as an instance attribute, creating it if necessary. '
'Thus, the\n'
' two occurrences of "a.x" do not necessarily refer to the '
'same\n'
' attribute: if the RHS expression refers to a class '
'attribute, the\n'
' LHS creates a new instance attribute as the target of the\n'
' assignment:\n'
'\n'
' class Cls:\n'
' x = 3 # class variable\n'
' inst = Cls()\n'
' inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x '
'as 3\n'
'\n'
' This description does not necessarily apply to descriptor\n'
' attributes, such as properties created with "property()".\n'
'\n'
'* If the target is a subscription: The primary expression in '
'the\n'
' reference is evaluated. It should yield either a mutable '
'sequence\n'
' object (such as a list) or a mapping object (such as a '
'dictionary).\n'
' Next, the subscript expression is evaluated.\n'
'\n'
' If the primary is a mutable sequence object (such as a '
'list), the\n'
' subscript must yield an integer. If it is negative, the '
'sequence’s\n'
' length is added to it. The resulting value must be a '
'nonnegative\n'
' integer less than the sequence’s length, and the sequence is '
'asked\n'
' to assign the assigned object to its item with that index. '
'If the\n'
' index is out of range, "IndexError" is raised (assignment to '
'a\n'
' subscripted sequence cannot add new items to a list).\n'
'\n'
' If the primary is a mapping object (such as a dictionary), '
'the\n'
' subscript must have a type compatible with the mapping’s key '
'type,\n'
' and the mapping is then asked to create a key/datum pair '
'which maps\n'
' the subscript to the assigned object. This can either '
'replace an\n'
' existing key/value pair with the same key value, or insert a '
'new\n'
' key/value pair (if no key with the same value existed).\n'
'\n'
' For user-defined objects, the "__setitem__()" method is '
'called with\n'
' appropriate arguments.\n'
'\n'
'* If the target is a slicing: The primary expression in the\n'
' reference is evaluated. It should yield a mutable sequence '
'object\n'
' (such as a list). The assigned object should be a sequence '
'object\n'
' of the same type. Next, the lower and upper bound '
'expressions are\n'
' evaluated, insofar they are present; defaults are zero and '
'the\n'
' sequence’s length. The bounds should evaluate to integers. '
'If\n'
' either bound is negative, the sequence’s length is added to '
'it. The\n'
' resulting bounds are clipped to lie between zero and the '
'sequence’s\n'
' length, inclusive. Finally, the sequence object is asked to '
'replace\n'
' the slice with the items of the assigned sequence. The '
'length of\n'
' the slice may be different from the length of the assigned '
'sequence,\n'
' thus changing the length of the target sequence, if the '
'target\n'
' sequence allows it.\n'
'\n'
'**CPython implementation detail:** In the current '
'implementation, the\n'
'syntax for targets is taken to be the same as for expressions, '
'and\n'
'invalid syntax is rejected during the code generation phase, '
'causing\n'
'less detailed error messages.\n'
'\n'
'Although the definition of assignment implies that overlaps '
'between\n'
'the left-hand side and the right-hand side are ‘simultaneous’ '
'(for\n'
'example "a, b = b, a" swaps two variables), overlaps *within* '
'the\n'
'collection of assigned-to variables occur left-to-right, '
'sometimes\n'
'resulting in confusion. For instance, the following program '
'prints\n'
'"[0, 2]":\n'
'\n'
' x = [0, 1]\n'
' i = 0\n'
' i, x[i] = 1, 2 # i is updated, then x[i] is '
'updated\n'
' print(x)\n'
'\n'
'See also:\n'
'\n'
' **PEP 3132** - Extended Iterable Unpacking\n'
' The specification for the "*target" feature.\n'
'\n'
'\n'
'Augmented assignment statements\n'
'===============================\n'
'\n'
'Augmented assignment is the combination, in a single '
'statement, of a\n'
'binary operation and an assignment statement:\n'
'\n'
' augmented_assignment_stmt ::= augtarget augop '
'(expression_list | yield_expression)\n'
' augtarget ::= identifier | attributeref | '
'subscription | slicing\n'
' augop ::= "+=" | "-=" | "*=" | "@=" | '
'"/=" | "//=" | "%=" | "**="\n'
' | ">>=" | "<<=" | "&=" | "^=" | "|="\n'
'\n'
'(See section Primaries for the syntax definitions of the last '
'three\n'
'symbols.)\n'
'\n'
'An augmented assignment evaluates the target (which, unlike '
'normal\n'
'assignment statements, cannot be an unpacking) and the '
'expression\n'
'list, performs the binary operation specific to the type of '
'assignment\n'
'on the two operands, and assigns the result to the original '
'target.\n'
'The target is only evaluated once.\n'
'\n'
'An augmented assignment expression like "x += 1" can be '
'rewritten as\n'
'"x = x + 1" to achieve a similar, but not exactly equal '
'effect. In the\n'
'augmented version, "x" is only evaluated once. Also, when '
'possible,\n'
'the actual operation is performed *in-place*, meaning that '
'rather than\n'
'creating a new object and assigning that to the target, the '
'old object\n'
'is modified instead.\n'
'\n'
'Unlike normal assignments, augmented assignments evaluate the '
'left-\n'
'hand side *before* evaluating the right-hand side. For '
'example, "a[i]\n'
'+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and '
'performs\n'
'the addition, and lastly, it writes the result back to '
'"a[i]".\n'
'\n'
'With the exception of assigning to tuples and multiple targets '
'in a\n'
'single statement, the assignment done by augmented assignment\n'
'statements is handled the same way as normal assignments. '
'Similarly,\n'
'with the exception of the possible *in-place* behavior, the '
'binary\n'
'operation performed by augmented assignment is the same as the '
'normal\n'
'binary operations.\n'
'\n'
'For targets which are attribute references, the same caveat '
'about\n'
'class and instance attributes applies as for regular '
'assignments.\n'
'\n'
'\n'
'Annotated assignment statements\n'
'===============================\n'
'\n'
'Annotation assignment is the combination, in a single '
'statement, of a\n'
'variable or attribute annotation and an optional assignment '
'statement:\n'
'\n'
' annotated_assignment_stmt ::= augtarget ":" expression ["=" '
'expression]\n'
'\n'
'The difference from normal Assignment statements is that only '
'single\n'
'target and only single right hand side value is allowed.\n'
'\n'
'For simple names as assignment targets, if in class or module '
'scope,\n'
'the annotations are evaluated and stored in a special class or '
'module\n'
'attribute "__annotations__" that is a dictionary mapping from '
'variable\n'
'names (mangled if private) to evaluated annotations. This '
'attribute is\n'
'writable and is automatically created at the start of class or '
'module\n'
'body execution, if annotations are found statically.\n'
'\n'
'For expressions as assignment targets, the annotations are '
'evaluated\n'
'if in class or module scope, but not stored.\n'
'\n'
'If a name is annotated in a function scope, then this name is '
'local\n'
'for that scope. Annotations are never evaluated and stored in '
'function\n'
'scopes.\n'
'\n'
'If the right hand side is present, an annotated assignment '
'performs\n'
'the actual assignment before evaluating annotations (where\n'
'applicable). If the right hand side is not present for an '
'expression\n'
'target, then the interpreter evaluates the target except for '
'the last\n'
'"__setitem__()" or "__setattr__()" call.\n'
'\n'
'See also:\n'
'\n'
' **PEP 526** - Syntax for Variable Annotations\n'
' The proposal that added syntax for annotating the types '
'of\n'
' variables (including class variables and instance '
'variables),\n'
' instead of expressing them through comments.\n'
'\n'
' **PEP 484** - Type hints\n'
' The proposal that added the "typing" module to provide a '
'standard\n'
' syntax for type annotations that can be used in static '
'analysis\n'
' tools and IDEs.\n',
'atom-identifiers': 'Identifiers (Names)\n'
'*******************\n'
'\n'
'An identifier occurring as an atom is a name. See '
'section Identifiers\n'
'and keywords for lexical definition and section Naming '
'and binding for\n'
'documentation of naming and binding.\n'
'\n'
'When the name is bound to an object, evaluation of the '
'atom yields\n'
'that object. When a name is not bound, an attempt to '
'evaluate it\n'
'raises a "NameError" exception.\n'
'\n'
'**Private name mangling:** When an identifier that '
'textually occurs in\n'
'a class definition begins with two or more underscore '
'characters and\n'
'does not end in two or more underscores, it is '
'considered a *private\n'
'name* of that class. Private names are transformed to a '
'longer form\n'
'before code is generated for them. The transformation '
'inserts the\n'
'class name, with leading underscores removed and a '
'single underscore\n'
'inserted, in front of the name. For example, the '
'identifier "__spam"\n'
'occurring in a class named "Ham" will be transformed to '
'"_Ham__spam".\n'
'This transformation is independent of the syntactical '
'context in which\n'
'the identifier is used. If the transformed name is '
'extremely long\n'
'(longer than 255 characters), implementation defined '
'truncation may\n'
'happen. If the class name consists only of underscores, '
'no\n'
'transformation is done.\n',
'atom-literals': 'Literals\n'
'********\n'
'\n'
'Python supports string and bytes literals and various '
'numeric\n'
'literals:\n'
'\n'
' literal ::= stringliteral | bytesliteral\n'
' | integer | floatnumber | imagnumber\n'
'\n'
'Evaluation of a literal yields an object of the given type '
'(string,\n'
'bytes, integer, floating point number, complex number) with '
'the given\n'
'value. The value may be approximated in the case of '
'floating point\n'
'and imaginary (complex) literals. See section Literals for '
'details.\n'
'\n'
'All literals correspond to immutable data types, and hence '
'the\n'
'object’s identity is less important than its value. '
'Multiple\n'
'evaluations of literals with the same value (either the '
'same\n'
'occurrence in the program text or a different occurrence) '
'may obtain\n'
'the same object or a different object with the same '
'value.\n',
'attribute-access': 'Customizing attribute access\n'
'****************************\n'
'\n'
'The following methods can be defined to customize the '
'meaning of\n'
'attribute access (use of, assignment to, or deletion of '
'"x.name") for\n'
'class instances.\n'
'\n'
'object.__getattr__(self, name)\n'
'\n'
' Called when the default attribute access fails with '
'an\n'
' "AttributeError" (either "__getattribute__()" raises '
'an\n'
' "AttributeError" because *name* is not an instance '
'attribute or an\n'
' attribute in the class tree for "self"; or '
'"__get__()" of a *name*\n'
' property raises "AttributeError"). This method '
'should either\n'
' return the (computed) attribute value or raise an '
'"AttributeError"\n'
' exception.\n'
'\n'
' Note that if the attribute is found through the '
'normal mechanism,\n'
' "__getattr__()" is not called. (This is an '
'intentional asymmetry\n'
' between "__getattr__()" and "__setattr__()".) This is '
'done both for\n'
' efficiency reasons and because otherwise '
'"__getattr__()" would have\n'
' no way to access other attributes of the instance. '
'Note that at\n'
' least for instance variables, you can fake total '
'control by not\n'
' inserting any values in the instance attribute '
'dictionary (but\n'
' instead inserting them in another object). See the\n'
' "__getattribute__()" method below for a way to '
'actually get total\n'
' control over attribute access.\n'
'\n'
'object.__getattribute__(self, name)\n'
'\n'
' Called unconditionally to implement attribute '
'accesses for\n'
' instances of the class. If the class also defines '
'"__getattr__()",\n'
' the latter will not be called unless '
'"__getattribute__()" either\n'
' calls it explicitly or raises an "AttributeError". '
'This method\n'
' should return the (computed) attribute value or raise '
'an\n'
' "AttributeError" exception. In order to avoid '
'infinite recursion in\n'
' this method, its implementation should always call '
'the base class\n'
' method with the same name to access any attributes it '
'needs, for\n'
' example, "object.__getattribute__(self, name)".\n'
'\n'
' Note: This method may still be bypassed when looking '
'up special\n'
' methods as the result of implicit invocation via '
'language syntax\n'
' or built-in functions. See Special method lookup.\n'
'\n'
'object.__setattr__(self, name, value)\n'
'\n'
' Called when an attribute assignment is attempted. '
'This is called\n'
' instead of the normal mechanism (i.e. store the value '
'in the\n'
' instance dictionary). *name* is the attribute name, '
'*value* is the\n'
' value to be assigned to it.\n'
'\n'
' If "__setattr__()" wants to assign to an instance '
'attribute, it\n'
' should call the base class method with the same name, '
'for example,\n'
' "object.__setattr__(self, name, value)".\n'
'\n'
'object.__delattr__(self, name)\n'
'\n'
' Like "__setattr__()" but for attribute deletion '
'instead of\n'
' assignment. This should only be implemented if "del '
'obj.name" is\n'
' meaningful for the object.\n'
'\n'
'object.__dir__(self)\n'
'\n'
' Called when "dir()" is called on the object. A '
'sequence must be\n'
' returned. "dir()" converts the returned sequence to a '
'list and\n'
' sorts it.\n'
'\n'
'\n'
'Customizing module attribute access\n'
'===================================\n'
'\n'
'For a more fine grained customization of the module '
'behavior (setting\n'
'attributes, properties, etc.), one can set the '
'"__class__" attribute\n'
'of a module object to a subclass of "types.ModuleType". '
'For example:\n'
'\n'
' import sys\n'
' from types import ModuleType\n'
'\n'
' class VerboseModule(ModuleType):\n'
' def __repr__(self):\n'
" return f'Verbose {self.__name__}'\n"
'\n'
' def __setattr__(self, attr, value):\n'
" print(f'Setting {attr}...')\n"
' setattr(self, attr, value)\n'
'\n'
' sys.modules[__name__].__class__ = VerboseModule\n'
'\n'
'Note: Setting module "__class__" only affects lookups '
'made using the\n'
' attribute access syntax – directly accessing the '
'module globals\n'
' (whether by code within the module, or via a reference '
'to the\n'
' module’s globals dictionary) is unaffected.\n'
'\n'
'Changed in version 3.5: "__class__" module attribute is '
'now writable.\n'
'\n'
'\n'
'Implementing Descriptors\n'
'========================\n'
'\n'
'The following methods only apply when an instance of the '
'class\n'
'containing the method (a so-called *descriptor* class) '
'appears in an\n'
'*owner* class (the descriptor must be in either the '
'owner’s class\n'
'dictionary or in the class dictionary for one of its '
'parents). In the\n'
'examples below, “the attribute” refers to the attribute '
'whose name is\n'
'the key of the property in the owner class’ "__dict__".\n'
'\n'
'object.__get__(self, instance, owner)\n'
'\n'
' Called to get the attribute of the owner class (class '
'attribute\n'
' access) or of an instance of that class (instance '
'attribute\n'
' access). *owner* is always the owner class, while '
'*instance* is the\n'
' instance that the attribute was accessed through, or '
'"None" when\n'
' the attribute is accessed through the *owner*. This '
'method should\n'
' return the (computed) attribute value or raise an '
'"AttributeError"\n'
' exception.\n'
'\n'
'object.__set__(self, instance, value)\n'
'\n'
' Called to set the attribute on an instance *instance* '
'of the owner\n'
' class to a new value, *value*.\n'
'\n'
'object.__delete__(self, instance)\n'
'\n'
' Called to delete the attribute on an instance '
'*instance* of the\n'
' owner class.\n'
'\n'
'object.__set_name__(self, owner, name)\n'
'\n'
' Called at the time the owning class *owner* is '
'created. The\n'
' descriptor has been assigned to *name*.\n'
'\n'
' New in version 3.6.\n'
'\n'
'The attribute "__objclass__" is interpreted by the '
'"inspect" module as\n'
'specifying the class where this object was defined '
'(setting this\n'
'appropriately can assist in runtime introspection of '
'dynamic class\n'
'attributes). For callables, it may indicate that an '
'instance of the\n'
'given type (or a subclass) is expected or required as '
'the first\n'
'positional argument (for example, CPython sets this '
'attribute for\n'
'unbound methods that are implemented in C).\n'
'\n'
'\n'
'Invoking Descriptors\n'
'====================\n'
'\n'
'In general, a descriptor is an object attribute with '
'“binding\n'
'behavior”, one whose attribute access has been '
'overridden by methods\n'
'in the descriptor protocol: "__get__()", "__set__()", '
'and\n'
'"__delete__()". If any of those methods are defined for '
'an object, it\n'
'is said to be a descriptor.\n'
'\n'
'The default behavior for attribute access is to get, '
'set, or delete\n'
'the attribute from an object’s dictionary. For instance, '
'"a.x" has a\n'
'lookup chain starting with "a.__dict__[\'x\']", then\n'
'"type(a).__dict__[\'x\']", and continuing through the '
'base classes of\n'
'"type(a)" excluding metaclasses.\n'
'\n'
'However, if the looked-up value is an object defining '
'one of the\n'
'descriptor methods, then Python may override the default '
'behavior and\n'
'invoke the descriptor method instead. Where this occurs '
'in the\n'
'precedence chain depends on which descriptor methods '
'were defined and\n'
'how they were called.\n'
'\n'
'The starting point for descriptor invocation is a '
'binding, "a.x". How\n'
'the arguments are assembled depends on "a":\n'
'\n'
'Direct Call\n'
' The simplest and least common call is when user code '
'directly\n'
' invokes a descriptor method: "x.__get__(a)".\n'
'\n'
'Instance Binding\n'
' If binding to an object instance, "a.x" is '
'transformed into the\n'
' call: "type(a).__dict__[\'x\'].__get__(a, type(a))".\n'
'\n'
'Class Binding\n'
' If binding to a class, "A.x" is transformed into the '
'call:\n'
' "A.__dict__[\'x\'].__get__(None, A)".\n'
'\n'
'Super Binding\n'
' If "a" is an instance of "super", then the binding '
'"super(B,\n'
' obj).m()" searches "obj.__class__.__mro__" for the '
'base class "A"\n'
' immediately preceding "B" and then invokes the '
'descriptor with the\n'
' call: "A.__dict__[\'m\'].__get__(obj, '
'obj.__class__)".\n'
'\n'
'For instance bindings, the precedence of descriptor '
'invocation depends\n'
'on the which descriptor methods are defined. A '
'descriptor can define\n'
'any combination of "__get__()", "__set__()" and '
'"__delete__()". If it\n'
'does not define "__get__()", then accessing the '
'attribute will return\n'
'the descriptor object itself unless there is a value in '
'the object’s\n'
'instance dictionary. If the descriptor defines '
'"__set__()" and/or\n'
'"__delete__()", it is a data descriptor; if it defines '
'neither, it is\n'
'a non-data descriptor. Normally, data descriptors '
'define both\n'
'"__get__()" and "__set__()", while non-data descriptors '
'have just the\n'
'"__get__()" method. Data descriptors with "__set__()" '
'and "__get__()"\n'
'defined always override a redefinition in an instance '
'dictionary. In\n'
'contrast, non-data descriptors can be overridden by '
'instances.\n'
'\n'
'Python methods (including "staticmethod()" and '
'"classmethod()") are\n'
'implemented as non-data descriptors. Accordingly, '
'instances can\n'
'redefine and override methods. This allows individual '
'instances to\n'
'acquire behaviors that differ from other instances of '
'the same class.\n'
'\n'
'The "property()" function is implemented as a data '
'descriptor.\n'
'Accordingly, instances cannot override the behavior of a '
'property.\n'
'\n'
'\n'
'__slots__\n'
'=========\n'
'\n'
'*__slots__* allow us to explicitly declare data members '
'(like\n'
'properties) and deny the creation of *__dict__* and '
'*__weakref__*\n'
'(unless explicitly declared in *__slots__* or available '
'in a parent.)\n'
'\n'
'The space saved over using *__dict__* can be '
'significant.\n'
'\n'
'object.__slots__\n'
'\n'
' This class variable can be assigned a string, '
'iterable, or sequence\n'
' of strings with variable names used by instances. '
'*__slots__*\n'
' reserves space for the declared variables and '
'prevents the\n'
' automatic creation of *__dict__* and *__weakref__* '
'for each\n'
' instance.\n'
'\n'
'\n'
'Notes on using *__slots__*\n'
'--------------------------\n'
'\n'
'* When inheriting from a class without *__slots__*, the '
'*__dict__*\n'
' and *__weakref__* attribute of the instances will '
'always be\n'
' accessible.\n'
'\n'
'* Without a *__dict__* variable, instances cannot be '
'assigned new\n'
' variables not listed in the *__slots__* definition. '
'Attempts to\n'
' assign to an unlisted variable name raises '
'"AttributeError". If\n'
' dynamic assignment of new variables is desired, then '
'add\n'
' "\'__dict__\'" to the sequence of strings in the '
'*__slots__*\n'
' declaration.\n'
'\n'
'* Without a *__weakref__* variable for each instance, '
'classes\n'
' defining *__slots__* do not support weak references to '
'its\n'
' instances. If weak reference support is needed, then '
'add\n'
' "\'__weakref__\'" to the sequence of strings in the '
'*__slots__*\n'
' declaration.\n'
'\n'
'* *__slots__* are implemented at the class level by '
'creating\n'
' descriptors (Implementing Descriptors) for each '
'variable name. As a\n'
' result, class attributes cannot be used to set default '
'values for\n'
' instance variables defined by *__slots__*; otherwise, '
'the class\n'
' attribute would overwrite the descriptor assignment.\n'
'\n'
'* The action of a *__slots__* declaration is not limited '
'to the\n'
' class where it is defined. *__slots__* declared in '
'parents are\n'
' available in child classes. However, child subclasses '
'will get a\n'
' *__dict__* and *__weakref__* unless they also define '
'*__slots__*\n'
' (which should only contain names of any *additional* '
'slots).\n'
'\n'
'* If a class defines a slot also defined in a base '
'class, the\n'
' instance variable defined by the base class slot is '
'inaccessible\n'
' (except by retrieving its descriptor directly from the '
'base class).\n'
' This renders the meaning of the program undefined. In '
'the future, a\n'
' check may be added to prevent this.\n'
'\n'
'* Nonempty *__slots__* does not work for classes derived '
'from\n'
' “variable-length” built-in types such as "int", '
'"bytes" and "tuple".\n'
'\n'
'* Any non-string iterable may be assigned to '
'*__slots__*. Mappings\n'
' may also be used; however, in the future, special '
'meaning may be\n'
' assigned to the values corresponding to each key.\n'
'\n'
'* *__class__* assignment works only if both classes have '
'the same\n'
' *__slots__*.\n'
'\n'
'* Multiple inheritance with multiple slotted parent '
'classes can be\n'
' used, but only one parent is allowed to have '
'attributes created by\n'
' slots (the other bases must have empty slot layouts) - '
'violations\n'
' raise "TypeError".\n',
'attribute-references': 'Attribute references\n'
'********************\n'
'\n'
'An attribute reference is a primary followed by a '
'period and a name:\n'
'\n'
' attributeref ::= primary "." identifier\n'
'\n'
'The primary must evaluate to an object of a type '
'that supports\n'
'attribute references, which most objects do. This '
'object is then\n'
'asked to produce the attribute whose name is the '
'identifier. This\n'
'production can be customized by overriding the '
'"__getattr__()" method.\n'
'If this attribute is not available, the exception '
'"AttributeError" is\n'
'raised. Otherwise, the type and value of the object '
'produced is\n'
'determined by the object. Multiple evaluations of '
'the same attribute\n'
'reference may yield different objects.\n',
'augassign': 'Augmented assignment statements\n'
'*******************************\n'
'\n'
'Augmented assignment is the combination, in a single statement, '
'of a\n'
'binary operation and an assignment statement:\n'
'\n'
' augmented_assignment_stmt ::= augtarget augop '
'(expression_list | yield_expression)\n'
' augtarget ::= identifier | attributeref | '
'subscription | slicing\n'
' augop ::= "+=" | "-=" | "*=" | "@=" | '
'"/=" | "//=" | "%=" | "**="\n'
' | ">>=" | "<<=" | "&=" | "^=" | "|="\n'
'\n'
'(See section Primaries for the syntax definitions of the last '
'three\n'
'symbols.)\n'
'\n'
'An augmented assignment evaluates the target (which, unlike '
'normal\n'
'assignment statements, cannot be an unpacking) and the '
'expression\n'
'list, performs the binary operation specific to the type of '
'assignment\n'
'on the two operands, and assigns the result to the original '
'target.\n'
'The target is only evaluated once.\n'
'\n'
'An augmented assignment expression like "x += 1" can be '
'rewritten as\n'
'"x = x + 1" to achieve a similar, but not exactly equal effect. '
'In the\n'
'augmented version, "x" is only evaluated once. Also, when '
'possible,\n'
'the actual operation is performed *in-place*, meaning that '
'rather than\n'
'creating a new object and assigning that to the target, the old '
'object\n'
'is modified instead.\n'
'\n'
'Unlike normal assignments, augmented assignments evaluate the '
'left-\n'
'hand side *before* evaluating the right-hand side. For '
'example, "a[i]\n'
'+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and '
'performs\n'
'the addition, and lastly, it writes the result back to "a[i]".\n'
'\n'
'With the exception of assigning to tuples and multiple targets '
'in a\n'
'single statement, the assignment done by augmented assignment\n'
'statements is handled the same way as normal assignments. '
'Similarly,\n'
'with the exception of the possible *in-place* behavior, the '
'binary\n'
'operation performed by augmented assignment is the same as the '
'normal\n'
'binary operations.\n'
'\n'
'For targets which are attribute references, the same caveat '
'about\n'
'class and instance attributes applies as for regular '
'assignments.\n',
'binary': 'Binary arithmetic operations\n'
'****************************\n'
'\n'
'The binary arithmetic operations have the conventional priority\n'
'levels. Note that some of these operations also apply to certain '
'non-\n'
'numeric types. Apart from the power operator, there are only two\n'
'levels, one for multiplicative operators and one for additive\n'
'operators:\n'
'\n'
' m_expr ::= u_expr | m_expr "*" u_expr | m_expr "@" m_expr |\n'
' m_expr "//" u_expr | m_expr "/" u_expr |\n'
' m_expr "%" u_expr\n'
' a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr\n'
'\n'
'The "*" (multiplication) operator yields the product of its '
'arguments.\n'
'The arguments must either both be numbers, or one argument must be '
'an\n'
'integer and the other must be a sequence. In the former case, the\n'
'numbers are converted to a common type and then multiplied '
'together.\n'
'In the latter case, sequence repetition is performed; a negative\n'
'repetition factor yields an empty sequence.\n'
'\n'
'The "@" (at) operator is intended to be used for matrix\n'
'multiplication. No builtin Python types implement this operator.\n'
'\n'
'New in version 3.5.\n'
'\n'
'The "/" (division) and "//" (floor division) operators yield the\n'
'quotient of their arguments. The numeric arguments are first\n'
'converted to a common type. Division of integers yields a float, '
'while\n'
'floor division of integers results in an integer; the result is '
'that\n'
'of mathematical division with the ‘floor’ function applied to the\n'
'result. Division by zero raises the "ZeroDivisionError" '
'exception.\n'
'\n'
'The "%" (modulo) operator yields the remainder from the division '
'of\n'
'the first argument by the second. The numeric arguments are '
'first\n'
'converted to a common type. A zero right argument raises the\n'
'"ZeroDivisionError" exception. The arguments may be floating '
'point\n'
'numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals '
'"4*0.7 +\n'
'0.34".) The modulo operator always yields a result with the same '
'sign\n'
'as its second operand (or zero); the absolute value of the result '
'is\n'
'strictly smaller than the absolute value of the second operand '
'[1].\n'
'\n'
'The floor division and modulo operators are connected by the '
'following\n'
'identity: "x == (x//y)*y + (x%y)". Floor division and modulo are '
'also\n'
'connected with the built-in function "divmod()": "divmod(x, y) ==\n'
'(x//y, x%y)". [2].\n'
'\n'
'In addition to performing the modulo operation on numbers, the '
'"%"\n'
'operator is also overloaded by string objects to perform '
'old-style\n'
'string formatting (also known as interpolation). The syntax for\n'
'string formatting is described in the Python Library Reference,\n'
'section printf-style String Formatting.\n'
'\n'
'The floor division operator, the modulo operator, and the '
'"divmod()"\n'
'function are not defined for complex numbers. Instead, convert to '
'a\n'
'floating point number using the "abs()" function if appropriate.\n'
'\n'
'The "+" (addition) operator yields the sum of its arguments. The\n'
'arguments must either both be numbers or both be sequences of the '
'same\n'
'type. In the former case, the numbers are converted to a common '
'type\n'
'and then added together. In the latter case, the sequences are\n'
'concatenated.\n'
'\n'
'The "-" (subtraction) operator yields the difference of its '
'arguments.\n'
'The numeric arguments are first converted to a common type.\n',
'bitwise': 'Binary bitwise operations\n'
'*************************\n'
'\n'
'Each of the three bitwise operations has a different priority '
'level:\n'
'\n'
' and_expr ::= shift_expr | and_expr "&" shift_expr\n'
' xor_expr ::= and_expr | xor_expr "^" and_expr\n'
' or_expr ::= xor_expr | or_expr "|" xor_expr\n'
'\n'
'The "&" operator yields the bitwise AND of its arguments, which '
'must\n'
'be integers.\n'
'\n'
'The "^" operator yields the bitwise XOR (exclusive OR) of its\n'
'arguments, which must be integers.\n'
'\n'
'The "|" operator yields the bitwise (inclusive) OR of its '
'arguments,\n'
'which must be integers.\n',
'bltin-code-objects': 'Code Objects\n'
'************\n'
'\n'
'Code objects are used by the implementation to '
'represent “pseudo-\n'
'compiled” executable Python code such as a function '
'body. They differ\n'
'from function objects because they don’t contain a '
'reference to their\n'
'global execution environment. Code objects are '
'returned by the built-\n'
'in "compile()" function and can be extracted from '
'function objects\n'
'through their "__code__" attribute. See also the '
'"code" module.\n'
'\n'
'A code object can be executed or evaluated by passing '
'it (instead of a\n'
'source string) to the "exec()" or "eval()" built-in '
'functions.\n'
'\n'
'See The standard type hierarchy for more '
'information.\n',
'bltin-ellipsis-object': 'The Ellipsis Object\n'
'*******************\n'
'\n'
'This object is commonly used by slicing (see '
'Slicings). It supports\n'
'no special operations. There is exactly one '
'ellipsis object, named\n'
'"Ellipsis" (a built-in name). "type(Ellipsis)()" '
'produces the\n'
'"Ellipsis" singleton.\n'
'\n'
'It is written as "Ellipsis" or "...".\n',
'bltin-null-object': 'The Null Object\n'
'***************\n'
'\n'
'This object is returned by functions that don’t '
'explicitly return a\n'
'value. It supports no special operations. There is '
'exactly one null\n'
'object, named "None" (a built-in name). "type(None)()" '
'produces the\n'
'same singleton.\n'
'\n'
'It is written as "None".\n',
'bltin-type-objects': 'Type Objects\n'
'************\n'
'\n'
'Type objects represent the various object types. An '
'object’s type is\n'
'accessed by the built-in function "type()". There are '
'no special\n'
'operations on types. The standard module "types" '
'defines names for\n'
'all standard built-in types.\n'
'\n'
'Types are written like this: "<class \'int\'>".\n',
'booleans': 'Boolean operations\n'
'******************\n'
'\n'
' or_test ::= and_test | or_test "or" and_test\n'
' and_test ::= not_test | and_test "and" not_test\n'
' not_test ::= comparison | "not" not_test\n'
'\n'
'In the context of Boolean operations, and also when expressions '
'are\n'
'used by control flow statements, the following values are '
'interpreted\n'
'as false: "False", "None", numeric zero of all types, and empty\n'
'strings and containers (including strings, tuples, lists,\n'
'dictionaries, sets and frozensets). All other values are '
'interpreted\n'
'as true. User-defined objects can customize their truth value '
'by\n'
'providing a "__bool__()" method.\n'
'\n'
'The operator "not" yields "True" if its argument is false, '
'"False"\n'
'otherwise.\n'
'\n'
'The expression "x and y" first evaluates *x*; if *x* is false, '
'its\n'
'value is returned; otherwise, *y* is evaluated and the resulting '
'value\n'
'is returned.\n'
'\n'
'The expression "x or y" first evaluates *x*; if *x* is true, its '
'value\n'
'is returned; otherwise, *y* is evaluated and the resulting value '
'is\n'
'returned.\n'
'\n'
'Note that neither "and" nor "or" restrict the value and type '
'they\n'
'return to "False" and "True", but rather return the last '
'evaluated\n'
'argument. This is sometimes useful, e.g., if "s" is a string '
'that\n'
'should be replaced by a default value if it is empty, the '
'expression\n'
'"s or \'foo\'" yields the desired value. Because "not" has to '
'create a\n'
'new value, it returns a boolean value regardless of the type of '
'its\n'
'argument (for example, "not \'foo\'" produces "False" rather '
'than "\'\'".)\n',
'break': 'The "break" statement\n'
'*********************\n'
'\n'
' break_stmt ::= "break"\n'
'\n'
'"break" may only occur syntactically nested in a "for" or "while"\n'
'loop, but not nested in a function or class definition within that\n'
'loop.\n'
'\n'
'It terminates the nearest enclosing loop, skipping the optional '
'"else"\n'
'clause if the loop has one.\n'
'\n'
'If a "for" loop is terminated by "break", the loop control target\n'
'keeps its current value.\n'
'\n'
'When "break" passes control out of a "try" statement with a '
'"finally"\n'
'clause, that "finally" clause is executed before really leaving '
'the\n'
'loop.\n',
'callable-types': 'Emulating callable objects\n'
'**************************\n'
'\n'
'object.__call__(self[, args...])\n'
'\n'
' Called when the instance is “called” as a function; if '
'this method\n'
' is defined, "x(arg1, arg2, ...)" is a shorthand for\n'
' "x.__call__(arg1, arg2, ...)".\n',
'calls': 'Calls\n'
'*****\n'
'\n'
'A call calls a callable object (e.g., a *function*) with a '
'possibly\n'
'empty series of *arguments*:\n'
'\n'
' call ::= primary "(" [argument_list [","] | '
'comprehension] ")"\n'
' argument_list ::= positional_arguments ["," '
'starred_and_keywords]\n'
' ["," keywords_arguments]\n'
' | starred_and_keywords ["," '
'keywords_arguments]\n'
' | keywords_arguments\n'
' positional_arguments ::= ["*"] expression ("," ["*"] '
'expression)*\n'
' starred_and_keywords ::= ("*" expression | keyword_item)\n'
' ("," "*" expression | "," '
'keyword_item)*\n'
' keywords_arguments ::= (keyword_item | "**" expression)\n'
' ("," keyword_item | "," "**" '
'expression)*\n'
' keyword_item ::= identifier "=" expression\n'
'\n'
'An optional trailing comma may be present after the positional and\n'
'keyword arguments but does not affect the semantics.\n'
'\n'
'The primary must evaluate to a callable object (user-defined\n'
'functions, built-in functions, methods of built-in objects, class\n'
'objects, methods of class instances, and all objects having a\n'
'"__call__()" method are callable). All argument expressions are\n'
'evaluated before the call is attempted. Please refer to section\n'
'Function definitions for the syntax of formal *parameter* lists.\n'
'\n'
'If keyword arguments are present, they are first converted to\n'
'positional arguments, as follows. First, a list of unfilled slots '
'is\n'
'created for the formal parameters. If there are N positional\n'
'arguments, they are placed in the first N slots. Next, for each\n'
'keyword argument, the identifier is used to determine the\n'
'corresponding slot (if the identifier is the same as the first '
'formal\n'
'parameter name, the first slot is used, and so on). If the slot '
'is\n'
'already filled, a "TypeError" exception is raised. Otherwise, the\n'
'value of the argument is placed in the slot, filling it (even if '
'the\n'
'expression is "None", it fills the slot). When all arguments have\n'
'been processed, the slots that are still unfilled are filled with '
'the\n'
'corresponding default value from the function definition. '
'(Default\n'
'values are calculated, once, when the function is defined; thus, a\n'
'mutable object such as a list or dictionary used as default value '
'will\n'
'be shared by all calls that don’t specify an argument value for '
'the\n'
'corresponding slot; this should usually be avoided.) If there are '
'any\n'
'unfilled slots for which no default value is specified, a '
'"TypeError"\n'
'exception is raised. Otherwise, the list of filled slots is used '
'as\n'
'the argument list for the call.\n'
'\n'
'**CPython implementation detail:** An implementation may provide\n'
'built-in functions whose positional parameters do not have names, '
'even\n'
'if they are ‘named’ for the purpose of documentation, and which\n'
'therefore cannot be supplied by keyword. In CPython, this is the '
'case\n'
'for functions implemented in C that use "PyArg_ParseTuple()" to '
'parse\n'
'their arguments.\n'
'\n'
'If there are more positional arguments than there are formal '
'parameter\n'
'slots, a "TypeError" exception is raised, unless a formal '
'parameter\n'
'using the syntax "*identifier" is present; in this case, that '
'formal\n'
'parameter receives a tuple containing the excess positional '
'arguments\n'
'(or an empty tuple if there were no excess positional arguments).\n'
'\n'
'If any keyword argument does not correspond to a formal parameter\n'
'name, a "TypeError" exception is raised, unless a formal parameter\n'
'using the syntax "**identifier" is present; in this case, that '
'formal\n'
'parameter receives a dictionary containing the excess keyword\n'
'arguments (using the keywords as keys and the argument values as\n'
'corresponding values), or a (new) empty dictionary if there were '
'no\n'
'excess keyword arguments.\n'
'\n'
'If the syntax "*expression" appears in the function call, '
'"expression"\n'
'must evaluate to an *iterable*. Elements from these iterables are\n'
'treated as if they were additional positional arguments. For the '
'call\n'
'"f(x1, x2, *y, x3, x4)", if *y* evaluates to a sequence *y1*, …, '
'*yM*,\n'
'this is equivalent to a call with M+4 positional arguments *x1*, '
'*x2*,\n'
'*y1*, …, *yM*, *x3*, *x4*.\n'
'\n'
'A consequence of this is that although the "*expression" syntax '
'may\n'
'appear *after* explicit keyword arguments, it is processed '
'*before*\n'
'the keyword arguments (and any "**expression" arguments – see '
'below).\n'
'So:\n'
'\n'
' >>> def f(a, b):\n'
' ... print(a, b)\n'
' ...\n'
' >>> f(b=1, *(2,))\n'
' 2 1\n'
' >>> f(a=1, *(2,))\n'
' Traceback (most recent call last):\n'
' File "<stdin>", line 1, in <module>\n'
" TypeError: f() got multiple values for keyword argument 'a'\n"
' >>> f(1, *(2,))\n'
' 1 2\n'
'\n'
'It is unusual for both keyword arguments and the "*expression" '
'syntax\n'
'to be used in the same call, so in practice this confusion does '
'not\n'
'arise.\n'
'\n'
'If the syntax "**expression" appears in the function call,\n'
'"expression" must evaluate to a *mapping*, the contents of which '
'are\n'
'treated as additional keyword arguments. If a keyword is already\n'
'present (as an explicit keyword argument, or from another '
'unpacking),\n'
'a "TypeError" exception is raised.\n'
'\n'
'Formal parameters using the syntax "*identifier" or "**identifier"\n'
'cannot be used as positional argument slots or as keyword argument\n'
'names.\n'
'\n'
'Changed in version 3.5: Function calls accept any number of "*" '
'and\n'
'"**" unpackings, positional arguments may follow iterable '
'unpackings\n'
'("*"), and keyword arguments may follow dictionary unpackings '
'("**").\n'
'Originally proposed by **PEP 448**.\n'
'\n'
'A call always returns some value, possibly "None", unless it raises '
'an\n'
'exception. How this value is computed depends on the type of the\n'
'callable object.\n'
'\n'
'If it is—\n'
'\n'
'a user-defined function:\n'
' The code block for the function is executed, passing it the\n'
' argument list. The first thing the code block will do is bind '
'the\n'
' formal parameters to the arguments; this is described in '
'section\n'
' Function definitions. When the code block executes a "return"\n'
' statement, this specifies the return value of the function '
'call.\n'
'\n'
'a built-in function or method:\n'
' The result is up to the interpreter; see Built-in Functions for '
'the\n'
' descriptions of built-in functions and methods.\n'
'\n'
'a class object:\n'
' A new instance of that class is returned.\n'
'\n'
'a class instance method:\n'
' The corresponding user-defined function is called, with an '
'argument\n'
' list that is one longer than the argument list of the call: the\n'
' instance becomes the first argument.\n'
'\n'
'a class instance:\n'
' The class must define a "__call__()" method; the effect is then '
'the\n'
' same as if that method was called.\n',
'class': 'Class definitions\n'
'*****************\n'
'\n'
'A class definition defines a class object (see section The '
'standard\n'
'type hierarchy):\n'
'\n'
' classdef ::= [decorators] "class" classname [inheritance] ":" '
'suite\n'
' inheritance ::= "(" [argument_list] ")"\n'
' classname ::= identifier\n'
'\n'
'A class definition is an executable statement. The inheritance '
'list\n'
'usually gives a list of base classes (see Metaclasses for more\n'
'advanced uses), so each item in the list should evaluate to a '
'class\n'
'object which allows subclassing. Classes without an inheritance '
'list\n'
'inherit, by default, from the base class "object"; hence,\n'
'\n'
' class Foo:\n'
' pass\n'
'\n'
'is equivalent to\n'
'\n'
' class Foo(object):\n'
' pass\n'
'\n'
'The class’s suite is then executed in a new execution frame (see\n'
'Naming and binding), using a newly created local namespace and the\n'
'original global namespace. (Usually, the suite contains mostly\n'
'function definitions.) When the class’s suite finishes execution, '
'its\n'
'execution frame is discarded but its local namespace is saved. [3] '
'A\n'
'class object is then created using the inheritance list for the '
'base\n'
'classes and the saved local namespace for the attribute '
'dictionary.\n'
'The class name is bound to this class object in the original local\n'
'namespace.\n'
'\n'
'The order in which attributes are defined in the class body is\n'
'preserved in the new class’s "__dict__". Note that this is '
'reliable\n'
'only right after the class is created and only for classes that '
'were\n'
'defined using the definition syntax.\n'
'\n'
'Class creation can be customized heavily using metaclasses.\n'
'\n'
'Classes can also be decorated: just like when decorating '
'functions,\n'
'\n'
' @f1(arg)\n'
' @f2\n'
' class Foo: pass\n'
'\n'
'is roughly equivalent to\n'
'\n'
' class Foo: pass\n'
' Foo = f1(arg)(f2(Foo))\n'
'\n'
'The evaluation rules for the decorator expressions are the same as '
'for\n'
'function decorators. The result is then bound to the class name.\n'
'\n'
'**Programmer’s note:** Variables defined in the class definition '
'are\n'
'class attributes; they are shared by instances. Instance '
'attributes\n'
'can be set in a method with "self.name = value". Both class and\n'
'instance attributes are accessible through the notation '
'“"self.name"”,\n'
'and an instance attribute hides a class attribute with the same '
'name\n'
'when accessed in this way. Class attributes can be used as '
'defaults\n'
'for instance attributes, but using mutable values there can lead '
'to\n'
'unexpected results. Descriptors can be used to create instance\n'
'variables with different implementation details.\n'
'\n'
'See also:\n'
'\n'
' **PEP 3115** - Metaclasses in Python 3000\n'
' The proposal that changed the declaration of metaclasses to '
'the\n'
' current syntax, and the semantics for how classes with\n'
' metaclasses are constructed.\n'
'\n'
' **PEP 3129** - Class Decorators\n'
' The proposal that added class decorators. Function and '
'method\n'
' decorators were introduced in **PEP 318**.\n',
'comparisons': 'Comparisons\n'
'***********\n'
'\n'
'Unlike C, all comparison operations in Python have the same '
'priority,\n'
'which is lower than that of any arithmetic, shifting or '
'bitwise\n'
'operation. Also unlike C, expressions like "a < b < c" have '
'the\n'
'interpretation that is conventional in mathematics:\n'
'\n'
' comparison ::= or_expr (comp_operator or_expr)*\n'
' comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="\n'
' | "is" ["not"] | ["not"] "in"\n'
'\n'
'Comparisons yield boolean values: "True" or "False".\n'
'\n'
'Comparisons can be chained arbitrarily, e.g., "x < y <= z" '
'is\n'
'equivalent to "x < y and y <= z", except that "y" is '
'evaluated only\n'
'once (but in both cases "z" is not evaluated at all when "x < '
'y" is\n'
'found to be false).\n'
'\n'
'Formally, if *a*, *b*, *c*, …, *y*, *z* are expressions and '
'*op1*,\n'
'*op2*, …, *opN* are comparison operators, then "a op1 b op2 c '
'... y\n'
'opN z" is equivalent to "a op1 b and b op2 c and ... y opN '
'z", except\n'
'that each expression is evaluated at most once.\n'
'\n'
'Note that "a op1 b op2 c" doesn’t imply any kind of '
'comparison between\n'
'*a* and *c*, so that, e.g., "x < y > z" is perfectly legal '
'(though\n'
'perhaps not pretty).\n'
'\n'
'\n'
'Value comparisons\n'
'=================\n'
'\n'
'The operators "<", ">", "==", ">=", "<=", and "!=" compare '
'the values\n'
'of two objects. The objects do not need to have the same '
'type.\n'
'\n'
'Chapter Objects, values and types states that objects have a '
'value (in\n'
'addition to type and identity). The value of an object is a '
'rather\n'
'abstract notion in Python: For example, there is no canonical '
'access\n'
'method for an object’s value. Also, there is no requirement '
'that the\n'
'value of an object should be constructed in a particular way, '
'e.g.\n'
'comprised of all its data attributes. Comparison operators '
'implement a\n'
'particular notion of what the value of an object is. One can '
'think of\n'
'them as defining the value of an object indirectly, by means '
'of their\n'
'comparison implementation.\n'
'\n'
'Because all types are (direct or indirect) subtypes of '
'"object", they\n'
'inherit the default comparison behavior from "object". Types '
'can\n'
'customize their comparison behavior by implementing *rich '
'comparison\n'
'methods* like "__lt__()", described in Basic customization.\n'
'\n'
'The default behavior for equality comparison ("==" and "!=") '
'is based\n'
'on the identity of the objects. Hence, equality comparison '
'of\n'
'instances with the same identity results in equality, and '
'equality\n'
'comparison of instances with different identities results in\n'
'inequality. A motivation for this default behavior is the '
'desire that\n'
'all objects should be reflexive (i.e. "x is y" implies "x == '
'y").\n'
'\n'
'A default order comparison ("<", ">", "<=", and ">=") is not '
'provided;\n'
'an attempt raises "TypeError". A motivation for this default '
'behavior\n'
'is the lack of a similar invariant as for equality.\n'
'\n'
'The behavior of the default equality comparison, that '
'instances with\n'
'different identities are always unequal, may be in contrast '
'to what\n'
'types will need that have a sensible definition of object '
'value and\n'
'value-based equality. Such types will need to customize '
'their\n'
'comparison behavior, and in fact, a number of built-in types '
'have done\n'
'that.\n'
'\n'
'The following list describes the comparison behavior of the '
'most\n'
'important built-in types.\n'
'\n'
'* Numbers of built-in numeric types (Numeric Types — int, '
'float,\n'
' complex) and of the standard library types '
'"fractions.Fraction" and\n'
' "decimal.Decimal" can be compared within and across their '
'types,\n'
' with the restriction that complex numbers do not support '
'order\n'
' comparison. Within the limits of the types involved, they '
'compare\n'
' mathematically (algorithmically) correct without loss of '
'precision.\n'
'\n'
' The not-a-number values "float(\'NaN\')" and '
'"Decimal(\'NaN\')" are\n'
' special. They are identical to themselves ("x is x" is '
'true) but\n'
' are not equal to themselves ("x == x" is false). '
'Additionally,\n'
' comparing any number to a not-a-number value will return '
'"False".\n'
' For example, both "3 < float(\'NaN\')" and "float(\'NaN\') '
'< 3" will\n'
' return "False".\n'
'\n'
'* Binary sequences (instances of "bytes" or "bytearray") can '
'be\n'
' compared within and across their types. They compare\n'
' lexicographically using the numeric values of their '
'elements.\n'
'\n'
'* Strings (instances of "str") compare lexicographically '
'using the\n'
' numerical Unicode code points (the result of the built-in '
'function\n'
' "ord()") of their characters. [3]\n'
'\n'
' Strings and binary sequences cannot be directly compared.\n'
'\n'
'* Sequences (instances of "tuple", "list", or "range") can '
'be\n'
' compared only within each of their types, with the '
'restriction that\n'
' ranges do not support order comparison. Equality '
'comparison across\n'
' these types results in inequality, and ordering comparison '
'across\n'
' these types raises "TypeError".\n'
'\n'
' Sequences compare lexicographically using comparison of\n'
' corresponding elements, whereby reflexivity of the elements '
'is\n'
' enforced.\n'
'\n'
' In enforcing reflexivity of elements, the comparison of '
'collections\n'
' assumes that for a collection element "x", "x == x" is '
'always true.\n'
' Based on that assumption, element identity is compared '
'first, and\n'
' element comparison is performed only for distinct '
'elements. This\n'
' approach yields the same result as a strict element '
'comparison\n'
' would, if the compared elements are reflexive. For '
'non-reflexive\n'
' elements, the result is different than for strict element\n'
' comparison, and may be surprising: The non-reflexive '
'not-a-number\n'
' values for example result in the following comparison '
'behavior when\n'
' used in a list:\n'
'\n'
" >>> nan = float('NaN')\n"
' >>> nan is nan\n'
' True\n'
' >>> nan == nan\n'
' False <-- the defined non-reflexive '
'behavior of NaN\n'
' >>> [nan] == [nan]\n'
' True <-- list enforces reflexivity and '
'tests identity first\n'
'\n'
' Lexicographical comparison between built-in collections '
'works as\n'
' follows:\n'
'\n'
' * For two collections to compare equal, they must be of the '
'same\n'
' type, have the same length, and each pair of '
'corresponding\n'
' elements must compare equal (for example, "[1,2] == '
'(1,2)" is\n'
' false because the type is not the same).\n'
'\n'
' * Collections that support order comparison are ordered the '
'same\n'
' as their first unequal elements (for example, "[1,2,x] <= '
'[1,2,y]"\n'
' has the same value as "x <= y"). If a corresponding '
'element does\n'
' not exist, the shorter collection is ordered first (for '
'example,\n'
' "[1,2] < [1,2,3]" is true).\n'
'\n'
'* Mappings (instances of "dict") compare equal if and only if '
'they\n'
' have equal *(key, value)* pairs. Equality comparison of the '
'keys and\n'
' values enforces reflexivity.\n'
'\n'
' Order comparisons ("<", ">", "<=", and ">=") raise '
'"TypeError".\n'
'\n'
'* Sets (instances of "set" or "frozenset") can be compared '
'within\n'
' and across their types.\n'
'\n'
' They define order comparison operators to mean subset and '
'superset\n'
' tests. Those relations do not define total orderings (for '
'example,\n'
' the two sets "{1,2}" and "{2,3}" are not equal, nor subsets '
'of one\n'
' another, nor supersets of one another). Accordingly, sets '
'are not\n'
' appropriate arguments for functions which depend on total '
'ordering\n'
' (for example, "min()", "max()", and "sorted()" produce '
'undefined\n'
' results given a list of sets as inputs).\n'
'\n'
' Comparison of sets enforces reflexivity of its elements.\n'
'\n'
'* Most other built-in types have no comparison methods '
'implemented,\n'
' so they inherit the default comparison behavior.\n'
'\n'
'User-defined classes that customize their comparison behavior '
'should\n'
'follow some consistency rules, if possible:\n'
'\n'
'* Equality comparison should be reflexive. In other words, '
'identical\n'
' objects should compare equal:\n'
'\n'
' "x is y" implies "x == y"\n'
'\n'
'* Comparison should be symmetric. In other words, the '
'following\n'
' expressions should have the same result:\n'
'\n'
' "x == y" and "y == x"\n'
'\n'
' "x != y" and "y != x"\n'
'\n'
' "x < y" and "y > x"\n'
'\n'
' "x <= y" and "y >= x"\n'
'\n'
'* Comparison should be transitive. The following '
'(non-exhaustive)\n'
' examples illustrate that:\n'
'\n'
' "x > y and y > z" implies "x > z"\n'
'\n'
' "x < y and y <= z" implies "x < z"\n'
'\n'
'* Inverse comparison should result in the boolean negation. '
'In other\n'
' words, the following expressions should have the same '
'result:\n'
'\n'
' "x == y" and "not x != y"\n'
'\n'
' "x < y" and "not x >= y" (for total ordering)\n'
'\n'
' "x > y" and "not x <= y" (for total ordering)\n'
'\n'
' The last two expressions apply to totally ordered '
'collections (e.g.\n'
' to sequences, but not to sets or mappings). See also the\n'
' "total_ordering()" decorator.\n'
'\n'
'* The "hash()" result should be consistent with equality. '
'Objects\n'
' that are equal should either have the same hash value, or '
'be marked\n'
' as unhashable.\n'
'\n'
'Python does not enforce these consistency rules. In fact, '
'the\n'
'not-a-number values are an example for not following these '
'rules.\n'
'\n'
'\n'
'Membership test operations\n'
'==========================\n'
'\n'
'The operators "in" and "not in" test for membership. "x in '
's"\n'
'evaluates to "True" if *x* is a member of *s*, and "False" '
'otherwise.\n'
'"x not in s" returns the negation of "x in s". All built-in '
'sequences\n'
'and set types support this as well as dictionary, for which '
'"in" tests\n'
'whether the dictionary has a given key. For container types '
'such as\n'
'list, tuple, set, frozenset, dict, or collections.deque, the\n'
'expression "x in y" is equivalent to "any(x is e or x == e '
'for e in\n'
'y)".\n'
'\n'
'For the string and bytes types, "x in y" is "True" if and '
'only if *x*\n'
'is a substring of *y*. An equivalent test is "y.find(x) != '
'-1".\n'
'Empty strings are always considered to be a substring of any '
'other\n'
'string, so """ in "abc"" will return "True".\n'
'\n'
'For user-defined classes which define the "__contains__()" '
'method, "x\n'
'in y" returns "True" if "y.__contains__(x)" returns a true '
'value, and\n'
'"False" otherwise.\n'
'\n'
'For user-defined classes which do not define "__contains__()" '
'but do\n'
'define "__iter__()", "x in y" is "True" if some value "z" '
'with "x ==\n'
'z" is produced while iterating over "y". If an exception is '
'raised\n'
'during the iteration, it is as if "in" raised that '
'exception.\n'
'\n'
'Lastly, the old-style iteration protocol is tried: if a class '
'defines\n'
'"__getitem__()", "x in y" is "True" if and only if there is a '
'non-\n'
'negative integer index *i* such that "x == y[i]", and all '
'lower\n'
'integer indices do not raise "IndexError" exception. (If any '
'other\n'
'exception is raised, it is as if "in" raised that '
'exception).\n'
'\n'
'The operator "not in" is defined to have the inverse true '
'value of\n'
'"in".\n'
'\n'
'\n'
'Identity comparisons\n'
'====================\n'
'\n'
'The operators "is" and "is not" test for object identity: "x '
'is y" is\n'
'true if and only if *x* and *y* are the same object. Object '
'identity\n'
'is determined using the "id()" function. "x is not y" yields '
'the\n'
'inverse truth value. [4]\n',
'compound': 'Compound statements\n'
'*******************\n'
'\n'
'Compound statements contain (groups of) other statements; they '
'affect\n'
'or control the execution of those other statements in some way. '
'In\n'
'general, compound statements span multiple lines, although in '
'simple\n'
'incarnations a whole compound statement may be contained in one '
'line.\n'
'\n'
'The "if", "while" and "for" statements implement traditional '
'control\n'
'flow constructs. "try" specifies exception handlers and/or '
'cleanup\n'
'code for a group of statements, while the "with" statement '
'allows the\n'
'execution of initialization and finalization code around a block '
'of\n'
'code. Function and class definitions are also syntactically '
'compound\n'
'statements.\n'
'\n'
'A compound statement consists of one or more ‘clauses.’ A '
'clause\n'
'consists of a header and a ‘suite.’ The clause headers of a\n'
'particular compound statement are all at the same indentation '
'level.\n'
'Each clause header begins with a uniquely identifying keyword '
'and ends\n'
'with a colon. A suite is a group of statements controlled by a\n'
'clause. A suite can be one or more semicolon-separated simple\n'
'statements on the same line as the header, following the '
'header’s\n'
'colon, or it can be one or more indented statements on '
'subsequent\n'
'lines. Only the latter form of a suite can contain nested '
'compound\n'
'statements; the following is illegal, mostly because it wouldn’t '
'be\n'
'clear to which "if" clause a following "else" clause would '
'belong:\n'
'\n'
' if test1: if test2: print(x)\n'
'\n'
'Also note that the semicolon binds tighter than the colon in '
'this\n'
'context, so that in the following example, either all or none of '
'the\n'
'"print()" calls are executed:\n'
'\n'
' if x < y < z: print(x); print(y); print(z)\n'
'\n'
'Summarizing:\n'
'\n'
' compound_stmt ::= if_stmt\n'
' | while_stmt\n'
' | for_stmt\n'
' | try_stmt\n'
' | with_stmt\n'
' | funcdef\n'
' | classdef\n'
' | async_with_stmt\n'
' | async_for_stmt\n'
' | async_funcdef\n'
' suite ::= stmt_list NEWLINE | NEWLINE INDENT '
'statement+ DEDENT\n'
' statement ::= stmt_list NEWLINE | compound_stmt\n'
' stmt_list ::= simple_stmt (";" simple_stmt)* [";"]\n'
'\n'
'Note that statements always end in a "NEWLINE" possibly followed '
'by a\n'
'"DEDENT". Also note that optional continuation clauses always '
'begin\n'
'with a keyword that cannot start a statement, thus there are no\n'
'ambiguities (the ‘dangling "else"’ problem is solved in Python '
'by\n'
'requiring nested "if" statements to be indented).\n'
'\n'
'The formatting of the grammar rules in the following sections '
'places\n'
'each clause on a separate line for clarity.\n'
'\n'
'\n'
'The "if" statement\n'
'==================\n'
'\n'
'The "if" statement is used for conditional execution:\n'
'\n'
' if_stmt ::= "if" expression ":" suite\n'
' ("elif" expression ":" suite)*\n'
' ["else" ":" suite]\n'
'\n'
'It selects exactly one of the suites by evaluating the '
'expressions one\n'
'by one until one is found to be true (see section Boolean '
'operations\n'
'for the definition of true and false); then that suite is '
'executed\n'
'(and no other part of the "if" statement is executed or '
'evaluated).\n'
'If all expressions are false, the suite of the "else" clause, '
'if\n'
'present, is executed.\n'
'\n'
'\n'
'The "while" statement\n'
'=====================\n'
'\n'
'The "while" statement is used for repeated execution as long as '
'an\n'
'expression is true:\n'
'\n'
' while_stmt ::= "while" expression ":" suite\n'
' ["else" ":" suite]\n'
'\n'
'This repeatedly tests the expression and, if it is true, '
'executes the\n'
'first suite; if the expression is false (which may be the first '
'time\n'
'it is tested) the suite of the "else" clause, if present, is '
'executed\n'
'and the loop terminates.\n'
'\n'
'A "break" statement executed in the first suite terminates the '
'loop\n'
'without executing the "else" clause’s suite. A "continue" '
'statement\n'
'executed in the first suite skips the rest of the suite and goes '
'back\n'
'to testing the expression.\n'
'\n'
'\n'
'The "for" statement\n'
'===================\n'
'\n'
'The "for" statement is used to iterate over the elements of a '
'sequence\n'
'(such as a string, tuple or list) or other iterable object:\n'
'\n'
' for_stmt ::= "for" target_list "in" expression_list ":" '
'suite\n'
' ["else" ":" suite]\n'
'\n'
'The expression list is evaluated once; it should yield an '
'iterable\n'
'object. An iterator is created for the result of the\n'
'"expression_list". The suite is then executed once for each '
'item\n'
'provided by the iterator, in the order returned by the '
'iterator. Each\n'
'item in turn is assigned to the target list using the standard '
'rules\n'
'for assignments (see Assignment statements), and then the suite '
'is\n'
'executed. When the items are exhausted (which is immediately '
'when the\n'
'sequence is empty or an iterator raises a "StopIteration" '
'exception),\n'
'the suite in the "else" clause, if present, is executed, and the '
'loop\n'
'terminates.\n'
'\n'
'A "break" statement executed in the first suite terminates the '
'loop\n'
'without executing the "else" clause’s suite. A "continue" '
'statement\n'
'executed in the first suite skips the rest of the suite and '
'continues\n'
'with the next item, or with the "else" clause if there is no '
'next\n'
'item.\n'
'\n'
'The for-loop makes assignments to the variables(s) in the target '
'list.\n'
'This overwrites all previous assignments to those variables '
'including\n'
'those made in the suite of the for-loop:\n'
'\n'
' for i in range(10):\n'
' print(i)\n'
' i = 5 # this will not affect the for-loop\n'
' # because i will be overwritten with '
'the next\n'
' # index in the range\n'
'\n'
'Names in the target list are not deleted when the loop is '
'finished,\n'
'but if the sequence is empty, they will not have been assigned '
'to at\n'
'all by the loop. Hint: the built-in function "range()" returns '
'an\n'
'iterator of integers suitable to emulate the effect of Pascal’s '
'"for i\n'
':= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, '
'2]".\n'
'\n'
'Note: There is a subtlety when the sequence is being modified by '
'the\n'
' loop (this can only occur for mutable sequences, e.g. lists). '
'An\n'
' internal counter is used to keep track of which item is used '
'next,\n'
' and this is incremented on each iteration. When this counter '
'has\n'
' reached the length of the sequence the loop terminates. This '
'means\n'
' that if the suite deletes the current (or a previous) item '
'from the\n'
' sequence, the next item will be skipped (since it gets the '
'index of\n'
' the current item which has already been treated). Likewise, '
'if the\n'
' suite inserts an item in the sequence before the current item, '
'the\n'
' current item will be treated again the next time through the '
'loop.\n'
' This can lead to nasty bugs that can be avoided by making a\n'
' temporary copy using a slice of the whole sequence, e.g.,\n'
'\n'
' for x in a[:]:\n'
' if x < 0: a.remove(x)\n'
'\n'
'\n'
'The "try" statement\n'
'===================\n'
'\n'
'The "try" statement specifies exception handlers and/or cleanup '
'code\n'
'for a group of statements:\n'
'\n'
' try_stmt ::= try1_stmt | try2_stmt\n'
' try1_stmt ::= "try" ":" suite\n'
' ("except" [expression ["as" identifier]] ":" '
'suite)+\n'
' ["else" ":" suite]\n'
' ["finally" ":" suite]\n'
' try2_stmt ::= "try" ":" suite\n'
' "finally" ":" suite\n'
'\n'
'The "except" clause(s) specify one or more exception handlers. '
'When no\n'
'exception occurs in the "try" clause, no exception handler is\n'
'executed. When an exception occurs in the "try" suite, a search '
'for an\n'
'exception handler is started. This search inspects the except '
'clauses\n'
'in turn until one is found that matches the exception. An '
'expression-\n'
'less except clause, if present, must be last; it matches any\n'
'exception. For an except clause with an expression, that '
'expression\n'
'is evaluated, and the clause matches the exception if the '
'resulting\n'
'object is “compatible” with the exception. An object is '
'compatible\n'
'with an exception if it is the class or a base class of the '
'exception\n'
'object or a tuple containing an item compatible with the '
'exception.\n'
'\n'
'If no except clause matches the exception, the search for an '
'exception\n'
'handler continues in the surrounding code and on the invocation '
'stack.\n'
'[1]\n'
'\n'
'If the evaluation of an expression in the header of an except '
'clause\n'
'raises an exception, the original search for a handler is '
'canceled and\n'
'a search starts for the new exception in the surrounding code '
'and on\n'
'the call stack (it is treated as if the entire "try" statement '
'raised\n'
'the exception).\n'
'\n'
'When a matching except clause is found, the exception is '
'assigned to\n'
'the target specified after the "as" keyword in that except '
'clause, if\n'
'present, and the except clause’s suite is executed. All except\n'
'clauses must have an executable block. When the end of this '
'block is\n'
'reached, execution continues normally after the entire try '
'statement.\n'
'(This means that if two nested handlers exist for the same '
'exception,\n'
'and the exception occurs in the try clause of the inner handler, '
'the\n'
'outer handler will not handle the exception.)\n'
'\n'
'When an exception has been assigned using "as target", it is '
'cleared\n'
'at the end of the except clause. This is as if\n'
'\n'
' except E as N:\n'
' foo\n'
'\n'
'was translated to\n'
'\n'
' except E as N:\n'
' try:\n'
' foo\n'
' finally:\n'
' del N\n'
'\n'
'This means the exception must be assigned to a different name to '
'be\n'
'able to refer to it after the except clause. Exceptions are '
'cleared\n'
'because with the traceback attached to them, they form a '
'reference\n'
'cycle with the stack frame, keeping all locals in that frame '
'alive\n'
'until the next garbage collection occurs.\n'
'\n'
'Before an except clause’s suite is executed, details about the\n'
'exception are stored in the "sys" module and can be accessed '
'via\n'
'"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting '
'of the\n'
'exception class, the exception instance and a traceback object '
'(see\n'
'section The standard type hierarchy) identifying the point in '
'the\n'
'program where the exception occurred. "sys.exc_info()" values '
'are\n'
'restored to their previous values (before the call) when '
'returning\n'
'from a function that handled an exception.\n'
'\n'
'The optional "else" clause is executed if the control flow '
'leaves the\n'
'"try" suite, no exception was raised, and no "return", '
'"continue", or\n'
'"break" statement was executed. Exceptions in the "else" clause '
'are\n'
'not handled by the preceding "except" clauses.\n'
'\n'
'If "finally" is present, it specifies a ‘cleanup’ handler. The '
'"try"\n'
'clause is executed, including any "except" and "else" clauses. '
'If an\n'
'exception occurs in any of the clauses and is not handled, the\n'
'exception is temporarily saved. The "finally" clause is '
'executed. If\n'
'there is a saved exception it is re-raised at the end of the '
'"finally"\n'
'clause. If the "finally" clause raises another exception, the '
'saved\n'
'exception is set as the context of the new exception. If the '
'"finally"\n'
'clause executes a "return" or "break" statement, the saved '
'exception\n'
'is discarded:\n'
'\n'
' >>> def f():\n'
' ... try:\n'
' ... 1/0\n'
' ... finally:\n'
' ... return 42\n'
' ...\n'
' >>> f()\n'
' 42\n'
'\n'
'The exception information is not available to the program '
'during\n'
'execution of the "finally" clause.\n'
'\n'
'When a "return", "break" or "continue" statement is executed in '
'the\n'
'"try" suite of a "try"…"finally" statement, the "finally" clause '
'is\n'
'also executed ‘on the way out.’ A "continue" statement is '
'illegal in\n'
'the "finally" clause. (The reason is a problem with the current\n'
'implementation — this restriction may be lifted in the future).\n'
'\n'
'The return value of a function is determined by the last '
'"return"\n'
'statement executed. Since the "finally" clause always executes, '
'a\n'
'"return" statement executed in the "finally" clause will always '
'be the\n'
'last one executed:\n'
'\n'
' >>> def foo():\n'
' ... try:\n'
" ... return 'try'\n"
' ... finally:\n'
" ... return 'finally'\n"
' ...\n'
' >>> foo()\n'
" 'finally'\n"
'\n'
'Additional information on exceptions can be found in section\n'
'Exceptions, and information on using the "raise" statement to '
'generate\n'
'exceptions may be found in section The raise statement.\n'
'\n'
'\n'
'The "with" statement\n'
'====================\n'
'\n'
'The "with" statement is used to wrap the execution of a block '
'with\n'
'methods defined by a context manager (see section With '
'Statement\n'
'Context Managers). This allows common "try"…"except"…"finally" '
'usage\n'
'patterns to be encapsulated for convenient reuse.\n'
'\n'
' with_stmt ::= "with" with_item ("," with_item)* ":" suite\n'
' with_item ::= expression ["as" target]\n'
'\n'
'The execution of the "with" statement with one “item” proceeds '
'as\n'
'follows:\n'
'\n'
'1. The context expression (the expression given in the '
'"with_item")\n'
' is evaluated to obtain a context manager.\n'
'\n'
'2. The context manager’s "__exit__()" is loaded for later use.\n'
'\n'
'3. The context manager’s "__enter__()" method is invoked.\n'
'\n'
'4. If a target was included in the "with" statement, the return\n'
' value from "__enter__()" is assigned to it.\n'
'\n'
' Note: The "with" statement guarantees that if the '
'"__enter__()"\n'
' method returns without an error, then "__exit__()" will '
'always be\n'
' called. Thus, if an error occurs during the assignment to '
'the\n'
' target list, it will be treated the same as an error '
'occurring\n'
' within the suite would be. See step 6 below.\n'
'\n'
'5. The suite is executed.\n'
'\n'
'6. The context manager’s "__exit__()" method is invoked. If an\n'
' exception caused the suite to be exited, its type, value, '
'and\n'
' traceback are passed as arguments to "__exit__()". Otherwise, '
'three\n'
' "None" arguments are supplied.\n'
'\n'
' If the suite was exited due to an exception, and the return '
'value\n'
' from the "__exit__()" method was false, the exception is '
'reraised.\n'
' If the return value was true, the exception is suppressed, '
'and\n'
' execution continues with the statement following the "with"\n'
' statement.\n'
'\n'
' If the suite was exited for any reason other than an '
'exception, the\n'
' return value from "__exit__()" is ignored, and execution '
'proceeds\n'
' at the normal location for the kind of exit that was taken.\n'
'\n'
'With more than one item, the context managers are processed as '
'if\n'
'multiple "with" statements were nested:\n'
'\n'
' with A() as a, B() as b:\n'
' suite\n'
'\n'
'is equivalent to\n'
'\n'
' with A() as a:\n'
' with B() as b:\n'
' suite\n'
'\n'
'Changed in version 3.1: Support for multiple context '
'expressions.\n'
'\n'
'See also:\n'
'\n'
' **PEP 343** - The “with” statement\n'
' The specification, background, and examples for the Python '
'"with"\n'
' statement.\n'
'\n'
'\n'
'Function definitions\n'
'====================\n'
'\n'
'A function definition defines a user-defined function object '
'(see\n'
'section The standard type hierarchy):\n'
'\n'
' funcdef ::= [decorators] "def" funcname "(" '
'[parameter_list] ")"\n'
' ["->" expression] ":" suite\n'
' decorators ::= decorator+\n'
' decorator ::= "@" dotted_name ["(" '
'[argument_list [","]] ")"] NEWLINE\n'
' dotted_name ::= identifier ("." identifier)*\n'
' parameter_list ::= defparameter ("," defparameter)* '
'["," [parameter_list_starargs]]\n'
' | parameter_list_starargs\n'
' parameter_list_starargs ::= "*" [parameter] ("," '
'defparameter)* ["," ["**" parameter [","]]]\n'
' | "**" parameter [","]\n'
' parameter ::= identifier [":" expression]\n'
' defparameter ::= parameter ["=" expression]\n'
' funcname ::= identifier\n'
'\n'
'A function definition is an executable statement. Its execution '
'binds\n'
'the function name in the current local namespace to a function '
'object\n'
'(a wrapper around the executable code for the function). This\n'
'function object contains a reference to the current global '
'namespace\n'
'as the global namespace to be used when the function is called.\n'
'\n'
'The function definition does not execute the function body; this '
'gets\n'
'executed only when the function is called. [2]\n'
'\n'
'A function definition may be wrapped by one or more *decorator*\n'
'expressions. Decorator expressions are evaluated when the '
'function is\n'
'defined, in the scope that contains the function definition. '
'The\n'
'result must be a callable, which is invoked with the function '
'object\n'
'as the only argument. The returned value is bound to the '
'function name\n'
'instead of the function object. Multiple decorators are applied '
'in\n'
'nested fashion. For example, the following code\n'
'\n'
' @f1(arg)\n'
' @f2\n'
' def func(): pass\n'
'\n'
'is roughly equivalent to\n'
'\n'
' def func(): pass\n'
' func = f1(arg)(f2(func))\n'
'\n'
'except that the original function is not temporarily bound to '
'the name\n'
'"func".\n'
'\n'
'When one or more *parameters* have the form *parameter* "="\n'
'*expression*, the function is said to have “default parameter '
'values.”\n'
'For a parameter with a default value, the corresponding '
'*argument* may\n'
'be omitted from a call, in which case the parameter’s default '
'value is\n'
'substituted. If a parameter has a default value, all following\n'
'parameters up until the “"*"” must also have a default value — '
'this is\n'
'a syntactic restriction that is not expressed by the grammar.\n'
'\n'
'**Default parameter values are evaluated from left to right when '
'the\n'
'function definition is executed.** This means that the '
'expression is\n'
'evaluated once, when the function is defined, and that the same '
'“pre-\n'
'computed” value is used for each call. This is especially '
'important\n'
'to understand when a default parameter is a mutable object, such '
'as a\n'
'list or a dictionary: if the function modifies the object (e.g. '
'by\n'
'appending an item to a list), the default value is in effect '
'modified.\n'
'This is generally not what was intended. A way around this is '
'to use\n'
'"None" as the default, and explicitly test for it in the body of '
'the\n'
'function, e.g.:\n'
'\n'
' def whats_on_the_telly(penguin=None):\n'
' if penguin is None:\n'
' penguin = []\n'
' penguin.append("property of the zoo")\n'
' return penguin\n'
'\n'
'Function call semantics are described in more detail in section '
'Calls.\n'
'A function call always assigns values to all parameters '
'mentioned in\n'
'the parameter list, either from position arguments, from '
'keyword\n'
'arguments, or from default values. If the form “"*identifier"” '
'is\n'
'present, it is initialized to a tuple receiving any excess '
'positional\n'
'parameters, defaulting to the empty tuple. If the form\n'
'“"**identifier"” is present, it is initialized to a new ordered\n'
'mapping receiving any excess keyword arguments, defaulting to a '
'new\n'
'empty mapping of the same type. Parameters after “"*"” or\n'
'“"*identifier"” are keyword-only parameters and may only be '
'passed\n'
'used keyword arguments.\n'
'\n'
'Parameters may have annotations of the form “": expression"” '
'following\n'
'the parameter name. Any parameter may have an annotation even '
'those\n'
'of the form "*identifier" or "**identifier". Functions may '
'have\n'
'“return” annotation of the form “"-> expression"” after the '
'parameter\n'
'list. These annotations can be any valid Python expression and '
'are\n'
'evaluated when the function definition is executed. Annotations '
'may\n'
'be evaluated in a different order than they appear in the source '
'code.\n'
'The presence of annotations does not change the semantics of a\n'
'function. The annotation values are available as values of a\n'
'dictionary keyed by the parameters’ names in the '
'"__annotations__"\n'
'attribute of the function object.\n'
'\n'
'It is also possible to create anonymous functions (functions not '
'bound\n'
'to a name), for immediate use in expressions. This uses lambda\n'
'expressions, described in section Lambdas. Note that the '
'lambda\n'
'expression is merely a shorthand for a simplified function '
'definition;\n'
'a function defined in a “"def"” statement can be passed around '
'or\n'
'assigned to another name just like a function defined by a '
'lambda\n'
'expression. The “"def"” form is actually more powerful since '
'it\n'
'allows the execution of multiple statements and annotations.\n'
'\n'
'**Programmer’s note:** Functions are first-class objects. A '
'“"def"”\n'
'statement executed inside a function definition defines a local\n'
'function that can be returned or passed around. Free variables '
'used\n'
'in the nested function can access the local variables of the '
'function\n'
'containing the def. See section Naming and binding for '
'details.\n'
'\n'
'See also:\n'
'\n'
' **PEP 3107** - Function Annotations\n'
' The original specification for function annotations.\n'
'\n'
'\n'
'Class definitions\n'
'=================\n'
'\n'
'A class definition defines a class object (see section The '
'standard\n'
'type hierarchy):\n'
'\n'
' classdef ::= [decorators] "class" classname [inheritance] '
'":" suite\n'
' inheritance ::= "(" [argument_list] ")"\n'
' classname ::= identifier\n'
'\n'
'A class definition is an executable statement. The inheritance '
'list\n'
'usually gives a list of base classes (see Metaclasses for more\n'
'advanced uses), so each item in the list should evaluate to a '
'class\n'
'object which allows subclassing. Classes without an inheritance '
'list\n'
'inherit, by default, from the base class "object"; hence,\n'
'\n'
' class Foo:\n'
' pass\n'
'\n'
'is equivalent to\n'
'\n'
' class Foo(object):\n'
' pass\n'
'\n'
'The class’s suite is then executed in a new execution frame '
'(see\n'
'Naming and binding), using a newly created local namespace and '
'the\n'
'original global namespace. (Usually, the suite contains mostly\n'
'function definitions.) When the class’s suite finishes '
'execution, its\n'
'execution frame is discarded but its local namespace is saved. '
'[3] A\n'
'class object is then created using the inheritance list for the '
'base\n'
'classes and the saved local namespace for the attribute '
'dictionary.\n'
'The class name is bound to this class object in the original '
'local\n'
'namespace.\n'
'\n'
'The order in which attributes are defined in the class body is\n'
'preserved in the new class’s "__dict__". Note that this is '
'reliable\n'
'only right after the class is created and only for classes that '
'were\n'
'defined using the definition syntax.\n'
'\n'
'Class creation can be customized heavily using metaclasses.\n'
'\n'
'Classes can also be decorated: just like when decorating '
'functions,\n'
'\n'
' @f1(arg)\n'
' @f2\n'
' class Foo: pass\n'
'\n'
'is roughly equivalent to\n'
'\n'
' class Foo: pass\n'
' Foo = f1(arg)(f2(Foo))\n'
'\n'
'The evaluation rules for the decorator expressions are the same '
'as for\n'
'function decorators. The result is then bound to the class '
'name.\n'
'\n'
'**Programmer’s note:** Variables defined in the class definition '
'are\n'
'class attributes; they are shared by instances. Instance '
'attributes\n'
'can be set in a method with "self.name = value". Both class '
'and\n'
'instance attributes are accessible through the notation '
'“"self.name"”,\n'
'and an instance attribute hides a class attribute with the same '
'name\n'
'when accessed in this way. Class attributes can be used as '
'defaults\n'
'for instance attributes, but using mutable values there can lead '
'to\n'
'unexpected results. Descriptors can be used to create instance\n'
'variables with different implementation details.\n'
'\n'
'See also:\n'
'\n'
' **PEP 3115** - Metaclasses in Python 3000\n'
' The proposal that changed the declaration of metaclasses to '
'the\n'
' current syntax, and the semantics for how classes with\n'
' metaclasses are constructed.\n'
'\n'
' **PEP 3129** - Class Decorators\n'
' The proposal that added class decorators. Function and '
'method\n'
' decorators were introduced in **PEP 318**.\n'
'\n'
'\n'
'Coroutines\n'
'==========\n'
'\n'
'New in version 3.5.\n'
'\n'
'\n'
'Coroutine function definition\n'
'-----------------------------\n'
'\n'
' async_funcdef ::= [decorators] "async" "def" funcname "(" '
'[parameter_list] ")"\n'
' ["->" expression] ":" suite\n'
'\n'
'Execution of Python coroutines can be suspended and resumed at '
'many\n'
'points (see *coroutine*). In the body of a coroutine, any '
'"await" and\n'
'"async" identifiers become reserved keywords; "await" '
'expressions,\n'
'"async for" and "async with" can only be used in coroutine '
'bodies.\n'
'\n'
'Functions defined with "async def" syntax are always coroutine\n'
'functions, even if they do not contain "await" or "async" '
'keywords.\n'
'\n'
'It is a "SyntaxError" to use "yield from" expressions in "async '
'def"\n'
'coroutines.\n'
'\n'
'An example of a coroutine function:\n'
'\n'
' async def func(param1, param2):\n'
' do_stuff()\n'
' await some_coroutine()\n'
'\n'
'\n'
'The "async for" statement\n'
'-------------------------\n'
'\n'
' async_for_stmt ::= "async" for_stmt\n'
'\n'
'An *asynchronous iterable* is able to call asynchronous code in '
'its\n'
'*iter* implementation, and *asynchronous iterator* can call\n'
'asynchronous code in its *next* method.\n'
'\n'
'The "async for" statement allows convenient iteration over\n'
'asynchronous iterators.\n'
'\n'
'The following code:\n'
'\n'
' async for TARGET in ITER:\n'
' BLOCK\n'
' else:\n'
' BLOCK2\n'
'\n'
'Is semantically equivalent to:\n'
'\n'
' iter = (ITER)\n'
' iter = type(iter).__aiter__(iter)\n'
' running = True\n'
' while running:\n'
' try:\n'
' TARGET = await type(iter).__anext__(iter)\n'
' except StopAsyncIteration:\n'
' running = False\n'
' else:\n'
' BLOCK\n'
' else:\n'
' BLOCK2\n'
'\n'
'See also "__aiter__()" and "__anext__()" for details.\n'
'\n'
'It is a "SyntaxError" to use "async for" statement outside of '
'an\n'
'"async def" function.\n'
'\n'
'\n'
'The "async with" statement\n'
'--------------------------\n'
'\n'
' async_with_stmt ::= "async" with_stmt\n'
'\n'
'An *asynchronous context manager* is a *context manager* that is '
'able\n'
'to suspend execution in its *enter* and *exit* methods.\n'
'\n'
'The following code:\n'
'\n'
' async with EXPR as VAR:\n'
' BLOCK\n'
'\n'
'Is semantically equivalent to:\n'
'\n'
' mgr = (EXPR)\n'
' aexit = type(mgr).__aexit__\n'
' aenter = type(mgr).__aenter__(mgr)\n'
'\n'
' VAR = await aenter\n'
' try:\n'
' BLOCK\n'
' except:\n'
' if not await aexit(mgr, *sys.exc_info()):\n'
' raise\n'
' else:\n'
' await aexit(mgr, None, None, None)\n'
'\n'
'See also "__aenter__()" and "__aexit__()" for details.\n'
'\n'
'It is a "SyntaxError" to use "async with" statement outside of '
'an\n'
'"async def" function.\n'
'\n'
'See also:\n'
'\n'
' **PEP 492** - Coroutines with async and await syntax\n'
' The proposal that made coroutines a proper standalone '
'concept in\n'
' Python, and added supporting syntax.\n'
'\n'
'-[ Footnotes ]-\n'
'\n'
'[1] The exception is propagated to the invocation stack unless\n'
' there is a "finally" clause which happens to raise another\n'
' exception. That new exception causes the old one to be '
'lost.\n'
'\n'
'[2] A string literal appearing as the first statement in the\n'
' function body is transformed into the function’s "__doc__"\n'
' attribute and therefore the function’s *docstring*.\n'
'\n'
'[3] A string literal appearing as the first statement in the '
'class\n'
' body is transformed into the namespace’s "__doc__" item and\n'
' therefore the class’s *docstring*.\n',
'context-managers': 'With Statement Context Managers\n'
'*******************************\n'
'\n'
'A *context manager* is an object that defines the '
'runtime context to\n'
'be established when executing a "with" statement. The '
'context manager\n'
'handles the entry into, and the exit from, the desired '
'runtime context\n'
'for the execution of the block of code. Context '
'managers are normally\n'
'invoked using the "with" statement (described in section '
'The with\n'
'statement), but can also be used by directly invoking '
'their methods.\n'
'\n'
'Typical uses of context managers include saving and '
'restoring various\n'
'kinds of global state, locking and unlocking resources, '
'closing opened\n'
'files, etc.\n'
'\n'
'For more information on context managers, see Context '
'Manager Types.\n'
'\n'
'object.__enter__(self)\n'
'\n'
' Enter the runtime context related to this object. The '
'"with"\n'
' statement will bind this method’s return value to the '
'target(s)\n'
' specified in the "as" clause of the statement, if '
'any.\n'
'\n'
'object.__exit__(self, exc_type, exc_value, traceback)\n'
'\n'
' Exit the runtime context related to this object. The '
'parameters\n'
' describe the exception that caused the context to be '
'exited. If the\n'
' context was exited without an exception, all three '
'arguments will\n'
' be "None".\n'
'\n'
' If an exception is supplied, and the method wishes to '
'suppress the\n'
' exception (i.e., prevent it from being propagated), '
'it should\n'
' return a true value. Otherwise, the exception will be '
'processed\n'
' normally upon exit from this method.\n'
'\n'
' Note that "__exit__()" methods should not reraise the '
'passed-in\n'
' exception; this is the caller’s responsibility.\n'
'\n'
'See also:\n'
'\n'
' **PEP 343** - The “with” statement\n'
' The specification, background, and examples for the '
'Python "with"\n'
' statement.\n',
'continue': 'The "continue" statement\n'
'************************\n'
'\n'
' continue_stmt ::= "continue"\n'
'\n'
'"continue" may only occur syntactically nested in a "for" or '
'"while"\n'
'loop, but not nested in a function or class definition or '
'"finally"\n'
'clause within that loop. It continues with the next cycle of '
'the\n'
'nearest enclosing loop.\n'
'\n'
'When "continue" passes control out of a "try" statement with a\n'
'"finally" clause, that "finally" clause is executed before '
'really\n'
'starting the next loop cycle.\n',
'conversions': 'Arithmetic conversions\n'
'**********************\n'
'\n'
'When a description of an arithmetic operator below uses the '
'phrase\n'
'“the numeric arguments are converted to a common type,” this '
'means\n'
'that the operator implementation for built-in types works as '
'follows:\n'
'\n'
'* If either argument is a complex number, the other is '
'converted to\n'
' complex;\n'
'\n'
'* otherwise, if either argument is a floating point number, '
'the\n'
' other is converted to floating point;\n'
'\n'
'* otherwise, both must be integers and no conversion is '
'necessary.\n'
'\n'
'Some additional rules apply for certain operators (e.g., a '
'string as a\n'
'left argument to the ‘%’ operator). Extensions must define '
'their own\n'
'conversion behavior.\n',
'customization': 'Basic customization\n'
'*******************\n'
'\n'
'object.__new__(cls[, ...])\n'
'\n'
' Called to create a new instance of class *cls*. '
'"__new__()" is a\n'
' static method (special-cased so you need not declare it '
'as such)\n'
' that takes the class of which an instance was requested '
'as its\n'
' first argument. The remaining arguments are those '
'passed to the\n'
' object constructor expression (the call to the class). '
'The return\n'
' value of "__new__()" should be the new object instance '
'(usually an\n'
' instance of *cls*).\n'
'\n'
' Typical implementations create a new instance of the '
'class by\n'
' invoking the superclass’s "__new__()" method using\n'
' "super().__new__(cls[, ...])" with appropriate arguments '
'and then\n'
' modifying the newly-created instance as necessary before '
'returning\n'
' it.\n'
'\n'
' If "__new__()" returns an instance of *cls*, then the '
'new\n'
' instance’s "__init__()" method will be invoked like\n'
' "__init__(self[, ...])", where *self* is the new '
'instance and the\n'
' remaining arguments are the same as were passed to '
'"__new__()".\n'
'\n'
' If "__new__()" does not return an instance of *cls*, '
'then the new\n'
' instance’s "__init__()" method will not be invoked.\n'
'\n'
' "__new__()" is intended mainly to allow subclasses of '
'immutable\n'
' types (like int, str, or tuple) to customize instance '
'creation. It\n'
' is also commonly overridden in custom metaclasses in '
'order to\n'
' customize class creation.\n'
'\n'
'object.__init__(self[, ...])\n'
'\n'
' Called after the instance has been created (by '
'"__new__()"), but\n'
' before it is returned to the caller. The arguments are '
'those\n'
' passed to the class constructor expression. If a base '
'class has an\n'
' "__init__()" method, the derived class’s "__init__()" '
'method, if\n'
' any, must explicitly call it to ensure proper '
'initialization of the\n'
' base class part of the instance; for example:\n'
' "super().__init__([args...])".\n'
'\n'
' Because "__new__()" and "__init__()" work together in '
'constructing\n'
' objects ("__new__()" to create it, and "__init__()" to '
'customize\n'
' it), no non-"None" value may be returned by '
'"__init__()"; doing so\n'
' will cause a "TypeError" to be raised at runtime.\n'
'\n'
'object.__del__(self)\n'
'\n'
' Called when the instance is about to be destroyed. This '
'is also\n'
' called a finalizer or (improperly) a destructor. If a '
'base class\n'
' has a "__del__()" method, the derived class’s '
'"__del__()" method,\n'
' if any, must explicitly call it to ensure proper '
'deletion of the\n'
' base class part of the instance.\n'
'\n'
' It is possible (though not recommended!) for the '
'"__del__()" method\n'
' to postpone destruction of the instance by creating a '
'new reference\n'
' to it. This is called object *resurrection*. It is\n'
' implementation-dependent whether "__del__()" is called a '
'second\n'
' time when a resurrected object is about to be destroyed; '
'the\n'
' current *CPython* implementation only calls it once.\n'
'\n'
' It is not guaranteed that "__del__()" methods are called '
'for\n'
' objects that still exist when the interpreter exits.\n'
'\n'
' Note: "del x" doesn’t directly call "x.__del__()" — the '
'former\n'
' decrements the reference count for "x" by one, and the '
'latter is\n'
' only called when "x"’s reference count reaches zero.\n'
'\n'
' **CPython implementation detail:** It is possible for a '
'reference\n'
' cycle to prevent the reference count of an object from '
'going to\n'
' zero. In this case, the cycle will be later detected '
'and deleted\n'
' by the *cyclic garbage collector*. A common cause of '
'reference\n'
' cycles is when an exception has been caught in a local '
'variable.\n'
' The frame’s locals then reference the exception, which '
'references\n'
' its own traceback, which references the locals of all '
'frames caught\n'
' in the traceback.\n'
'\n'
' See also: Documentation for the "gc" module.\n'
'\n'
' Warning: Due to the precarious circumstances under '
'which\n'
' "__del__()" methods are invoked, exceptions that occur '
'during\n'
' their execution are ignored, and a warning is printed '
'to\n'
' "sys.stderr" instead. In particular:\n'
'\n'
' * "__del__()" can be invoked when arbitrary code is '
'being\n'
' executed, including from any arbitrary thread. If '
'"__del__()"\n'
' needs to take a lock or invoke any other blocking '
'resource, it\n'
' may deadlock as the resource may already be taken by '
'the code\n'
' that gets interrupted to execute "__del__()".\n'
'\n'
' * "__del__()" can be executed during interpreter '
'shutdown. As\n'
' a consequence, the global variables it needs to '
'access\n'
' (including other modules) may already have been '
'deleted or set\n'
' to "None". Python guarantees that globals whose name '
'begins\n'
' with a single underscore are deleted from their '
'module before\n'
' other globals are deleted; if no other references to '
'such\n'
' globals exist, this may help in assuring that '
'imported modules\n'
' are still available at the time when the "__del__()" '
'method is\n'
' called.\n'
'\n'
'object.__repr__(self)\n'
'\n'
' Called by the "repr()" built-in function to compute the '
'“official”\n'
' string representation of an object. If at all possible, '
'this\n'
' should look like a valid Python expression that could be '
'used to\n'
' recreate an object with the same value (given an '
'appropriate\n'
' environment). If this is not possible, a string of the '
'form\n'
' "<...some useful description...>" should be returned. '
'The return\n'
' value must be a string object. If a class defines '
'"__repr__()" but\n'
' not "__str__()", then "__repr__()" is also used when an '
'“informal”\n'
' string representation of instances of that class is '
'required.\n'
'\n'
' This is typically used for debugging, so it is important '
'that the\n'
' representation is information-rich and unambiguous.\n'
'\n'
'object.__str__(self)\n'
'\n'
' Called by "str(object)" and the built-in functions '
'"format()" and\n'
' "print()" to compute the “informal” or nicely printable '
'string\n'
' representation of an object. The return value must be a '
'string\n'
' object.\n'
'\n'
' This method differs from "object.__repr__()" in that '
'there is no\n'
' expectation that "__str__()" return a valid Python '
'expression: a\n'
' more convenient or concise representation can be used.\n'
'\n'
' The default implementation defined by the built-in type '
'"object"\n'
' calls "object.__repr__()".\n'
'\n'
'object.__bytes__(self)\n'
'\n'
' Called by bytes to compute a byte-string representation '
'of an\n'
' object. This should return a "bytes" object.\n'
'\n'
'object.__format__(self, format_spec)\n'
'\n'
' Called by the "format()" built-in function, and by '
'extension,\n'
' evaluation of formatted string literals and the '
'"str.format()"\n'
' method, to produce a “formatted” string representation '
'of an\n'
' object. The "format_spec" argument is a string that '
'contains a\n'
' description of the formatting options desired. The '
'interpretation\n'
' of the "format_spec" argument is up to the type '
'implementing\n'
' "__format__()", however most classes will either '
'delegate\n'
' formatting to one of the built-in types, or use a '
'similar\n'
' formatting option syntax.\n'
'\n'
' See Format Specification Mini-Language for a description '
'of the\n'
' standard formatting syntax.\n'
'\n'
' The return value must be a string object.\n'
'\n'
' Changed in version 3.4: The __format__ method of '
'"object" itself\n'
' raises a "TypeError" if passed any non-empty string.\n'
'\n'
'object.__lt__(self, other)\n'
'object.__le__(self, other)\n'
'object.__eq__(self, other)\n'
'object.__ne__(self, other)\n'
'object.__gt__(self, other)\n'
'object.__ge__(self, other)\n'
'\n'
' These are the so-called “rich comparison” methods. The\n'
' correspondence between operator symbols and method names '
'is as\n'
' follows: "x<y" calls "x.__lt__(y)", "x<=y" calls '
'"x.__le__(y)",\n'
' "x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", '
'"x>y" calls\n'
' "x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".\n'
'\n'
' A rich comparison method may return the singleton '
'"NotImplemented"\n'
' if it does not implement the operation for a given pair '
'of\n'
' arguments. By convention, "False" and "True" are '
'returned for a\n'
' successful comparison. However, these methods can return '
'any value,\n'
' so if the comparison operator is used in a Boolean '
'context (e.g.,\n'
' in the condition of an "if" statement), Python will call '
'"bool()"\n'
' on the value to determine if the result is true or '
'false.\n'
'\n'
' By default, "__ne__()" delegates to "__eq__()" and '
'inverts the\n'
' result unless it is "NotImplemented". There are no '
'other implied\n'
' relationships among the comparison operators, for '
'example, the\n'
' truth of "(x<y or x==y)" does not imply "x<=y". To '
'automatically\n'
' generate ordering operations from a single root '
'operation, see\n'
' "functools.total_ordering()".\n'
'\n'
' See the paragraph on "__hash__()" for some important '
'notes on\n'
' creating *hashable* objects which support custom '
'comparison\n'
' operations and are usable as dictionary keys.\n'
'\n'
' There are no swapped-argument versions of these methods '
'(to be used\n'
' when the left argument does not support the operation '
'but the right\n'
' argument does); rather, "__lt__()" and "__gt__()" are '
'each other’s\n'
' reflection, "__le__()" and "__ge__()" are each other’s '
'reflection,\n'
' and "__eq__()" and "__ne__()" are their own reflection. '
'If the\n'
' operands are of different types, and right operand’s '
'type is a\n'
' direct or indirect subclass of the left operand’s type, '
'the\n'
' reflected method of the right operand has priority, '
'otherwise the\n'
' left operand’s method has priority. Virtual subclassing '
'is not\n'
' considered.\n'
'\n'
'object.__hash__(self)\n'
'\n'
' Called by built-in function "hash()" and for operations '
'on members\n'
' of hashed collections including "set", "frozenset", and '
'"dict".\n'
' "__hash__()" should return an integer. The only required '
'property\n'
' is that objects which compare equal have the same hash '
'value; it is\n'
' advised to mix together the hash values of the '
'components of the\n'
' object that also play a part in comparison of objects by '
'packing\n'
' them into a tuple and hashing the tuple. Example:\n'
'\n'
' def __hash__(self):\n'
' return hash((self.name, self.nick, self.color))\n'
'\n'
' Note: "hash()" truncates the value returned from an '
'object’s\n'
' custom "__hash__()" method to the size of a '
'"Py_ssize_t". This\n'
' is typically 8 bytes on 64-bit builds and 4 bytes on '
'32-bit\n'
' builds. If an object’s "__hash__()" must '
'interoperate on builds\n'
' of different bit sizes, be sure to check the width on '
'all\n'
' supported builds. An easy way to do this is with '
'"python -c\n'
' "import sys; print(sys.hash_info.width)"".\n'
'\n'
' If a class does not define an "__eq__()" method it '
'should not\n'
' define a "__hash__()" operation either; if it defines '
'"__eq__()"\n'
' but not "__hash__()", its instances will not be usable '
'as items in\n'
' hashable collections. If a class defines mutable '
'objects and\n'
' implements an "__eq__()" method, it should not '
'implement\n'
' "__hash__()", since the implementation of hashable '
'collections\n'
' requires that a key’s hash value is immutable (if the '
'object’s hash\n'
' value changes, it will be in the wrong hash bucket).\n'
'\n'
' User-defined classes have "__eq__()" and "__hash__()" '
'methods by\n'
' default; with them, all objects compare unequal (except '
'with\n'
' themselves) and "x.__hash__()" returns an appropriate '
'value such\n'
' that "x == y" implies both that "x is y" and "hash(x) == '
'hash(y)".\n'
'\n'
' A class that overrides "__eq__()" and does not define '
'"__hash__()"\n'
' will have its "__hash__()" implicitly set to "None". '
'When the\n'
' "__hash__()" method of a class is "None", instances of '
'the class\n'
' will raise an appropriate "TypeError" when a program '
'attempts to\n'
' retrieve their hash value, and will also be correctly '
'identified as\n'
' unhashable when checking "isinstance(obj, '
'collections.Hashable)".\n'
'\n'
' If a class that overrides "__eq__()" needs to retain '
'the\n'
' implementation of "__hash__()" from a parent class, the '
'interpreter\n'
' must be told this explicitly by setting "__hash__ =\n'
' <ParentClass>.__hash__".\n'
'\n'
' If a class that does not override "__eq__()" wishes to '
'suppress\n'
' hash support, it should include "__hash__ = None" in the '
'class\n'
' definition. A class which defines its own "__hash__()" '
'that\n'
' explicitly raises a "TypeError" would be incorrectly '
'identified as\n'
' hashable by an "isinstance(obj, collections.Hashable)" '
'call.\n'
'\n'
' Note: By default, the "__hash__()" values of str, bytes '
'and\n'
' datetime objects are “salted” with an unpredictable '
'random value.\n'
' Although they remain constant within an individual '
'Python\n'
' process, they are not predictable between repeated '
'invocations of\n'
' Python.This is intended to provide protection against '
'a denial-\n'
' of-service caused by carefully-chosen inputs that '
'exploit the\n'
' worst case performance of a dict insertion, O(n^2) '
'complexity.\n'
' See '
'http://www.ocert.org/advisories/ocert-2011-003.html for\n'
' details.Changing hash values affects the iteration '
'order of\n'
' dicts, sets and other mappings. Python has never made '
'guarantees\n'
' about this ordering (and it typically varies between '
'32-bit and\n'
' 64-bit builds).See also "PYTHONHASHSEED".\n'
'\n'
' Changed in version 3.3: Hash randomization is enabled by '
'default.\n'
'\n'
'object.__bool__(self)\n'
'\n'
' Called to implement truth value testing and the built-in '
'operation\n'
' "bool()"; should return "False" or "True". When this '
'method is not\n'
' defined, "__len__()" is called, if it is defined, and '
'the object is\n'
' considered true if its result is nonzero. If a class '
'defines\n'
' neither "__len__()" nor "__bool__()", all its instances '
'are\n'
' considered true.\n',
'debugger': '"pdb" — The Python Debugger\n'
'***************************\n'
'\n'
'**Source code:** Lib/pdb.py\n'
'\n'
'======================================================================\n'
'\n'
'The module "pdb" defines an interactive source code debugger '
'for\n'
'Python programs. It supports setting (conditional) breakpoints '
'and\n'
'single stepping at the source line level, inspection of stack '
'frames,\n'
'source code listing, and evaluation of arbitrary Python code in '
'the\n'
'context of any stack frame. It also supports post-mortem '
'debugging\n'
'and can be called under program control.\n'
'\n'
'The debugger is extensible – it is actually defined as the '
'class\n'
'"Pdb". This is currently undocumented but easily understood by '
'reading\n'
'the source. The extension interface uses the modules "bdb" and '
'"cmd".\n'
'\n'
'The debugger’s prompt is "(Pdb)". Typical usage to run a program '
'under\n'
'control of the debugger is:\n'
'\n'
' >>> import pdb\n'
' >>> import mymodule\n'
" >>> pdb.run('mymodule.test()')\n"
' > <string>(0)?()\n'
' (Pdb) continue\n'
' > <string>(1)?()\n'
' (Pdb) continue\n'
" NameError: 'spam'\n"
' > <string>(1)?()\n'
' (Pdb)\n'
'\n'
'Changed in version 3.3: Tab-completion via the "readline" module '
'is\n'
'available for commands and command arguments, e.g. the current '
'global\n'
'and local names are offered as arguments of the "p" command.\n'
'\n'
'"pdb.py" can also be invoked as a script to debug other '
'scripts. For\n'
'example:\n'
'\n'
' python3 -m pdb myscript.py\n'
'\n'
'When invoked as a script, pdb will automatically enter '
'post-mortem\n'
'debugging if the program being debugged exits abnormally. After '
'post-\n'
'mortem debugging (or after normal exit of the program), pdb '
'will\n'
'restart the program. Automatic restarting preserves pdb’s state '
'(such\n'
'as breakpoints) and in most cases is more useful than quitting '
'the\n'
'debugger upon program’s exit.\n'
'\n'
'New in version 3.2: "pdb.py" now accepts a "-c" option that '
'executes\n'
'commands as if given in a ".pdbrc" file, see Debugger Commands.\n'
'\n'
'The typical usage to break into the debugger from a running '
'program is\n'
'to insert\n'
'\n'
' import pdb; pdb.set_trace()\n'
'\n'
'at the location you want to break into the debugger. You can '
'then\n'
'step through the code following this statement, and continue '
'running\n'
'without the debugger using the "continue" command.\n'
'\n'
'The typical usage to inspect a crashed program is:\n'
'\n'
' >>> import pdb\n'
' >>> import mymodule\n'
' >>> mymodule.test()\n'
' Traceback (most recent call last):\n'
' File "<stdin>", line 1, in <module>\n'
' File "./mymodule.py", line 4, in test\n'
' test2()\n'
' File "./mymodule.py", line 3, in test2\n'
' print(spam)\n'
' NameError: spam\n'
' >>> pdb.pm()\n'
' > ./mymodule.py(3)test2()\n'
' -> print(spam)\n'
' (Pdb)\n'
'\n'
'The module defines the following functions; each enters the '
'debugger\n'
'in a slightly different way:\n'
'\n'
'pdb.run(statement, globals=None, locals=None)\n'
'\n'
' Execute the *statement* (given as a string or a code object) '
'under\n'
' debugger control. The debugger prompt appears before any '
'code is\n'
' executed; you can set breakpoints and type "continue", or you '
'can\n'
' step through the statement using "step" or "next" (all these\n'
' commands are explained below). The optional *globals* and '
'*locals*\n'
' arguments specify the environment in which the code is '
'executed; by\n'
' default the dictionary of the module "__main__" is used. '
'(See the\n'
' explanation of the built-in "exec()" or "eval()" functions.)\n'
'\n'
'pdb.runeval(expression, globals=None, locals=None)\n'
'\n'
' Evaluate the *expression* (given as a string or a code '
'object)\n'
' under debugger control. When "runeval()" returns, it returns '
'the\n'
' value of the expression. Otherwise this function is similar '
'to\n'
' "run()".\n'
'\n'
'pdb.runcall(function, *args, **kwds)\n'
'\n'
' Call the *function* (a function or method object, not a '
'string)\n'
' with the given arguments. When "runcall()" returns, it '
'returns\n'
' whatever the function call returned. The debugger prompt '
'appears\n'
' as soon as the function is entered.\n'
'\n'
'pdb.set_trace()\n'
'\n'
' Enter the debugger at the calling stack frame. This is '
'useful to\n'
' hard-code a breakpoint at a given point in a program, even if '
'the\n'
' code is not otherwise being debugged (e.g. when an assertion\n'
' fails).\n'
'\n'
'pdb.post_mortem(traceback=None)\n'
'\n'
' Enter post-mortem debugging of the given *traceback* object. '
'If no\n'
' *traceback* is given, it uses the one of the exception that '
'is\n'
' currently being handled (an exception must be being handled '
'if the\n'
' default is to be used).\n'
'\n'
'pdb.pm()\n'
'\n'
' Enter post-mortem debugging of the traceback found in\n'
' "sys.last_traceback".\n'
'\n'
'The "run*" functions and "set_trace()" are aliases for '
'instantiating\n'
'the "Pdb" class and calling the method of the same name. If you '
'want\n'
'to access further features, you have to do this yourself:\n'
'\n'
"class pdb.Pdb(completekey='tab', stdin=None, stdout=None, "
'skip=None, nosigint=False, readrc=True)\n'
'\n'
' "Pdb" is the debugger class.\n'
'\n'
' The *completekey*, *stdin* and *stdout* arguments are passed '
'to the\n'
' underlying "cmd.Cmd" class; see the description there.\n'
'\n'
' The *skip* argument, if given, must be an iterable of '
'glob-style\n'
' module name patterns. The debugger will not step into frames '
'that\n'
' originate in a module that matches one of these patterns. '
'[1]\n'
'\n'
' By default, Pdb sets a handler for the SIGINT signal (which '
'is sent\n'
' when the user presses "Ctrl-C" on the console) when you give '
'a\n'
' "continue" command. This allows you to break into the '
'debugger\n'
' again by pressing "Ctrl-C". If you want Pdb not to touch '
'the\n'
' SIGINT handler, set *nosigint* to true.\n'
'\n'
' The *readrc* argument defaults to true and controls whether '
'Pdb\n'
' will load .pdbrc files from the filesystem.\n'
'\n'
' Example call to enable tracing with *skip*:\n'
'\n'
" import pdb; pdb.Pdb(skip=['django.*']).set_trace()\n"
'\n'
' New in version 3.1: The *skip* argument.\n'
'\n'
' New in version 3.2: The *nosigint* argument. Previously, a '
'SIGINT\n'
' handler was never set by Pdb.\n'
'\n'
' Changed in version 3.6: The *readrc* argument.\n'
'\n'
' run(statement, globals=None, locals=None)\n'
' runeval(expression, globals=None, locals=None)\n'
' runcall(function, *args, **kwds)\n'
' set_trace()\n'
'\n'
' See the documentation for the functions explained above.\n'
'\n'
'\n'
'Debugger Commands\n'
'=================\n'
'\n'
'The commands recognized by the debugger are listed below. Most\n'
'commands can be abbreviated to one or two letters as indicated; '
'e.g.\n'
'"h(elp)" means that either "h" or "help" can be used to enter '
'the help\n'
'command (but not "he" or "hel", nor "H" or "Help" or "HELP").\n'
'Arguments to commands must be separated by whitespace (spaces '
'or\n'
'tabs). Optional arguments are enclosed in square brackets '
'("[]") in\n'
'the command syntax; the square brackets must not be typed.\n'
'Alternatives in the command syntax are separated by a vertical '
'bar\n'
'("|").\n'
'\n'
'Entering a blank line repeats the last command entered. '
'Exception: if\n'
'the last command was a "list" command, the next 11 lines are '
'listed.\n'
'\n'
'Commands that the debugger doesn’t recognize are assumed to be '
'Python\n'
'statements and are executed in the context of the program being\n'
'debugged. Python statements can also be prefixed with an '
'exclamation\n'
'point ("!"). This is a powerful way to inspect the program '
'being\n'
'debugged; it is even possible to change a variable or call a '
'function.\n'
'When an exception occurs in such a statement, the exception name '
'is\n'
'printed but the debugger’s state is not changed.\n'
'\n'
'The debugger supports aliases. Aliases can have parameters '
'which\n'
'allows one a certain level of adaptability to the context under\n'
'examination.\n'
'\n'
'Multiple commands may be entered on a single line, separated by '
'";;".\n'
'(A single ";" is not used as it is the separator for multiple '
'commands\n'
'in a line that is passed to the Python parser.) No intelligence '
'is\n'
'applied to separating the commands; the input is split at the '
'first\n'
'";;" pair, even if it is in the middle of a quoted string.\n'
'\n'
'If a file ".pdbrc" exists in the user’s home directory or in '
'the\n'
'current directory, it is read in and executed as if it had been '
'typed\n'
'at the debugger prompt. This is particularly useful for '
'aliases. If\n'
'both files exist, the one in the home directory is read first '
'and\n'
'aliases defined there can be overridden by the local file.\n'
'\n'
'Changed in version 3.2: ".pdbrc" can now contain commands that\n'
'continue debugging, such as "continue" or "next". Previously, '
'these\n'
'commands had no effect.\n'
'\n'
'h(elp) [command]\n'
'\n'
' Without argument, print the list of available commands. With '
'a\n'
' *command* as argument, print help about that command. "help '
'pdb"\n'
' displays the full documentation (the docstring of the "pdb"\n'
' module). Since the *command* argument must be an identifier, '
'"help\n'
' exec" must be entered to get help on the "!" command.\n'
'\n'
'w(here)\n'
'\n'
' Print a stack trace, with the most recent frame at the '
'bottom. An\n'
' arrow indicates the current frame, which determines the '
'context of\n'
' most commands.\n'
'\n'
'd(own) [count]\n'
'\n'
' Move the current frame *count* (default one) levels down in '
'the\n'
' stack trace (to a newer frame).\n'
'\n'
'u(p) [count]\n'
'\n'
' Move the current frame *count* (default one) levels up in the '
'stack\n'
' trace (to an older frame).\n'
'\n'
'b(reak) [([filename:]lineno | function) [, condition]]\n'
'\n'
' With a *lineno* argument, set a break there in the current '
'file.\n'
' With a *function* argument, set a break at the first '
'executable\n'
' statement within that function. The line number may be '
'prefixed\n'
' with a filename and a colon, to specify a breakpoint in '
'another\n'
' file (probably one that hasn’t been loaded yet). The file '
'is\n'
' searched on "sys.path". Note that each breakpoint is '
'assigned a\n'
' number to which all the other breakpoint commands refer.\n'
'\n'
' If a second argument is present, it is an expression which '
'must\n'
' evaluate to true before the breakpoint is honored.\n'
'\n'
' Without argument, list all breaks, including for each '
'breakpoint,\n'
' the number of times that breakpoint has been hit, the '
'current\n'
' ignore count, and the associated condition if any.\n'
'\n'
'tbreak [([filename:]lineno | function) [, condition]]\n'
'\n'
' Temporary breakpoint, which is removed automatically when it '
'is\n'
' first hit. The arguments are the same as for "break".\n'
'\n'
'cl(ear) [filename:lineno | bpnumber [bpnumber ...]]\n'
'\n'
' With a *filename:lineno* argument, clear all the breakpoints '
'at\n'
' this line. With a space separated list of breakpoint numbers, '
'clear\n'
' those breakpoints. Without argument, clear all breaks (but '
'first\n'
' ask confirmation).\n'
'\n'
'disable [bpnumber [bpnumber ...]]\n'
'\n'
' Disable the breakpoints given as a space separated list of\n'
' breakpoint numbers. Disabling a breakpoint means it cannot '
'cause\n'
' the program to stop execution, but unlike clearing a '
'breakpoint, it\n'
' remains in the list of breakpoints and can be (re-)enabled.\n'
'\n'
'enable [bpnumber [bpnumber ...]]\n'
'\n'
' Enable the breakpoints specified.\n'
'\n'
'ignore bpnumber [count]\n'
'\n'
' Set the ignore count for the given breakpoint number. If '
'count is\n'
' omitted, the ignore count is set to 0. A breakpoint becomes '
'active\n'
' when the ignore count is zero. When non-zero, the count is\n'
' decremented each time the breakpoint is reached and the '
'breakpoint\n'
' is not disabled and any associated condition evaluates to '
'true.\n'
'\n'
'condition bpnumber [condition]\n'
'\n'
' Set a new *condition* for the breakpoint, an expression which '
'must\n'
' evaluate to true before the breakpoint is honored. If '
'*condition*\n'
' is absent, any existing condition is removed; i.e., the '
'breakpoint\n'
' is made unconditional.\n'
'\n'
'commands [bpnumber]\n'
'\n'
' Specify a list of commands for breakpoint number *bpnumber*. '
'The\n'
' commands themselves appear on the following lines. Type a '
'line\n'
' containing just "end" to terminate the commands. An example:\n'
'\n'
' (Pdb) commands 1\n'
' (com) p some_variable\n'
' (com) end\n'
' (Pdb)\n'
'\n'
' To remove all commands from a breakpoint, type commands and '
'follow\n'
' it immediately with "end"; that is, give no commands.\n'
'\n'
' With no *bpnumber* argument, commands refers to the last '
'breakpoint\n'
' set.\n'
'\n'
' You can use breakpoint commands to start your program up '
'again.\n'
' Simply use the continue command, or step, or any other '
'command that\n'
' resumes execution.\n'
'\n'
' Specifying any command resuming execution (currently '
'continue,\n'
' step, next, return, jump, quit and their abbreviations) '
'terminates\n'
' the command list (as if that command was immediately followed '
'by\n'
' end). This is because any time you resume execution (even '
'with a\n'
' simple next or step), you may encounter another '
'breakpoint—which\n'
' could have its own command list, leading to ambiguities about '
'which\n'
' list to execute.\n'
'\n'
' If you use the ‘silent’ command in the command list, the '
'usual\n'
' message about stopping at a breakpoint is not printed. This '
'may be\n'
' desirable for breakpoints that are to print a specific '
'message and\n'
' then continue. If none of the other commands print anything, '
'you\n'
' see no sign that the breakpoint was reached.\n'
'\n'
's(tep)\n'
'\n'
' Execute the current line, stop at the first possible '
'occasion\n'
' (either in a function that is called or on the next line in '
'the\n'
' current function).\n'
'\n'
'n(ext)\n'
'\n'
' Continue execution until the next line in the current '
'function is\n'
' reached or it returns. (The difference between "next" and '
'"step"\n'
' is that "step" stops inside a called function, while "next"\n'
' executes called functions at (nearly) full speed, only '
'stopping at\n'
' the next line in the current function.)\n'
'\n'
'unt(il) [lineno]\n'
'\n'
' Without argument, continue execution until the line with a '
'number\n'
' greater than the current one is reached.\n'
'\n'
' With a line number, continue execution until a line with a '
'number\n'
' greater or equal to that is reached. In both cases, also '
'stop when\n'
' the current frame returns.\n'
'\n'
' Changed in version 3.2: Allow giving an explicit line '
'number.\n'
'\n'
'r(eturn)\n'
'\n'
' Continue execution until the current function returns.\n'
'\n'
'c(ont(inue))\n'
'\n'
' Continue execution, only stop when a breakpoint is '
'encountered.\n'
'\n'
'j(ump) lineno\n'
'\n'
' Set the next line that will be executed. Only available in '
'the\n'
' bottom-most frame. This lets you jump back and execute code '
'again,\n'
' or jump forward to skip code that you don’t want to run.\n'
'\n'
' It should be noted that not all jumps are allowed – for '
'instance it\n'
' is not possible to jump into the middle of a "for" loop or '
'out of a\n'
' "finally" clause.\n'
'\n'
'l(ist) [first[, last]]\n'
'\n'
' List source code for the current file. Without arguments, '
'list 11\n'
' lines around the current line or continue the previous '
'listing.\n'
' With "." as argument, list 11 lines around the current line. '
'With\n'
' one argument, list 11 lines around at that line. With two\n'
' arguments, list the given range; if the second argument is '
'less\n'
' than the first, it is interpreted as a count.\n'
'\n'
' The current line in the current frame is indicated by "->". '
'If an\n'
' exception is being debugged, the line where the exception '
'was\n'
' originally raised or propagated is indicated by ">>", if it '
'differs\n'
' from the current line.\n'
'\n'
' New in version 3.2: The ">>" marker.\n'
'\n'
'll | longlist\n'
'\n'
' List all source code for the current function or frame.\n'
' Interesting lines are marked as for "list".\n'
'\n'
' New in version 3.2.\n'
'\n'
'a(rgs)\n'
'\n'
' Print the argument list of the current function.\n'
'\n'
'p expression\n'
'\n'
' Evaluate the *expression* in the current context and print '
'its\n'
' value.\n'
'\n'
' Note: "print()" can also be used, but is not a debugger '
'command —\n'
' this executes the Python "print()" function.\n'
'\n'
'pp expression\n'
'\n'
' Like the "p" command, except the value of the expression is '
'pretty-\n'
' printed using the "pprint" module.\n'
'\n'
'whatis expression\n'
'\n'
' Print the type of the *expression*.\n'
'\n'
'source expression\n'
'\n'
' Try to get source code for the given object and display it.\n'
'\n'
' New in version 3.2.\n'
'\n'
'display [expression]\n'
'\n'
' Display the value of the expression if it changed, each time\n'
' execution stops in the current frame.\n'
'\n'
' Without expression, list all display expressions for the '
'current\n'
' frame.\n'
'\n'
' New in version 3.2.\n'
'\n'
'undisplay [expression]\n'
'\n'
' Do not display the expression any more in the current frame.\n'
' Without expression, clear all display expressions for the '
'current\n'
' frame.\n'
'\n'
' New in version 3.2.\n'
'\n'
'interact\n'
'\n'
' Start an interactive interpreter (using the "code" module) '
'whose\n'
' global namespace contains all the (global and local) names '
'found in\n'
' the current scope.\n'
'\n'
' New in version 3.2.\n'
'\n'
'alias [name [command]]\n'
'\n'
' Create an alias called *name* that executes *command*. The '
'command\n'
' must *not* be enclosed in quotes. Replaceable parameters can '
'be\n'
' indicated by "%1", "%2", and so on, while "%*" is replaced by '
'all\n'
' the parameters. If no command is given, the current alias '
'for\n'
' *name* is shown. If no arguments are given, all aliases are '
'listed.\n'
'\n'
' Aliases may be nested and can contain anything that can be '
'legally\n'
' typed at the pdb prompt. Note that internal pdb commands '
'*can* be\n'
' overridden by aliases. Such a command is then hidden until '
'the\n'
' alias is removed. Aliasing is recursively applied to the '
'first\n'
' word of the command line; all other words in the line are '
'left\n'
' alone.\n'
'\n'
' As an example, here are two useful aliases (especially when '
'placed\n'
' in the ".pdbrc" file):\n'
'\n'
' # Print instance variables (usage "pi classInst")\n'
' alias pi for k in %1.__dict__.keys(): '
'print("%1.",k,"=",%1.__dict__[k])\n'
' # Print instance variables in self\n'
' alias ps pi self\n'
'\n'
'unalias name\n'
'\n'
' Delete the specified alias.\n'
'\n'
'! statement\n'
'\n'
' Execute the (one-line) *statement* in the context of the '
'current\n'
' stack frame. The exclamation point can be omitted unless the '
'first\n'
' word of the statement resembles a debugger command. To set '
'a\n'
' global variable, you can prefix the assignment command with '
'a\n'
' "global" statement on the same line, e.g.:\n'
'\n'
" (Pdb) global list_options; list_options = ['-l']\n"
' (Pdb)\n'
'\n'
'run [args ...]\n'
'restart [args ...]\n'
'\n'
' Restart the debugged Python program. If an argument is '
'supplied,\n'
' it is split with "shlex" and the result is used as the new\n'
' "sys.argv". History, breakpoints, actions and debugger '
'options are\n'
' preserved. "restart" is an alias for "run".\n'
'\n'
'q(uit)\n'
'\n'
' Quit from the debugger. The program being executed is '
'aborted.\n'
'\n'
'-[ Footnotes ]-\n'
'\n'
'[1] Whether a frame is considered to originate in a certain '
'module\n'
' is determined by the "__name__" in the frame globals.\n',
'del': 'The "del" statement\n'
'*******************\n'
'\n'
' del_stmt ::= "del" target_list\n'
'\n'
'Deletion is recursively defined very similar to the way assignment '
'is\n'
'defined. Rather than spelling it out in full details, here are some\n'
'hints.\n'
'\n'
'Deletion of a target list recursively deletes each target, from left\n'
'to right.\n'
'\n'
'Deletion of a name removes the binding of that name from the local '
'or\n'
'global namespace, depending on whether the name occurs in a "global"\n'
'statement in the same code block. If the name is unbound, a\n'
'"NameError" exception will be raised.\n'
'\n'
'Deletion of attribute references, subscriptions and slicings is '
'passed\n'
'to the primary object involved; deletion of a slicing is in general\n'
'equivalent to assignment of an empty slice of the right type (but '
'even\n'
'this is determined by the sliced object).\n'
'\n'
'Changed in version 3.2: Previously it was illegal to delete a name\n'
'from the local namespace if it occurs as a free variable in a nested\n'
'block.\n',
'dict': 'Dictionary displays\n'
'*******************\n'
'\n'
'A dictionary display is a possibly empty series of key/datum pairs\n'
'enclosed in curly braces:\n'
'\n'
' dict_display ::= "{" [key_datum_list | dict_comprehension] '
'"}"\n'
' key_datum_list ::= key_datum ("," key_datum)* [","]\n'
' key_datum ::= expression ":" expression | "**" or_expr\n'
' dict_comprehension ::= expression ":" expression comp_for\n'
'\n'
'A dictionary display yields a new dictionary object.\n'
'\n'
'If a comma-separated sequence of key/datum pairs is given, they are\n'
'evaluated from left to right to define the entries of the '
'dictionary:\n'
'each key object is used as a key into the dictionary to store the\n'
'corresponding datum. This means that you can specify the same key\n'
'multiple times in the key/datum list, and the final dictionary’s '
'value\n'
'for that key will be the last one given.\n'
'\n'
'A double asterisk "**" denotes *dictionary unpacking*. Its operand\n'
'must be a *mapping*. Each mapping item is added to the new\n'
'dictionary. Later values replace values already set by earlier\n'
'key/datum pairs and earlier dictionary unpackings.\n'
'\n'
'New in version 3.5: Unpacking into dictionary displays, originally\n'
'proposed by **PEP 448**.\n'
'\n'
'A dict comprehension, in contrast to list and set comprehensions,\n'
'needs two expressions separated with a colon followed by the usual\n'
'“for” and “if” clauses. When the comprehension is run, the '
'resulting\n'
'key and value elements are inserted in the new dictionary in the '
'order\n'
'they are produced.\n'
'\n'
'Restrictions on the types of the key values are listed earlier in\n'
'section The standard type hierarchy. (To summarize, the key type\n'
'should be *hashable*, which excludes all mutable objects.) Clashes\n'
'between duplicate keys are not detected; the last datum (textually\n'
'rightmost in the display) stored for a given key value prevails.\n',
'dynamic-features': 'Interaction with dynamic features\n'
'*********************************\n'
'\n'
'Name resolution of free variables occurs at runtime, not '
'at compile\n'
'time. This means that the following code will print 42:\n'
'\n'
' i = 10\n'
' def f():\n'
' print(i)\n'
' i = 42\n'
' f()\n'
'\n'
'The "eval()" and "exec()" functions do not have access '
'to the full\n'
'environment for resolving names. Names may be resolved '
'in the local\n'
'and global namespaces of the caller. Free variables are '
'not resolved\n'
'in the nearest enclosing namespace, but in the global '
'namespace. [1]\n'
'The "exec()" and "eval()" functions have optional '
'arguments to\n'
'override the global and local namespace. If only one '
'namespace is\n'
'specified, it is used for both.\n',
'else': 'The "if" statement\n'
'******************\n'
'\n'
'The "if" statement is used for conditional execution:\n'
'\n'
' if_stmt ::= "if" expression ":" suite\n'
' ("elif" expression ":" suite)*\n'
' ["else" ":" suite]\n'
'\n'
'It selects exactly one of the suites by evaluating the expressions '
'one\n'
'by one until one is found to be true (see section Boolean '
'operations\n'
'for the definition of true and false); then that suite is executed\n'
'(and no other part of the "if" statement is executed or evaluated).\n'
'If all expressions are false, the suite of the "else" clause, if\n'
'present, is executed.\n',
'exceptions': 'Exceptions\n'
'**********\n'
'\n'
'Exceptions are a means of breaking out of the normal flow of '
'control\n'
'of a code block in order to handle errors or other '
'exceptional\n'
'conditions. An exception is *raised* at the point where the '
'error is\n'
'detected; it may be *handled* by the surrounding code block or '
'by any\n'
'code block that directly or indirectly invoked the code block '
'where\n'
'the error occurred.\n'
'\n'
'The Python interpreter raises an exception when it detects a '
'run-time\n'
'error (such as division by zero). A Python program can also\n'
'explicitly raise an exception with the "raise" statement. '
'Exception\n'
'handlers are specified with the "try" … "except" statement. '
'The\n'
'"finally" clause of such a statement can be used to specify '
'cleanup\n'
'code which does not handle the exception, but is executed '
'whether an\n'
'exception occurred or not in the preceding code.\n'
'\n'
'Python uses the “termination” model of error handling: an '
'exception\n'
'handler can find out what happened and continue execution at '
'an outer\n'
'level, but it cannot repair the cause of the error and retry '
'the\n'
'failing operation (except by re-entering the offending piece '
'of code\n'
'from the top).\n'
'\n'
'When an exception is not handled at all, the interpreter '
'terminates\n'
'execution of the program, or returns to its interactive main '
'loop. In\n'
'either case, it prints a stack backtrace, except when the '
'exception is\n'
'"SystemExit".\n'
'\n'
'Exceptions are identified by class instances. The "except" '
'clause is\n'
'selected depending on the class of the instance: it must '
'reference the\n'
'class of the instance or a base class thereof. The instance '
'can be\n'
'received by the handler and can carry additional information '
'about the\n'
'exceptional condition.\n'
'\n'
'Note: Exception messages are not part of the Python API. '
'Their\n'
' contents may change from one version of Python to the next '
'without\n'
' warning and should not be relied on by code which will run '
'under\n'
' multiple versions of the interpreter.\n'
'\n'
'See also the description of the "try" statement in section The '
'try\n'
'statement and "raise" statement in section The raise '
'statement.\n'
'\n'
'-[ Footnotes ]-\n'
'\n'
'[1] This limitation occurs because the code that is executed '
'by\n'
' these operations is not available at the time the module '
'is\n'
' compiled.\n',
'execmodel': 'Execution model\n'
'***************\n'
'\n'
'\n'
'Structure of a program\n'
'======================\n'
'\n'
'A Python program is constructed from code blocks. A *block* is '
'a piece\n'
'of Python program text that is executed as a unit. The '
'following are\n'
'blocks: a module, a function body, and a class definition. '
'Each\n'
'command typed interactively is a block. A script file (a file '
'given\n'
'as standard input to the interpreter or specified as a command '
'line\n'
'argument to the interpreter) is a code block. A script command '
'(a\n'
'command specified on the interpreter command line with the '
'"-c"\n'
'option) is a code block. The string argument passed to the '
'built-in\n'
'functions "eval()" and "exec()" is a code block.\n'
'\n'
'A code block is executed in an *execution frame*. A frame '
'contains\n'
'some administrative information (used for debugging) and '
'determines\n'
'where and how execution continues after the code block’s '
'execution has\n'
'completed.\n'
'\n'
'\n'
'Naming and binding\n'
'==================\n'
'\n'
'\n'
'Binding of names\n'
'----------------\n'
'\n'
'*Names* refer to objects. Names are introduced by name '
'binding\n'
'operations.\n'
'\n'
'The following constructs bind names: formal parameters to '
'functions,\n'
'"import" statements, class and function definitions (these bind '
'the\n'
'class or function name in the defining block), and targets that '
'are\n'
'identifiers if occurring in an assignment, "for" loop header, '
'or after\n'
'"as" in a "with" statement or "except" clause. The "import" '
'statement\n'
'of the form "from ... import *" binds all names defined in the\n'
'imported module, except those beginning with an underscore. '
'This form\n'
'may only be used at the module level.\n'
'\n'
'A target occurring in a "del" statement is also considered '
'bound for\n'
'this purpose (though the actual semantics are to unbind the '
'name).\n'
'\n'
'Each assignment or import statement occurs within a block '
'defined by a\n'
'class or function definition or at the module level (the '
'top-level\n'
'code block).\n'
'\n'
'If a name is bound in a block, it is a local variable of that '
'block,\n'
'unless declared as "nonlocal" or "global". If a name is bound '
'at the\n'
'module level, it is a global variable. (The variables of the '
'module\n'
'code block are local and global.) If a variable is used in a '
'code\n'
'block but not defined there, it is a *free variable*.\n'
'\n'
'Each occurrence of a name in the program text refers to the '
'*binding*\n'
'of that name established by the following name resolution '
'rules.\n'
'\n'
'\n'
'Resolution of names\n'
'-------------------\n'
'\n'
'A *scope* defines the visibility of a name within a block. If '
'a local\n'
'variable is defined in a block, its scope includes that block. '
'If the\n'
'definition occurs in a function block, the scope extends to any '
'blocks\n'
'contained within the defining one, unless a contained block '
'introduces\n'
'a different binding for the name.\n'
'\n'
'When a name is used in a code block, it is resolved using the '
'nearest\n'
'enclosing scope. The set of all such scopes visible to a code '
'block\n'
'is called the block’s *environment*.\n'
'\n'
'When a name is not found at all, a "NameError" exception is '
'raised. If\n'
'the current scope is a function scope, and the name refers to a '
'local\n'
'variable that has not yet been bound to a value at the point '
'where the\n'
'name is used, an "UnboundLocalError" exception is raised.\n'
'"UnboundLocalError" is a subclass of "NameError".\n'
'\n'
'If a name binding operation occurs anywhere within a code '
'block, all\n'
'uses of the name within the block are treated as references to '
'the\n'
'current block. This can lead to errors when a name is used '
'within a\n'
'block before it is bound. This rule is subtle. Python lacks\n'
'declarations and allows name binding operations to occur '
'anywhere\n'
'within a code block. The local variables of a code block can '
'be\n'
'determined by scanning the entire text of the block for name '
'binding\n'
'operations.\n'
'\n'
'If the "global" statement occurs within a block, all uses of '
'the name\n'
'specified in the statement refer to the binding of that name in '
'the\n'
'top-level namespace. Names are resolved in the top-level '
'namespace by\n'
'searching the global namespace, i.e. the namespace of the '
'module\n'
'containing the code block, and the builtins namespace, the '
'namespace\n'
'of the module "builtins". The global namespace is searched '
'first. If\n'
'the name is not found there, the builtins namespace is '
'searched. The\n'
'"global" statement must precede all uses of the name.\n'
'\n'
'The "global" statement has the same scope as a name binding '
'operation\n'
'in the same block. If the nearest enclosing scope for a free '
'variable\n'
'contains a global statement, the free variable is treated as a '
'global.\n'
'\n'
'The "nonlocal" statement causes corresponding names to refer '
'to\n'
'previously bound variables in the nearest enclosing function '
'scope.\n'
'"SyntaxError" is raised at compile time if the given name does '
'not\n'
'exist in any enclosing function scope.\n'
'\n'
'The namespace for a module is automatically created the first '
'time a\n'
'module is imported. The main module for a script is always '
'called\n'
'"__main__".\n'
'\n'
'Class definition blocks and arguments to "exec()" and "eval()" '
'are\n'
'special in the context of name resolution. A class definition '
'is an\n'
'executable statement that may use and define names. These '
'references\n'
'follow the normal rules for name resolution with an exception '
'that\n'
'unbound local variables are looked up in the global namespace. '
'The\n'
'namespace of the class definition becomes the attribute '
'dictionary of\n'
'the class. The scope of names defined in a class block is '
'limited to\n'
'the class block; it does not extend to the code blocks of '
'methods –\n'
'this includes comprehensions and generator expressions since '
'they are\n'
'implemented using a function scope. This means that the '
'following\n'
'will fail:\n'
'\n'
' class A:\n'
' a = 42\n'
' b = list(a + i for i in range(10))\n'
'\n'
'\n'
'Builtins and restricted execution\n'
'---------------------------------\n'
'\n'
'**CPython implementation detail:** Users should not touch\n'
'"__builtins__"; it is strictly an implementation detail. '
'Users\n'
'wanting to override values in the builtins namespace should '
'"import"\n'
'the "builtins" module and modify its attributes appropriately.\n'
'\n'
'The builtins namespace associated with the execution of a code '
'block\n'
'is actually found by looking up the name "__builtins__" in its '
'global\n'
'namespace; this should be a dictionary or a module (in the '
'latter case\n'
'the module’s dictionary is used). By default, when in the '
'"__main__"\n'
'module, "__builtins__" is the built-in module "builtins"; when '
'in any\n'
'other module, "__builtins__" is an alias for the dictionary of '
'the\n'
'"builtins" module itself.\n'
'\n'
'\n'
'Interaction with dynamic features\n'
'---------------------------------\n'
'\n'
'Name resolution of free variables occurs at runtime, not at '
'compile\n'
'time. This means that the following code will print 42:\n'
'\n'
' i = 10\n'
' def f():\n'
' print(i)\n'
' i = 42\n'
' f()\n'
'\n'
'The "eval()" and "exec()" functions do not have access to the '
'full\n'
'environment for resolving names. Names may be resolved in the '
'local\n'
'and global namespaces of the caller. Free variables are not '
'resolved\n'
'in the nearest enclosing namespace, but in the global '
'namespace. [1]\n'
'The "exec()" and "eval()" functions have optional arguments to\n'
'override the global and local namespace. If only one namespace '
'is\n'
'specified, it is used for both.\n'
'\n'
'\n'
'Exceptions\n'
'==========\n'
'\n'
'Exceptions are a means of breaking out of the normal flow of '
'control\n'
'of a code block in order to handle errors or other exceptional\n'
'conditions. An exception is *raised* at the point where the '
'error is\n'
'detected; it may be *handled* by the surrounding code block or '
'by any\n'
'code block that directly or indirectly invoked the code block '
'where\n'
'the error occurred.\n'
'\n'
'The Python interpreter raises an exception when it detects a '
'run-time\n'
'error (such as division by zero). A Python program can also\n'
'explicitly raise an exception with the "raise" statement. '
'Exception\n'
'handlers are specified with the "try" … "except" statement. '
'The\n'
'"finally" clause of such a statement can be used to specify '
'cleanup\n'
'code which does not handle the exception, but is executed '
'whether an\n'
'exception occurred or not in the preceding code.\n'
'\n'
'Python uses the “termination” model of error handling: an '
'exception\n'
'handler can find out what happened and continue execution at an '
'outer\n'
'level, but it cannot repair the cause of the error and retry '
'the\n'
'failing operation (except by re-entering the offending piece of '
'code\n'
'from the top).\n'
'\n'
'When an exception is not handled at all, the interpreter '
'terminates\n'
'execution of the program, or returns to its interactive main '
'loop. In\n'
'either case, it prints a stack backtrace, except when the '
'exception is\n'
'"SystemExit".\n'
'\n'
'Exceptions are identified by class instances. The "except" '
'clause is\n'
'selected depending on the class of the instance: it must '
'reference the\n'
'class of the instance or a base class thereof. The instance '
'can be\n'
'received by the handler and can carry additional information '
'about the\n'
'exceptional condition.\n'
'\n'
'Note: Exception messages are not part of the Python API. '
'Their\n'
' contents may change from one version of Python to the next '
'without\n'
' warning and should not be relied on by code which will run '
'under\n'
' multiple versions of the interpreter.\n'
'\n'
'See also the description of the "try" statement in section The '
'try\n'
'statement and "raise" statement in section The raise '
'statement.\n'
'\n'
'-[ Footnotes ]-\n'
'\n'
'[1] This limitation occurs because the code that is executed '
'by\n'
' these operations is not available at the time the module '
'is\n'
' compiled.\n',
'exprlists': 'Expression lists\n'
'****************\n'
'\n'
' expression_list ::= expression ("," expression)* [","]\n'
' starred_list ::= starred_item ("," starred_item)* '
'[","]\n'
' starred_expression ::= expression | (starred_item ",")* '
'[starred_item]\n'
' starred_item ::= expression | "*" or_expr\n'
'\n'
'Except when part of a list or set display, an expression list\n'
'containing at least one comma yields a tuple. The length of '
'the tuple\n'
'is the number of expressions in the list. The expressions are\n'
'evaluated from left to right.\n'
'\n'
'An asterisk "*" denotes *iterable unpacking*. Its operand must '
'be an\n'
'*iterable*. The iterable is expanded into a sequence of items, '
'which\n'
'are included in the new tuple, list, or set, at the site of '
'the\n'
'unpacking.\n'
'\n'
'New in version 3.5: Iterable unpacking in expression lists, '
'originally\n'
'proposed by **PEP 448**.\n'
'\n'
'The trailing comma is required only to create a single tuple '
'(a.k.a. a\n'
'*singleton*); it is optional in all other cases. A single '
'expression\n'
'without a trailing comma doesn’t create a tuple, but rather '
'yields the\n'
'value of that expression. (To create an empty tuple, use an '
'empty pair\n'
'of parentheses: "()".)\n',
'floating': 'Floating point literals\n'
'***********************\n'
'\n'
'Floating point literals are described by the following lexical\n'
'definitions:\n'
'\n'
' floatnumber ::= pointfloat | exponentfloat\n'
' pointfloat ::= [digitpart] fraction | digitpart "."\n'
' exponentfloat ::= (digitpart | pointfloat) exponent\n'
' digitpart ::= digit (["_"] digit)*\n'
' fraction ::= "." digitpart\n'
' exponent ::= ("e" | "E") ["+" | "-"] digitpart\n'
'\n'
'Note that the integer and exponent parts are always interpreted '
'using\n'
'radix 10. For example, "077e010" is legal, and denotes the same '
'number\n'
'as "77e10". The allowed range of floating point literals is\n'
'implementation-dependent. As in integer literals, underscores '
'are\n'
'supported for digit grouping.\n'
'\n'
'Some examples of floating point literals:\n'
'\n'
' 3.14 10. .001 1e100 3.14e-10 0e0 '
'3.14_15_93\n'
'\n'
'Changed in version 3.6: Underscores are now allowed for '
'grouping\n'
'purposes in literals.\n',
'for': 'The "for" statement\n'
'*******************\n'
'\n'
'The "for" statement is used to iterate over the elements of a '
'sequence\n'
'(such as a string, tuple or list) or other iterable object:\n'
'\n'
' for_stmt ::= "for" target_list "in" expression_list ":" suite\n'
' ["else" ":" suite]\n'
'\n'
'The expression list is evaluated once; it should yield an iterable\n'
'object. An iterator is created for the result of the\n'
'"expression_list". The suite is then executed once for each item\n'
'provided by the iterator, in the order returned by the iterator. '
'Each\n'
'item in turn is assigned to the target list using the standard rules\n'
'for assignments (see Assignment statements), and then the suite is\n'
'executed. When the items are exhausted (which is immediately when '
'the\n'
'sequence is empty or an iterator raises a "StopIteration" '
'exception),\n'
'the suite in the "else" clause, if present, is executed, and the '
'loop\n'
'terminates.\n'
'\n'
'A "break" statement executed in the first suite terminates the loop\n'
'without executing the "else" clause’s suite. A "continue" statement\n'
'executed in the first suite skips the rest of the suite and '
'continues\n'
'with the next item, or with the "else" clause if there is no next\n'
'item.\n'
'\n'
'The for-loop makes assignments to the variables(s) in the target '
'list.\n'
'This overwrites all previous assignments to those variables '
'including\n'
'those made in the suite of the for-loop:\n'
'\n'
' for i in range(10):\n'
' print(i)\n'
' i = 5 # this will not affect the for-loop\n'
' # because i will be overwritten with the '
'next\n'
' # index in the range\n'
'\n'
'Names in the target list are not deleted when the loop is finished,\n'
'but if the sequence is empty, they will not have been assigned to at\n'
'all by the loop. Hint: the built-in function "range()" returns an\n'
'iterator of integers suitable to emulate the effect of Pascal’s "for '
'i\n'
':= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".\n'
'\n'
'Note: There is a subtlety when the sequence is being modified by the\n'
' loop (this can only occur for mutable sequences, e.g. lists). An\n'
' internal counter is used to keep track of which item is used next,\n'
' and this is incremented on each iteration. When this counter has\n'
' reached the length of the sequence the loop terminates. This '
'means\n'
' that if the suite deletes the current (or a previous) item from '
'the\n'
' sequence, the next item will be skipped (since it gets the index '
'of\n'
' the current item which has already been treated). Likewise, if '
'the\n'
' suite inserts an item in the sequence before the current item, the\n'
' current item will be treated again the next time through the loop.\n'
' This can lead to nasty bugs that can be avoided by making a\n'
' temporary copy using a slice of the whole sequence, e.g.,\n'
'\n'
' for x in a[:]:\n'
' if x < 0: a.remove(x)\n',
'formatstrings': 'Format String Syntax\n'
'********************\n'
'\n'
'The "str.format()" method and the "Formatter" class share '
'the same\n'
'syntax for format strings (although in the case of '
'"Formatter",\n'
'subclasses can define their own format string syntax). The '
'syntax is\n'
'related to that of formatted string literals, but there '
'are\n'
'differences.\n'
'\n'
'Format strings contain “replacement fields” surrounded by '
'curly braces\n'
'"{}". Anything that is not contained in braces is '
'considered literal\n'
'text, which is copied unchanged to the output. If you need '
'to include\n'
'a brace character in the literal text, it can be escaped by '
'doubling:\n'
'"{{" and "}}".\n'
'\n'
'The grammar for a replacement field is as follows:\n'
'\n'
' replacement_field ::= "{" [field_name] ["!" '
'conversion] [":" format_spec] "}"\n'
' field_name ::= arg_name ("." attribute_name | '
'"[" element_index "]")*\n'
' arg_name ::= [identifier | digit+]\n'
' attribute_name ::= identifier\n'
' element_index ::= digit+ | index_string\n'
' index_string ::= <any source character except '
'"]"> +\n'
' conversion ::= "r" | "s" | "a"\n'
' format_spec ::= <described in the next '
'section>\n'
'\n'
'In less formal terms, the replacement field can start with '
'a\n'
'*field_name* that specifies the object whose value is to be '
'formatted\n'
'and inserted into the output instead of the replacement '
'field. The\n'
'*field_name* is optionally followed by a *conversion* '
'field, which is\n'
'preceded by an exclamation point "\'!\'", and a '
'*format_spec*, which is\n'
'preceded by a colon "\':\'". These specify a non-default '
'format for the\n'
'replacement value.\n'
'\n'
'See also the Format Specification Mini-Language section.\n'
'\n'
'The *field_name* itself begins with an *arg_name* that is '
'either a\n'
'number or a keyword. If it’s a number, it refers to a '
'positional\n'
'argument, and if it’s a keyword, it refers to a named '
'keyword\n'
'argument. If the numerical arg_names in a format string '
'are 0, 1, 2,\n'
'… in sequence, they can all be omitted (not just some) and '
'the numbers\n'
'0, 1, 2, … will be automatically inserted in that order. '
'Because\n'
'*arg_name* is not quote-delimited, it is not possible to '
'specify\n'
'arbitrary dictionary keys (e.g., the strings "\'10\'" or '
'"\':-]\'") within\n'
'a format string. The *arg_name* can be followed by any '
'number of index\n'
'or attribute expressions. An expression of the form '
'"\'.name\'" selects\n'
'the named attribute using "getattr()", while an expression '
'of the form\n'
'"\'[index]\'" does an index lookup using "__getitem__()".\n'
'\n'
'Changed in version 3.1: The positional argument specifiers '
'can be\n'
'omitted for "str.format()", so "\'{} {}\'.format(a, b)" is '
'equivalent to\n'
'"\'{0} {1}\'.format(a, b)".\n'
'\n'
'Changed in version 3.4: The positional argument specifiers '
'can be\n'
'omitted for "Formatter".\n'
'\n'
'Some simple format string examples:\n'
'\n'
' "First, thou shalt count to {0}" # References first '
'positional argument\n'
' "Bring me a {}" # Implicitly '
'references the first positional argument\n'
' "From {} to {}" # Same as "From {0} to '
'{1}"\n'
' "My quest is {name}" # References keyword '
"argument 'name'\n"
' "Weight in tons {0.weight}" # \'weight\' attribute '
'of first positional arg\n'
' "Units destroyed: {players[0]}" # First element of '
"keyword argument 'players'.\n"
'\n'
'The *conversion* field causes a type coercion before '
'formatting.\n'
'Normally, the job of formatting a value is done by the '
'"__format__()"\n'
'method of the value itself. However, in some cases it is '
'desirable to\n'
'force a type to be formatted as a string, overriding its '
'own\n'
'definition of formatting. By converting the value to a '
'string before\n'
'calling "__format__()", the normal formatting logic is '
'bypassed.\n'
'\n'
'Three conversion flags are currently supported: "\'!s\'" '
'which calls\n'
'"str()" on the value, "\'!r\'" which calls "repr()" and '
'"\'!a\'" which\n'
'calls "ascii()".\n'
'\n'
'Some examples:\n'
'\n'
' "Harold\'s a clever {0!s}" # Calls str() on the '
'argument first\n'
' "Bring out the holy {name!r}" # Calls repr() on the '
'argument first\n'
' "More {!a}" # Calls ascii() on the '
'argument first\n'
'\n'
'The *format_spec* field contains a specification of how the '
'value\n'
'should be presented, including such details as field width, '
'alignment,\n'
'padding, decimal precision and so on. Each value type can '
'define its\n'
'own “formatting mini-language” or interpretation of the '
'*format_spec*.\n'
'\n'
'Most built-in types support a common formatting '
'mini-language, which\n'
'is described in the next section.\n'
'\n'
'A *format_spec* field can also include nested replacement '
'fields\n'
'within it. These nested replacement fields may contain a '
'field name,\n'
'conversion flag and format specification, but deeper '
'nesting is not\n'
'allowed. The replacement fields within the format_spec '
'are\n'
'substituted before the *format_spec* string is interpreted. '
'This\n'
'allows the formatting of a value to be dynamically '
'specified.\n'
'\n'
'See the Format examples section for some examples.\n'
'\n'
'\n'
'Format Specification Mini-Language\n'
'==================================\n'
'\n'
'“Format specifications” are used within replacement fields '
'contained\n'
'within a format string to define how individual values are '
'presented\n'
'(see Format String Syntax and Formatted string literals). '
'They can\n'
'also be passed directly to the built-in "format()" '
'function. Each\n'
'formattable type may define how the format specification is '
'to be\n'
'interpreted.\n'
'\n'
'Most built-in types implement the following options for '
'format\n'
'specifications, although some of the formatting options are '
'only\n'
'supported by the numeric types.\n'
'\n'
'A general convention is that an empty format string ("""") '
'produces\n'
'the same result as if you had called "str()" on the value. '
'A non-empty\n'
'format string typically modifies the result.\n'
'\n'
'The general form of a *standard format specifier* is:\n'
'\n'
' format_spec ::= '
'[[fill]align][sign][#][0][width][grouping_option][.precision][type]\n'
' fill ::= <any character>\n'
' align ::= "<" | ">" | "=" | "^"\n'
' sign ::= "+" | "-" | " "\n'
' width ::= digit+\n'
' grouping_option ::= "_" | ","\n'
' precision ::= digit+\n'
' type ::= "b" | "c" | "d" | "e" | "E" | "f" | '
'"F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"\n'
'\n'
'If a valid *align* value is specified, it can be preceded '
'by a *fill*\n'
'character that can be any character and defaults to a space '
'if\n'
'omitted. It is not possible to use a literal curly brace '
'(“"{"” or\n'
'“"}"”) as the *fill* character in a formatted string '
'literal or when\n'
'using the "str.format()" method. However, it is possible '
'to insert a\n'
'curly brace with a nested replacement field. This '
'limitation doesn’t\n'
'affect the "format()" function.\n'
'\n'
'The meaning of the various alignment options is as '
'follows:\n'
'\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | Option | '
'Meaning '
'|\n'
' '
'+===========+============================================================+\n'
' | "\'<\'" | Forces the field to be left-aligned '
'within the available |\n'
' | | space (this is the default for most '
'objects). |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'>\'" | Forces the field to be right-aligned '
'within the available |\n'
' | | space (this is the default for '
'numbers). |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'=\'" | Forces the padding to be placed after '
'the sign (if any) |\n'
' | | but before the digits. This is used for '
'printing fields |\n'
' | | in the form ‘+000000120’. This alignment '
'option is only |\n'
' | | valid for numeric types. It becomes the '
'default when ‘0’ |\n'
' | | immediately precedes the field '
'width. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'^\'" | Forces the field to be centered within '
'the available |\n'
' | | '
'space. '
'|\n'
' '
'+-----------+------------------------------------------------------------+\n'
'\n'
'Note that unless a minimum field width is defined, the '
'field width\n'
'will always be the same size as the data to fill it, so '
'that the\n'
'alignment option has no meaning in this case.\n'
'\n'
'The *sign* option is only valid for number types, and can '
'be one of\n'
'the following:\n'
'\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | Option | '
'Meaning '
'|\n'
' '
'+===========+============================================================+\n'
' | "\'+\'" | indicates that a sign should be used for '
'both positive as |\n'
' | | well as negative '
'numbers. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'-\'" | indicates that a sign should be used '
'only for negative |\n'
' | | numbers (this is the default '
'behavior). |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | space | indicates that a leading space should be '
'used on positive |\n'
' | | numbers, and a minus sign on negative '
'numbers. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
'\n'
'The "\'#\'" option causes the “alternate form” to be used '
'for the\n'
'conversion. The alternate form is defined differently for '
'different\n'
'types. This option is only valid for integer, float, '
'complex and\n'
'Decimal types. For integers, when binary, octal, or '
'hexadecimal output\n'
'is used, this option adds the prefix respective "\'0b\'", '
'"\'0o\'", or\n'
'"\'0x\'" to the output value. For floats, complex and '
'Decimal the\n'
'alternate form causes the result of the conversion to '
'always contain a\n'
'decimal-point character, even if no digits follow it. '
'Normally, a\n'
'decimal-point character appears in the result of these '
'conversions\n'
'only if a digit follows it. In addition, for "\'g\'" and '
'"\'G\'"\n'
'conversions, trailing zeros are not removed from the '
'result.\n'
'\n'
'The "\',\'" option signals the use of a comma for a '
'thousands separator.\n'
'For a locale aware separator, use the "\'n\'" integer '
'presentation type\n'
'instead.\n'
'\n'
'Changed in version 3.1: Added the "\',\'" option (see also '
'**PEP 378**).\n'
'\n'
'The "\'_\'" option signals the use of an underscore for a '
'thousands\n'
'separator for floating point presentation types and for '
'integer\n'
'presentation type "\'d\'". For integer presentation types '
'"\'b\'", "\'o\'",\n'
'"\'x\'", and "\'X\'", underscores will be inserted every 4 '
'digits. For\n'
'other presentation types, specifying this option is an '
'error.\n'
'\n'
'Changed in version 3.6: Added the "\'_\'" option (see also '
'**PEP 515**).\n'
'\n'
'*width* is a decimal integer defining the minimum field '
'width. If not\n'
'specified, then the field width will be determined by the '
'content.\n'
'\n'
'When no explicit alignment is given, preceding the *width* '
'field by a\n'
'zero ("\'0\'") character enables sign-aware zero-padding '
'for numeric\n'
'types. This is equivalent to a *fill* character of "\'0\'" '
'with an\n'
'*alignment* type of "\'=\'".\n'
'\n'
'The *precision* is a decimal number indicating how many '
'digits should\n'
'be displayed after the decimal point for a floating point '
'value\n'
'formatted with "\'f\'" and "\'F\'", or before and after the '
'decimal point\n'
'for a floating point value formatted with "\'g\'" or '
'"\'G\'". For non-\n'
'number types the field indicates the maximum field size - '
'in other\n'
'words, how many characters will be used from the field '
'content. The\n'
'*precision* is not allowed for integer values.\n'
'\n'
'Finally, the *type* determines how the data should be '
'presented.\n'
'\n'
'The available string presentation types are:\n'
'\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | Type | '
'Meaning '
'|\n'
' '
'+===========+============================================================+\n'
' | "\'s\'" | String format. This is the default type '
'for strings and |\n'
' | | may be '
'omitted. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | None | The same as '
'"\'s\'". |\n'
' '
'+-----------+------------------------------------------------------------+\n'
'\n'
'The available integer presentation types are:\n'
'\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | Type | '
'Meaning '
'|\n'
' '
'+===========+============================================================+\n'
' | "\'b\'" | Binary format. Outputs the number in '
'base 2. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'c\'" | Character. Converts the integer to the '
'corresponding |\n'
' | | unicode character before '
'printing. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'d\'" | Decimal Integer. Outputs the number in '
'base 10. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'o\'" | Octal format. Outputs the number in base '
'8. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'x\'" | Hex format. Outputs the number in base '
'16, using lower- |\n'
' | | case letters for the digits above '
'9. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'X\'" | Hex format. Outputs the number in base '
'16, using upper- |\n'
' | | case letters for the digits above '
'9. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'n\'" | Number. This is the same as "\'d\'", '
'except that it uses the |\n'
' | | current locale setting to insert the '
'appropriate number |\n'
' | | separator '
'characters. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | None | The same as '
'"\'d\'". |\n'
' '
'+-----------+------------------------------------------------------------+\n'
'\n'
'In addition to the above presentation types, integers can '
'be formatted\n'
'with the floating point presentation types listed below '
'(except "\'n\'"\n'
'and "None"). When doing so, "float()" is used to convert '
'the integer\n'
'to a floating point number before formatting.\n'
'\n'
'The available presentation types for floating point and '
'decimal values\n'
'are:\n'
'\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | Type | '
'Meaning '
'|\n'
' '
'+===========+============================================================+\n'
' | "\'e\'" | Exponent notation. Prints the number in '
'scientific |\n'
' | | notation using the letter ‘e’ to indicate '
'the exponent. |\n'
' | | The default precision is '
'"6". |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'E\'" | Exponent notation. Same as "\'e\'" '
'except it uses an upper |\n'
' | | case ‘E’ as the separator '
'character. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'f\'" | Fixed-point notation. Displays the '
'number as a fixed-point |\n'
' | | number. The default precision is '
'"6". |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'F\'" | Fixed-point notation. Same as "\'f\'", '
'but converts "nan" to |\n'
' | | "NAN" and "inf" to '
'"INF". |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'g\'" | General format. For a given precision '
'"p >= 1", this |\n'
' | | rounds the number to "p" significant '
'digits and then |\n'
' | | formats the result in either fixed-point '
'format or in |\n'
' | | scientific notation, depending on its '
'magnitude. The |\n'
' | | precise rules are as follows: suppose that '
'the result |\n'
' | | formatted with presentation type "\'e\'" '
'and precision "p-1" |\n'
' | | would have exponent "exp". Then if "-4 <= '
'exp < p", the |\n'
' | | number is formatted with presentation type '
'"\'f\'" and |\n'
' | | precision "p-1-exp". Otherwise, the '
'number is formatted |\n'
' | | with presentation type "\'e\'" and '
'precision "p-1". In both |\n'
' | | cases insignificant trailing zeros are '
'removed from the |\n'
' | | significand, and the decimal point is also '
'removed if |\n'
' | | there are no remaining digits following '
'it. Positive and |\n'
' | | negative infinity, positive and negative '
'zero, and nans, |\n'
' | | are formatted as "inf", "-inf", "0", "-0" '
'and "nan" |\n'
' | | respectively, regardless of the '
'precision. A precision of |\n'
' | | "0" is treated as equivalent to a '
'precision of "1". The |\n'
' | | default precision is '
'"6". |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'G\'" | General format. Same as "\'g\'" except '
'switches to "\'E\'" if |\n'
' | | the number gets too large. The '
'representations of infinity |\n'
' | | and NaN are uppercased, '
'too. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'n\'" | Number. This is the same as "\'g\'", '
'except that it uses the |\n'
' | | current locale setting to insert the '
'appropriate number |\n'
' | | separator '
'characters. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | "\'%\'" | Percentage. Multiplies the number by 100 '
'and displays in |\n'
' | | fixed ("\'f\'") format, followed by a '
'percent sign. |\n'
' '
'+-----------+------------------------------------------------------------+\n'
' | None | Similar to "\'g\'", except that '
'fixed-point notation, when |\n'
' | | used, has at least one digit past the '
'decimal point. The |\n'
' | | default precision is as high as needed to '
'represent the |\n'
' | | particular value. The overall effect is to '
'match the |\n'
' | | output of "str()" as altered by the other '
'format |\n'
' | | '
'modifiers. '
'|\n'
' '
'+-----------+------------------------------------------------------------+\n'
'\n'
'\n'
'Format examples\n'
'===============\n'
'\n'
'This section contains examples of the "str.format()" syntax '
'and\n'
'comparison with the old "%"-formatting.\n'
'\n'
'In most of the cases the syntax is similar to the old '
'"%"-formatting,\n'
'with the addition of the "{}" and with ":" used instead of '
'"%". For\n'
'example, "\'%03.2f\'" can be translated to "\'{:03.2f}\'".\n'
'\n'
'The new format syntax also supports new and different '
'options, shown\n'
'in the following examples.\n'
'\n'
'Accessing arguments by position:\n'
'\n'
" >>> '{0}, {1}, {2}'.format('a', 'b', 'c')\n"
" 'a, b, c'\n"
" >>> '{}, {}, {}'.format('a', 'b', 'c') # 3.1+ only\n"
" 'a, b, c'\n"
" >>> '{2}, {1}, {0}'.format('a', 'b', 'c')\n"
" 'c, b, a'\n"
" >>> '{2}, {1}, {0}'.format(*'abc') # unpacking "
'argument sequence\n'
" 'c, b, a'\n"
" >>> '{0}{1}{0}'.format('abra', 'cad') # arguments' "
'indices can be repeated\n'
" 'abracadabra'\n"
'\n'
'Accessing arguments by name:\n'
'\n'
" >>> 'Coordinates: {latitude}, "
"{longitude}'.format(latitude='37.24N', "
"longitude='-115.81W')\n"
" 'Coordinates: 37.24N, -115.81W'\n"
" >>> coord = {'latitude': '37.24N', 'longitude': "
"'-115.81W'}\n"
" >>> 'Coordinates: {latitude}, "
"{longitude}'.format(**coord)\n"
" 'Coordinates: 37.24N, -115.81W'\n"
'\n'
'Accessing arguments’ attributes:\n'
'\n'
' >>> c = 3-5j\n'
" >>> ('The complex number {0} is formed from the real "
"part {0.real} '\n"
" ... 'and the imaginary part {0.imag}.').format(c)\n"
" 'The complex number (3-5j) is formed from the real part "
"3.0 and the imaginary part -5.0.'\n"
' >>> class Point:\n'
' ... def __init__(self, x, y):\n'
' ... self.x, self.y = x, y\n'
' ... def __str__(self):\n'
" ... return 'Point({self.x}, "
"{self.y})'.format(self=self)\n"
' ...\n'
' >>> str(Point(4, 2))\n'
" 'Point(4, 2)'\n"
'\n'
'Accessing arguments’ items:\n'
'\n'
' >>> coord = (3, 5)\n'
" >>> 'X: {0[0]}; Y: {0[1]}'.format(coord)\n"
" 'X: 3; Y: 5'\n"
'\n'
'Replacing "%s" and "%r":\n'
'\n'
' >>> "repr() shows quotes: {!r}; str() doesn\'t: '
'{!s}".format(\'test1\', \'test2\')\n'
' "repr() shows quotes: \'test1\'; str() doesn\'t: test2"\n'
'\n'
'Aligning the text and specifying a width:\n'
'\n'
" >>> '{:<30}'.format('left aligned')\n"
" 'left aligned '\n"
" >>> '{:>30}'.format('right aligned')\n"
" ' right aligned'\n"
" >>> '{:^30}'.format('centered')\n"
" ' centered '\n"
" >>> '{:*^30}'.format('centered') # use '*' as a fill "
'char\n'
" '***********centered***********'\n"
'\n'
'Replacing "%+f", "%-f", and "% f" and specifying a sign:\n'
'\n'
" >>> '{:+f}; {:+f}'.format(3.14, -3.14) # show it "
'always\n'
" '+3.140000; -3.140000'\n"
" >>> '{: f}; {: f}'.format(3.14, -3.14) # show a space "
'for positive numbers\n'
" ' 3.140000; -3.140000'\n"
" >>> '{:-f}; {:-f}'.format(3.14, -3.14) # show only the "
"minus -- same as '{:f}; {:f}'\n"
" '3.140000; -3.140000'\n"
'\n'
'Replacing "%x" and "%o" and converting the value to '
'different bases:\n'
'\n'
' >>> # format also supports binary numbers\n'
' >>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: '
'{0:b}".format(42)\n'
" 'int: 42; hex: 2a; oct: 52; bin: 101010'\n"
' >>> # with 0x, 0o, or 0b as prefix:\n'
' >>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: '
'{0:#b}".format(42)\n'
" 'int: 42; hex: 0x2a; oct: 0o52; bin: 0b101010'\n"
'\n'
'Using the comma as a thousands separator:\n'
'\n'
" >>> '{:,}'.format(1234567890)\n"
" '1,234,567,890'\n"
'\n'
'Expressing a percentage:\n'
'\n'
' >>> points = 19\n'
' >>> total = 22\n'
" >>> 'Correct answers: {:.2%}'.format(points/total)\n"
" 'Correct answers: 86.36%'\n"
'\n'
'Using type-specific formatting:\n'
'\n'
' >>> import datetime\n'
' >>> d = datetime.datetime(2010, 7, 4, 12, 15, 58)\n'
" >>> '{:%Y-%m-%d %H:%M:%S}'.format(d)\n"
" '2010-07-04 12:15:58'\n"
'\n'
'Nesting arguments and more complex examples:\n'
'\n'
" >>> for align, text in zip('<^>', ['left', 'center', "
"'right']):\n"
" ... '{0:{fill}{align}16}'.format(text, fill=align, "
'align=align)\n'
' ...\n'
" 'left<<<<<<<<<<<<'\n"
" '^^^^^center^^^^^'\n"
" '>>>>>>>>>>>right'\n"
' >>>\n'
' >>> octets = [192, 168, 0, 1]\n'
" >>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets)\n"
" 'C0A80001'\n"
' >>> int(_, 16)\n'
' 3232235521\n'
' >>>\n'
' >>> width = 5\n'
' >>> for num in range(5,12): \n'
" ... for base in 'dXob':\n"
" ... print('{0:{width}{base}}'.format(num, "
"base=base, width=width), end=' ')\n"
' ... print()\n'
' ...\n'
' 5 5 5 101\n'
' 6 6 6 110\n'
' 7 7 7 111\n'
' 8 8 10 1000\n'
' 9 9 11 1001\n'
' 10 A 12 1010\n'
' 11 B 13 1011\n',
'function': 'Function definitions\n'
'********************\n'
'\n'
'A function definition defines a user-defined function object '
'(see\n'
'section The standard type hierarchy):\n'
'\n'
' funcdef ::= [decorators] "def" funcname "(" '
'[parameter_list] ")"\n'
' ["->" expression] ":" suite\n'
' decorators ::= decorator+\n'
' decorator ::= "@" dotted_name ["(" '
'[argument_list [","]] ")"] NEWLINE\n'
' dotted_name ::= identifier ("." identifier)*\n'
' parameter_list ::= defparameter ("," defparameter)* '
'["," [parameter_list_starargs]]\n'
' | parameter_list_starargs\n'
' parameter_list_starargs ::= "*" [parameter] ("," '
'defparameter)* ["," ["**" parameter [","]]]\n'
' | "**" parameter [","]\n'
' parameter ::= identifier [":" expression]\n'
' defparameter ::= parameter ["=" expression]\n'
' funcname ::= identifier\n'
'\n'
'A function definition is an executable statement. Its execution '
'binds\n'
'the function name in the current local namespace to a function '
'object\n'
'(a wrapper around the executable code for the function). This\n'
'function object contains a reference to the current global '
'namespace\n'
'as the global namespace to be used when the function is called.\n'
'\n'
'The function definition does not execute the function body; this '
'gets\n'
'executed only when the function is called. [2]\n'
'\n'
'A function definition may be wrapped by one or more *decorator*\n'
'expressions. Decorator expressions are evaluated when the '
'function is\n'
'defined, in the scope that contains the function definition. '
'The\n'
'result must be a callable, which is invoked with the function '
'object\n'
'as the only argument. The returned value is bound to the '
'function name\n'
'instead of the function object. Multiple decorators are applied '
'in\n'
'nested fashion. For example, the following code\n'
'\n'
' @f1(arg)\n'
' @f2\n'
' def func(): pass\n'
'\n'
'is roughly equivalent to\n'
'\n'
' def func(): pass\n'
' func = f1(arg)(f2(func))\n'
'\n'
'except that the original function is not temporarily bound to '
'the name\n'
'"func".\n'
'\n'
'When one or more *parameters* have the form *parameter* "="\n'
'*expression*, the function is said to have “default parameter '
'values.”\n'
'For a parameter with a default value, the corresponding '
'*argument* may\n'
'be omitted from a call, in which case the parameter’s default '
'value is\n'
'substituted. If a parameter has a default value, all following\n'
'parameters up until the “"*"” must also have a default value — '
'this is\n'
'a syntactic restriction that is not expressed by the grammar.\n'
'\n'
'**Default parameter values are evaluated from left to right when '
'the\n'
'function definition is executed.** This means that the '
'expression is\n'
'evaluated once, when the function is defined, and that the same '
'“pre-\n'
'computed” value is used for each call. This is especially '
'important\n'
'to understand when a default parameter is a mutable object, such '
'as a\n'
'list or a dictionary: if the function modifies the object (e.g. '
'by\n'
'appending an item to a list), the default value is in effect '
'modified.\n'
'This is generally not what was intended. A way around this is '
'to use\n'
'"None" as the default, and explicitly test for it in the body of '
'the\n'
'function, e.g.:\n'
'\n'
' def whats_on_the_telly(penguin=None):\n'
' if penguin is None:\n'
' penguin = []\n'
' penguin.append("property of the zoo")\n'
' return penguin\n'
'\n'
'Function call semantics are described in more detail in section '
'Calls.\n'
'A function call always assigns values to all parameters '
'mentioned in\n'
'the parameter list, either from position arguments, from '
'keyword\n'
'arguments, or from default values. If the form “"*identifier"” '
'is\n'
'present, it is initialized to a tuple receiving any excess '
'positional\n'
'parameters, defaulting to the empty tuple. If the form\n'
'“"**identifier"” is present, it is initialized to a new ordered\n'
'mapping receiving any excess keyword arguments, defaulting to a '
'new\n'
'empty mapping of the same type. Parameters after “"*"” or\n'
'“"*identifier"” are keyword-only parameters and may only be '
'passed\n'
'used keyword arguments.\n'
'\n'
'Parameters may have annotations of the form “": expression"” '
'following\n'
'the parameter name. Any parameter may have an annotation even '
'those\n'
'of the form "*identifier" or "**identifier". Functions may '
'have\n'
'“return” annotation of the form “"-> expression"” after the '
'parameter\n'
'list. These annotations can be any valid Python expression and '
'are\n'
'evaluated when the function definition is executed. Annotations '
'may\n'
'be evaluated in a different order than they appear in the source '
'code.\n'
'The presence of annotations does not change the semantics of a\n'
'function. The annotation values are available as values of a\n'
'dictionary keyed by the parameters’ names in the '
'"__annotations__"\n'
'attribute of the function object.\n'
'\n'
'It is also possible to create anonymous functions (functions not '
'bound\n'
'to a name), for immediate use in expressions. This uses lambda\n'
'expressions, described in section Lambdas. Note that the '
'lambda\n'
'expression is merely a shorthand for a simplified function '
'definition;\n'
'a function defined in a “"def"” statement can be passed around '
'or\n'
'assigned to another name just like a function defined by a '
'lambda\n'
'expression. The “"def"” form is actually more powerful since '
'it\n'
'allows the execution of multiple statements and annotations.\n'
'\n'
'**Programmer’s note:** Functions are first-class objects. A '
'“"def"”\n'
'statement executed inside a function definition defines a local\n'
'function that can be returned or passed around. Free variables '
'used\n'
'in the nested function can access the local variables of the '
'function\n'
'containing the def. See section Naming and binding for '
'details.\n'
'\n'
'See also:\n'
'\n'
' **PEP 3107** - Function Annotations\n'
' The original specification for function annotations.\n',
'global': 'The "global" statement\n'
'**********************\n'
'\n'
' global_stmt ::= "global" identifier ("," identifier)*\n'
'\n'
'The "global" statement is a declaration which holds for the '
'entire\n'
'current code block. It means that the listed identifiers are to '
'be\n'
'interpreted as globals. It would be impossible to assign to a '
'global\n'
'variable without "global", although free variables may refer to\n'
'globals without being declared global.\n'
'\n'
'Names listed in a "global" statement must not be used in the same '
'code\n'
'block textually preceding that "global" statement.\n'
'\n'
'Names listed in a "global" statement must not be defined as '
'formal\n'
'parameters or in a "for" loop control target, "class" definition,\n'
'function definition, "import" statement, or variable annotation.\n'
'\n'
'**CPython implementation detail:** The current implementation does '
'not\n'
'enforce some of these restrictions, but programs should not abuse '
'this\n'
'freedom, as future implementations may enforce them or silently '
'change\n'
'the meaning of the program.\n'
'\n'
'**Programmer’s note:** "global" is a directive to the parser. It\n'
'applies only to code parsed at the same time as the "global"\n'
'statement. In particular, a "global" statement contained in a '
'string\n'
'or code object supplied to the built-in "exec()" function does '
'not\n'
'affect the code block *containing* the function call, and code\n'
'contained in such a string is unaffected by "global" statements in '
'the\n'
'code containing the function call. The same applies to the '
'"eval()"\n'
'and "compile()" functions.\n',
'id-classes': 'Reserved classes of identifiers\n'
'*******************************\n'
'\n'
'Certain classes of identifiers (besides keywords) have '
'special\n'
'meanings. These classes are identified by the patterns of '
'leading and\n'
'trailing underscore characters:\n'
'\n'
'"_*"\n'
' Not imported by "from module import *". The special '
'identifier "_"\n'
' is used in the interactive interpreter to store the result '
'of the\n'
' last evaluation; it is stored in the "builtins" module. '
'When not\n'
' in interactive mode, "_" has no special meaning and is not '
'defined.\n'
' See section The import statement.\n'
'\n'
' Note: The name "_" is often used in conjunction with\n'
' internationalization; refer to the documentation for the\n'
' "gettext" module for more information on this '
'convention.\n'
'\n'
'"__*__"\n'
' System-defined names. These names are defined by the '
'interpreter\n'
' and its implementation (including the standard library). '
'Current\n'
' system names are discussed in the Special method names '
'section and\n'
' elsewhere. More will likely be defined in future versions '
'of\n'
' Python. *Any* use of "__*__" names, in any context, that '
'does not\n'
' follow explicitly documented use, is subject to breakage '
'without\n'
' warning.\n'
'\n'
'"__*"\n'
' Class-private names. Names in this category, when used '
'within the\n'
' context of a class definition, are re-written to use a '
'mangled form\n'
' to help avoid name clashes between “private” attributes of '
'base and\n'
' derived classes. See section Identifiers (Names).\n',
'identifiers': 'Identifiers and keywords\n'
'************************\n'
'\n'
'Identifiers (also referred to as *names*) are described by '
'the\n'
'following lexical definitions.\n'
'\n'
'The syntax of identifiers in Python is based on the Unicode '
'standard\n'
'annex UAX-31, with elaboration and changes as defined below; '
'see also\n'
'**PEP 3131** for further details.\n'
'\n'
'Within the ASCII range (U+0001..U+007F), the valid characters '
'for\n'
'identifiers are the same as in Python 2.x: the uppercase and '
'lowercase\n'
'letters "A" through "Z", the underscore "_" and, except for '
'the first\n'
'character, the digits "0" through "9".\n'
'\n'
'Python 3.0 introduces additional characters from outside the '
'ASCII\n'
'range (see **PEP 3131**). For these characters, the '
'classification\n'
'uses the version of the Unicode Character Database as '
'included in the\n'
'"unicodedata" module.\n'
'\n'
'Identifiers are unlimited in length. Case is significant.\n'
'\n'
' identifier ::= xid_start xid_continue*\n'
' id_start ::= <all characters in general categories Lu, '
'Ll, Lt, Lm, Lo, Nl, the underscore, and characters with the '
'Other_ID_Start property>\n'
' id_continue ::= <all characters in id_start, plus '
'characters in the categories Mn, Mc, Nd, Pc and others with '
'the Other_ID_Continue property>\n'
' xid_start ::= <all characters in id_start whose NFKC '
'normalization is in "id_start xid_continue*">\n'
' xid_continue ::= <all characters in id_continue whose NFKC '
'normalization is in "id_continue*">\n'
'\n'
'The Unicode category codes mentioned above stand for:\n'
'\n'
'* *Lu* - uppercase letters\n'
'\n'
'* *Ll* - lowercase letters\n'
'\n'
'* *Lt* - titlecase letters\n'
'\n'
'* *Lm* - modifier letters\n'
'\n'
'* *Lo* - other letters\n'
'\n'
'* *Nl* - letter numbers\n'
'\n'
'* *Mn* - nonspacing marks\n'
'\n'
'* *Mc* - spacing combining marks\n'
'\n'
'* *Nd* - decimal numbers\n'
'\n'
'* *Pc* - connector punctuations\n'
'\n'
'* *Other_ID_Start* - explicit list of characters in '
'PropList.txt to\n'
' support backwards compatibility\n'
'\n'
'* *Other_ID_Continue* - likewise\n'
'\n'
'All identifiers are converted into the normal form NFKC while '
'parsing;\n'
'comparison of identifiers is based on NFKC.\n'
'\n'
'A non-normative HTML file listing all valid identifier '
'characters for\n'
'Unicode 4.1 can be found at https://www.dcl.hpi.uni-\n'
'potsdam.de/home/loewis/table-3131.html.\n'
'\n'
'\n'
'Keywords\n'
'========\n'
'\n'
'The following identifiers are used as reserved words, or '
'*keywords* of\n'
'the language, and cannot be used as ordinary identifiers. '
'They must\n'
'be spelled exactly as written here:\n'
'\n'
' False class finally is return\n'
' None continue for lambda try\n'
' True def from nonlocal while\n'
' and del global not with\n'
' as elif if or yield\n'
' assert else import pass\n'
' break except in raise\n'
'\n'
'\n'
'Reserved classes of identifiers\n'
'===============================\n'
'\n'
'Certain classes of identifiers (besides keywords) have '
'special\n'
'meanings. These classes are identified by the patterns of '
'leading and\n'
'trailing underscore characters:\n'
'\n'
'"_*"\n'
' Not imported by "from module import *". The special '
'identifier "_"\n'
' is used in the interactive interpreter to store the result '
'of the\n'
' last evaluation; it is stored in the "builtins" module. '
'When not\n'
' in interactive mode, "_" has no special meaning and is not '
'defined.\n'
' See section The import statement.\n'
'\n'
' Note: The name "_" is often used in conjunction with\n'
' internationalization; refer to the documentation for '
'the\n'
' "gettext" module for more information on this '
'convention.\n'
'\n'
'"__*__"\n'
' System-defined names. These names are defined by the '
'interpreter\n'
' and its implementation (including the standard library). '
'Current\n'
' system names are discussed in the Special method names '
'section and\n'
' elsewhere. More will likely be defined in future versions '
'of\n'
' Python. *Any* use of "__*__" names, in any context, that '
'does not\n'
' follow explicitly documented use, is subject to breakage '
'without\n'
' warning.\n'
'\n'
'"__*"\n'
' Class-private names. Names in this category, when used '
'within the\n'
' context of a class definition, are re-written to use a '
'mangled form\n'
' to help avoid name clashes between “private” attributes of '
'base and\n'
' derived classes. See section Identifiers (Names).\n',
'if': 'The "if" statement\n'
'******************\n'
'\n'
'The "if" statement is used for conditional execution:\n'
'\n'
' if_stmt ::= "if" expression ":" suite\n'
' ("elif" expression ":" suite)*\n'
' ["else" ":" suite]\n'
'\n'
'It selects exactly one of the suites by evaluating the expressions '
'one\n'
'by one until one is found to be true (see section Boolean operations\n'
'for the definition of true and false); then that suite is executed\n'
'(and no other part of the "if" statement is executed or evaluated).\n'
'If all expressions are false, the suite of the "else" clause, if\n'
'present, is executed.\n',
'imaginary': 'Imaginary literals\n'
'******************\n'
'\n'
'Imaginary literals are described by the following lexical '
'definitions:\n'
'\n'
' imagnumber ::= (floatnumber | digitpart) ("j" | "J")\n'
'\n'
'An imaginary literal yields a complex number with a real part '
'of 0.0.\n'
'Complex numbers are represented as a pair of floating point '
'numbers\n'
'and have the same restrictions on their range. To create a '
'complex\n'
'number with a nonzero real part, add a floating point number to '
'it,\n'
'e.g., "(3+4j)". Some examples of imaginary literals:\n'
'\n'
' 3.14j 10.j 10j .001j 1e100j 3.14e-10j '
'3.14_15_93j\n',
'import': 'The "import" statement\n'
'**********************\n'
'\n'
' import_stmt ::= "import" module ["as" identifier] ("," '
'module ["as" identifier])*\n'
' | "from" relative_module "import" identifier '
'["as" identifier]\n'
' ("," identifier ["as" identifier])*\n'
' | "from" relative_module "import" "(" '
'identifier ["as" identifier]\n'
' ("," identifier ["as" identifier])* [","] ")"\n'
' | "from" module "import" "*"\n'
' module ::= (identifier ".")* identifier\n'
' relative_module ::= "."* module | "."+\n'
'\n'
'The basic import statement (no "from" clause) is executed in two\n'
'steps:\n'
'\n'
'1. find a module, loading and initializing it if necessary\n'
'\n'
'2. define a name or names in the local namespace for the scope\n'
' where the "import" statement occurs.\n'
'\n'
'When the statement contains multiple clauses (separated by commas) '
'the\n'
'two steps are carried out separately for each clause, just as '
'though\n'
'the clauses had been separated out into individual import '
'statements.\n'
'\n'
'The details of the first step, finding and loading modules are\n'
'described in greater detail in the section on the import system, '
'which\n'
'also describes the various types of packages and modules that can '
'be\n'
'imported, as well as all the hooks that can be used to customize '
'the\n'
'import system. Note that failures in this step may indicate '
'either\n'
'that the module could not be located, *or* that an error occurred\n'
'while initializing the module, which includes execution of the\n'
'module’s code.\n'
'\n'
'If the requested module is retrieved successfully, it will be '
'made\n'
'available in the local namespace in one of three ways:\n'
'\n'
'* If the module name is followed by "as", then the name following\n'
' "as" is bound directly to the imported module.\n'
'\n'
'* If no other name is specified, and the module being imported is '
'a\n'
' top level module, the module’s name is bound in the local '
'namespace\n'
' as a reference to the imported module\n'
'\n'
'* If the module being imported is *not* a top level module, then '
'the\n'
' name of the top level package that contains the module is bound '
'in\n'
' the local namespace as a reference to the top level package. '
'The\n'
' imported module must be accessed using its full qualified name\n'
' rather than directly\n'
'\n'
'The "from" form uses a slightly more complex process:\n'
'\n'
'1. find the module specified in the "from" clause, loading and\n'
' initializing it if necessary;\n'
'\n'
'2. for each of the identifiers specified in the "import" clauses:\n'
'\n'
' 1. check if the imported module has an attribute by that name\n'
'\n'
' 2. if not, attempt to import a submodule with that name and '
'then\n'
' check the imported module again for that attribute\n'
'\n'
' 3. if the attribute is not found, "ImportError" is raised.\n'
'\n'
' 4. otherwise, a reference to that value is stored in the local\n'
' namespace, using the name in the "as" clause if it is '
'present,\n'
' otherwise using the attribute name\n'
'\n'
'Examples:\n'
'\n'
' import foo # foo imported and bound locally\n'
' import foo.bar.baz # foo.bar.baz imported, foo bound '
'locally\n'
' import foo.bar.baz as fbb # foo.bar.baz imported and bound as '
'fbb\n'
' from foo.bar import baz # foo.bar.baz imported and bound as '
'baz\n'
' from foo import attr # foo imported and foo.attr bound as '
'attr\n'
'\n'
'If the list of identifiers is replaced by a star ("\'*\'"), all '
'public\n'
'names defined in the module are bound in the local namespace for '
'the\n'
'scope where the "import" statement occurs.\n'
'\n'
'The *public names* defined by a module are determined by checking '
'the\n'
'module’s namespace for a variable named "__all__"; if defined, it '
'must\n'
'be a sequence of strings which are names defined or imported by '
'that\n'
'module. The names given in "__all__" are all considered public '
'and\n'
'are required to exist. If "__all__" is not defined, the set of '
'public\n'
'names includes all names found in the module’s namespace which do '
'not\n'
'begin with an underscore character ("\'_\'"). "__all__" should '
'contain\n'
'the entire public API. It is intended to avoid accidentally '
'exporting\n'
'items that are not part of the API (such as library modules which '
'were\n'
'imported and used within the module).\n'
'\n'
'The wild card form of import — "from module import *" — is only\n'
'allowed at the module level. Attempting to use it in class or\n'
'function definitions will raise a "SyntaxError".\n'
'\n'
'When specifying what module to import you do not have to specify '
'the\n'
'absolute name of the module. When a module or package is '
'contained\n'
'within another package it is possible to make a relative import '
'within\n'
'the same top package without having to mention the package name. '
'By\n'
'using leading dots in the specified module or package after "from" '
'you\n'
'can specify how high to traverse up the current package hierarchy\n'
'without specifying exact names. One leading dot means the current\n'
'package where the module making the import exists. Two dots means '
'up\n'
'one package level. Three dots is up two levels, etc. So if you '
'execute\n'
'"from . import mod" from a module in the "pkg" package then you '
'will\n'
'end up importing "pkg.mod". If you execute "from ..subpkg2 import '
'mod"\n'
'from within "pkg.subpkg1" you will import "pkg.subpkg2.mod". The\n'
'specification for relative imports is contained within **PEP '
'328**.\n'
'\n'
'"importlib.import_module()" is provided to support applications '
'that\n'
'determine dynamically the modules to be loaded.\n'
'\n'
'\n'
'Future statements\n'
'=================\n'
'\n'
'A *future statement* is a directive to the compiler that a '
'particular\n'
'module should be compiled using syntax or semantics that will be\n'
'available in a specified future release of Python where the '
'feature\n'
'becomes standard.\n'
'\n'
'The future statement is intended to ease migration to future '
'versions\n'
'of Python that introduce incompatible changes to the language. '
'It\n'
'allows use of the new features on a per-module basis before the\n'
'release in which the feature becomes standard.\n'
'\n'
' future_stmt ::= "from" "__future__" "import" feature ["as" '
'identifier]\n'
' ("," feature ["as" identifier])*\n'
' | "from" "__future__" "import" "(" feature '
'["as" identifier]\n'
' ("," feature ["as" identifier])* [","] ")"\n'
' feature ::= identifier\n'
'\n'
'A future statement must appear near the top of the module. The '
'only\n'
'lines that can appear before a future statement are:\n'
'\n'
'* the module docstring (if any),\n'
'\n'
'* comments,\n'
'\n'
'* blank lines, and\n'
'\n'
'* other future statements.\n'
'\n'
'The features recognized by Python 3.0 are "absolute_import",\n'
'"division", "generators", "unicode_literals", "print_function",\n'
'"nested_scopes" and "with_statement". They are all redundant '
'because\n'
'they are always enabled, and only kept for backwards '
'compatibility.\n'
'\n'
'A future statement is recognized and treated specially at compile\n'
'time: Changes to the semantics of core constructs are often\n'
'implemented by generating different code. It may even be the '
'case\n'
'that a new feature introduces new incompatible syntax (such as a '
'new\n'
'reserved word), in which case the compiler may need to parse the\n'
'module differently. Such decisions cannot be pushed off until\n'
'runtime.\n'
'\n'
'For any given release, the compiler knows which feature names '
'have\n'
'been defined, and raises a compile-time error if a future '
'statement\n'
'contains a feature not known to it.\n'
'\n'
'The direct runtime semantics are the same as for any import '
'statement:\n'
'there is a standard module "__future__", described later, and it '
'will\n'
'be imported in the usual way at the time the future statement is\n'
'executed.\n'
'\n'
'The interesting runtime semantics depend on the specific feature\n'
'enabled by the future statement.\n'
'\n'
'Note that there is nothing special about the statement:\n'
'\n'
' import __future__ [as name]\n'
'\n'
'That is not a future statement; it’s an ordinary import statement '
'with\n'
'no special semantics or syntax restrictions.\n'
'\n'
'Code compiled by calls to the built-in functions "exec()" and\n'
'"compile()" that occur in a module "M" containing a future '
'statement\n'
'will, by default, use the new syntax or semantics associated with '
'the\n'
'future statement. This can be controlled by optional arguments '
'to\n'
'"compile()" — see the documentation of that function for details.\n'
'\n'
'A future statement typed at an interactive interpreter prompt '
'will\n'
'take effect for the rest of the interpreter session. If an\n'
'interpreter is started with the "-i" option, is passed a script '
'name\n'
'to execute, and the script includes a future statement, it will be '
'in\n'
'effect in the interactive session started after the script is\n'
'executed.\n'
'\n'
'See also:\n'
'\n'
' **PEP 236** - Back to the __future__\n'
' The original proposal for the __future__ mechanism.\n',
'in': 'Membership test operations\n'
'**************************\n'
'\n'
'The operators "in" and "not in" test for membership. "x in s"\n'
'evaluates to "True" if *x* is a member of *s*, and "False" otherwise.\n'
'"x not in s" returns the negation of "x in s". All built-in '
'sequences\n'
'and set types support this as well as dictionary, for which "in" '
'tests\n'
'whether the dictionary has a given key. For container types such as\n'
'list, tuple, set, frozenset, dict, or collections.deque, the\n'
'expression "x in y" is equivalent to "any(x is e or x == e for e in\n'
'y)".\n'
'\n'
'For the string and bytes types, "x in y" is "True" if and only if *x*\n'
'is a substring of *y*. An equivalent test is "y.find(x) != -1".\n'
'Empty strings are always considered to be a substring of any other\n'
'string, so """ in "abc"" will return "True".\n'
'\n'
'For user-defined classes which define the "__contains__()" method, "x\n'
'in y" returns "True" if "y.__contains__(x)" returns a true value, and\n'
'"False" otherwise.\n'
'\n'
'For user-defined classes which do not define "__contains__()" but do\n'
'define "__iter__()", "x in y" is "True" if some value "z" with "x ==\n'
'z" is produced while iterating over "y". If an exception is raised\n'
'during the iteration, it is as if "in" raised that exception.\n'
'\n'
'Lastly, the old-style iteration protocol is tried: if a class defines\n'
'"__getitem__()", "x in y" is "True" if and only if there is a non-\n'
'negative integer index *i* such that "x == y[i]", and all lower\n'
'integer indices do not raise "IndexError" exception. (If any other\n'
'exception is raised, it is as if "in" raised that exception).\n'
'\n'
'The operator "not in" is defined to have the inverse true value of\n'
'"in".\n',
'integers': 'Integer literals\n'
'****************\n'
'\n'
'Integer literals are described by the following lexical '
'definitions:\n'
'\n'
' integer ::= decinteger | bininteger | octinteger | '
'hexinteger\n'
' decinteger ::= nonzerodigit (["_"] digit)* | "0"+ (["_"] '
'"0")*\n'
' bininteger ::= "0" ("b" | "B") (["_"] bindigit)+\n'
' octinteger ::= "0" ("o" | "O") (["_"] octdigit)+\n'
' hexinteger ::= "0" ("x" | "X") (["_"] hexdigit)+\n'
' nonzerodigit ::= "1"..."9"\n'
' digit ::= "0"..."9"\n'
' bindigit ::= "0" | "1"\n'
' octdigit ::= "0"..."7"\n'
' hexdigit ::= digit | "a"..."f" | "A"..."F"\n'
'\n'
'There is no limit for the length of integer literals apart from '
'what\n'
'can be stored in available memory.\n'
'\n'
'Underscores are ignored for determining the numeric value of '
'the\n'
'literal. They can be used to group digits for enhanced '
'readability.\n'
'One underscore can occur between digits, and after base '
'specifiers\n'
'like "0x".\n'
'\n'
'Note that leading zeros in a non-zero decimal number are not '
'allowed.\n'
'This is for disambiguation with C-style octal literals, which '
'Python\n'
'used before version 3.0.\n'
'\n'
'Some examples of integer literals:\n'
'\n'
' 7 2147483647 0o177 0b100110111\n'
' 3 79228162514264337593543950336 0o377 0xdeadbeef\n'
' 100_000_000_000 0b_1110_0101\n'
'\n'
'Changed in version 3.6: Underscores are now allowed for '
'grouping\n'
'purposes in literals.\n',
'lambda': 'Lambdas\n'
'*******\n'
'\n'
' lambda_expr ::= "lambda" [parameter_list] ":" '
'expression\n'
' lambda_expr_nocond ::= "lambda" [parameter_list] ":" '
'expression_nocond\n'
'\n'
'Lambda expressions (sometimes called lambda forms) are used to '
'create\n'
'anonymous functions. The expression "lambda parameters: '
'expression"\n'
'yields a function object. The unnamed object behaves like a '
'function\n'
'object defined with:\n'
'\n'
' def <lambda>(parameters):\n'
' return expression\n'
'\n'
'See section Function definitions for the syntax of parameter '
'lists.\n'
'Note that functions created with lambda expressions cannot '
'contain\n'
'statements or annotations.\n',
'lists': 'List displays\n'
'*************\n'
'\n'
'A list display is a possibly empty series of expressions enclosed '
'in\n'
'square brackets:\n'
'\n'
' list_display ::= "[" [starred_list | comprehension] "]"\n'
'\n'
'A list display yields a new list object, the contents being '
'specified\n'
'by either a list of expressions or a comprehension. When a comma-\n'
'separated list of expressions is supplied, its elements are '
'evaluated\n'
'from left to right and placed into the list object in that order.\n'
'When a comprehension is supplied, the list is constructed from the\n'
'elements resulting from the comprehension.\n',
'naming': 'Naming and binding\n'
'******************\n'
'\n'
'\n'
'Binding of names\n'
'================\n'
'\n'
'*Names* refer to objects. Names are introduced by name binding\n'
'operations.\n'
'\n'
'The following constructs bind names: formal parameters to '
'functions,\n'
'"import" statements, class and function definitions (these bind '
'the\n'
'class or function name in the defining block), and targets that '
'are\n'
'identifiers if occurring in an assignment, "for" loop header, or '
'after\n'
'"as" in a "with" statement or "except" clause. The "import" '
'statement\n'
'of the form "from ... import *" binds all names defined in the\n'
'imported module, except those beginning with an underscore. This '
'form\n'
'may only be used at the module level.\n'
'\n'
'A target occurring in a "del" statement is also considered bound '
'for\n'
'this purpose (though the actual semantics are to unbind the '
'name).\n'
'\n'
'Each assignment or import statement occurs within a block defined '
'by a\n'
'class or function definition or at the module level (the '
'top-level\n'
'code block).\n'
'\n'
'If a name is bound in a block, it is a local variable of that '
'block,\n'
'unless declared as "nonlocal" or "global". If a name is bound at '
'the\n'
'module level, it is a global variable. (The variables of the '
'module\n'
'code block are local and global.) If a variable is used in a '
'code\n'
'block but not defined there, it is a *free variable*.\n'
'\n'
'Each occurrence of a name in the program text refers to the '
'*binding*\n'
'of that name established by the following name resolution rules.\n'
'\n'
'\n'
'Resolution of names\n'
'===================\n'
'\n'
'A *scope* defines the visibility of a name within a block. If a '
'local\n'
'variable is defined in a block, its scope includes that block. If '
'the\n'
'definition occurs in a function block, the scope extends to any '
'blocks\n'
'contained within the defining one, unless a contained block '
'introduces\n'
'a different binding for the name.\n'
'\n'
'When a name is used in a code block, it is resolved using the '
'nearest\n'
'enclosing scope. The set of all such scopes visible to a code '
'block\n'
'is called the block’s *environment*.\n'
'\n'
'When a name is not found at all, a "NameError" exception is '
'raised. If\n'
'the current scope is a function scope, and the name refers to a '
'local\n'
'variable that has not yet been bound to a value at the point where '
'the\n'
'name is used, an "UnboundLocalError" exception is raised.\n'
'"UnboundLocalError" is a subclass of "NameError".\n'
'\n'
'If a name binding operation occurs anywhere within a code block, '
'all\n'
'uses of the name within the block are treated as references to '
'the\n'
'current block. This can lead to errors when a name is used within '
'a\n'
'block before it is bound. This rule is subtle. Python lacks\n'
'declarations and allows name binding operations to occur anywhere\n'
'within a code block. The local variables of a code block can be\n'
'determined by scanning the entire text of the block for name '
'binding\n'
'operations.\n'
'\n'
'If the "global" statement occurs within a block, all uses of the '
'name\n'
'specified in the statement refer to the binding of that name in '
'the\n'
'top-level namespace. Names are resolved in the top-level '
'namespace by\n'
'searching the global namespace, i.e. the namespace of the module\n'
'containing the code block, and the builtins namespace, the '
'namespace\n'
'of the module "builtins". The global namespace is searched '
'first. If\n'
'the name is not found there, the builtins namespace is searched. '
'The\n'
'"global" statement must precede all uses of the name.\n'
'\n'
'The "global" statement has the same scope as a name binding '
'operation\n'
'in the same block. If the nearest enclosing scope for a free '
'variable\n'
'contains a global statement, the free variable is treated as a '
'global.\n'
'\n'
'The "nonlocal" statement causes corresponding names to refer to\n'
'previously bound variables in the nearest enclosing function '
'scope.\n'
'"SyntaxError" is raised at compile time if the given name does '
'not\n'
'exist in any enclosing function scope.\n'
'\n'
'The namespace for a module is automatically created the first time '
'a\n'
'module is imported. The main module for a script is always '
'called\n'
'"__main__".\n'
'\n'
'Class definition blocks and arguments to "exec()" and "eval()" '
'are\n'
'special in the context of name resolution. A class definition is '
'an\n'
'executable statement that may use and define names. These '
'references\n'
'follow the normal rules for name resolution with an exception '
'that\n'
'unbound local variables are looked up in the global namespace. '
'The\n'
'namespace of the class definition becomes the attribute dictionary '
'of\n'
'the class. The scope of names defined in a class block is limited '
'to\n'
'the class block; it does not extend to the code blocks of methods '
'–\n'
'this includes comprehensions and generator expressions since they '
'are\n'
'implemented using a function scope. This means that the '
'following\n'
'will fail:\n'
'\n'
' class A:\n'
' a = 42\n'
' b = list(a + i for i in range(10))\n'
'\n'
'\n'
'Builtins and restricted execution\n'
'=================================\n'
'\n'
'**CPython implementation detail:** Users should not touch\n'
'"__builtins__"; it is strictly an implementation detail. Users\n'
'wanting to override values in the builtins namespace should '
'"import"\n'
'the "builtins" module and modify its attributes appropriately.\n'
'\n'
'The builtins namespace associated with the execution of a code '
'block\n'
'is actually found by looking up the name "__builtins__" in its '
'global\n'
'namespace; this should be a dictionary or a module (in the latter '
'case\n'
'the module’s dictionary is used). By default, when in the '
'"__main__"\n'
'module, "__builtins__" is the built-in module "builtins"; when in '
'any\n'
'other module, "__builtins__" is an alias for the dictionary of '
'the\n'
'"builtins" module itself.\n'
'\n'
'\n'
'Interaction with dynamic features\n'
'=================================\n'
'\n'
'Name resolution of free variables occurs at runtime, not at '
'compile\n'
'time. This means that the following code will print 42:\n'
'\n'
' i = 10\n'
' def f():\n'
' print(i)\n'
' i = 42\n'
' f()\n'
'\n'
'The "eval()" and "exec()" functions do not have access to the '
'full\n'
'environment for resolving names. Names may be resolved in the '
'local\n'
'and global namespaces of the caller. Free variables are not '
'resolved\n'
'in the nearest enclosing namespace, but in the global namespace. '
'[1]\n'
'The "exec()" and "eval()" functions have optional arguments to\n'
'override the global and local namespace. If only one namespace '
'is\n'
'specified, it is used for both.\n',
'nonlocal': 'The "nonlocal" statement\n'
'************************\n'
'\n'
' nonlocal_stmt ::= "nonlocal" identifier ("," identifier)*\n'
'\n'
'The "nonlocal" statement causes the listed identifiers to refer '
'to\n'
'previously bound variables in the nearest enclosing scope '
'excluding\n'
'globals. This is important because the default behavior for '
'binding is\n'
'to search the local namespace first. The statement allows\n'
'encapsulated code to rebind variables outside of the local '
'scope\n'
'besides the global (module) scope.\n'
'\n'
'Names listed in a "nonlocal" statement, unlike those listed in '
'a\n'
'"global" statement, must refer to pre-existing bindings in an\n'
'enclosing scope (the scope in which a new binding should be '
'created\n'
'cannot be determined unambiguously).\n'
'\n'
'Names listed in a "nonlocal" statement must not collide with '
'pre-\n'
'existing bindings in the local scope.\n'
'\n'
'See also:\n'
'\n'
' **PEP 3104** - Access to Names in Outer Scopes\n'
' The specification for the "nonlocal" statement.\n',
'numbers': 'Numeric literals\n'
'****************\n'
'\n'
'There are three types of numeric literals: integers, floating '
'point\n'
'numbers, and imaginary numbers. There are no complex literals\n'
'(complex numbers can be formed by adding a real number and an\n'
'imaginary number).\n'
'\n'
'Note that numeric literals do not include a sign; a phrase like '
'"-1"\n'
'is actually an expression composed of the unary operator ‘"-"‘ '
'and the\n'
'literal "1".\n',
'numeric-types': 'Emulating numeric types\n'
'***********************\n'
'\n'
'The following methods can be defined to emulate numeric '
'objects.\n'
'Methods corresponding to operations that are not supported '
'by the\n'
'particular kind of number implemented (e.g., bitwise '
'operations for\n'
'non-integral numbers) should be left undefined.\n'
'\n'
'object.__add__(self, other)\n'
'object.__sub__(self, other)\n'
'object.__mul__(self, other)\n'
'object.__matmul__(self, other)\n'
'object.__truediv__(self, other)\n'
'object.__floordiv__(self, other)\n'
'object.__mod__(self, other)\n'
'object.__divmod__(self, other)\n'
'object.__pow__(self, other[, modulo])\n'
'object.__lshift__(self, other)\n'
'object.__rshift__(self, other)\n'
'object.__and__(self, other)\n'
'object.__xor__(self, other)\n'
'object.__or__(self, other)\n'
'\n'
' These methods are called to implement the binary '
'arithmetic\n'
' operations ("+", "-", "*", "@", "/", "//", "%", '
'"divmod()",\n'
' "pow()", "**", "<<", ">>", "&", "^", "|"). For '
'instance, to\n'
' evaluate the expression "x + y", where *x* is an '
'instance of a\n'
' class that has an "__add__()" method, "x.__add__(y)" is '
'called.\n'
' The "__divmod__()" method should be the equivalent to '
'using\n'
' "__floordiv__()" and "__mod__()"; it should not be '
'related to\n'
' "__truediv__()". Note that "__pow__()" should be '
'defined to accept\n'
' an optional third argument if the ternary version of the '
'built-in\n'
' "pow()" function is to be supported.\n'
'\n'
' If one of those methods does not support the operation '
'with the\n'
' supplied arguments, it should return "NotImplemented".\n'
'\n'
'object.__radd__(self, other)\n'
'object.__rsub__(self, other)\n'
'object.__rmul__(self, other)\n'
'object.__rmatmul__(self, other)\n'
'object.__rtruediv__(self, other)\n'
'object.__rfloordiv__(self, other)\n'
'object.__rmod__(self, other)\n'
'object.__rdivmod__(self, other)\n'
'object.__rpow__(self, other)\n'
'object.__rlshift__(self, other)\n'
'object.__rrshift__(self, other)\n'
'object.__rand__(self, other)\n'
'object.__rxor__(self, other)\n'
'object.__ror__(self, other)\n'
'\n'
' These methods are called to implement the binary '
'arithmetic\n'
' operations ("+", "-", "*", "@", "/", "//", "%", '
'"divmod()",\n'
' "pow()", "**", "<<", ">>", "&", "^", "|") with reflected '
'(swapped)\n'
' operands. These functions are only called if the left '
'operand does\n'
' not support the corresponding operation [3] and the '
'operands are of\n'
' different types. [4] For instance, to evaluate the '
'expression "x -\n'
' y", where *y* is an instance of a class that has an '
'"__rsub__()"\n'
' method, "y.__rsub__(x)" is called if "x.__sub__(y)" '
'returns\n'
' *NotImplemented*.\n'
'\n'
' Note that ternary "pow()" will not try calling '
'"__rpow__()" (the\n'
' coercion rules would become too complicated).\n'
'\n'
' Note: If the right operand’s type is a subclass of the '
'left\n'
' operand’s type and that subclass provides the '
'reflected method\n'
' for the operation, this method will be called before '
'the left\n'
' operand’s non-reflected method. This behavior allows '
'subclasses\n'
' to override their ancestors’ operations.\n'
'\n'
'object.__iadd__(self, other)\n'
'object.__isub__(self, other)\n'
'object.__imul__(self, other)\n'
'object.__imatmul__(self, other)\n'
'object.__itruediv__(self, other)\n'
'object.__ifloordiv__(self, other)\n'
'object.__imod__(self, other)\n'
'object.__ipow__(self, other[, modulo])\n'
'object.__ilshift__(self, other)\n'
'object.__irshift__(self, other)\n'
'object.__iand__(self, other)\n'
'object.__ixor__(self, other)\n'
'object.__ior__(self, other)\n'
'\n'
' These methods are called to implement the augmented '
'arithmetic\n'
' assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", '
'"**=",\n'
' "<<=", ">>=", "&=", "^=", "|="). These methods should '
'attempt to\n'
' do the operation in-place (modifying *self*) and return '
'the result\n'
' (which could be, but does not have to be, *self*). If a '
'specific\n'
' method is not defined, the augmented assignment falls '
'back to the\n'
' normal methods. For instance, if *x* is an instance of '
'a class\n'
' with an "__iadd__()" method, "x += y" is equivalent to '
'"x =\n'
' x.__iadd__(y)" . Otherwise, "x.__add__(y)" and '
'"y.__radd__(x)" are\n'
' considered, as with the evaluation of "x + y". In '
'certain\n'
' situations, augmented assignment can result in '
'unexpected errors\n'
' (see Why does a_tuple[i] += [‘item’] raise an exception '
'when the\n'
' addition works?), but this behavior is in fact part of '
'the data\n'
' model.\n'
'\n'
'object.__neg__(self)\n'
'object.__pos__(self)\n'
'object.__abs__(self)\n'
'object.__invert__(self)\n'
'\n'
' Called to implement the unary arithmetic operations '
'("-", "+",\n'
' "abs()" and "~").\n'
'\n'
'object.__complex__(self)\n'
'object.__int__(self)\n'
'object.__float__(self)\n'
'\n'
' Called to implement the built-in functions "complex()", '
'"int()" and\n'
' "float()". Should return a value of the appropriate '
'type.\n'
'\n'
'object.__index__(self)\n'
'\n'
' Called to implement "operator.index()", and whenever '
'Python needs\n'
' to losslessly convert the numeric object to an integer '
'object (such\n'
' as in slicing, or in the built-in "bin()", "hex()" and '
'"oct()"\n'
' functions). Presence of this method indicates that the '
'numeric\n'
' object is an integer type. Must return an integer.\n'
'\n'
' Note: In order to have a coherent integer type class, '
'when\n'
' "__index__()" is defined "__int__()" should also be '
'defined, and\n'
' both should return the same value.\n'
'\n'
'object.__round__(self[, ndigits])\n'
'object.__trunc__(self)\n'
'object.__floor__(self)\n'
'object.__ceil__(self)\n'
'\n'
' Called to implement the built-in function "round()" and '
'"math"\n'
' functions "trunc()", "floor()" and "ceil()". Unless '
'*ndigits* is\n'
' passed to "__round__()" all these methods should return '
'the value\n'
' of the object truncated to an "Integral" (typically an '
'"int").\n'
'\n'
' If "__int__()" is not defined then the built-in function '
'"int()"\n'
' falls back to "__trunc__()".\n',
'objects': 'Objects, values and types\n'
'*************************\n'
'\n'
'*Objects* are Python’s abstraction for data. All data in a '
'Python\n'
'program is represented by objects or by relations between '
'objects. (In\n'
'a sense, and in conformance to Von Neumann’s model of a “stored\n'
'program computer,” code is also represented by objects.)\n'
'\n'
'Every object has an identity, a type and a value. An object’s\n'
'*identity* never changes once it has been created; you may think '
'of it\n'
'as the object’s address in memory. The ‘"is"’ operator compares '
'the\n'
'identity of two objects; the "id()" function returns an integer\n'
'representing its identity.\n'
'\n'
'**CPython implementation detail:** For CPython, "id(x)" is the '
'memory\n'
'address where "x" is stored.\n'
'\n'
'An object’s type determines the operations that the object '
'supports\n'
'(e.g., “does it have a length?”) and also defines the possible '
'values\n'
'for objects of that type. The "type()" function returns an '
'object’s\n'
'type (which is an object itself). Like its identity, an '
'object’s\n'
'*type* is also unchangeable. [1]\n'
'\n'
'The *value* of some objects can change. Objects whose value can\n'
'change are said to be *mutable*; objects whose value is '
'unchangeable\n'
'once they are created are called *immutable*. (The value of an\n'
'immutable container object that contains a reference to a '
'mutable\n'
'object can change when the latter’s value is changed; however '
'the\n'
'container is still considered immutable, because the collection '
'of\n'
'objects it contains cannot be changed. So, immutability is not\n'
'strictly the same as having an unchangeable value, it is more '
'subtle.)\n'
'An object’s mutability is determined by its type; for instance,\n'
'numbers, strings and tuples are immutable, while dictionaries '
'and\n'
'lists are mutable.\n'
'\n'
'Objects are never explicitly destroyed; however, when they '
'become\n'
'unreachable they may be garbage-collected. An implementation is\n'
'allowed to postpone garbage collection or omit it altogether — it '
'is a\n'
'matter of implementation quality how garbage collection is\n'
'implemented, as long as no objects are collected that are still\n'
'reachable.\n'
'\n'
'**CPython implementation detail:** CPython currently uses a '
'reference-\n'
'counting scheme with (optional) delayed detection of cyclically '
'linked\n'
'garbage, which collects most objects as soon as they become\n'
'unreachable, but is not guaranteed to collect garbage containing\n'
'circular references. See the documentation of the "gc" module '
'for\n'
'information on controlling the collection of cyclic garbage. '
'Other\n'
'implementations act differently and CPython may change. Do not '
'depend\n'
'on immediate finalization of objects when they become unreachable '
'(so\n'
'you should always close files explicitly).\n'
'\n'
'Note that the use of the implementation’s tracing or debugging\n'
'facilities may keep objects alive that would normally be '
'collectable.\n'
'Also note that catching an exception with a ‘"try"…"except"’ '
'statement\n'
'may keep objects alive.\n'
'\n'
'Some objects contain references to “external” resources such as '
'open\n'
'files or windows. It is understood that these resources are '
'freed\n'
'when the object is garbage-collected, but since garbage '
'collection is\n'
'not guaranteed to happen, such objects also provide an explicit '
'way to\n'
'release the external resource, usually a "close()" method. '
'Programs\n'
'are strongly recommended to explicitly close such objects. The\n'
'‘"try"…"finally"’ statement and the ‘"with"’ statement provide\n'
'convenient ways to do this.\n'
'\n'
'Some objects contain references to other objects; these are '
'called\n'
'*containers*. Examples of containers are tuples, lists and\n'
'dictionaries. The references are part of a container’s value. '
'In\n'
'most cases, when we talk about the value of a container, we imply '
'the\n'
'values, not the identities of the contained objects; however, '
'when we\n'
'talk about the mutability of a container, only the identities of '
'the\n'
'immediately contained objects are implied. So, if an immutable\n'
'container (like a tuple) contains a reference to a mutable '
'object, its\n'
'value changes if that mutable object is changed.\n'
'\n'
'Types affect almost all aspects of object behavior. Even the\n'
'importance of object identity is affected in some sense: for '
'immutable\n'
'types, operations that compute new values may actually return a\n'
'reference to any existing object with the same type and value, '
'while\n'
'for mutable objects this is not allowed. E.g., after "a = 1; b = '
'1",\n'
'"a" and "b" may or may not refer to the same object with the '
'value\n'
'one, depending on the implementation, but after "c = []; d = []", '
'"c"\n'
'and "d" are guaranteed to refer to two different, unique, newly\n'
'created empty lists. (Note that "c = d = []" assigns the same '
'object\n'
'to both "c" and "d".)\n',
'operator-summary': 'Operator precedence\n'
'*******************\n'
'\n'
'The following table summarizes the operator precedence '
'in Python, from\n'
'lowest precedence (least binding) to highest precedence '
'(most\n'
'binding). Operators in the same box have the same '
'precedence. Unless\n'
'the syntax is explicitly given, operators are binary. '
'Operators in\n'
'the same box group left to right (except for '
'exponentiation, which\n'
'groups from right to left).\n'
'\n'
'Note that comparisons, membership tests, and identity '
'tests, all have\n'
'the same precedence and have a left-to-right chaining '
'feature as\n'
'described in the Comparisons section.\n'
'\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| Operator | '
'Description |\n'
'+=================================================+=======================================+\n'
'| "lambda" | '
'Lambda expression |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "if" – "else" | '
'Conditional expression |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "or" | '
'Boolean OR |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "and" | '
'Boolean AND |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "not" "x" | '
'Boolean NOT |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "in", "not in", "is", "is not", "<", "<=", ">", | '
'Comparisons, including membership |\n'
'| ">=", "!=", "==" | '
'tests and identity tests |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "|" | '
'Bitwise OR |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "^" | '
'Bitwise XOR |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "&" | '
'Bitwise AND |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "<<", ">>" | '
'Shifts |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "+", "-" | '
'Addition and subtraction |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "*", "@", "/", "//", "%" | '
'Multiplication, matrix |\n'
'| | '
'multiplication, division, floor |\n'
'| | '
'division, remainder [5] |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "+x", "-x", "~x" | '
'Positive, negative, bitwise NOT |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "**" | '
'Exponentiation [6] |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "await" "x" | '
'Await expression |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "x[index]", "x[index:index]", | '
'Subscription, slicing, call, |\n'
'| "x(arguments...)", "x.attribute" | '
'attribute reference |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'| "(expressions...)", "[expressions...]", "{key: | '
'Binding or tuple display, list |\n'
'| value...}", "{expressions...}" | '
'display, dictionary display, set |\n'
'| | '
'display |\n'
'+-------------------------------------------------+---------------------------------------+\n'
'\n'
'-[ Footnotes ]-\n'
'\n'
'[1] While "abs(x%y) < abs(y)" is true mathematically, '
'for floats\n'
' it may not be true numerically due to roundoff. For '
'example, and\n'
' assuming a platform on which a Python float is an '
'IEEE 754 double-\n'
' precision number, in order that "-1e-100 % 1e100" '
'have the same\n'
' sign as "1e100", the computed result is "-1e-100 + '
'1e100", which\n'
' is numerically exactly equal to "1e100". The '
'function\n'
' "math.fmod()" returns a result whose sign matches '
'the sign of the\n'
' first argument instead, and so returns "-1e-100" in '
'this case.\n'
' Which approach is more appropriate depends on the '
'application.\n'
'\n'
'[2] If x is very close to an exact integer multiple of '
'y, it’s\n'
' possible for "x//y" to be one larger than '
'"(x-x%y)//y" due to\n'
' rounding. In such cases, Python returns the latter '
'result, in\n'
' order to preserve that "divmod(x,y)[0] * y + x % y" '
'be very close\n'
' to "x".\n'
'\n'
'[3] The Unicode standard distinguishes between *code '
'points* (e.g.\n'
' U+0041) and *abstract characters* (e.g. “LATIN '
'CAPITAL LETTER A”).\n'
' While most abstract characters in Unicode are only '
'represented\n'
' using one code point, there is a number of abstract '
'characters\n'
' that can in addition be represented using a sequence '
'of more than\n'
' one code point. For example, the abstract character '
'“LATIN\n'
' CAPITAL LETTER C WITH CEDILLA” can be represented as '
'a single\n'
' *precomposed character* at code position U+00C7, or '
'as a sequence\n'
' of a *base character* at code position U+0043 (LATIN '
'CAPITAL\n'
' LETTER C), followed by a *combining character* at '
'code position\n'
' U+0327 (COMBINING CEDILLA).\n'
'\n'
' The comparison operators on strings compare at the '
'level of\n'
' Unicode code points. This may be counter-intuitive '
'to humans. For\n'
' example, ""\\u00C7" == "\\u0043\\u0327"" is "False", '
'even though both\n'
' strings represent the same abstract character “LATIN '
'CAPITAL\n'
' LETTER C WITH CEDILLA”.\n'
'\n'
' To compare strings at the level of abstract '
'characters (that is,\n'
' in a way intuitive to humans), use '
'"unicodedata.normalize()".\n'
'\n'
'[4] Due to automatic garbage-collection, free lists, and '
'the\n'
' dynamic nature of descriptors, you may notice '
'seemingly unusual\n'
' behaviour in certain uses of the "is" operator, like '
'those\n'
' involving comparisons between instance methods, or '
'constants.\n'
' Check their documentation for more info.\n'
'\n'
'[5] The "%" operator is also used for string formatting; '
'the same\n'
' precedence applies.\n'
'\n'
'[6] The power operator "**" binds less tightly than an '
'arithmetic\n'
' or bitwise unary operator on its right, that is, '
'"2**-1" is "0.5".\n',
'pass': 'The "pass" statement\n'
'********************\n'
'\n'
' pass_stmt ::= "pass"\n'
'\n'
'"pass" is a null operation — when it is executed, nothing happens. '
'It\n'
'is useful as a placeholder when a statement is required '
'syntactically,\n'
'but no code needs to be executed, for example:\n'
'\n'
' def f(arg): pass # a function that does nothing (yet)\n'
'\n'
' class C: pass # a class with no methods (yet)\n',
'power': 'The power operator\n'
'******************\n'
'\n'
'The power operator binds more tightly than unary operators on its\n'
'left; it binds less tightly than unary operators on its right. '
'The\n'
'syntax is:\n'
'\n'
' power ::= (await_expr | primary) ["**" u_expr]\n'
'\n'
'Thus, in an unparenthesized sequence of power and unary operators, '
'the\n'
'operators are evaluated from right to left (this does not '
'constrain\n'
'the evaluation order for the operands): "-1**2" results in "-1".\n'
'\n'
'The power operator has the same semantics as the built-in "pow()"\n'
'function, when called with two arguments: it yields its left '
'argument\n'
'raised to the power of its right argument. The numeric arguments '
'are\n'
'first converted to a common type, and the result is of that type.\n'
'\n'
'For int operands, the result has the same type as the operands '
'unless\n'
'the second argument is negative; in that case, all arguments are\n'
'converted to float and a float result is delivered. For example,\n'
'"10**2" returns "100", but "10**-2" returns "0.01".\n'
'\n'
'Raising "0.0" to a negative power results in a '
'"ZeroDivisionError".\n'
'Raising a negative number to a fractional power results in a '
'"complex"\n'
'number. (In earlier versions it raised a "ValueError".)\n',
'raise': 'The "raise" statement\n'
'*********************\n'
'\n'
' raise_stmt ::= "raise" [expression ["from" expression]]\n'
'\n'
'If no expressions are present, "raise" re-raises the last '
'exception\n'
'that was active in the current scope. If no exception is active '
'in\n'
'the current scope, a "RuntimeError" exception is raised indicating\n'
'that this is an error.\n'
'\n'
'Otherwise, "raise" evaluates the first expression as the exception\n'
'object. It must be either a subclass or an instance of\n'
'"BaseException". If it is a class, the exception instance will be\n'
'obtained when needed by instantiating the class with no arguments.\n'
'\n'
'The *type* of the exception is the exception instance’s class, the\n'
'*value* is the instance itself.\n'
'\n'
'A traceback object is normally created automatically when an '
'exception\n'
'is raised and attached to it as the "__traceback__" attribute, '
'which\n'
'is writable. You can create an exception and set your own traceback '
'in\n'
'one step using the "with_traceback()" exception method (which '
'returns\n'
'the same exception instance, with its traceback set to its '
'argument),\n'
'like so:\n'
'\n'
' raise Exception("foo occurred").with_traceback(tracebackobj)\n'
'\n'
'The "from" clause is used for exception chaining: if given, the '
'second\n'
'*expression* must be another exception class or instance, which '
'will\n'
'then be attached to the raised exception as the "__cause__" '
'attribute\n'
'(which is writable). If the raised exception is not handled, both\n'
'exceptions will be printed:\n'
'\n'
' >>> try:\n'
' ... print(1 / 0)\n'
' ... except Exception as exc:\n'
' ... raise RuntimeError("Something bad happened") from exc\n'
' ...\n'
' Traceback (most recent call last):\n'
' File "<stdin>", line 2, in <module>\n'
' ZeroDivisionError: division by zero\n'
'\n'
' The above exception was the direct cause of the following '
'exception:\n'
'\n'
' Traceback (most recent call last):\n'
' File "<stdin>", line 4, in <module>\n'
' RuntimeError: Something bad happened\n'
'\n'
'A similar mechanism works implicitly if an exception is raised '
'inside\n'
'an exception handler or a "finally" clause: the previous exception '
'is\n'
'then attached as the new exception’s "__context__" attribute:\n'
'\n'
' >>> try:\n'
' ... print(1 / 0)\n'
' ... except:\n'
' ... raise RuntimeError("Something bad happened")\n'
' ...\n'
' Traceback (most recent call last):\n'
' File "<stdin>", line 2, in <module>\n'
' ZeroDivisionError: division by zero\n'
'\n'
' During handling of the above exception, another exception '
'occurred:\n'
'\n'
' Traceback (most recent call last):\n'
' File "<stdin>", line 4, in <module>\n'
' RuntimeError: Something bad happened\n'
'\n'
'Exception chaining can be explicitly suppressed by specifying '
'"None"\n'
'in the "from" clause:\n'
'\n'
' >>> try:\n'
' ... print(1 / 0)\n'
' ... except:\n'
' ... raise RuntimeError("Something bad happened") from None\n'
' ...\n'
' Traceback (most recent call last):\n'
' File "<stdin>", line 4, in <module>\n'
' RuntimeError: Something bad happened\n'
'\n'
'Additional information on exceptions can be found in section\n'
'Exceptions, and information about handling exceptions is in '
'section\n'
'The try statement.\n'
'\n'
'Changed in version 3.3: "None" is now permitted as "Y" in "raise X\n'
'from Y".\n'
'\n'
'New in version 3.3: The "__suppress_context__" attribute to '
'suppress\n'
'automatic display of the exception context.\n',
'return': 'The "return" statement\n'
'**********************\n'
'\n'
' return_stmt ::= "return" [expression_list]\n'
'\n'
'"return" may only occur syntactically nested in a function '
'definition,\n'
'not within a nested class definition.\n'
'\n'
'If an expression list is present, it is evaluated, else "None" is\n'
'substituted.\n'
'\n'
'"return" leaves the current function call with the expression list '
'(or\n'
'"None") as return value.\n'
'\n'
'When "return" passes control out of a "try" statement with a '
'"finally"\n'
'clause, that "finally" clause is executed before really leaving '
'the\n'
'function.\n'
'\n'
'In a generator function, the "return" statement indicates that '
'the\n'
'generator is done and will cause "StopIteration" to be raised. '
'The\n'
'returned value (if any) is used as an argument to construct\n'
'"StopIteration" and becomes the "StopIteration.value" attribute.\n'
'\n'
'In an asynchronous generator function, an empty "return" '
'statement\n'
'indicates that the asynchronous generator is done and will cause\n'
'"StopAsyncIteration" to be raised. A non-empty "return" statement '
'is\n'
'a syntax error in an asynchronous generator function.\n',
'sequence-types': 'Emulating container types\n'
'*************************\n'
'\n'
'The following methods can be defined to implement '
'container objects.\n'
'Containers usually are sequences (such as lists or tuples) '
'or mappings\n'
'(like dictionaries), but can represent other containers as '
'well. The\n'
'first set of methods is used either to emulate a sequence '
'or to\n'
'emulate a mapping; the difference is that for a sequence, '
'the\n'
'allowable keys should be the integers *k* for which "0 <= '
'k < N" where\n'
'*N* is the length of the sequence, or slice objects, which '
'define a\n'
'range of items. It is also recommended that mappings '
'provide the\n'
'methods "keys()", "values()", "items()", "get()", '
'"clear()",\n'
'"setdefault()", "pop()", "popitem()", "copy()", and '
'"update()"\n'
'behaving similar to those for Python’s standard dictionary '
'objects.\n'
'The "collections" module provides a "MutableMapping" '
'abstract base\n'
'class to help create those methods from a base set of '
'"__getitem__()",\n'
'"__setitem__()", "__delitem__()", and "keys()". Mutable '
'sequences\n'
'should provide methods "append()", "count()", "index()", '
'"extend()",\n'
'"insert()", "pop()", "remove()", "reverse()" and "sort()", '
'like Python\n'
'standard list objects. Finally, sequence types should '
'implement\n'
'addition (meaning concatenation) and multiplication '
'(meaning\n'
'repetition) by defining the methods "__add__()", '
'"__radd__()",\n'
'"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" '
'described\n'
'below; they should not define other numerical operators. '
'It is\n'
'recommended that both mappings and sequences implement '
'the\n'
'"__contains__()" method to allow efficient use of the "in" '
'operator;\n'
'for mappings, "in" should search the mapping’s keys; for '
'sequences, it\n'
'should search through the values. It is further '
'recommended that both\n'
'mappings and sequences implement the "__iter__()" method '
'to allow\n'
'efficient iteration through the container; for mappings, '
'"__iter__()"\n'
'should be the same as "keys()"; for sequences, it should '
'iterate\n'
'through the values.\n'
'\n'
'object.__len__(self)\n'
'\n'
' Called to implement the built-in function "len()". '
'Should return\n'
' the length of the object, an integer ">=" 0. Also, an '
'object that\n'
' doesn’t define a "__bool__()" method and whose '
'"__len__()" method\n'
' returns zero is considered to be false in a Boolean '
'context.\n'
'\n'
' **CPython implementation detail:** In CPython, the '
'length is\n'
' required to be at most "sys.maxsize". If the length is '
'larger than\n'
' "sys.maxsize" some features (such as "len()") may '
'raise\n'
' "OverflowError". To prevent raising "OverflowError" by '
'truth value\n'
' testing, an object must define a "__bool__()" method.\n'
'\n'
'object.__length_hint__(self)\n'
'\n'
' Called to implement "operator.length_hint()". Should '
'return an\n'
' estimated length for the object (which may be greater '
'or less than\n'
' the actual length). The length must be an integer ">=" '
'0. This\n'
' method is purely an optimization and is never required '
'for\n'
' correctness.\n'
'\n'
' New in version 3.4.\n'
'\n'
'Note: Slicing is done exclusively with the following three '
'methods.\n'
' A call like\n'
'\n'
' a[1:2] = b\n'
'\n'
' is translated to\n'
'\n'
' a[slice(1, 2, None)] = b\n'
'\n'
' and so forth. Missing slice items are always filled in '
'with "None".\n'
'\n'
'object.__getitem__(self, key)\n'
'\n'
' Called to implement evaluation of "self[key]". For '
'sequence types,\n'
' the accepted keys should be integers and slice '
'objects. Note that\n'
' the special interpretation of negative indexes (if the '
'class wishes\n'
' to emulate a sequence type) is up to the '
'"__getitem__()" method. If\n'
' *key* is of an inappropriate type, "TypeError" may be '
'raised; if of\n'
' a value outside the set of indexes for the sequence '
'(after any\n'
' special interpretation of negative values), '
'"IndexError" should be\n'
' raised. For mapping types, if *key* is missing (not in '
'the\n'
' container), "KeyError" should be raised.\n'
'\n'
' Note: "for" loops expect that an "IndexError" will be '
'raised for\n'
' illegal indexes to allow proper detection of the end '
'of the\n'
' sequence.\n'
'\n'
'object.__setitem__(self, key, value)\n'
'\n'
' Called to implement assignment to "self[key]". Same '
'note as for\n'
' "__getitem__()". This should only be implemented for '
'mappings if\n'
' the objects support changes to the values for keys, or '
'if new keys\n'
' can be added, or for sequences if elements can be '
'replaced. The\n'
' same exceptions should be raised for improper *key* '
'values as for\n'
' the "__getitem__()" method.\n'
'\n'
'object.__delitem__(self, key)\n'
'\n'
' Called to implement deletion of "self[key]". Same note '
'as for\n'
' "__getitem__()". This should only be implemented for '
'mappings if\n'
' the objects support removal of keys, or for sequences '
'if elements\n'
' can be removed from the sequence. The same exceptions '
'should be\n'
' raised for improper *key* values as for the '
'"__getitem__()" method.\n'
'\n'
'object.__missing__(self, key)\n'
'\n'
' Called by "dict"."__getitem__()" to implement '
'"self[key]" for dict\n'
' subclasses when key is not in the dictionary.\n'
'\n'
'object.__iter__(self)\n'
'\n'
' This method is called when an iterator is required for '
'a container.\n'
' This method should return a new iterator object that '
'can iterate\n'
' over all the objects in the container. For mappings, '
'it should\n'
' iterate over the keys of the container.\n'
'\n'
' Iterator objects also need to implement this method; '
'they are\n'
' required to return themselves. For more information on '
'iterator\n'
' objects, see Iterator Types.\n'
'\n'
'object.__reversed__(self)\n'
'\n'
' Called (if present) by the "reversed()" built-in to '
'implement\n'
' reverse iteration. It should return a new iterator '
'object that\n'
' iterates over all the objects in the container in '
'reverse order.\n'
'\n'
' If the "__reversed__()" method is not provided, the '
'"reversed()"\n'
' built-in will fall back to using the sequence protocol '
'("__len__()"\n'
' and "__getitem__()"). Objects that support the '
'sequence protocol\n'
' should only provide "__reversed__()" if they can '
'provide an\n'
' implementation that is more efficient than the one '
'provided by\n'
' "reversed()".\n'
'\n'
'The membership test operators ("in" and "not in") are '
'normally\n'
'implemented as an iteration through a sequence. However, '
'container\n'
'objects can supply the following special method with a '
'more efficient\n'
'implementation, which also does not require the object be '
'a sequence.\n'
'\n'
'object.__contains__(self, item)\n'
'\n'
' Called to implement membership test operators. Should '
'return true\n'
' if *item* is in *self*, false otherwise. For mapping '
'objects, this\n'
' should consider the keys of the mapping rather than the '
'values or\n'
' the key-item pairs.\n'
'\n'
' For objects that don’t define "__contains__()", the '
'membership test\n'
' first tries iteration via "__iter__()", then the old '
'sequence\n'
' iteration protocol via "__getitem__()", see this '
'section in the\n'
' language reference.\n',
'shifting': 'Shifting operations\n'
'*******************\n'
'\n'
'The shifting operations have lower priority than the arithmetic\n'
'operations:\n'
'\n'
' shift_expr ::= a_expr | shift_expr ("<<" | ">>") a_expr\n'
'\n'
'These operators accept integers as arguments. They shift the '
'first\n'
'argument to the left or right by the number of bits given by '
'the\n'
'second argument.\n'
'\n'
'A right shift by *n* bits is defined as floor division by '
'"pow(2,n)".\n'
'A left shift by *n* bits is defined as multiplication with '
'"pow(2,n)".\n'
'\n'
'Note: In the current implementation, the right-hand operand is\n'
' required to be at most "sys.maxsize". If the right-hand '
'operand is\n'
' larger than "sys.maxsize" an "OverflowError" exception is '
'raised.\n',
'slicings': 'Slicings\n'
'********\n'
'\n'
'A slicing selects a range of items in a sequence object (e.g., '
'a\n'
'string, tuple or list). Slicings may be used as expressions or '
'as\n'
'targets in assignment or "del" statements. The syntax for a '
'slicing:\n'
'\n'
' slicing ::= primary "[" slice_list "]"\n'
' slice_list ::= slice_item ("," slice_item)* [","]\n'
' slice_item ::= expression | proper_slice\n'
' proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" '
'[stride] ]\n'
' lower_bound ::= expression\n'
' upper_bound ::= expression\n'
' stride ::= expression\n'
'\n'
'There is ambiguity in the formal syntax here: anything that '
'looks like\n'
'an expression list also looks like a slice list, so any '
'subscription\n'
'can be interpreted as a slicing. Rather than further '
'complicating the\n'
'syntax, this is disambiguated by defining that in this case the\n'
'interpretation as a subscription takes priority over the\n'
'interpretation as a slicing (this is the case if the slice list\n'
'contains no proper slice).\n'
'\n'
'The semantics for a slicing are as follows. The primary is '
'indexed\n'
'(using the same "__getitem__()" method as normal subscription) '
'with a\n'
'key that is constructed from the slice list, as follows. If the '
'slice\n'
'list contains at least one comma, the key is a tuple containing '
'the\n'
'conversion of the slice items; otherwise, the conversion of the '
'lone\n'
'slice item is the key. The conversion of a slice item that is '
'an\n'
'expression is that expression. The conversion of a proper slice '
'is a\n'
'slice object (see section The standard type hierarchy) whose '
'"start",\n'
'"stop" and "step" attributes are the values of the expressions '
'given\n'
'as lower bound, upper bound and stride, respectively, '
'substituting\n'
'"None" for missing expressions.\n',
'specialattrs': 'Special Attributes\n'
'******************\n'
'\n'
'The implementation adds a few special read-only attributes '
'to several\n'
'object types, where they are relevant. Some of these are '
'not reported\n'
'by the "dir()" built-in function.\n'
'\n'
'object.__dict__\n'
'\n'
' A dictionary or other mapping object used to store an '
'object’s\n'
' (writable) attributes.\n'
'\n'
'instance.__class__\n'
'\n'
' The class to which a class instance belongs.\n'
'\n'
'class.__bases__\n'
'\n'
' The tuple of base classes of a class object.\n'
'\n'
'definition.__name__\n'
'\n'
' The name of the class, function, method, descriptor, or '
'generator\n'
' instance.\n'
'\n'
'definition.__qualname__\n'
'\n'
' The *qualified name* of the class, function, method, '
'descriptor, or\n'
' generator instance.\n'
'\n'
' New in version 3.3.\n'
'\n'
'class.__mro__\n'
'\n'
' This attribute is a tuple of classes that are considered '
'when\n'
' looking for base classes during method resolution.\n'
'\n'
'class.mro()\n'
'\n'
' This method can be overridden by a metaclass to customize '
'the\n'
' method resolution order for its instances. It is called '
'at class\n'
' instantiation, and its result is stored in "__mro__".\n'
'\n'
'class.__subclasses__()\n'
'\n'
' Each class keeps a list of weak references to its '
'immediate\n'
' subclasses. This method returns a list of all those '
'references\n'
' still alive. Example:\n'
'\n'
' >>> int.__subclasses__()\n'
" [<class 'bool'>]\n"
'\n'
'-[ Footnotes ]-\n'
'\n'
'[1] Additional information on these special methods may be '
'found\n'
' in the Python Reference Manual (Basic customization).\n'
'\n'
'[2] As a consequence, the list "[1, 2]" is considered equal '
'to\n'
' "[1.0, 2.0]", and similarly for tuples.\n'
'\n'
'[3] They must have since the parser can’t tell the type of '
'the\n'
' operands.\n'
'\n'
'[4] Cased characters are those with general category '
'property\n'
' being one of “Lu” (Letter, uppercase), “Ll” (Letter, '
'lowercase),\n'
' or “Lt” (Letter, titlecase).\n'
'\n'
'[5] To format only a tuple you should therefore provide a\n'
' singleton tuple whose only element is the tuple to be '
'formatted.\n',
'specialnames': 'Special method names\n'
'********************\n'
'\n'
'A class can implement certain operations that are invoked by '
'special\n'
'syntax (such as arithmetic operations or subscripting and '
'slicing) by\n'
'defining methods with special names. This is Python’s '
'approach to\n'
'*operator overloading*, allowing classes to define their own '
'behavior\n'
'with respect to language operators. For instance, if a '
'class defines\n'
'a method named "__getitem__()", and "x" is an instance of '
'this class,\n'
'then "x[i]" is roughly equivalent to "type(x).__getitem__(x, '
'i)".\n'
'Except where mentioned, attempts to execute an operation '
'raise an\n'
'exception when no appropriate method is defined (typically\n'
'"AttributeError" or "TypeError").\n'
'\n'
'Setting a special method to "None" indicates that the '
'corresponding\n'
'operation is not available. For example, if a class sets '
'"__iter__()"\n'
'to "None", the class is not iterable, so calling "iter()" on '
'its\n'
'instances will raise a "TypeError" (without falling back to\n'
'"__getitem__()"). [2]\n'
'\n'
'When implementing a class that emulates any built-in type, '
'it is\n'
'important that the emulation only be implemented to the '
'degree that it\n'
'makes sense for the object being modelled. For example, '
'some\n'
'sequences may work well with retrieval of individual '
'elements, but\n'
'extracting a slice may not make sense. (One example of this '
'is the\n'
'"NodeList" interface in the W3C’s Document Object Model.)\n'
'\n'
'\n'
'Basic customization\n'
'===================\n'
'\n'
'object.__new__(cls[, ...])\n'
'\n'
' Called to create a new instance of class *cls*. '
'"__new__()" is a\n'
' static method (special-cased so you need not declare it '
'as such)\n'
' that takes the class of which an instance was requested '
'as its\n'
' first argument. The remaining arguments are those passed '
'to the\n'
' object constructor expression (the call to the class). '
'The return\n'
' value of "__new__()" should be the new object instance '
'(usually an\n'
' instance of *cls*).\n'
'\n'
' Typical implementations create a new instance of the '
'class by\n'
' invoking the superclass’s "__new__()" method using\n'
' "super().__new__(cls[, ...])" with appropriate arguments '
'and then\n'
' modifying the newly-created instance as necessary before '
'returning\n'
' it.\n'
'\n'
' If "__new__()" returns an instance of *cls*, then the '
'new\n'
' instance’s "__init__()" method will be invoked like\n'
' "__init__(self[, ...])", where *self* is the new instance '
'and the\n'
' remaining arguments are the same as were passed to '
'"__new__()".\n'
'\n'
' If "__new__()" does not return an instance of *cls*, then '
'the new\n'
' instance’s "__init__()" method will not be invoked.\n'
'\n'
' "__new__()" is intended mainly to allow subclasses of '
'immutable\n'
' types (like int, str, or tuple) to customize instance '
'creation. It\n'
' is also commonly overridden in custom metaclasses in '
'order to\n'
' customize class creation.\n'
'\n'
'object.__init__(self[, ...])\n'
'\n'
' Called after the instance has been created (by '
'"__new__()"), but\n'
' before it is returned to the caller. The arguments are '
'those\n'
' passed to the class constructor expression. If a base '
'class has an\n'
' "__init__()" method, the derived class’s "__init__()" '
'method, if\n'
' any, must explicitly call it to ensure proper '
'initialization of the\n'
' base class part of the instance; for example:\n'
' "super().__init__([args...])".\n'
'\n'
' Because "__new__()" and "__init__()" work together in '
'constructing\n'
' objects ("__new__()" to create it, and "__init__()" to '
'customize\n'
' it), no non-"None" value may be returned by "__init__()"; '
'doing so\n'
' will cause a "TypeError" to be raised at runtime.\n'
'\n'
'object.__del__(self)\n'
'\n'
' Called when the instance is about to be destroyed. This '
'is also\n'
' called a finalizer or (improperly) a destructor. If a '
'base class\n'
' has a "__del__()" method, the derived class’s "__del__()" '
'method,\n'
' if any, must explicitly call it to ensure proper deletion '
'of the\n'
' base class part of the instance.\n'
'\n'
' It is possible (though not recommended!) for the '
'"__del__()" method\n'
' to postpone destruction of the instance by creating a new '
'reference\n'
' to it. This is called object *resurrection*. It is\n'
' implementation-dependent whether "__del__()" is called a '
'second\n'
' time when a resurrected object is about to be destroyed; '
'the\n'
' current *CPython* implementation only calls it once.\n'
'\n'
' It is not guaranteed that "__del__()" methods are called '
'for\n'
' objects that still exist when the interpreter exits.\n'
'\n'
' Note: "del x" doesn’t directly call "x.__del__()" — the '
'former\n'
' decrements the reference count for "x" by one, and the '
'latter is\n'
' only called when "x"’s reference count reaches zero.\n'
'\n'
' **CPython implementation detail:** It is possible for a '
'reference\n'
' cycle to prevent the reference count of an object from '
'going to\n'
' zero. In this case, the cycle will be later detected and '
'deleted\n'
' by the *cyclic garbage collector*. A common cause of '
'reference\n'
' cycles is when an exception has been caught in a local '
'variable.\n'
' The frame’s locals then reference the exception, which '
'references\n'
' its own traceback, which references the locals of all '
'frames caught\n'
' in the traceback.\n'
'\n'
' See also: Documentation for the "gc" module.\n'
'\n'
' Warning: Due to the precarious circumstances under which\n'
' "__del__()" methods are invoked, exceptions that occur '
'during\n'
' their execution are ignored, and a warning is printed '
'to\n'
' "sys.stderr" instead. In particular:\n'
'\n'
' * "__del__()" can be invoked when arbitrary code is '
'being\n'
' executed, including from any arbitrary thread. If '
'"__del__()"\n'
' needs to take a lock or invoke any other blocking '
'resource, it\n'
' may deadlock as the resource may already be taken by '
'the code\n'
' that gets interrupted to execute "__del__()".\n'
'\n'
' * "__del__()" can be executed during interpreter '
'shutdown. As\n'
' a consequence, the global variables it needs to '
'access\n'
' (including other modules) may already have been '
'deleted or set\n'
' to "None". Python guarantees that globals whose name '
'begins\n'
' with a single underscore are deleted from their '
'module before\n'
' other globals are deleted; if no other references to '
'such\n'
' globals exist, this may help in assuring that '
'imported modules\n'
' are still available at the time when the "__del__()" '
'method is\n'
' called.\n'
'\n'
'object.__repr__(self)\n'
'\n'
' Called by the "repr()" built-in function to compute the '
'“official”\n'
' string representation of an object. If at all possible, '
'this\n'
' should look like a valid Python expression that could be '
'used to\n'
' recreate an object with the same value (given an '
'appropriate\n'
' environment). If this is not possible, a string of the '
'form\n'
' "<...some useful description...>" should be returned. The '
'return\n'
' value must be a string object. If a class defines '
'"__repr__()" but\n'
' not "__str__()", then "__repr__()" is also used when an '
'“informal”\n'
' string representation of instances of that class is '
'required.\n'
'\n'
' This is typically used for debugging, so it is important '
'that the\n'
' representation is information-rich and unambiguous.\n'
'\n'
'object.__str__(self)\n'
'\n'
' Called by "str(object)" and the built-in functions '
'"format()" and\n'
' "print()" to compute the “informal” or nicely printable '
'string\n'
' representation of an object. The return value must be a '
'string\n'
' object.\n'
'\n'
' This method differs from "object.__repr__()" in that '
'there is no\n'
' expectation that "__str__()" return a valid Python '
'expression: a\n'
' more convenient or concise representation can be used.\n'
'\n'
' The default implementation defined by the built-in type '
'"object"\n'
' calls "object.__repr__()".\n'
'\n'
'object.__bytes__(self)\n'
'\n'
' Called by bytes to compute a byte-string representation '
'of an\n'
' object. This should return a "bytes" object.\n'
'\n'
'object.__format__(self, format_spec)\n'
'\n'
' Called by the "format()" built-in function, and by '
'extension,\n'
' evaluation of formatted string literals and the '
'"str.format()"\n'
' method, to produce a “formatted” string representation of '
'an\n'
' object. The "format_spec" argument is a string that '
'contains a\n'
' description of the formatting options desired. The '
'interpretation\n'
' of the "format_spec" argument is up to the type '
'implementing\n'
' "__format__()", however most classes will either '
'delegate\n'
' formatting to one of the built-in types, or use a '
'similar\n'
' formatting option syntax.\n'
'\n'
' See Format Specification Mini-Language for a description '
'of the\n'
' standard formatting syntax.\n'
'\n'
' The return value must be a string object.\n'
'\n'
' Changed in version 3.4: The __format__ method of "object" '
'itself\n'
' raises a "TypeError" if passed any non-empty string.\n'
'\n'
'object.__lt__(self, other)\n'
'object.__le__(self, other)\n'
'object.__eq__(self, other)\n'
'object.__ne__(self, other)\n'
'object.__gt__(self, other)\n'
'object.__ge__(self, other)\n'
'\n'
' These are the so-called “rich comparison” methods. The\n'
' correspondence between operator symbols and method names '
'is as\n'
' follows: "x<y" calls "x.__lt__(y)", "x<=y" calls '
'"x.__le__(y)",\n'
' "x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", '
'"x>y" calls\n'
' "x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".\n'
'\n'
' A rich comparison method may return the singleton '
'"NotImplemented"\n'
' if it does not implement the operation for a given pair '
'of\n'
' arguments. By convention, "False" and "True" are returned '
'for a\n'
' successful comparison. However, these methods can return '
'any value,\n'
' so if the comparison operator is used in a Boolean '
'context (e.g.,\n'
' in the condition of an "if" statement), Python will call '
'"bool()"\n'
' on the value to determine if the result is true or '
'false.\n'
'\n'
' By default, "__ne__()" delegates to "__eq__()" and '
'inverts the\n'
' result unless it is "NotImplemented". There are no other '
'implied\n'
' relationships among the comparison operators, for '
'example, the\n'
' truth of "(x<y or x==y)" does not imply "x<=y". To '
'automatically\n'
' generate ordering operations from a single root '
'operation, see\n'
' "functools.total_ordering()".\n'
'\n'
' See the paragraph on "__hash__()" for some important '
'notes on\n'
' creating *hashable* objects which support custom '
'comparison\n'
' operations and are usable as dictionary keys.\n'
'\n'
' There are no swapped-argument versions of these methods '
'(to be used\n'
' when the left argument does not support the operation but '
'the right\n'
' argument does); rather, "__lt__()" and "__gt__()" are '
'each other’s\n'
' reflection, "__le__()" and "__ge__()" are each other’s '
'reflection,\n'
' and "__eq__()" and "__ne__()" are their own reflection. '
'If the\n'
' operands are of different types, and right operand’s type '
'is a\n'
' direct or indirect subclass of the left operand’s type, '
'the\n'
' reflected method of the right operand has priority, '
'otherwise the\n'
' left operand’s method has priority. Virtual subclassing '
'is not\n'
' considered.\n'
'\n'
'object.__hash__(self)\n'
'\n'
' Called by built-in function "hash()" and for operations '
'on members\n'
' of hashed collections including "set", "frozenset", and '
'"dict".\n'
' "__hash__()" should return an integer. The only required '
'property\n'
' is that objects which compare equal have the same hash '
'value; it is\n'
' advised to mix together the hash values of the components '
'of the\n'
' object that also play a part in comparison of objects by '
'packing\n'
' them into a tuple and hashing the tuple. Example:\n'
'\n'
' def __hash__(self):\n'
' return hash((self.name, self.nick, self.color))\n'
'\n'
' Note: "hash()" truncates the value returned from an '
'object’s\n'
' custom "__hash__()" method to the size of a '
'"Py_ssize_t". This\n'
' is typically 8 bytes on 64-bit builds and 4 bytes on '
'32-bit\n'
' builds. If an object’s "__hash__()" must interoperate '
'on builds\n'
' of different bit sizes, be sure to check the width on '
'all\n'
' supported builds. An easy way to do this is with '
'"python -c\n'
' "import sys; print(sys.hash_info.width)"".\n'
'\n'
' If a class does not define an "__eq__()" method it should '
'not\n'
' define a "__hash__()" operation either; if it defines '
'"__eq__()"\n'
' but not "__hash__()", its instances will not be usable as '
'items in\n'
' hashable collections. If a class defines mutable objects '
'and\n'
' implements an "__eq__()" method, it should not implement\n'
' "__hash__()", since the implementation of hashable '
'collections\n'
' requires that a key’s hash value is immutable (if the '
'object’s hash\n'
' value changes, it will be in the wrong hash bucket).\n'
'\n'
' User-defined classes have "__eq__()" and "__hash__()" '
'methods by\n'
' default; with them, all objects compare unequal (except '
'with\n'
' themselves) and "x.__hash__()" returns an appropriate '
'value such\n'
' that "x == y" implies both that "x is y" and "hash(x) == '
'hash(y)".\n'
'\n'
' A class that overrides "__eq__()" and does not define '
'"__hash__()"\n'
' will have its "__hash__()" implicitly set to "None". '
'When the\n'
' "__hash__()" method of a class is "None", instances of '
'the class\n'
' will raise an appropriate "TypeError" when a program '
'attempts to\n'
' retrieve their hash value, and will also be correctly '
'identified as\n'
' unhashable when checking "isinstance(obj, '
'collections.Hashable)".\n'
'\n'
' If a class that overrides "__eq__()" needs to retain the\n'
' implementation of "__hash__()" from a parent class, the '
'interpreter\n'
' must be told this explicitly by setting "__hash__ =\n'
' <ParentClass>.__hash__".\n'
'\n'
' If a class that does not override "__eq__()" wishes to '
'suppress\n'
' hash support, it should include "__hash__ = None" in the '
'class\n'
' definition. A class which defines its own "__hash__()" '
'that\n'
' explicitly raises a "TypeError" would be incorrectly '
'identified as\n'
' hashable by an "isinstance(obj, collections.Hashable)" '
'call.\n'
'\n'
' Note: By default, the "__hash__()" values of str, bytes '
'and\n'
' datetime objects are “salted” with an unpredictable '
'random value.\n'
' Although they remain constant within an individual '
'Python\n'
' process, they are not predictable between repeated '
'invocations of\n'
' Python.This is intended to provide protection against a '
'denial-\n'
' of-service caused by carefully-chosen inputs that '
'exploit the\n'
' worst case performance of a dict insertion, O(n^2) '
'complexity.\n'
' See http://www.ocert.org/advisories/ocert-2011-003.html '
'for\n'
' details.Changing hash values affects the iteration '
'order of\n'
' dicts, sets and other mappings. Python has never made '
'guarantees\n'
' about this ordering (and it typically varies between '
'32-bit and\n'
' 64-bit builds).See also "PYTHONHASHSEED".\n'
'\n'
' Changed in version 3.3: Hash randomization is enabled by '
'default.\n'
'\n'
'object.__bool__(self)\n'
'\n'
' Called to implement truth value testing and the built-in '
'operation\n'
' "bool()"; should return "False" or "True". When this '
'method is not\n'
' defined, "__len__()" is called, if it is defined, and the '
'object is\n'
' considered true if its result is nonzero. If a class '
'defines\n'
' neither "__len__()" nor "__bool__()", all its instances '
'are\n'
' considered true.\n'
'\n'
'\n'
'Customizing attribute access\n'
'============================\n'
'\n'
'The following methods can be defined to customize the '
'meaning of\n'
'attribute access (use of, assignment to, or deletion of '
'"x.name") for\n'
'class instances.\n'
'\n'
'object.__getattr__(self, name)\n'
'\n'
' Called when the default attribute access fails with an\n'
' "AttributeError" (either "__getattribute__()" raises an\n'
' "AttributeError" because *name* is not an instance '
'attribute or an\n'
' attribute in the class tree for "self"; or "__get__()" of '
'a *name*\n'
' property raises "AttributeError"). This method should '
'either\n'
' return the (computed) attribute value or raise an '
'"AttributeError"\n'
' exception.\n'
'\n'
' Note that if the attribute is found through the normal '
'mechanism,\n'
' "__getattr__()" is not called. (This is an intentional '
'asymmetry\n'
' between "__getattr__()" and "__setattr__()".) This is '
'done both for\n'
' efficiency reasons and because otherwise "__getattr__()" '
'would have\n'
' no way to access other attributes of the instance. Note '
'that at\n'
' least for instance variables, you can fake total control '
'by not\n'
' inserting any values in the instance attribute dictionary '
'(but\n'
' instead inserting them in another object). See the\n'
' "__getattribute__()" method below for a way to actually '
'get total\n'
' control over attribute access.\n'
'\n'
'object.__getattribute__(self, name)\n'
'\n'
' Called unconditionally to implement attribute accesses '
'for\n'
' instances of the class. If the class also defines '
'"__getattr__()",\n'
' the latter will not be called unless "__getattribute__()" '
'either\n'
' calls it explicitly or raises an "AttributeError". This '
'method\n'
' should return the (computed) attribute value or raise an\n'
' "AttributeError" exception. In order to avoid infinite '
'recursion in\n'
' this method, its implementation should always call the '
'base class\n'
' method with the same name to access any attributes it '
'needs, for\n'
' example, "object.__getattribute__(self, name)".\n'
'\n'
' Note: This method may still be bypassed when looking up '
'special\n'
' methods as the result of implicit invocation via '
'language syntax\n'
' or built-in functions. See Special method lookup.\n'
'\n'
'object.__setattr__(self, name, value)\n'
'\n'
' Called when an attribute assignment is attempted. This '
'is called\n'
' instead of the normal mechanism (i.e. store the value in '
'the\n'
' instance dictionary). *name* is the attribute name, '
'*value* is the\n'
' value to be assigned to it.\n'
'\n'
' If "__setattr__()" wants to assign to an instance '
'attribute, it\n'
' should call the base class method with the same name, for '
'example,\n'
' "object.__setattr__(self, name, value)".\n'
'\n'
'object.__delattr__(self, name)\n'
'\n'
' Like "__setattr__()" but for attribute deletion instead '
'of\n'
' assignment. This should only be implemented if "del '
'obj.name" is\n'
' meaningful for the object.\n'
'\n'
'object.__dir__(self)\n'
'\n'
' Called when "dir()" is called on the object. A sequence '
'must be\n'
' returned. "dir()" converts the returned sequence to a '
'list and\n'
' sorts it.\n'
'\n'
'\n'
'Customizing module attribute access\n'
'-----------------------------------\n'
'\n'
'For a more fine grained customization of the module behavior '
'(setting\n'
'attributes, properties, etc.), one can set the "__class__" '
'attribute\n'
'of a module object to a subclass of "types.ModuleType". For '
'example:\n'
'\n'
' import sys\n'
' from types import ModuleType\n'
'\n'
' class VerboseModule(ModuleType):\n'
' def __repr__(self):\n'
" return f'Verbose {self.__name__}'\n"
'\n'
' def __setattr__(self, attr, value):\n'
" print(f'Setting {attr}...')\n"
' setattr(self, attr, value)\n'
'\n'
' sys.modules[__name__].__class__ = VerboseModule\n'
'\n'
'Note: Setting module "__class__" only affects lookups made '
'using the\n'
' attribute access syntax – directly accessing the module '
'globals\n'
' (whether by code within the module, or via a reference to '
'the\n'
' module’s globals dictionary) is unaffected.\n'
'\n'
'Changed in version 3.5: "__class__" module attribute is now '
'writable.\n'
'\n'
'\n'
'Implementing Descriptors\n'
'------------------------\n'
'\n'
'The following methods only apply when an instance of the '
'class\n'
'containing the method (a so-called *descriptor* class) '
'appears in an\n'
'*owner* class (the descriptor must be in either the owner’s '
'class\n'
'dictionary or in the class dictionary for one of its '
'parents). In the\n'
'examples below, “the attribute” refers to the attribute '
'whose name is\n'
'the key of the property in the owner class’ "__dict__".\n'
'\n'
'object.__get__(self, instance, owner)\n'
'\n'
' Called to get the attribute of the owner class (class '
'attribute\n'
' access) or of an instance of that class (instance '
'attribute\n'
' access). *owner* is always the owner class, while '
'*instance* is the\n'
' instance that the attribute was accessed through, or '
'"None" when\n'
' the attribute is accessed through the *owner*. This '
'method should\n'
' return the (computed) attribute value or raise an '
'"AttributeError"\n'
' exception.\n'
'\n'
'object.__set__(self, instance, value)\n'
'\n'
' Called to set the attribute on an instance *instance* of '
'the owner\n'
' class to a new value, *value*.\n'
'\n'
'object.__delete__(self, instance)\n'
'\n'
' Called to delete the attribute on an instance *instance* '
'of the\n'
' owner class.\n'
'\n'
'object.__set_name__(self, owner, name)\n'
'\n'
' Called at the time the owning class *owner* is created. '
'The\n'
' descriptor has been assigned to *name*.\n'
'\n'
' New in version 3.6.\n'
'\n'
'The attribute "__objclass__" is interpreted by the "inspect" '
'module as\n'
'specifying the class where this object was defined (setting '
'this\n'
'appropriately can assist in runtime introspection of dynamic '
'class\n'
'attributes). For callables, it may indicate that an instance '
'of the\n'
'given type (or a subclass) is expected or required as the '
'first\n'
'positional argument (for example, CPython sets this '
'attribute for\n'
'unbound methods that are implemented in C).\n'
'\n'
'\n'
'Invoking Descriptors\n'
'--------------------\n'
'\n'
'In general, a descriptor is an object attribute with '
'“binding\n'
'behavior”, one whose attribute access has been overridden by '
'methods\n'
'in the descriptor protocol: "__get__()", "__set__()", and\n'
'"__delete__()". If any of those methods are defined for an '
'object, it\n'
'is said to be a descriptor.\n'
'\n'
'The default behavior for attribute access is to get, set, or '
'delete\n'
'the attribute from an object’s dictionary. For instance, '
'"a.x" has a\n'
'lookup chain starting with "a.__dict__[\'x\']", then\n'
'"type(a).__dict__[\'x\']", and continuing through the base '
'classes of\n'
'"type(a)" excluding metaclasses.\n'
'\n'
'However, if the looked-up value is an object defining one of '
'the\n'
'descriptor methods, then Python may override the default '
'behavior and\n'
'invoke the descriptor method instead. Where this occurs in '
'the\n'
'precedence chain depends on which descriptor methods were '
'defined and\n'
'how they were called.\n'
'\n'
'The starting point for descriptor invocation is a binding, '
'"a.x". How\n'
'the arguments are assembled depends on "a":\n'
'\n'
'Direct Call\n'
' The simplest and least common call is when user code '
'directly\n'
' invokes a descriptor method: "x.__get__(a)".\n'
'\n'
'Instance Binding\n'
' If binding to an object instance, "a.x" is transformed '
'into the\n'
' call: "type(a).__dict__[\'x\'].__get__(a, type(a))".\n'
'\n'
'Class Binding\n'
' If binding to a class, "A.x" is transformed into the '
'call:\n'
' "A.__dict__[\'x\'].__get__(None, A)".\n'
'\n'
'Super Binding\n'
' If "a" is an instance of "super", then the binding '
'"super(B,\n'
' obj).m()" searches "obj.__class__.__mro__" for the base '
'class "A"\n'
' immediately preceding "B" and then invokes the descriptor '
'with the\n'
' call: "A.__dict__[\'m\'].__get__(obj, obj.__class__)".\n'
'\n'
'For instance bindings, the precedence of descriptor '
'invocation depends\n'
'on the which descriptor methods are defined. A descriptor '
'can define\n'
'any combination of "__get__()", "__set__()" and '
'"__delete__()". If it\n'
'does not define "__get__()", then accessing the attribute '
'will return\n'
'the descriptor object itself unless there is a value in the '
'object’s\n'
'instance dictionary. If the descriptor defines "__set__()" '
'and/or\n'
'"__delete__()", it is a data descriptor; if it defines '
'neither, it is\n'
'a non-data descriptor. Normally, data descriptors define '
'both\n'
'"__get__()" and "__set__()", while non-data descriptors have '
'just the\n'
'"__get__()" method. Data descriptors with "__set__()" and '
'"__get__()"\n'
'defined always override a redefinition in an instance '
'dictionary. In\n'
'contrast, non-data descriptors can be overridden by '
'instances.\n'
'\n'
'Python methods (including "staticmethod()" and '
'"classmethod()") are\n'
'implemented as non-data descriptors. Accordingly, instances '
'can\n'
'redefine and override methods. This allows individual '
'instances to\n'
'acquire behaviors that differ from other instances of the '
'same class.\n'
'\n'
'The "property()" function is implemented as a data '
'descriptor.\n'
'Accordingly, instances cannot override the behavior of a '
'property.\n'
'\n'
'\n'
'__slots__\n'
'---------\n'
'\n'
'*__slots__* allow us to explicitly declare data members '
'(like\n'
'properties) and deny the creation of *__dict__* and '
'*__weakref__*\n'
'(unless explicitly declared in *__slots__* or available in a '
'parent.)\n'
'\n'
'The space saved over using *__dict__* can be significant.\n'
'\n'
'object.__slots__\n'
'\n'
' This class variable can be assigned a string, iterable, '
'or sequence\n'
' of strings with variable names used by instances. '
'*__slots__*\n'
' reserves space for the declared variables and prevents '
'the\n'
' automatic creation of *__dict__* and *__weakref__* for '
'each\n'
' instance.\n'
'\n'
'\n'
'Notes on using *__slots__*\n'
'~~~~~~~~~~~~~~~~~~~~~~~~~~\n'
'\n'
'* When inheriting from a class without *__slots__*, the '
'*__dict__*\n'
' and *__weakref__* attribute of the instances will always '
'be\n'
' accessible.\n'
'\n'
'* Without a *__dict__* variable, instances cannot be '
'assigned new\n'
' variables not listed in the *__slots__* definition. '
'Attempts to\n'
' assign to an unlisted variable name raises '
'"AttributeError". If\n'
' dynamic assignment of new variables is desired, then add\n'
' "\'__dict__\'" to the sequence of strings in the '
'*__slots__*\n'
' declaration.\n'
'\n'
'* Without a *__weakref__* variable for each instance, '
'classes\n'
' defining *__slots__* do not support weak references to '
'its\n'
' instances. If weak reference support is needed, then add\n'
' "\'__weakref__\'" to the sequence of strings in the '
'*__slots__*\n'
' declaration.\n'
'\n'
'* *__slots__* are implemented at the class level by '
'creating\n'
' descriptors (Implementing Descriptors) for each variable '
'name. As a\n'
' result, class attributes cannot be used to set default '
'values for\n'
' instance variables defined by *__slots__*; otherwise, the '
'class\n'
' attribute would overwrite the descriptor assignment.\n'
'\n'
'* The action of a *__slots__* declaration is not limited to '
'the\n'
' class where it is defined. *__slots__* declared in '
'parents are\n'
' available in child classes. However, child subclasses will '
'get a\n'
' *__dict__* and *__weakref__* unless they also define '
'*__slots__*\n'
' (which should only contain names of any *additional* '
'slots).\n'
'\n'
'* If a class defines a slot also defined in a base class, '
'the\n'
' instance variable defined by the base class slot is '
'inaccessible\n'
' (except by retrieving its descriptor directly from the '
'base class).\n'
' This renders the meaning of the program undefined. In the '
'future, a\n'
' check may be added to prevent this.\n'
'\n'
'* Nonempty *__slots__* does not work for classes derived '
'from\n'
' “variable-length” built-in types such as "int", "bytes" '
'and "tuple".\n'
'\n'
'* Any non-string iterable may be assigned to *__slots__*. '
'Mappings\n'
' may also be used; however, in the future, special meaning '
'may be\n'
' assigned to the values corresponding to each key.\n'
'\n'
'* *__class__* assignment works only if both classes have the '
'same\n'
' *__slots__*.\n'
'\n'
'* Multiple inheritance with multiple slotted parent classes '
'can be\n'
' used, but only one parent is allowed to have attributes '
'created by\n'
' slots (the other bases must have empty slot layouts) - '
'violations\n'
' raise "TypeError".\n'
'\n'
'\n'
'Customizing class creation\n'
'==========================\n'
'\n'
'Whenever a class inherits from another class, '
'*__init_subclass__* is\n'
'called on that class. This way, it is possible to write '
'classes which\n'
'change the behavior of subclasses. This is closely related '
'to class\n'
'decorators, but where class decorators only affect the '
'specific class\n'
'they’re applied to, "__init_subclass__" solely applies to '
'future\n'
'subclasses of the class defining the method.\n'
'\n'
'classmethod object.__init_subclass__(cls)\n'
'\n'
' This method is called whenever the containing class is '
'subclassed.\n'
' *cls* is then the new subclass. If defined as a normal '
'instance\n'
' method, this method is implicitly converted to a class '
'method.\n'
'\n'
' Keyword arguments which are given to a new class are '
'passed to the\n'
' parent’s class "__init_subclass__". For compatibility '
'with other\n'
' classes using "__init_subclass__", one should take out '
'the needed\n'
' keyword arguments and pass the others over to the base '
'class, as\n'
' in:\n'
'\n'
' class Philosopher:\n'
' def __init_subclass__(cls, default_name, '
'**kwargs):\n'
' super().__init_subclass__(**kwargs)\n'
' cls.default_name = default_name\n'
'\n'
' class AustralianPhilosopher(Philosopher, '
'default_name="Bruce"):\n'
' pass\n'
'\n'
' The default implementation "object.__init_subclass__" '
'does nothing,\n'
' but raises an error if it is called with any arguments.\n'
'\n'
' Note: The metaclass hint "metaclass" is consumed by the '
'rest of\n'
' the type machinery, and is never passed to '
'"__init_subclass__"\n'
' implementations. The actual metaclass (rather than the '
'explicit\n'
' hint) can be accessed as "type(cls)".\n'
'\n'
' New in version 3.6.\n'
'\n'
'\n'
'Metaclasses\n'
'-----------\n'
'\n'
'By default, classes are constructed using "type()". The '
'class body is\n'
'executed in a new namespace and the class name is bound '
'locally to the\n'
'result of "type(name, bases, namespace)".\n'
'\n'
'The class creation process can be customized by passing the\n'
'"metaclass" keyword argument in the class definition line, '
'or by\n'
'inheriting from an existing class that included such an '
'argument. In\n'
'the following example, both "MyClass" and "MySubclass" are '
'instances\n'
'of "Meta":\n'
'\n'
' class Meta(type):\n'
' pass\n'
'\n'
' class MyClass(metaclass=Meta):\n'
' pass\n'
'\n'
' class MySubclass(MyClass):\n'
' pass\n'
'\n'
'Any other keyword arguments that are specified in the class '
'definition\n'
'are passed through to all metaclass operations described '
'below.\n'
'\n'
'When a class definition is executed, the following steps '
'occur:\n'
'\n'
'* the appropriate metaclass is determined\n'
'\n'
'* the class namespace is prepared\n'
'\n'
'* the class body is executed\n'
'\n'
'* the class object is created\n'
'\n'
'\n'
'Determining the appropriate metaclass\n'
'-------------------------------------\n'
'\n'
'The appropriate metaclass for a class definition is '
'determined as\n'
'follows:\n'
'\n'
'* if no bases and no explicit metaclass are given, then '
'"type()" is\n'
' used\n'
'\n'
'* if an explicit metaclass is given and it is *not* an '
'instance of\n'
' "type()", then it is used directly as the metaclass\n'
'\n'
'* if an instance of "type()" is given as the explicit '
'metaclass, or\n'
' bases are defined, then the most derived metaclass is '
'used\n'
'\n'
'The most derived metaclass is selected from the explicitly '
'specified\n'
'metaclass (if any) and the metaclasses (i.e. "type(cls)") of '
'all\n'
'specified base classes. The most derived metaclass is one '
'which is a\n'
'subtype of *all* of these candidate metaclasses. If none of '
'the\n'
'candidate metaclasses meets that criterion, then the class '
'definition\n'
'will fail with "TypeError".\n'
'\n'
'\n'
'Preparing the class namespace\n'
'-----------------------------\n'
'\n'
'Once the appropriate metaclass has been identified, then the '
'class\n'
'namespace is prepared. If the metaclass has a "__prepare__" '
'attribute,\n'
'it is called as "namespace = metaclass.__prepare__(name, '
'bases,\n'
'**kwds)" (where the additional keyword arguments, if any, '
'come from\n'
'the class definition).\n'
'\n'
'If the metaclass has no "__prepare__" attribute, then the '
'class\n'
'namespace is initialised as an empty ordered mapping.\n'
'\n'
'See also:\n'
'\n'
' **PEP 3115** - Metaclasses in Python 3000\n'
' Introduced the "__prepare__" namespace hook\n'
'\n'
'\n'
'Executing the class body\n'
'------------------------\n'
'\n'
'The class body is executed (approximately) as "exec(body, '
'globals(),\n'
'namespace)". The key difference from a normal call to '
'"exec()" is that\n'
'lexical scoping allows the class body (including any '
'methods) to\n'
'reference names from the current and outer scopes when the '
'class\n'
'definition occurs inside a function.\n'
'\n'
'However, even when the class definition occurs inside the '
'function,\n'
'methods defined inside the class still cannot see names '
'defined at the\n'
'class scope. Class variables must be accessed through the '
'first\n'
'parameter of instance or class methods, or through the '
'implicit\n'
'lexically scoped "__class__" reference described in the next '
'section.\n'
'\n'
'\n'
'Creating the class object\n'
'-------------------------\n'
'\n'
'Once the class namespace has been populated by executing the '
'class\n'
'body, the class object is created by calling '
'"metaclass(name, bases,\n'
'namespace, **kwds)" (the additional keywords passed here are '
'the same\n'
'as those passed to "__prepare__").\n'
'\n'
'This class object is the one that will be referenced by the '
'zero-\n'
'argument form of "super()". "__class__" is an implicit '
'closure\n'
'reference created by the compiler if any methods in a class '
'body refer\n'
'to either "__class__" or "super". This allows the zero '
'argument form\n'
'of "super()" to correctly identify the class being defined '
'based on\n'
'lexical scoping, while the class or instance that was used '
'to make the\n'
'current call is identified based on the first argument '
'passed to the\n'
'method.\n'
'\n'
'**CPython implementation detail:** In CPython 3.6 and later, '
'the\n'
'"__class__" cell is passed to the metaclass as a '
'"__classcell__" entry\n'
'in the class namespace. If present, this must be propagated '
'up to the\n'
'"type.__new__" call in order for the class to be '
'initialised\n'
'correctly. Failing to do so will result in a '
'"DeprecationWarning" in\n'
'Python 3.6, and a "RuntimeError" in Python 3.8.\n'
'\n'
'When using the default metaclass "type", or any metaclass '
'that\n'
'ultimately calls "type.__new__", the following additional\n'
'customisation steps are invoked after creating the class '
'object:\n'
'\n'
'* first, "type.__new__" collects all of the descriptors in '
'the class\n'
' namespace that define a "__set_name__()" method;\n'
'\n'
'* second, all of these "__set_name__" methods are called '
'with the\n'
' class being defined and the assigned name of that '
'particular\n'
' descriptor; and\n'
'\n'
'* finally, the "__init_subclass__()" hook is called on the '
'immediate\n'
' parent of the new class in its method resolution order.\n'
'\n'
'After the class object is created, it is passed to the '
'class\n'
'decorators included in the class definition (if any) and the '
'resulting\n'
'object is bound in the local namespace as the defined '
'class.\n'
'\n'
'When a new class is created by "type.__new__", the object '
'provided as\n'
'the namespace parameter is copied to a new ordered mapping '
'and the\n'
'original object is discarded. The new copy is wrapped in a '
'read-only\n'
'proxy, which becomes the "__dict__" attribute of the class '
'object.\n'
'\n'
'See also:\n'
'\n'
' **PEP 3135** - New super\n'
' Describes the implicit "__class__" closure reference\n'
'\n'
'\n'
'Uses for metaclasses\n'
'--------------------\n'
'\n'
'The potential uses for metaclasses are boundless. Some ideas '
'that have\n'
'been explored include enum, logging, interface checking, '
'automatic\n'
'delegation, automatic property creation, proxies, '
'frameworks, and\n'
'automatic resource locking/synchronization.\n'
'\n'
'\n'
'Customizing instance and subclass checks\n'
'========================================\n'
'\n'
'The following methods are used to override the default '
'behavior of the\n'
'"isinstance()" and "issubclass()" built-in functions.\n'
'\n'
'In particular, the metaclass "abc.ABCMeta" implements these '
'methods in\n'
'order to allow the addition of Abstract Base Classes (ABCs) '
'as\n'
'“virtual base classes” to any class or type (including '
'built-in\n'
'types), including other ABCs.\n'
'\n'
'class.__instancecheck__(self, instance)\n'
'\n'
' Return true if *instance* should be considered a (direct '
'or\n'
' indirect) instance of *class*. If defined, called to '
'implement\n'
' "isinstance(instance, class)".\n'
'\n'
'class.__subclasscheck__(self, subclass)\n'
'\n'
' Return true if *subclass* should be considered a (direct '
'or\n'
' indirect) subclass of *class*. If defined, called to '
'implement\n'
' "issubclass(subclass, class)".\n'
'\n'
'Note that these methods are looked up on the type '
'(metaclass) of a\n'
'class. They cannot be defined as class methods in the '
'actual class.\n'
'This is consistent with the lookup of special methods that '
'are called\n'
'on instances, only in this case the instance is itself a '
'class.\n'
'\n'
'See also:\n'
'\n'
' **PEP 3119** - Introducing Abstract Base Classes\n'
' Includes the specification for customizing '
'"isinstance()" and\n'
' "issubclass()" behavior through "__instancecheck__()" '
'and\n'
' "__subclasscheck__()", with motivation for this '
'functionality in\n'
' the context of adding Abstract Base Classes (see the '
'"abc"\n'
' module) to the language.\n'
'\n'
'\n'
'Emulating callable objects\n'
'==========================\n'
'\n'
'object.__call__(self[, args...])\n'
'\n'
' Called when the instance is “called” as a function; if '
'this method\n'
' is defined, "x(arg1, arg2, ...)" is a shorthand for\n'
' "x.__call__(arg1, arg2, ...)".\n'
'\n'
'\n'
'Emulating container types\n'
'=========================\n'
'\n'
'The following methods can be defined to implement container '
'objects.\n'
'Containers usually are sequences (such as lists or tuples) '
'or mappings\n'
'(like dictionaries), but can represent other containers as '
'well. The\n'
'first set of methods is used either to emulate a sequence or '
'to\n'
'emulate a mapping; the difference is that for a sequence, '
'the\n'
'allowable keys should be the integers *k* for which "0 <= k '
'< N" where\n'
'*N* is the length of the sequence, or slice objects, which '
'define a\n'
'range of items. It is also recommended that mappings '
'provide the\n'
'methods "keys()", "values()", "items()", "get()", '
'"clear()",\n'
'"setdefault()", "pop()", "popitem()", "copy()", and '
'"update()"\n'
'behaving similar to those for Python’s standard dictionary '
'objects.\n'
'The "collections" module provides a "MutableMapping" '
'abstract base\n'
'class to help create those methods from a base set of '
'"__getitem__()",\n'
'"__setitem__()", "__delitem__()", and "keys()". Mutable '
'sequences\n'
'should provide methods "append()", "count()", "index()", '
'"extend()",\n'
'"insert()", "pop()", "remove()", "reverse()" and "sort()", '
'like Python\n'
'standard list objects. Finally, sequence types should '
'implement\n'
'addition (meaning concatenation) and multiplication '
'(meaning\n'
'repetition) by defining the methods "__add__()", '
'"__radd__()",\n'
'"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" '
'described\n'
'below; they should not define other numerical operators. It '
'is\n'
'recommended that both mappings and sequences implement the\n'
'"__contains__()" method to allow efficient use of the "in" '
'operator;\n'
'for mappings, "in" should search the mapping’s keys; for '
'sequences, it\n'
'should search through the values. It is further recommended '
'that both\n'
'mappings and sequences implement the "__iter__()" method to '
'allow\n'
'efficient iteration through the container; for mappings, '
'"__iter__()"\n'
'should be the same as "keys()"; for sequences, it should '
'iterate\n'
'through the values.\n'
'\n'
'object.__len__(self)\n'
'\n'
' Called to implement the built-in function "len()". '
'Should return\n'
' the length of the object, an integer ">=" 0. Also, an '
'object that\n'
' doesn’t define a "__bool__()" method and whose '
'"__len__()" method\n'
' returns zero is considered to be false in a Boolean '
'context.\n'
'\n'
' **CPython implementation detail:** In CPython, the length '
'is\n'
' required to be at most "sys.maxsize". If the length is '
'larger than\n'
' "sys.maxsize" some features (such as "len()") may raise\n'
' "OverflowError". To prevent raising "OverflowError" by '
'truth value\n'
' testing, an object must define a "__bool__()" method.\n'
'\n'
'object.__length_hint__(self)\n'
'\n'
' Called to implement "operator.length_hint()". Should '
'return an\n'
' estimated length for the object (which may be greater or '
'less than\n'
' the actual length). The length must be an integer ">=" 0. '
'This\n'
' method is purely an optimization and is never required '
'for\n'
' correctness.\n'
'\n'
' New in version 3.4.\n'
'\n'
'Note: Slicing is done exclusively with the following three '
'methods.\n'
' A call like\n'
'\n'
' a[1:2] = b\n'
'\n'
' is translated to\n'
'\n'
' a[slice(1, 2, None)] = b\n'
'\n'
' and so forth. Missing slice items are always filled in '
'with "None".\n'
'\n'
'object.__getitem__(self, key)\n'
'\n'
' Called to implement evaluation of "self[key]". For '
'sequence types,\n'
' the accepted keys should be integers and slice objects. '
'Note that\n'
' the special interpretation of negative indexes (if the '
'class wishes\n'
' to emulate a sequence type) is up to the "__getitem__()" '
'method. If\n'
' *key* is of an inappropriate type, "TypeError" may be '
'raised; if of\n'
' a value outside the set of indexes for the sequence '
'(after any\n'
' special interpretation of negative values), "IndexError" '
'should be\n'
' raised. For mapping types, if *key* is missing (not in '
'the\n'
' container), "KeyError" should be raised.\n'
'\n'
' Note: "for" loops expect that an "IndexError" will be '
'raised for\n'
' illegal indexes to allow proper detection of the end of '
'the\n'
' sequence.\n'
'\n'
'object.__setitem__(self, key, value)\n'
'\n'
' Called to implement assignment to "self[key]". Same note '
'as for\n'
' "__getitem__()". This should only be implemented for '
'mappings if\n'
' the objects support changes to the values for keys, or if '
'new keys\n'
' can be added, or for sequences if elements can be '
'replaced. The\n'
' same exceptions should be raised for improper *key* '
'values as for\n'
' the "__getitem__()" method.\n'
'\n'
'object.__delitem__(self, key)\n'
'\n'
' Called to implement deletion of "self[key]". Same note '
'as for\n'
' "__getitem__()". This should only be implemented for '
'mappings if\n'
' the objects support removal of keys, or for sequences if '
'elements\n'
' can be removed from the sequence. The same exceptions '
'should be\n'
' raised for improper *key* values as for the '
'"__getitem__()" method.\n'
'\n'
'object.__missing__(self, key)\n'
'\n'
' Called by "dict"."__getitem__()" to implement "self[key]" '
'for dict\n'
' subclasses when key is not in the dictionary.\n'
'\n'
'object.__iter__(self)\n'
'\n'
' This method is called when an iterator is required for a '
'container.\n'
' This method should return a new iterator object that can '
'iterate\n'
' over all the objects in the container. For mappings, it '
'should\n'
' iterate over the keys of the container.\n'
'\n'
' Iterator objects also need to implement this method; they '
'are\n'
' required to return themselves. For more information on '
'iterator\n'
' objects, see Iterator Types.\n'
'\n'
'object.__reversed__(self)\n'
'\n'
' Called (if present) by the "reversed()" built-in to '
'implement\n'
' reverse iteration. It should return a new iterator '
'object that\n'
' iterates over all the objects in the container in reverse '
'order.\n'
'\n'
' If the "__reversed__()" method is not provided, the '
'"reversed()"\n'
' built-in will fall back to using the sequence protocol '
'("__len__()"\n'
' and "__getitem__()"). Objects that support the sequence '
'protocol\n'
' should only provide "__reversed__()" if they can provide '
'an\n'
' implementation that is more efficient than the one '
'provided by\n'
' "reversed()".\n'
'\n'
'The membership test operators ("in" and "not in") are '
'normally\n'
'implemented as an iteration through a sequence. However, '
'container\n'
'objects can supply the following special method with a more '
'efficient\n'
'implementation, which also does not require the object be a '
'sequence.\n'
'\n'
'object.__contains__(self, item)\n'
'\n'
' Called to implement membership test operators. Should '
'return true\n'
' if *item* is in *self*, false otherwise. For mapping '
'objects, this\n'
' should consider the keys of the mapping rather than the '
'values or\n'
' the key-item pairs.\n'
'\n'
' For objects that don’t define "__contains__()", the '
'membership test\n'
' first tries iteration via "__iter__()", then the old '
'sequence\n'
' iteration protocol via "__getitem__()", see this section '
'in the\n'
' language reference.\n'
'\n'
'\n'
'Emulating numeric types\n'
'=======================\n'
'\n'
'The following methods can be defined to emulate numeric '
'objects.\n'
'Methods corresponding to operations that are not supported '
'by the\n'
'particular kind of number implemented (e.g., bitwise '
'operations for\n'
'non-integral numbers) should be left undefined.\n'
'\n'
'object.__add__(self, other)\n'
'object.__sub__(self, other)\n'
'object.__mul__(self, other)\n'
'object.__matmul__(self, other)\n'
'object.__truediv__(self, other)\n'
'object.__floordiv__(self, other)\n'
'object.__mod__(self, other)\n'
'object.__divmod__(self, other)\n'
'object.__pow__(self, other[, modulo])\n'
'object.__lshift__(self, other)\n'
'object.__rshift__(self, other)\n'
'object.__and__(self, other)\n'
'object.__xor__(self, other)\n'
'object.__or__(self, other)\n'
'\n'
' These methods are called to implement the binary '
'arithmetic\n'
' operations ("+", "-", "*", "@", "/", "//", "%", '
'"divmod()",\n'
' "pow()", "**", "<<", ">>", "&", "^", "|"). For instance, '
'to\n'
' evaluate the expression "x + y", where *x* is an instance '
'of a\n'
' class that has an "__add__()" method, "x.__add__(y)" is '
'called.\n'
' The "__divmod__()" method should be the equivalent to '
'using\n'
' "__floordiv__()" and "__mod__()"; it should not be '
'related to\n'
' "__truediv__()". Note that "__pow__()" should be defined '
'to accept\n'
' an optional third argument if the ternary version of the '
'built-in\n'
' "pow()" function is to be supported.\n'
'\n'
' If one of those methods does not support the operation '
'with the\n'
' supplied arguments, it should return "NotImplemented".\n'
'\n'
'object.__radd__(self, other)\n'
'object.__rsub__(self, other)\n'
'object.__rmul__(self, other)\n'
'object.__rmatmul__(self, other)\n'
'object.__rtruediv__(self, other)\n'
'object.__rfloordiv__(self, other)\n'
'object.__rmod__(self, other)\n'
'object.__rdivmod__(self, other)\n'
'object.__rpow__(self, other)\n'
'object.__rlshift__(self, other)\n'
'object.__rrshift__(self, other)\n'
'object.__rand__(self, other)\n'
'object.__rxor__(self, other)\n'
'object.__ror__(self, other)\n'
'\n'
' These methods are called to implement the binary '
'arithmetic\n'
' operations ("+", "-", "*", "@", "/", "//", "%", '
'"divmod()",\n'
' "pow()", "**", "<<", ">>", "&", "^", "|") with reflected '
'(swapped)\n'
' operands. These functions are only called if the left '
'operand does\n'
' not support the corresponding operation [3] and the '
'operands are of\n'
' different types. [4] For instance, to evaluate the '
'expression "x -\n'
' y", where *y* is an instance of a class that has an '
'"__rsub__()"\n'
' method, "y.__rsub__(x)" is called if "x.__sub__(y)" '
'returns\n'
' *NotImplemented*.\n'
'\n'
' Note that ternary "pow()" will not try calling '
'"__rpow__()" (the\n'
' coercion rules would become too complicated).\n'
'\n'
' Note: If the right operand’s type is a subclass of the '
'left\n'
' operand’s type and that subclass provides the reflected '
'method\n'
' for the operation, this method will be called before '
'the left\n'
' operand’s non-reflected method. This behavior allows '
'subclasses\n'
' to override their ancestors’ operations.\n'
'\n'
'object.__iadd__(self, other)\n'
'object.__isub__(self, other)\n'
'object.__imul__(self, other)\n'
'object.__imatmul__(self, other)\n'
'object.__itruediv__(self, other)\n'
'object.__ifloordiv__(self, other)\n'
'object.__imod__(self, other)\n'
'object.__ipow__(self, other[, modulo])\n'
'object.__ilshift__(self, other)\n'
'object.__irshift__(self, other)\n'
'object.__iand__(self, other)\n'
'object.__ixor__(self, other)\n'
'object.__ior__(self, other)\n'
'\n'
' These methods are called to implement the augmented '
'arithmetic\n'
' assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", '
'"**=",\n'
' "<<=", ">>=", "&=", "^=", "|="). These methods should '
'attempt to\n'
' do the operation in-place (modifying *self*) and return '
'the result\n'
' (which could be, but does not have to be, *self*). If a '
'specific\n'
' method is not defined, the augmented assignment falls '
'back to the\n'
' normal methods. For instance, if *x* is an instance of a '
'class\n'
' with an "__iadd__()" method, "x += y" is equivalent to "x '
'=\n'
' x.__iadd__(y)" . Otherwise, "x.__add__(y)" and '
'"y.__radd__(x)" are\n'
' considered, as with the evaluation of "x + y". In '
'certain\n'
' situations, augmented assignment can result in unexpected '
'errors\n'
' (see Why does a_tuple[i] += [‘item’] raise an exception '
'when the\n'
' addition works?), but this behavior is in fact part of '
'the data\n'
' model.\n'
'\n'
'object.__neg__(self)\n'
'object.__pos__(self)\n'
'object.__abs__(self)\n'
'object.__invert__(self)\n'
'\n'
' Called to implement the unary arithmetic operations ("-", '
'"+",\n'
' "abs()" and "~").\n'
'\n'
'object.__complex__(self)\n'
'object.__int__(self)\n'
'object.__float__(self)\n'
'\n'
' Called to implement the built-in functions "complex()", '
'"int()" and\n'
' "float()". Should return a value of the appropriate '
'type.\n'
'\n'
'object.__index__(self)\n'
'\n'
' Called to implement "operator.index()", and whenever '
'Python needs\n'
' to losslessly convert the numeric object to an integer '
'object (such\n'
' as in slicing, or in the built-in "bin()", "hex()" and '
'"oct()"\n'
' functions). Presence of this method indicates that the '
'numeric\n'
' object is an integer type. Must return an integer.\n'
'\n'
' Note: In order to have a coherent integer type class, '
'when\n'
' "__index__()" is defined "__int__()" should also be '
'defined, and\n'
' both should return the same value.\n'
'\n'
'object.__round__(self[, ndigits])\n'
'object.__trunc__(self)\n'
'object.__floor__(self)\n'
'object.__ceil__(self)\n'
'\n'
' Called to implement the built-in function "round()" and '
'"math"\n'
' functions "trunc()", "floor()" and "ceil()". Unless '
'*ndigits* is\n'
' passed to "__round__()" all these methods should return '
'the value\n'
' of the object truncated to an "Integral" (typically an '
'"int").\n'
'\n'
' If "__int__()" is not defined then the built-in function '
'"int()"\n'
' falls back to "__trunc__()".\n'
'\n'
'\n'
'With Statement Context Managers\n'
'===============================\n'
'\n'
'A *context manager* is an object that defines the runtime '
'context to\n'
'be established when executing a "with" statement. The '
'context manager\n'
'handles the entry into, and the exit from, the desired '
'runtime context\n'
'for the execution of the block of code. Context managers '
'are normally\n'
'invoked using the "with" statement (described in section The '
'with\n'
'statement), but can also be used by directly invoking their '
'methods.\n'
'\n'
'Typical uses of context managers include saving and '
'restoring various\n'
'kinds of global state, locking and unlocking resources, '
'closing opened\n'
'files, etc.\n'
'\n'
'For more information on context managers, see Context '
'Manager Types.\n'
'\n'
'object.__enter__(self)\n'
'\n'
' Enter the runtime context related to this object. The '
'"with"\n'
' statement will bind this method’s return value to the '
'target(s)\n'
' specified in the "as" clause of the statement, if any.\n'
'\n'
'object.__exit__(self, exc_type, exc_value, traceback)\n'
'\n'
' Exit the runtime context related to this object. The '
'parameters\n'
' describe the exception that caused the context to be '
'exited. If the\n'
' context was exited without an exception, all three '
'arguments will\n'
' be "None".\n'
'\n'
' If an exception is supplied, and the method wishes to '
'suppress the\n'
' exception (i.e., prevent it from being propagated), it '
'should\n'
' return a true value. Otherwise, the exception will be '
'processed\n'
' normally upon exit from this method.\n'
'\n'
' Note that "__exit__()" methods should not reraise the '
'passed-in\n'
' exception; this is the caller’s responsibility.\n'
'\n'
'See also:\n'
'\n'
' **PEP 343** - The “with” statement\n'
' The specification, background, and examples for the '
'Python "with"\n'
' statement.\n'
'\n'
'\n'
'Special method lookup\n'
'=====================\n'
'\n'
'For custom classes, implicit invocations of special methods '
'are only\n'
'guaranteed to work correctly if defined on an object’s type, '
'not in\n'
'the object’s instance dictionary. That behaviour is the '
'reason why\n'
'the following code raises an exception:\n'
'\n'
' >>> class C:\n'
' ... pass\n'
' ...\n'
' >>> c = C()\n'
' >>> c.__len__ = lambda: 5\n'
' >>> len(c)\n'
' Traceback (most recent call last):\n'
' File "<stdin>", line 1, in <module>\n'
" TypeError: object of type 'C' has no len()\n"
'\n'
'The rationale behind this behaviour lies with a number of '
'special\n'
'methods such as "__hash__()" and "__repr__()" that are '
'implemented by\n'
'all objects, including type objects. If the implicit lookup '
'of these\n'
'methods used the conventional lookup process, they would '
'fail when\n'
'invoked on the type object itself:\n'
'\n'
' >>> 1 .__hash__() == hash(1)\n'
' True\n'
' >>> int.__hash__() == hash(int)\n'
' Traceback (most recent call last):\n'
' File "<stdin>", line 1, in <module>\n'
" TypeError: descriptor '__hash__' of 'int' object needs an "
'argument\n'
'\n'
'Incorrectly attempting to invoke an unbound method of a '
'class in this\n'
'way is sometimes referred to as ‘metaclass confusion’, and '
'is avoided\n'
'by bypassing the instance when looking up special methods:\n'
'\n'
' >>> type(1).__hash__(1) == hash(1)\n'
' True\n'
' >>> type(int).__hash__(int) == hash(int)\n'
' True\n'
'\n'
'In addition to bypassing any instance attributes in the '
'interest of\n'
'correctness, implicit special method lookup generally also '
'bypasses\n'
'the "__getattribute__()" method even of the object’s '
'metaclass:\n'
'\n'
' >>> class Meta(type):\n'
' ... def __getattribute__(*args):\n'
' ... print("Metaclass getattribute invoked")\n'
' ... return type.__getattribute__(*args)\n'
' ...\n'
' >>> class C(object, metaclass=Meta):\n'
' ... def __len__(self):\n'
' ... return 10\n'
' ... def __getattribute__(*args):\n'
' ... print("Class getattribute invoked")\n'
' ... return object.__getattribute__(*args)\n'
' ...\n'
' >>> c = C()\n'
' >>> c.__len__() # Explicit lookup via '
'instance\n'
' Class getattribute invoked\n'
' 10\n'
' >>> type(c).__len__(c) # Explicit lookup via '
'type\n'
' Metaclass getattribute invoked\n'
' 10\n'
' >>> len(c) # Implicit lookup\n'
' 10\n'
'\n'
'Bypassing the "__getattribute__()" machinery in this fashion '
'provides\n'
'significant scope for speed optimisations within the '
'interpreter, at\n'
'the cost of some flexibility in the handling of special '
'methods (the\n'
'special method *must* be set on the class object itself in '
'order to be\n'
'consistently invoked by the interpreter).\n',
'string-methods': 'String Methods\n'
'**************\n'
'\n'
'Strings implement all of the common sequence operations, '
'along with\n'
'the additional methods described below.\n'
'\n'
'Strings also support two styles of string formatting, one '
'providing a\n'
'large degree of flexibility and customization (see '
'"str.format()",\n'
'Format String Syntax and Custom String Formatting) and the '
'other based\n'
'on C "printf" style formatting that handles a narrower '
'range of types\n'
'and is slightly harder to use correctly, but is often '
'faster for the\n'
'cases it can handle (printf-style String Formatting).\n'
'\n'
'The Text Processing Services section of the standard '
'library covers a\n'
'number of other modules that provide various text related '
'utilities\n'
'(including regular expression support in the "re" '
'module).\n'
'\n'
'str.capitalize()\n'
'\n'
' Return a copy of the string with its first character '
'capitalized\n'
' and the rest lowercased.\n'
'\n'
'str.casefold()\n'
'\n'
' Return a casefolded copy of the string. Casefolded '
'strings may be\n'
' used for caseless matching.\n'
'\n'
' Casefolding is similar to lowercasing but more '
'aggressive because\n'
' it is intended to remove all case distinctions in a '
'string. For\n'
' example, the German lowercase letter "\'ß\'" is '
'equivalent to ""ss"".\n'
' Since it is already lowercase, "lower()" would do '
'nothing to "\'ß\'";\n'
' "casefold()" converts it to ""ss"".\n'
'\n'
' The casefolding algorithm is described in section 3.13 '
'of the\n'
' Unicode Standard.\n'
'\n'
' New in version 3.3.\n'
'\n'
'str.center(width[, fillchar])\n'
'\n'
' Return centered in a string of length *width*. Padding '
'is done\n'
' using the specified *fillchar* (default is an ASCII '
'space). The\n'
' original string is returned if *width* is less than or '
'equal to\n'
' "len(s)".\n'
'\n'
'str.count(sub[, start[, end]])\n'
'\n'
' Return the number of non-overlapping occurrences of '
'substring *sub*\n'
' in the range [*start*, *end*]. Optional arguments '
'*start* and\n'
' *end* are interpreted as in slice notation.\n'
'\n'
'str.encode(encoding="utf-8", errors="strict")\n'
'\n'
' Return an encoded version of the string as a bytes '
'object. Default\n'
' encoding is "\'utf-8\'". *errors* may be given to set a '
'different\n'
' error handling scheme. The default for *errors* is '
'"\'strict\'",\n'
' meaning that encoding errors raise a "UnicodeError". '
'Other possible\n'
' values are "\'ignore\'", "\'replace\'", '
'"\'xmlcharrefreplace\'",\n'
' "\'backslashreplace\'" and any other name registered '
'via\n'
' "codecs.register_error()", see section Error Handlers. '
'For a list\n'
' of possible encodings, see section Standard Encodings.\n'
'\n'
' Changed in version 3.1: Support for keyword arguments '
'added.\n'
'\n'
'str.endswith(suffix[, start[, end]])\n'
'\n'
' Return "True" if the string ends with the specified '
'*suffix*,\n'
' otherwise return "False". *suffix* can also be a tuple '
'of suffixes\n'
' to look for. With optional *start*, test beginning at '
'that\n'
' position. With optional *end*, stop comparing at that '
'position.\n'
'\n'
'str.expandtabs(tabsize=8)\n'
'\n'
' Return a copy of the string where all tab characters '
'are replaced\n'
' by one or more spaces, depending on the current column '
'and the\n'
' given tab size. Tab positions occur every *tabsize* '
'characters\n'
' (default is 8, giving tab positions at columns 0, 8, 16 '
'and so on).\n'
' To expand the string, the current column is set to zero '
'and the\n'
' string is examined character by character. If the '
'character is a\n'
' tab ("\\t"), one or more space characters are inserted '
'in the result\n'
' until the current column is equal to the next tab '
'position. (The\n'
' tab character itself is not copied.) If the character '
'is a newline\n'
' ("\\n") or return ("\\r"), it is copied and the current '
'column is\n'
' reset to zero. Any other character is copied unchanged '
'and the\n'
' current column is incremented by one regardless of how '
'the\n'
' character is represented when printed.\n'
'\n'
" >>> '01\\t012\\t0123\\t01234'.expandtabs()\n"
" '01 012 0123 01234'\n"
" >>> '01\\t012\\t0123\\t01234'.expandtabs(4)\n"
" '01 012 0123 01234'\n"
'\n'
'str.find(sub[, start[, end]])\n'
'\n'
' Return the lowest index in the string where substring '
'*sub* is\n'
' found within the slice "s[start:end]". Optional '
'arguments *start*\n'
' and *end* are interpreted as in slice notation. Return '
'"-1" if\n'
' *sub* is not found.\n'
'\n'
' Note: The "find()" method should be used only if you '
'need to know\n'
' the position of *sub*. To check if *sub* is a '
'substring or not,\n'
' use the "in" operator:\n'
'\n'
" >>> 'Py' in 'Python'\n"
' True\n'
'\n'
'str.format(*args, **kwargs)\n'
'\n'
' Perform a string formatting operation. The string on '
'which this\n'
' method is called can contain literal text or '
'replacement fields\n'
' delimited by braces "{}". Each replacement field '
'contains either\n'
' the numeric index of a positional argument, or the name '
'of a\n'
' keyword argument. Returns a copy of the string where '
'each\n'
' replacement field is replaced with the string value of '
'the\n'
' corresponding argument.\n'
'\n'
' >>> "The sum of 1 + 2 is {0}".format(1+2)\n'
" 'The sum of 1 + 2 is 3'\n"
'\n'
' See Format String Syntax for a description of the '
'various\n'
' formatting options that can be specified in format '
'strings.\n'
'\n'
' Note: When formatting a number ("int", "float", '
'"complex",\n'
' "decimal.Decimal" and subclasses) with the "n" type '
'(ex:\n'
' "\'{:n}\'.format(1234)"), the function temporarily '
'sets the\n'
' "LC_CTYPE" locale to the "LC_NUMERIC" locale to '
'decode\n'
' "decimal_point" and "thousands_sep" fields of '
'"localeconv()" if\n'
' they are non-ASCII or longer than 1 byte, and the '
'"LC_NUMERIC"\n'
' locale is different than the "LC_CTYPE" locale. This '
'temporary\n'
' change affects other threads.\n'
'\n'
' Changed in version 3.6.5: When formatting a number with '
'the "n"\n'
' type, the function sets temporarily the "LC_CTYPE" '
'locale to the\n'
' "LC_NUMERIC" locale in some cases.\n'
'\n'
'str.format_map(mapping)\n'
'\n'
' Similar to "str.format(**mapping)", except that '
'"mapping" is used\n'
' directly and not copied to a "dict". This is useful if '
'for example\n'
' "mapping" is a dict subclass:\n'
'\n'
' >>> class Default(dict):\n'
' ... def __missing__(self, key):\n'
' ... return key\n'
' ...\n'
" >>> '{name} was born in "
"{country}'.format_map(Default(name='Guido'))\n"
" 'Guido was born in country'\n"
'\n'
' New in version 3.2.\n'
'\n'
'str.index(sub[, start[, end]])\n'
'\n'
' Like "find()", but raise "ValueError" when the '
'substring is not\n'
' found.\n'
'\n'
'str.isalnum()\n'
'\n'
' Return true if all characters in the string are '
'alphanumeric and\n'
' there is at least one character, false otherwise. A '
'character "c"\n'
' is alphanumeric if one of the following returns '
'"True":\n'
' "c.isalpha()", "c.isdecimal()", "c.isdigit()", or '
'"c.isnumeric()".\n'
'\n'
'str.isalpha()\n'
'\n'
' Return true if all characters in the string are '
'alphabetic and\n'
' there is at least one character, false otherwise. '
'Alphabetic\n'
' characters are those characters defined in the Unicode '
'character\n'
' database as “Letter”, i.e., those with general category '
'property\n'
' being one of “Lm”, “Lt”, “Lu”, “Ll”, or “Lo”. Note '
'that this is\n'
' different from the “Alphabetic” property defined in the '
'Unicode\n'
' Standard.\n'
'\n'
'str.isdecimal()\n'
'\n'
' Return true if all characters in the string are decimal '
'characters\n'
' and there is at least one character, false otherwise. '
'Decimal\n'
' characters are those that can be used to form numbers '
'in base 10,\n'
' e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Formally a '
'decimal character\n'
' is a character in the Unicode General Category “Nd”.\n'
'\n'
'str.isdigit()\n'
'\n'
' Return true if all characters in the string are digits '
'and there is\n'
' at least one character, false otherwise. Digits '
'include decimal\n'
' characters and digits that need special handling, such '
'as the\n'
' compatibility superscript digits. This covers digits '
'which cannot\n'
' be used to form numbers in base 10, like the Kharosthi '
'numbers.\n'
' Formally, a digit is a character that has the property '
'value\n'
' Numeric_Type=Digit or Numeric_Type=Decimal.\n'
'\n'
'str.isidentifier()\n'
'\n'
' Return true if the string is a valid identifier '
'according to the\n'
' language definition, section Identifiers and keywords.\n'
'\n'
' Use "keyword.iskeyword()" to test for reserved '
'identifiers such as\n'
' "def" and "class".\n'
'\n'
'str.islower()\n'
'\n'
' Return true if all cased characters [4] in the string '
'are lowercase\n'
' and there is at least one cased character, false '
'otherwise.\n'
'\n'
'str.isnumeric()\n'
'\n'
' Return true if all characters in the string are numeric '
'characters,\n'
' and there is at least one character, false otherwise. '
'Numeric\n'
' characters include digit characters, and all characters '
'that have\n'
' the Unicode numeric value property, e.g. U+2155, VULGAR '
'FRACTION\n'
' ONE FIFTH. Formally, numeric characters are those with '
'the\n'
' property value Numeric_Type=Digit, Numeric_Type=Decimal '
'or\n'
' Numeric_Type=Numeric.\n'
'\n'
'str.isprintable()\n'
'\n'
' Return true if all characters in the string are '
'printable or the\n'
' string is empty, false otherwise. Nonprintable '
'characters are\n'
' those characters defined in the Unicode character '
'database as\n'
' “Other” or “Separator”, excepting the ASCII space '
'(0x20) which is\n'
' considered printable. (Note that printable characters '
'in this\n'
' context are those which should not be escaped when '
'"repr()" is\n'
' invoked on a string. It has no bearing on the handling '
'of strings\n'
' written to "sys.stdout" or "sys.stderr".)\n'
'\n'
'str.isspace()\n'
'\n'
' Return true if there are only whitespace characters in '
'the string\n'
' and there is at least one character, false otherwise. '
'Whitespace\n'
' characters are those characters defined in the Unicode '
'character\n'
' database as “Other” or “Separator” and those with '
'bidirectional\n'
' property being one of “WS”, “B”, or “S”.\n'
'\n'
'str.istitle()\n'
'\n'
' Return true if the string is a titlecased string and '
'there is at\n'
' least one character, for example uppercase characters '
'may only\n'
' follow uncased characters and lowercase characters only '
'cased ones.\n'
' Return false otherwise.\n'
'\n'
'str.isupper()\n'
'\n'
' Return true if all cased characters [4] in the string '
'are uppercase\n'
' and there is at least one cased character, false '
'otherwise.\n'
'\n'
'str.join(iterable)\n'
'\n'
' Return a string which is the concatenation of the '
'strings in\n'
' *iterable*. A "TypeError" will be raised if there are '
'any non-\n'
' string values in *iterable*, including "bytes" '
'objects. The\n'
' separator between elements is the string providing this '
'method.\n'
'\n'
'str.ljust(width[, fillchar])\n'
'\n'
' Return the string left justified in a string of length '
'*width*.\n'
' Padding is done using the specified *fillchar* (default '
'is an ASCII\n'
' space). The original string is returned if *width* is '
'less than or\n'
' equal to "len(s)".\n'
'\n'
'str.lower()\n'
'\n'
' Return a copy of the string with all the cased '
'characters [4]\n'
' converted to lowercase.\n'
'\n'
' The lowercasing algorithm used is described in section '
'3.13 of the\n'
' Unicode Standard.\n'
'\n'
'str.lstrip([chars])\n'
'\n'
' Return a copy of the string with leading characters '
'removed. The\n'
' *chars* argument is a string specifying the set of '
'characters to be\n'
' removed. If omitted or "None", the *chars* argument '
'defaults to\n'
' removing whitespace. The *chars* argument is not a '
'prefix; rather,\n'
' all combinations of its values are stripped:\n'
'\n'
" >>> ' spacious '.lstrip()\n"
" 'spacious '\n"
" >>> 'www.example.com'.lstrip('cmowz.')\n"
" 'example.com'\n"
'\n'
'static str.maketrans(x[, y[, z]])\n'
'\n'
' This static method returns a translation table usable '
'for\n'
' "str.translate()".\n'
'\n'
' If there is only one argument, it must be a dictionary '
'mapping\n'
' Unicode ordinals (integers) or characters (strings of '
'length 1) to\n'
' Unicode ordinals, strings (of arbitrary lengths) or '
'"None".\n'
' Character keys will then be converted to ordinals.\n'
'\n'
' If there are two arguments, they must be strings of '
'equal length,\n'
' and in the resulting dictionary, each character in x '
'will be mapped\n'
' to the character at the same position in y. If there '
'is a third\n'
' argument, it must be a string, whose characters will be '
'mapped to\n'
' "None" in the result.\n'
'\n'
'str.partition(sep)\n'
'\n'
' Split the string at the first occurrence of *sep*, and '
'return a\n'
' 3-tuple containing the part before the separator, the '
'separator\n'
' itself, and the part after the separator. If the '
'separator is not\n'
' found, return a 3-tuple containing the string itself, '
'followed by\n'
' two empty strings.\n'
'\n'
'str.replace(old, new[, count])\n'
'\n'
' Return a copy of the string with all occurrences of '
'substring *old*\n'
' replaced by *new*. If the optional argument *count* is '
'given, only\n'
' the first *count* occurrences are replaced.\n'
'\n'
'str.rfind(sub[, start[, end]])\n'
'\n'
' Return the highest index in the string where substring '
'*sub* is\n'
' found, such that *sub* is contained within '
'"s[start:end]".\n'
' Optional arguments *start* and *end* are interpreted as '
'in slice\n'
' notation. Return "-1" on failure.\n'
'\n'
'str.rindex(sub[, start[, end]])\n'
'\n'
' Like "rfind()" but raises "ValueError" when the '
'substring *sub* is\n'
' not found.\n'
'\n'
'str.rjust(width[, fillchar])\n'
'\n'
' Return the string right justified in a string of length '
'*width*.\n'
' Padding is done using the specified *fillchar* (default '
'is an ASCII\n'
' space). The original string is returned if *width* is '
'less than or\n'
' equal to "len(s)".\n'
'\n'
'str.rpartition(sep)\n'
'\n'
' Split the string at the last occurrence of *sep*, and '
'return a\n'
' 3-tuple containing the part before the separator, the '
'separator\n'
' itself, and the part after the separator. If the '
'separator is not\n'
' found, return a 3-tuple containing two empty strings, '
'followed by\n'
' the string itself.\n'
'\n'
'str.rsplit(sep=None, maxsplit=-1)\n'
'\n'
' Return a list of the words in the string, using *sep* '
'as the\n'
' delimiter string. If *maxsplit* is given, at most '
'*maxsplit* splits\n'
' are done, the *rightmost* ones. If *sep* is not '
'specified or\n'
' "None", any whitespace string is a separator. Except '
'for splitting\n'
' from the right, "rsplit()" behaves like "split()" which '
'is\n'
' described in detail below.\n'
'\n'
'str.rstrip([chars])\n'
'\n'
' Return a copy of the string with trailing characters '
'removed. The\n'
' *chars* argument is a string specifying the set of '
'characters to be\n'
' removed. If omitted or "None", the *chars* argument '
'defaults to\n'
' removing whitespace. The *chars* argument is not a '
'suffix; rather,\n'
' all combinations of its values are stripped:\n'
'\n'
" >>> ' spacious '.rstrip()\n"
" ' spacious'\n"
" >>> 'mississippi'.rstrip('ipz')\n"
" 'mississ'\n"
'\n'
'str.split(sep=None, maxsplit=-1)\n'
'\n'
' Return a list of the words in the string, using *sep* '
'as the\n'
' delimiter string. If *maxsplit* is given, at most '
'*maxsplit*\n'
' splits are done (thus, the list will have at most '
'"maxsplit+1"\n'
' elements). If *maxsplit* is not specified or "-1", '
'then there is\n'
' no limit on the number of splits (all possible splits '
'are made).\n'
'\n'
' If *sep* is given, consecutive delimiters are not '
'grouped together\n'
' and are deemed to delimit empty strings (for example,\n'
' "\'1,,2\'.split(\',\')" returns "[\'1\', \'\', '
'\'2\']"). The *sep* argument\n'
' may consist of multiple characters (for example,\n'
' "\'1<>2<>3\'.split(\'<>\')" returns "[\'1\', \'2\', '
'\'3\']"). Splitting an\n'
' empty string with a specified separator returns '
'"[\'\']".\n'
'\n'
' For example:\n'
'\n'
" >>> '1,2,3'.split(',')\n"
" ['1', '2', '3']\n"
" >>> '1,2,3'.split(',', maxsplit=1)\n"
" ['1', '2,3']\n"
" >>> '1,2,,3,'.split(',')\n"
" ['1', '2', '', '3', '']\n"
'\n'
' If *sep* is not specified or is "None", a different '
'splitting\n'
' algorithm is applied: runs of consecutive whitespace '
'are regarded\n'
' as a single separator, and the result will contain no '
'empty strings\n'
' at the start or end if the string has leading or '
'trailing\n'
' whitespace. Consequently, splitting an empty string or '
'a string\n'
' consisting of just whitespace with a "None" separator '
'returns "[]".\n'
'\n'
' For example:\n'
'\n'
" >>> '1 2 3'.split()\n"
" ['1', '2', '3']\n"
" >>> '1 2 3'.split(maxsplit=1)\n"
" ['1', '2 3']\n"
" >>> ' 1 2 3 '.split()\n"
" ['1', '2', '3']\n"
'\n'
'str.splitlines([keepends])\n'
'\n'
' Return a list of the lines in the string, breaking at '
'line\n'
' boundaries. Line breaks are not included in the '
'resulting list\n'
' unless *keepends* is given and true.\n'
'\n'
' This method splits on the following line boundaries. '
'In\n'
' particular, the boundaries are a superset of *universal '
'newlines*.\n'
'\n'
' '
'+-------------------------+-------------------------------+\n'
' | Representation | '
'Description |\n'
' '
'+=========================+===============================+\n'
' | "\\n" | Line '
'Feed |\n'
' '
'+-------------------------+-------------------------------+\n'
' | "\\r" | Carriage '
'Return |\n'
' '
'+-------------------------+-------------------------------+\n'
' | "\\r\\n" | Carriage Return + Line '
'Feed |\n'
' '
'+-------------------------+-------------------------------+\n'
' | "\\v" or "\\x0b" | Line '
'Tabulation |\n'
' '
'+-------------------------+-------------------------------+\n'
' | "\\f" or "\\x0c" | Form '
'Feed |\n'
' '
'+-------------------------+-------------------------------+\n'
' | "\\x1c" | File '
'Separator |\n'
' '
'+-------------------------+-------------------------------+\n'
' | "\\x1d" | Group '
'Separator |\n'
' '
'+-------------------------+-------------------------------+\n'
' | "\\x1e" | Record '
'Separator |\n'
' '
'+-------------------------+-------------------------------+\n'
' | "\\x85" | Next Line (C1 Control '
'Code) |\n'
' '
'+-------------------------+-------------------------------+\n'
' | "\\u2028" | Line '
'Separator |\n'
' '
'+-------------------------+-------------------------------+\n'
' | "\\u2029" | Paragraph '
'Separator |\n'
' '
'+-------------------------+-------------------------------+\n'
'\n'
' Changed in version 3.2: "\\v" and "\\f" added to list '
'of line\n'
' boundaries.\n'
'\n'
' For example:\n'
'\n'
" >>> 'ab c\\n\\nde fg\\rkl\\r\\n'.splitlines()\n"
" ['ab c', '', 'de fg', 'kl']\n"
" >>> 'ab c\\n\\nde "
"fg\\rkl\\r\\n'.splitlines(keepends=True)\n"
" ['ab c\\n', '\\n', 'de fg\\r', 'kl\\r\\n']\n"
'\n'
' Unlike "split()" when a delimiter string *sep* is '
'given, this\n'
' method returns an empty list for the empty string, and '
'a terminal\n'
' line break does not result in an extra line:\n'
'\n'
' >>> "".splitlines()\n'
' []\n'
' >>> "One line\\n".splitlines()\n'
" ['One line']\n"
'\n'
' For comparison, "split(\'\\n\')" gives:\n'
'\n'
" >>> ''.split('\\n')\n"
" ['']\n"
" >>> 'Two lines\\n'.split('\\n')\n"
" ['Two lines', '']\n"
'\n'
'str.startswith(prefix[, start[, end]])\n'
'\n'
' Return "True" if string starts with the *prefix*, '
'otherwise return\n'
' "False". *prefix* can also be a tuple of prefixes to '
'look for.\n'
' With optional *start*, test string beginning at that '
'position.\n'
' With optional *end*, stop comparing string at that '
'position.\n'
'\n'
'str.strip([chars])\n'
'\n'
' Return a copy of the string with the leading and '
'trailing\n'
' characters removed. The *chars* argument is a string '
'specifying the\n'
' set of characters to be removed. If omitted or "None", '
'the *chars*\n'
' argument defaults to removing whitespace. The *chars* '
'argument is\n'
' not a prefix or suffix; rather, all combinations of its '
'values are\n'
' stripped:\n'
'\n'
" >>> ' spacious '.strip()\n"
" 'spacious'\n"
" >>> 'www.example.com'.strip('cmowz.')\n"
" 'example'\n"
'\n'
' The outermost leading and trailing *chars* argument '
'values are\n'
' stripped from the string. Characters are removed from '
'the leading\n'
' end until reaching a string character that is not '
'contained in the\n'
' set of characters in *chars*. A similar action takes '
'place on the\n'
' trailing end. For example:\n'
'\n'
" >>> comment_string = '#....... Section 3.2.1 Issue "
"#32 .......'\n"
" >>> comment_string.strip('.#! ')\n"
" 'Section 3.2.1 Issue #32'\n"
'\n'
'str.swapcase()\n'
'\n'
' Return a copy of the string with uppercase characters '
'converted to\n'
' lowercase and vice versa. Note that it is not '
'necessarily true that\n'
' "s.swapcase().swapcase() == s".\n'
'\n'
'str.title()\n'
'\n'
' Return a titlecased version of the string where words '
'start with an\n'
' uppercase character and the remaining characters are '
'lowercase.\n'
'\n'
' For example:\n'
'\n'
" >>> 'Hello world'.title()\n"
" 'Hello World'\n"
'\n'
' The algorithm uses a simple language-independent '
'definition of a\n'
' word as groups of consecutive letters. The definition '
'works in\n'
' many contexts but it means that apostrophes in '
'contractions and\n'
' possessives form word boundaries, which may not be the '
'desired\n'
' result:\n'
'\n'
' >>> "they\'re bill\'s friends from the UK".title()\n'
' "They\'Re Bill\'S Friends From The Uk"\n'
'\n'
' A workaround for apostrophes can be constructed using '
'regular\n'
' expressions:\n'
'\n'
' >>> import re\n'
' >>> def titlecase(s):\n'
' ... return re.sub(r"[A-Za-z]+(\'[A-Za-z]+)?",\n'
' ... lambda mo: '
'mo.group(0)[0].upper() +\n'
' ... '
'mo.group(0)[1:].lower(),\n'
' ... s)\n'
' ...\n'
' >>> titlecase("they\'re bill\'s friends.")\n'
' "They\'re Bill\'s Friends."\n'
'\n'
'str.translate(table)\n'
'\n'
' Return a copy of the string in which each character has '
'been mapped\n'
' through the given translation table. The table must be '
'an object\n'
' that implements indexing via "__getitem__()", typically '
'a *mapping*\n'
' or *sequence*. When indexed by a Unicode ordinal (an '
'integer), the\n'
' table object can do any of the following: return a '
'Unicode ordinal\n'
' or a string, to map the character to one or more other '
'characters;\n'
' return "None", to delete the character from the return '
'string; or\n'
' raise a "LookupError" exception, to map the character '
'to itself.\n'
'\n'
' You can use "str.maketrans()" to create a translation '
'map from\n'
' character-to-character mappings in different formats.\n'
'\n'
' See also the "codecs" module for a more flexible '
'approach to custom\n'
' character mappings.\n'
'\n'
'str.upper()\n'
'\n'
' Return a copy of the string with all the cased '
'characters [4]\n'
' converted to uppercase. Note that '
'"s.upper().isupper()" might be\n'
' "False" if "s" contains uncased characters or if the '
'Unicode\n'
' category of the resulting character(s) is not “Lu” '
'(Letter,\n'
' uppercase), but e.g. “Lt” (Letter, titlecase).\n'
'\n'
' The uppercasing algorithm used is described in section '
'3.13 of the\n'
' Unicode Standard.\n'
'\n'
'str.zfill(width)\n'
'\n'
' Return a copy of the string left filled with ASCII '
'"\'0\'" digits to\n'
' make a string of length *width*. A leading sign prefix\n'
' ("\'+\'"/"\'-\'") is handled by inserting the padding '
'*after* the sign\n'
' character rather than before. The original string is '
'returned if\n'
' *width* is less than or equal to "len(s)".\n'
'\n'
' For example:\n'
'\n'
' >>> "42".zfill(5)\n'
" '00042'\n"
' >>> "-42".zfill(5)\n'
" '-0042'\n",
'strings': 'String and Bytes literals\n'
'*************************\n'
'\n'
'String literals are described by the following lexical '
'definitions:\n'
'\n'
' stringliteral ::= [stringprefix](shortstring | longstring)\n'
' stringprefix ::= "r" | "u" | "R" | "U" | "f" | "F"\n'
' | "fr" | "Fr" | "fR" | "FR" | "rf" | "rF" | '
'"Rf" | "RF"\n'
' shortstring ::= "\'" shortstringitem* "\'" | \'"\' '
'shortstringitem* \'"\'\n'
' longstring ::= "\'\'\'" longstringitem* "\'\'\'" | '
'\'"""\' longstringitem* \'"""\'\n'
' shortstringitem ::= shortstringchar | stringescapeseq\n'
' longstringitem ::= longstringchar | stringescapeseq\n'
' shortstringchar ::= <any source character except "\\" or '
'newline or the quote>\n'
' longstringchar ::= <any source character except "\\">\n'
' stringescapeseq ::= "\\" <any source character>\n'
'\n'
' bytesliteral ::= bytesprefix(shortbytes | longbytes)\n'
' bytesprefix ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | '
'"rb" | "rB" | "Rb" | "RB"\n'
' shortbytes ::= "\'" shortbytesitem* "\'" | \'"\' '
'shortbytesitem* \'"\'\n'
' longbytes ::= "\'\'\'" longbytesitem* "\'\'\'" | \'"""\' '
'longbytesitem* \'"""\'\n'
' shortbytesitem ::= shortbyteschar | bytesescapeseq\n'
' longbytesitem ::= longbyteschar | bytesescapeseq\n'
' shortbyteschar ::= <any ASCII character except "\\" or newline '
'or the quote>\n'
' longbyteschar ::= <any ASCII character except "\\">\n'
' bytesescapeseq ::= "\\" <any ASCII character>\n'
'\n'
'One syntactic restriction not indicated by these productions is '
'that\n'
'whitespace is not allowed between the "stringprefix" or '
'"bytesprefix"\n'
'and the rest of the literal. The source character set is defined '
'by\n'
'the encoding declaration; it is UTF-8 if no encoding declaration '
'is\n'
'given in the source file; see section Encoding declarations.\n'
'\n'
'In plain English: Both types of literals can be enclosed in '
'matching\n'
'single quotes ("\'") or double quotes ("""). They can also be '
'enclosed\n'
'in matching groups of three single or double quotes (these are\n'
'generally referred to as *triple-quoted strings*). The '
'backslash\n'
'("\\") character is used to escape characters that otherwise have '
'a\n'
'special meaning, such as newline, backslash itself, or the quote\n'
'character.\n'
'\n'
'Bytes literals are always prefixed with "\'b\'" or "\'B\'"; they '
'produce\n'
'an instance of the "bytes" type instead of the "str" type. They '
'may\n'
'only contain ASCII characters; bytes with a numeric value of 128 '
'or\n'
'greater must be expressed with escapes.\n'
'\n'
'Both string and bytes literals may optionally be prefixed with a\n'
'letter "\'r\'" or "\'R\'"; such strings are called *raw strings* '
'and treat\n'
'backslashes as literal characters. As a result, in string '
'literals,\n'
'"\'\\U\'" and "\'\\u\'" escapes in raw strings are not treated '
'specially.\n'
'Given that Python 2.x’s raw unicode literals behave differently '
'than\n'
'Python 3.x’s the "\'ur\'" syntax is not supported.\n'
'\n'
'New in version 3.3: The "\'rb\'" prefix of raw bytes literals has '
'been\n'
'added as a synonym of "\'br\'".\n'
'\n'
'New in version 3.3: Support for the unicode legacy literal\n'
'("u\'value\'") was reintroduced to simplify the maintenance of '
'dual\n'
'Python 2.x and 3.x codebases. See **PEP 414** for more '
'information.\n'
'\n'
'A string literal with "\'f\'" or "\'F\'" in its prefix is a '
'*formatted\n'
'string literal*; see Formatted string literals. The "\'f\'" may '
'be\n'
'combined with "\'r\'", but not with "\'b\'" or "\'u\'", therefore '
'raw\n'
'formatted strings are possible, but formatted bytes literals are '
'not.\n'
'\n'
'In triple-quoted literals, unescaped newlines and quotes are '
'allowed\n'
'(and are retained), except that three unescaped quotes in a row\n'
'terminate the literal. (A “quote” is the character used to open '
'the\n'
'literal, i.e. either "\'" or """.)\n'
'\n'
'Unless an "\'r\'" or "\'R\'" prefix is present, escape sequences '
'in string\n'
'and bytes literals are interpreted according to rules similar to '
'those\n'
'used by Standard C. The recognized escape sequences are:\n'
'\n'
'+-------------------+-----------------------------------+---------+\n'
'| Escape Sequence | Meaning | Notes '
'|\n'
'+===================+===================================+=========+\n'
'| "\\newline" | Backslash and newline ignored '
'| |\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\\\" | Backslash ("\\") '
'| |\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\\'" | Single quote ("\'") '
'| |\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\"" | Double quote (""") '
'| |\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\a" | ASCII Bell (BEL) '
'| |\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\b" | ASCII Backspace (BS) '
'| |\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\f" | ASCII Formfeed (FF) '
'| |\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\n" | ASCII Linefeed (LF) '
'| |\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\r" | ASCII Carriage Return (CR) '
'| |\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\t" | ASCII Horizontal Tab (TAB) '
'| |\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\v" | ASCII Vertical Tab (VT) '
'| |\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\ooo" | Character with octal value *ooo* | '
'(1,3) |\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\xhh" | Character with hex value *hh* | '
'(2,3) |\n'
'+-------------------+-----------------------------------+---------+\n'
'\n'
'Escape sequences only recognized in string literals are:\n'
'\n'
'+-------------------+-----------------------------------+---------+\n'
'| Escape Sequence | Meaning | Notes '
'|\n'
'+===================+===================================+=========+\n'
'| "\\N{name}" | Character named *name* in the | '
'(4) |\n'
'| | Unicode database | '
'|\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\uxxxx" | Character with 16-bit hex value | '
'(5) |\n'
'| | *xxxx* | '
'|\n'
'+-------------------+-----------------------------------+---------+\n'
'| "\\Uxxxxxxxx" | Character with 32-bit hex value | '
'(6) |\n'
'| | *xxxxxxxx* | '
'|\n'
'+-------------------+-----------------------------------+---------+\n'
'\n'
'Notes:\n'
'\n'
'1. As in Standard C, up to three octal digits are accepted.\n'
'\n'
'2. Unlike in Standard C, exactly two hex digits are required.\n'
'\n'
'3. In a bytes literal, hexadecimal and octal escapes denote the\n'
' byte with the given value. In a string literal, these escapes\n'
' denote a Unicode character with the given value.\n'
'\n'
'4. Changed in version 3.3: Support for name aliases [1] has been\n'
' added.\n'
'\n'
'5. Exactly four hex digits are required.\n'
'\n'
'6. Any Unicode character can be encoded this way. Exactly eight\n'
' hex digits are required.\n'
'\n'
'Unlike Standard C, all unrecognized escape sequences are left in '
'the\n'
'string unchanged, i.e., *the backslash is left in the result*. '
'(This\n'
'behavior is useful when debugging: if an escape sequence is '
'mistyped,\n'
'the resulting output is more easily recognized as broken.) It is '
'also\n'
'important to note that the escape sequences only recognized in '
'string\n'
'literals fall into the category of unrecognized escapes for '
'bytes\n'
'literals.\n'
'\n'
' Changed in version 3.6: Unrecognized escape sequences produce '
'a\n'
' DeprecationWarning. In some future version of Python they '
'will be\n'
' a SyntaxError.\n'
'\n'
'Even in a raw literal, quotes can be escaped with a backslash, '
'but the\n'
'backslash remains in the result; for example, "r"\\""" is a '
'valid\n'
'string literal consisting of two characters: a backslash and a '
'double\n'
'quote; "r"\\"" is not a valid string literal (even a raw string '
'cannot\n'
'end in an odd number of backslashes). Specifically, *a raw '
'literal\n'
'cannot end in a single backslash* (since the backslash would '
'escape\n'
'the following quote character). Note also that a single '
'backslash\n'
'followed by a newline is interpreted as those two characters as '
'part\n'
'of the literal, *not* as a line continuation.\n',
'subscriptions': 'Subscriptions\n'
'*************\n'
'\n'
'A subscription selects an item of a sequence (string, tuple '
'or list)\n'
'or mapping (dictionary) object:\n'
'\n'
' subscription ::= primary "[" expression_list "]"\n'
'\n'
'The primary must evaluate to an object that supports '
'subscription\n'
'(lists or dictionaries for example). User-defined objects '
'can support\n'
'subscription by defining a "__getitem__()" method.\n'
'\n'
'For built-in objects, there are two types of objects that '
'support\n'
'subscription:\n'
'\n'
'If the primary is a mapping, the expression list must '
'evaluate to an\n'
'object whose value is one of the keys of the mapping, and '
'the\n'
'subscription selects the value in the mapping that '
'corresponds to that\n'
'key. (The expression list is a tuple except if it has '
'exactly one\n'
'item.)\n'
'\n'
'If the primary is a sequence, the expression list must '
'evaluate to an\n'
'integer or a slice (as discussed in the following '
'section).\n'
'\n'
'The formal syntax makes no special provision for negative '
'indices in\n'
'sequences; however, built-in sequences all provide a '
'"__getitem__()"\n'
'method that interprets negative indices by adding the '
'length of the\n'
'sequence to the index (so that "x[-1]" selects the last '
'item of "x").\n'
'The resulting value must be a nonnegative integer less than '
'the number\n'
'of items in the sequence, and the subscription selects the '
'item whose\n'
'index is that value (counting from zero). Since the support '
'for\n'
'negative indices and slicing occurs in the object’s '
'"__getitem__()"\n'
'method, subclasses overriding this method will need to '
'explicitly add\n'
'that support.\n'
'\n'
'A string’s items are characters. A character is not a '
'separate data\n'
'type but a string of exactly one character.\n',
'truth': 'Truth Value Testing\n'
'*******************\n'
'\n'
'Any object can be tested for truth value, for use in an "if" or\n'
'"while" condition or as operand of the Boolean operations below.\n'
'\n'
'By default, an object is considered true unless its class defines\n'
'either a "__bool__()" method that returns "False" or a "__len__()"\n'
'method that returns zero, when called with the object. [1] Here '
'are\n'
'most of the built-in objects considered false:\n'
'\n'
'* constants defined to be false: "None" and "False".\n'
'\n'
'* zero of any numeric type: "0", "0.0", "0j", "Decimal(0)",\n'
' "Fraction(0, 1)"\n'
'\n'
'* empty sequences and collections: "\'\'", "()", "[]", "{}", '
'"set()",\n'
' "range(0)"\n'
'\n'
'Operations and built-in functions that have a Boolean result '
'always\n'
'return "0" or "False" for false and "1" or "True" for true, unless\n'
'otherwise stated. (Important exception: the Boolean operations '
'"or"\n'
'and "and" always return one of their operands.)\n',
'try': 'The "try" statement\n'
'*******************\n'
'\n'
'The "try" statement specifies exception handlers and/or cleanup code\n'
'for a group of statements:\n'
'\n'
' try_stmt ::= try1_stmt | try2_stmt\n'
' try1_stmt ::= "try" ":" suite\n'
' ("except" [expression ["as" identifier]] ":" '
'suite)+\n'
' ["else" ":" suite]\n'
' ["finally" ":" suite]\n'
' try2_stmt ::= "try" ":" suite\n'
' "finally" ":" suite\n'
'\n'
'The "except" clause(s) specify one or more exception handlers. When '
'no\n'
'exception occurs in the "try" clause, no exception handler is\n'
'executed. When an exception occurs in the "try" suite, a search for '
'an\n'
'exception handler is started. This search inspects the except '
'clauses\n'
'in turn until one is found that matches the exception. An '
'expression-\n'
'less except clause, if present, must be last; it matches any\n'
'exception. For an except clause with an expression, that expression\n'
'is evaluated, and the clause matches the exception if the resulting\n'
'object is “compatible” with the exception. An object is compatible\n'
'with an exception if it is the class or a base class of the '
'exception\n'
'object or a tuple containing an item compatible with the exception.\n'
'\n'
'If no except clause matches the exception, the search for an '
'exception\n'
'handler continues in the surrounding code and on the invocation '
'stack.\n'
'[1]\n'
'\n'
'If the evaluation of an expression in the header of an except clause\n'
'raises an exception, the original search for a handler is canceled '
'and\n'
'a search starts for the new exception in the surrounding code and on\n'
'the call stack (it is treated as if the entire "try" statement '
'raised\n'
'the exception).\n'
'\n'
'When a matching except clause is found, the exception is assigned to\n'
'the target specified after the "as" keyword in that except clause, '
'if\n'
'present, and the except clause’s suite is executed. All except\n'
'clauses must have an executable block. When the end of this block '
'is\n'
'reached, execution continues normally after the entire try '
'statement.\n'
'(This means that if two nested handlers exist for the same '
'exception,\n'
'and the exception occurs in the try clause of the inner handler, the\n'
'outer handler will not handle the exception.)\n'
'\n'
'When an exception has been assigned using "as target", it is cleared\n'
'at the end of the except clause. This is as if\n'
'\n'
' except E as N:\n'
' foo\n'
'\n'
'was translated to\n'
'\n'
' except E as N:\n'
' try:\n'
' foo\n'
' finally:\n'
' del N\n'
'\n'
'This means the exception must be assigned to a different name to be\n'
'able to refer to it after the except clause. Exceptions are cleared\n'
'because with the traceback attached to them, they form a reference\n'
'cycle with the stack frame, keeping all locals in that frame alive\n'
'until the next garbage collection occurs.\n'
'\n'
'Before an except clause’s suite is executed, details about the\n'
'exception are stored in the "sys" module and can be accessed via\n'
'"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of '
'the\n'
'exception class, the exception instance and a traceback object (see\n'
'section The standard type hierarchy) identifying the point in the\n'
'program where the exception occurred. "sys.exc_info()" values are\n'
'restored to their previous values (before the call) when returning\n'
'from a function that handled an exception.\n'
'\n'
'The optional "else" clause is executed if the control flow leaves '
'the\n'
'"try" suite, no exception was raised, and no "return", "continue", '
'or\n'
'"break" statement was executed. Exceptions in the "else" clause are\n'
'not handled by the preceding "except" clauses.\n'
'\n'
'If "finally" is present, it specifies a ‘cleanup’ handler. The '
'"try"\n'
'clause is executed, including any "except" and "else" clauses. If '
'an\n'
'exception occurs in any of the clauses and is not handled, the\n'
'exception is temporarily saved. The "finally" clause is executed. '
'If\n'
'there is a saved exception it is re-raised at the end of the '
'"finally"\n'
'clause. If the "finally" clause raises another exception, the saved\n'
'exception is set as the context of the new exception. If the '
'"finally"\n'
'clause executes a "return" or "break" statement, the saved exception\n'
'is discarded:\n'
'\n'
' >>> def f():\n'
' ... try:\n'
' ... 1/0\n'
' ... finally:\n'
' ... return 42\n'
' ...\n'
' >>> f()\n'
' 42\n'
'\n'
'The exception information is not available to the program during\n'
'execution of the "finally" clause.\n'
'\n'
'When a "return", "break" or "continue" statement is executed in the\n'
'"try" suite of a "try"…"finally" statement, the "finally" clause is\n'
'also executed ‘on the way out.’ A "continue" statement is illegal in\n'
'the "finally" clause. (The reason is a problem with the current\n'
'implementation — this restriction may be lifted in the future).\n'
'\n'
'The return value of a function is determined by the last "return"\n'
'statement executed. Since the "finally" clause always executes, a\n'
'"return" statement executed in the "finally" clause will always be '
'the\n'
'last one executed:\n'
'\n'
' >>> def foo():\n'
' ... try:\n'
" ... return 'try'\n"
' ... finally:\n'
" ... return 'finally'\n"
' ...\n'
' >>> foo()\n'
" 'finally'\n"
'\n'
'Additional information on exceptions can be found in section\n'
'Exceptions, and information on using the "raise" statement to '
'generate\n'
'exceptions may be found in section The raise statement.\n',
'types': 'The standard type hierarchy\n'
'***************************\n'
'\n'
'Below is a list of the types that are built into Python. '
'Extension\n'
'modules (written in C, Java, or other languages, depending on the\n'
'implementation) can define additional types. Future versions of\n'
'Python may add types to the type hierarchy (e.g., rational '
'numbers,\n'
'efficiently stored arrays of integers, etc.), although such '
'additions\n'
'will often be provided via the standard library instead.\n'
'\n'
'Some of the type descriptions below contain a paragraph listing\n'
'‘special attributes.’ These are attributes that provide access to '
'the\n'
'implementation and are not intended for general use. Their '
'definition\n'
'may change in the future.\n'
'\n'
'None\n'
' This type has a single value. There is a single object with '
'this\n'
' value. This object is accessed through the built-in name "None". '
'It\n'
' is used to signify the absence of a value in many situations, '
'e.g.,\n'
' it is returned from functions that don’t explicitly return\n'
' anything. Its truth value is false.\n'
'\n'
'NotImplemented\n'
' This type has a single value. There is a single object with '
'this\n'
' value. This object is accessed through the built-in name\n'
' "NotImplemented". Numeric methods and rich comparison methods\n'
' should return this value if they do not implement the operation '
'for\n'
' the operands provided. (The interpreter will then try the\n'
' reflected operation, or some other fallback, depending on the\n'
' operator.) Its truth value is true.\n'
'\n'
' See Implementing the arithmetic operations for more details.\n'
'\n'
'Ellipsis\n'
' This type has a single value. There is a single object with '
'this\n'
' value. This object is accessed through the literal "..." or the\n'
' built-in name "Ellipsis". Its truth value is true.\n'
'\n'
'"numbers.Number"\n'
' These are created by numeric literals and returned as results '
'by\n'
' arithmetic operators and arithmetic built-in functions. '
'Numeric\n'
' objects are immutable; once created their value never changes.\n'
' Python numbers are of course strongly related to mathematical\n'
' numbers, but subject to the limitations of numerical '
'representation\n'
' in computers.\n'
'\n'
' Python distinguishes between integers, floating point numbers, '
'and\n'
' complex numbers:\n'
'\n'
' "numbers.Integral"\n'
' These represent elements from the mathematical set of '
'integers\n'
' (positive and negative).\n'
'\n'
' There are two types of integers:\n'
'\n'
' Integers ("int")\n'
'\n'
' These represent numbers in an unlimited range, subject to\n'
' available (virtual) memory only. For the purpose of '
'shift\n'
' and mask operations, a binary representation is assumed, '
'and\n'
' negative numbers are represented in a variant of 2’s\n'
' complement which gives the illusion of an infinite string '
'of\n'
' sign bits extending to the left.\n'
'\n'
' Booleans ("bool")\n'
' These represent the truth values False and True. The two\n'
' objects representing the values "False" and "True" are '
'the\n'
' only Boolean objects. The Boolean type is a subtype of '
'the\n'
' integer type, and Boolean values behave like the values 0 '
'and\n'
' 1, respectively, in almost all contexts, the exception '
'being\n'
' that when converted to a string, the strings ""False"" or\n'
' ""True"" are returned, respectively.\n'
'\n'
' The rules for integer representation are intended to give '
'the\n'
' most meaningful interpretation of shift and mask operations\n'
' involving negative integers.\n'
'\n'
' "numbers.Real" ("float")\n'
' These represent machine-level double precision floating '
'point\n'
' numbers. You are at the mercy of the underlying machine\n'
' architecture (and C or Java implementation) for the accepted\n'
' range and handling of overflow. Python does not support '
'single-\n'
' precision floating point numbers; the savings in processor '
'and\n'
' memory usage that are usually the reason for using these are\n'
' dwarfed by the overhead of using objects in Python, so there '
'is\n'
' no reason to complicate the language with two kinds of '
'floating\n'
' point numbers.\n'
'\n'
' "numbers.Complex" ("complex")\n'
' These represent complex numbers as a pair of machine-level\n'
' double precision floating point numbers. The same caveats '
'apply\n'
' as for floating point numbers. The real and imaginary parts '
'of a\n'
' complex number "z" can be retrieved through the read-only\n'
' attributes "z.real" and "z.imag".\n'
'\n'
'Sequences\n'
' These represent finite ordered sets indexed by non-negative\n'
' numbers. The built-in function "len()" returns the number of '
'items\n'
' of a sequence. When the length of a sequence is *n*, the index '
'set\n'
' contains the numbers 0, 1, …, *n*-1. Item *i* of sequence *a* '
'is\n'
' selected by "a[i]".\n'
'\n'
' Sequences also support slicing: "a[i:j]" selects all items with\n'
' index *k* such that *i* "<=" *k* "<" *j*. When used as an\n'
' expression, a slice is a sequence of the same type. This '
'implies\n'
' that the index set is renumbered so that it starts at 0.\n'
'\n'
' Some sequences also support “extended slicing” with a third '
'“step”\n'
' parameter: "a[i:j:k]" selects all items of *a* with index *x* '
'where\n'
' "x = i + n*k", *n* ">=" "0" and *i* "<=" *x* "<" *j*.\n'
'\n'
' Sequences are distinguished according to their mutability:\n'
'\n'
' Immutable sequences\n'
' An object of an immutable sequence type cannot change once it '
'is\n'
' created. (If the object contains references to other '
'objects,\n'
' these other objects may be mutable and may be changed; '
'however,\n'
' the collection of objects directly referenced by an '
'immutable\n'
' object cannot change.)\n'
'\n'
' The following types are immutable sequences:\n'
'\n'
' Strings\n'
' A string is a sequence of values that represent Unicode '
'code\n'
' points. All the code points in the range "U+0000 - '
'U+10FFFF"\n'
' can be represented in a string. Python doesn’t have a '
'"char"\n'
' type; instead, every code point in the string is '
'represented\n'
' as a string object with length "1". The built-in '
'function\n'
' "ord()" converts a code point from its string form to an\n'
' integer in the range "0 - 10FFFF"; "chr()" converts an\n'
' integer in the range "0 - 10FFFF" to the corresponding '
'length\n'
' "1" string object. "str.encode()" can be used to convert '
'a\n'
' "str" to "bytes" using the given text encoding, and\n'
' "bytes.decode()" can be used to achieve the opposite.\n'
'\n'
' Tuples\n'
' The items of a tuple are arbitrary Python objects. Tuples '
'of\n'
' two or more items are formed by comma-separated lists of\n'
' expressions. A tuple of one item (a ‘singleton’) can be\n'
' formed by affixing a comma to an expression (an expression '
'by\n'
' itself does not create a tuple, since parentheses must be\n'
' usable for grouping of expressions). An empty tuple can '
'be\n'
' formed by an empty pair of parentheses.\n'
'\n'
' Bytes\n'
' A bytes object is an immutable array. The items are '
'8-bit\n'
' bytes, represented by integers in the range 0 <= x < 256.\n'
' Bytes literals (like "b\'abc\'") and the built-in '
'"bytes()"\n'
' constructor can be used to create bytes objects. Also, '
'bytes\n'
' objects can be decoded to strings via the "decode()" '
'method.\n'
'\n'
' Mutable sequences\n'
' Mutable sequences can be changed after they are created. '
'The\n'
' subscription and slicing notations can be used as the target '
'of\n'
' assignment and "del" (delete) statements.\n'
'\n'
' There are currently two intrinsic mutable sequence types:\n'
'\n'
' Lists\n'
' The items of a list are arbitrary Python objects. Lists '
'are\n'
' formed by placing a comma-separated list of expressions '
'in\n'
' square brackets. (Note that there are no special cases '
'needed\n'
' to form lists of length 0 or 1.)\n'
'\n'
' Byte Arrays\n'
' A bytearray object is a mutable array. They are created '
'by\n'
' the built-in "bytearray()" constructor. Aside from being\n'
' mutable (and hence unhashable), byte arrays otherwise '
'provide\n'
' the same interface and functionality as immutable "bytes"\n'
' objects.\n'
'\n'
' The extension module "array" provides an additional example '
'of a\n'
' mutable sequence type, as does the "collections" module.\n'
'\n'
'Set types\n'
' These represent unordered, finite sets of unique, immutable\n'
' objects. As such, they cannot be indexed by any subscript. '
'However,\n'
' they can be iterated over, and the built-in function "len()"\n'
' returns the number of items in a set. Common uses for sets are '
'fast\n'
' membership testing, removing duplicates from a sequence, and\n'
' computing mathematical operations such as intersection, union,\n'
' difference, and symmetric difference.\n'
'\n'
' For set elements, the same immutability rules apply as for\n'
' dictionary keys. Note that numeric types obey the normal rules '
'for\n'
' numeric comparison: if two numbers compare equal (e.g., "1" and\n'
' "1.0"), only one of them can be contained in a set.\n'
'\n'
' There are currently two intrinsic set types:\n'
'\n'
' Sets\n'
' These represent a mutable set. They are created by the '
'built-in\n'
' "set()" constructor and can be modified afterwards by '
'several\n'
' methods, such as "add()".\n'
'\n'
' Frozen sets\n'
' These represent an immutable set. They are created by the\n'
' built-in "frozenset()" constructor. As a frozenset is '
'immutable\n'
' and *hashable*, it can be used again as an element of '
'another\n'
' set, or as a dictionary key.\n'
'\n'
'Mappings\n'
' These represent finite sets of objects indexed by arbitrary '
'index\n'
' sets. The subscript notation "a[k]" selects the item indexed by '
'"k"\n'
' from the mapping "a"; this can be used in expressions and as '
'the\n'
' target of assignments or "del" statements. The built-in '
'function\n'
' "len()" returns the number of items in a mapping.\n'
'\n'
' There is currently a single intrinsic mapping type:\n'
'\n'
' Dictionaries\n'
' These represent finite sets of objects indexed by nearly\n'
' arbitrary values. The only types of values not acceptable '
'as\n'
' keys are values containing lists or dictionaries or other\n'
' mutable types that are compared by value rather than by '
'object\n'
' identity, the reason being that the efficient implementation '
'of\n'
' dictionaries requires a key’s hash value to remain constant.\n'
' Numeric types used for keys obey the normal rules for '
'numeric\n'
' comparison: if two numbers compare equal (e.g., "1" and '
'"1.0")\n'
' then they can be used interchangeably to index the same\n'
' dictionary entry.\n'
'\n'
' Dictionaries are mutable; they can be created by the "{...}"\n'
' notation (see section Dictionary displays).\n'
'\n'
' The extension modules "dbm.ndbm" and "dbm.gnu" provide\n'
' additional examples of mapping types, as does the '
'"collections"\n'
' module.\n'
'\n'
'Callable types\n'
' These are the types to which the function call operation (see\n'
' section Calls) can be applied:\n'
'\n'
' User-defined functions\n'
' A user-defined function object is created by a function\n'
' definition (see section Function definitions). It should be\n'
' called with an argument list containing the same number of '
'items\n'
' as the function’s formal parameter list.\n'
'\n'
' Special attributes:\n'
'\n'
' '
'+---------------------------+---------------------------------+-------------+\n'
' | Attribute | Meaning '
'| |\n'
' '
'+===========================+=================================+=============+\n'
' | "__doc__" | The function’s documentation '
'| Writable |\n'
' | | string, or "None" if '
'| |\n'
' | | unavailable; not inherited by '
'| |\n'
' | | subclasses '
'| |\n'
' '
'+---------------------------+---------------------------------+-------------+\n'
' | "__name__" | The function’s name '
'| Writable |\n'
' '
'+---------------------------+---------------------------------+-------------+\n'
' | "__qualname__" | The function’s *qualified name* '
'| Writable |\n'
' | | New in version 3.3. '
'| |\n'
' '
'+---------------------------+---------------------------------+-------------+\n'
' | "__module__" | The name of the module the '
'| Writable |\n'
' | | function was defined in, or '
'| |\n'
' | | "None" if unavailable. '
'| |\n'
' '
'+---------------------------+---------------------------------+-------------+\n'
' | "__defaults__" | A tuple containing default '
'| Writable |\n'
' | | argument values for those '
'| |\n'
' | | arguments that have defaults, '
'| |\n'
' | | or "None" if no arguments have '
'| |\n'
' | | a default value '
'| |\n'
' '
'+---------------------------+---------------------------------+-------------+\n'
' | "__code__" | The code object representing '
'| Writable |\n'
' | | the compiled function body. '
'| |\n'
' '
'+---------------------------+---------------------------------+-------------+\n'
' | "__globals__" | A reference to the dictionary '
'| Read-only |\n'
' | | that holds the function’s '
'| |\n'
' | | global variables — the global '
'| |\n'
' | | namespace of the module in '
'| |\n'
' | | which the function was defined. '
'| |\n'
' '
'+---------------------------+---------------------------------+-------------+\n'
' | "__dict__" | The namespace supporting '
'| Writable |\n'
' | | arbitrary function attributes. '
'| |\n'
' '
'+---------------------------+---------------------------------+-------------+\n'
' | "__closure__" | "None" or a tuple of cells that '
'| Read-only |\n'
' | | contain bindings for the '
'| |\n'
' | | function’s free variables. '
'| |\n'
' '
'+---------------------------+---------------------------------+-------------+\n'
' | "__annotations__" | A dict containing annotations '
'| Writable |\n'
' | | of parameters. The keys of the '
'| |\n'
' | | dict are the parameter names, '
'| |\n'
' | | and "\'return\'" for the '
'return | |\n'
' | | annotation, if provided. '
'| |\n'
' '
'+---------------------------+---------------------------------+-------------+\n'
' | "__kwdefaults__" | A dict containing defaults for '
'| Writable |\n'
' | | keyword-only parameters. '
'| |\n'
' '
'+---------------------------+---------------------------------+-------------+\n'
'\n'
' Most of the attributes labelled “Writable” check the type of '
'the\n'
' assigned value.\n'
'\n'
' Function objects also support getting and setting arbitrary\n'
' attributes, which can be used, for example, to attach '
'metadata\n'
' to functions. Regular attribute dot-notation is used to get '
'and\n'
' set such attributes. *Note that the current implementation '
'only\n'
' supports function attributes on user-defined functions. '
'Function\n'
' attributes on built-in functions may be supported in the\n'
' future.*\n'
'\n'
' Additional information about a function’s definition can be\n'
' retrieved from its code object; see the description of '
'internal\n'
' types below.\n'
'\n'
' Instance methods\n'
' An instance method object combines a class, a class instance '
'and\n'
' any callable object (normally a user-defined function).\n'
'\n'
' Special read-only attributes: "__self__" is the class '
'instance\n'
' object, "__func__" is the function object; "__doc__" is the\n'
' method’s documentation (same as "__func__.__doc__"); '
'"__name__"\n'
' is the method name (same as "__func__.__name__"); '
'"__module__"\n'
' is the name of the module the method was defined in, or '
'"None"\n'
' if unavailable.\n'
'\n'
' Methods also support accessing (but not setting) the '
'arbitrary\n'
' function attributes on the underlying function object.\n'
'\n'
' User-defined method objects may be created when getting an\n'
' attribute of a class (perhaps via an instance of that class), '
'if\n'
' that attribute is a user-defined function object or a class\n'
' method object.\n'
'\n'
' When an instance method object is created by retrieving a '
'user-\n'
' defined function object from a class via one of its '
'instances,\n'
' its "__self__" attribute is the instance, and the method '
'object\n'
' is said to be bound. The new method’s "__func__" attribute '
'is\n'
' the original function object.\n'
'\n'
' When a user-defined method object is created by retrieving\n'
' another method object from a class or instance, the behaviour '
'is\n'
' the same as for a function object, except that the '
'"__func__"\n'
' attribute of the new instance is not the original method '
'object\n'
' but its "__func__" attribute.\n'
'\n'
' When an instance method object is created by retrieving a '
'class\n'
' method object from a class or instance, its "__self__" '
'attribute\n'
' is the class itself, and its "__func__" attribute is the\n'
' function object underlying the class method.\n'
'\n'
' When an instance method object is called, the underlying\n'
' function ("__func__") is called, inserting the class '
'instance\n'
' ("__self__") in front of the argument list. For instance, '
'when\n'
' "C" is a class which contains a definition for a function '
'"f()",\n'
' and "x" is an instance of "C", calling "x.f(1)" is equivalent '
'to\n'
' calling "C.f(x, 1)".\n'
'\n'
' When an instance method object is derived from a class '
'method\n'
' object, the “class instance” stored in "__self__" will '
'actually\n'
' be the class itself, so that calling either "x.f(1)" or '
'"C.f(1)"\n'
' is equivalent to calling "f(C,1)" where "f" is the '
'underlying\n'
' function.\n'
'\n'
' Note that the transformation from function object to '
'instance\n'
' method object happens each time the attribute is retrieved '
'from\n'
' the instance. In some cases, a fruitful optimization is to\n'
' assign the attribute to a local variable and call that local\n'
' variable. Also notice that this transformation only happens '
'for\n'
' user-defined functions; other callable objects (and all non-\n'
' callable objects) are retrieved without transformation. It '
'is\n'
' also important to note that user-defined functions which are\n'
' attributes of a class instance are not converted to bound\n'
' methods; this *only* happens when the function is an '
'attribute\n'
' of the class.\n'
'\n'
' Generator functions\n'
' A function or method which uses the "yield" statement (see\n'
' section The yield statement) is called a *generator '
'function*.\n'
' Such a function, when called, always returns an iterator '
'object\n'
' which can be used to execute the body of the function: '
'calling\n'
' the iterator’s "iterator.__next__()" method will cause the\n'
' function to execute until it provides a value using the '
'"yield"\n'
' statement. When the function executes a "return" statement '
'or\n'
' falls off the end, a "StopIteration" exception is raised and '
'the\n'
' iterator will have reached the end of the set of values to '
'be\n'
' returned.\n'
'\n'
' Coroutine functions\n'
' A function or method which is defined using "async def" is\n'
' called a *coroutine function*. Such a function, when '
'called,\n'
' returns a *coroutine* object. It may contain "await"\n'
' expressions, as well as "async with" and "async for" '
'statements.\n'
' See also the Coroutine Objects section.\n'
'\n'
' Asynchronous generator functions\n'
' A function or method which is defined using "async def" and\n'
' which uses the "yield" statement is called a *asynchronous\n'
' generator function*. Such a function, when called, returns '
'an\n'
' asynchronous iterator object which can be used in an "async '
'for"\n'
' statement to execute the body of the function.\n'
'\n'
' Calling the asynchronous iterator’s "aiterator.__anext__()"\n'
' method will return an *awaitable* which when awaited will\n'
' execute until it provides a value using the "yield" '
'expression.\n'
' When the function executes an empty "return" statement or '
'falls\n'
' off the end, a "StopAsyncIteration" exception is raised and '
'the\n'
' asynchronous iterator will have reached the end of the set '
'of\n'
' values to be yielded.\n'
'\n'
' Built-in functions\n'
' A built-in function object is a wrapper around a C function.\n'
' Examples of built-in functions are "len()" and "math.sin()"\n'
' ("math" is a standard built-in module). The number and type '
'of\n'
' the arguments are determined by the C function. Special '
'read-\n'
' only attributes: "__doc__" is the function’s documentation\n'
' string, or "None" if unavailable; "__name__" is the '
'function’s\n'
' name; "__self__" is set to "None" (but see the next item);\n'
' "__module__" is the name of the module the function was '
'defined\n'
' in or "None" if unavailable.\n'
'\n'
' Built-in methods\n'
' This is really a different disguise of a built-in function, '
'this\n'
' time containing an object passed to the C function as an\n'
' implicit extra argument. An example of a built-in method is\n'
' "alist.append()", assuming *alist* is a list object. In this\n'
' case, the special read-only attribute "__self__" is set to '
'the\n'
' object denoted by *alist*.\n'
'\n'
' Classes\n'
' Classes are callable. These objects normally act as '
'factories\n'
' for new instances of themselves, but variations are possible '
'for\n'
' class types that override "__new__()". The arguments of the\n'
' call are passed to "__new__()" and, in the typical case, to\n'
' "__init__()" to initialize the new instance.\n'
'\n'
' Class Instances\n'
' Instances of arbitrary classes can be made callable by '
'defining\n'
' a "__call__()" method in their class.\n'
'\n'
'Modules\n'
' Modules are a basic organizational unit of Python code, and are\n'
' created by the import system as invoked either by the "import"\n'
' statement (see "import"), or by calling functions such as\n'
' "importlib.import_module()" and built-in "__import__()". A '
'module\n'
' object has a namespace implemented by a dictionary object (this '
'is\n'
' the dictionary referenced by the "__globals__" attribute of\n'
' functions defined in the module). Attribute references are\n'
' translated to lookups in this dictionary, e.g., "m.x" is '
'equivalent\n'
' to "m.__dict__["x"]". A module object does not contain the code\n'
' object used to initialize the module (since it isn’t needed '
'once\n'
' the initialization is done).\n'
'\n'
' Attribute assignment updates the module’s namespace dictionary,\n'
' e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".\n'
'\n'
' Predefined (writable) attributes: "__name__" is the module’s '
'name;\n'
' "__doc__" is the module’s documentation string, or "None" if\n'
' unavailable; "__annotations__" (optional) is a dictionary\n'
' containing *variable annotations* collected during module body\n'
' execution; "__file__" is the pathname of the file from which '
'the\n'
' module was loaded, if it was loaded from a file. The "__file__"\n'
' attribute may be missing for certain types of modules, such as '
'C\n'
' modules that are statically linked into the interpreter; for\n'
' extension modules loaded dynamically from a shared library, it '
'is\n'
' the pathname of the shared library file.\n'
'\n'
' Special read-only attribute: "__dict__" is the module’s '
'namespace\n'
' as a dictionary object.\n'
'\n'
' **CPython implementation detail:** Because of the way CPython\n'
' clears module dictionaries, the module dictionary will be '
'cleared\n'
' when the module falls out of scope even if the dictionary still '
'has\n'
' live references. To avoid this, copy the dictionary or keep '
'the\n'
' module around while using its dictionary directly.\n'
'\n'
'Custom classes\n'
' Custom class types are typically created by class definitions '
'(see\n'
' section Class definitions). A class has a namespace implemented '
'by\n'
' a dictionary object. Class attribute references are translated '
'to\n'
' lookups in this dictionary, e.g., "C.x" is translated to\n'
' "C.__dict__["x"]" (although there are a number of hooks which '
'allow\n'
' for other means of locating attributes). When the attribute name '
'is\n'
' not found there, the attribute search continues in the base\n'
' classes. This search of the base classes uses the C3 method\n'
' resolution order which behaves correctly even in the presence '
'of\n'
' ‘diamond’ inheritance structures where there are multiple\n'
' inheritance paths leading back to a common ancestor. Additional\n'
' details on the C3 MRO used by Python can be found in the\n'
' documentation accompanying the 2.3 release at\n'
' https://www.python.org/download/releases/2.3/mro/.\n'
'\n'
' When a class attribute reference (for class "C", say) would '
'yield a\n'
' class method object, it is transformed into an instance method\n'
' object whose "__self__" attribute is "C". When it would yield '
'a\n'
' static method object, it is transformed into the object wrapped '
'by\n'
' the static method object. See section Implementing Descriptors '
'for\n'
' another way in which attributes retrieved from a class may '
'differ\n'
' from those actually contained in its "__dict__".\n'
'\n'
' Class attribute assignments update the class’s dictionary, '
'never\n'
' the dictionary of a base class.\n'
'\n'
' A class object can be called (see above) to yield a class '
'instance\n'
' (see below).\n'
'\n'
' Special attributes: "__name__" is the class name; "__module__" '
'is\n'
' the module name in which the class was defined; "__dict__" is '
'the\n'
' dictionary containing the class’s namespace; "__bases__" is a '
'tuple\n'
' containing the base classes, in the order of their occurrence '
'in\n'
' the base class list; "__doc__" is the class’s documentation '
'string,\n'
' or "None" if undefined; "__annotations__" (optional) is a\n'
' dictionary containing *variable annotations* collected during '
'class\n'
' body execution.\n'
'\n'
'Class instances\n'
' A class instance is created by calling a class object (see '
'above).\n'
' A class instance has a namespace implemented as a dictionary '
'which\n'
' is the first place in which attribute references are searched.\n'
' When an attribute is not found there, and the instance’s class '
'has\n'
' an attribute by that name, the search continues with the class\n'
' attributes. If a class attribute is found that is a '
'user-defined\n'
' function object, it is transformed into an instance method '
'object\n'
' whose "__self__" attribute is the instance. Static method and\n'
' class method objects are also transformed; see above under\n'
' “Classes”. See section Implementing Descriptors for another way '
'in\n'
' which attributes of a class retrieved via its instances may '
'differ\n'
' from the objects actually stored in the class’s "__dict__". If '
'no\n'
' class attribute is found, and the object’s class has a\n'
' "__getattr__()" method, that is called to satisfy the lookup.\n'
'\n'
' Attribute assignments and deletions update the instance’s\n'
' dictionary, never a class’s dictionary. If the class has a\n'
' "__setattr__()" or "__delattr__()" method, this is called '
'instead\n'
' of updating the instance dictionary directly.\n'
'\n'
' Class instances can pretend to be numbers, sequences, or '
'mappings\n'
' if they have methods with certain special names. See section\n'
' Special method names.\n'
'\n'
' Special attributes: "__dict__" is the attribute dictionary;\n'
' "__class__" is the instance’s class.\n'
'\n'
'I/O objects (also known as file objects)\n'
' A *file object* represents an open file. Various shortcuts are\n'
' available to create file objects: the "open()" built-in '
'function,\n'
' and also "os.popen()", "os.fdopen()", and the "makefile()" '
'method\n'
' of socket objects (and perhaps by other functions or methods\n'
' provided by extension modules).\n'
'\n'
' The objects "sys.stdin", "sys.stdout" and "sys.stderr" are\n'
' initialized to file objects corresponding to the interpreter’s\n'
' standard input, output and error streams; they are all open in '
'text\n'
' mode and therefore follow the interface defined by the\n'
' "io.TextIOBase" abstract class.\n'
'\n'
'Internal types\n'
' A few types used internally by the interpreter are exposed to '
'the\n'
' user. Their definitions may change with future versions of the\n'
' interpreter, but they are mentioned here for completeness.\n'
'\n'
' Code objects\n'
' Code objects represent *byte-compiled* executable Python '
'code,\n'
' or *bytecode*. The difference between a code object and a\n'
' function object is that the function object contains an '
'explicit\n'
' reference to the function’s globals (the module in which it '
'was\n'
' defined), while a code object contains no context; also the\n'
' default argument values are stored in the function object, '
'not\n'
' in the code object (because they represent values calculated '
'at\n'
' run-time). Unlike function objects, code objects are '
'immutable\n'
' and contain no references (directly or indirectly) to '
'mutable\n'
' objects.\n'
'\n'
' Special read-only attributes: "co_name" gives the function '
'name;\n'
' "co_argcount" is the number of positional arguments '
'(including\n'
' arguments with default values); "co_nlocals" is the number '
'of\n'
' local variables used by the function (including arguments);\n'
' "co_varnames" is a tuple containing the names of the local\n'
' variables (starting with the argument names); "co_cellvars" '
'is a\n'
' tuple containing the names of local variables that are\n'
' referenced by nested functions; "co_freevars" is a tuple\n'
' containing the names of free variables; "co_code" is a '
'string\n'
' representing the sequence of bytecode instructions; '
'"co_consts"\n'
' is a tuple containing the literals used by the bytecode;\n'
' "co_names" is a tuple containing the names used by the '
'bytecode;\n'
' "co_filename" is the filename from which the code was '
'compiled;\n'
' "co_firstlineno" is the first line number of the function;\n'
' "co_lnotab" is a string encoding the mapping from bytecode\n'
' offsets to line numbers (for details see the source code of '
'the\n'
' interpreter); "co_stacksize" is the required stack size\n'
' (including local variables); "co_flags" is an integer '
'encoding a\n'
' number of flags for the interpreter.\n'
'\n'
' The following flag bits are defined for "co_flags": bit '
'"0x04"\n'
' is set if the function uses the "*arguments" syntax to accept '
'an\n'
' arbitrary number of positional arguments; bit "0x08" is set '
'if\n'
' the function uses the "**keywords" syntax to accept '
'arbitrary\n'
' keyword arguments; bit "0x20" is set if the function is a\n'
' generator.\n'
'\n'
' Future feature declarations ("from __future__ import '
'division")\n'
' also use bits in "co_flags" to indicate whether a code '
'object\n'
' was compiled with a particular feature enabled: bit "0x2000" '
'is\n'
' set if the function was compiled with future division '
'enabled;\n'
' bits "0x10" and "0x1000" were used in earlier versions of\n'
' Python.\n'
'\n'
' Other bits in "co_flags" are reserved for internal use.\n'
'\n'
' If a code object represents a function, the first item in\n'
' "co_consts" is the documentation string of the function, or\n'
' "None" if undefined.\n'
'\n'
' Frame objects\n'
' Frame objects represent execution frames. They may occur in\n'
' traceback objects (see below).\n'
'\n'
' Special read-only attributes: "f_back" is to the previous '
'stack\n'
' frame (towards the caller), or "None" if this is the bottom\n'
' stack frame; "f_code" is the code object being executed in '
'this\n'
' frame; "f_locals" is the dictionary used to look up local\n'
' variables; "f_globals" is used for global variables;\n'
' "f_builtins" is used for built-in (intrinsic) names; '
'"f_lasti"\n'
' gives the precise instruction (this is an index into the\n'
' bytecode string of the code object).\n'
'\n'
' Special writable attributes: "f_trace", if not "None", is a\n'
' function called at the start of each source code line (this '
'is\n'
' used by the debugger); "f_lineno" is the current line number '
'of\n'
' the frame — writing to this from within a trace function '
'jumps\n'
' to the given line (only for the bottom-most frame). A '
'debugger\n'
' can implement a Jump command (aka Set Next Statement) by '
'writing\n'
' to f_lineno.\n'
'\n'
' Frame objects support one method:\n'
'\n'
' frame.clear()\n'
'\n'
' This method clears all references to local variables held '
'by\n'
' the frame. Also, if the frame belonged to a generator, '
'the\n'
' generator is finalized. This helps break reference '
'cycles\n'
' involving frame objects (for example when catching an\n'
' exception and storing its traceback for later use).\n'
'\n'
' "RuntimeError" is raised if the frame is currently '
'executing.\n'
'\n'
' New in version 3.4.\n'
'\n'
' Traceback objects\n'
' Traceback objects represent a stack trace of an exception. '
'A\n'
' traceback object is created when an exception occurs. When '
'the\n'
' search for an exception handler unwinds the execution stack, '
'at\n'
' each unwound level a traceback object is inserted in front '
'of\n'
' the current traceback. When an exception handler is '
'entered,\n'
' the stack trace is made available to the program. (See '
'section\n'
' The try statement.) It is accessible as the third item of '
'the\n'
' tuple returned by "sys.exc_info()". When the program contains '
'no\n'
' suitable handler, the stack trace is written (nicely '
'formatted)\n'
' to the standard error stream; if the interpreter is '
'interactive,\n'
' it is also made available to the user as '
'"sys.last_traceback".\n'
'\n'
' Special read-only attributes: "tb_next" is the next level in '
'the\n'
' stack trace (towards the frame where the exception occurred), '
'or\n'
' "None" if there is no next level; "tb_frame" points to the\n'
' execution frame of the current level; "tb_lineno" gives the '
'line\n'
' number where the exception occurred; "tb_lasti" indicates '
'the\n'
' precise instruction. The line number and last instruction '
'in\n'
' the traceback may differ from the line number of its frame\n'
' object if the exception occurred in a "try" statement with '
'no\n'
' matching except clause or with a finally clause.\n'
'\n'
' Slice objects\n'
' Slice objects are used to represent slices for '
'"__getitem__()"\n'
' methods. They are also created by the built-in "slice()"\n'
' function.\n'
'\n'
' Special read-only attributes: "start" is the lower bound; '
'"stop"\n'
' is the upper bound; "step" is the step value; each is "None" '
'if\n'
' omitted. These attributes can have any type.\n'
'\n'
' Slice objects support one method:\n'
'\n'
' slice.indices(self, length)\n'
'\n'
' This method takes a single integer argument *length* and\n'
' computes information about the slice that the slice '
'object\n'
' would describe if applied to a sequence of *length* '
'items.\n'
' It returns a tuple of three integers; respectively these '
'are\n'
' the *start* and *stop* indices and the *step* or stride\n'
' length of the slice. Missing or out-of-bounds indices are\n'
' handled in a manner consistent with regular slices.\n'
'\n'
' Static method objects\n'
' Static method objects provide a way of defeating the\n'
' transformation of function objects to method objects '
'described\n'
' above. A static method object is a wrapper around any other\n'
' object, usually a user-defined method object. When a static\n'
' method object is retrieved from a class or a class instance, '
'the\n'
' object actually returned is the wrapped object, which is not\n'
' subject to any further transformation. Static method objects '
'are\n'
' not themselves callable, although the objects they wrap '
'usually\n'
' are. Static method objects are created by the built-in\n'
' "staticmethod()" constructor.\n'
'\n'
' Class method objects\n'
' A class method object, like a static method object, is a '
'wrapper\n'
' around another object that alters the way in which that '
'object\n'
' is retrieved from classes and class instances. The behaviour '
'of\n'
' class method objects upon such retrieval is described above,\n'
' under “User-defined methods”. Class method objects are '
'created\n'
' by the built-in "classmethod()" constructor.\n',
'typesfunctions': 'Functions\n'
'*********\n'
'\n'
'Function objects are created by function definitions. The '
'only\n'
'operation on a function object is to call it: '
'"func(argument-list)".\n'
'\n'
'There are really two flavors of function objects: built-in '
'functions\n'
'and user-defined functions. Both support the same '
'operation (to call\n'
'the function), but the implementation is different, hence '
'the\n'
'different object types.\n'
'\n'
'See Function definitions for more information.\n',
'typesmapping': 'Mapping Types — "dict"\n'
'**********************\n'
'\n'
'A *mapping* object maps *hashable* values to arbitrary '
'objects.\n'
'Mappings are mutable objects. There is currently only one '
'standard\n'
'mapping type, the *dictionary*. (For other containers see '
'the built-\n'
'in "list", "set", and "tuple" classes, and the "collections" '
'module.)\n'
'\n'
'A dictionary’s keys are *almost* arbitrary values. Values '
'that are\n'
'not *hashable*, that is, values containing lists, '
'dictionaries or\n'
'other mutable types (that are compared by value rather than '
'by object\n'
'identity) may not be used as keys. Numeric types used for '
'keys obey\n'
'the normal rules for numeric comparison: if two numbers '
'compare equal\n'
'(such as "1" and "1.0") then they can be used '
'interchangeably to index\n'
'the same dictionary entry. (Note however, that since '
'computers store\n'
'floating-point numbers as approximations it is usually '
'unwise to use\n'
'them as dictionary keys.)\n'
'\n'
'Dictionaries can be created by placing a comma-separated '
'list of "key:\n'
'value" pairs within braces, for example: "{\'jack\': 4098, '
"'sjoerd':\n"
'4127}" or "{4098: \'jack\', 4127: \'sjoerd\'}", or by the '
'"dict"\n'
'constructor.\n'
'\n'
'class dict(**kwarg)\n'
'class dict(mapping, **kwarg)\n'
'class dict(iterable, **kwarg)\n'
'\n'
' Return a new dictionary initialized from an optional '
'positional\n'
' argument and a possibly empty set of keyword arguments.\n'
'\n'
' If no positional argument is given, an empty dictionary '
'is created.\n'
' If a positional argument is given and it is a mapping '
'object, a\n'
' dictionary is created with the same key-value pairs as '
'the mapping\n'
' object. Otherwise, the positional argument must be an '
'*iterable*\n'
' object. Each item in the iterable must itself be an '
'iterable with\n'
' exactly two objects. The first object of each item '
'becomes a key\n'
' in the new dictionary, and the second object the '
'corresponding\n'
' value. If a key occurs more than once, the last value '
'for that key\n'
' becomes the corresponding value in the new dictionary.\n'
'\n'
' If keyword arguments are given, the keyword arguments and '
'their\n'
' values are added to the dictionary created from the '
'positional\n'
' argument. If a key being added is already present, the '
'value from\n'
' the keyword argument replaces the value from the '
'positional\n'
' argument.\n'
'\n'
' To illustrate, the following examples all return a '
'dictionary equal\n'
' to "{"one": 1, "two": 2, "three": 3}":\n'
'\n'
' >>> a = dict(one=1, two=2, three=3)\n'
" >>> b = {'one': 1, 'two': 2, 'three': 3}\n"
" >>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))\n"
" >>> d = dict([('two', 2), ('one', 1), ('three', 3)])\n"
" >>> e = dict({'three': 3, 'one': 1, 'two': 2})\n"
' >>> a == b == c == d == e\n'
' True\n'
'\n'
' Providing keyword arguments as in the first example only '
'works for\n'
' keys that are valid Python identifiers. Otherwise, any '
'valid keys\n'
' can be used.\n'
'\n'
' These are the operations that dictionaries support (and '
'therefore,\n'
' custom mapping types should support too):\n'
'\n'
' len(d)\n'
'\n'
' Return the number of items in the dictionary *d*.\n'
'\n'
' d[key]\n'
'\n'
' Return the item of *d* with key *key*. Raises a '
'"KeyError" if\n'
' *key* is not in the map.\n'
'\n'
' If a subclass of dict defines a method "__missing__()" '
'and *key*\n'
' is not present, the "d[key]" operation calls that '
'method with\n'
' the key *key* as argument. The "d[key]" operation '
'then returns\n'
' or raises whatever is returned or raised by the\n'
' "__missing__(key)" call. No other operations or '
'methods invoke\n'
' "__missing__()". If "__missing__()" is not defined, '
'"KeyError"\n'
' is raised. "__missing__()" must be a method; it cannot '
'be an\n'
' instance variable:\n'
'\n'
' >>> class Counter(dict):\n'
' ... def __missing__(self, key):\n'
' ... return 0\n'
' >>> c = Counter()\n'
" >>> c['red']\n"
' 0\n'
" >>> c['red'] += 1\n"
" >>> c['red']\n"
' 1\n'
'\n'
' The example above shows part of the implementation of\n'
' "collections.Counter". A different "__missing__" '
'method is used\n'
' by "collections.defaultdict".\n'
'\n'
' d[key] = value\n'
'\n'
' Set "d[key]" to *value*.\n'
'\n'
' del d[key]\n'
'\n'
' Remove "d[key]" from *d*. Raises a "KeyError" if '
'*key* is not\n'
' in the map.\n'
'\n'
' key in d\n'
'\n'
' Return "True" if *d* has a key *key*, else "False".\n'
'\n'
' key not in d\n'
'\n'
' Equivalent to "not key in d".\n'
'\n'
' iter(d)\n'
'\n'
' Return an iterator over the keys of the dictionary. '
'This is a\n'
' shortcut for "iter(d.keys())".\n'
'\n'
' clear()\n'
'\n'
' Remove all items from the dictionary.\n'
'\n'
' copy()\n'
'\n'
' Return a shallow copy of the dictionary.\n'
'\n'
' classmethod fromkeys(seq[, value])\n'
'\n'
' Create a new dictionary with keys from *seq* and '
'values set to\n'
' *value*.\n'
'\n'
' "fromkeys()" is a class method that returns a new '
'dictionary.\n'
' *value* defaults to "None".\n'
'\n'
' get(key[, default])\n'
'\n'
' Return the value for *key* if *key* is in the '
'dictionary, else\n'
' *default*. If *default* is not given, it defaults to '
'"None", so\n'
' that this method never raises a "KeyError".\n'
'\n'
' items()\n'
'\n'
' Return a new view of the dictionary’s items ("(key, '
'value)"\n'
' pairs). See the documentation of view objects.\n'
'\n'
' keys()\n'
'\n'
' Return a new view of the dictionary’s keys. See the\n'
' documentation of view objects.\n'
'\n'
' pop(key[, default])\n'
'\n'
' If *key* is in the dictionary, remove it and return '
'its value,\n'
' else return *default*. If *default* is not given and '
'*key* is\n'
' not in the dictionary, a "KeyError" is raised.\n'
'\n'
' popitem()\n'
'\n'
' Remove and return an arbitrary "(key, value)" pair '
'from the\n'
' dictionary.\n'
'\n'
' "popitem()" is useful to destructively iterate over a\n'
' dictionary, as often used in set algorithms. If the '
'dictionary\n'
' is empty, calling "popitem()" raises a "KeyError".\n'
'\n'
' setdefault(key[, default])\n'
'\n'
' If *key* is in the dictionary, return its value. If '
'not, insert\n'
' *key* with a value of *default* and return *default*. '
'*default*\n'
' defaults to "None".\n'
'\n'
' update([other])\n'
'\n'
' Update the dictionary with the key/value pairs from '
'*other*,\n'
' overwriting existing keys. Return "None".\n'
'\n'
' "update()" accepts either another dictionary object or '
'an\n'
' iterable of key/value pairs (as tuples or other '
'iterables of\n'
' length two). If keyword arguments are specified, the '
'dictionary\n'
' is then updated with those key/value pairs: '
'"d.update(red=1,\n'
' blue=2)".\n'
'\n'
' values()\n'
'\n'
' Return a new view of the dictionary’s values. See '
'the\n'
' documentation of view objects.\n'
'\n'
' Dictionaries compare equal if and only if they have the '
'same "(key,\n'
' value)" pairs. Order comparisons (‘<’, ‘<=’, ‘>=’, ‘>’) '
'raise\n'
' "TypeError".\n'
'\n'
'See also: "types.MappingProxyType" can be used to create a '
'read-only\n'
' view of a "dict".\n'
'\n'
'\n'
'Dictionary view objects\n'
'=======================\n'
'\n'
'The objects returned by "dict.keys()", "dict.values()" and\n'
'"dict.items()" are *view objects*. They provide a dynamic '
'view on the\n'
'dictionary’s entries, which means that when the dictionary '
'changes,\n'
'the view reflects these changes.\n'
'\n'
'Dictionary views can be iterated over to yield their '
'respective data,\n'
'and support membership tests:\n'
'\n'
'len(dictview)\n'
'\n'
' Return the number of entries in the dictionary.\n'
'\n'
'iter(dictview)\n'
'\n'
' Return an iterator over the keys, values or items '
'(represented as\n'
' tuples of "(key, value)") in the dictionary.\n'
'\n'
' Keys and values are iterated over in an arbitrary order '
'which is\n'
' non-random, varies across Python implementations, and '
'depends on\n'
' the dictionary’s history of insertions and deletions. If '
'keys,\n'
' values and items views are iterated over with no '
'intervening\n'
' modifications to the dictionary, the order of items will '
'directly\n'
' correspond. This allows the creation of "(value, key)" '
'pairs using\n'
' "zip()": "pairs = zip(d.values(), d.keys())". Another '
'way to\n'
' create the same list is "pairs = [(v, k) for (k, v) in '
'd.items()]".\n'
'\n'
' Iterating views while adding or deleting entries in the '
'dictionary\n'
' may raise a "RuntimeError" or fail to iterate over all '
'entries.\n'
'\n'
'x in dictview\n'
'\n'
' Return "True" if *x* is in the underlying dictionary’s '
'keys, values\n'
' or items (in the latter case, *x* should be a "(key, '
'value)"\n'
' tuple).\n'
'\n'
'Keys views are set-like since their entries are unique and '
'hashable.\n'
'If all values are hashable, so that "(key, value)" pairs are '
'unique\n'
'and hashable, then the items view is also set-like. (Values '
'views are\n'
'not treated as set-like since the entries are generally not '
'unique.)\n'
'For set-like views, all of the operations defined for the '
'abstract\n'
'base class "collections.abc.Set" are available (for example, '
'"==",\n'
'"<", or "^").\n'
'\n'
'An example of dictionary view usage:\n'
'\n'
" >>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, "
"'spam': 500}\n"
' >>> keys = dishes.keys()\n'
' >>> values = dishes.values()\n'
'\n'
' >>> # iteration\n'
' >>> n = 0\n'
' >>> for val in values:\n'
' ... n += val\n'
' >>> print(n)\n'
' 504\n'
'\n'
' >>> # keys and values are iterated over in the same '
'order\n'
' >>> list(keys)\n'
" ['eggs', 'bacon', 'sausage', 'spam']\n"
' >>> list(values)\n'
' [2, 1, 1, 500]\n'
'\n'
' >>> # view objects are dynamic and reflect dict changes\n'
" >>> del dishes['eggs']\n"
" >>> del dishes['sausage']\n"
' >>> list(keys)\n'
" ['spam', 'bacon']\n"
'\n'
' >>> # set operations\n'
" >>> keys & {'eggs', 'bacon', 'salad'}\n"
" {'bacon'}\n"
" >>> keys ^ {'sausage', 'juice'}\n"
" {'juice', 'sausage', 'bacon', 'spam'}\n",
'typesmethods': 'Methods\n'
'*******\n'
'\n'
'Methods are functions that are called using the attribute '
'notation.\n'
'There are two flavors: built-in methods (such as "append()" '
'on lists)\n'
'and class instance methods. Built-in methods are described '
'with the\n'
'types that support them.\n'
'\n'
'If you access a method (a function defined in a class '
'namespace)\n'
'through an instance, you get a special object: a *bound '
'method* (also\n'
'called *instance method*) object. When called, it will add '
'the "self"\n'
'argument to the argument list. Bound methods have two '
'special read-\n'
'only attributes: "m.__self__" is the object on which the '
'method\n'
'operates, and "m.__func__" is the function implementing the '
'method.\n'
'Calling "m(arg-1, arg-2, ..., arg-n)" is completely '
'equivalent to\n'
'calling "m.__func__(m.__self__, arg-1, arg-2, ..., arg-n)".\n'
'\n'
'Like function objects, bound method objects support getting '
'arbitrary\n'
'attributes. However, since method attributes are actually '
'stored on\n'
'the underlying function object ("meth.__func__"), setting '
'method\n'
'attributes on bound methods is disallowed. Attempting to '
'set an\n'
'attribute on a method results in an "AttributeError" being '
'raised. In\n'
'order to set a method attribute, you need to explicitly set '
'it on the\n'
'underlying function object:\n'
'\n'
' >>> class C:\n'
' ... def method(self):\n'
' ... pass\n'
' ...\n'
' >>> c = C()\n'
" >>> c.method.whoami = 'my name is method' # can't set on "
'the method\n'
' Traceback (most recent call last):\n'
' File "<stdin>", line 1, in <module>\n'
" AttributeError: 'method' object has no attribute "
"'whoami'\n"
" >>> c.method.__func__.whoami = 'my name is method'\n"
' >>> c.method.whoami\n'
" 'my name is method'\n"
'\n'
'See The standard type hierarchy for more information.\n',
'typesmodules': 'Modules\n'
'*******\n'
'\n'
'The only special operation on a module is attribute access: '
'"m.name",\n'
'where *m* is a module and *name* accesses a name defined in '
'*m*’s\n'
'symbol table. Module attributes can be assigned to. (Note '
'that the\n'
'"import" statement is not, strictly speaking, an operation '
'on a module\n'
'object; "import foo" does not require a module object named '
'*foo* to\n'
'exist, rather it requires an (external) *definition* for a '
'module\n'
'named *foo* somewhere.)\n'
'\n'
'A special attribute of every module is "__dict__". This is '
'the\n'
'dictionary containing the module’s symbol table. Modifying '
'this\n'
'dictionary will actually change the module’s symbol table, '
'but direct\n'
'assignment to the "__dict__" attribute is not possible (you '
'can write\n'
'"m.__dict__[\'a\'] = 1", which defines "m.a" to be "1", but '
'you can’t\n'
'write "m.__dict__ = {}"). Modifying "__dict__" directly is '
'not\n'
'recommended.\n'
'\n'
'Modules built into the interpreter are written like this: '
'"<module\n'
'\'sys\' (built-in)>". If loaded from a file, they are '
'written as\n'
'"<module \'os\' from '
'\'/usr/local/lib/pythonX.Y/os.pyc\'>".\n',
'typesseq': 'Sequence Types — "list", "tuple", "range"\n'
'*****************************************\n'
'\n'
'There are three basic sequence types: lists, tuples, and range\n'
'objects. Additional sequence types tailored for processing of '
'binary\n'
'data and text strings are described in dedicated sections.\n'
'\n'
'\n'
'Common Sequence Operations\n'
'==========================\n'
'\n'
'The operations in the following table are supported by most '
'sequence\n'
'types, both mutable and immutable. The '
'"collections.abc.Sequence" ABC\n'
'is provided to make it easier to correctly implement these '
'operations\n'
'on custom sequence types.\n'
'\n'
'This table lists the sequence operations sorted in ascending '
'priority.\n'
'In the table, *s* and *t* are sequences of the same type, *n*, '
'*i*,\n'
'*j* and *k* are integers and *x* is an arbitrary object that '
'meets any\n'
'type and value restrictions imposed by *s*.\n'
'\n'
'The "in" and "not in" operations have the same priorities as '
'the\n'
'comparison operations. The "+" (concatenation) and "*" '
'(repetition)\n'
'operations have the same priority as the corresponding numeric\n'
'operations. [3]\n'
'\n'
'+----------------------------+----------------------------------+------------+\n'
'| Operation | Result '
'| Notes |\n'
'+============================+==================================+============+\n'
'| "x in s" | "True" if an item of *s* is '
'| (1) |\n'
'| | equal to *x*, else "False" '
'| |\n'
'+----------------------------+----------------------------------+------------+\n'
'| "x not in s" | "False" if an item of *s* is '
'| (1) |\n'
'| | equal to *x*, else "True" '
'| |\n'
'+----------------------------+----------------------------------+------------+\n'
'| "s + t" | the concatenation of *s* and *t* '
'| (6)(7) |\n'
'+----------------------------+----------------------------------+------------+\n'
'| "s * n" or "n * s" | equivalent to adding *s* to '
'| (2)(7) |\n'
'| | itself *n* times '
'| |\n'
'+----------------------------+----------------------------------+------------+\n'
'| "s[i]" | *i*th item of *s*, origin 0 '
'| (3) |\n'
'+----------------------------+----------------------------------+------------+\n'
'| "s[i:j]" | slice of *s* from *i* to *j* '
'| (3)(4) |\n'
'+----------------------------+----------------------------------+------------+\n'
'| "s[i:j:k]" | slice of *s* from *i* to *j* '
'| (3)(5) |\n'
'| | with step *k* '
'| |\n'
'+----------------------------+----------------------------------+------------+\n'
'| "len(s)" | length of *s* '
'| |\n'
'+----------------------------+----------------------------------+------------+\n'
'| "min(s)" | smallest item of *s* '
'| |\n'
'+----------------------------+----------------------------------+------------+\n'
'| "max(s)" | largest item of *s* '
'| |\n'
'+----------------------------+----------------------------------+------------+\n'
'| "s.index(x[, i[, j]])" | index of the first occurrence of '
'| (8) |\n'
'| | *x* in *s* (at or after index '
'| |\n'
'| | *i* and before index *j*) '
'| |\n'
'+----------------------------+----------------------------------+------------+\n'
'| "s.count(x)" | total number of occurrences of '
'| |\n'
'| | *x* in *s* '
'| |\n'
'+----------------------------+----------------------------------+------------+\n'
'\n'
'Sequences of the same type also support comparisons. In '
'particular,\n'
'tuples and lists are compared lexicographically by comparing\n'
'corresponding elements. This means that to compare equal, every\n'
'element must compare equal and the two sequences must be of the '
'same\n'
'type and have the same length. (For full details see '
'Comparisons in\n'
'the language reference.)\n'
'\n'
'Notes:\n'
'\n'
'1. While the "in" and "not in" operations are used only for '
'simple\n'
' containment testing in the general case, some specialised '
'sequences\n'
' (such as "str", "bytes" and "bytearray") also use them for\n'
' subsequence testing:\n'
'\n'
' >>> "gg" in "eggs"\n'
' True\n'
'\n'
'2. Values of *n* less than "0" are treated as "0" (which yields '
'an\n'
' empty sequence of the same type as *s*). Note that items in '
'the\n'
' sequence *s* are not copied; they are referenced multiple '
'times.\n'
' This often haunts new Python programmers; consider:\n'
'\n'
' >>> lists = [[]] * 3\n'
' >>> lists\n'
' [[], [], []]\n'
' >>> lists[0].append(3)\n'
' >>> lists\n'
' [[3], [3], [3]]\n'
'\n'
' What has happened is that "[[]]" is a one-element list '
'containing\n'
' an empty list, so all three elements of "[[]] * 3" are '
'references\n'
' to this single empty list. Modifying any of the elements of\n'
' "lists" modifies this single list. You can create a list of\n'
' different lists this way:\n'
'\n'
' >>> lists = [[] for i in range(3)]\n'
' >>> lists[0].append(3)\n'
' >>> lists[1].append(5)\n'
' >>> lists[2].append(7)\n'
' >>> lists\n'
' [[3], [5], [7]]\n'
'\n'
' Further explanation is available in the FAQ entry How do I '
'create a\n'
' multidimensional list?.\n'
'\n'
'3. If *i* or *j* is negative, the index is relative to the end '
'of\n'
' sequence *s*: "len(s) + i" or "len(s) + j" is substituted. '
'But\n'
' note that "-0" is still "0".\n'
'\n'
'4. The slice of *s* from *i* to *j* is defined as the sequence '
'of\n'
' items with index *k* such that "i <= k < j". If *i* or *j* '
'is\n'
' greater than "len(s)", use "len(s)". If *i* is omitted or '
'"None",\n'
' use "0". If *j* is omitted or "None", use "len(s)". If *i* '
'is\n'
' greater than or equal to *j*, the slice is empty.\n'
'\n'
'5. The slice of *s* from *i* to *j* with step *k* is defined as '
'the\n'
' sequence of items with index "x = i + n*k" such that "0 <= n '
'<\n'
' (j-i)/k". In other words, the indices are "i", "i+k", '
'"i+2*k",\n'
' "i+3*k" and so on, stopping when *j* is reached (but never\n'
' including *j*). When *k* is positive, *i* and *j* are '
'reduced to\n'
' "len(s)" if they are greater. When *k* is negative, *i* and '
'*j* are\n'
' reduced to "len(s) - 1" if they are greater. If *i* or *j* '
'are\n'
' omitted or "None", they become “end” values (which end '
'depends on\n'
' the sign of *k*). Note, *k* cannot be zero. If *k* is '
'"None", it\n'
' is treated like "1".\n'
'\n'
'6. Concatenating immutable sequences always results in a new\n'
' object. This means that building up a sequence by repeated\n'
' concatenation will have a quadratic runtime cost in the '
'total\n'
' sequence length. To get a linear runtime cost, you must '
'switch to\n'
' one of the alternatives below:\n'
'\n'
' * if concatenating "str" objects, you can build a list and '
'use\n'
' "str.join()" at the end or else write to an "io.StringIO"\n'
' instance and retrieve its value when complete\n'
'\n'
' * if concatenating "bytes" objects, you can similarly use\n'
' "bytes.join()" or "io.BytesIO", or you can do in-place\n'
' concatenation with a "bytearray" object. "bytearray" '
'objects are\n'
' mutable and have an efficient overallocation mechanism\n'
'\n'
' * if concatenating "tuple" objects, extend a "list" instead\n'
'\n'
' * for other types, investigate the relevant class '
'documentation\n'
'\n'
'7. Some sequence types (such as "range") only support item\n'
' sequences that follow specific patterns, and hence don’t '
'support\n'
' sequence concatenation or repetition.\n'
'\n'
'8. "index" raises "ValueError" when *x* is not found in *s*. '
'Not\n'
' all implementations support passing the additional arguments '
'*i*\n'
' and *j*. These arguments allow efficient searching of '
'subsections\n'
' of the sequence. Passing the extra arguments is roughly '
'equivalent\n'
' to using "s[i:j].index(x)", only without copying any data and '
'with\n'
' the returned index being relative to the start of the '
'sequence\n'
' rather than the start of the slice.\n'
'\n'
'\n'
'Immutable Sequence Types\n'
'========================\n'
'\n'
'The only operation that immutable sequence types generally '
'implement\n'
'that is not also implemented by mutable sequence types is '
'support for\n'
'the "hash()" built-in.\n'
'\n'
'This support allows immutable sequences, such as "tuple" '
'instances, to\n'
'be used as "dict" keys and stored in "set" and "frozenset" '
'instances.\n'
'\n'
'Attempting to hash an immutable sequence that contains '
'unhashable\n'
'values will result in "TypeError".\n'
'\n'
'\n'
'Mutable Sequence Types\n'
'======================\n'
'\n'
'The operations in the following table are defined on mutable '
'sequence\n'
'types. The "collections.abc.MutableSequence" ABC is provided to '
'make\n'
'it easier to correctly implement these operations on custom '
'sequence\n'
'types.\n'
'\n'
'In the table *s* is an instance of a mutable sequence type, *t* '
'is any\n'
'iterable object and *x* is an arbitrary object that meets any '
'type and\n'
'value restrictions imposed by *s* (for example, "bytearray" '
'only\n'
'accepts integers that meet the value restriction "0 <= x <= '
'255").\n'
'\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| Operation | '
'Result | Notes |\n'
'+================================+==================================+=======================+\n'
'| "s[i] = x" | item *i* of *s* is replaced '
'by | |\n'
'| | '
'*x* | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s[i:j] = t" | slice of *s* from *i* to *j* '
'is | |\n'
'| | replaced by the contents of '
'the | |\n'
'| | iterable '
'*t* | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "del s[i:j]" | same as "s[i:j] = '
'[]" | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s[i:j:k] = t" | the elements of "s[i:j:k]" '
'are | (1) |\n'
'| | replaced by those of '
'*t* | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "del s[i:j:k]" | removes the elements '
'of | |\n'
'| | "s[i:j:k]" from the '
'list | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.append(x)" | appends *x* to the end of '
'the | |\n'
'| | sequence (same '
'as | |\n'
'| | "s[len(s):len(s)] = '
'[x]") | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.clear()" | removes all items from *s* '
'(same | (5) |\n'
'| | as "del '
's[:]") | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.copy()" | creates a shallow copy of '
'*s* | (5) |\n'
'| | (same as '
'"s[:]") | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.extend(t)" or "s += t" | extends *s* with the contents '
'of | |\n'
'| | *t* (for the most part the '
'same | |\n'
'| | as "s[len(s):len(s)] = '
't") | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s *= n" | updates *s* with its '
'contents | (6) |\n'
'| | repeated *n* '
'times | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.insert(i, x)" | inserts *x* into *s* at '
'the | |\n'
'| | index given by *i* (same '
'as | |\n'
'| | "s[i:i] = '
'[x]") | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.pop([i])" | retrieves the item at *i* '
'and | (2) |\n'
'| | also removes it from '
'*s* | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.remove(x)" | remove the first item from '
'*s* | (3) |\n'
'| | where "s[i] == '
'x" | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.reverse()" | reverses the items of *s* '
'in | (4) |\n'
'| | '
'place | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'\n'
'Notes:\n'
'\n'
'1. *t* must have the same length as the slice it is replacing.\n'
'\n'
'2. The optional argument *i* defaults to "-1", so that by '
'default\n'
' the last item is removed and returned.\n'
'\n'
'3. "remove" raises "ValueError" when *x* is not found in *s*.\n'
'\n'
'4. The "reverse()" method modifies the sequence in place for\n'
' economy of space when reversing a large sequence. To remind '
'users\n'
' that it operates by side effect, it does not return the '
'reversed\n'
' sequence.\n'
'\n'
'5. "clear()" and "copy()" are included for consistency with the\n'
' interfaces of mutable containers that don’t support slicing\n'
' operations (such as "dict" and "set")\n'
'\n'
' New in version 3.3: "clear()" and "copy()" methods.\n'
'\n'
'6. The value *n* is an integer, or an object implementing\n'
' "__index__()". Zero and negative values of *n* clear the '
'sequence.\n'
' Items in the sequence are not copied; they are referenced '
'multiple\n'
' times, as explained for "s * n" under Common Sequence '
'Operations.\n'
'\n'
'\n'
'Lists\n'
'=====\n'
'\n'
'Lists are mutable sequences, typically used to store collections '
'of\n'
'homogeneous items (where the precise degree of similarity will '
'vary by\n'
'application).\n'
'\n'
'class list([iterable])\n'
'\n'
' Lists may be constructed in several ways:\n'
'\n'
' * Using a pair of square brackets to denote the empty list: '
'"[]"\n'
'\n'
' * Using square brackets, separating items with commas: '
'"[a]",\n'
' "[a, b, c]"\n'
'\n'
' * Using a list comprehension: "[x for x in iterable]"\n'
'\n'
' * Using the type constructor: "list()" or "list(iterable)"\n'
'\n'
' The constructor builds a list whose items are the same and in '
'the\n'
' same order as *iterable*’s items. *iterable* may be either '
'a\n'
' sequence, a container that supports iteration, or an '
'iterator\n'
' object. If *iterable* is already a list, a copy is made and\n'
' returned, similar to "iterable[:]". For example, '
'"list(\'abc\')"\n'
' returns "[\'a\', \'b\', \'c\']" and "list( (1, 2, 3) )" '
'returns "[1, 2,\n'
' 3]". If no argument is given, the constructor creates a new '
'empty\n'
' list, "[]".\n'
'\n'
' Many other operations also produce lists, including the '
'"sorted()"\n'
' built-in.\n'
'\n'
' Lists implement all of the common and mutable sequence '
'operations.\n'
' Lists also provide the following additional method:\n'
'\n'
' sort(*, key=None, reverse=False)\n'
'\n'
' This method sorts the list in place, using only "<" '
'comparisons\n'
' between items. Exceptions are not suppressed - if any '
'comparison\n'
' operations fail, the entire sort operation will fail (and '
'the\n'
' list will likely be left in a partially modified state).\n'
'\n'
' "sort()" accepts two arguments that can only be passed by\n'
' keyword (keyword-only arguments):\n'
'\n'
' *key* specifies a function of one argument that is used '
'to\n'
' extract a comparison key from each list element (for '
'example,\n'
' "key=str.lower"). The key corresponding to each item in '
'the list\n'
' is calculated once and then used for the entire sorting '
'process.\n'
' The default value of "None" means that list items are '
'sorted\n'
' directly without calculating a separate key value.\n'
'\n'
' The "functools.cmp_to_key()" utility is available to '
'convert a\n'
' 2.x style *cmp* function to a *key* function.\n'
'\n'
' *reverse* is a boolean value. If set to "True", then the '
'list\n'
' elements are sorted as if each comparison were reversed.\n'
'\n'
' This method modifies the sequence in place for economy of '
'space\n'
' when sorting a large sequence. To remind users that it '
'operates\n'
' by side effect, it does not return the sorted sequence '
'(use\n'
' "sorted()" to explicitly request a new sorted list '
'instance).\n'
'\n'
' The "sort()" method is guaranteed to be stable. A sort '
'is\n'
' stable if it guarantees not to change the relative order '
'of\n'
' elements that compare equal — this is helpful for sorting '
'in\n'
' multiple passes (for example, sort by department, then by '
'salary\n'
' grade).\n'
'\n'
' **CPython implementation detail:** While a list is being '
'sorted,\n'
' the effect of attempting to mutate, or even inspect, the '
'list is\n'
' undefined. The C implementation of Python makes the list '
'appear\n'
' empty for the duration, and raises "ValueError" if it can '
'detect\n'
' that the list has been mutated during a sort.\n'
'\n'
'\n'
'Tuples\n'
'======\n'
'\n'
'Tuples are immutable sequences, typically used to store '
'collections of\n'
'heterogeneous data (such as the 2-tuples produced by the '
'"enumerate()"\n'
'built-in). Tuples are also used for cases where an immutable '
'sequence\n'
'of homogeneous data is needed (such as allowing storage in a '
'"set" or\n'
'"dict" instance).\n'
'\n'
'class tuple([iterable])\n'
'\n'
' Tuples may be constructed in a number of ways:\n'
'\n'
' * Using a pair of parentheses to denote the empty tuple: '
'"()"\n'
'\n'
' * Using a trailing comma for a singleton tuple: "a," or '
'"(a,)"\n'
'\n'
' * Separating items with commas: "a, b, c" or "(a, b, c)"\n'
'\n'
' * Using the "tuple()" built-in: "tuple()" or '
'"tuple(iterable)"\n'
'\n'
' The constructor builds a tuple whose items are the same and '
'in the\n'
' same order as *iterable*’s items. *iterable* may be either '
'a\n'
' sequence, a container that supports iteration, or an '
'iterator\n'
' object. If *iterable* is already a tuple, it is returned\n'
' unchanged. For example, "tuple(\'abc\')" returns "(\'a\', '
'\'b\', \'c\')"\n'
' and "tuple( [1, 2, 3] )" returns "(1, 2, 3)". If no argument '
'is\n'
' given, the constructor creates a new empty tuple, "()".\n'
'\n'
' Note that it is actually the comma which makes a tuple, not '
'the\n'
' parentheses. The parentheses are optional, except in the '
'empty\n'
' tuple case, or when they are needed to avoid syntactic '
'ambiguity.\n'
' For example, "f(a, b, c)" is a function call with three '
'arguments,\n'
' while "f((a, b, c))" is a function call with a 3-tuple as the '
'sole\n'
' argument.\n'
'\n'
' Tuples implement all of the common sequence operations.\n'
'\n'
'For heterogeneous collections of data where access by name is '
'clearer\n'
'than access by index, "collections.namedtuple()" may be a more\n'
'appropriate choice than a simple tuple object.\n'
'\n'
'\n'
'Ranges\n'
'======\n'
'\n'
'The "range" type represents an immutable sequence of numbers and '
'is\n'
'commonly used for looping a specific number of times in "for" '
'loops.\n'
'\n'
'class range(stop)\n'
'class range(start, stop[, step])\n'
'\n'
' The arguments to the range constructor must be integers '
'(either\n'
' built-in "int" or any object that implements the "__index__"\n'
' special method). If the *step* argument is omitted, it '
'defaults to\n'
' "1". If the *start* argument is omitted, it defaults to "0". '
'If\n'
' *step* is zero, "ValueError" is raised.\n'
'\n'
' For a positive *step*, the contents of a range "r" are '
'determined\n'
' by the formula "r[i] = start + step*i" where "i >= 0" and '
'"r[i] <\n'
' stop".\n'
'\n'
' For a negative *step*, the contents of the range are still\n'
' determined by the formula "r[i] = start + step*i", but the\n'
' constraints are "i >= 0" and "r[i] > stop".\n'
'\n'
' A range object will be empty if "r[0]" does not meet the '
'value\n'
' constraint. Ranges do support negative indices, but these '
'are\n'
' interpreted as indexing from the end of the sequence '
'determined by\n'
' the positive indices.\n'
'\n'
' Ranges containing absolute values larger than "sys.maxsize" '
'are\n'
' permitted but some features (such as "len()") may raise\n'
' "OverflowError".\n'
'\n'
' Range examples:\n'
'\n'
' >>> list(range(10))\n'
' [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n'
' >>> list(range(1, 11))\n'
' [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n'
' >>> list(range(0, 30, 5))\n'
' [0, 5, 10, 15, 20, 25]\n'
' >>> list(range(0, 10, 3))\n'
' [0, 3, 6, 9]\n'
' >>> list(range(0, -10, -1))\n'
' [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]\n'
' >>> list(range(0))\n'
' []\n'
' >>> list(range(1, 0))\n'
' []\n'
'\n'
' Ranges implement all of the common sequence operations '
'except\n'
' concatenation and repetition (due to the fact that range '
'objects\n'
' can only represent sequences that follow a strict pattern '
'and\n'
' repetition and concatenation will usually violate that '
'pattern).\n'
'\n'
' start\n'
'\n'
' The value of the *start* parameter (or "0" if the '
'parameter was\n'
' not supplied)\n'
'\n'
' stop\n'
'\n'
' The value of the *stop* parameter\n'
'\n'
' step\n'
'\n'
' The value of the *step* parameter (or "1" if the parameter '
'was\n'
' not supplied)\n'
'\n'
'The advantage of the "range" type over a regular "list" or '
'"tuple" is\n'
'that a "range" object will always take the same (small) amount '
'of\n'
'memory, no matter the size of the range it represents (as it '
'only\n'
'stores the "start", "stop" and "step" values, calculating '
'individual\n'
'items and subranges as needed).\n'
'\n'
'Range objects implement the "collections.abc.Sequence" ABC, and\n'
'provide features such as containment tests, element index '
'lookup,\n'
'slicing and support for negative indices (see Sequence Types — '
'list,\n'
'tuple, range):\n'
'\n'
'>>> r = range(0, 20, 2)\n'
'>>> r\n'
'range(0, 20, 2)\n'
'>>> 11 in r\n'
'False\n'
'>>> 10 in r\n'
'True\n'
'>>> r.index(10)\n'
'5\n'
'>>> r[5]\n'
'10\n'
'>>> r[:5]\n'
'range(0, 10, 2)\n'
'>>> r[-1]\n'
'18\n'
'\n'
'Testing range objects for equality with "==" and "!=" compares '
'them as\n'
'sequences. That is, two range objects are considered equal if '
'they\n'
'represent the same sequence of values. (Note that two range '
'objects\n'
'that compare equal might have different "start", "stop" and '
'"step"\n'
'attributes, for example "range(0) == range(2, 1, 3)" or '
'"range(0, 3,\n'
'2) == range(0, 4, 2)".)\n'
'\n'
'Changed in version 3.2: Implement the Sequence ABC. Support '
'slicing\n'
'and negative indices. Test "int" objects for membership in '
'constant\n'
'time instead of iterating through all items.\n'
'\n'
'Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range '
'objects\n'
'based on the sequence of values they define (instead of '
'comparing\n'
'based on object identity).\n'
'\n'
'New in version 3.3: The "start", "stop" and "step" attributes.\n'
'\n'
'See also:\n'
'\n'
' * The linspace recipe shows how to implement a lazy version '
'of\n'
' range suitable for floating point applications.\n',
'typesseq-mutable': 'Mutable Sequence Types\n'
'**********************\n'
'\n'
'The operations in the following table are defined on '
'mutable sequence\n'
'types. The "collections.abc.MutableSequence" ABC is '
'provided to make\n'
'it easier to correctly implement these operations on '
'custom sequence\n'
'types.\n'
'\n'
'In the table *s* is an instance of a mutable sequence '
'type, *t* is any\n'
'iterable object and *x* is an arbitrary object that '
'meets any type and\n'
'value restrictions imposed by *s* (for example, '
'"bytearray" only\n'
'accepts integers that meet the value restriction "0 <= x '
'<= 255").\n'
'\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| Operation | '
'Result | Notes '
'|\n'
'+================================+==================================+=======================+\n'
'| "s[i] = x" | item *i* of *s* is '
'replaced by | |\n'
'| | '
'*x* | '
'|\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s[i:j] = t" | slice of *s* from *i* '
'to *j* is | |\n'
'| | replaced by the '
'contents of the | |\n'
'| | iterable '
'*t* | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "del s[i:j]" | same as "s[i:j] = '
'[]" | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s[i:j:k] = t" | the elements of '
'"s[i:j:k]" are | (1) |\n'
'| | replaced by those of '
'*t* | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "del s[i:j:k]" | removes the elements '
'of | |\n'
'| | "s[i:j:k]" from the '
'list | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.append(x)" | appends *x* to the '
'end of the | |\n'
'| | sequence (same '
'as | |\n'
'| | "s[len(s):len(s)] = '
'[x]") | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.clear()" | removes all items '
'from *s* (same | (5) |\n'
'| | as "del '
's[:]") | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.copy()" | creates a shallow '
'copy of *s* | (5) |\n'
'| | (same as '
'"s[:]") | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.extend(t)" or "s += t" | extends *s* with the '
'contents of | |\n'
'| | *t* (for the most '
'part the same | |\n'
'| | as "s[len(s):len(s)] '
'= t") | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s *= n" | updates *s* with its '
'contents | (6) |\n'
'| | repeated *n* '
'times | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.insert(i, x)" | inserts *x* into *s* '
'at the | |\n'
'| | index given by *i* '
'(same as | |\n'
'| | "s[i:i] = '
'[x]") | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.pop([i])" | retrieves the item at '
'*i* and | (2) |\n'
'| | also removes it from '
'*s* | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.remove(x)" | remove the first item '
'from *s* | (3) |\n'
'| | where "s[i] == '
'x" | |\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'| "s.reverse()" | reverses the items of '
'*s* in | (4) |\n'
'| | '
'place | '
'|\n'
'+--------------------------------+----------------------------------+-----------------------+\n'
'\n'
'Notes:\n'
'\n'
'1. *t* must have the same length as the slice it is '
'replacing.\n'
'\n'
'2. The optional argument *i* defaults to "-1", so that '
'by default\n'
' the last item is removed and returned.\n'
'\n'
'3. "remove" raises "ValueError" when *x* is not found in '
'*s*.\n'
'\n'
'4. The "reverse()" method modifies the sequence in place '
'for\n'
' economy of space when reversing a large sequence. To '
'remind users\n'
' that it operates by side effect, it does not return '
'the reversed\n'
' sequence.\n'
'\n'
'5. "clear()" and "copy()" are included for consistency '
'with the\n'
' interfaces of mutable containers that don’t support '
'slicing\n'
' operations (such as "dict" and "set")\n'
'\n'
' New in version 3.3: "clear()" and "copy()" methods.\n'
'\n'
'6. The value *n* is an integer, or an object '
'implementing\n'
' "__index__()". Zero and negative values of *n* clear '
'the sequence.\n'
' Items in the sequence are not copied; they are '
'referenced multiple\n'
' times, as explained for "s * n" under Common Sequence '
'Operations.\n',
'unary': 'Unary arithmetic and bitwise operations\n'
'***************************************\n'
'\n'
'All unary arithmetic and bitwise operations have the same '
'priority:\n'
'\n'
' u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr\n'
'\n'
'The unary "-" (minus) operator yields the negation of its numeric\n'
'argument.\n'
'\n'
'The unary "+" (plus) operator yields its numeric argument '
'unchanged.\n'
'\n'
'The unary "~" (invert) operator yields the bitwise inversion of '
'its\n'
'integer argument. The bitwise inversion of "x" is defined as\n'
'"-(x+1)". It only applies to integral numbers.\n'
'\n'
'In all three cases, if the argument does not have the proper type, '
'a\n'
'"TypeError" exception is raised.\n',
'while': 'The "while" statement\n'
'*********************\n'
'\n'
'The "while" statement is used for repeated execution as long as an\n'
'expression is true:\n'
'\n'
' while_stmt ::= "while" expression ":" suite\n'
' ["else" ":" suite]\n'
'\n'
'This repeatedly tests the expression and, if it is true, executes '
'the\n'
'first suite; if the expression is false (which may be the first '
'time\n'
'it is tested) the suite of the "else" clause, if present, is '
'executed\n'
'and the loop terminates.\n'
'\n'
'A "break" statement executed in the first suite terminates the '
'loop\n'
'without executing the "else" clause’s suite. A "continue" '
'statement\n'
'executed in the first suite skips the rest of the suite and goes '
'back\n'
'to testing the expression.\n',
'with': 'The "with" statement\n'
'********************\n'
'\n'
'The "with" statement is used to wrap the execution of a block with\n'
'methods defined by a context manager (see section With Statement\n'
'Context Managers). This allows common "try"…"except"…"finally" '
'usage\n'
'patterns to be encapsulated for convenient reuse.\n'
'\n'
' with_stmt ::= "with" with_item ("," with_item)* ":" suite\n'
' with_item ::= expression ["as" target]\n'
'\n'
'The execution of the "with" statement with one “item” proceeds as\n'
'follows:\n'
'\n'
'1. The context expression (the expression given in the "with_item")\n'
' is evaluated to obtain a context manager.\n'
'\n'
'2. The context manager’s "__exit__()" is loaded for later use.\n'
'\n'
'3. The context manager’s "__enter__()" method is invoked.\n'
'\n'
'4. If a target was included in the "with" statement, the return\n'
' value from "__enter__()" is assigned to it.\n'
'\n'
' Note: The "with" statement guarantees that if the "__enter__()"\n'
' method returns without an error, then "__exit__()" will always '
'be\n'
' called. Thus, if an error occurs during the assignment to the\n'
' target list, it will be treated the same as an error occurring\n'
' within the suite would be. See step 6 below.\n'
'\n'
'5. The suite is executed.\n'
'\n'
'6. The context manager’s "__exit__()" method is invoked. If an\n'
' exception caused the suite to be exited, its type, value, and\n'
' traceback are passed as arguments to "__exit__()". Otherwise, '
'three\n'
' "None" arguments are supplied.\n'
'\n'
' If the suite was exited due to an exception, and the return '
'value\n'
' from the "__exit__()" method was false, the exception is '
'reraised.\n'
' If the return value was true, the exception is suppressed, and\n'
' execution continues with the statement following the "with"\n'
' statement.\n'
'\n'
' If the suite was exited for any reason other than an exception, '
'the\n'
' return value from "__exit__()" is ignored, and execution '
'proceeds\n'
' at the normal location for the kind of exit that was taken.\n'
'\n'
'With more than one item, the context managers are processed as if\n'
'multiple "with" statements were nested:\n'
'\n'
' with A() as a, B() as b:\n'
' suite\n'
'\n'
'is equivalent to\n'
'\n'
' with A() as a:\n'
' with B() as b:\n'
' suite\n'
'\n'
'Changed in version 3.1: Support for multiple context expressions.\n'
'\n'
'See also:\n'
'\n'
' **PEP 343** - The “with” statement\n'
' The specification, background, and examples for the Python '
'"with"\n'
' statement.\n',
'yield': 'The "yield" statement\n'
'*********************\n'
'\n'
' yield_stmt ::= yield_expression\n'
'\n'
'A "yield" statement is semantically equivalent to a yield '
'expression.\n'
'The yield statement can be used to omit the parentheses that would\n'
'otherwise be required in the equivalent yield expression '
'statement.\n'
'For example, the yield statements\n'
'\n'
' yield <expr>\n'
' yield from <expr>\n'
'\n'
'are equivalent to the yield expression statements\n'
'\n'
' (yield <expr>)\n'
' (yield from <expr>)\n'
'\n'
'Yield expressions and statements are only used when defining a\n'
'*generator* function, and are only used in the body of the '
'generator\n'
'function. Using yield in a function definition is sufficient to '
'cause\n'
'that definition to create a generator function instead of a normal\n'
'function.\n'
'\n'
'For full details of "yield" semantics, refer to the Yield '
'expressions\n'
'section.\n'}
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The "assert" statement
**********************
Assert statements are a convenient way to insert debugging assertions
into a program:
assert_stmt ::= "assert" expression ["," expression]
The simple form, "assert expression", is equivalent to
if __debug__:
if not expression: raise AssertionError
The extended form, "assert expression1, expression2", is equivalent to
if __debug__:
if not expression1: raise AssertionError(expression2)
These equivalences assume that "__debug__" and "AssertionError" refer
to the built-in variables with those names. In the current
implementation, the built-in variable "__debug__" is "True" under
normal circumstances, "False" when optimization is requested (command
line option -O). The current code generator emits no code for an
assert statement when optimization is requested at compile time. Note
that it is unnecessary to include the source code for the expression
that failed in the error message; it will be displayed as part of the
stack trace.
Assignments to "__debug__" are illegal. The value for the built-in
variable is determined when the interpreter starts.
t asserts
Assignment statements
*********************
Assignment statements are used to (re)bind names to values and to
modify attributes or items of mutable objects:
assignment_stmt ::= (target_list "=")+ (expression_list | yield_expression)
target_list ::= target ("," target)* [","]
target ::= identifier
| "(" target_list ")"
| "[" [target_list] "]"
| attributeref
| subscription
| slicing
(See section Primaries for the syntax definitions for the last three
symbols.)
An assignment statement evaluates the expression list (remember that
this can be a single expression or a comma-separated list, the latter
yielding a tuple) and assigns the single resulting object to each of
the target lists, from left to right.
Assignment is defined recursively depending on the form of the target
(list). When a target is part of a mutable object (an attribute
reference, subscription or slicing), the mutable object must
ultimately perform the assignment and decide about its validity, and
may raise an exception if the assignment is unacceptable. The rules
observed by various types and the exceptions raised are given with the
definition of the object types (see section The standard type
hierarchy).
Assignment of an object to a target list is recursively defined as
follows.
* If the target list is a single target: The object is assigned to
that target.
* If the target list is a comma-separated list of targets: The
object must be an iterable with the same number of items as there
are targets in the target list, and the items are assigned, from
left to right, to the corresponding targets.
Assignment of an object to a single target is recursively defined as
follows.
* If the target is an identifier (name):
* If the name does not occur in a "global" statement in the
current code block: the name is bound to the object in the current
local namespace.
* Otherwise: the name is bound to the object in the current global
namespace.
The name is rebound if it was already bound. This may cause the
reference count for the object previously bound to the name to reach
zero, causing the object to be deallocated and its destructor (if it
has one) to be called.
* If the target is a target list enclosed in parentheses or in
square brackets: The object must be an iterable with the same number
of items as there are targets in the target list, and its items are
assigned, from left to right, to the corresponding targets.
* If the target is an attribute reference: The primary expression in
the reference is evaluated. It should yield an object with
assignable attributes; if this is not the case, "TypeError" is
raised. That object is then asked to assign the assigned object to
the given attribute; if it cannot perform the assignment, it raises
an exception (usually but not necessarily "AttributeError").
Note: If the object is a class instance and the attribute reference
occurs on both sides of the assignment operator, the RHS expression,
"a.x" can access either an instance attribute or (if no instance
attribute exists) a class attribute. The LHS target "a.x" is always
set as an instance attribute, creating it if necessary. Thus, the
two occurrences of "a.x" do not necessarily refer to the same
attribute: if the RHS expression refers to a class attribute, the
LHS creates a new instance attribute as the target of the
assignment:
class Cls:
x = 3 # class variable
inst = Cls()
inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3
This description does not necessarily apply to descriptor
attributes, such as properties created with "property()".
* If the target is a subscription: The primary expression in the
reference is evaluated. It should yield either a mutable sequence
object (such as a list) or a mapping object (such as a dictionary).
Next, the subscript expression is evaluated.
If the primary is a mutable sequence object (such as a list), the
subscript must yield a plain integer. If it is negative, the
sequence's length is added to it. The resulting value must be a
nonnegative integer less than the sequence's length, and the
sequence is asked to assign the assigned object to its item with
that index. If the index is out of range, "IndexError" is raised
(assignment to a subscripted sequence cannot add new items to a
list).
If the primary is a mapping object (such as a dictionary), the
subscript must have a type compatible with the mapping's key type,
and the mapping is then asked to create a key/datum pair which maps
the subscript to the assigned object. This can either replace an
existing key/value pair with the same key value, or insert a new
key/value pair (if no key with the same value existed).
* If the target is a slicing: The primary expression in the
reference is evaluated. It should yield a mutable sequence object
(such as a list). The assigned object should be a sequence object
of the same type. Next, the lower and upper bound expressions are
evaluated, insofar they are present; defaults are zero and the
sequence's length. The bounds should evaluate to (small) integers.
If either bound is negative, the sequence's length is added to it.
The resulting bounds are clipped to lie between zero and the
sequence's length, inclusive. Finally, the sequence object is asked
to replace the slice with the items of the assigned sequence. The
length of the slice may be different from the length of the assigned
sequence, thus changing the length of the target sequence, if the
object allows it.
**CPython implementation detail:** In the current implementation, the
syntax for targets is taken to be the same as for expressions, and
invalid syntax is rejected during the code generation phase, causing
less detailed error messages.
WARNING: Although the definition of assignment implies that overlaps
between the left-hand side and the right-hand side are 'safe' (for
example "a, b = b, a" swaps two variables), overlaps *within* the
collection of assigned-to variables are not safe! For instance, the
following program prints "[0, 2]":
x = [0, 1]
i = 0
i, x[i] = 1, 2
print x
Augmented assignment statements
===============================
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions for the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
t
assignments�
Identifiers (Names)
*******************
An identifier occurring as an atom is a name. See section Identifiers
and keywords for lexical definition and section Naming and binding for
documentation of naming and binding.
When the name is bound to an object, evaluation of the atom yields
that object. When a name is not bound, an attempt to evaluate it
raises a "NameError" exception.
**Private name mangling:** When an identifier that textually occurs in
a class definition begins with two or more underscore characters and
does not end in two or more underscores, it is considered a *private
name* of that class. Private names are transformed to a longer form
before code is generated for them. The transformation inserts the
class name, with leading underscores removed and a single underscore
inserted, in front of the name. For example, the identifier "__spam"
occurring in a class named "Ham" will be transformed to "_Ham__spam".
This transformation is independent of the syntactical context in which
the identifier is used. If the transformed name is extremely long
(longer than 255 characters), implementation defined truncation may
happen. If the class name consists only of underscores, no
transformation is done.
s atom-identifierss
Literals
********
Python supports string literals and various numeric literals:
literal ::= stringliteral | integer | longinteger
| floatnumber | imagnumber
Evaluation of a literal yields an object of the given type (string,
integer, long integer, floating point number, complex number) with the
given value. The value may be approximated in the case of floating
point and imaginary (complex) literals. See section Literals for
details.
All literals correspond to immutable data types, and hence the
object's identity is less important than its value. Multiple
evaluations of literals with the same value (either the same
occurrence in the program text or a different occurrence) may obtain
the same object or a different object with the same value.
s
atom-literalssU*
Customizing attribute access
****************************
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when an attribute lookup has not found the attribute in the
usual places (i.e. it is not an instance attribute nor is it found
in the class tree for "self"). "name" is the attribute name. This
method should return the (computed) attribute value or raise an
"AttributeError" exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control in new-style classes.
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should not simply execute "self.name = value" --- this would cause
a recursive call to itself. Instead, it should insert the value in
the dictionary of instance attributes, e.g., "self.__dict__[name] =
value". For new-style classes, rather than accessing the instance
dictionary, it should call the base class method with the same
name, for example, "object.__setattr__(self, name, value)".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
More attribute access for new-style classes
===========================================
The following methods only apply to new-style classes.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note: This method may still be bypassed when looking up special
methods as the result of implicit invocation via language syntax
or built-in functions. See Special method lookup for new-style
classes.
Implementing Descriptors
========================
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner's class
dictionary or in the class dictionary for one of its parents). In the
examples below, "the attribute" refers to the attribute whose name is
the key of the property in the owner class' "__dict__".
object.__get__(self, instance, owner)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). *owner* is always the owner class, while *instance* is the
instance that the attribute was accessed through, or "None" when
the attribute is accessed through the *owner*. This method should
return the (computed) attribute value or raise an "AttributeError"
exception.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
Invoking Descriptors
====================
In general, a descriptor is an object attribute with "binding
behavior", one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object's dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called. Note that descriptors are only invoked for new
style objects or classes (ones that subclass "object()" or "type()").
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to a new-style object instance, "a.x" is transformed
into the call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a new-style class, "A.x" is transformed into the
call: "A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined. A descriptor can define
any combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object's
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
=========
By default, instances of both old and new-style classes have a
dictionary for attribute storage. This wastes space for objects
having very few instance variables. The space consumption can become
acute when creating large numbers of instances.
The default can be overridden by defining *__slots__* in a new-style
class definition. The *__slots__* declaration takes a sequence of
instance variables and reserves just enough space in each instance to
hold a value for each variable. Space is saved because *__dict__* is
not created for each instance.
__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. If defined in a
new-style class, *__slots__* reserves space for the declared
variables and prevents the automatic creation of *__dict__* and
*__weakref__* for each instance.
New in version 2.2.
Notes on using *__slots__*
* When inheriting from a class without *__slots__*, the *__dict__*
attribute of that class will always be accessible, so a *__slots__*
definition in the subclass is meaningless.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
Changed in version 2.3: Previously, adding "'__dict__'" to the
*__slots__* declaration would not enable the assignment of new
attributes not specifically listed in the sequence of instance
variable names.
* Without a *__weakref__* variable for each instance, classes
defining *__slots__* do not support weak references to its
instances. If weak reference support is needed, then add
"'__weakref__'" to the sequence of strings in the *__slots__*
declaration.
Changed in version 2.3: Previously, adding "'__weakref__'" to the
*__slots__* declaration would not enable support for weak
references.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is limited to the class
where it is defined. As a result, subclasses will have a *__dict__*
unless they also define *__slots__* (which must only contain names
of any *additional* slots).
* If a class defines a slot also defined in a base class, the
instance variable defined by the base class slot is inaccessible
(except by retrieving its descriptor directly from the base class).
This renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
"variable-length" built-in types such as "long", "str" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings
may also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
Changed in version 2.6: Previously, *__class__* assignment raised an
error if either new or old class had *__slots__*.
s attribute-accesss_
Attribute references
********************
An attribute reference is a primary followed by a period and a name:
attributeref ::= primary "." identifier
The primary must evaluate to an object of a type that supports
attribute references, e.g., a module, list, or an instance. This
object is then asked to produce the attribute whose name is the
identifier. If this attribute is not available, the exception
"AttributeError" is raised. Otherwise, the type and value of the
object produced is determined by the object. Multiple evaluations of
the same attribute reference may yield different objects.
s attribute-referencess�
Augmented assignment statements
*******************************
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions for the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
t augassignsn
Binary arithmetic operations
****************************
The binary arithmetic operations have the conventional priority
levels. Note that some of these operations also apply to certain non-
numeric types. Apart from the power operator, there are only two
levels, one for multiplicative operators and one for additive
operators:
m_expr ::= u_expr | m_expr "*" u_expr | m_expr "//" u_expr | m_expr "/" u_expr
| m_expr "%" u_expr
a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr
The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer (plain or long) and the other must be a sequence. In the
former case, the numbers are converted to a common type and then
multiplied together. In the latter case, sequence repetition is
performed; a negative repetition factor yields an empty sequence.
The "/" (division) and "//" (floor division) operators yield the
quotient of their arguments. The numeric arguments are first
converted to a common type. Plain or long integer division yields an
integer of the same type; the result is that of mathematical division
with the 'floor' function applied to the result. Division by zero
raises the "ZeroDivisionError" exception.
The "%" (modulo) operator yields the remainder from the division of
the first argument by the second. The numeric arguments are first
converted to a common type. A zero right argument raises the
"ZeroDivisionError" exception. The arguments may be floating point
numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 +
0.34".) The modulo operator always yields a result with the same sign
as its second operand (or zero); the absolute value of the result is
strictly smaller than the absolute value of the second operand [2].
The integer division and modulo operators are connected by the
following identity: "x == (x/y)*y + (x%y)". Integer division and
modulo are also connected with the built-in function "divmod()":
"divmod(x, y) == (x/y, x%y)". These identities don't hold for
floating point numbers; there similar identities hold approximately
where "x/y" is replaced by "floor(x/y)" or "floor(x/y) - 1" [3].
In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string and unicode objects to perform
string formatting (also known as interpolation). The syntax for string
formatting is described in the Python Library Reference, section
String Formatting Operations.
Deprecated since version 2.3: The floor division operator, the modulo
operator, and the "divmod()" function are no longer defined for
complex numbers. Instead, convert to a floating point number using
the "abs()" function if appropriate.
The "+" (addition) operator yields the sum of its arguments. The
arguments must either both be numbers or both sequences of the same
type. In the former case, the numbers are converted to a common type
and then added together. In the latter case, the sequences are
concatenated.
The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.
t binarys�
Binary bitwise operations
*************************
Each of the three bitwise operations has a different priority level:
and_expr ::= shift_expr | and_expr "&" shift_expr
xor_expr ::= and_expr | xor_expr "^" and_expr
or_expr ::= xor_expr | or_expr "|" xor_expr
The "&" operator yields the bitwise AND of its arguments, which must
be plain or long integers. The arguments are converted to a common
type.
The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be plain or long integers. The arguments are
converted to a common type.
The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be plain or long integers. The arguments are converted to
a common type.
t bitwises~
Code Objects
************
Code objects are used by the implementation to represent "pseudo-
compiled" executable Python code such as a function body. They differ
from function objects because they don't contain a reference to their
global execution environment. Code objects are returned by the built-
in "compile()" function and can be extracted from function objects
through their "func_code" attribute. See also the "code" module.
A code object can be executed or evaluated by passing it (instead of a
source string) to the "exec" statement or the built-in "eval()"
function.
See The standard type hierarchy for more information.
s bltin-code-objectssE
The Ellipsis Object
*******************
This object is used by extended slice notation (see Slicings). It
supports no special operations. There is exactly one ellipsis object,
named "Ellipsis" (a built-in name).
It is written as "Ellipsis". When in a subscript, it can also be
written as "...", for example "seq[...]".
s bltin-ellipsis-objects�+
File Objects
************
File objects are implemented using C's "stdio" package and can be
created with the built-in "open()" function. File objects are also
returned by some other built-in functions and methods, such as
"os.popen()" and "os.fdopen()" and the "makefile()" method of socket
objects. Temporary files can be created using the "tempfile" module,
and high-level file operations such as copying, moving, and deleting
files and directories can be achieved with the "shutil" module.
When a file operation fails for an I/O-related reason, the exception
"IOError" is raised. This includes situations where the operation is
not defined for some reason, like "seek()" on a tty device or writing
a file opened for reading.
Files have the following methods:
file.close()
Close the file. A closed file cannot be read or written any more.
Any operation which requires that the file be open will raise a
"ValueError" after the file has been closed. Calling "close()"
more than once is allowed.
As of Python 2.5, you can avoid having to call this method
explicitly if you use the "with" statement. For example, the
following code will automatically close *f* when the "with" block
is exited:
from __future__ import with_statement # This isn't required in Python 2.6
with open("hello.txt") as f:
for line in f:
print line,
In older versions of Python, you would have needed to do this to
get the same effect:
f = open("hello.txt")
try:
for line in f:
print line,
finally:
f.close()
Note: Not all "file-like" types in Python support use as a
context manager for the "with" statement. If your code is
intended to work with any file-like object, you can use the
function "contextlib.closing()" instead of using the object
directly.
file.flush()
Flush the internal buffer, like "stdio"'s "fflush()". This may be
a no-op on some file-like objects.
Note: "flush()" does not necessarily write the file's data to
disk. Use "flush()" followed by "os.fsync()" to ensure this
behavior.
file.fileno()
Return the integer "file descriptor" that is used by the underlying
implementation to request I/O operations from the operating system.
This can be useful for other, lower level interfaces that use file
descriptors, such as the "fcntl" module or "os.read()" and friends.
Note: File-like objects which do not have a real file descriptor
should *not* provide this method!
file.isatty()
Return "True" if the file is connected to a tty(-like) device, else
"False".
Note: If a file-like object is not associated with a real file,
this method should *not* be implemented.
file.next()
A file object is its own iterator, for example "iter(f)" returns
*f* (unless *f* is closed). When a file is used as an iterator,
typically in a "for" loop (for example, "for line in f: print
line.strip()"), the "next()" method is called repeatedly. This
method returns the next input line, or raises "StopIteration" when
EOF is hit when the file is open for reading (behavior is undefined
when the file is open for writing). In order to make a "for" loop
the most efficient way of looping over the lines of a file (a very
common operation), the "next()" method uses a hidden read-ahead
buffer. As a consequence of using a read-ahead buffer, combining
"next()" with other file methods (like "readline()") does not work
right. However, using "seek()" to reposition the file to an
absolute position will flush the read-ahead buffer.
New in version 2.3.
file.read([size])
Read at most *size* bytes from the file (less if the read hits EOF
before obtaining *size* bytes). If the *size* argument is negative
or omitted, read all data until EOF is reached. The bytes are
returned as a string object. An empty string is returned when EOF
is encountered immediately. (For certain files, like ttys, it
makes sense to continue reading after an EOF is hit.) Note that
this method may call the underlying C function "fread()" more than
once in an effort to acquire as close to *size* bytes as possible.
Also note that when in non-blocking mode, less data than was
requested may be returned, even if no *size* parameter was given.
Note: This function is simply a wrapper for the underlying
"fread()" C function, and will behave the same in corner cases,
such as whether the EOF value is cached.
file.readline([size])
Read one entire line from the file. A trailing newline character
is kept in the string (but may be absent when a file ends with an
incomplete line). [6] If the *size* argument is present and non-
negative, it is a maximum byte count (including the trailing
newline) and an incomplete line may be returned. When *size* is not
0, an empty string is returned *only* when EOF is encountered
immediately.
Note: Unlike "stdio"'s "fgets()", the returned string contains
null characters ("'\0'") if they occurred in the input.
file.readlines([sizehint])
Read until EOF using "readline()" and return a list containing the
lines thus read. If the optional *sizehint* argument is present,
instead of reading up to EOF, whole lines totalling approximately
*sizehint* bytes (possibly after rounding up to an internal buffer
size) are read. Objects implementing a file-like interface may
choose to ignore *sizehint* if it cannot be implemented, or cannot
be implemented efficiently.
file.xreadlines()
This method returns the same thing as "iter(f)".
New in version 2.1.
Deprecated since version 2.3: Use "for line in file" instead.
file.seek(offset[, whence])
Set the file's current position, like "stdio"'s "fseek()". The
*whence* argument is optional and defaults to "os.SEEK_SET" or "0"
(absolute file positioning); other values are "os.SEEK_CUR" or "1"
(seek relative to the current position) and "os.SEEK_END" or "2"
(seek relative to the file's end). There is no return value.
For example, "f.seek(2, os.SEEK_CUR)" advances the position by two
and "f.seek(-3, os.SEEK_END)" sets the position to the third to
last.
Note that if the file is opened for appending (mode "'a'" or
"'a+'"), any "seek()" operations will be undone at the next write.
If the file is only opened for writing in append mode (mode "'a'"),
this method is essentially a no-op, but it remains useful for files
opened in append mode with reading enabled (mode "'a+'"). If the
file is opened in text mode (without "'b'"), only offsets returned
by "tell()" are legal. Use of other offsets causes undefined
behavior.
Note that not all file objects are seekable.
Changed in version 2.6: Passing float values as offset has been
deprecated.
file.tell()
Return the file's current position, like "stdio"'s "ftell()".
Note: On Windows, "tell()" can return illegal values (after an
"fgets()") when reading files with Unix-style line-endings. Use
binary mode ("'rb'") to circumvent this problem.
file.truncate([size])
Truncate the file's size. If the optional *size* argument is
present, the file is truncated to (at most) that size. The size
defaults to the current position. The current file position is not
changed. Note that if a specified size exceeds the file's current
size, the result is platform-dependent: possibilities include that
the file may remain unchanged, increase to the specified size as if
zero-filled, or increase to the specified size with undefined new
content. Availability: Windows, many Unix variants.
file.write(str)
Write a string to the file. There is no return value. Due to
buffering, the string may not actually show up in the file until
the "flush()" or "close()" method is called.
file.writelines(sequence)
Write a sequence of strings to the file. The sequence can be any
iterable object producing strings, typically a list of strings.
There is no return value. (The name is intended to match
"readlines()"; "writelines()" does not add line separators.)
Files support the iterator protocol. Each iteration returns the same
result as "readline()", and iteration ends when the "readline()"
method returns an empty string.
File objects also offer a number of other interesting attributes.
These are not required for file-like objects, but should be
implemented if they make sense for the particular object.
file.closed
bool indicating the current state of the file object. This is a
read-only attribute; the "close()" method changes the value. It may
not be available on all file-like objects.
file.encoding
The encoding that this file uses. When Unicode strings are written
to a file, they will be converted to byte strings using this
encoding. In addition, when the file is connected to a terminal,
the attribute gives the encoding that the terminal is likely to use
(that information might be incorrect if the user has misconfigured
the terminal). The attribute is read-only and may not be present
on all file-like objects. It may also be "None", in which case the
file uses the system default encoding for converting Unicode
strings.
New in version 2.3.
file.errors
The Unicode error handler used along with the encoding.
New in version 2.6.
file.mode
The I/O mode for the file. If the file was created using the
"open()" built-in function, this will be the value of the *mode*
parameter. This is a read-only attribute and may not be present on
all file-like objects.
file.name
If the file object was created using "open()", the name of the
file. Otherwise, some string that indicates the source of the file
object, of the form "<...>". This is a read-only attribute and may
not be present on all file-like objects.
file.newlines
If Python was built with *universal newlines* enabled (the default)
this read-only attribute exists, and for files opened in universal
newline read mode it keeps track of the types of newlines
encountered while reading the file. The values it can take are
"'\r'", "'\n'", "'\r\n'", "None" (unknown, no newlines read yet) or
a tuple containing all the newline types seen, to indicate that
multiple newline conventions were encountered. For files not opened
in universal newlines read mode the value of this attribute will be
"None".
file.softspace
Boolean that indicates whether a space character needs to be
printed before another value when using the "print" statement.
Classes that are trying to simulate a file object should also have
a writable "softspace" attribute, which should be initialized to
zero. This will be automatic for most classes implemented in
Python (care may be needed for objects that override attribute
access); types implemented in C will have to provide a writable
"softspace" attribute.
Note: This attribute is not used to control the "print"
statement, but to allow the implementation of "print" to keep
track of its internal state.
s bltin-file-objectss�
The Null Object
***************
This object is returned by functions that don't explicitly return a
value. It supports no special operations. There is exactly one null
object, named "None" (a built-in name).
It is written as "None".
s bltin-null-objects3
Type Objects
************
Type objects represent the various object types. An object's type is
accessed by the built-in function "type()". There are no special
operations on types. The standard module "types" defines names for
all standard built-in types.
Types are written like this: "<type 'int'>".
s bltin-type-objectss�
Boolean operations
******************
or_test ::= and_test | or_test "or" and_test
and_test ::= not_test | and_test "and" not_test
not_test ::= comparison | "not" not_test
In the context of Boolean operations, and also when expressions are
used by control flow statements, the following values are interpreted
as false: "False", "None", numeric zero of all types, and empty
strings and containers (including strings, tuples, lists,
dictionaries, sets and frozensets). All other values are interpreted
as true. (See the "__nonzero__()" special method for a way to change
this.)
The operator "not" yields "True" if its argument is false, "False"
otherwise.
The expression "x and y" first evaluates *x*; if *x* is false, its
value is returned; otherwise, *y* is evaluated and the resulting value
is returned.
The expression "x or y" first evaluates *x*; if *x* is true, its value
is returned; otherwise, *y* is evaluated and the resulting value is
returned.
(Note that neither "and" nor "or" restrict the value and type they
return to "False" and "True", but rather return the last evaluated
argument. This is sometimes useful, e.g., if "s" is a string that
should be replaced by a default value if it is empty, the expression
"s or 'foo'" yields the desired value. Because "not" has to invent a
value anyway, it does not bother to return a value of the same type as
its argument, so e.g., "not 'foo'" yields "False", not "''".)
t booleanss%
The "break" statement
*********************
break_stmt ::= "break"
"break" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop.
It terminates the nearest enclosing loop, skipping the optional "else"
clause if the loop has one.
If a "for" loop is terminated by "break", the loop control target
keeps its current value.
When "break" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
loop.
t breaks�
Emulating callable objects
**************************
object.__call__(self[, args...])
Called when the instance is "called" as a function; if this method
is defined, "x(arg1, arg2, ...)" is a shorthand for
"x.__call__(arg1, arg2, ...)".
s callable-typess�
Calls
*****
A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:
call ::= primary "(" [argument_list [","]
| expression genexpr_for] ")"
argument_list ::= positional_arguments ["," keyword_arguments]
["," "*" expression] ["," keyword_arguments]
["," "**" expression]
| keyword_arguments ["," "*" expression]
["," "**" expression]
| "*" expression ["," keyword_arguments] ["," "**" expression]
| "**" expression
positional_arguments ::= expression ("," expression)*
keyword_arguments ::= keyword_item ("," keyword_item)*
keyword_item ::= identifier "=" expression
A trailing comma may be present after the positional and keyword
arguments but does not affect the semantics.
The primary must evaluate to a callable object (user-defined
functions, built-in functions, methods of built-in objects, class
objects, methods of class instances, and certain class instances
themselves are callable; extensions may define additional callable
object types). All argument expressions are evaluated before the call
is attempted. Please refer to section Function definitions for the
syntax of formal *parameter* lists.
If keyword arguments are present, they are first converted to
positional arguments, as follows. First, a list of unfilled slots is
created for the formal parameters. If there are N positional
arguments, they are placed in the first N slots. Next, for each
keyword argument, the identifier is used to determine the
corresponding slot (if the identifier is the same as the first formal
parameter name, the first slot is used, and so on). If the slot is
already filled, a "TypeError" exception is raised. Otherwise, the
value of the argument is placed in the slot, filling it (even if the
expression is "None", it fills the slot). When all arguments have
been processed, the slots that are still unfilled are filled with the
corresponding default value from the function definition. (Default
values are calculated, once, when the function is defined; thus, a
mutable object such as a list or dictionary used as default value will
be shared by all calls that don't specify an argument value for the
corresponding slot; this should usually be avoided.) If there are any
unfilled slots for which no default value is specified, a "TypeError"
exception is raised. Otherwise, the list of filled slots is used as
the argument list for the call.
**CPython implementation detail:** An implementation may provide
built-in functions whose positional parameters do not have names, even
if they are 'named' for the purpose of documentation, and which
therefore cannot be supplied by keyword. In CPython, this is the case
for functions implemented in C that use "PyArg_ParseTuple()" to parse
their arguments.
If there are more positional arguments than there are formal parameter
slots, a "TypeError" exception is raised, unless a formal parameter
using the syntax "*identifier" is present; in this case, that formal
parameter receives a tuple containing the excess positional arguments
(or an empty tuple if there were no excess positional arguments).
If any keyword argument does not correspond to a formal parameter
name, a "TypeError" exception is raised, unless a formal parameter
using the syntax "**identifier" is present; in this case, that formal
parameter receives a dictionary containing the excess keyword
arguments (using the keywords as keys and the argument values as
corresponding values), or a (new) empty dictionary if there were no
excess keyword arguments.
If the syntax "*expression" appears in the function call, "expression"
must evaluate to an iterable. Elements from this iterable are treated
as if they were additional positional arguments; if there are
positional arguments *x1*, ..., *xN*, and "expression" evaluates to a
sequence *y1*, ..., *yM*, this is equivalent to a call with M+N
positional arguments *x1*, ..., *xN*, *y1*, ..., *yM*.
A consequence of this is that although the "*expression" syntax may
appear *after* some keyword arguments, it is processed *before* the
keyword arguments (and the "**expression" argument, if any -- see
below). So:
>>> def f(a, b):
... print a, b
...
>>> f(b=1, *(2,))
2 1
>>> f(a=1, *(2,))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: f() got multiple values for keyword argument 'a'
>>> f(1, *(2,))
1 2
It is unusual for both keyword arguments and the "*expression" syntax
to be used in the same call, so in practice this confusion does not
arise.
If the syntax "**expression" appears in the function call,
"expression" must evaluate to a mapping, the contents of which are
treated as additional keyword arguments. In the case of a keyword
appearing in both "expression" and as an explicit keyword argument, a
"TypeError" exception is raised.
Formal parameters using the syntax "*identifier" or "**identifier"
cannot be used as positional argument slots or as keyword argument
names. Formal parameters using the syntax "(sublist)" cannot be used
as keyword argument names; the outermost sublist corresponds to a
single unnamed argument slot, and the argument value is assigned to
the sublist using the usual tuple assignment rules after all other
parameter processing is done.
A call always returns some value, possibly "None", unless it raises an
exception. How this value is computed depends on the type of the
callable object.
If it is---
a user-defined function:
The code block for the function is executed, passing it the
argument list. The first thing the code block will do is bind the
formal parameters to the arguments; this is described in section
Function definitions. When the code block executes a "return"
statement, this specifies the return value of the function call.
a built-in function or method:
The result is up to the interpreter; see Built-in Functions for the
descriptions of built-in functions and methods.
a class object:
A new instance of that class is returned.
a class instance method:
The corresponding user-defined function is called, with an argument
list that is one longer than the argument list of the call: the
instance becomes the first argument.
a class instance:
The class must define a "__call__()" method; the effect is then the
same as if that method was called.
t callssJ
Class definitions
*****************
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= "class" classname [inheritance] ":" suite
inheritance ::= "(" [expression_list] ")"
classname ::= identifier
A class definition is an executable statement. It first evaluates the
inheritance list, if present. Each item in the inheritance list
should evaluate to a class object or class type which allows
subclassing. The class's suite is then executed in a new execution
frame (see section Naming and binding), using a newly created local
namespace and the original global namespace. (Usually, the suite
contains only function definitions.) When the class's suite finishes
execution, its execution frame is discarded but its local namespace is
saved. [4] A class object is then created using the inheritance list
for the base classes and the saved local namespace for the attribute
dictionary. The class name is bound to this class object in the
original local namespace.
**Programmer's note:** Variables defined in the class definition are
class variables; they are shared by all instances. To create instance
variables, they can be set in a method with "self.name = value". Both
class and instance variables are accessible through the notation
""self.name"", and an instance variable hides a class variable with
the same name when accessed in this way. Class variables can be used
as defaults for instance variables, but using mutable values there can
lead to unexpected results. For *new-style class*es, descriptors can
be used to create instance variables with different implementation
details.
Class definitions, like function definitions, may be wrapped by one or
more *decorator* expressions. The evaluation rules for the decorator
expressions are the same as for functions. The result must be a class
object, which is then bound to the class name.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless
there is a "finally" clause which happens to raise another
exception. That new exception causes the old one to be lost.
[2] Currently, control "flows off the end" except in the case of
an exception or the execution of a "return", "continue", or
"break" statement.
[3] A string literal appearing as the first statement in the
function body is transformed into the function's "__doc__"
attribute and therefore the function's *docstring*.
[4] A string literal appearing as the first statement in the class
body is transformed into the namespace's "__doc__" item and
therefore the class's *docstring*.
t classs$
Comparisons
***********
Unlike C, all comparison operations in Python have the same priority,
which is lower than that of any arithmetic, shifting or bitwise
operation. Also unlike C, expressions like "a < b < c" have the
interpretation that is conventional in mathematics:
comparison ::= or_expr ( comp_operator or_expr )*
comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "<>" | "!="
| "is" ["not"] | ["not"] "in"
Comparisons yield boolean values: "True" or "False".
Comparisons can be chained arbitrarily, e.g., "x < y <= z" is
equivalent to "x < y and y <= z", except that "y" is evaluated only
once (but in both cases "z" is not evaluated at all when "x < y" is
found to be false).
Formally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,
*op2*, ..., *opN* are comparison operators, then "a op1 b op2 c ... y
opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except
that each expression is evaluated at most once.
Note that "a op1 b op2 c" doesn't imply any kind of comparison between
*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though
perhaps not pretty).
The forms "<>" and "!=" are equivalent; for consistency with C, "!="
is preferred; where "!=" is mentioned below "<>" is also accepted.
The "<>" spelling is considered obsolescent.
Value comparisons
=================
The operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects. The objects do not need to have the same type.
Chapter Objects, values and types states that objects have a value (in
addition to type and identity). The value of an object is a rather
abstract notion in Python: For example, there is no canonical access
method for an object's value. Also, there is no requirement that the
value of an object should be constructed in a particular way, e.g.
comprised of all its data attributes. Comparison operators implement a
particular notion of what the value of an object is. One can think of
them as defining the value of an object indirectly, by means of their
comparison implementation.
Types can customize their comparison behavior by implementing a
"__cmp__()" method or *rich comparison methods* like "__lt__()",
described in Basic customization.
The default behavior for equality comparison ("==" and "!=") is based
on the identity of the objects. Hence, equality comparison of
instances with the same identity results in equality, and equality
comparison of instances with different identities results in
inequality. A motivation for this default behavior is the desire that
all objects should be reflexive (i.e. "x is y" implies "x == y").
The default order comparison ("<", ">", "<=", and ">=") gives a
consistent but arbitrary order.
(This unusual definition of comparison was used to simplify the
definition of operations like sorting and the "in" and "not in"
operators. In the future, the comparison rules for objects of
different types are likely to change.)
The behavior of the default equality comparison, that instances with
different identities are always unequal, may be in contrast to what
types will need that have a sensible definition of object value and
value-based equality. Such types will need to customize their
comparison behavior, and in fact, a number of built-in types have done
that.
The following list describes the comparison behavior of the most
important built-in types.
* Numbers of built-in numeric types (Numeric Types --- int, float,
long, complex) and of the standard library types
"fractions.Fraction" and "decimal.Decimal" can be compared within
and across their types, with the restriction that complex numbers do
not support order comparison. Within the limits of the types
involved, they compare mathematically (algorithmically) correct
without loss of precision.
* Strings (instances of "str" or "unicode") compare
lexicographically using the numeric equivalents (the result of the
built-in function "ord()") of their characters. [4] When comparing
an 8-bit string and a Unicode string, the 8-bit string is converted
to Unicode. If the conversion fails, the strings are considered
unequal.
* Instances of "tuple" or "list" can be compared only within each of
their types. Equality comparison across these types results in
unequality, and ordering comparison across these types gives an
arbitrary order.
These sequences compare lexicographically using comparison of
corresponding elements, whereby reflexivity of the elements is
enforced.
In enforcing reflexivity of elements, the comparison of collections
assumes that for a collection element "x", "x == x" is always true.
Based on that assumption, element identity is compared first, and
element comparison is performed only for distinct elements. This
approach yields the same result as a strict element comparison
would, if the compared elements are reflexive. For non-reflexive
elements, the result is different than for strict element
comparison.
Lexicographical comparison between built-in collections works as
follows:
* For two collections to compare equal, they must be of the same
type, have the same length, and each pair of corresponding
elements must compare equal (for example, "[1,2] == (1,2)" is
false because the type is not the same).
* Collections are ordered the same as their first unequal elements
(for example, "cmp([1,2,x], [1,2,y])" returns the same as
"cmp(x,y)"). If a corresponding element does not exist, the
shorter collection is ordered first (for example, "[1,2] <
[1,2,3]" is true).
* Mappings (instances of "dict") compare equal if and only if they
have equal *(key, value)* pairs. Equality comparison of the keys and
values enforces reflexivity.
Outcomes other than equality are resolved consistently, but are not
otherwise defined. [5]
* Most other objects of built-in types compare unequal unless they
are the same object; the choice whether one object is considered
smaller or larger than another one is made arbitrarily but
consistently within one execution of a program.
User-defined classes that customize their comparison behavior should
follow some consistency rules, if possible:
* Equality comparison should be reflexive. In other words, identical
objects should compare equal:
"x is y" implies "x == y"
* Comparison should be symmetric. In other words, the following
expressions should have the same result:
"x == y" and "y == x"
"x != y" and "y != x"
"x < y" and "y > x"
"x <= y" and "y >= x"
* Comparison should be transitive. The following (non-exhaustive)
examples illustrate that:
"x > y and y > z" implies "x > z"
"x < y and y <= z" implies "x < z"
* Inverse comparison should result in the boolean negation. In other
words, the following expressions should have the same result:
"x == y" and "not x != y"
"x < y" and "not x >= y" (for total ordering)
"x > y" and "not x <= y" (for total ordering)
The last two expressions apply to totally ordered collections (e.g.
to sequences, but not to sets or mappings). See also the
"total_ordering()" decorator.
* The "hash()" result should be consistent with equality. Objects
that are equal should either have the same hash value, or be marked
as unhashable.
Python does not enforce these consistency rules.
Membership test operations
==========================
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y". If an exception is raised
during the iteration, it is as if "in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse true value of
"in".
Identity comparisons
====================
The operators "is" and "is not" test for object identity: "x is y" is
true if and only if *x* and *y* are the same object. "x is not y"
yields the inverse truth value. [6]
t comparisonsspP
Compound statements
*******************
Compound statements contain (groups of) other statements; they affect
or control the execution of those other statements in some way. In
general, compound statements span multiple lines, although in simple
incarnations a whole compound statement may be contained in one line.
The "if", "while" and "for" statements implement traditional control
flow constructs. "try" specifies exception handlers and/or cleanup
code for a group of statements. Function and class definitions are
also syntactically compound statements.
Compound statements consist of one or more 'clauses.' A clause
consists of a header and a 'suite.' The clause headers of a
particular compound statement are all at the same indentation level.
Each clause header begins with a uniquely identifying keyword and ends
with a colon. A suite is a group of statements controlled by a
clause. A suite can be one or more semicolon-separated simple
statements on the same line as the header, following the header's
colon, or it can be one or more indented statements on subsequent
lines. Only the latter form of suite can contain nested compound
statements; the following is illegal, mostly because it wouldn't be
clear to which "if" clause a following "else" clause would belong:
if test1: if test2: print x
Also note that the semicolon binds tighter than the colon in this
context, so that in the following example, either all or none of the
"print" statements are executed:
if x < y < z: print x; print y; print z
Summarizing:
compound_stmt ::= if_stmt
| while_stmt
| for_stmt
| try_stmt
| with_stmt
| funcdef
| classdef
| decorated
suite ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT
statement ::= stmt_list NEWLINE | compound_stmt
stmt_list ::= simple_stmt (";" simple_stmt)* [";"]
Note that statements always end in a "NEWLINE" possibly followed by a
"DEDENT". Also note that optional continuation clauses always begin
with a keyword that cannot start a statement, thus there are no
ambiguities (the 'dangling "else"' problem is solved in Python by
requiring nested "if" statements to be indented).
The formatting of the grammar rules in the following sections places
each clause on a separate line for clarity.
The "if" statement
==================
The "if" statement is used for conditional execution:
if_stmt ::= "if" expression ":" suite
( "elif" expression ":" suite )*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
The "while" statement
=====================
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause's suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
The "for" statement
===================
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order of ascending indices. Each
item in turn is assigned to the target list using the standard rules
for assignments, and then the suite is executed. When the items are
exhausted (which is immediately when the sequence is empty), the suite
in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause's suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there was no next
item.
The suite may assign to the variable(s) in the target list; this does
not affect the next item assigned to it.
The target list is not deleted when the loop is finished, but if the
sequence is empty, it will not have been assigned to at all by the
loop. Hint: the built-in function "range()" returns a sequence of
integers suitable to emulate the effect of Pascal's "for i := a to b
do"; e.g., "range(3)" returns the list "[0, 1, 2]".
Note: There is a subtlety when the sequence is being modified by the
loop (this can only occur for mutable sequences, i.e. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
The "try" statement
===================
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression [("as" | ",") identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
Changed in version 2.5: In previous versions of Python,
"try"..."except"..."finally" did not work. "try"..."except" had to be
nested in "try"..."finally".
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is "compatible" with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object, or a tuple containing an item compatible with the exception.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified in that except clause, if present, and the except
clause's suite is executed. All except clauses must have an
executable block. When the end of this block is reached, execution
continues normally after the entire try statement. (This means that
if two nested handlers exist for the same exception, and the exception
occurs in the try clause of the inner handler, the outer handler will
not handle the exception.)
Before an except clause's suite is executed, details about the
exception are assigned to three variables in the "sys" module:
"sys.exc_type" receives the object identifying the exception;
"sys.exc_value" receives the exception's parameter;
"sys.exc_traceback" receives a traceback object (see section The
standard type hierarchy) identifying the point in the program where
the exception occurred. These details are also available through the
"sys.exc_info()" function, which returns a tuple "(exc_type,
exc_value, exc_traceback)". Use of the corresponding variables is
deprecated in favor of this function, since their use is unsafe in a
threaded program. As of Python 1.5, the variables are restored to
their previous values (before the call) when returning from a function
that handled an exception.
The optional "else" clause is executed if and when control flows off
the end of the "try" clause. [2] Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a 'cleanup' handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception, it is re-raised at the end of the
"finally" clause. If the "finally" clause raises another exception or
executes a "return" or "break" statement, the saved exception is
discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"..."finally" statement, the "finally" clause is
also executed 'on the way out.' A "continue" statement is illegal in
the "finally" clause. (The reason is a problem with the current
implementation --- this restriction may be lifted in the future).
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
The "with" statement
====================
New in version 2.5.
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"..."except"..."finally"
usage patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one "item" proceeds as
follows:
1. The context expression (the expression given in the "with_item")
is evaluated to obtain a context manager.
2. The context manager's "__exit__()" is loaded for later use.
3. The context manager's "__enter__()" method is invoked.
4. If a target was included in the "with" statement, the return
value from "__enter__()" is assigned to it.
Note: The "with" statement guarantees that if the "__enter__()"
method returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
5. The suite is executed.
6. The context manager's "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
suite
is equivalent to
with A() as a:
with B() as b:
suite
Note: In Python 2.5, the "with" statement is only allowed when the
"with_statement" feature has been enabled. It is always enabled in
Python 2.6.
Changed in version 2.7: Support for multiple context expressions.
See also:
**PEP 343** - The "with" statement
The specification, background, and examples for the Python "with"
statement.
Function definitions
====================
A function definition defines a user-defined function object (see
section The standard type hierarchy):
decorated ::= decorators (classdef | funcdef)
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
funcdef ::= "def" funcname "(" [parameter_list] ")" ":" suite
dotted_name ::= identifier ("." identifier)*
parameter_list ::= (defparameter ",")*
( "*" identifier ["," "**" identifier]
| "**" identifier
| defparameter [","] )
defparameter ::= parameter ["=" expression]
sublist ::= parameter ("," parameter)* [","]
parameter ::= identifier | "(" sublist ")"
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [3]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code:
@f1(arg)
@f2
def func(): pass
is equivalent to:
def func(): pass
func = f1(arg)(f2(func))
When one or more top-level *parameters* have the form *parameter* "="
*expression*, the function is said to have "default parameter values."
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter's default value is
substituted. If a parameter has a default value, all following
parameters must also have a default value --- this is a syntactic
restriction that is not expressed by the grammar.
**Default parameter values are evaluated when the function definition
is executed.** This means that the expression is evaluated once, when
the function is defined, and that the same "pre-computed" value is
used for each call. This is especially important to understand when a
default parameter is a mutable object, such as a list or a dictionary:
if the function modifies the object (e.g. by appending an item to a
list), the default value is in effect modified. This is generally not
what was intended. A way around this is to use "None" as the
default, and explicitly test for it in the body of the function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from position arguments, from keyword
arguments, or from default values. If the form ""*identifier"" is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
""**identifier"" is present, it is initialized to a new dictionary
receiving any excess keyword arguments, defaulting to a new empty
dictionary.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a ""def"" statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The ""def"" form is actually more powerful since it
allows the execution of multiple statements.
**Programmer's note:** Functions are first-class objects. A ""def""
form executed inside a function definition defines a local function
that can be returned or passed around. Free variables used in the
nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
Class definitions
=================
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= "class" classname [inheritance] ":" suite
inheritance ::= "(" [expression_list] ")"
classname ::= identifier
A class definition is an executable statement. It first evaluates the
inheritance list, if present. Each item in the inheritance list
should evaluate to a class object or class type which allows
subclassing. The class's suite is then executed in a new execution
frame (see section Naming and binding), using a newly created local
namespace and the original global namespace. (Usually, the suite
contains only function definitions.) When the class's suite finishes
execution, its execution frame is discarded but its local namespace is
saved. [4] A class object is then created using the inheritance list
for the base classes and the saved local namespace for the attribute
dictionary. The class name is bound to this class object in the
original local namespace.
**Programmer's note:** Variables defined in the class definition are
class variables; they are shared by all instances. To create instance
variables, they can be set in a method with "self.name = value". Both
class and instance variables are accessible through the notation
""self.name"", and an instance variable hides a class variable with
the same name when accessed in this way. Class variables can be used
as defaults for instance variables, but using mutable values there can
lead to unexpected results. For *new-style class*es, descriptors can
be used to create instance variables with different implementation
details.
Class definitions, like function definitions, may be wrapped by one or
more *decorator* expressions. The evaluation rules for the decorator
expressions are the same as for functions. The result must be a class
object, which is then bound to the class name.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless
there is a "finally" clause which happens to raise another
exception. That new exception causes the old one to be lost.
[2] Currently, control "flows off the end" except in the case of
an exception or the execution of a "return", "continue", or
"break" statement.
[3] A string literal appearing as the first statement in the
function body is transformed into the function's "__doc__"
attribute and therefore the function's *docstring*.
[4] A string literal appearing as the first statement in the class
body is transformed into the namespace's "__doc__" item and
therefore the class's *docstring*.
t compounds�
With Statement Context Managers
*******************************
New in version 2.5.
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method's return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller's responsibility.
See also:
**PEP 343** - The "with" statement
The specification, background, and examples for the Python "with"
statement.
s context-managerss�
The "continue" statement
************************
continue_stmt ::= "continue"
"continue" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition or "finally"
clause within that loop. It continues with the next cycle of the
nearest enclosing loop.
When "continue" passes control out of a "try" statement with a
"finally" clause, that "finally" clause is executed before really
starting the next loop cycle.
t continuesB
Arithmetic conversions
**********************
When a description of an arithmetic operator below uses the phrase
"the numeric arguments are converted to a common type," the arguments
are coerced using the coercion rules listed at Coercion rules. If
both arguments are standard numeric types, the following coercions are
applied:
* If either argument is a complex number, the other is converted to
complex;
* otherwise, if either argument is a floating point number, the
other is converted to floating point;
* otherwise, if either argument is a long integer, the other is
converted to long integer;
* otherwise, both must be plain integers and no conversion is
necessary.
Some additional rules apply for certain operators (e.g., a string left
argument to the '%' operator). Extensions can define their own
coercions.
t conversionss�/
Basic customization
*******************
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass's "__new__()" method using
"super(currentclass, cls).__new__(cls[, ...])" with appropriate
arguments and then modifying the newly-created instance as
necessary before returning it.
If "__new__()" returns an instance of *cls*, then the new
instance's "__init__()" method will be invoked like
"__init__(self[, ...])", where *self* is the new instance and the
remaining arguments are the same as were passed to "__new__()".
If "__new__()" does not return an instance of *cls*, then the new
instance's "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class's "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"BaseClass.__init__(self, [args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customise
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a destructor. If a base class has a "__del__()" method, the
derived class's "__del__()" method, if any, must explicitly call it
to ensure proper deletion of the base class part of the instance.
Note that it is possible (though not recommended!) for the
"__del__()" method to postpone destruction of the instance by
creating a new reference to it. It may then be called at a later
time when this new reference is deleted. It is not guaranteed that
"__del__()" methods are called for objects that still exist when
the interpreter exits.
Note: "del x" doesn't directly call "x.__del__()" --- the former
decrements the reference count for "x" by one, and the latter is
only called when "x"'s reference count reaches zero. Some common
situations that may prevent the reference count of an object from
going to zero include: circular references between objects (e.g.,
a doubly-linked list or a tree data structure with parent and
child pointers); a reference to the object on the stack frame of
a function that caught an exception (the traceback stored in
"sys.exc_traceback" keeps the stack frame alive); or a reference
to the object on the stack frame that raised an unhandled
exception in interactive mode (the traceback stored in
"sys.last_traceback" keeps the stack frame alive). The first
situation can only be remedied by explicitly breaking the cycles;
the latter two situations can be resolved by storing "None" in
"sys.exc_traceback" or "sys.last_traceback". Circular references
which are garbage are detected when the option cycle detector is
enabled (it's on by default), but can only be cleaned up if there
are no Python-level "__del__()" methods involved. Refer to the
documentation for the "gc" module for more information about how
"__del__()" methods are handled by the cycle detector,
particularly the description of the "garbage" value.
Warning: Due to the precarious circumstances under which
"__del__()" methods are invoked, exceptions that occur during
their execution are ignored, and a warning is printed to
"sys.stderr" instead. Also, when "__del__()" is invoked in
response to a module being deleted (e.g., when execution of the
program is done), other globals referenced by the "__del__()"
method may already have been deleted or in the process of being
torn down (e.g. the import machinery shutting down). For this
reason, "__del__()" methods should do the absolute minimum needed
to maintain external invariants. Starting with version 1.5,
Python guarantees that globals whose name begins with a single
underscore are deleted from their module before other globals are
deleted; if no other references to such globals exist, this may
help in assuring that imported modules are still available at the
time when the "__del__()" method is called.
See also the "-R" command-line option.
object.__repr__(self)
Called by the "repr()" built-in function and by string conversions
(reverse quotes) to compute the "official" string representation of
an object. If at all possible, this should look like a valid
Python expression that could be used to recreate an object with the
same value (given an appropriate environment). If this is not
possible, a string of the form "<...some useful description...>"
should be returned. The return value must be a string object. If a
class defines "__repr__()" but not "__str__()", then "__repr__()"
is also used when an "informal" string representation of instances
of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by the "str()" built-in function and by the "print"
statement to compute the "informal" string representation of an
object. This differs from "__repr__()" in that it does not have to
be a valid Python expression: a more convenient or concise
representation may be used instead. The return value must be a
string object.
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
New in version 2.1.
These are the so-called "rich comparison" methods, and are called
for comparison operators in preference to "__cmp__()" below. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" and "x<>y" call "x.__ne__(y)",
"x>y" calls "x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
There are no implied relationships among the comparison operators.
The truth of "x==y" does not imply that "x!=y" is false.
Accordingly, when defining "__eq__()", one should also define
"__ne__()" so that the operators will behave as expected. See the
paragraph on "__hash__()" for some important notes on creating
*hashable* objects which support custom comparison operations and
are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other's
reflection, "__le__()" and "__ge__()" are each other's reflection,
and "__eq__()" and "__ne__()" are their own reflection.
Arguments to rich comparison methods are never coerced.
To automatically generate ordering operations from a single root
operation, see "functools.total_ordering()".
object.__cmp__(self, other)
Called by comparison operations if rich comparison (see above) is
not defined. Should return a negative integer if "self < other",
zero if "self == other", a positive integer if "self > other". If
no "__cmp__()", "__eq__()" or "__ne__()" operation is defined,
class instances are compared by object identity ("address"). See
also the description of "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys. (Note: the
restriction that exceptions are not propagated by "__cmp__()" has
been removed since Python 1.5.)
object.__rcmp__(self, other)
Changed in version 2.1: No longer supported.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
If a class does not define a "__cmp__()" or "__eq__()" method it
should not define a "__hash__()" operation either; if it defines
"__cmp__()" or "__eq__()" but not "__hash__()", its instances will
not be usable in hashed collections. If a class defines mutable
objects and implements a "__cmp__()" or "__eq__()" method, it
should not implement "__hash__()", since hashable collection
implementations require that an object's hash value is immutable
(if the object's hash value changes, it will be in the wrong hash
bucket).
User-defined classes have "__cmp__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns a result derived from
"id(x)".
Classes which inherit a "__hash__()" method from a parent class but
change the meaning of "__cmp__()" or "__eq__()" such that the hash
value returned is no longer appropriate (e.g. by switching to a
value-based concept of equality instead of the default identity
based equality) can explicitly flag themselves as being unhashable
by setting "__hash__ = None" in the class definition. Doing so
means that not only will instances of the class raise an
appropriate "TypeError" when a program attempts to retrieve their
hash value, but they will also be correctly identified as
unhashable when checking "isinstance(obj, collections.Hashable)"
(unlike classes which define their own "__hash__()" to explicitly
raise "TypeError").
Changed in version 2.5: "__hash__()" may now also return a long
integer object; the 32-bit integer is then derived from the hash of
that object.
Changed in version 2.6: "__hash__" may now be set to "None" to
explicitly flag instances of a class as unhashable.
object.__nonzero__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True", or their integer
equivalents "0" or "1". When this method is not defined,
"__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__nonzero__()", all its instances are
considered true.
object.__unicode__(self)
Called to implement "unicode()" built-in; should return a Unicode
object. When this method is not defined, string conversion is
attempted, and the result of string conversion is converted to
Unicode using the system default encoding.
t
customizations�
"pdb" --- The Python Debugger
*****************************
**Source code:** Lib/pdb.py
======================================================================
The module "pdb" defines an interactive source code debugger for
Python programs. It supports setting (conditional) breakpoints and
single stepping at the source line level, inspection of stack frames,
source code listing, and evaluation of arbitrary Python code in the
context of any stack frame. It also supports post-mortem debugging
and can be called under program control.
The debugger is extensible --- it is actually defined as the class
"Pdb". This is currently undocumented but easily understood by reading
the source. The extension interface uses the modules "bdb" and "cmd".
The debugger's prompt is "(Pdb)". Typical usage to run a program under
control of the debugger is:
>>> import pdb
>>> import mymodule
>>> pdb.run('mymodule.test()')
> <string>(0)?()
(Pdb) continue
> <string>(1)?()
(Pdb) continue
NameError: 'spam'
> <string>(1)?()
(Pdb)
"pdb.py" can also be invoked as a script to debug other scripts. For
example:
python -m pdb myscript.py
When invoked as a script, pdb will automatically enter post-mortem
debugging if the program being debugged exits abnormally. After post-
mortem debugging (or after normal exit of the program), pdb will
restart the program. Automatic restarting preserves pdb's state (such
as breakpoints) and in most cases is more useful than quitting the
debugger upon program's exit.
New in version 2.4: Restarting post-mortem behavior added.
The typical usage to break into the debugger from a running program is
to insert
import pdb; pdb.set_trace()
at the location you want to break into the debugger. You can then
step through the code following this statement, and continue running
without the debugger using the "c" command.
The typical usage to inspect a crashed program is:
>>> import pdb
>>> import mymodule
>>> mymodule.test()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "./mymodule.py", line 4, in test
test2()
File "./mymodule.py", line 3, in test2
print spam
NameError: spam
>>> pdb.pm()
> ./mymodule.py(3)test2()
-> print spam
(Pdb)
The module defines the following functions; each enters the debugger
in a slightly different way:
pdb.run(statement[, globals[, locals]])
Execute the *statement* (given as a string) under debugger control.
The debugger prompt appears before any code is executed; you can
set breakpoints and type "continue", or you can step through the
statement using "step" or "next" (all these commands are explained
below). The optional *globals* and *locals* arguments specify the
environment in which the code is executed; by default the
dictionary of the module "__main__" is used. (See the explanation
of the "exec" statement or the "eval()" built-in function.)
pdb.runeval(expression[, globals[, locals]])
Evaluate the *expression* (given as a string) under debugger
control. When "runeval()" returns, it returns the value of the
expression. Otherwise this function is similar to "run()".
pdb.runcall(function[, argument, ...])
Call the *function* (a function or method object, not a string)
with the given arguments. When "runcall()" returns, it returns
whatever the function call returned. The debugger prompt appears
as soon as the function is entered.
pdb.set_trace()
Enter the debugger at the calling stack frame. This is useful to
hard-code a breakpoint at a given point in a program, even if the
code is not otherwise being debugged (e.g. when an assertion
fails).
pdb.post_mortem([traceback])
Enter post-mortem debugging of the given *traceback* object. If no
*traceback* is given, it uses the one of the exception that is
currently being handled (an exception must be being handled if the
default is to be used).
pdb.pm()
Enter post-mortem debugging of the traceback found in
"sys.last_traceback".
The "run*" functions and "set_trace()" are aliases for instantiating
the "Pdb" class and calling the method of the same name. If you want
to access further features, you have to do this yourself:
class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None)
"Pdb" is the debugger class.
The *completekey*, *stdin* and *stdout* arguments are passed to the
underlying "cmd.Cmd" class; see the description there.
The *skip* argument, if given, must be an iterable of glob-style
module name patterns. The debugger will not step into frames that
originate in a module that matches one of these patterns. [1]
Example call to enable tracing with *skip*:
import pdb; pdb.Pdb(skip=['django.*']).set_trace()
New in version 2.7: The *skip* argument.
run(statement[, globals[, locals]])
runeval(expression[, globals[, locals]])
runcall(function[, argument, ...])
set_trace()
See the documentation for the functions explained above.
t debuggers�
The "del" statement
*******************
del_stmt ::= "del" target_list
Deletion is recursively defined very similar to the way assignment is
defined. Rather than spelling it out in full details, here are some
hints.
Deletion of a target list recursively deletes each target, from left
to right.
Deletion of a name removes the binding of that name from the local or
global namespace, depending on whether the name occurs in a "global"
statement in the same code block. If the name is unbound, a
"NameError" exception will be raised.
It is illegal to delete a name from the local namespace if it occurs
as a free variable in a nested block.
Deletion of attribute references, subscriptions and slicings is passed
to the primary object involved; deletion of a slicing is in general
equivalent to assignment of an empty slice of the right type (but even
this is determined by the sliced object).
t dels�
Dictionary displays
*******************
A dictionary display is a possibly empty series of key/datum pairs
enclosed in curly braces:
dict_display ::= "{" [key_datum_list | dict_comprehension] "}"
key_datum_list ::= key_datum ("," key_datum)* [","]
key_datum ::= expression ":" expression
dict_comprehension ::= expression ":" expression comp_for
A dictionary display yields a new dictionary object.
If a comma-separated sequence of key/datum pairs is given, they are
evaluated from left to right to define the entries of the dictionary:
each key object is used as a key into the dictionary to store the
corresponding datum. This means that you can specify the same key
multiple times in the key/datum list, and the final dictionary's value
for that key will be the last one given.
A dict comprehension, in contrast to list and set comprehensions,
needs two expressions separated with a colon followed by the usual
"for" and "if" clauses. When the comprehension is run, the resulting
key and value elements are inserted in the new dictionary in the order
they are produced.
Restrictions on the types of the key values are listed earlier in
section The standard type hierarchy. (To summarize, the key type
should be *hashable*, which excludes all mutable objects.) Clashes
between duplicate keys are not detected; the last datum (textually
rightmost in the display) stored for a given key value prevails.
t dicts+
Interaction with dynamic features
*********************************
There are several cases where Python statements are illegal when used
in conjunction with nested scopes that contain free variables.
If a variable is referenced in an enclosing scope, it is illegal to
delete the name. An error will be reported at compile time.
If the wild card form of import --- "import *" --- is used in a
function and the function contains or is a nested block with free
variables, the compiler will raise a "SyntaxError".
If "exec" is used in a function and the function contains or is a
nested block with free variables, the compiler will raise a
"SyntaxError" unless the exec explicitly specifies the local namespace
for the "exec". (In other words, "exec obj" would be illegal, but
"exec obj in ns" would be legal.)
The "eval()", "execfile()", and "input()" functions and the "exec"
statement do not have access to the full environment for resolving
names. Names may be resolved in the local and global namespaces of
the caller. Free variables are not resolved in the nearest enclosing
namespace, but in the global namespace. [1] The "exec" statement and
the "eval()" and "execfile()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
s dynamic-featuressE
The "if" statement
******************
The "if" statement is used for conditional execution:
if_stmt ::= "if" expression ":" suite
( "elif" expression ":" suite )*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
t elsesh
Exceptions
**********
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" ... "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the "termination" model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Exceptions can also be identified by strings, in which case the
"except" clause is selected by object identity. An arbitrary value
can be raised along with the identifying string which can be passed to
the handler.
Note: Messages to exceptions are not part of the Python API. Their
contents may change from one version of Python to the next without
warning and should not be relied on by code which will run under
multiple versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by
these operations is not available at the time the module is
compiled.
t
exceptionss�
The "exec" statement
********************
exec_stmt ::= "exec" or_expr ["in" expression ["," expression]]
This statement supports dynamic execution of Python code. The first
expression should evaluate to either a Unicode string, a *Latin-1*
encoded string, an open file object, a code object, or a tuple. If it
is a string, the string is parsed as a suite of Python statements
which is then executed (unless a syntax error occurs). [1] If it is an
open file, the file is parsed until EOF and executed. If it is a code
object, it is simply executed. For the interpretation of a tuple, see
below. In all cases, the code that's executed is expected to be valid
as file input (see section File input). Be aware that the "return"
and "yield" statements may not be used outside of function definitions
even within the context of code passed to the "exec" statement.
In all cases, if the optional parts are omitted, the code is executed
in the current scope. If only the first expression after "in" is
specified, it should be a dictionary, which will be used for both the
global and the local variables. If two expressions are given, they
are used for the global and local variables, respectively. If
provided, *locals* can be any mapping object. Remember that at module
level, globals and locals are the same dictionary. If two separate
objects are given as *globals* and *locals*, the code will be executed
as if it were embedded in a class definition.
The first expression may also be a tuple of length 2 or 3. In this
case, the optional parts must be omitted. The form "exec(expr,
globals)" is equivalent to "exec expr in globals", while the form
"exec(expr, globals, locals)" is equivalent to "exec expr in globals,
locals". The tuple form of "exec" provides compatibility with Python
3, where "exec" is a function rather than a statement.
Changed in version 2.4: Formerly, *locals* was required to be a
dictionary.
As a side effect, an implementation may insert additional keys into
the dictionaries given besides those corresponding to variable names
set by the executed code. For example, the current implementation may
add a reference to the dictionary of the built-in module "__builtin__"
under the key "__builtins__" (!).
**Programmer's hints:** dynamic evaluation of expressions is supported
by the built-in function "eval()". The built-in functions "globals()"
and "locals()" return the current global and local dictionary,
respectively, which may be useful to pass around for use by "exec".
-[ Footnotes ]-
[1] Note that the parser only accepts the Unix-style end of line
convention. If you are reading the code from a file, make sure to
use *universal newlines* mode to convert Windows or Mac-style
newlines.
t execs &
Execution model
***************
Naming and binding
==================
*Names* refer to objects. Names are introduced by name binding
operations. Each occurrence of a name in the program text refers to
the *binding* of that name established in the innermost function block
containing the use.
A *block* is a piece of Python program text that is executed as a
unit. The following are blocks: a module, a function body, and a class
definition. Each command typed interactively is a block. A script
file (a file given as standard input to the interpreter or specified
on the interpreter command line the first argument) is a code block.
A script command (a command specified on the interpreter command line
with the '**-c**' option) is a code block. The file read by the
built-in function "execfile()" is a code block. The string argument
passed to the built-in function "eval()" and to the "exec" statement
is a code block. The expression read and evaluated by the built-in
function "input()" is a code block.
A code block is executed in an *execution frame*. A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block's execution has
completed.
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name. The scope of names defined in a
class block is limited to the class block; it does not extend to the
code blocks of methods -- this includes generator expressions since
they are implemented using a function scope. This means that the
following will fail:
class A:
a = 42
b = list(a + i for i in range(10))
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block's *environment*.
If a name is bound in a block, it is a local variable of that block.
If a name is bound at the module level, it is a global variable. (The
variables of the module code block are local and global.) If a
variable is used in a code block but not defined there, it is a *free
variable*.
When a name is not found at all, a "NameError" exception is raised.
If the name refers to a local variable that has not been bound, a
"UnboundLocalError" exception is raised. "UnboundLocalError" is a
subclass of "NameError".
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, in the
second position of an "except" clause header or after "as" in a "with"
statement. The "import" statement of the form "from ... import *"
binds all names defined in the imported module, except those beginning
with an underscore. This form may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name). It
is illegal to unbind a name that is referenced by an enclosing scope;
the compiler will report a "SyntaxError".
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the global statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "__builtin__". The global namespace is searched first.
If the name is not found there, the builtins namespace is searched.
The global statement must precede all uses of the name.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module's dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "__builtin__" (note: no
's'); when in any other module, "__builtins__" is an alias for the
dictionary of the "__builtin__" module itself. "__builtins__" can be
set to a user-created dictionary to create a weak form of restricted
execution.
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "__builtin__" (no 's') module and modify its attributes
appropriately.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
A class definition is an executable statement that may use and define
names. These references follow the normal rules for name resolution.
The namespace of the class definition becomes the attribute dictionary
of the class. Names defined at the class scope are not visible in
methods.
Interaction with dynamic features
---------------------------------
There are several cases where Python statements are illegal when used
in conjunction with nested scopes that contain free variables.
If a variable is referenced in an enclosing scope, it is illegal to
delete the name. An error will be reported at compile time.
If the wild card form of import --- "import *" --- is used in a
function and the function contains or is a nested block with free
variables, the compiler will raise a "SyntaxError".
If "exec" is used in a function and the function contains or is a
nested block with free variables, the compiler will raise a
"SyntaxError" unless the exec explicitly specifies the local namespace
for the "exec". (In other words, "exec obj" would be illegal, but
"exec obj in ns" would be legal.)
The "eval()", "execfile()", and "input()" functions and the "exec"
statement do not have access to the full environment for resolving
names. Names may be resolved in the local and global namespaces of
the caller. Free variables are not resolved in the nearest enclosing
namespace, but in the global namespace. [1] The "exec" statement and
the "eval()" and "execfile()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
Exceptions
==========
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" ... "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the "termination" model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Exceptions can also be identified by strings, in which case the
"except" clause is selected by object identity. An arbitrary value
can be raised along with the identifying string which can be passed to
the handler.
Note: Messages to exceptions are not part of the Python API. Their
contents may change from one version of Python to the next without
warning and should not be relied on by code which will run under
multiple versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by
these operations is not available at the time the module is
compiled.
t execmodelsK
Expression lists
****************
expression_list ::= expression ( "," expression )* [","]
An expression list containing at least one comma yields a tuple. The
length of the tuple is the number of expressions in the list. The
expressions are evaluated from left to right.
The trailing comma is required only to create a single tuple (a.k.a. a
*singleton*); it is optional in all other cases. A single expression
without a trailing comma doesn't create a tuple, but rather yields the
value of that expression. (To create an empty tuple, use an empty pair
of parentheses: "()".)
t exprlistss�
Floating point literals
***********************
Floating point literals are described by the following lexical
definitions:
floatnumber ::= pointfloat | exponentfloat
pointfloat ::= [intpart] fraction | intpart "."
exponentfloat ::= (intpart | pointfloat) exponent
intpart ::= digit+
fraction ::= "." digit+
exponent ::= ("e" | "E") ["+" | "-"] digit+
Note that the integer and exponent parts of floating point numbers can
look like octal integers, but are interpreted using radix 10. For
example, "077e010" is legal, and denotes the same number as "77e10".
The allowed range of floating point literals is implementation-
dependent. Some examples of floating point literals:
3.14 10. .001 1e100 3.14e-10 0e0
Note that numeric literals do not include a sign; a phrase like "-1"
is actually an expression composed of the unary operator "-" and the
literal "1".
t floatingsZ
The "for" statement
*******************
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order of ascending indices. Each
item in turn is assigned to the target list using the standard rules
for assignments, and then the suite is executed. When the items are
exhausted (which is immediately when the sequence is empty), the suite
in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause's suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there was no next
item.
The suite may assign to the variable(s) in the target list; this does
not affect the next item assigned to it.
The target list is not deleted when the loop is finished, but if the
sequence is empty, it will not have been assigned to at all by the
loop. Hint: the built-in function "range()" returns a sequence of
integers suitable to emulate the effect of Pascal's "for i := a to b
do"; e.g., "range(3)" returns the list "[0, 1, 2]".
Note: There is a subtlety when the sequence is being modified by the
loop (this can only occur for mutable sequences, i.e. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
t fors�Q
Format String Syntax
********************
The "str.format()" method and the "Formatter" class share the same
syntax for format strings (although in the case of "Formatter",
subclasses can define their own format string syntax).
Format strings contain "replacement fields" surrounded by curly braces
"{}". Anything that is not contained in braces is considered literal
text, which is copied unchanged to the output. If you need to include
a brace character in the literal text, it can be escaped by doubling:
"{{" and "}}".
The grammar for a replacement field is as follows:
replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}"
field_name ::= arg_name ("." attribute_name | "[" element_index "]")*
arg_name ::= [identifier | integer]
attribute_name ::= identifier
element_index ::= integer | index_string
index_string ::= <any source character except "]"> +
conversion ::= "r" | "s"
format_spec ::= <described in the next section>
In less formal terms, the replacement field can start with a
*field_name* that specifies the object whose value is to be formatted
and inserted into the output instead of the replacement field. The
*field_name* is optionally followed by a *conversion* field, which is
preceded by an exclamation point "'!'", and a *format_spec*, which is
preceded by a colon "':'". These specify a non-default format for the
replacement value.
See also the Format Specification Mini-Language section.
The *field_name* itself begins with an *arg_name* that is either a
number or a keyword. If it's a number, it refers to a positional
argument, and if it's a keyword, it refers to a named keyword
argument. If the numerical arg_names in a format string are 0, 1, 2,
... in sequence, they can all be omitted (not just some) and the
numbers 0, 1, 2, ... will be automatically inserted in that order.
Because *arg_name* is not quote-delimited, it is not possible to
specify arbitrary dictionary keys (e.g., the strings "'10'" or
"':-]'") within a format string. The *arg_name* can be followed by any
number of index or attribute expressions. An expression of the form
"'.name'" selects the named attribute using "getattr()", while an
expression of the form "'[index]'" does an index lookup using
"__getitem__()".
Changed in version 2.7: The positional argument specifiers can be
omitted, so "'{} {}'" is equivalent to "'{0} {1}'".
Some simple format string examples:
"First, thou shalt count to {0}" # References first positional argument
"Bring me a {}" # Implicitly references the first positional argument
"From {} to {}" # Same as "From {0} to {1}"
"My quest is {name}" # References keyword argument 'name'
"Weight in tons {0.weight}" # 'weight' attribute of first positional arg
"Units destroyed: {players[0]}" # First element of keyword argument 'players'.
The *conversion* field causes a type coercion before formatting.
Normally, the job of formatting a value is done by the "__format__()"
method of the value itself. However, in some cases it is desirable to
force a type to be formatted as a string, overriding its own
definition of formatting. By converting the value to a string before
calling "__format__()", the normal formatting logic is bypassed.
Two conversion flags are currently supported: "'!s'" which calls
"str()" on the value, and "'!r'" which calls "repr()".
Some examples:
"Harold's a clever {0!s}" # Calls str() on the argument first
"Bring out the holy {name!r}" # Calls repr() on the argument first
The *format_spec* field contains a specification of how the value
should be presented, including such details as field width, alignment,
padding, decimal precision and so on. Each value type can define its
own "formatting mini-language" or interpretation of the *format_spec*.
Most built-in types support a common formatting mini-language, which
is described in the next section.
A *format_spec* field can also include nested replacement fields
within it. These nested replacement fields may contain a field name,
conversion flag and format specification, but deeper nesting is not
allowed. The replacement fields within the format_spec are
substituted before the *format_spec* string is interpreted. This
allows the formatting of a value to be dynamically specified.
See the Format examples section for some examples.
Format Specification Mini-Language
==================================
"Format specifications" are used within replacement fields contained
within a format string to define how individual values are presented
(see Format String Syntax). They can also be passed directly to the
built-in "format()" function. Each formattable type may define how
the format specification is to be interpreted.
Most built-in types implement the following options for format
specifications, although some of the formatting options are only
supported by the numeric types.
A general convention is that an empty format string ("""") produces
the same result as if you had called "str()" on the value. A non-empty
format string typically modifies the result.
The general form of a *standard format specifier* is:
format_spec ::= [[fill]align][sign][#][0][width][,][.precision][type]
fill ::= <any character>
align ::= "<" | ">" | "=" | "^"
sign ::= "+" | "-" | " "
width ::= integer
precision ::= integer
type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"
If a valid *align* value is specified, it can be preceded by a *fill*
character that can be any character and defaults to a space if
omitted. It is not possible to use a literal curly brace (""{"" or
""}"") as the *fill* character when using the "str.format()" method.
However, it is possible to insert a curly brace with a nested
replacement field. This limitation doesn't affect the "format()"
function.
The meaning of the various alignment options is as follows:
+-----------+------------------------------------------------------------+
| Option | Meaning |
+===========+============================================================+
| "'<'" | Forces the field to be left-aligned within the available |
| | space (this is the default for most objects). |
+-----------+------------------------------------------------------------+
| "'>'" | Forces the field to be right-aligned within the available |
| | space (this is the default for numbers). |
+-----------+------------------------------------------------------------+
| "'='" | Forces the padding to be placed after the sign (if any) |
| | but before the digits. This is used for printing fields |
| | in the form '+000000120'. This alignment option is only |
| | valid for numeric types. It becomes the default when '0' |
| | immediately precedes the field width. |
+-----------+------------------------------------------------------------+
| "'^'" | Forces the field to be centered within the available |
| | space. |
+-----------+------------------------------------------------------------+
Note that unless a minimum field width is defined, the field width
will always be the same size as the data to fill it, so that the
alignment option has no meaning in this case.
The *sign* option is only valid for number types, and can be one of
the following:
+-----------+------------------------------------------------------------+
| Option | Meaning |
+===========+============================================================+
| "'+'" | indicates that a sign should be used for both positive as |
| | well as negative numbers. |
+-----------+------------------------------------------------------------+
| "'-'" | indicates that a sign should be used only for negative |
| | numbers (this is the default behavior). |
+-----------+------------------------------------------------------------+
| space | indicates that a leading space should be used on positive |
| | numbers, and a minus sign on negative numbers. |
+-----------+------------------------------------------------------------+
The "'#'" option is only valid for integers, and only for binary,
octal, or hexadecimal output. If present, it specifies that the
output will be prefixed by "'0b'", "'0o'", or "'0x'", respectively.
The "','" option signals the use of a comma for a thousands separator.
For a locale aware separator, use the "'n'" integer presentation type
instead.
Changed in version 2.7: Added the "','" option (see also **PEP 378**).
*width* is a decimal integer defining the minimum field width. If not
specified, then the field width will be determined by the content.
When no explicit alignment is given, preceding the *width* field by a
zero ("'0'") character enables sign-aware zero-padding for numeric
types. This is equivalent to a *fill* character of "'0'" with an
*alignment* type of "'='".
The *precision* is a decimal number indicating how many digits should
be displayed after the decimal point for a floating point value
formatted with "'f'" and "'F'", or before and after the decimal point
for a floating point value formatted with "'g'" or "'G'". For non-
number types the field indicates the maximum field size - in other
words, how many characters will be used from the field content. The
*precision* is not allowed for integer values.
Finally, the *type* determines how the data should be presented.
The available string presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'s'" | String format. This is the default type for strings and |
| | may be omitted. |
+-----------+------------------------------------------------------------+
| None | The same as "'s'". |
+-----------+------------------------------------------------------------+
The available integer presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'b'" | Binary format. Outputs the number in base 2. |
+-----------+------------------------------------------------------------+
| "'c'" | Character. Converts the integer to the corresponding |
| | unicode character before printing. |
+-----------+------------------------------------------------------------+
| "'d'" | Decimal Integer. Outputs the number in base 10. |
+-----------+------------------------------------------------------------+
| "'o'" | Octal format. Outputs the number in base 8. |
+-----------+------------------------------------------------------------+
| "'x'" | Hex format. Outputs the number in base 16, using lower- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'X'" | Hex format. Outputs the number in base 16, using upper- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'d'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| None | The same as "'d'". |
+-----------+------------------------------------------------------------+
In addition to the above presentation types, integers can be formatted
with the floating point presentation types listed below (except "'n'"
and "None"). When doing so, "float()" is used to convert the integer
to a floating point number before formatting.
The available presentation types for floating point and decimal values
are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'e'" | Exponent notation. Prints the number in scientific |
| | notation using the letter 'e' to indicate the exponent. |
| | The default precision is "6". |
+-----------+------------------------------------------------------------+
| "'E'" | Exponent notation. Same as "'e'" except it uses an upper |
| | case 'E' as the separator character. |
+-----------+------------------------------------------------------------+
| "'f'" | Fixed point. Displays the number as a fixed-point number. |
| | The default precision is "6". |
+-----------+------------------------------------------------------------+
| "'F'" | Fixed point. Same as "'f'". |
+-----------+------------------------------------------------------------+
| "'g'" | General format. For a given precision "p >= 1", this |
| | rounds the number to "p" significant digits and then |
| | formats the result in either fixed-point format or in |
| | scientific notation, depending on its magnitude. The |
| | precise rules are as follows: suppose that the result |
| | formatted with presentation type "'e'" and precision "p-1" |
| | would have exponent "exp". Then if "-4 <= exp < p", the |
| | number is formatted with presentation type "'f'" and |
| | precision "p-1-exp". Otherwise, the number is formatted |
| | with presentation type "'e'" and precision "p-1". In both |
| | cases insignificant trailing zeros are removed from the |
| | significand, and the decimal point is also removed if |
| | there are no remaining digits following it. Positive and |
| | negative infinity, positive and negative zero, and nans, |
| | are formatted as "inf", "-inf", "0", "-0" and "nan" |
| | respectively, regardless of the precision. A precision of |
| | "0" is treated as equivalent to a precision of "1". The |
| | default precision is "6". |
+-----------+------------------------------------------------------------+
| "'G'" | General format. Same as "'g'" except switches to "'E'" if |
| | the number gets too large. The representations of infinity |
| | and NaN are uppercased, too. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'g'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| "'%'" | Percentage. Multiplies the number by 100 and displays in |
| | fixed ("'f'") format, followed by a percent sign. |
+-----------+------------------------------------------------------------+
| None | The same as "'g'". |
+-----------+------------------------------------------------------------+
Format examples
===============
This section contains examples of the "str.format()" syntax and
comparison with the old "%"-formatting.
In most of the cases the syntax is similar to the old "%"-formatting,
with the addition of the "{}" and with ":" used instead of "%". For
example, "'%03.2f'" can be translated to "'{:03.2f}'".
The new format syntax also supports new and different options, shown
in the follow examples.
Accessing arguments by position:
>>> '{0}, {1}, {2}'.format('a', 'b', 'c')
'a, b, c'
>>> '{}, {}, {}'.format('a', 'b', 'c') # 2.7+ only
'a, b, c'
>>> '{2}, {1}, {0}'.format('a', 'b', 'c')
'c, b, a'
>>> '{2}, {1}, {0}'.format(*'abc') # unpacking argument sequence
'c, b, a'
>>> '{0}{1}{0}'.format('abra', 'cad') # arguments' indices can be repeated
'abracadabra'
Accessing arguments by name:
>>> 'Coordinates: {latitude}, {longitude}'.format(latitude='37.24N', longitude='-115.81W')
'Coordinates: 37.24N, -115.81W'
>>> coord = {'latitude': '37.24N', 'longitude': '-115.81W'}
>>> 'Coordinates: {latitude}, {longitude}'.format(**coord)
'Coordinates: 37.24N, -115.81W'
Accessing arguments' attributes:
>>> c = 3-5j
>>> ('The complex number {0} is formed from the real part {0.real} '
... 'and the imaginary part {0.imag}.').format(c)
'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.'
>>> class Point(object):
... def __init__(self, x, y):
... self.x, self.y = x, y
... def __str__(self):
... return 'Point({self.x}, {self.y})'.format(self=self)
...
>>> str(Point(4, 2))
'Point(4, 2)'
Accessing arguments' items:
>>> coord = (3, 5)
>>> 'X: {0[0]}; Y: {0[1]}'.format(coord)
'X: 3; Y: 5'
Replacing "%s" and "%r":
>>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2')
"repr() shows quotes: 'test1'; str() doesn't: test2"
Aligning the text and specifying a width:
>>> '{:<30}'.format('left aligned')
'left aligned '
>>> '{:>30}'.format('right aligned')
' right aligned'
>>> '{:^30}'.format('centered')
' centered '
>>> '{:*^30}'.format('centered') # use '*' as a fill char
'***********centered***********'
Replacing "%+f", "%-f", and "% f" and specifying a sign:
>>> '{:+f}; {:+f}'.format(3.14, -3.14) # show it always
'+3.140000; -3.140000'
>>> '{: f}; {: f}'.format(3.14, -3.14) # show a space for positive numbers
' 3.140000; -3.140000'
>>> '{:-f}; {:-f}'.format(3.14, -3.14) # show only the minus -- same as '{:f}; {:f}'
'3.140000; -3.140000'
Replacing "%x" and "%o" and converting the value to different bases:
>>> # format also supports binary numbers
>>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: {0:b}".format(42)
'int: 42; hex: 2a; oct: 52; bin: 101010'
>>> # with 0x, 0o, or 0b as prefix:
>>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: {0:#b}".format(42)
'int: 42; hex: 0x2a; oct: 0o52; bin: 0b101010'
Using the comma as a thousands separator:
>>> '{:,}'.format(1234567890)
'1,234,567,890'
Expressing a percentage:
>>> points = 19.5
>>> total = 22
>>> 'Correct answers: {:.2%}'.format(points/total)
'Correct answers: 88.64%'
Using type-specific formatting:
>>> import datetime
>>> d = datetime.datetime(2010, 7, 4, 12, 15, 58)
>>> '{:%Y-%m-%d %H:%M:%S}'.format(d)
'2010-07-04 12:15:58'
Nesting arguments and more complex examples:
>>> for align, text in zip('<^>', ['left', 'center', 'right']):
... '{0:{fill}{align}16}'.format(text, fill=align, align=align)
...
'left<<<<<<<<<<<<'
'^^^^^center^^^^^'
'>>>>>>>>>>>right'
>>>
>>> octets = [192, 168, 0, 1]
>>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets)
'C0A80001'
>>> int(_, 16)
3232235521
>>>
>>> width = 5
>>> for num in range(5,12):
... for base in 'dXob':
... print '{0:{width}{base}}'.format(num, base=base, width=width),
... print
...
5 5 5 101
6 6 6 110
7 7 7 111
8 8 10 1000
9 9 11 1001
10 A 12 1010
11 B 13 1011
t
formatstringssz
Function definitions
********************
A function definition defines a user-defined function object (see
section The standard type hierarchy):
decorated ::= decorators (classdef | funcdef)
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
funcdef ::= "def" funcname "(" [parameter_list] ")" ":" suite
dotted_name ::= identifier ("." identifier)*
parameter_list ::= (defparameter ",")*
( "*" identifier ["," "**" identifier]
| "**" identifier
| defparameter [","] )
defparameter ::= parameter ["=" expression]
sublist ::= parameter ("," parameter)* [","]
parameter ::= identifier | "(" sublist ")"
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [3]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code:
@f1(arg)
@f2
def func(): pass
is equivalent to:
def func(): pass
func = f1(arg)(f2(func))
When one or more top-level *parameters* have the form *parameter* "="
*expression*, the function is said to have "default parameter values."
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter's default value is
substituted. If a parameter has a default value, all following
parameters must also have a default value --- this is a syntactic
restriction that is not expressed by the grammar.
**Default parameter values are evaluated when the function definition
is executed.** This means that the expression is evaluated once, when
the function is defined, and that the same "pre-computed" value is
used for each call. This is especially important to understand when a
default parameter is a mutable object, such as a list or a dictionary:
if the function modifies the object (e.g. by appending an item to a
list), the default value is in effect modified. This is generally not
what was intended. A way around this is to use "None" as the
default, and explicitly test for it in the body of the function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from position arguments, from keyword
arguments, or from default values. If the form ""*identifier"" is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
""**identifier"" is present, it is initialized to a new dictionary
receiving any excess keyword arguments, defaulting to a new empty
dictionary.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a ""def"" statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The ""def"" form is actually more powerful since it
allows the execution of multiple statements.
**Programmer's note:** Functions are first-class objects. A ""def""
form executed inside a function definition defines a local function
that can be returned or passed around. Free variables used in the
nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
t functions�
The "global" statement
**********************
global_stmt ::= "global" identifier ("," identifier)*
The "global" statement is a declaration which holds for the entire
current code block. It means that the listed identifiers are to be
interpreted as globals. It would be impossible to assign to a global
variable without "global", although free variables may refer to
globals without being declared global.
Names listed in a "global" statement must not be used in the same code
block textually preceding that "global" statement.
Names listed in a "global" statement must not be defined as formal
parameters or in a "for" loop control target, "class" definition,
function definition, or "import" statement.
**CPython implementation detail:** The current implementation does not
enforce the latter two restrictions, but programs should not abuse
this freedom, as future implementations may enforce them or silently
change the meaning of the program.
**Programmer's note:** "global" is a directive to the parser. It
applies only to code parsed at the same time as the "global"
statement. In particular, a "global" statement contained in an "exec"
statement does not affect the code block *containing* the "exec"
statement, and code contained in an "exec" statement is unaffected by
"global" statements in the code containing the "exec" statement. The
same applies to the "eval()", "execfile()" and "compile()" functions.
t globals�
Reserved classes of identifiers
*******************************
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "__builtin__" module. When
not in interactive mode, "_" has no special meaning and is not
defined. See section The import statement.
Note: The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names. These names are defined by the interpreter
and its implementation (including the standard library). Current
system names are discussed in the Special method names section and
elsewhere. More will likely be defined in future versions of
Python. *Any* use of "__*__" names, in any context, that does not
follow explicitly documented use, is subject to breakage without
warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between "private" attributes of base and
derived classes. See section Identifiers (Names).
s
id-classess�
Identifiers and keywords
************************
Identifiers (also referred to as *names*) are described by the
following lexical definitions:
identifier ::= (letter|"_") (letter | digit | "_")*
letter ::= lowercase | uppercase
lowercase ::= "a"..."z"
uppercase ::= "A"..."Z"
digit ::= "0"..."9"
Identifiers are unlimited in length. Case is significant.
Keywords
========
The following identifiers are used as reserved words, or *keywords* of
the language, and cannot be used as ordinary identifiers. They must
be spelled exactly as written here:
and del from not while
as elif global or with
assert else if pass yield
break except import print
class exec in raise
continue finally is return
def for lambda try
Changed in version 2.4: "None" became a constant and is now recognized
by the compiler as a name for the built-in object "None". Although it
is not a keyword, you cannot assign a different object to it.
Changed in version 2.5: Using "as" and "with" as identifiers triggers
a warning. To use them as keywords, enable the "with_statement"
future feature .
Changed in version 2.6: "as" and "with" are full keywords.
Reserved classes of identifiers
===============================
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "__builtin__" module. When
not in interactive mode, "_" has no special meaning and is not
defined. See section The import statement.
Note: The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names. These names are defined by the interpreter
and its implementation (including the standard library). Current
system names are discussed in the Special method names section and
elsewhere. More will likely be defined in future versions of
Python. *Any* use of "__*__" names, in any context, that does not
follow explicitly documented use, is subject to breakage without
warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between "private" attributes of base and
derived classes. See section Identifiers (Names).
t identifierst ifs%
Imaginary literals
******************
Imaginary literals are described by the following lexical definitions:
imagnumber ::= (floatnumber | intpart) ("j" | "J")
An imaginary literal yields a complex number with a real part of 0.0.
Complex numbers are represented as a pair of floating point numbers
and have the same restrictions on their range. To create a complex
number with a nonzero real part, add a floating point number to it,
e.g., "(3+4j)". Some examples of imaginary literals:
3.14j 10.j 10j .001j 1e100j 3.14e-10j
t imaginarysK.
The "import" statement
**********************
import_stmt ::= "import" module ["as" name] ( "," module ["as" name] )*
| "from" relative_module "import" identifier ["as" name]
( "," identifier ["as" name] )*
| "from" relative_module "import" "(" identifier ["as" name]
( "," identifier ["as" name] )* [","] ")"
| "from" module "import" "*"
module ::= (identifier ".")* identifier
relative_module ::= "."* module | "."+
name ::= identifier
Import statements are executed in two steps: (1) find a module, and
initialize it if necessary; (2) define a name or names in the local
namespace (of the scope where the "import" statement occurs). The
statement comes in two forms differing on whether it uses the "from"
keyword. The first form (without "from") repeats these steps for each
identifier in the list. The form with "from" performs step (1) once,
and then performs step (2) repeatedly.
To understand how step (1) occurs, one must first understand how
Python handles hierarchical naming of modules. To help organize
modules and provide a hierarchy in naming, Python has a concept of
packages. A package can contain other packages and modules while
modules cannot contain other modules or packages. From a file system
perspective, packages are directories and modules are files.
Once the name of the module is known (unless otherwise specified, the
term "module" will refer to both packages and modules), searching for
the module or package can begin. The first place checked is
"sys.modules", the cache of all modules that have been imported
previously. If the module is found there then it is used in step (2)
of import.
If the module is not found in the cache, then "sys.meta_path" is
searched (the specification for "sys.meta_path" can be found in **PEP
302**). The object is a list of *finder* objects which are queried in
order as to whether they know how to load the module by calling their
"find_module()" method with the name of the module. If the module
happens to be contained within a package (as denoted by the existence
of a dot in the name), then a second argument to "find_module()" is
given as the value of the "__path__" attribute from the parent package
(everything up to the last dot in the name of the module being
imported). If a finder can find the module it returns a *loader*
(discussed later) or returns "None".
If none of the finders on "sys.meta_path" are able to find the module
then some implicitly defined finders are queried. Implementations of
Python vary in what implicit meta path finders are defined. The one
they all do define, though, is one that handles "sys.path_hooks",
"sys.path_importer_cache", and "sys.path".
The implicit finder searches for the requested module in the "paths"
specified in one of two places ("paths" do not have to be file system
paths). If the module being imported is supposed to be contained
within a package then the second argument passed to "find_module()",
"__path__" on the parent package, is used as the source of paths. If
the module is not contained in a package then "sys.path" is used as
the source of paths.
Once the source of paths is chosen it is iterated over to find a
finder that can handle that path. The dict at
"sys.path_importer_cache" caches finders for paths and is checked for
a finder. If the path does not have a finder cached then
"sys.path_hooks" is searched by calling each object in the list with a
single argument of the path, returning a finder or raises
"ImportError". If a finder is returned then it is cached in
"sys.path_importer_cache" and then used for that path entry. If no
finder can be found but the path exists then a value of "None" is
stored in "sys.path_importer_cache" to signify that an implicit, file-
based finder that handles modules stored as individual files should be
used for that path. If the path does not exist then a finder which
always returns "None" is placed in the cache for the path.
If no finder can find the module then "ImportError" is raised.
Otherwise some finder returned a loader whose "load_module()" method
is called with the name of the module to load (see **PEP 302** for the
original definition of loaders). A loader has several responsibilities
to perform on a module it loads. First, if the module already exists
in "sys.modules" (a possibility if the loader is called outside of the
import machinery) then it is to use that module for initialization and
not a new module. But if the module does not exist in "sys.modules"
then it is to be added to that dict before initialization begins. If
an error occurs during loading of the module and it was added to
"sys.modules" it is to be removed from the dict. If an error occurs
but the module was already in "sys.modules" it is left in the dict.
The loader must set several attributes on the module. "__name__" is to
be set to the name of the module. "__file__" is to be the "path" to
the file unless the module is built-in (and thus listed in
"sys.builtin_module_names") in which case the attribute is not set. If
what is being imported is a package then "__path__" is to be set to a
list of paths to be searched when looking for modules and packages
contained within the package being imported. "__package__" is optional
but should be set to the name of package that contains the module or
package (the empty string is used for module not contained in a
package). "__loader__" is also optional but should be set to the
loader object that is loading the module.
If an error occurs during loading then the loader raises "ImportError"
if some other exception is not already being propagated. Otherwise the
loader returns the module that was loaded and initialized.
When step (1) finishes without raising an exception, step (2) can
begin.
The first form of "import" statement binds the module name in the
local namespace to the module object, and then goes on to import the
next identifier, if any. If the module name is followed by "as", the
name following "as" is used as the local name for the module.
The "from" form does not bind the module name: it goes through the
list of identifiers, looks each one of them up in the module found in
step (1), and binds the name in the local namespace to the object thus
found. As with the first form of "import", an alternate local name
can be supplied by specifying ""as" localname". If a name is not
found, "ImportError" is raised. If the list of identifiers is
replaced by a star ("'*'"), all public names defined in the module are
bound in the local namespace of the "import" statement..
The *public names* defined by a module are determined by checking the
module's namespace for a variable named "__all__"; if defined, it must
be a sequence of strings which are names defined or imported by that
module. The names given in "__all__" are all considered public and
are required to exist. If "__all__" is not defined, the set of public
names includes all names found in the module's namespace which do not
begin with an underscore character ("'_'"). "__all__" should contain
the entire public API. It is intended to avoid accidentally exporting
items that are not part of the API (such as library modules which were
imported and used within the module).
The "from" form with "*" may only occur in a module scope. If the
wild card form of import --- "import *" --- is used in a function and
the function contains or is a nested block with free variables, the
compiler will raise a "SyntaxError".
When specifying what module to import you do not have to specify the
absolute name of the module. When a module or package is contained
within another package it is possible to make a relative import within
the same top package without having to mention the package name. By
using leading dots in the specified module or package after "from" you
can specify how high to traverse up the current package hierarchy
without specifying exact names. One leading dot means the current
package where the module making the import exists. Two dots means up
one package level. Three dots is up two levels, etc. So if you execute
"from . import mod" from a module in the "pkg" package then you will
end up importing "pkg.mod". If you execute "from ..subpkg2 import mod"
from within "pkg.subpkg1" you will import "pkg.subpkg2.mod". The
specification for relative imports is contained within **PEP 328**.
"importlib.import_module()" is provided to support applications that
determine which modules need to be loaded dynamically.
Future statements
=================
A *future statement* is a directive to the compiler that a particular
module should be compiled using syntax or semantics that will be
available in a specified future release of Python. The future
statement is intended to ease migration to future versions of Python
that introduce incompatible changes to the language. It allows use of
the new features on a per-module basis before the release in which the
feature becomes standard.
future_statement ::= "from" "__future__" "import" feature ["as" name]
("," feature ["as" name])*
| "from" "__future__" "import" "(" feature ["as" name]
("," feature ["as" name])* [","] ")"
feature ::= identifier
name ::= identifier
A future statement must appear near the top of the module. The only
lines that can appear before a future statement are:
* the module docstring (if any),
* comments,
* blank lines, and
* other future statements.
The features recognized by Python 2.6 are "unicode_literals",
"print_function", "absolute_import", "division", "generators",
"nested_scopes" and "with_statement". "generators", "with_statement",
"nested_scopes" are redundant in Python version 2.6 and above because
they are always enabled.
A future statement is recognized and treated specially at compile
time: Changes to the semantics of core constructs are often
implemented by generating different code. It may even be the case
that a new feature introduces new incompatible syntax (such as a new
reserved word), in which case the compiler may need to parse the
module differently. Such decisions cannot be pushed off until
runtime.
For any given release, the compiler knows which feature names have
been defined, and raises a compile-time error if a future statement
contains a feature not known to it.
The direct runtime semantics are the same as for any import statement:
there is a standard module "__future__", described later, and it will
be imported in the usual way at the time the future statement is
executed.
The interesting runtime semantics depend on the specific feature
enabled by the future statement.
Note that there is nothing special about the statement:
import __future__ [as name]
That is not a future statement; it's an ordinary import statement with
no special semantics or syntax restrictions.
Code compiled by an "exec" statement or calls to the built-in
functions "compile()" and "execfile()" that occur in a module "M"
containing a future statement will, by default, use the new syntax or
semantics associated with the future statement. This can, starting
with Python 2.2 be controlled by optional arguments to "compile()" ---
see the documentation of that function for details.
A future statement typed at an interactive interpreter prompt will
take effect for the rest of the interpreter session. If an
interpreter is started with the "-i" option, is passed a script name
to execute, and the script includes a future statement, it will be in
effect in the interactive session started after the script is
executed.
See also:
**PEP 236** - Back to the __future__
The original proposal for the __future__ mechanism.
t importsO
Membership test operations
**************************
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y". If an exception is raised
during the iteration, it is as if "in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse true value of
"in".
t inso
Integer and long integer literals
*********************************
Integer and long integer literals are described by the following
lexical definitions:
longinteger ::= integer ("l" | "L")
integer ::= decimalinteger | octinteger | hexinteger | bininteger
decimalinteger ::= nonzerodigit digit* | "0"
octinteger ::= "0" ("o" | "O") octdigit+ | "0" octdigit+
hexinteger ::= "0" ("x" | "X") hexdigit+
bininteger ::= "0" ("b" | "B") bindigit+
nonzerodigit ::= "1"..."9"
octdigit ::= "0"..."7"
bindigit ::= "0" | "1"
hexdigit ::= digit | "a"..."f" | "A"..."F"
Although both lower case "'l'" and upper case "'L'" are allowed as
suffix for long integers, it is strongly recommended to always use
"'L'", since the letter "'l'" looks too much like the digit "'1'".
Plain integer literals that are above the largest representable plain
integer (e.g., 2147483647 when using 32-bit arithmetic) are accepted
as if they were long integers instead. [1] There is no limit for long
integer literals apart from what can be stored in available memory.
Some examples of plain integer literals (first row) and long integer
literals (second and third rows):
7 2147483647 0177
3L 79228162514264337593543950336L 0377L 0x100000000L
79228162514264337593543950336 0xdeadbeef
t integerssx
Lambdas
*******
lambda_expr ::= "lambda" [parameter_list]: expression
old_lambda_expr ::= "lambda" [parameter_list]: old_expression
Lambda expressions (sometimes called lambda forms) have the same
syntactic position as expressions. They are a shorthand to create
anonymous functions; the expression "lambda arguments: expression"
yields a function object. The unnamed object behaves like a function
object defined with
def name(arguments):
return expression
See section Function definitions for the syntax of parameter lists.
Note that functions created with lambda expressions cannot contain
statements.
t lambdas�
List displays
*************
A list display is a possibly empty series of expressions enclosed in
square brackets:
list_display ::= "[" [expression_list | list_comprehension] "]"
list_comprehension ::= expression list_for
list_for ::= "for" target_list "in" old_expression_list [list_iter]
old_expression_list ::= old_expression [("," old_expression)+ [","]]
old_expression ::= or_test | old_lambda_expr
list_iter ::= list_for | list_if
list_if ::= "if" old_expression [list_iter]
A list display yields a new list object. Its contents are specified
by providing either a list of expressions or a list comprehension.
When a comma-separated list of expressions is supplied, its elements
are evaluated from left to right and placed into the list object in
that order. When a list comprehension is supplied, it consists of a
single expression followed by at least one "for" clause and zero or
more "for" or "if" clauses. In this case, the elements of the new
list are those that would be produced by considering each of the "for"
or "if" clauses a block, nesting from left to right, and evaluating
the expression to produce a list element each time the innermost block
is reached [1].
t listss�
Naming and binding
******************
*Names* refer to objects. Names are introduced by name binding
operations. Each occurrence of a name in the program text refers to
the *binding* of that name established in the innermost function block
containing the use.
A *block* is a piece of Python program text that is executed as a
unit. The following are blocks: a module, a function body, and a class
definition. Each command typed interactively is a block. A script
file (a file given as standard input to the interpreter or specified
on the interpreter command line the first argument) is a code block.
A script command (a command specified on the interpreter command line
with the '**-c**' option) is a code block. The file read by the
built-in function "execfile()" is a code block. The string argument
passed to the built-in function "eval()" and to the "exec" statement
is a code block. The expression read and evaluated by the built-in
function "input()" is a code block.
A code block is executed in an *execution frame*. A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block's execution has
completed.
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name. The scope of names defined in a
class block is limited to the class block; it does not extend to the
code blocks of methods -- this includes generator expressions since
they are implemented using a function scope. This means that the
following will fail:
class A:
a = 42
b = list(a + i for i in range(10))
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block's *environment*.
If a name is bound in a block, it is a local variable of that block.
If a name is bound at the module level, it is a global variable. (The
variables of the module code block are local and global.) If a
variable is used in a code block but not defined there, it is a *free
variable*.
When a name is not found at all, a "NameError" exception is raised.
If the name refers to a local variable that has not been bound, a
"UnboundLocalError" exception is raised. "UnboundLocalError" is a
subclass of "NameError".
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, in the
second position of an "except" clause header or after "as" in a "with"
statement. The "import" statement of the form "from ... import *"
binds all names defined in the imported module, except those beginning
with an underscore. This form may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name). It
is illegal to unbind a name that is referenced by an enclosing scope;
the compiler will report a "SyntaxError".
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the global statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "__builtin__". The global namespace is searched first.
If the name is not found there, the builtins namespace is searched.
The global statement must precede all uses of the name.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module's dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "__builtin__" (note: no
's'); when in any other module, "__builtins__" is an alias for the
dictionary of the "__builtin__" module itself. "__builtins__" can be
set to a user-created dictionary to create a weak form of restricted
execution.
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "__builtin__" (no 's') module and modify its attributes
appropriately.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
A class definition is an executable statement that may use and define
names. These references follow the normal rules for name resolution.
The namespace of the class definition becomes the attribute dictionary
of the class. Names defined at the class scope are not visible in
methods.
Interaction with dynamic features
=================================
There are several cases where Python statements are illegal when used
in conjunction with nested scopes that contain free variables.
If a variable is referenced in an enclosing scope, it is illegal to
delete the name. An error will be reported at compile time.
If the wild card form of import --- "import *" --- is used in a
function and the function contains or is a nested block with free
variables, the compiler will raise a "SyntaxError".
If "exec" is used in a function and the function contains or is a
nested block with free variables, the compiler will raise a
"SyntaxError" unless the exec explicitly specifies the local namespace
for the "exec". (In other words, "exec obj" would be illegal, but
"exec obj in ns" would be legal.)
The "eval()", "execfile()", and "input()" functions and the "exec"
statement do not have access to the full environment for resolving
names. Names may be resolved in the local and global namespaces of
the caller. Free variables are not resolved in the nearest enclosing
namespace, but in the global namespace. [1] The "exec" statement and
the "eval()" and "execfile()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
t namings�
Numeric literals
****************
There are four types of numeric literals: plain integers, long
integers, floating point numbers, and imaginary numbers. There are no
complex literals (complex numbers can be formed by adding a real
number and an imaginary number).
Note that numeric literals do not include a sign; a phrase like "-1"
is actually an expression composed of the unary operator '"-"' and the
literal "1".
t numberssy
Emulating numeric types
***********************
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "//", "%", "divmod()", "pow()", "**",
"<<", ">>", "&", "^", "|"). For instance, to evaluate the
expression "x + y", where *x* is an instance of a class that has an
"__add__()" method, "x.__add__(y)" is called. The "__divmod__()"
method should be the equivalent to using "__floordiv__()" and
"__mod__()"; it should not be related to "__truediv__()" (described
below). Note that "__pow__()" should be defined to accept an
optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__div__(self, other)
object.__truediv__(self, other)
The division operator ("/") is implemented by these methods. The
"__truediv__()" method is used when "__future__.division" is in
effect, otherwise "__div__()" is used. If only one of these two
methods is defined, the object will not support division in the
alternate context; "TypeError" will be raised instead.
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rdiv__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other)
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "/", "%", "divmod()", "pow()", "**",
"<<", ">>", "&", "^", "|") with reflected (swapped) operands.
These functions are only called if the left operand does not
support the corresponding operation and the operands are of
different types. [2] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note: If the right operand's type is a subclass of the left
operand's type and that subclass provides the reflected method
for the operation, this method will be called before the left
operand's non-reflected method. This behavior allows subclasses
to override their ancestors' operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__idiv__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "/=", "//=", "%=", "**=", "<<=",
">>=", "&=", "^=", "|="). These methods should attempt to do the
operation in-place (modifying *self*) and return the result (which
could be, but does not have to be, *self*). If a specific method
is not defined, the augmented assignment falls back to the normal
methods. For instance, to execute the statement "x += y", where
*x* is an instance of a class that has an "__iadd__()" method,
"x.__iadd__(y)" is called. If *x* is an instance of a class that
does not define a "__iadd__()" method, "x.__add__(y)" and
"y.__radd__(x)" are considered, as with the evaluation of "x + y".
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__long__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()",
"long()", and "float()". Should return a value of the appropriate
type.
object.__oct__(self)
object.__hex__(self)
Called to implement the built-in functions "oct()" and "hex()".
Should return a string value.
object.__index__(self)
Called to implement "operator.index()". Also called whenever
Python needs an integer object (such as in slicing). Must return
an integer (int or long).
New in version 2.5.
object.__coerce__(self, other)
Called to implement "mixed-mode" numeric arithmetic. Should either
return a 2-tuple containing *self* and *other* converted to a
common numeric type, or "None" if conversion is impossible. When
the common type would be the type of "other", it is sufficient to
return "None", since the interpreter will also ask the other object
to attempt a coercion (but sometimes, if the implementation of the
other type cannot be changed, it is useful to do the conversion to
the other type here). A return value of "NotImplemented" is
equivalent to returning "None".
s
numeric-typessZ
Objects, values and types
*************************
*Objects* are Python's abstraction for data. All data in a Python
program is represented by objects or by relations between objects. (In
a sense, and in conformance to Von Neumann's model of a "stored
program computer," code is also represented by objects.)
Every object has an identity, a type and a value. An object's
*identity* never changes once it has been created; you may think of it
as the object's address in memory. The '"is"' operator compares the
identity of two objects; the "id()" function returns an integer
representing its identity (currently implemented as its address). An
object's *type* is also unchangeable. [1] An object's type determines
the operations that the object supports (e.g., "does it have a
length?") and also defines the possible values for objects of that
type. The "type()" function returns an object's type (which is an
object itself). The *value* of some objects can change. Objects
whose value can change are said to be *mutable*; objects whose value
is unchangeable once they are created are called *immutable*. (The
value of an immutable container object that contains a reference to a
mutable object can change when the latter's value is changed; however
the container is still considered immutable, because the collection of
objects it contains cannot be changed. So, immutability is not
strictly the same as having an unchangeable value, it is more subtle.)
An object's mutability is determined by its type; for instance,
numbers, strings and tuples are immutable, while dictionaries and
lists are mutable.
Objects are never explicitly destroyed; however, when they become
unreachable they may be garbage-collected. An implementation is
allowed to postpone garbage collection or omit it altogether --- it is
a matter of implementation quality how garbage collection is
implemented, as long as no objects are collected that are still
reachable.
**CPython implementation detail:** CPython currently uses a reference-
counting scheme with (optional) delayed detection of cyclically linked
garbage, which collects most objects as soon as they become
unreachable, but is not guaranteed to collect garbage containing
circular references. See the documentation of the "gc" module for
information on controlling the collection of cyclic garbage. Other
implementations act differently and CPython may change. Do not depend
on immediate finalization of objects when they become unreachable (ex:
always close files).
Note that the use of the implementation's tracing or debugging
facilities may keep objects alive that would normally be collectable.
Also note that catching an exception with a '"try"..."except"'
statement may keep objects alive.
Some objects contain references to "external" resources such as open
files or windows. It is understood that these resources are freed
when the object is garbage-collected, but since garbage collection is
not guaranteed to happen, such objects also provide an explicit way to
release the external resource, usually a "close()" method. Programs
are strongly recommended to explicitly close such objects. The
'"try"..."finally"' statement provides a convenient way to do this.
Some objects contain references to other objects; these are called
*containers*. Examples of containers are tuples, lists and
dictionaries. The references are part of a container's value. In
most cases, when we talk about the value of a container, we imply the
values, not the identities of the contained objects; however, when we
talk about the mutability of a container, only the identities of the
immediately contained objects are implied. So, if an immutable
container (like a tuple) contains a reference to a mutable object, its
value changes if that mutable object is changed.
Types affect almost all aspects of object behavior. Even the
importance of object identity is affected in some sense: for immutable
types, operations that compute new values may actually return a
reference to any existing object with the same type and value, while
for mutable objects this is not allowed. E.g., after "a = 1; b = 1",
"a" and "b" may or may not refer to the same object with the value
one, depending on the implementation, but after "c = []; d = []", "c"
and "d" are guaranteed to refer to two different, unique, newly
created empty lists. (Note that "c = d = []" assigns the same object
to both "c" and "d".)
t objectss
Operator precedence
*******************
The following table summarizes the operator precedences in Python,
from lowest precedence (least binding) to highest precedence (most
binding). Operators in the same box have the same precedence. Unless
the syntax is explicitly given, operators are binary. Operators in
the same box group left to right (except for comparisons, including
tests, which all have the same precedence and chain from left to right
--- see section Comparisons --- and exponentiation, which groups from
right to left).
+-------------------------------------------------+---------------------------------------+
| Operator | Description |
+=================================================+=======================================+
| "lambda" | Lambda expression |
+-------------------------------------------------+---------------------------------------+
| "if" -- "else" | Conditional expression |
+-------------------------------------------------+---------------------------------------+
| "or" | Boolean OR |
+-------------------------------------------------+---------------------------------------+
| "and" | Boolean AND |
+-------------------------------------------------+---------------------------------------+
| "not" "x" | Boolean NOT |
+-------------------------------------------------+---------------------------------------+
| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership |
| ">=", "<>", "!=", "==" | tests and identity tests |
+-------------------------------------------------+---------------------------------------+
| "|" | Bitwise OR |
+-------------------------------------------------+---------------------------------------+
| "^" | Bitwise XOR |
+-------------------------------------------------+---------------------------------------+
| "&" | Bitwise AND |
+-------------------------------------------------+---------------------------------------+
| "<<", ">>" | Shifts |
+-------------------------------------------------+---------------------------------------+
| "+", "-" | Addition and subtraction |
+-------------------------------------------------+---------------------------------------+
| "*", "/", "//", "%" | Multiplication, division, remainder |
| | [7] |
+-------------------------------------------------+---------------------------------------+
| "+x", "-x", "~x" | Positive, negative, bitwise NOT |
+-------------------------------------------------+---------------------------------------+
| "**" | Exponentiation [8] |
+-------------------------------------------------+---------------------------------------+
| "x[index]", "x[index:index]", | Subscription, slicing, call, |
| "x(arguments...)", "x.attribute" | attribute reference |
+-------------------------------------------------+---------------------------------------+
| "(expressions...)", "[expressions...]", "{key: | Binding or tuple display, list |
| value...}", "`expressions...`" | display, dictionary display, string |
| | conversion |
+-------------------------------------------------+---------------------------------------+
-[ Footnotes ]-
[1] In Python 2.3 and later releases, a list comprehension "leaks"
the control variables of each "for" it contains into the
containing scope. However, this behavior is deprecated, and
relying on it will not work in Python 3.
[2] While "abs(x%y) < abs(y)" is true mathematically, for floats
it may not be true numerically due to roundoff. For example, and
assuming a platform on which a Python float is an IEEE 754 double-
precision number, in order that "-1e-100 % 1e100" have the same
sign as "1e100", the computed result is "-1e-100 + 1e100", which
is numerically exactly equal to "1e100". The function
"math.fmod()" returns a result whose sign matches the sign of the
first argument instead, and so returns "-1e-100" in this case.
Which approach is more appropriate depends on the application.
[3] If x is very close to an exact integer multiple of y, it's
possible for "floor(x/y)" to be one larger than "(x-x%y)/y" due to
rounding. In such cases, Python returns the latter result, in
order to preserve that "divmod(x,y)[0] * y + x % y" be very close
to "x".
[4] The Unicode standard distinguishes between *code points* (e.g.
U+0041) and *abstract characters* (e.g. "LATIN CAPITAL LETTER A").
While most abstract characters in Unicode are only represented
using one code point, there is a number of abstract characters
that can in addition be represented using a sequence of more than
one code point. For example, the abstract character "LATIN
CAPITAL LETTER C WITH CEDILLA" can be represented as a single
*precomposed character* at code position U+00C7, or as a sequence
of a *base character* at code position U+0043 (LATIN CAPITAL
LETTER C), followed by a *combining character* at code position
U+0327 (COMBINING CEDILLA).
The comparison operators on unicode strings compare at the level
of Unicode code points. This may be counter-intuitive to humans.
For example, "u"\u00C7" == u"\u0043\u0327"" is "False", even
though both strings represent the same abstract character "LATIN
CAPITAL LETTER C WITH CEDILLA".
To compare strings at the level of abstract characters (that is,
in a way intuitive to humans), use "unicodedata.normalize()".
[5] Earlier versions of Python used lexicographic comparison of
the sorted (key, value) lists, but this was very expensive for the
common case of comparing for equality. An even earlier version of
Python compared dictionaries by identity only, but this caused
surprises because people expected to be able to test a dictionary
for emptiness by comparing it to "{}".
[6] Due to automatic garbage-collection, free lists, and the
dynamic nature of descriptors, you may notice seemingly unusual
behaviour in certain uses of the "is" operator, like those
involving comparisons between instance methods, or constants.
Check their documentation for more info.
[7] The "%" operator is also used for string formatting; the same
precedence applies.
[8] The power operator "**" binds less tightly than an arithmetic
or bitwise unary operator on its right, that is, "2**-1" is "0.5".
s operator-summarysx
The "pass" statement
********************
pass_stmt ::= "pass"
"pass" is a null operation --- when it is executed, nothing happens.
It is useful as a placeholder when a statement is required
syntactically, but no code needs to be executed, for example:
def f(arg): pass # a function that does nothing (yet)
class C: pass # a class with no methods (yet)
t passs�
The power operator
******************
The power operator binds more tightly than unary operators on its
left; it binds less tightly than unary operators on its right. The
syntax is:
power ::= primary ["**" u_expr]
Thus, in an unparenthesized sequence of power and unary operators, the
operators are evaluated from right to left (this does not constrain
the evaluation order for the operands): "-1**2" results in "-1".
The power operator has the same semantics as the built-in "pow()"
function, when called with two arguments: it yields its left argument
raised to the power of its right argument. The numeric arguments are
first converted to a common type. The result type is that of the
arguments after coercion.
With mixed operand types, the coercion rules for binary arithmetic
operators apply. For int and long int operands, the result has the
same type as the operands (after coercion) unless the second argument
is negative; in that case, all arguments are converted to float and a
float result is delivered. For example, "10**2" returns "100", but
"10**-2" returns "0.01". (This last feature was added in Python 2.2.
In Python 2.1 and before, if both arguments were of integer types and
the second argument was negative, an exception was raised).
Raising "0.0" to a negative power results in a "ZeroDivisionError".
Raising a negative number to a fractional power results in a
"ValueError".
t powers�
The "print" statement
*********************
print_stmt ::= "print" ([expression ("," expression)* [","]]
| ">>" expression [("," expression)+ [","]])
"print" evaluates each expression in turn and writes the resulting
object to standard output (see below). If an object is not a string,
it is first converted to a string using the rules for string
conversions. The (resulting or original) string is then written. A
space is written before each object is (converted and) written, unless
the output system believes it is positioned at the beginning of a
line. This is the case (1) when no characters have yet been written
to standard output, (2) when the last character written to standard
output is a whitespace character except "' '", or (3) when the last
write operation on standard output was not a "print" statement. (In
some cases it may be functional to write an empty string to standard
output for this reason.)
Note: Objects which act like file objects but which are not the
built-in file objects often do not properly emulate this aspect of
the file object's behavior, so it is best not to rely on this.
A "'\n'" character is written at the end, unless the "print" statement
ends with a comma. This is the only action if the statement contains
just the keyword "print".
Standard output is defined as the file object named "stdout" in the
built-in module "sys". If no such object exists, or if it does not
have a "write()" method, a "RuntimeError" exception is raised.
"print" also has an extended form, defined by the second portion of
the syntax described above. This form is sometimes referred to as
""print" chevron." In this form, the first expression after the ">>"
must evaluate to a "file-like" object, specifically an object that has
a "write()" method as described above. With this extended form, the
subsequent expressions are printed to this file object. If the first
expression evaluates to "None", then "sys.stdout" is used as the file
for output.
t prints�
The "raise" statement
*********************
raise_stmt ::= "raise" [expression ["," expression ["," expression]]]
If no expressions are present, "raise" re-raises the last exception
that was active in the current scope. If no exception is active in
the current scope, a "TypeError" exception is raised indicating that
this is an error (if running under IDLE, a "Queue.Empty" exception is
raised instead).
Otherwise, "raise" evaluates the expressions to get three objects,
using "None" as the value of omitted expressions. The first two
objects are used to determine the *type* and *value* of the exception.
If the first object is an instance, the type of the exception is the
class of the instance, the instance itself is the value, and the
second object must be "None".
If the first object is a class, it becomes the type of the exception.
The second object is used to determine the exception value: If it is
an instance of the class, the instance becomes the exception value. If
the second object is a tuple, it is used as the argument list for the
class constructor; if it is "None", an empty argument list is used,
and any other object is treated as a single argument to the
constructor. The instance so created by calling the constructor is
used as the exception value.
If a third object is present and not "None", it must be a traceback
object (see section The standard type hierarchy), and it is
substituted instead of the current location as the place where the
exception occurred. If the third object is present and not a
traceback object or "None", a "TypeError" exception is raised. The
three-expression form of "raise" is useful to re-raise an exception
transparently in an except clause, but "raise" with no expressions
should be preferred if the exception to be re-raised was the most
recently active exception in the current scope.
Additional information on exceptions can be found in section
Exceptions, and information about handling exceptions is in section
The try statement.
t raises�
The "return" statement
**********************
return_stmt ::= "return" [expression_list]
"return" may only occur syntactically nested in a function definition,
not within a nested class definition.
If an expression list is present, it is evaluated, else "None" is
substituted.
"return" leaves the current function call with the expression list (or
"None") as return value.
When "return" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
function.
In a generator function, the "return" statement is not allowed to
include an "expression_list". In that context, a bare "return"
indicates that the generator is done and will cause "StopIteration" to
be raised.
t returns�
Emulating container types
*************************
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. (For backwards compatibility, the method
"__getslice__()" (see below) can also be defined to handle simple, but
not extended slices.) It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "has_key()", "get()",
"clear()", "setdefault()", "iterkeys()", "itervalues()",
"iteritems()", "pop()", "popitem()", "copy()", and "update()" behaving
similar to those for Python's standard dictionary objects. The
"UserDict" module provides a "DictMixin" class to help create those
methods from a base set of "__getitem__()", "__setitem__()",
"__delitem__()", and "keys()". Mutable sequences should provide
methods "append()", "count()", "index()", "extend()", "insert()",
"pop()", "remove()", "reverse()" and "sort()", like Python standard
list objects. Finally, sequence types should implement addition
(meaning concatenation) and multiplication (meaning repetition) by
defining the methods "__add__()", "__radd__()", "__iadd__()",
"__mul__()", "__rmul__()" and "__imul__()" described below; they
should not define "__coerce__()" or other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should be equivalent of "has_key()"; for sequences,
it should search through the values. It is further recommended that
both mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should be the same as "iterkeys()"; for sequences, it should iterate
through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn't define a "__nonzero__()" method and whose "__len__()"
method returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__nonzero__()" method.
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note: "for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container, and should also be made
available as the method "iterkeys()".
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
New in version 2.6.
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a sequence. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don't define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
s sequence-typess
Shifting operations
*******************
The shifting operations have lower priority than the arithmetic
operations:
shift_expr ::= a_expr | shift_expr ( "<<" | ">>" ) a_expr
These operators accept plain or long integers as arguments. The
arguments are converted to a common type. They shift the first
argument to the left or right by the number of bits given by the
second argument.
A right shift by *n* bits is defined as division by "pow(2, n)". A
left shift by *n* bits is defined as multiplication with "pow(2, n)".
Negative shift counts raise a "ValueError" exception.
Note: In the current implementation, the right-hand operand is
required to be at most "sys.maxsize". If the right-hand operand is
larger than "sys.maxsize" an "OverflowError" exception is raised.
t shiftings�
Slicings
********
A slicing selects a range of items in a sequence object (e.g., a
string, tuple or list). Slicings may be used as expressions or as
targets in assignment or "del" statements. The syntax for a slicing:
slicing ::= simple_slicing | extended_slicing
simple_slicing ::= primary "[" short_slice "]"
extended_slicing ::= primary "[" slice_list "]"
slice_list ::= slice_item ("," slice_item)* [","]
slice_item ::= expression | proper_slice | ellipsis
proper_slice ::= short_slice | long_slice
short_slice ::= [lower_bound] ":" [upper_bound]
long_slice ::= short_slice ":" [stride]
lower_bound ::= expression
upper_bound ::= expression
stride ::= expression
ellipsis ::= "..."
There is ambiguity in the formal syntax here: anything that looks like
an expression list also looks like a slice list, so any subscription
can be interpreted as a slicing. Rather than further complicating the
syntax, this is disambiguated by defining that in this case the
interpretation as a subscription takes priority over the
interpretation as a slicing (this is the case if the slice list
contains no proper slice nor ellipses). Similarly, when the slice
list has exactly one short slice and no trailing comma, the
interpretation as a simple slicing takes priority over that as an
extended slicing.
The semantics for a simple slicing are as follows. The primary must
evaluate to a sequence object. The lower and upper bound expressions,
if present, must evaluate to plain integers; defaults are zero and the
"sys.maxint", respectively. If either bound is negative, the
sequence's length is added to it. The slicing now selects all items
with index *k* such that "i <= k < j" where *i* and *j* are the
specified lower and upper bounds. This may be an empty sequence. It
is not an error if *i* or *j* lie outside the range of valid indexes
(such items don't exist so they aren't selected).
The semantics for an extended slicing are as follows. The primary
must evaluate to a mapping object, and it is indexed with a key that
is constructed from the slice list, as follows. If the slice list
contains at least one comma, the key is a tuple containing the
conversion of the slice items; otherwise, the conversion of the lone
slice item is the key. The conversion of a slice item that is an
expression is that expression. The conversion of an ellipsis slice
item is the built-in "Ellipsis" object. The conversion of a proper
slice is a slice object (see section The standard type hierarchy)
whose "start", "stop" and "step" attributes are the values of the
expressions given as lower bound, upper bound and stride,
respectively, substituting "None" for missing expressions.
t slicingss�
Special Attributes
******************
The implementation adds a few special read-only attributes to several
object types, where they are relevant. Some of these are not reported
by the "dir()" built-in function.
object.__dict__
A dictionary or other mapping object used to store an object's
(writable) attributes.
object.__methods__
Deprecated since version 2.2: Use the built-in function "dir()" to
get a list of an object's attributes. This attribute is no longer
available.
object.__members__
Deprecated since version 2.2: Use the built-in function "dir()" to
get a list of an object's attributes. This attribute is no longer
available.
instance.__class__
The class to which a class instance belongs.
class.__bases__
The tuple of base classes of a class object.
definition.__name__
The name of the class, type, function, method, descriptor, or
generator instance.
The following attributes are only supported by *new-style class*es.
class.__mro__
This attribute is a tuple of classes that are considered when
looking for base classes during method resolution.
class.mro()
This method can be overridden by a metaclass to customize the
method resolution order for its instances. It is called at class
instantiation, and its result is stored in "__mro__".
class.__subclasses__()
Each new-style class keeps a list of weak references to its
immediate subclasses. This method returns a list of all those
references still alive. Example:
>>> int.__subclasses__()
[<type 'bool'>]
-[ Footnotes ]-
[1] Additional information on these special methods may be found
in the Python Reference Manual (Basic customization).
[2] As a consequence, the list "[1, 2]" is considered equal to
"[1.0, 2.0]", and similarly for tuples.
[3] They must have since the parser can't tell the type of the
operands.
[4] Cased characters are those with general category property
being one of "Lu" (Letter, uppercase), "Ll" (Letter, lowercase),
or "Lt" (Letter, titlecase).
[5] To format only a tuple you should therefore provide a
singleton tuple whose only element is the tuple to be formatted.
[6] The advantage of leaving the newline on is that returning an
empty string is then an unambiguous EOF indication. It is also
possible (in cases where it might matter, for example, if you want
to make an exact copy of a file while scanning its lines) to tell
whether the last line of a file ended in a newline or not (yes
this happens!).
t specialattrssa�
Special method names
********************
A class can implement certain operations that are invoked by special
syntax (such as arithmetic operations or subscripting and slicing) by
defining methods with special names. This is Python's approach to
*operator overloading*, allowing classes to define their own behavior
with respect to language operators. For instance, if a class defines
a method named "__getitem__()", and "x" is an instance of this class,
then "x[i]" is roughly equivalent to "x.__getitem__(i)" for old-style
classes and "type(x).__getitem__(x, i)" for new-style classes. Except
where mentioned, attempts to execute an operation raise an exception
when no appropriate method is defined (typically "AttributeError" or
"TypeError").
When implementing a class that emulates any built-in type, it is
important that the emulation only be implemented to the degree that it
makes sense for the object being modelled. For example, some
sequences may work well with retrieval of individual elements, but
extracting a slice may not make sense. (One example of this is the
"NodeList" interface in the W3C's Document Object Model.)
Basic customization
===================
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass's "__new__()" method using
"super(currentclass, cls).__new__(cls[, ...])" with appropriate
arguments and then modifying the newly-created instance as
necessary before returning it.
If "__new__()" returns an instance of *cls*, then the new
instance's "__init__()" method will be invoked like
"__init__(self[, ...])", where *self* is the new instance and the
remaining arguments are the same as were passed to "__new__()".
If "__new__()" does not return an instance of *cls*, then the new
instance's "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class's "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"BaseClass.__init__(self, [args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customise
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a destructor. If a base class has a "__del__()" method, the
derived class's "__del__()" method, if any, must explicitly call it
to ensure proper deletion of the base class part of the instance.
Note that it is possible (though not recommended!) for the
"__del__()" method to postpone destruction of the instance by
creating a new reference to it. It may then be called at a later
time when this new reference is deleted. It is not guaranteed that
"__del__()" methods are called for objects that still exist when
the interpreter exits.
Note: "del x" doesn't directly call "x.__del__()" --- the former
decrements the reference count for "x" by one, and the latter is
only called when "x"'s reference count reaches zero. Some common
situations that may prevent the reference count of an object from
going to zero include: circular references between objects (e.g.,
a doubly-linked list or a tree data structure with parent and
child pointers); a reference to the object on the stack frame of
a function that caught an exception (the traceback stored in
"sys.exc_traceback" keeps the stack frame alive); or a reference
to the object on the stack frame that raised an unhandled
exception in interactive mode (the traceback stored in
"sys.last_traceback" keeps the stack frame alive). The first
situation can only be remedied by explicitly breaking the cycles;
the latter two situations can be resolved by storing "None" in
"sys.exc_traceback" or "sys.last_traceback". Circular references
which are garbage are detected when the option cycle detector is
enabled (it's on by default), but can only be cleaned up if there
are no Python-level "__del__()" methods involved. Refer to the
documentation for the "gc" module for more information about how
"__del__()" methods are handled by the cycle detector,
particularly the description of the "garbage" value.
Warning: Due to the precarious circumstances under which
"__del__()" methods are invoked, exceptions that occur during
their execution are ignored, and a warning is printed to
"sys.stderr" instead. Also, when "__del__()" is invoked in
response to a module being deleted (e.g., when execution of the
program is done), other globals referenced by the "__del__()"
method may already have been deleted or in the process of being
torn down (e.g. the import machinery shutting down). For this
reason, "__del__()" methods should do the absolute minimum needed
to maintain external invariants. Starting with version 1.5,
Python guarantees that globals whose name begins with a single
underscore are deleted from their module before other globals are
deleted; if no other references to such globals exist, this may
help in assuring that imported modules are still available at the
time when the "__del__()" method is called.
See also the "-R" command-line option.
object.__repr__(self)
Called by the "repr()" built-in function and by string conversions
(reverse quotes) to compute the "official" string representation of
an object. If at all possible, this should look like a valid
Python expression that could be used to recreate an object with the
same value (given an appropriate environment). If this is not
possible, a string of the form "<...some useful description...>"
should be returned. The return value must be a string object. If a
class defines "__repr__()" but not "__str__()", then "__repr__()"
is also used when an "informal" string representation of instances
of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by the "str()" built-in function and by the "print"
statement to compute the "informal" string representation of an
object. This differs from "__repr__()" in that it does not have to
be a valid Python expression: a more convenient or concise
representation may be used instead. The return value must be a
string object.
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
New in version 2.1.
These are the so-called "rich comparison" methods, and are called
for comparison operators in preference to "__cmp__()" below. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" and "x<>y" call "x.__ne__(y)",
"x>y" calls "x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
There are no implied relationships among the comparison operators.
The truth of "x==y" does not imply that "x!=y" is false.
Accordingly, when defining "__eq__()", one should also define
"__ne__()" so that the operators will behave as expected. See the
paragraph on "__hash__()" for some important notes on creating
*hashable* objects which support custom comparison operations and
are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other's
reflection, "__le__()" and "__ge__()" are each other's reflection,
and "__eq__()" and "__ne__()" are their own reflection.
Arguments to rich comparison methods are never coerced.
To automatically generate ordering operations from a single root
operation, see "functools.total_ordering()".
object.__cmp__(self, other)
Called by comparison operations if rich comparison (see above) is
not defined. Should return a negative integer if "self < other",
zero if "self == other", a positive integer if "self > other". If
no "__cmp__()", "__eq__()" or "__ne__()" operation is defined,
class instances are compared by object identity ("address"). See
also the description of "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys. (Note: the
restriction that exceptions are not propagated by "__cmp__()" has
been removed since Python 1.5.)
object.__rcmp__(self, other)
Changed in version 2.1: No longer supported.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
If a class does not define a "__cmp__()" or "__eq__()" method it
should not define a "__hash__()" operation either; if it defines
"__cmp__()" or "__eq__()" but not "__hash__()", its instances will
not be usable in hashed collections. If a class defines mutable
objects and implements a "__cmp__()" or "__eq__()" method, it
should not implement "__hash__()", since hashable collection
implementations require that an object's hash value is immutable
(if the object's hash value changes, it will be in the wrong hash
bucket).
User-defined classes have "__cmp__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns a result derived from
"id(x)".
Classes which inherit a "__hash__()" method from a parent class but
change the meaning of "__cmp__()" or "__eq__()" such that the hash
value returned is no longer appropriate (e.g. by switching to a
value-based concept of equality instead of the default identity
based equality) can explicitly flag themselves as being unhashable
by setting "__hash__ = None" in the class definition. Doing so
means that not only will instances of the class raise an
appropriate "TypeError" when a program attempts to retrieve their
hash value, but they will also be correctly identified as
unhashable when checking "isinstance(obj, collections.Hashable)"
(unlike classes which define their own "__hash__()" to explicitly
raise "TypeError").
Changed in version 2.5: "__hash__()" may now also return a long
integer object; the 32-bit integer is then derived from the hash of
that object.
Changed in version 2.6: "__hash__" may now be set to "None" to
explicitly flag instances of a class as unhashable.
object.__nonzero__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True", or their integer
equivalents "0" or "1". When this method is not defined,
"__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__nonzero__()", all its instances are
considered true.
object.__unicode__(self)
Called to implement "unicode()" built-in; should return a Unicode
object. When this method is not defined, string conversion is
attempted, and the result of string conversion is converted to
Unicode using the system default encoding.
Customizing attribute access
============================
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when an attribute lookup has not found the attribute in the
usual places (i.e. it is not an instance attribute nor is it found
in the class tree for "self"). "name" is the attribute name. This
method should return the (computed) attribute value or raise an
"AttributeError" exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control in new-style classes.
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should not simply execute "self.name = value" --- this would cause
a recursive call to itself. Instead, it should insert the value in
the dictionary of instance attributes, e.g., "self.__dict__[name] =
value". For new-style classes, rather than accessing the instance
dictionary, it should call the base class method with the same
name, for example, "object.__setattr__(self, name, value)".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
More attribute access for new-style classes
-------------------------------------------
The following methods only apply to new-style classes.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note: This method may still be bypassed when looking up special
methods as the result of implicit invocation via language syntax
or built-in functions. See Special method lookup for new-style
classes.
Implementing Descriptors
------------------------
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner's class
dictionary or in the class dictionary for one of its parents). In the
examples below, "the attribute" refers to the attribute whose name is
the key of the property in the owner class' "__dict__".
object.__get__(self, instance, owner)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). *owner* is always the owner class, while *instance* is the
instance that the attribute was accessed through, or "None" when
the attribute is accessed through the *owner*. This method should
return the (computed) attribute value or raise an "AttributeError"
exception.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
Invoking Descriptors
--------------------
In general, a descriptor is an object attribute with "binding
behavior", one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object's dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called. Note that descriptors are only invoked for new
style objects or classes (ones that subclass "object()" or "type()").
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to a new-style object instance, "a.x" is transformed
into the call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a new-style class, "A.x" is transformed into the
call: "A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined. A descriptor can define
any combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object's
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
---------
By default, instances of both old and new-style classes have a
dictionary for attribute storage. This wastes space for objects
having very few instance variables. The space consumption can become
acute when creating large numbers of instances.
The default can be overridden by defining *__slots__* in a new-style
class definition. The *__slots__* declaration takes a sequence of
instance variables and reserves just enough space in each instance to
hold a value for each variable. Space is saved because *__dict__* is
not created for each instance.
__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. If defined in a
new-style class, *__slots__* reserves space for the declared
variables and prevents the automatic creation of *__dict__* and
*__weakref__* for each instance.
New in version 2.2.
Notes on using *__slots__*
* When inheriting from a class without *__slots__*, the *__dict__*
attribute of that class will always be accessible, so a *__slots__*
definition in the subclass is meaningless.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
Changed in version 2.3: Previously, adding "'__dict__'" to the
*__slots__* declaration would not enable the assignment of new
attributes not specifically listed in the sequence of instance
variable names.
* Without a *__weakref__* variable for each instance, classes
defining *__slots__* do not support weak references to its
instances. If weak reference support is needed, then add
"'__weakref__'" to the sequence of strings in the *__slots__*
declaration.
Changed in version 2.3: Previously, adding "'__weakref__'" to the
*__slots__* declaration would not enable support for weak
references.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is limited to the class
where it is defined. As a result, subclasses will have a *__dict__*
unless they also define *__slots__* (which must only contain names
of any *additional* slots).
* If a class defines a slot also defined in a base class, the
instance variable defined by the base class slot is inaccessible
(except by retrieving its descriptor directly from the base class).
This renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
"variable-length" built-in types such as "long", "str" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings
may also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
Changed in version 2.6: Previously, *__class__* assignment raised an
error if either new or old class had *__slots__*.
Customizing class creation
==========================
By default, new-style classes are constructed using "type()". A class
definition is read into a separate namespace and the value of class
name is bound to the result of "type(name, bases, dict)".
When the class definition is read, if *__metaclass__* is defined then
the callable assigned to it will be called instead of "type()". This
allows classes or functions to be written which monitor or alter the
class creation process:
* Modifying the class dictionary prior to the class being created.
* Returning an instance of another class -- essentially performing
the role of a factory function.
These steps will have to be performed in the metaclass's "__new__()"
method -- "type.__new__()" can then be called from this method to
create a class with different properties. This example adds a new
element to the class dictionary before creating the class:
class metacls(type):
def __new__(mcs, name, bases, dict):
dict['foo'] = 'metacls was here'
return type.__new__(mcs, name, bases, dict)
You can of course also override other class methods (or add new
methods); for example defining a custom "__call__()" method in the
metaclass allows custom behavior when the class is called, e.g. not
always creating a new instance.
__metaclass__
This variable can be any callable accepting arguments for "name",
"bases", and "dict". Upon class creation, the callable is used
instead of the built-in "type()".
New in version 2.2.
The appropriate metaclass is determined by the following precedence
rules:
* If "dict['__metaclass__']" exists, it is used.
* Otherwise, if there is at least one base class, its metaclass is
used (this looks for a *__class__* attribute first and if not found,
uses its type).
* Otherwise, if a global variable named __metaclass__ exists, it is
used.
* Otherwise, the old-style, classic metaclass (types.ClassType) is
used.
The potential uses for metaclasses are boundless. Some ideas that have
been explored including logging, interface checking, automatic
delegation, automatic property creation, proxies, frameworks, and
automatic resource locking/synchronization.
Customizing instance and subclass checks
========================================
New in version 2.6.
The following methods are used to override the default behavior of the
"isinstance()" and "issubclass()" built-in functions.
In particular, the metaclass "abc.ABCMeta" implements these methods in
order to allow the addition of Abstract Base Classes (ABCs) as
"virtual base classes" to any class or type (including built-in
types), including other ABCs.
class.__instancecheck__(self, instance)
Return true if *instance* should be considered a (direct or
indirect) instance of *class*. If defined, called to implement
"isinstance(instance, class)".
class.__subclasscheck__(self, subclass)
Return true if *subclass* should be considered a (direct or
indirect) subclass of *class*. If defined, called to implement
"issubclass(subclass, class)".
Note that these methods are looked up on the type (metaclass) of a
class. They cannot be defined as class methods in the actual class.
This is consistent with the lookup of special methods that are called
on instances, only in this case the instance is itself a class.
See also:
**PEP 3119** - Introducing Abstract Base Classes
Includes the specification for customizing "isinstance()" and
"issubclass()" behavior through "__instancecheck__()" and
"__subclasscheck__()", with motivation for this functionality in
the context of adding Abstract Base Classes (see the "abc"
module) to the language.
Emulating callable objects
==========================
object.__call__(self[, args...])
Called when the instance is "called" as a function; if this method
is defined, "x(arg1, arg2, ...)" is a shorthand for
"x.__call__(arg1, arg2, ...)".
Emulating container types
=========================
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. (For backwards compatibility, the method
"__getslice__()" (see below) can also be defined to handle simple, but
not extended slices.) It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "has_key()", "get()",
"clear()", "setdefault()", "iterkeys()", "itervalues()",
"iteritems()", "pop()", "popitem()", "copy()", and "update()" behaving
similar to those for Python's standard dictionary objects. The
"UserDict" module provides a "DictMixin" class to help create those
methods from a base set of "__getitem__()", "__setitem__()",
"__delitem__()", and "keys()". Mutable sequences should provide
methods "append()", "count()", "index()", "extend()", "insert()",
"pop()", "remove()", "reverse()" and "sort()", like Python standard
list objects. Finally, sequence types should implement addition
(meaning concatenation) and multiplication (meaning repetition) by
defining the methods "__add__()", "__radd__()", "__iadd__()",
"__mul__()", "__rmul__()" and "__imul__()" described below; they
should not define "__coerce__()" or other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should be equivalent of "has_key()"; for sequences,
it should search through the values. It is further recommended that
both mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should be the same as "iterkeys()"; for sequences, it should iterate
through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn't define a "__nonzero__()" method and whose "__len__()"
method returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__nonzero__()" method.
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note: "for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container, and should also be made
available as the method "iterkeys()".
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
New in version 2.6.
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a sequence. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don't define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
Additional methods for emulation of sequence types
==================================================
The following optional methods can be defined to further emulate
sequence objects. Immutable sequences methods should at most only
define "__getslice__()"; mutable sequences might define all three
methods.
object.__getslice__(self, i, j)
Deprecated since version 2.0: Support slice objects as parameters
to the "__getitem__()" method. (However, built-in types in CPython
currently still implement "__getslice__()". Therefore, you have to
override it in derived classes when implementing slicing.)
Called to implement evaluation of "self[i:j]". The returned object
should be of the same type as *self*. Note that missing *i* or *j*
in the slice expression are replaced by zero or "sys.maxsize",
respectively. If negative indexes are used in the slice, the
length of the sequence is added to that index. If the instance does
not implement the "__len__()" method, an "AttributeError" is
raised. No guarantee is made that indexes adjusted this way are not
still negative. Indexes which are greater than the length of the
sequence are not modified. If no "__getslice__()" is found, a slice
object is created instead, and passed to "__getitem__()" instead.
object.__setslice__(self, i, j, sequence)
Called to implement assignment to "self[i:j]". Same notes for *i*
and *j* as for "__getslice__()".
This method is deprecated. If no "__setslice__()" is found, or for
extended slicing of the form "self[i:j:k]", a slice object is
created, and passed to "__setitem__()", instead of "__setslice__()"
being called.
object.__delslice__(self, i, j)
Called to implement deletion of "self[i:j]". Same notes for *i* and
*j* as for "__getslice__()". This method is deprecated. If no
"__delslice__()" is found, or for extended slicing of the form
"self[i:j:k]", a slice object is created, and passed to
"__delitem__()", instead of "__delslice__()" being called.
Notice that these methods are only invoked when a single slice with a
single colon is used, and the slice method is available. For slice
operations involving extended slice notation, or in absence of the
slice methods, "__getitem__()", "__setitem__()" or "__delitem__()" is
called with a slice object as argument.
The following example demonstrate how to make your program or module
compatible with earlier versions of Python (assuming that methods
"__getitem__()", "__setitem__()" and "__delitem__()" support slice
objects as arguments):
class MyClass:
...
def __getitem__(self, index):
...
def __setitem__(self, index, value):
...
def __delitem__(self, index):
...
if sys.version_info < (2, 0):
# They won't be defined if version is at least 2.0 final
def __getslice__(self, i, j):
return self[max(0, i):max(0, j):]
def __setslice__(self, i, j, seq):
self[max(0, i):max(0, j):] = seq
def __delslice__(self, i, j):
del self[max(0, i):max(0, j):]
...
Note the calls to "max()"; these are necessary because of the handling
of negative indices before the "__*slice__()" methods are called.
When negative indexes are used, the "__*item__()" methods receive them
as provided, but the "__*slice__()" methods get a "cooked" form of the
index values. For each negative index value, the length of the
sequence is added to the index before calling the method (which may
still result in a negative index); this is the customary handling of
negative indexes by the built-in sequence types, and the "__*item__()"
methods are expected to do this as well. However, since they should
already be doing that, negative indexes cannot be passed in; they must
be constrained to the bounds of the sequence before being passed to
the "__*item__()" methods. Calling "max(0, i)" conveniently returns
the proper value.
Emulating numeric types
=======================
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "//", "%", "divmod()", "pow()", "**",
"<<", ">>", "&", "^", "|"). For instance, to evaluate the
expression "x + y", where *x* is an instance of a class that has an
"__add__()" method, "x.__add__(y)" is called. The "__divmod__()"
method should be the equivalent to using "__floordiv__()" and
"__mod__()"; it should not be related to "__truediv__()" (described
below). Note that "__pow__()" should be defined to accept an
optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__div__(self, other)
object.__truediv__(self, other)
The division operator ("/") is implemented by these methods. The
"__truediv__()" method is used when "__future__.division" is in
effect, otherwise "__div__()" is used. If only one of these two
methods is defined, the object will not support division in the
alternate context; "TypeError" will be raised instead.
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rdiv__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other)
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "/", "%", "divmod()", "pow()", "**",
"<<", ">>", "&", "^", "|") with reflected (swapped) operands.
These functions are only called if the left operand does not
support the corresponding operation and the operands are of
different types. [2] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note: If the right operand's type is a subclass of the left
operand's type and that subclass provides the reflected method
for the operation, this method will be called before the left
operand's non-reflected method. This behavior allows subclasses
to override their ancestors' operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__idiv__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "/=", "//=", "%=", "**=", "<<=",
">>=", "&=", "^=", "|="). These methods should attempt to do the
operation in-place (modifying *self*) and return the result (which
could be, but does not have to be, *self*). If a specific method
is not defined, the augmented assignment falls back to the normal
methods. For instance, to execute the statement "x += y", where
*x* is an instance of a class that has an "__iadd__()" method,
"x.__iadd__(y)" is called. If *x* is an instance of a class that
does not define a "__iadd__()" method, "x.__add__(y)" and
"y.__radd__(x)" are considered, as with the evaluation of "x + y".
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__long__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()",
"long()", and "float()". Should return a value of the appropriate
type.
object.__oct__(self)
object.__hex__(self)
Called to implement the built-in functions "oct()" and "hex()".
Should return a string value.
object.__index__(self)
Called to implement "operator.index()". Also called whenever
Python needs an integer object (such as in slicing). Must return
an integer (int or long).
New in version 2.5.
object.__coerce__(self, other)
Called to implement "mixed-mode" numeric arithmetic. Should either
return a 2-tuple containing *self* and *other* converted to a
common numeric type, or "None" if conversion is impossible. When
the common type would be the type of "other", it is sufficient to
return "None", since the interpreter will also ask the other object
to attempt a coercion (but sometimes, if the implementation of the
other type cannot be changed, it is useful to do the conversion to
the other type here). A return value of "NotImplemented" is
equivalent to returning "None".
Coercion rules
==============
This section used to document the rules for coercion. As the language
has evolved, the coercion rules have become hard to document
precisely; documenting what one version of one particular
implementation does is undesirable. Instead, here are some informal
guidelines regarding coercion. In Python 3, coercion will not be
supported.
* If the left operand of a % operator is a string or Unicode object,
no coercion takes place and the string formatting operation is
invoked instead.
* It is no longer recommended to define a coercion operation. Mixed-
mode operations on types that don't define coercion pass the
original arguments to the operation.
* New-style classes (those derived from "object") never invoke the
"__coerce__()" method in response to a binary operator; the only
time "__coerce__()" is invoked is when the built-in function
"coerce()" is called.
* For most intents and purposes, an operator that returns
"NotImplemented" is treated the same as one that is not implemented
at all.
* Below, "__op__()" and "__rop__()" are used to signify the generic
method names corresponding to an operator; "__iop__()" is used for
the corresponding in-place operator. For example, for the operator
'"+"', "__add__()" and "__radd__()" are used for the left and right
variant of the binary operator, and "__iadd__()" for the in-place
variant.
* For objects *x* and *y*, first "x.__op__(y)" is tried. If this is
not implemented or returns "NotImplemented", "y.__rop__(x)" is
tried. If this is also not implemented or returns "NotImplemented",
a "TypeError" exception is raised. But see the following exception:
* Exception to the previous item: if the left operand is an instance
of a built-in type or a new-style class, and the right operand is an
instance of a proper subclass of that type or class and overrides
the base's "__rop__()" method, the right operand's "__rop__()"
method is tried *before* the left operand's "__op__()" method.
This is done so that a subclass can completely override binary
operators. Otherwise, the left operand's "__op__()" method would
always accept the right operand: when an instance of a given class
is expected, an instance of a subclass of that class is always
acceptable.
* When either operand type defines a coercion, this coercion is
called before that type's "__op__()" or "__rop__()" method is
called, but no sooner. If the coercion returns an object of a
different type for the operand whose coercion is invoked, part of
the process is redone using the new object.
* When an in-place operator (like '"+="') is used, if the left
operand implements "__iop__()", it is invoked without any coercion.
When the operation falls back to "__op__()" and/or "__rop__()", the
normal coercion rules apply.
* In "x + y", if *x* is a sequence that implements sequence
concatenation, sequence concatenation is invoked.
* In "x * y", if one operand is a sequence that implements sequence
repetition, and the other is an integer ("int" or "long"), sequence
repetition is invoked.
* Rich comparisons (implemented by methods "__eq__()" and so on)
never use coercion. Three-way comparison (implemented by
"__cmp__()") does use coercion under the same conditions as other
binary operations use it.
* In the current implementation, the built-in numeric types "int",
"long", "float", and "complex" do not use coercion. All these types
implement a "__coerce__()" method, for use by the built-in
"coerce()" function.
Changed in version 2.7: The complex type no longer makes implicit
calls to the "__coerce__()" method for mixed-type binary arithmetic
operations.
With Statement Context Managers
===============================
New in version 2.5.
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method's return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller's responsibility.
See also:
**PEP 343** - The "with" statement
The specification, background, and examples for the Python "with"
statement.
Special method lookup for old-style classes
===========================================
For old-style classes, special methods are always looked up in exactly
the same way as any other method or attribute. This is the case
regardless of whether the method is being looked up explicitly as in
"x.__getitem__(i)" or implicitly as in "x[i]".
This behaviour means that special methods may exhibit different
behaviour for different instances of a single old-style class if the
appropriate special attributes are set differently:
>>> class C:
... pass
...
>>> c1 = C()
>>> c2 = C()
>>> c1.__len__ = lambda: 5
>>> c2.__len__ = lambda: 9
>>> len(c1)
5
>>> len(c2)
9
Special method lookup for new-style classes
===========================================
For new-style classes, implicit invocations of special methods are
only guaranteed to work correctly if defined on an object's type, not
in the object's instance dictionary. That behaviour is the reason why
the following code raises an exception (unlike the equivalent example
with old-style classes):
>>> class C(object):
... pass
...
>>> c = C()
>>> c.__len__ = lambda: 5
>>> len(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: object of type 'C' has no len()
The rationale behind this behaviour lies with a number of special
methods such as "__hash__()" and "__repr__()" that are implemented by
all objects, including type objects. If the implicit lookup of these
methods used the conventional lookup process, they would fail when
invoked on the type object itself:
>>> 1 .__hash__() == hash(1)
True
>>> int.__hash__() == hash(int)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: descriptor '__hash__' of 'int' object needs an argument
Incorrectly attempting to invoke an unbound method of a class in this
way is sometimes referred to as 'metaclass confusion', and is avoided
by bypassing the instance when looking up special methods:
>>> type(1).__hash__(1) == hash(1)
True
>>> type(int).__hash__(int) == hash(int)
True
In addition to bypassing any instance attributes in the interest of
correctness, implicit special method lookup generally also bypasses
the "__getattribute__()" method even of the object's metaclass:
>>> class Meta(type):
... def __getattribute__(*args):
... print "Metaclass getattribute invoked"
... return type.__getattribute__(*args)
...
>>> class C(object):
... __metaclass__ = Meta
... def __len__(self):
... return 10
... def __getattribute__(*args):
... print "Class getattribute invoked"
... return object.__getattribute__(*args)
...
>>> c = C()
>>> c.__len__() # Explicit lookup via instance
Class getattribute invoked
10
>>> type(c).__len__(c) # Explicit lookup via type
Metaclass getattribute invoked
10
>>> len(c) # Implicit lookup
10
Bypassing the "__getattribute__()" machinery in this fashion provides
significant scope for speed optimisations within the interpreter, at
the cost of some flexibility in the handling of special methods (the
special method *must* be set on the class object itself in order to be
consistently invoked by the interpreter).
-[ Footnotes ]-
[1] It *is* possible in some cases to change an object's type,
under certain controlled conditions. It generally isn't a good
idea though, since it can lead to some very strange behaviour if
it is handled incorrectly.
[2] For operands of the same type, it is assumed that if the non-
reflected method (such as "__add__()") fails the operation is not
supported, which is why the reflected method is not called.
t specialnamess�K
String Methods
**************
Below are listed the string methods which both 8-bit strings and
Unicode objects support. Some of them are also available on
"bytearray" objects.
In addition, Python's strings support the sequence type methods
described in the Sequence Types --- str, unicode, list, tuple,
bytearray, buffer, xrange section. To output formatted strings use
template strings or the "%" operator described in the String
Formatting Operations section. Also, see the "re" module for string
functions based on regular expressions.
str.capitalize()
Return a copy of the string with its first character capitalized
and the rest lowercased.
For 8-bit strings, this method is locale-dependent.
str.center(width[, fillchar])
Return centered in a string of length *width*. Padding is done
using the specified *fillchar* (default is a space).
Changed in version 2.4: Support for the *fillchar* argument.
str.count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring *sub*
in the range [*start*, *end*]. Optional arguments *start* and
*end* are interpreted as in slice notation.
str.decode([encoding[, errors]])
Decodes the string using the codec registered for *encoding*.
*encoding* defaults to the default string encoding. *errors* may
be given to set a different error handling scheme. The default is
"'strict'", meaning that encoding errors raise "UnicodeError".
Other possible values are "'ignore'", "'replace'" and any other
name registered via "codecs.register_error()", see section Codec
Base Classes.
New in version 2.2.
Changed in version 2.3: Support for other error handling schemes
added.
Changed in version 2.7: Support for keyword arguments added.
str.encode([encoding[, errors]])
Return an encoded version of the string. Default encoding is the
current default string encoding. *errors* may be given to set a
different error handling scheme. The default for *errors* is
"'strict'", meaning that encoding errors raise a "UnicodeError".
Other possible values are "'ignore'", "'replace'",
"'xmlcharrefreplace'", "'backslashreplace'" and any other name
registered via "codecs.register_error()", see section Codec Base
Classes. For a list of possible encodings, see section Standard
Encodings.
New in version 2.0.
Changed in version 2.3: Support for "'xmlcharrefreplace'" and
"'backslashreplace'" and other error handling schemes added.
Changed in version 2.7: Support for keyword arguments added.
str.endswith(suffix[, start[, end]])
Return "True" if the string ends with the specified *suffix*,
otherwise return "False". *suffix* can also be a tuple of suffixes
to look for. With optional *start*, test beginning at that
position. With optional *end*, stop comparing at that position.
Changed in version 2.5: Accept tuples as *suffix*.
str.expandtabs([tabsize])
Return a copy of the string where all tab characters are replaced
by one or more spaces, depending on the current column and the
given tab size. Tab positions occur every *tabsize* characters
(default is 8, giving tab positions at columns 0, 8, 16 and so on).
To expand the string, the current column is set to zero and the
string is examined character by character. If the character is a
tab ("\t"), one or more space characters are inserted in the result
until the current column is equal to the next tab position. (The
tab character itself is not copied.) If the character is a newline
("\n") or return ("\r"), it is copied and the current column is
reset to zero. Any other character is copied unchanged and the
current column is incremented by one regardless of how the
character is represented when printed.
>>> '01\t012\t0123\t01234'.expandtabs()
'01 012 0123 01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01 012 0123 01234'
str.find(sub[, start[, end]])
Return the lowest index in the string where substring *sub* is
found within the slice "s[start:end]". Optional arguments *start*
and *end* are interpreted as in slice notation. Return "-1" if
*sub* is not found.
Note: The "find()" method should be used only if you need to know
the position of *sub*. To check if *sub* is a substring or not,
use the "in" operator:
>>> 'Py' in 'Python'
True
str.format(*args, **kwargs)
Perform a string formatting operation. The string on which this
method is called can contain literal text or replacement fields
delimited by braces "{}". Each replacement field contains either
the numeric index of a positional argument, or the name of a
keyword argument. Returns a copy of the string where each
replacement field is replaced with the string value of the
corresponding argument.
>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'
See Format String Syntax for a description of the various
formatting options that can be specified in format strings.
This method of string formatting is the new standard in Python 3,
and should be preferred to the "%" formatting described in String
Formatting Operations in new code.
New in version 2.6.
str.index(sub[, start[, end]])
Like "find()", but raise "ValueError" when the substring is not
found.
str.isalnum()
Return true if all characters in the string are alphanumeric and
there is at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isalpha()
Return true if all characters in the string are alphabetic and
there is at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isdigit()
Return true if all characters in the string are digits and there is
at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.islower()
Return true if all cased characters [4] in the string are lowercase
and there is at least one cased character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isspace()
Return true if there are only whitespace characters in the string
and there is at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.istitle()
Return true if the string is a titlecased string and there is at
least one character, for example uppercase characters may only
follow uncased characters and lowercase characters only cased ones.
Return false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isupper()
Return true if all cased characters [4] in the string are uppercase
and there is at least one cased character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.join(iterable)
Return a string which is the concatenation of the strings in
*iterable*. A "TypeError" will be raised if there are any non-
string values in *iterable*, including "bytes" objects. The
separator between elements is the string providing this method.
str.ljust(width[, fillchar])
Return the string left justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is a
space). The original string is returned if *width* is less than or
equal to "len(s)".
Changed in version 2.4: Support for the *fillchar* argument.
str.lower()
Return a copy of the string with all the cased characters [4]
converted to lowercase.
For 8-bit strings, this method is locale-dependent.
str.lstrip([chars])
Return a copy of the string with leading characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a prefix; rather,
all combinations of its values are stripped:
>>> ' spacious '.lstrip()
'spacious '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'
Changed in version 2.2.2: Support for the *chars* argument.
str.partition(sep)
Split the string at the first occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing the string itself, followed by
two empty strings.
New in version 2.5.
str.replace(old, new[, count])
Return a copy of the string with all occurrences of substring *old*
replaced by *new*. If the optional argument *count* is given, only
the first *count* occurrences are replaced.
str.rfind(sub[, start[, end]])
Return the highest index in the string where substring *sub* is
found, such that *sub* is contained within "s[start:end]".
Optional arguments *start* and *end* are interpreted as in slice
notation. Return "-1" on failure.
str.rindex(sub[, start[, end]])
Like "rfind()" but raises "ValueError" when the substring *sub* is
not found.
str.rjust(width[, fillchar])
Return the string right justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is a
space). The original string is returned if *width* is less than or
equal to "len(s)".
Changed in version 2.4: Support for the *fillchar* argument.
str.rpartition(sep)
Split the string at the last occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing two empty strings, followed by
the string itself.
New in version 2.5.
str.rsplit([sep[, maxsplit]])
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit* splits
are done, the *rightmost* ones. If *sep* is not specified or
"None", any whitespace string is a separator. Except for splitting
from the right, "rsplit()" behaves like "split()" which is
described in detail below.
New in version 2.4.
str.rstrip([chars])
Return a copy of the string with trailing characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a suffix; rather,
all combinations of its values are stripped:
>>> ' spacious '.rstrip()
' spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'
Changed in version 2.2.2: Support for the *chars* argument.
str.split([sep[, maxsplit]])
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit*
splits are done (thus, the list will have at most "maxsplit+1"
elements). If *maxsplit* is not specified or "-1", then there is
no limit on the number of splits (all possible splits are made).
If *sep* is given, consecutive delimiters are not grouped together
and are deemed to delimit empty strings (for example,
"'1,,2'.split(',')" returns "['1', '', '2']"). The *sep* argument
may consist of multiple characters (for example,
"'1<>2<>3'.split('<>')" returns "['1', '2', '3']"). Splitting an
empty string with a specified separator returns "['']".
If *sep* is not specified or is "None", a different splitting
algorithm is applied: runs of consecutive whitespace are regarded
as a single separator, and the result will contain no empty strings
at the start or end if the string has leading or trailing
whitespace. Consequently, splitting an empty string or a string
consisting of just whitespace with a "None" separator returns "[]".
For example, "' 1 2 3 '.split()" returns "['1', '2', '3']", and
"' 1 2 3 '.split(None, 1)" returns "['1', '2 3 ']".
str.splitlines([keepends])
Return a list of the lines in the string, breaking at line
boundaries. This method uses the *universal newlines* approach to
splitting lines. Line breaks are not included in the resulting list
unless *keepends* is given and true.
Python recognizes ""\r"", ""\n"", and ""\r\n"" as line boundaries
for 8-bit strings.
For example:
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
['ab c', '', 'de fg', 'kl']
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(True)
['ab c\n', '\n', 'de fg\r', 'kl\r\n']
Unlike "split()" when a delimiter string *sep* is given, this
method returns an empty list for the empty string, and a terminal
line break does not result in an extra line:
>>> "".splitlines()
[]
>>> "One line\n".splitlines()
['One line']
For comparison, "split('\n')" gives:
>>> ''.split('\n')
['']
>>> 'Two lines\n'.split('\n')
['Two lines', '']
unicode.splitlines([keepends])
Return a list of the lines in the string, like "str.splitlines()".
However, the Unicode method splits on the following line
boundaries, which are a superset of the *universal newlines*
recognized for 8-bit strings.
+-------------------------+-------------------------------+
| Representation | Description |
+=========================+===============================+
| "\n" | Line Feed |
+-------------------------+-------------------------------+
| "\r" | Carriage Return |
+-------------------------+-------------------------------+
| "\r\n" | Carriage Return + Line Feed |
+-------------------------+-------------------------------+
| "\v" or "\x0b" | Line Tabulation |
+-------------------------+-------------------------------+
| "\f" or "\x0c" | Form Feed |
+-------------------------+-------------------------------+
| "\x1c" | File Separator |
+-------------------------+-------------------------------+
| "\x1d" | Group Separator |
+-------------------------+-------------------------------+
| "\x1e" | Record Separator |
+-------------------------+-------------------------------+
| "\x85" | Next Line (C1 Control Code) |
+-------------------------+-------------------------------+
| "\u2028" | Line Separator |
+-------------------------+-------------------------------+
| "\u2029" | Paragraph Separator |
+-------------------------+-------------------------------+
Changed in version 2.7: "\v" and "\f" added to list of line
boundaries.
str.startswith(prefix[, start[, end]])
Return "True" if string starts with the *prefix*, otherwise return
"False". *prefix* can also be a tuple of prefixes to look for.
With optional *start*, test string beginning at that position.
With optional *end*, stop comparing string at that position.
Changed in version 2.5: Accept tuples as *prefix*.
str.strip([chars])
Return a copy of the string with the leading and trailing
characters removed. The *chars* argument is a string specifying the
set of characters to be removed. If omitted or "None", the *chars*
argument defaults to removing whitespace. The *chars* argument is
not a prefix or suffix; rather, all combinations of its values are
stripped:
>>> ' spacious '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'
Changed in version 2.2.2: Support for the *chars* argument.
str.swapcase()
Return a copy of the string with uppercase characters converted to
lowercase and vice versa.
For 8-bit strings, this method is locale-dependent.
str.title()
Return a titlecased version of the string where words start with an
uppercase character and the remaining characters are lowercase.
The algorithm uses a simple language-independent definition of a
word as groups of consecutive letters. The definition works in
many contexts but it means that apostrophes in contractions and
possessives form word boundaries, which may not be the desired
result:
>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"
A workaround for apostrophes can be constructed using regular
expressions:
>>> import re
>>> def titlecase(s):
... return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
... lambda mo: mo.group(0)[0].upper() +
... mo.group(0)[1:].lower(),
... s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."
For 8-bit strings, this method is locale-dependent.
str.translate(table[, deletechars])
Return a copy of the string where all characters occurring in the
optional argument *deletechars* are removed, and the remaining
characters have been mapped through the given translation table,
which must be a string of length 256.
You can use the "maketrans()" helper function in the "string"
module to create a translation table. For string objects, set the
*table* argument to "None" for translations that only delete
characters:
>>> 'read this short text'.translate(None, 'aeiou')
'rd ths shrt txt'
New in version 2.6: Support for a "None" *table* argument.
For Unicode objects, the "translate()" method does not accept the
optional *deletechars* argument. Instead, it returns a copy of the
*s* where all characters have been mapped through the given
translation table which must be a mapping of Unicode ordinals to
Unicode ordinals, Unicode strings or "None". Unmapped characters
are left untouched. Characters mapped to "None" are deleted. Note,
a more flexible approach is to create a custom character mapping
codec using the "codecs" module (see "encodings.cp1251" for an
example).
str.upper()
Return a copy of the string with all the cased characters [4]
converted to uppercase. Note that "str.upper().isupper()" might be
"False" if "s" contains uncased characters or if the Unicode
category of the resulting character(s) is not "Lu" (Letter,
uppercase), but e.g. "Lt" (Letter, titlecase).
For 8-bit strings, this method is locale-dependent.
str.zfill(width)
Return the numeric string left filled with zeros in a string of
length *width*. A sign prefix is handled correctly. The original
string is returned if *width* is less than or equal to "len(s)".
New in version 2.2.2.
The following methods are present only on unicode objects:
unicode.isnumeric()
Return "True" if there are only numeric characters in S, "False"
otherwise. Numeric characters include digit characters, and all
characters that have the Unicode numeric value property, e.g.
U+2155, VULGAR FRACTION ONE FIFTH.
unicode.isdecimal()
Return "True" if there are only decimal characters in S, "False"
otherwise. Decimal characters include digit characters, and all
characters that can be used to form decimal-radix numbers, e.g.
U+0660, ARABIC-INDIC DIGIT ZERO.
s string-methodssF
String literals
***************
String literals are described by the following lexical definitions:
stringliteral ::= [stringprefix](shortstring | longstring)
stringprefix ::= "r" | "u" | "ur" | "R" | "U" | "UR" | "Ur" | "uR"
| "b" | "B" | "br" | "Br" | "bR" | "BR"
shortstring ::= "'" shortstringitem* "'" | '"' shortstringitem* '"'
longstring ::= "'''" longstringitem* "'''"
| '"""' longstringitem* '"""'
shortstringitem ::= shortstringchar | escapeseq
longstringitem ::= longstringchar | escapeseq
shortstringchar ::= <any source character except "\" or newline or the quote>
longstringchar ::= <any source character except "\">
escapeseq ::= "\" <any ASCII character>
One syntactic restriction not indicated by these productions is that
whitespace is not allowed between the "stringprefix" and the rest of
the string literal. The source character set is defined by the
encoding declaration; it is ASCII if no encoding declaration is given
in the source file; see section Encoding declarations.
In plain English: String literals can be enclosed in matching single
quotes ("'") or double quotes ("""). They can also be enclosed in
matching groups of three single or double quotes (these are generally
referred to as *triple-quoted strings*). The backslash ("\")
character is used to escape characters that otherwise have a special
meaning, such as newline, backslash itself, or the quote character.
String literals may optionally be prefixed with a letter "'r'" or
"'R'"; such strings are called *raw strings* and use different rules
for interpreting backslash escape sequences. A prefix of "'u'" or
"'U'" makes the string a Unicode string. Unicode strings use the
Unicode character set as defined by the Unicode Consortium and ISO
10646. Some additional escape sequences, described below, are
available in Unicode strings. A prefix of "'b'" or "'B'" is ignored in
Python 2; it indicates that the literal should become a bytes literal
in Python 3 (e.g. when code is automatically converted with 2to3). A
"'u'" or "'b'" prefix may be followed by an "'r'" prefix.
In triple-quoted strings, unescaped newlines and quotes are allowed
(and are retained), except that three unescaped quotes in a row
terminate the string. (A "quote" is the character used to open the
string, i.e. either "'" or """.)
Unless an "'r'" or "'R'" prefix is present, escape sequences in
strings are interpreted according to rules similar to those used by
Standard C. The recognized escape sequences are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
+===================+===================================+=========+
| "\newline" | Ignored | |
+-------------------+-----------------------------------+---------+
| "\\" | Backslash ("\") | |
+-------------------+-----------------------------------+---------+
| "\'" | Single quote ("'") | |
+-------------------+-----------------------------------+---------+
| "\"" | Double quote (""") | |
+-------------------+-----------------------------------+---------+
| "\a" | ASCII Bell (BEL) | |
+-------------------+-----------------------------------+---------+
| "\b" | ASCII Backspace (BS) | |
+-------------------+-----------------------------------+---------+
| "\f" | ASCII Formfeed (FF) | |
+-------------------+-----------------------------------+---------+
| "\n" | ASCII Linefeed (LF) | |
+-------------------+-----------------------------------+---------+
| "\N{name}" | Character named *name* in the | |
| | Unicode database (Unicode only) | |
+-------------------+-----------------------------------+---------+
| "\r" | ASCII Carriage Return (CR) | |
+-------------------+-----------------------------------+---------+
| "\t" | ASCII Horizontal Tab (TAB) | |
+-------------------+-----------------------------------+---------+
| "\uxxxx" | Character with 16-bit hex value | (1) |
| | *xxxx* (Unicode only) | |
+-------------------+-----------------------------------+---------+
| "\Uxxxxxxxx" | Character with 32-bit hex value | (2) |
| | *xxxxxxxx* (Unicode only) | |
+-------------------+-----------------------------------+---------+
| "\v" | ASCII Vertical Tab (VT) | |
+-------------------+-----------------------------------+---------+
| "\ooo" | Character with octal value *ooo* | (3,5) |
+-------------------+-----------------------------------+---------+
| "\xhh" | Character with hex value *hh* | (4,5) |
+-------------------+-----------------------------------+---------+
Notes:
1. Individual code units which form parts of a surrogate pair can
be encoded using this escape sequence.
2. Any Unicode character can be encoded this way, but characters
outside the Basic Multilingual Plane (BMP) will be encoded using a
surrogate pair if Python is compiled to use 16-bit code units (the
default).
3. As in Standard C, up to three octal digits are accepted.
4. Unlike in Standard C, exactly two hex digits are required.
5. In a string literal, hexadecimal and octal escapes denote the
byte with the given value; it is not necessary that the byte
encodes a character in the source character set. In a Unicode
literal, these escapes denote a Unicode character with the given
value.
Unlike Standard C, all unrecognized escape sequences are left in the
string unchanged, i.e., *the backslash is left in the string*. (This
behavior is useful when debugging: if an escape sequence is mistyped,
the resulting output is more easily recognized as broken.) It is also
important to note that the escape sequences marked as "(Unicode only)"
in the table above fall into the category of unrecognized escapes for
non-Unicode string literals.
When an "'r'" or "'R'" prefix is present, a character following a
backslash is included in the string without change, and *all
backslashes are left in the string*. For example, the string literal
"r"\n"" consists of two characters: a backslash and a lowercase "'n'".
String quotes can be escaped with a backslash, but the backslash
remains in the string; for example, "r"\""" is a valid string literal
consisting of two characters: a backslash and a double quote; "r"\""
is not a valid string literal (even a raw string cannot end in an odd
number of backslashes). Specifically, *a raw string cannot end in a
single backslash* (since the backslash would escape the following
quote character). Note also that a single backslash followed by a
newline is interpreted as those two characters as part of the string,
*not* as a line continuation.
When an "'r'" or "'R'" prefix is used in conjunction with a "'u'" or
"'U'" prefix, then the "\uXXXX" and "\UXXXXXXXX" escape sequences are
processed while *all other backslashes are left in the string*. For
example, the string literal "ur"\u0062\n"" consists of three Unicode
characters: 'LATIN SMALL LETTER B', 'REVERSE SOLIDUS', and 'LATIN
SMALL LETTER N'. Backslashes can be escaped with a preceding
backslash; however, both remain in the string. As a result, "\uXXXX"
escape sequences are only recognized when there are an odd number of
backslashes.
t stringss
Subscriptions
*************
A subscription selects an item of a sequence (string, tuple or list)
or mapping (dictionary) object:
subscription ::= primary "[" expression_list "]"
The primary must evaluate to an object of a sequence or mapping type.
If the primary is a mapping, the expression list must evaluate to an
object whose value is one of the keys of the mapping, and the
subscription selects the value in the mapping that corresponds to that
key. (The expression list is a tuple except if it has exactly one
item.)
If the primary is a sequence, the expression (list) must evaluate to a
plain integer. If this value is negative, the length of the sequence
is added to it (so that, e.g., "x[-1]" selects the last item of "x".)
The resulting value must be a nonnegative integer less than the number
of items in the sequence, and the subscription selects the item whose
index is that value (counting from zero).
A string's items are characters. A character is not a separate data
type but a string of exactly one character.
t
subscriptionss�
Truth Value Testing
*******************
Any object can be tested for truth value, for use in an "if" or
"while" condition or as operand of the Boolean operations below. The
following values are considered false:
* "None"
* "False"
* zero of any numeric type, for example, "0", "0L", "0.0", "0j".
* any empty sequence, for example, "''", "()", "[]".
* any empty mapping, for example, "{}".
* instances of user-defined classes, if the class defines a
"__nonzero__()" or "__len__()" method, when that method returns the
integer zero or "bool" value "False". [1]
All other values are considered true --- so objects of many types are
always true.
Operations and built-in functions that have a Boolean result always
return "0" or "False" for false and "1" or "True" for true, unless
otherwise stated. (Important exception: the Boolean operations "or"
and "and" always return one of their operands.)
t truths
The "try" statement
*******************
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression [("as" | ",") identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
Changed in version 2.5: In previous versions of Python,
"try"..."except"..."finally" did not work. "try"..."except" had to be
nested in "try"..."finally".
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is "compatible" with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object, or a tuple containing an item compatible with the exception.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified in that except clause, if present, and the except
clause's suite is executed. All except clauses must have an
executable block. When the end of this block is reached, execution
continues normally after the entire try statement. (This means that
if two nested handlers exist for the same exception, and the exception
occurs in the try clause of the inner handler, the outer handler will
not handle the exception.)
Before an except clause's suite is executed, details about the
exception are assigned to three variables in the "sys" module:
"sys.exc_type" receives the object identifying the exception;
"sys.exc_value" receives the exception's parameter;
"sys.exc_traceback" receives a traceback object (see section The
standard type hierarchy) identifying the point in the program where
the exception occurred. These details are also available through the
"sys.exc_info()" function, which returns a tuple "(exc_type,
exc_value, exc_traceback)". Use of the corresponding variables is
deprecated in favor of this function, since their use is unsafe in a
threaded program. As of Python 1.5, the variables are restored to
their previous values (before the call) when returning from a function
that handled an exception.
The optional "else" clause is executed if and when control flows off
the end of the "try" clause. [2] Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a 'cleanup' handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception, it is re-raised at the end of the
"finally" clause. If the "finally" clause raises another exception or
executes a "return" or "break" statement, the saved exception is
discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"..."finally" statement, the "finally" clause is
also executed 'on the way out.' A "continue" statement is illegal in
the "finally" clause. (The reason is a problem with the current
implementation --- this restriction may be lifted in the future).
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
t trys��
The standard type hierarchy
***************************
Below is a list of the types that are built into Python. Extension
modules (written in C, Java, or other languages, depending on the
implementation) can define additional types. Future versions of
Python may add types to the type hierarchy (e.g., rational numbers,
efficiently stored arrays of integers, etc.).
Some of the type descriptions below contain a paragraph listing
'special attributes.' These are attributes that provide access to the
implementation and are not intended for general use. Their definition
may change in the future.
None
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name "None". It
is used to signify the absence of a value in many situations, e.g.,
it is returned from functions that don't explicitly return
anything. Its truth value is false.
NotImplemented
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name
"NotImplemented". Numeric methods and rich comparison methods may
return this value if they do not implement the operation for the
operands provided. (The interpreter will then try the reflected
operation, or some other fallback, depending on the operator.) Its
truth value is true.
Ellipsis
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name
"Ellipsis". It is used to indicate the presence of the "..." syntax
in a slice. Its truth value is true.
"numbers.Number"
These are created by numeric literals and returned as results by
arithmetic operators and arithmetic built-in functions. Numeric
objects are immutable; once created their value never changes.
Python numbers are of course strongly related to mathematical
numbers, but subject to the limitations of numerical representation
in computers.
Python distinguishes between integers, floating point numbers, and
complex numbers:
"numbers.Integral"
These represent elements from the mathematical set of integers
(positive and negative).
There are three types of integers:
Plain integers
These represent numbers in the range -2147483648 through
2147483647. (The range may be larger on machines with a
larger natural word size, but not smaller.) When the result
of an operation would fall outside this range, the result is
normally returned as a long integer (in some cases, the
exception "OverflowError" is raised instead). For the
purpose of shift and mask operations, integers are assumed to
have a binary, 2's complement notation using 32 or more bits,
and hiding no bits from the user (i.e., all 4294967296
different bit patterns correspond to different values).
Long integers
These represent numbers in an unlimited range, subject to
available (virtual) memory only. For the purpose of shift
and mask operations, a binary representation is assumed, and
negative numbers are represented in a variant of 2's
complement which gives the illusion of an infinite string of
sign bits extending to the left.
Booleans
These represent the truth values False and True. The two
objects representing the values "False" and "True" are the
only Boolean objects. The Boolean type is a subtype of plain
integers, and Boolean values behave like the values 0 and 1,
respectively, in almost all contexts, the exception being
that when converted to a string, the strings ""False"" or
""True"" are returned, respectively.
The rules for integer representation are intended to give the
most meaningful interpretation of shift and mask operations
involving negative integers and the least surprises when
switching between the plain and long integer domains. Any
operation, if it yields a result in the plain integer domain,
will yield the same result in the long integer domain or when
using mixed operands. The switch between domains is transparent
to the programmer.
"numbers.Real" ("float")
These represent machine-level double precision floating point
numbers. You are at the mercy of the underlying machine
architecture (and C or Java implementation) for the accepted
range and handling of overflow. Python does not support single-
precision floating point numbers; the savings in processor and
memory usage that are usually the reason for using these are
dwarfed by the overhead of using objects in Python, so there is
no reason to complicate the language with two kinds of floating
point numbers.
"numbers.Complex"
These represent complex numbers as a pair of machine-level
double precision floating point numbers. The same caveats apply
as for floating point numbers. The real and imaginary parts of a
complex number "z" can be retrieved through the read-only
attributes "z.real" and "z.imag".
Sequences
These represent finite ordered sets indexed by non-negative
numbers. The built-in function "len()" returns the number of items
of a sequence. When the length of a sequence is *n*, the index set
contains the numbers 0, 1, ..., *n*-1. Item *i* of sequence *a* is
selected by "a[i]".
Sequences also support slicing: "a[i:j]" selects all items with
index *k* such that *i* "<=" *k* "<" *j*. When used as an
expression, a slice is a sequence of the same type. This implies
that the index set is renumbered so that it starts at 0.
Some sequences also support "extended slicing" with a third "step"
parameter: "a[i:j:k]" selects all items of *a* with index *x* where
"x = i + n*k", *n* ">=" "0" and *i* "<=" *x* "<" *j*.
Sequences are distinguished according to their mutability:
Immutable sequences
An object of an immutable sequence type cannot change once it is
created. (If the object contains references to other objects,
these other objects may be mutable and may be changed; however,
the collection of objects directly referenced by an immutable
object cannot change.)
The following types are immutable sequences:
Strings
The items of a string are characters. There is no separate
character type; a character is represented by a string of one
item. Characters represent (at least) 8-bit bytes. The
built-in functions "chr()" and "ord()" convert between
characters and nonnegative integers representing the byte
values. Bytes with the values 0--127 usually represent the
corresponding ASCII values, but the interpretation of values
is up to the program. The string data type is also used to
represent arrays of bytes, e.g., to hold data read from a
file.
(On systems whose native character set is not ASCII, strings
may use EBCDIC in their internal representation, provided the
functions "chr()" and "ord()" implement a mapping between
ASCII and EBCDIC, and string comparison preserves the ASCII
order. Or perhaps someone can propose a better rule?)
Unicode
The items of a Unicode object are Unicode code units. A
Unicode code unit is represented by a Unicode object of one
item and can hold either a 16-bit or 32-bit value
representing a Unicode ordinal (the maximum value for the
ordinal is given in "sys.maxunicode", and depends on how
Python is configured at compile time). Surrogate pairs may
be present in the Unicode object, and will be reported as two
separate items. The built-in functions "unichr()" and
"ord()" convert between code units and nonnegative integers
representing the Unicode ordinals as defined in the Unicode
Standard 3.0. Conversion from and to other encodings are
possible through the Unicode method "encode()" and the built-
in function "unicode()".
Tuples
The items of a tuple are arbitrary Python objects. Tuples of
two or more items are formed by comma-separated lists of
expressions. A tuple of one item (a 'singleton') can be
formed by affixing a comma to an expression (an expression by
itself does not create a tuple, since parentheses must be
usable for grouping of expressions). An empty tuple can be
formed by an empty pair of parentheses.
Mutable sequences
Mutable sequences can be changed after they are created. The
subscription and slicing notations can be used as the target of
assignment and "del" (delete) statements.
There are currently two intrinsic mutable sequence types:
Lists
The items of a list are arbitrary Python objects. Lists are
formed by placing a comma-separated list of expressions in
square brackets. (Note that there are no special cases needed
to form lists of length 0 or 1.)
Byte Arrays
A bytearray object is a mutable array. They are created by
the built-in "bytearray()" constructor. Aside from being
mutable (and hence unhashable), byte arrays otherwise provide
the same interface and functionality as immutable bytes
objects.
The extension module "array" provides an additional example of a
mutable sequence type.
Set types
These represent unordered, finite sets of unique, immutable
objects. As such, they cannot be indexed by any subscript. However,
they can be iterated over, and the built-in function "len()"
returns the number of items in a set. Common uses for sets are fast
membership testing, removing duplicates from a sequence, and
computing mathematical operations such as intersection, union,
difference, and symmetric difference.
For set elements, the same immutability rules apply as for
dictionary keys. Note that numeric types obey the normal rules for
numeric comparison: if two numbers compare equal (e.g., "1" and
"1.0"), only one of them can be contained in a set.
There are currently two intrinsic set types:
Sets
These represent a mutable set. They are created by the built-in
"set()" constructor and can be modified afterwards by several
methods, such as "add()".
Frozen sets
These represent an immutable set. They are created by the
built-in "frozenset()" constructor. As a frozenset is immutable
and *hashable*, it can be used again as an element of another
set, or as a dictionary key.
Mappings
These represent finite sets of objects indexed by arbitrary index
sets. The subscript notation "a[k]" selects the item indexed by "k"
from the mapping "a"; this can be used in expressions and as the
target of assignments or "del" statements. The built-in function
"len()" returns the number of items in a mapping.
There is currently a single intrinsic mapping type:
Dictionaries
These represent finite sets of objects indexed by nearly
arbitrary values. The only types of values not acceptable as
keys are values containing lists or dictionaries or other
mutable types that are compared by value rather than by object
identity, the reason being that the efficient implementation of
dictionaries requires a key's hash value to remain constant.
Numeric types used for keys obey the normal rules for numeric
comparison: if two numbers compare equal (e.g., "1" and "1.0")
then they can be used interchangeably to index the same
dictionary entry.
Dictionaries are mutable; they can be created by the "{...}"
notation (see section Dictionary displays).
The extension modules "dbm", "gdbm", and "bsddb" provide
additional examples of mapping types.
Callable types
These are the types to which the function call operation (see
section Calls) can be applied:
User-defined functions
A user-defined function object is created by a function
definition (see section Function definitions). It should be
called with an argument list containing the same number of items
as the function's formal parameter list.
Special attributes:
+-------------------------+---------------------------------+-------------+
| Attribute | Meaning | |
+=========================+=================================+=============+
| "__doc__" "func_doc" | The function's documentation | Writable |
| | string, or "None" if | |
| | unavailable. | |
+-------------------------+---------------------------------+-------------+
| "__name__" "func_name" | The function's name | Writable |
+-------------------------+---------------------------------+-------------+
| "__module__" | The name of the module the | Writable |
| | function was defined in, or | |
| | "None" if unavailable. | |
+-------------------------+---------------------------------+-------------+
| "__defaults__" | A tuple containing default | Writable |
| "func_defaults" | argument values for those | |
| | arguments that have defaults, | |
| | or "None" if no arguments have | |
| | a default value. | |
+-------------------------+---------------------------------+-------------+
| "__code__" "func_code" | The code object representing | Writable |
| | the compiled function body. | |
+-------------------------+---------------------------------+-------------+
| "__globals__" | A reference to the dictionary | Read-only |
| "func_globals" | that holds the function's | |
| | global variables --- the global | |
| | namespace of the module in | |
| | which the function was defined. | |
+-------------------------+---------------------------------+-------------+
| "__dict__" "func_dict" | The namespace supporting | Writable |
| | arbitrary function attributes. | |
+-------------------------+---------------------------------+-------------+
| "__closure__" | "None" or a tuple of cells that | Read-only |
| "func_closure" | contain bindings for the | |
| | function's free variables. | |
+-------------------------+---------------------------------+-------------+
Most of the attributes labelled "Writable" check the type of the
assigned value.
Changed in version 2.4: "func_name" is now writable.
Changed in version 2.6: The double-underscore attributes
"__closure__", "__code__", "__defaults__", and "__globals__"
were introduced as aliases for the corresponding "func_*"
attributes for forwards compatibility with Python 3.
Function objects also support getting and setting arbitrary
attributes, which can be used, for example, to attach metadata
to functions. Regular attribute dot-notation is used to get and
set such attributes. *Note that the current implementation only
supports function attributes on user-defined functions. Function
attributes on built-in functions may be supported in the
future.*
Additional information about a function's definition can be
retrieved from its code object; see the description of internal
types below.
User-defined methods
A user-defined method object combines a class, a class instance
(or "None") and any callable object (normally a user-defined
function).
Special read-only attributes: "im_self" is the class instance
object, "im_func" is the function object; "im_class" is the
class of "im_self" for bound methods or the class that asked for
the method for unbound methods; "__doc__" is the method's
documentation (same as "im_func.__doc__"); "__name__" is the
method name (same as "im_func.__name__"); "__module__" is the
name of the module the method was defined in, or "None" if
unavailable.
Changed in version 2.2: "im_self" used to refer to the class
that defined the method.
Changed in version 2.6: For Python 3 forward-compatibility,
"im_func" is also available as "__func__", and "im_self" as
"__self__".
Methods also support accessing (but not setting) the arbitrary
function attributes on the underlying function object.
User-defined method objects may be created when getting an
attribute of a class (perhaps via an instance of that class), if
that attribute is a user-defined function object, an unbound
user-defined method object, or a class method object. When the
attribute is a user-defined method object, a new method object
is only created if the class from which it is being retrieved is
the same as, or a derived class of, the class stored in the
original method object; otherwise, the original method object is
used as it is.
When a user-defined method object is created by retrieving a
user-defined function object from a class, its "im_self"
attribute is "None" and the method object is said to be unbound.
When one is created by retrieving a user-defined function object
from a class via one of its instances, its "im_self" attribute
is the instance, and the method object is said to be bound. In
either case, the new method's "im_class" attribute is the class
from which the retrieval takes place, and its "im_func"
attribute is the original function object.
When a user-defined method object is created by retrieving
another method object from a class or instance, the behaviour is
the same as for a function object, except that the "im_func"
attribute of the new instance is not the original method object
but its "im_func" attribute.
When a user-defined method object is created by retrieving a
class method object from a class or instance, its "im_self"
attribute is the class itself, and its "im_func" attribute is
the function object underlying the class method.
When an unbound user-defined method object is called, the
underlying function ("im_func") is called, with the restriction
that the first argument must be an instance of the proper class
("im_class") or of a derived class thereof.
When a bound user-defined method object is called, the
underlying function ("im_func") is called, inserting the class
instance ("im_self") in front of the argument list. For
instance, when "C" is a class which contains a definition for a
function "f()", and "x" is an instance of "C", calling "x.f(1)"
is equivalent to calling "C.f(x, 1)".
When a user-defined method object is derived from a class method
object, the "class instance" stored in "im_self" will actually
be the class itself, so that calling either "x.f(1)" or "C.f(1)"
is equivalent to calling "f(C,1)" where "f" is the underlying
function.
Note that the transformation from function object to (unbound or
bound) method object happens each time the attribute is
retrieved from the class or instance. In some cases, a fruitful
optimization is to assign the attribute to a local variable and
call that local variable. Also notice that this transformation
only happens for user-defined functions; other callable objects
(and all non-callable objects) are retrieved without
transformation. It is also important to note that user-defined
functions which are attributes of a class instance are not
converted to bound methods; this *only* happens when the
function is an attribute of the class.
Generator functions
A function or method which uses the "yield" statement (see
section The yield statement) is called a *generator function*.
Such a function, when called, always returns an iterator object
which can be used to execute the body of the function: calling
the iterator's "next()" method will cause the function to
execute until it provides a value using the "yield" statement.
When the function executes a "return" statement or falls off the
end, a "StopIteration" exception is raised and the iterator will
have reached the end of the set of values to be returned.
Built-in functions
A built-in function object is a wrapper around a C function.
Examples of built-in functions are "len()" and "math.sin()"
("math" is a standard built-in module). The number and type of
the arguments are determined by the C function. Special read-
only attributes: "__doc__" is the function's documentation
string, or "None" if unavailable; "__name__" is the function's
name; "__self__" is set to "None" (but see the next item);
"__module__" is the name of the module the function was defined
in or "None" if unavailable.
Built-in methods
This is really a different disguise of a built-in function, this
time containing an object passed to the C function as an
implicit extra argument. An example of a built-in method is
"alist.append()", assuming *alist* is a list object. In this
case, the special read-only attribute "__self__" is set to the
object denoted by *alist*.
Class Types
Class types, or "new-style classes," are callable. These
objects normally act as factories for new instances of
themselves, but variations are possible for class types that
override "__new__()". The arguments of the call are passed to
"__new__()" and, in the typical case, to "__init__()" to
initialize the new instance.
Classic Classes
Class objects are described below. When a class object is
called, a new class instance (also described below) is created
and returned. This implies a call to the class's "__init__()"
method if it has one. Any arguments are passed on to the
"__init__()" method. If there is no "__init__()" method, the
class must be called without arguments.
Class instances
Class instances are described below. Class instances are
callable only when the class has a "__call__()" method;
"x(arguments)" is a shorthand for "x.__call__(arguments)".
Modules
Modules are imported by the "import" statement (see section The
import statement). A module object has a namespace implemented by a
dictionary object (this is the dictionary referenced by the
func_globals attribute of functions defined in the module).
Attribute references are translated to lookups in this dictionary,
e.g., "m.x" is equivalent to "m.__dict__["x"]". A module object
does not contain the code object used to initialize the module
(since it isn't needed once the initialization is done).
Attribute assignment updates the module's namespace dictionary,
e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".
Special read-only attribute: "__dict__" is the module's namespace
as a dictionary object.
**CPython implementation detail:** Because of the way CPython
clears module dictionaries, the module dictionary will be cleared
when the module falls out of scope even if the dictionary still has
live references. To avoid this, copy the dictionary or keep the
module around while using its dictionary directly.
Predefined (writable) attributes: "__name__" is the module's name;
"__doc__" is the module's documentation string, or "None" if
unavailable; "__file__" is the pathname of the file from which the
module was loaded, if it was loaded from a file. The "__file__"
attribute is not present for C modules that are statically linked
into the interpreter; for extension modules loaded dynamically from
a shared library, it is the pathname of the shared library file.
Classes
Both class types (new-style classes) and class objects (old-
style/classic classes) are typically created by class definitions
(see section Class definitions). A class has a namespace
implemented by a dictionary object. Class attribute references are
translated to lookups in this dictionary, e.g., "C.x" is translated
to "C.__dict__["x"]" (although for new-style classes in particular
there are a number of hooks which allow for other means of locating
attributes). When the attribute name is not found there, the
attribute search continues in the base classes. For old-style
classes, the search is depth-first, left-to-right in the order of
occurrence in the base class list. New-style classes use the more
complex C3 method resolution order which behaves correctly even in
the presence of 'diamond' inheritance structures where there are
multiple inheritance paths leading back to a common ancestor.
Additional details on the C3 MRO used by new-style classes can be
found in the documentation accompanying the 2.3 release at
https://www.python.org/download/releases/2.3/mro/.
When a class attribute reference (for class "C", say) would yield a
user-defined function object or an unbound user-defined method
object whose associated class is either "C" or one of its base
classes, it is transformed into an unbound user-defined method
object whose "im_class" attribute is "C". When it would yield a
class method object, it is transformed into a bound user-defined
method object whose "im_self" attribute is "C". When it would
yield a static method object, it is transformed into the object
wrapped by the static method object. See section Implementing
Descriptors for another way in which attributes retrieved from a
class may differ from those actually contained in its "__dict__"
(note that only new-style classes support descriptors).
Class attribute assignments update the class's dictionary, never
the dictionary of a base class.
A class object can be called (see above) to yield a class instance
(see below).
Special attributes: "__name__" is the class name; "__module__" is
the module name in which the class was defined; "__dict__" is the
dictionary containing the class's namespace; "__bases__" is a tuple
(possibly empty or a singleton) containing the base classes, in the
order of their occurrence in the base class list; "__doc__" is the
class's documentation string, or "None" if undefined.
Class instances
A class instance is created by calling a class object (see above).
A class instance has a namespace implemented as a dictionary which
is the first place in which attribute references are searched.
When an attribute is not found there, and the instance's class has
an attribute by that name, the search continues with the class
attributes. If a class attribute is found that is a user-defined
function object or an unbound user-defined method object whose
associated class is the class (call it "C") of the instance for
which the attribute reference was initiated or one of its bases, it
is transformed into a bound user-defined method object whose
"im_class" attribute is "C" and whose "im_self" attribute is the
instance. Static method and class method objects are also
transformed, as if they had been retrieved from class "C"; see
above under "Classes". See section Implementing Descriptors for
another way in which attributes of a class retrieved via its
instances may differ from the objects actually stored in the
class's "__dict__". If no class attribute is found, and the
object's class has a "__getattr__()" method, that is called to
satisfy the lookup.
Attribute assignments and deletions update the instance's
dictionary, never a class's dictionary. If the class has a
"__setattr__()" or "__delattr__()" method, this is called instead
of updating the instance dictionary directly.
Class instances can pretend to be numbers, sequences, or mappings
if they have methods with certain special names. See section
Special method names.
Special attributes: "__dict__" is the attribute dictionary;
"__class__" is the instance's class.
Files
A file object represents an open file. File objects are created by
the "open()" built-in function, and also by "os.popen()",
"os.fdopen()", and the "makefile()" method of socket objects (and
perhaps by other functions or methods provided by extension
modules). The objects "sys.stdin", "sys.stdout" and "sys.stderr"
are initialized to file objects corresponding to the interpreter's
standard input, output and error streams. See File Objects for
complete documentation of file objects.
Internal types
A few types used internally by the interpreter are exposed to the
user. Their definitions may change with future versions of the
interpreter, but they are mentioned here for completeness.
Code objects
Code objects represent *byte-compiled* executable Python code,
or *bytecode*. The difference between a code object and a
function object is that the function object contains an explicit
reference to the function's globals (the module in which it was
defined), while a code object contains no context; also the
default argument values are stored in the function object, not
in the code object (because they represent values calculated at
run-time). Unlike function objects, code objects are immutable
and contain no references (directly or indirectly) to mutable
objects.
Special read-only attributes: "co_name" gives the function name;
"co_argcount" is the number of positional arguments (including
arguments with default values); "co_nlocals" is the number of
local variables used by the function (including arguments);
"co_varnames" is a tuple containing the names of the local
variables (starting with the argument names); "co_cellvars" is a
tuple containing the names of local variables that are
referenced by nested functions; "co_freevars" is a tuple
containing the names of free variables; "co_code" is a string
representing the sequence of bytecode instructions; "co_consts"
is a tuple containing the literals used by the bytecode;
"co_names" is a tuple containing the names used by the bytecode;
"co_filename" is the filename from which the code was compiled;
"co_firstlineno" is the first line number of the function;
"co_lnotab" is a string encoding the mapping from bytecode
offsets to line numbers (for details see the source code of the
interpreter); "co_stacksize" is the required stack size
(including local variables); "co_flags" is an integer encoding a
number of flags for the interpreter.
The following flag bits are defined for "co_flags": bit "0x04"
is set if the function uses the "*arguments" syntax to accept an
arbitrary number of positional arguments; bit "0x08" is set if
the function uses the "**keywords" syntax to accept arbitrary
keyword arguments; bit "0x20" is set if the function is a
generator.
Future feature declarations ("from __future__ import division")
also use bits in "co_flags" to indicate whether a code object
was compiled with a particular feature enabled: bit "0x2000" is
set if the function was compiled with future division enabled;
bits "0x10" and "0x1000" were used in earlier versions of
Python.
Other bits in "co_flags" are reserved for internal use.
If a code object represents a function, the first item in
"co_consts" is the documentation string of the function, or
"None" if undefined.
Frame objects
Frame objects represent execution frames. They may occur in
traceback objects (see below).
Special read-only attributes: "f_back" is to the previous stack
frame (towards the caller), or "None" if this is the bottom
stack frame; "f_code" is the code object being executed in this
frame; "f_locals" is the dictionary used to look up local
variables; "f_globals" is used for global variables;
"f_builtins" is used for built-in (intrinsic) names;
"f_restricted" is a flag indicating whether the function is
executing in restricted execution mode; "f_lasti" gives the
precise instruction (this is an index into the bytecode string
of the code object).
Special writable attributes: "f_trace", if not "None", is a
function called at the start of each source code line (this is
used by the debugger); "f_exc_type", "f_exc_value",
"f_exc_traceback" represent the last exception raised in the
parent frame provided another exception was ever raised in the
current frame (in all other cases they are "None"); "f_lineno"
is the current line number of the frame --- writing to this from
within a trace function jumps to the given line (only for the
bottom-most frame). A debugger can implement a Jump command
(aka Set Next Statement) by writing to f_lineno.
Traceback objects
Traceback objects represent a stack trace of an exception. A
traceback object is created when an exception occurs. When the
search for an exception handler unwinds the execution stack, at
each unwound level a traceback object is inserted in front of
the current traceback. When an exception handler is entered,
the stack trace is made available to the program. (See section
The try statement.) It is accessible as "sys.exc_traceback", and
also as the third item of the tuple returned by
"sys.exc_info()". The latter is the preferred interface, since
it works correctly when the program is using multiple threads.
When the program contains no suitable handler, the stack trace
is written (nicely formatted) to the standard error stream; if
the interpreter is interactive, it is also made available to the
user as "sys.last_traceback".
Special read-only attributes: "tb_next" is the next level in the
stack trace (towards the frame where the exception occurred), or
"None" if there is no next level; "tb_frame" points to the
execution frame of the current level; "tb_lineno" gives the line
number where the exception occurred; "tb_lasti" indicates the
precise instruction. The line number and last instruction in
the traceback may differ from the line number of its frame
object if the exception occurred in a "try" statement with no
matching except clause or with a finally clause.
Slice objects
Slice objects are used to represent slices when *extended slice
syntax* is used. This is a slice using two colons, or multiple
slices or ellipses separated by commas, e.g., "a[i:j:step]",
"a[i:j, k:l]", or "a[..., i:j]". They are also created by the
built-in "slice()" function.
Special read-only attributes: "start" is the lower bound; "stop"
is the upper bound; "step" is the step value; each is "None" if
omitted. These attributes can have any type.
Slice objects support one method:
slice.indices(self, length)
This method takes a single integer argument *length* and
computes information about the extended slice that the slice
object would describe if applied to a sequence of *length*
items. It returns a tuple of three integers; respectively
these are the *start* and *stop* indices and the *step* or
stride length of the slice. Missing or out-of-bounds indices
are handled in a manner consistent with regular slices.
New in version 2.3.
Static method objects
Static method objects provide a way of defeating the
transformation of function objects to method objects described
above. A static method object is a wrapper around any other
object, usually a user-defined method object. When a static
method object is retrieved from a class or a class instance, the
object actually returned is the wrapped object, which is not
subject to any further transformation. Static method objects are
not themselves callable, although the objects they wrap usually
are. Static method objects are created by the built-in
"staticmethod()" constructor.
Class method objects
A class method object, like a static method object, is a wrapper
around another object that alters the way in which that object
is retrieved from classes and class instances. The behaviour of
class method objects upon such retrieval is described above,
under "User-defined methods". Class method objects are created
by the built-in "classmethod()" constructor.
t typess�
Functions
*********
Function objects are created by function definitions. The only
operation on a function object is to call it: "func(argument-list)".
There are really two flavors of function objects: built-in functions
and user-defined functions. Both support the same operation (to call
the function), but the implementation is different, hence the
different object types.
See Function definitions for more information.
t typesfunctionss�/
Mapping Types --- "dict"
************************
A *mapping* object maps *hashable* values to arbitrary objects.
Mappings are mutable objects. There is currently only one standard
mapping type, the *dictionary*. (For other containers see the built
in "list", "set", and "tuple" classes, and the "collections" module.)
A dictionary's keys are *almost* arbitrary values. Values that are
not *hashable*, that is, values containing lists, dictionaries or
other mutable types (that are compared by value rather than by object
identity) may not be used as keys. Numeric types used for keys obey
the normal rules for numeric comparison: if two numbers compare equal
(such as "1" and "1.0") then they can be used interchangeably to index
the same dictionary entry. (Note however, that since computers store
floating-point numbers as approximations it is usually unwise to use
them as dictionary keys.)
Dictionaries can be created by placing a comma-separated list of "key:
value" pairs within braces, for example: "{'jack': 4098, 'sjoerd':
4127}" or "{4098: 'jack', 4127: 'sjoerd'}", or by the "dict"
constructor.
class dict(**kwarg)
class dict(mapping, **kwarg)
class dict(iterable, **kwarg)
Return a new dictionary initialized from an optional positional
argument and a possibly empty set of keyword arguments.
If no positional argument is given, an empty dictionary is created.
If a positional argument is given and it is a mapping object, a
dictionary is created with the same key-value pairs as the mapping
object. Otherwise, the positional argument must be an *iterable*
object. Each item in the iterable must itself be an iterable with
exactly two objects. The first object of each item becomes a key
in the new dictionary, and the second object the corresponding
value. If a key occurs more than once, the last value for that key
becomes the corresponding value in the new dictionary.
If keyword arguments are given, the keyword arguments and their
values are added to the dictionary created from the positional
argument. If a key being added is already present, the value from
the keyword argument replaces the value from the positional
argument.
To illustrate, the following examples all return a dictionary equal
to "{"one": 1, "two": 2, "three": 3}":
>>> a = dict(one=1, two=2, three=3)
>>> b = {'one': 1, 'two': 2, 'three': 3}
>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
>>> e = dict({'three': 3, 'one': 1, 'two': 2})
>>> a == b == c == d == e
True
Providing keyword arguments as in the first example only works for
keys that are valid Python identifiers. Otherwise, any valid keys
can be used.
New in version 2.2.
Changed in version 2.3: Support for building a dictionary from
keyword arguments added.
These are the operations that dictionaries support (and therefore,
custom mapping types should support too):
len(d)
Return the number of items in the dictionary *d*.
d[key]
Return the item of *d* with key *key*. Raises a "KeyError" if
*key* is not in the map.
If a subclass of dict defines a method "__missing__()" and *key*
is not present, the "d[key]" operation calls that method with
the key *key* as argument. The "d[key]" operation then returns
or raises whatever is returned or raised by the
"__missing__(key)" call. No other operations or methods invoke
"__missing__()". If "__missing__()" is not defined, "KeyError"
is raised. "__missing__()" must be a method; it cannot be an
instance variable:
>>> class Counter(dict):
... def __missing__(self, key):
... return 0
>>> c = Counter()
>>> c['red']
0
>>> c['red'] += 1
>>> c['red']
1
The example above shows part of the implementation of
"collections.Counter". A different "__missing__" method is used
by "collections.defaultdict".
New in version 2.5: Recognition of __missing__ methods of dict
subclasses.
d[key] = value
Set "d[key]" to *value*.
del d[key]
Remove "d[key]" from *d*. Raises a "KeyError" if *key* is not
in the map.
key in d
Return "True" if *d* has a key *key*, else "False".
New in version 2.2.
key not in d
Equivalent to "not key in d".
New in version 2.2.
iter(d)
Return an iterator over the keys of the dictionary. This is a
shortcut for "iterkeys()".
clear()
Remove all items from the dictionary.
copy()
Return a shallow copy of the dictionary.
fromkeys(seq[, value])
Create a new dictionary with keys from *seq* and values set to
*value*.
"fromkeys()" is a class method that returns a new dictionary.
*value* defaults to "None".
New in version 2.3.
get(key[, default])
Return the value for *key* if *key* is in the dictionary, else
*default*. If *default* is not given, it defaults to "None", so
that this method never raises a "KeyError".
has_key(key)
Test for the presence of *key* in the dictionary. "has_key()"
is deprecated in favor of "key in d".
items()
Return a copy of the dictionary's list of "(key, value)" pairs.
**CPython implementation detail:** Keys and values are listed in
an arbitrary order which is non-random, varies across Python
implementations, and depends on the dictionary's history of
insertions and deletions.
If "items()", "keys()", "values()", "iteritems()", "iterkeys()",
and "itervalues()" are called with no intervening modifications
to the dictionary, the lists will directly correspond. This
allows the creation of "(value, key)" pairs using "zip()":
"pairs = zip(d.values(), d.keys())". The same relationship
holds for the "iterkeys()" and "itervalues()" methods: "pairs =
zip(d.itervalues(), d.iterkeys())" provides the same value for
"pairs". Another way to create the same list is "pairs = [(v, k)
for (k, v) in d.iteritems()]".
iteritems()
Return an iterator over the dictionary's "(key, value)" pairs.
See the note for "dict.items()".
Using "iteritems()" while adding or deleting entries in the
dictionary may raise a "RuntimeError" or fail to iterate over
all entries.
New in version 2.2.
iterkeys()
Return an iterator over the dictionary's keys. See the note for
"dict.items()".
Using "iterkeys()" while adding or deleting entries in the
dictionary may raise a "RuntimeError" or fail to iterate over
all entries.
New in version 2.2.
itervalues()
Return an iterator over the dictionary's values. See the note
for "dict.items()".
Using "itervalues()" while adding or deleting entries in the
dictionary may raise a "RuntimeError" or fail to iterate over
all entries.
New in version 2.2.
keys()
Return a copy of the dictionary's list of keys. See the note
for "dict.items()".
pop(key[, default])
If *key* is in the dictionary, remove it and return its value,
else return *default*. If *default* is not given and *key* is
not in the dictionary, a "KeyError" is raised.
New in version 2.3.
popitem()
Remove and return an arbitrary "(key, value)" pair from the
dictionary.
"popitem()" is useful to destructively iterate over a
dictionary, as often used in set algorithms. If the dictionary
is empty, calling "popitem()" raises a "KeyError".
setdefault(key[, default])
If *key* is in the dictionary, return its value. If not, insert
*key* with a value of *default* and return *default*. *default*
defaults to "None".
update([other])
Update the dictionary with the key/value pairs from *other*,
overwriting existing keys. Return "None".
"update()" accepts either another dictionary object or an
iterable of key/value pairs (as tuples or other iterables of
length two). If keyword arguments are specified, the dictionary
is then updated with those key/value pairs: "d.update(red=1,
blue=2)".
Changed in version 2.4: Allowed the argument to be an iterable
of key/value pairs and allowed keyword arguments.
values()
Return a copy of the dictionary's list of values. See the note
for "dict.items()".
viewitems()
Return a new view of the dictionary's items ("(key, value)"
pairs). See below for documentation of view objects.
New in version 2.7.
viewkeys()
Return a new view of the dictionary's keys. See below for
documentation of view objects.
New in version 2.7.
viewvalues()
Return a new view of the dictionary's values. See below for
documentation of view objects.
New in version 2.7.
Dictionaries compare equal if and only if they have the same "(key,
value)" pairs.
Dictionary view objects
=======================
The objects returned by "dict.viewkeys()", "dict.viewvalues()" and
"dict.viewitems()" are *view objects*. They provide a dynamic view on
the dictionary's entries, which means that when the dictionary
changes, the view reflects these changes.
Dictionary views can be iterated over to yield their respective data,
and support membership tests:
len(dictview)
Return the number of entries in the dictionary.
iter(dictview)
Return an iterator over the keys, values or items (represented as
tuples of "(key, value)") in the dictionary.
Keys and values are iterated over in an arbitrary order which is
non-random, varies across Python implementations, and depends on
the dictionary's history of insertions and deletions. If keys,
values and items views are iterated over with no intervening
modifications to the dictionary, the order of items will directly
correspond. This allows the creation of "(value, key)" pairs using
"zip()": "pairs = zip(d.values(), d.keys())". Another way to
create the same list is "pairs = [(v, k) for (k, v) in d.items()]".
Iterating views while adding or deleting entries in the dictionary
may raise a "RuntimeError" or fail to iterate over all entries.
x in dictview
Return "True" if *x* is in the underlying dictionary's keys, values
or items (in the latter case, *x* should be a "(key, value)"
tuple).
Keys views are set-like since their entries are unique and hashable.
If all values are hashable, so that (key, value) pairs are unique and
hashable, then the items view is also set-like. (Values views are not
treated as set-like since the entries are generally not unique.) Then
these set operations are available ("other" refers either to another
view or a set):
dictview & other
Return the intersection of the dictview and the other object as a
new set.
dictview | other
Return the union of the dictview and the other object as a new set.
dictview - other
Return the difference between the dictview and the other object
(all elements in *dictview* that aren't in *other*) as a new set.
dictview ^ other
Return the symmetric difference (all elements either in *dictview*
or *other*, but not in both) of the dictview and the other object
as a new set.
An example of dictionary view usage:
>>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500}
>>> keys = dishes.viewkeys()
>>> values = dishes.viewvalues()
>>> # iteration
>>> n = 0
>>> for val in values:
... n += val
>>> print(n)
504
>>> # keys and values are iterated over in the same order
>>> list(keys)
['eggs', 'bacon', 'sausage', 'spam']
>>> list(values)
[2, 1, 1, 500]
>>> # view objects are dynamic and reflect dict changes
>>> del dishes['eggs']
>>> del dishes['sausage']
>>> list(keys)
['spam', 'bacon']
>>> # set operations
>>> keys & {'eggs', 'bacon', 'salad'}
{'bacon'}
t typesmappingsz
Methods
*******
Methods are functions that are called using the attribute notation.
There are two flavors: built-in methods (such as "append()" on lists)
and class instance methods. Built-in methods are described with the
types that support them.
The implementation adds two special read-only attributes to class
instance methods: "m.im_self" is the object on which the method
operates, and "m.im_func" is the function implementing the method.
Calling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to
calling "m.im_func(m.im_self, arg-1, arg-2, ..., arg-n)".
Class instance methods are either *bound* or *unbound*, referring to
whether the method was accessed through an instance or a class,
respectively. When a method is unbound, its "im_self" attribute will
be "None" and if called, an explicit "self" object must be passed as
the first argument. In this case, "self" must be an instance of the
unbound method's class (or a subclass of that class), otherwise a
"TypeError" is raised.
Like function objects, methods objects support getting arbitrary
attributes. However, since method attributes are actually stored on
the underlying function object ("meth.im_func"), setting method
attributes on either bound or unbound methods is disallowed.
Attempting to set an attribute on a method results in an
"AttributeError" being raised. In order to set a method attribute,
you need to explicitly set it on the underlying function object:
>>> class C:
... def method(self):
... pass
...
>>> c = C()
>>> c.method.whoami = 'my name is method' # can't set on the method
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'instancemethod' object has no attribute 'whoami'
>>> c.method.im_func.whoami = 'my name is method'
>>> c.method.whoami
'my name is method'
See The standard type hierarchy for more information.
t typesmethodss
Modules
*******
The only special operation on a module is attribute access: "m.name",
where *m* is a module and *name* accesses a name defined in *m*'s
symbol table. Module attributes can be assigned to. (Note that the
"import" statement is not, strictly speaking, an operation on a module
object; "import foo" does not require a module object named *foo* to
exist, rather it requires an (external) *definition* for a module
named *foo* somewhere.)
A special attribute of every module is "__dict__". This is the
dictionary containing the module's symbol table. Modifying this
dictionary will actually change the module's symbol table, but direct
assignment to the "__dict__" attribute is not possible (you can write
"m.__dict__['a'] = 1", which defines "m.a" to be "1", but you can't
write "m.__dict__ = {}"). Modifying "__dict__" directly is not
recommended.
Modules built into the interpreter are written like this: "<module
'sys' (built-in)>". If loaded from a file, they are written as
"<module 'os' from '/usr/local/lib/pythonX.Y/os.pyc'>".
t typesmodulessy�
Sequence Types --- "str", "unicode", "list", "tuple", "bytearray", "buffer", "xrange"
*************************************************************************************
There are seven sequence types: strings, Unicode strings, lists,
tuples, bytearrays, buffers, and xrange objects.
For other containers see the built in "dict" and "set" classes, and
the "collections" module.
String literals are written in single or double quotes: "'xyzzy'",
""frobozz"". See String literals for more about string literals.
Unicode strings are much like strings, but are specified in the syntax
using a preceding "'u'" character: "u'abc'", "u"def"". In addition to
the functionality described here, there are also string-specific
methods described in the String Methods section. Lists are constructed
with square brackets, separating items with commas: "[a, b, c]".
Tuples are constructed by the comma operator (not within square
brackets), with or without enclosing parentheses, but an empty tuple
must have the enclosing parentheses, such as "a, b, c" or "()". A
single item tuple must have a trailing comma, such as "(d,)".
Bytearray objects are created with the built-in function
"bytearray()".
Buffer objects are not directly supported by Python syntax, but can be
created by calling the built-in function "buffer()". They don't
support concatenation or repetition.
Objects of type xrange are similar to buffers in that there is no
specific syntax to create them, but they are created using the
"xrange()" function. They don't support slicing, concatenation or
repetition, and using "in", "not in", "min()" or "max()" on them is
inefficient.
Most sequence types support the following operations. The "in" and
"not in" operations have the same priorities as the comparison
operations. The "+" and "*" operations have the same priority as the
corresponding numeric operations. [3] Additional methods are provided
for Mutable Sequence Types.
This table lists the sequence operations sorted in ascending priority.
In the table, *s* and *t* are sequences of the same type; *n*, *i* and
*j* are integers:
+--------------------+----------------------------------+------------+
| Operation | Result | Notes |
+====================+==================================+============+
| "x in s" | "True" if an item of *s* is | (1) |
| | equal to *x*, else "False" | |
+--------------------+----------------------------------+------------+
| "x not in s" | "False" if an item of *s* is | (1) |
| | equal to *x*, else "True" | |
+--------------------+----------------------------------+------------+
| "s + t" | the concatenation of *s* and *t* | (6) |
+--------------------+----------------------------------+------------+
| "s * n, n * s" | equivalent to adding *s* to | (2) |
| | itself *n* times | |
+--------------------+----------------------------------+------------+
| "s[i]" | *i*th item of *s*, origin 0 | (3) |
+--------------------+----------------------------------+------------+
| "s[i:j]" | slice of *s* from *i* to *j* | (3)(4) |
+--------------------+----------------------------------+------------+
| "s[i:j:k]" | slice of *s* from *i* to *j* | (3)(5) |
| | with step *k* | |
+--------------------+----------------------------------+------------+
| "len(s)" | length of *s* | |
+--------------------+----------------------------------+------------+
| "min(s)" | smallest item of *s* | |
+--------------------+----------------------------------+------------+
| "max(s)" | largest item of *s* | |
+--------------------+----------------------------------+------------+
| "s.index(x)" | index of the first occurrence of | |
| | *x* in *s* | |
+--------------------+----------------------------------+------------+
| "s.count(x)" | total number of occurrences of | |
| | *x* in *s* | |
+--------------------+----------------------------------+------------+
Sequence types also support comparisons. In particular, tuples and
lists are compared lexicographically by comparing corresponding
elements. This means that to compare equal, every element must compare
equal and the two sequences must be of the same type and have the same
length. (For full details see Comparisons in the language reference.)
Notes:
1. When *s* is a string or Unicode string object the "in" and "not
in" operations act like a substring test. In Python versions
before 2.3, *x* had to be a string of length 1. In Python 2.3 and
beyond, *x* may be a string of any length.
2. Values of *n* less than "0" are treated as "0" (which yields an
empty sequence of the same type as *s*). Note that items in the
sequence *s* are not copied; they are referenced multiple times.
This often haunts new Python programmers; consider:
>>> lists = [[]] * 3
>>> lists
[[], [], []]
>>> lists[0].append(3)
>>> lists
[[3], [3], [3]]
What has happened is that "[[]]" is a one-element list containing
an empty list, so all three elements of "[[]] * 3" are references
to this single empty list. Modifying any of the elements of
"lists" modifies this single list. You can create a list of
different lists this way:
>>> lists = [[] for i in range(3)]
>>> lists[0].append(3)
>>> lists[1].append(5)
>>> lists[2].append(7)
>>> lists
[[3], [5], [7]]
Further explanation is available in the FAQ entry How do I create a
multidimensional list?.
3. If *i* or *j* is negative, the index is relative to the end of
sequence *s*: "len(s) + i" or "len(s) + j" is substituted. But
note that "-0" is still "0".
4. The slice of *s* from *i* to *j* is defined as the sequence of
items with index *k* such that "i <= k < j". If *i* or *j* is
greater than "len(s)", use "len(s)". If *i* is omitted or "None",
use "0". If *j* is omitted or "None", use "len(s)". If *i* is
greater than or equal to *j*, the slice is empty.
5. The slice of *s* from *i* to *j* with step *k* is defined as the
sequence of items with index "x = i + n*k" such that "0 <= n <
(j-i)/k". In other words, the indices are "i", "i+k", "i+2*k",
"i+3*k" and so on, stopping when *j* is reached (but never
including *j*). When *k* is positive, *i* and *j* are reduced to
"len(s)" if they are greater. When *k* is negative, *i* and *j* are
reduced to "len(s) - 1" if they are greater. If *i* or *j* are
omitted or "None", they become "end" values (which end depends on
the sign of *k*). Note, *k* cannot be zero. If *k* is "None", it
is treated like "1".
6. **CPython implementation detail:** If *s* and *t* are both
strings, some Python implementations such as CPython can usually
perform an in-place optimization for assignments of the form "s = s
+ t" or "s += t". When applicable, this optimization makes
quadratic run-time much less likely. This optimization is both
version and implementation dependent. For performance sensitive
code, it is preferable to use the "str.join()" method which assures
consistent linear concatenation performance across versions and
implementations.
Changed in version 2.4: Formerly, string concatenation never
occurred in-place.
String Methods
==============
Below are listed the string methods which both 8-bit strings and
Unicode objects support. Some of them are also available on
"bytearray" objects.
In addition, Python's strings support the sequence type methods
described in the Sequence Types --- str, unicode, list, tuple,
bytearray, buffer, xrange section. To output formatted strings use
template strings or the "%" operator described in the String
Formatting Operations section. Also, see the "re" module for string
functions based on regular expressions.
str.capitalize()
Return a copy of the string with its first character capitalized
and the rest lowercased.
For 8-bit strings, this method is locale-dependent.
str.center(width[, fillchar])
Return centered in a string of length *width*. Padding is done
using the specified *fillchar* (default is a space).
Changed in version 2.4: Support for the *fillchar* argument.
str.count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring *sub*
in the range [*start*, *end*]. Optional arguments *start* and
*end* are interpreted as in slice notation.
str.decode([encoding[, errors]])
Decodes the string using the codec registered for *encoding*.
*encoding* defaults to the default string encoding. *errors* may
be given to set a different error handling scheme. The default is
"'strict'", meaning that encoding errors raise "UnicodeError".
Other possible values are "'ignore'", "'replace'" and any other
name registered via "codecs.register_error()", see section Codec
Base Classes.
New in version 2.2.
Changed in version 2.3: Support for other error handling schemes
added.
Changed in version 2.7: Support for keyword arguments added.
str.encode([encoding[, errors]])
Return an encoded version of the string. Default encoding is the
current default string encoding. *errors* may be given to set a
different error handling scheme. The default for *errors* is
"'strict'", meaning that encoding errors raise a "UnicodeError".
Other possible values are "'ignore'", "'replace'",
"'xmlcharrefreplace'", "'backslashreplace'" and any other name
registered via "codecs.register_error()", see section Codec Base
Classes. For a list of possible encodings, see section Standard
Encodings.
New in version 2.0.
Changed in version 2.3: Support for "'xmlcharrefreplace'" and
"'backslashreplace'" and other error handling schemes added.
Changed in version 2.7: Support for keyword arguments added.
str.endswith(suffix[, start[, end]])
Return "True" if the string ends with the specified *suffix*,
otherwise return "False". *suffix* can also be a tuple of suffixes
to look for. With optional *start*, test beginning at that
position. With optional *end*, stop comparing at that position.
Changed in version 2.5: Accept tuples as *suffix*.
str.expandtabs([tabsize])
Return a copy of the string where all tab characters are replaced
by one or more spaces, depending on the current column and the
given tab size. Tab positions occur every *tabsize* characters
(default is 8, giving tab positions at columns 0, 8, 16 and so on).
To expand the string, the current column is set to zero and the
string is examined character by character. If the character is a
tab ("\t"), one or more space characters are inserted in the result
until the current column is equal to the next tab position. (The
tab character itself is not copied.) If the character is a newline
("\n") or return ("\r"), it is copied and the current column is
reset to zero. Any other character is copied unchanged and the
current column is incremented by one regardless of how the
character is represented when printed.
>>> '01\t012\t0123\t01234'.expandtabs()
'01 012 0123 01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01 012 0123 01234'
str.find(sub[, start[, end]])
Return the lowest index in the string where substring *sub* is
found within the slice "s[start:end]". Optional arguments *start*
and *end* are interpreted as in slice notation. Return "-1" if
*sub* is not found.
Note: The "find()" method should be used only if you need to know
the position of *sub*. To check if *sub* is a substring or not,
use the "in" operator:
>>> 'Py' in 'Python'
True
str.format(*args, **kwargs)
Perform a string formatting operation. The string on which this
method is called can contain literal text or replacement fields
delimited by braces "{}". Each replacement field contains either
the numeric index of a positional argument, or the name of a
keyword argument. Returns a copy of the string where each
replacement field is replaced with the string value of the
corresponding argument.
>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'
See Format String Syntax for a description of the various
formatting options that can be specified in format strings.
This method of string formatting is the new standard in Python 3,
and should be preferred to the "%" formatting described in String
Formatting Operations in new code.
New in version 2.6.
str.index(sub[, start[, end]])
Like "find()", but raise "ValueError" when the substring is not
found.
str.isalnum()
Return true if all characters in the string are alphanumeric and
there is at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isalpha()
Return true if all characters in the string are alphabetic and
there is at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isdigit()
Return true if all characters in the string are digits and there is
at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.islower()
Return true if all cased characters [4] in the string are lowercase
and there is at least one cased character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isspace()
Return true if there are only whitespace characters in the string
and there is at least one character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.istitle()
Return true if the string is a titlecased string and there is at
least one character, for example uppercase characters may only
follow uncased characters and lowercase characters only cased ones.
Return false otherwise.
For 8-bit strings, this method is locale-dependent.
str.isupper()
Return true if all cased characters [4] in the string are uppercase
and there is at least one cased character, false otherwise.
For 8-bit strings, this method is locale-dependent.
str.join(iterable)
Return a string which is the concatenation of the strings in
*iterable*. A "TypeError" will be raised if there are any non-
string values in *iterable*, including "bytes" objects. The
separator between elements is the string providing this method.
str.ljust(width[, fillchar])
Return the string left justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is a
space). The original string is returned if *width* is less than or
equal to "len(s)".
Changed in version 2.4: Support for the *fillchar* argument.
str.lower()
Return a copy of the string with all the cased characters [4]
converted to lowercase.
For 8-bit strings, this method is locale-dependent.
str.lstrip([chars])
Return a copy of the string with leading characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a prefix; rather,
all combinations of its values are stripped:
>>> ' spacious '.lstrip()
'spacious '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'
Changed in version 2.2.2: Support for the *chars* argument.
str.partition(sep)
Split the string at the first occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing the string itself, followed by
two empty strings.
New in version 2.5.
str.replace(old, new[, count])
Return a copy of the string with all occurrences of substring *old*
replaced by *new*. If the optional argument *count* is given, only
the first *count* occurrences are replaced.
str.rfind(sub[, start[, end]])
Return the highest index in the string where substring *sub* is
found, such that *sub* is contained within "s[start:end]".
Optional arguments *start* and *end* are interpreted as in slice
notation. Return "-1" on failure.
str.rindex(sub[, start[, end]])
Like "rfind()" but raises "ValueError" when the substring *sub* is
not found.
str.rjust(width[, fillchar])
Return the string right justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is a
space). The original string is returned if *width* is less than or
equal to "len(s)".
Changed in version 2.4: Support for the *fillchar* argument.
str.rpartition(sep)
Split the string at the last occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing two empty strings, followed by
the string itself.
New in version 2.5.
str.rsplit([sep[, maxsplit]])
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit* splits
are done, the *rightmost* ones. If *sep* is not specified or
"None", any whitespace string is a separator. Except for splitting
from the right, "rsplit()" behaves like "split()" which is
described in detail below.
New in version 2.4.
str.rstrip([chars])
Return a copy of the string with trailing characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a suffix; rather,
all combinations of its values are stripped:
>>> ' spacious '.rstrip()
' spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'
Changed in version 2.2.2: Support for the *chars* argument.
str.split([sep[, maxsplit]])
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit*
splits are done (thus, the list will have at most "maxsplit+1"
elements). If *maxsplit* is not specified or "-1", then there is
no limit on the number of splits (all possible splits are made).
If *sep* is given, consecutive delimiters are not grouped together
and are deemed to delimit empty strings (for example,
"'1,,2'.split(',')" returns "['1', '', '2']"). The *sep* argument
may consist of multiple characters (for example,
"'1<>2<>3'.split('<>')" returns "['1', '2', '3']"). Splitting an
empty string with a specified separator returns "['']".
If *sep* is not specified or is "None", a different splitting
algorithm is applied: runs of consecutive whitespace are regarded
as a single separator, and the result will contain no empty strings
at the start or end if the string has leading or trailing
whitespace. Consequently, splitting an empty string or a string
consisting of just whitespace with a "None" separator returns "[]".
For example, "' 1 2 3 '.split()" returns "['1', '2', '3']", and
"' 1 2 3 '.split(None, 1)" returns "['1', '2 3 ']".
str.splitlines([keepends])
Return a list of the lines in the string, breaking at line
boundaries. This method uses the *universal newlines* approach to
splitting lines. Line breaks are not included in the resulting list
unless *keepends* is given and true.
Python recognizes ""\r"", ""\n"", and ""\r\n"" as line boundaries
for 8-bit strings.
For example:
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
['ab c', '', 'de fg', 'kl']
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(True)
['ab c\n', '\n', 'de fg\r', 'kl\r\n']
Unlike "split()" when a delimiter string *sep* is given, this
method returns an empty list for the empty string, and a terminal
line break does not result in an extra line:
>>> "".splitlines()
[]
>>> "One line\n".splitlines()
['One line']
For comparison, "split('\n')" gives:
>>> ''.split('\n')
['']
>>> 'Two lines\n'.split('\n')
['Two lines', '']
unicode.splitlines([keepends])
Return a list of the lines in the string, like "str.splitlines()".
However, the Unicode method splits on the following line
boundaries, which are a superset of the *universal newlines*
recognized for 8-bit strings.
+-------------------------+-------------------------------+
| Representation | Description |
+=========================+===============================+
| "\n" | Line Feed |
+-------------------------+-------------------------------+
| "\r" | Carriage Return |
+-------------------------+-------------------------------+
| "\r\n" | Carriage Return + Line Feed |
+-------------------------+-------------------------------+
| "\v" or "\x0b" | Line Tabulation |
+-------------------------+-------------------------------+
| "\f" or "\x0c" | Form Feed |
+-------------------------+-------------------------------+
| "\x1c" | File Separator |
+-------------------------+-------------------------------+
| "\x1d" | Group Separator |
+-------------------------+-------------------------------+
| "\x1e" | Record Separator |
+-------------------------+-------------------------------+
| "\x85" | Next Line (C1 Control Code) |
+-------------------------+-------------------------------+
| "\u2028" | Line Separator |
+-------------------------+-------------------------------+
| "\u2029" | Paragraph Separator |
+-------------------------+-------------------------------+
Changed in version 2.7: "\v" and "\f" added to list of line
boundaries.
str.startswith(prefix[, start[, end]])
Return "True" if string starts with the *prefix*, otherwise return
"False". *prefix* can also be a tuple of prefixes to look for.
With optional *start*, test string beginning at that position.
With optional *end*, stop comparing string at that position.
Changed in version 2.5: Accept tuples as *prefix*.
str.strip([chars])
Return a copy of the string with the leading and trailing
characters removed. The *chars* argument is a string specifying the
set of characters to be removed. If omitted or "None", the *chars*
argument defaults to removing whitespace. The *chars* argument is
not a prefix or suffix; rather, all combinations of its values are
stripped:
>>> ' spacious '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'
Changed in version 2.2.2: Support for the *chars* argument.
str.swapcase()
Return a copy of the string with uppercase characters converted to
lowercase and vice versa.
For 8-bit strings, this method is locale-dependent.
str.title()
Return a titlecased version of the string where words start with an
uppercase character and the remaining characters are lowercase.
The algorithm uses a simple language-independent definition of a
word as groups of consecutive letters. The definition works in
many contexts but it means that apostrophes in contractions and
possessives form word boundaries, which may not be the desired
result:
>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"
A workaround for apostrophes can be constructed using regular
expressions:
>>> import re
>>> def titlecase(s):
... return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
... lambda mo: mo.group(0)[0].upper() +
... mo.group(0)[1:].lower(),
... s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."
For 8-bit strings, this method is locale-dependent.
str.translate(table[, deletechars])
Return a copy of the string where all characters occurring in the
optional argument *deletechars* are removed, and the remaining
characters have been mapped through the given translation table,
which must be a string of length 256.
You can use the "maketrans()" helper function in the "string"
module to create a translation table. For string objects, set the
*table* argument to "None" for translations that only delete
characters:
>>> 'read this short text'.translate(None, 'aeiou')
'rd ths shrt txt'
New in version 2.6: Support for a "None" *table* argument.
For Unicode objects, the "translate()" method does not accept the
optional *deletechars* argument. Instead, it returns a copy of the
*s* where all characters have been mapped through the given
translation table which must be a mapping of Unicode ordinals to
Unicode ordinals, Unicode strings or "None". Unmapped characters
are left untouched. Characters mapped to "None" are deleted. Note,
a more flexible approach is to create a custom character mapping
codec using the "codecs" module (see "encodings.cp1251" for an
example).
str.upper()
Return a copy of the string with all the cased characters [4]
converted to uppercase. Note that "str.upper().isupper()" might be
"False" if "s" contains uncased characters or if the Unicode
category of the resulting character(s) is not "Lu" (Letter,
uppercase), but e.g. "Lt" (Letter, titlecase).
For 8-bit strings, this method is locale-dependent.
str.zfill(width)
Return the numeric string left filled with zeros in a string of
length *width*. A sign prefix is handled correctly. The original
string is returned if *width* is less than or equal to "len(s)".
New in version 2.2.2.
The following methods are present only on unicode objects:
unicode.isnumeric()
Return "True" if there are only numeric characters in S, "False"
otherwise. Numeric characters include digit characters, and all
characters that have the Unicode numeric value property, e.g.
U+2155, VULGAR FRACTION ONE FIFTH.
unicode.isdecimal()
Return "True" if there are only decimal characters in S, "False"
otherwise. Decimal characters include digit characters, and all
characters that can be used to form decimal-radix numbers, e.g.
U+0660, ARABIC-INDIC DIGIT ZERO.
String Formatting Operations
============================
String and Unicode objects have one unique built-in operation: the "%"
operator (modulo). This is also known as the string *formatting* or
*interpolation* operator. Given "format % values" (where *format* is
a string or Unicode object), "%" conversion specifications in *format*
are replaced with zero or more elements of *values*. The effect is
similar to the using "sprintf()" in the C language. If *format* is a
Unicode object, or if any of the objects being converted using the
"%s" conversion are Unicode objects, the result will also be a Unicode
object.
If *format* requires a single argument, *values* may be a single non-
tuple object. [5] Otherwise, *values* must be a tuple with exactly
the number of items specified by the format string, or a single
mapping object (for example, a dictionary).
A conversion specifier contains two or more characters and has the
following components, which must occur in this order:
1. The "'%'" character, which marks the start of the specifier.
2. Mapping key (optional), consisting of a parenthesised sequence
of characters (for example, "(somename)").
3. Conversion flags (optional), which affect the result of some
conversion types.
4. Minimum field width (optional). If specified as an "'*'"
(asterisk), the actual width is read from the next element of the
tuple in *values*, and the object to convert comes after the
minimum field width and optional precision.
5. Precision (optional), given as a "'.'" (dot) followed by the
precision. If specified as "'*'" (an asterisk), the actual width
is read from the next element of the tuple in *values*, and the
value to convert comes after the precision.
6. Length modifier (optional).
7. Conversion type.
When the right argument is a dictionary (or other mapping type), then
the formats in the string *must* include a parenthesised mapping key
into that dictionary inserted immediately after the "'%'" character.
The mapping key selects the value to be formatted from the mapping.
For example:
>>> print '%(language)s has %(number)03d quote types.' % \
... {"language": "Python", "number": 2}
Python has 002 quote types.
In this case no "*" specifiers may occur in a format (since they
require a sequential parameter list).
The conversion flag characters are:
+-----------+-----------------------------------------------------------------------+
| Flag | Meaning |
+===========+=======================================================================+
| "'#'" | The value conversion will use the "alternate form" (where defined |
| | below). |
+-----------+-----------------------------------------------------------------------+
| "'0'" | The conversion will be zero padded for numeric values. |
+-----------+-----------------------------------------------------------------------+
| "'-'" | The converted value is left adjusted (overrides the "'0'" conversion |
| | if both are given). |
+-----------+-----------------------------------------------------------------------+
| "' '" | (a space) A blank should be left before a positive number (or empty |
| | string) produced by a signed conversion. |
+-----------+-----------------------------------------------------------------------+
| "'+'" | A sign character ("'+'" or "'-'") will precede the conversion |
| | (overrides a "space" flag). |
+-----------+-----------------------------------------------------------------------+
A length modifier ("h", "l", or "L") may be present, but is ignored as
it is not necessary for Python -- so e.g. "%ld" is identical to "%d".
The conversion types are:
+--------------+-------------------------------------------------------+---------+
| Conversion | Meaning | Notes |
+==============+=======================================================+=========+
| "'d'" | Signed integer decimal. | |
+--------------+-------------------------------------------------------+---------+
| "'i'" | Signed integer decimal. | |
+--------------+-------------------------------------------------------+---------+
| "'o'" | Signed octal value. | (1) |
+--------------+-------------------------------------------------------+---------+
| "'u'" | Obsolete type -- it is identical to "'d'". | (7) |
+--------------+-------------------------------------------------------+---------+
| "'x'" | Signed hexadecimal (lowercase). | (2) |
+--------------+-------------------------------------------------------+---------+
| "'X'" | Signed hexadecimal (uppercase). | (2) |
+--------------+-------------------------------------------------------+---------+
| "'e'" | Floating point exponential format (lowercase). | (3) |
+--------------+-------------------------------------------------------+---------+
| "'E'" | Floating point exponential format (uppercase). | (3) |
+--------------+-------------------------------------------------------+---------+
| "'f'" | Floating point decimal format. | (3) |
+--------------+-------------------------------------------------------+---------+
| "'F'" | Floating point decimal format. | (3) |
+--------------+-------------------------------------------------------+---------+
| "'g'" | Floating point format. Uses lowercase exponential | (4) |
| | format if exponent is less than -4 or not less than | |
| | precision, decimal format otherwise. | |
+--------------+-------------------------------------------------------+---------+
| "'G'" | Floating point format. Uses uppercase exponential | (4) |
| | format if exponent is less than -4 or not less than | |
| | precision, decimal format otherwise. | |
+--------------+-------------------------------------------------------+---------+
| "'c'" | Single character (accepts integer or single character | |
| | string). | |
+--------------+-------------------------------------------------------+---------+
| "'r'" | String (converts any Python object using repr()). | (5) |
+--------------+-------------------------------------------------------+---------+
| "'s'" | String (converts any Python object using "str()"). | (6) |
+--------------+-------------------------------------------------------+---------+
| "'%'" | No argument is converted, results in a "'%'" | |
| | character in the result. | |
+--------------+-------------------------------------------------------+---------+
Notes:
1. The alternate form causes a leading zero ("'0'") to be inserted
between left-hand padding and the formatting of the number if the
leading character of the result is not already a zero.
2. The alternate form causes a leading "'0x'" or "'0X'" (depending
on whether the "'x'" or "'X'" format was used) to be inserted
before the first digit.
3. The alternate form causes the result to always contain a decimal
point, even if no digits follow it.
The precision determines the number of digits after the decimal
point and defaults to 6.
4. The alternate form causes the result to always contain a decimal
point, and trailing zeroes are not removed as they would otherwise
be.
The precision determines the number of significant digits before
and after the decimal point and defaults to 6.
5. The "%r" conversion was added in Python 2.0.
The precision determines the maximal number of characters used.
6. If the object or format provided is a "unicode" string, the
resulting string will also be "unicode".
The precision determines the maximal number of characters used.
7. See **PEP 237**.
Since Python strings have an explicit length, "%s" conversions do not
assume that "'\0'" is the end of the string.
Changed in version 2.7: "%f" conversions for numbers whose absolute
value is over 1e50 are no longer replaced by "%g" conversions.
Additional string operations are defined in standard modules "string"
and "re".
XRange Type
===========
The "xrange" type is an immutable sequence which is commonly used for
looping. The advantage of the "xrange" type is that an "xrange"
object will always take the same amount of memory, no matter the size
of the range it represents. There are no consistent performance
advantages.
XRange objects have very little behavior: they only support indexing,
iteration, and the "len()" function.
Mutable Sequence Types
======================
List and "bytearray" objects support additional operations that allow
in-place modification of the object. Other mutable sequence types
(when added to the language) should also support these operations.
Strings and tuples are immutable sequence types: such objects cannot
be modified once created. The following operations are defined on
mutable sequence types (where *x* is an arbitrary object):
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
+================================+==================================+=======================+
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | same as "s[len(s):len(s)] = [x]" | (2) |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | for the most part the same as | (3) |
| | "s[len(s):len(s)] = t" | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (11) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.count(x)" | return number of *i*'s for which | |
| | "s[i] == x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.index(x[, i[, j]])" | return smallest *k* such that | (4) |
| | "s[k] == x" and "i <= k < j" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | same as "s[i:i] = [x]" | (5) |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop([i])" | same as "x = s[i]; del s[i]; | (6) |
| | return x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | same as "del s[s.index(x)]" | (4) |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (7) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
| "s.sort([cmp[, key[, | sort the items of *s* in place | (7)(8)(9)(10) |
| reverse]]])" | | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The C implementation of Python has historically accepted
multiple parameters and implicitly joined them into a tuple; this
no longer works in Python 2.0. Use of this misfeature has been
deprecated since Python 1.4.
3. *t* can be any iterable object.
4. Raises "ValueError" when *x* is not found in *s*. When a
negative index is passed as the second or third parameter to the
"index()" method, the list length is added, as for slice indices.
If it is still negative, it is truncated to zero, as for slice
indices.
Changed in version 2.3: Previously, "index()" didn't have arguments
for specifying start and stop positions.
5. When a negative index is passed as the first parameter to the
"insert()" method, the list length is added, as for slice indices.
If it is still negative, it is truncated to zero, as for slice
indices.
Changed in version 2.3: Previously, all negative indices were
truncated to zero.
6. The "pop()" method's optional argument *i* defaults to "-1", so
that by default the last item is removed and returned.
7. The "sort()" and "reverse()" methods modify the list in place
for economy of space when sorting or reversing a large list. To
remind you that they operate by side effect, they don't return the
sorted or reversed list.
8. The "sort()" method takes optional arguments for controlling the
comparisons.
*cmp* specifies a custom comparison function of two arguments (list
items) which should return a negative, zero or positive number
depending on whether the first argument is considered smaller than,
equal to, or larger than the second argument: "cmp=lambda x,y:
cmp(x.lower(), y.lower())". The default value is "None".
*key* specifies a function of one argument that is used to extract
a comparison key from each list element: "key=str.lower". The
default value is "None".
*reverse* is a boolean value. If set to "True", then the list
elements are sorted as if each comparison were reversed.
In general, the *key* and *reverse* conversion processes are much
faster than specifying an equivalent *cmp* function. This is
because *cmp* is called multiple times for each list element while
*key* and *reverse* touch each element only once. Use
"functools.cmp_to_key()" to convert an old-style *cmp* function to
a *key* function.
Changed in version 2.3: Support for "None" as an equivalent to
omitting *cmp* was added.
Changed in version 2.4: Support for *key* and *reverse* was added.
9. Starting with Python 2.3, the "sort()" method is guaranteed to
be stable. A sort is stable if it guarantees not to change the
relative order of elements that compare equal --- this is helpful
for sorting in multiple passes (for example, sort by department,
then by salary grade).
10. **CPython implementation detail:** While a list is being
sorted, the effect of attempting to mutate, or even inspect, the
list is undefined. The C implementation of Python 2.3 and newer
makes the list appear empty for the duration, and raises
"ValueError" if it can detect that the list has been mutated
during a sort.
11. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the
sequence. Items in the sequence are not copied; they are
referenced multiple times, as explained for "s * n" under Sequence
Types --- str, unicode, list, tuple, bytearray, buffer, xrange.
t typesseqsI
Mutable Sequence Types
**********************
List and "bytearray" objects support additional operations that allow
in-place modification of the object. Other mutable sequence types
(when added to the language) should also support these operations.
Strings and tuples are immutable sequence types: such objects cannot
be modified once created. The following operations are defined on
mutable sequence types (where *x* is an arbitrary object):
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
+================================+==================================+=======================+
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | same as "s[len(s):len(s)] = [x]" | (2) |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | for the most part the same as | (3) |
| | "s[len(s):len(s)] = t" | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (11) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.count(x)" | return number of *i*'s for which | |
| | "s[i] == x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.index(x[, i[, j]])" | return smallest *k* such that | (4) |
| | "s[k] == x" and "i <= k < j" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | same as "s[i:i] = [x]" | (5) |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop([i])" | same as "x = s[i]; del s[i]; | (6) |
| | return x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | same as "del s[s.index(x)]" | (4) |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (7) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
| "s.sort([cmp[, key[, | sort the items of *s* in place | (7)(8)(9)(10) |
| reverse]]])" | | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The C implementation of Python has historically accepted
multiple parameters and implicitly joined them into a tuple; this
no longer works in Python 2.0. Use of this misfeature has been
deprecated since Python 1.4.
3. *t* can be any iterable object.
4. Raises "ValueError" when *x* is not found in *s*. When a
negative index is passed as the second or third parameter to the
"index()" method, the list length is added, as for slice indices.
If it is still negative, it is truncated to zero, as for slice
indices.
Changed in version 2.3: Previously, "index()" didn't have arguments
for specifying start and stop positions.
5. When a negative index is passed as the first parameter to the
"insert()" method, the list length is added, as for slice indices.
If it is still negative, it is truncated to zero, as for slice
indices.
Changed in version 2.3: Previously, all negative indices were
truncated to zero.
6. The "pop()" method's optional argument *i* defaults to "-1", so
that by default the last item is removed and returned.
7. The "sort()" and "reverse()" methods modify the list in place
for economy of space when sorting or reversing a large list. To
remind you that they operate by side effect, they don't return the
sorted or reversed list.
8. The "sort()" method takes optional arguments for controlling the
comparisons.
*cmp* specifies a custom comparison function of two arguments (list
items) which should return a negative, zero or positive number
depending on whether the first argument is considered smaller than,
equal to, or larger than the second argument: "cmp=lambda x,y:
cmp(x.lower(), y.lower())". The default value is "None".
*key* specifies a function of one argument that is used to extract
a comparison key from each list element: "key=str.lower". The
default value is "None".
*reverse* is a boolean value. If set to "True", then the list
elements are sorted as if each comparison were reversed.
In general, the *key* and *reverse* conversion processes are much
faster than specifying an equivalent *cmp* function. This is
because *cmp* is called multiple times for each list element while
*key* and *reverse* touch each element only once. Use
"functools.cmp_to_key()" to convert an old-style *cmp* function to
a *key* function.
Changed in version 2.3: Support for "None" as an equivalent to
omitting *cmp* was added.
Changed in version 2.4: Support for *key* and *reverse* was added.
9. Starting with Python 2.3, the "sort()" method is guaranteed to
be stable. A sort is stable if it guarantees not to change the
relative order of elements that compare equal --- this is helpful
for sorting in multiple passes (for example, sort by department,
then by salary grade).
10. **CPython implementation detail:** While a list is being
sorted, the effect of attempting to mutate, or even inspect, the
list is undefined. The C implementation of Python 2.3 and newer
makes the list appear empty for the duration, and raises
"ValueError" if it can detect that the list has been mutated
during a sort.
11. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the
sequence. Items in the sequence are not copied; they are
referenced multiple times, as explained for "s * n" under Sequence
Types --- str, unicode, list, tuple, bytearray, buffer, xrange.
s typesseq-mutables�
Unary arithmetic and bitwise operations
***************************************
All unary arithmetic and bitwise operations have the same priority:
u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr
The unary "-" (minus) operator yields the negation of its numeric
argument.
The unary "+" (plus) operator yields its numeric argument unchanged.
The unary "~" (invert) operator yields the bitwise inversion of its
plain or long integer argument. The bitwise inversion of "x" is
defined as "-(x+1)". It only applies to integral numbers.
In all three cases, if the argument does not have the proper type, a
"TypeError" exception is raised.
t unarys�
The "while" statement
*********************
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause's suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
t whiles�
The "with" statement
********************
New in version 2.5.
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"..."except"..."finally"
usage patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one "item" proceeds as
follows:
1. The context expression (the expression given in the "with_item")
is evaluated to obtain a context manager.
2. The context manager's "__exit__()" is loaded for later use.
3. The context manager's "__enter__()" method is invoked.
4. If a target was included in the "with" statement, the return
value from "__enter__()" is assigned to it.
Note: The "with" statement guarantees that if the "__enter__()"
method returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
5. The suite is executed.
6. The context manager's "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
suite
is equivalent to
with A() as a:
with B() as b:
suite
Note: In Python 2.5, the "with" statement is only allowed when the
"with_statement" feature has been enabled. It is always enabled in
Python 2.6.
Changed in version 2.7: Support for multiple context expressions.
See also:
**PEP 343** - The "with" statement
The specification, background, and examples for the Python "with"
statement.
t withs
The "yield" statement
*********************
yield_stmt ::= yield_expression
The "yield" statement is only used when defining a generator function,
and is only used in the body of the generator function. Using a
"yield" statement in a function definition is sufficient to cause that
definition to create a generator function instead of a normal
function.
When a generator function is called, it returns an iterator known as a
generator iterator, or more commonly, a generator. The body of the
generator function is executed by calling the generator's "next()"
method repeatedly until it raises an exception.
When a "yield" statement is executed, the state of the generator is
frozen and the value of "expression_list" is returned to "next()"'s
caller. By "frozen" we mean that all local state is retained,
including the current bindings of local variables, the instruction
pointer, and the internal evaluation stack: enough information is
saved so that the next time "next()" is invoked, the function can
proceed exactly as if the "yield" statement were just another external
call.
As of Python version 2.5, the "yield" statement is now allowed in the
"try" clause of a "try" ... "finally" construct. If the generator is
not resumed before it is finalized (by reaching a zero reference count
or by being garbage collected), the generator-iterator's "close()"
method will be called, allowing any pending "finally" clauses to
execute.
For full details of "yield" semantics, refer to the Yield expressions
section.
Note: In Python 2.2, the "yield" statement was only allowed when the
"generators" feature has been enabled. This "__future__" import
statement was used to enable the feature:
from __future__ import generators
See also:
**PEP 255** - Simple Generators
The proposal for adding generators and the "yield" statement to
Python.
**PEP 342** - Coroutines via Enhanced Generators
The proposal that, among other generator enhancements, proposed
allowing "yield" to appear inside a "try" ... "finally" block.
t yieldN( t topics( ( ( s) /usr/lib64/python2.7/pydoc_data/topics.pyt <module> s�
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| fc @ s d S( N( ( ( ( s+ /usr/lib64/python2.7/pydoc_data/__init__.pyt <module> t PK a��Z �W �~ �~ ' __pycache__/topics.cpython-38.opt-2.pycnu �[��� U
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� P @ s� d ddddddddd d
d d d
ddddddddddddddd d d d d d!d"d#d$d%d&d'd(d d)d*d+d,d-d.d/d0d1d2d3d4d5d6d7d8d9d:d;d<d=d>d?d@dAdBdCdDdEdFdGdHdIdJdKdLdMdN�OZ dOS )Pau The "assert" statement
**********************
Assert statements are a convenient way to insert debugging assertions
into a program:
assert_stmt ::= "assert" expression ["," expression]
The simple form, "assert expression", is equivalent to
if __debug__:
if not expression: raise AssertionError
The extended form, "assert expression1, expression2", is equivalent to
if __debug__:
if not expression1: raise AssertionError(expression2)
These equivalences assume that "__debug__" and "AssertionError" refer
to the built-in variables with those names. In the current
implementation, the built-in variable "__debug__" is "True" under
normal circumstances, "False" when optimization is requested (command
line option "-O"). The current code generator emits no code for an
assert statement when optimization is requested at compile time. Note
that it is unnecessary to include the source code for the expression
that failed in the error message; it will be displayed as part of the
stack trace.
Assignments to "__debug__" are illegal. The value for the built-in
variable is determined when the interpreter starts.
u�, Assignment statements
*********************
Assignment statements are used to (re)bind names to values and to
modify attributes or items of mutable objects:
assignment_stmt ::= (target_list "=")+ (starred_expression | yield_expression)
target_list ::= target ("," target)* [","]
target ::= identifier
| "(" [target_list] ")"
| "[" [target_list] "]"
| attributeref
| subscription
| slicing
| "*" target
(See section Primaries for the syntax definitions for *attributeref*,
*subscription*, and *slicing*.)
An assignment statement evaluates the expression list (remember that
this can be a single expression or a comma-separated list, the latter
yielding a tuple) and assigns the single resulting object to each of
the target lists, from left to right.
Assignment is defined recursively depending on the form of the target
(list). When a target is part of a mutable object (an attribute
reference, subscription or slicing), the mutable object must
ultimately perform the assignment and decide about its validity, and
may raise an exception if the assignment is unacceptable. The rules
observed by various types and the exceptions raised are given with the
definition of the object types (see section The standard type
hierarchy).
Assignment of an object to a target list, optionally enclosed in
parentheses or square brackets, is recursively defined as follows.
* If the target list is a single target with no trailing comma,
optionally in parentheses, the object is assigned to that target.
* Else: The object must be an iterable with the same number of items
as there are targets in the target list, and the items are assigned,
from left to right, to the corresponding targets.
* If the target list contains one target prefixed with an asterisk,
called a “starred” target: The object must be an iterable with at
least as many items as there are targets in the target list, minus
one. The first items of the iterable are assigned, from left to
right, to the targets before the starred target. The final items
of the iterable are assigned to the targets after the starred
target. A list of the remaining items in the iterable is then
assigned to the starred target (the list can be empty).
* Else: The object must be an iterable with the same number of items
as there are targets in the target list, and the items are
assigned, from left to right, to the corresponding targets.
Assignment of an object to a single target is recursively defined as
follows.
* If the target is an identifier (name):
* If the name does not occur in a "global" or "nonlocal" statement
in the current code block: the name is bound to the object in the
current local namespace.
* Otherwise: the name is bound to the object in the global namespace
or the outer namespace determined by "nonlocal", respectively.
The name is rebound if it was already bound. This may cause the
reference count for the object previously bound to the name to reach
zero, causing the object to be deallocated and its destructor (if it
has one) to be called.
* If the target is an attribute reference: The primary expression in
the reference is evaluated. It should yield an object with
assignable attributes; if this is not the case, "TypeError" is
raised. That object is then asked to assign the assigned object to
the given attribute; if it cannot perform the assignment, it raises
an exception (usually but not necessarily "AttributeError").
Note: If the object is a class instance and the attribute reference
occurs on both sides of the assignment operator, the right-hand side
expression, "a.x" can access either an instance attribute or (if no
instance attribute exists) a class attribute. The left-hand side
target "a.x" is always set as an instance attribute, creating it if
necessary. Thus, the two occurrences of "a.x" do not necessarily
refer to the same attribute: if the right-hand side expression
refers to a class attribute, the left-hand side creates a new
instance attribute as the target of the assignment:
class Cls:
x = 3 # class variable
inst = Cls()
inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3
This description does not necessarily apply to descriptor
attributes, such as properties created with "property()".
* If the target is a subscription: The primary expression in the
reference is evaluated. It should yield either a mutable sequence
object (such as a list) or a mapping object (such as a dictionary).
Next, the subscript expression is evaluated.
If the primary is a mutable sequence object (such as a list), the
subscript must yield an integer. If it is negative, the sequence’s
length is added to it. The resulting value must be a nonnegative
integer less than the sequence’s length, and the sequence is asked
to assign the assigned object to its item with that index. If the
index is out of range, "IndexError" is raised (assignment to a
subscripted sequence cannot add new items to a list).
If the primary is a mapping object (such as a dictionary), the
subscript must have a type compatible with the mapping’s key type,
and the mapping is then asked to create a key/datum pair which maps
the subscript to the assigned object. This can either replace an
existing key/value pair with the same key value, or insert a new
key/value pair (if no key with the same value existed).
For user-defined objects, the "__setitem__()" method is called with
appropriate arguments.
* If the target is a slicing: The primary expression in the reference
is evaluated. It should yield a mutable sequence object (such as a
list). The assigned object should be a sequence object of the same
type. Next, the lower and upper bound expressions are evaluated,
insofar they are present; defaults are zero and the sequence’s
length. The bounds should evaluate to integers. If either bound is
negative, the sequence’s length is added to it. The resulting
bounds are clipped to lie between zero and the sequence’s length,
inclusive. Finally, the sequence object is asked to replace the
slice with the items of the assigned sequence. The length of the
slice may be different from the length of the assigned sequence,
thus changing the length of the target sequence, if the target
sequence allows it.
**CPython implementation detail:** In the current implementation, the
syntax for targets is taken to be the same as for expressions, and
invalid syntax is rejected during the code generation phase, causing
less detailed error messages.
Although the definition of assignment implies that overlaps between
the left-hand side and the right-hand side are ‘simultaneous’ (for
example "a, b = b, a" swaps two variables), overlaps *within* the
collection of assigned-to variables occur left-to-right, sometimes
resulting in confusion. For instance, the following program prints
"[0, 2]":
x = [0, 1]
i = 0
i, x[i] = 1, 2 # i is updated, then x[i] is updated
print(x)
See also:
**PEP 3132** - Extended Iterable Unpacking
The specification for the "*target" feature.
Augmented assignment statements
===============================
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions of the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side. For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
Annotated assignment statements
===============================
*Annotation* assignment is the combination, in a single statement, of
a variable or attribute annotation and an optional assignment
statement:
annotated_assignment_stmt ::= augtarget ":" expression
["=" (starred_expression | yield_expression)]
The difference from normal Assignment statements is that only single
target is allowed.
For simple names as assignment targets, if in class or module scope,
the annotations are evaluated and stored in a special class or module
attribute "__annotations__" that is a dictionary mapping from variable
names (mangled if private) to evaluated annotations. This attribute is
writable and is automatically created at the start of class or module
body execution, if annotations are found statically.
For expressions as assignment targets, the annotations are evaluated
if in class or module scope, but not stored.
If a name is annotated in a function scope, then this name is local
for that scope. Annotations are never evaluated and stored in function
scopes.
If the right hand side is present, an annotated assignment performs
the actual assignment before evaluating annotations (where
applicable). If the right hand side is not present for an expression
target, then the interpreter evaluates the target except for the last
"__setitem__()" or "__setattr__()" call.
See also:
**PEP 526** - Syntax for Variable Annotations
The proposal that added syntax for annotating the types of
variables (including class variables and instance variables),
instead of expressing them through comments.
**PEP 484** - Type hints
The proposal that added the "typing" module to provide a standard
syntax for type annotations that can be used in static analysis
tools and IDEs.
Changed in version 3.8: Now annotated assignments allow same
expressions in the right hand side as the regular assignments.
Previously, some expressions (like un-parenthesized tuple expressions)
caused a syntax error.
u>
Coroutines
**********
New in version 3.5.
Coroutine function definition
=============================
async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*). Inside the body of a coroutine function,
"await" and "async" identifiers become reserved keywords; "await"
expressions, "async for" and "async with" can only be used in
coroutine function bodies.
Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.
It is a "SyntaxError" to use a "yield from" expression inside the body
of a coroutine function.
An example of a coroutine function:
async def func(param1, param2):
do_stuff()
await some_coroutine()
The "async for" statement
=========================
async_for_stmt ::= "async" for_stmt
An *asynchronous iterable* is able to call asynchronous code in its
*iter* implementation, and *asynchronous iterator* can call
asynchronous code in its *next* method.
The "async for" statement allows convenient iteration over
asynchronous iterators.
The following code:
async for TARGET in ITER:
SUITE
else:
SUITE2
Is semantically equivalent to:
iter = (ITER)
iter = type(iter).__aiter__(iter)
running = True
while running:
try:
TARGET = await type(iter).__anext__(iter)
except StopAsyncIteration:
running = False
else:
SUITE
else:
SUITE2
See also "__aiter__()" and "__anext__()" for details.
It is a "SyntaxError" to use an "async for" statement outside the body
of a coroutine function.
The "async with" statement
==========================
async_with_stmt ::= "async" with_stmt
An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.
The following code:
async with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
aexit = type(manager).__aexit__
aenter = type(manager).__aenter__
value = await aenter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not await aexit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
await aexit(manager, None, None, None)
See also "__aenter__()" and "__aexit__()" for details.
It is a "SyntaxError" to use an "async with" statement outside the
body of a coroutine function.
See also:
**PEP 492** - Coroutines with async and await syntax
The proposal that made coroutines a proper standalone concept in
Python, and added supporting syntax.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless there
is a "finally" clause which happens to raise another exception.
That new exception causes the old one to be lost.
[2] A string literal appearing as the first statement in the function
body is transformed into the function’s "__doc__" attribute and
therefore the function’s *docstring*.
[3] A string literal appearing as the first statement in the class
body is transformed into the namespace’s "__doc__" item and
therefore the class’s *docstring*.
a� Identifiers (Names)
*******************
An identifier occurring as an atom is a name. See section Identifiers
and keywords for lexical definition and section Naming and binding for
documentation of naming and binding.
When the name is bound to an object, evaluation of the atom yields
that object. When a name is not bound, an attempt to evaluate it
raises a "NameError" exception.
**Private name mangling:** When an identifier that textually occurs in
a class definition begins with two or more underscore characters and
does not end in two or more underscores, it is considered a *private
name* of that class. Private names are transformed to a longer form
before code is generated for them. The transformation inserts the
class name, with leading underscores removed and a single underscore
inserted, in front of the name. For example, the identifier "__spam"
occurring in a class named "Ham" will be transformed to "_Ham__spam".
This transformation is independent of the syntactical context in which
the identifier is used. If the transformed name is extremely long
(longer than 255 characters), implementation defined truncation may
happen. If the class name consists only of underscores, no
transformation is done.
u
Literals
********
Python supports string and bytes literals and various numeric
literals:
literal ::= stringliteral | bytesliteral
| integer | floatnumber | imagnumber
Evaluation of a literal yields an object of the given type (string,
bytes, integer, floating point number, complex number) with the given
value. The value may be approximated in the case of floating point
and imaginary (complex) literals. See section Literals for details.
All literals correspond to immutable data types, and hence the
object’s identity is less important than its value. Multiple
evaluations of literals with the same value (either the same
occurrence in the program text or a different occurrence) may obtain
the same object or a different object with the same value.
uA7 Customizing attribute access
****************************
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
return the (computed) attribute value or raise an "AttributeError"
exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note:
This method may still be bypassed when looking up special methods
as the result of implicit invocation via language syntax or
built-in functions. See Special method lookup.
For certain sensitive attribute accesses, raises an auditing event
"object.__getattr__" with arguments "obj" and "name".
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should call the base class method with the same name, for example,
"object.__setattr__(self, name, value)".
For certain sensitive attribute assignments, raises an auditing
event "object.__setattr__" with arguments "obj", "name", "value".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
For certain sensitive attribute deletions, raises an auditing event
"object.__delattr__" with arguments "obj" and "name".
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
Customizing module attribute access
===================================
Special names "__getattr__" and "__dir__" can be also used to
customize access to module attributes. The "__getattr__" function at
the module level should accept one argument which is the name of an
attribute and return the computed value or raise an "AttributeError".
If an attribute is not found on a module object through the normal
lookup, i.e. "object.__getattribute__()", then "__getattr__" is
searched in the module "__dict__" before raising an "AttributeError".
If found, it is called with the attribute name and the result is
returned.
The "__dir__" function should accept no arguments, and return a
sequence of strings that represents the names accessible on module. If
present, this function overrides the standard "dir()" search on a
module.
For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
super().__setattr__(attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note:
Defining module "__getattr__" and setting module "__class__" only
affect lookups made using the attribute access syntax – directly
accessing the module globals (whether by code within the module, or
via a reference to the module’s globals dictionary) is unaffected.
Changed in version 3.5: "__class__" module attribute is now writable.
New in version 3.7: "__getattr__" and "__dir__" module attributes.
See also:
**PEP 562** - Module __getattr__ and __dir__
Describes the "__getattr__" and "__dir__" functions on modules.
Implementing Descriptors
========================
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents). In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".
object.__get__(self, instance, owner=None)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). The optional *owner* argument is the owner class, while
*instance* is the instance that the attribute was accessed through,
or "None" when the attribute is accessed through the *owner*.
This method should return the computed attribute value or raise an
"AttributeError" exception.
**PEP 252** specifies that "__get__()" is callable with one or two
arguments. Python’s own built-in descriptors support this
specification; however, it is likely that some third-party tools
have descriptors that require both arguments. Python’s own
"__getattribute__()" implementation always passes in both arguments
whether they are required or not.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
Note, adding "__set__()" or "__delete__()" changes the kind of
descriptor to a “data descriptor”. See Invoking Descriptors for
more details.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
object.__set_name__(self, owner, name)
Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.
Note:
"__set_name__()" is only called implicitly as part of the "type"
constructor, so it will need to be called explicitly with the
appropriate parameters when a descriptor is added to a class
after initial creation:
class A:
pass
descr = custom_descriptor()
A.attr = descr
descr.__set_name__(A, 'attr')
See Creating the class object for more details.
New in version 3.6.
The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).
Invoking Descriptors
====================
In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a class, "A.x" is transformed into the call:
"A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on which descriptor methods are defined. A descriptor can define any
combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
=========
*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of *__dict__* and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)
The space saved over using *__dict__* can be significant. Attribute
lookup speed can be significantly improved as well.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. *__slots__*
reserves space for the declared variables and prevents the
automatic creation of *__dict__* and *__weakref__* for each
instance.
Notes on using *__slots__*
--------------------------
* When inheriting from a class without *__slots__*, the *__dict__* and
*__weakref__* attribute of the instances will always be accessible.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
* Without a *__weakref__* variable for each instance, classes defining
*__slots__* do not support weak references to its instances. If weak
reference support is needed, then add "'__weakref__'" to the
sequence of strings in the *__slots__* declaration.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is not limited to the class
where it is defined. *__slots__* declared in parents are available
in child classes. However, child subclasses will get a *__dict__*
and *__weakref__* unless they also define *__slots__* (which should
only contain names of any *additional* slots).
* If a class defines a slot also defined in a base class, the instance
variable defined by the base class slot is inaccessible (except by
retrieving its descriptor directly from the base class). This
renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
“variable-length” built-in types such as "int", "bytes" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings may
also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".
* If an iterator is used for *__slots__* then a descriptor is created
for each of the iterator’s values. However, the *__slots__*
attribute will be an empty iterator.
a� Attribute references
********************
An attribute reference is a primary followed by a period and a name:
attributeref ::= primary "." identifier
The primary must evaluate to an object of a type that supports
attribute references, which most objects do. This object is then
asked to produce the attribute whose name is the identifier. This
production can be customized by overriding the "__getattr__()" method.
If this attribute is not available, the exception "AttributeError" is
raised. Otherwise, the type and value of the object produced is
determined by the object. Multiple evaluations of the same attribute
reference may yield different objects.
a� Augmented assignment statements
*******************************
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions of the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side. For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
z�Await expression
****************
Suspend the execution of *coroutine* on an *awaitable* object. Can
only be used inside a *coroutine function*.
await_expr ::= "await" primary
New in version 3.5.
uj Binary arithmetic operations
****************************
The binary arithmetic operations have the conventional priority
levels. Note that some of these operations also apply to certain non-
numeric types. Apart from the power operator, there are only two
levels, one for multiplicative operators and one for additive
operators:
m_expr ::= u_expr | m_expr "*" u_expr | m_expr "@" m_expr |
m_expr "//" u_expr | m_expr "/" u_expr |
m_expr "%" u_expr
a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr
The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer and the other must be a sequence. In the former case, the
numbers are converted to a common type and then multiplied together.
In the latter case, sequence repetition is performed; a negative
repetition factor yields an empty sequence.
The "@" (at) operator is intended to be used for matrix
multiplication. No builtin Python types implement this operator.
New in version 3.5.
The "/" (division) and "//" (floor division) operators yield the
quotient of their arguments. The numeric arguments are first
converted to a common type. Division of integers yields a float, while
floor division of integers results in an integer; the result is that
of mathematical division with the ‘floor’ function applied to the
result. Division by zero raises the "ZeroDivisionError" exception.
The "%" (modulo) operator yields the remainder from the division of
the first argument by the second. The numeric arguments are first
converted to a common type. A zero right argument raises the
"ZeroDivisionError" exception. The arguments may be floating point
numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 +
0.34".) The modulo operator always yields a result with the same sign
as its second operand (or zero); the absolute value of the result is
strictly smaller than the absolute value of the second operand [1].
The floor division and modulo operators are connected by the following
identity: "x == (x//y)*y + (x%y)". Floor division and modulo are also
connected with the built-in function "divmod()": "divmod(x, y) ==
(x//y, x%y)". [2].
In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string objects to perform old-style
string formatting (also known as interpolation). The syntax for
string formatting is described in the Python Library Reference,
section printf-style String Formatting.
The floor division operator, the modulo operator, and the "divmod()"
function are not defined for complex numbers. Instead, convert to a
floating point number using the "abs()" function if appropriate.
The "+" (addition) operator yields the sum of its arguments. The
arguments must either both be numbers or both be sequences of the same
type. In the former case, the numbers are converted to a common type
and then added together. In the latter case, the sequences are
concatenated.
The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.
a$ Binary bitwise operations
*************************
Each of the three bitwise operations has a different priority level:
and_expr ::= shift_expr | and_expr "&" shift_expr
xor_expr ::= and_expr | xor_expr "^" and_expr
or_expr ::= xor_expr | or_expr "|" xor_expr
The "&" operator yields the bitwise AND of its arguments, which must
be integers.
The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be integers.
The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be integers.
u� Code Objects
************
Code objects are used by the implementation to represent “pseudo-
compiled” executable Python code such as a function body. They differ
from function objects because they don’t contain a reference to their
global execution environment. Code objects are returned by the built-
in "compile()" function and can be extracted from function objects
through their "__code__" attribute. See also the "code" module.
Accessing "__code__" raises an auditing event "object.__getattr__"
with arguments "obj" and ""__code__"".
A code object can be executed or evaluated by passing it (instead of a
source string) to the "exec()" or "eval()" built-in functions.
See The standard type hierarchy for more information.
a. The Ellipsis Object
*******************
This object is commonly used by slicing (see Slicings). It supports
no special operations. There is exactly one ellipsis object, named
"Ellipsis" (a built-in name). "type(Ellipsis)()" produces the
"Ellipsis" singleton.
It is written as "Ellipsis" or "...".
u The Null Object
***************
This object is returned by functions that don’t explicitly return a
value. It supports no special operations. There is exactly one null
object, named "None" (a built-in name). "type(None)()" produces the
same singleton.
It is written as "None".
u5 Type Objects
************
Type objects represent the various object types. An object’s type is
accessed by the built-in function "type()". There are no special
operations on types. The standard module "types" defines names for
all standard built-in types.
Types are written like this: "<class 'int'>".
a� Boolean operations
******************
or_test ::= and_test | or_test "or" and_test
and_test ::= not_test | and_test "and" not_test
not_test ::= comparison | "not" not_test
In the context of Boolean operations, and also when expressions are
used by control flow statements, the following values are interpreted
as false: "False", "None", numeric zero of all types, and empty
strings and containers (including strings, tuples, lists,
dictionaries, sets and frozensets). All other values are interpreted
as true. User-defined objects can customize their truth value by
providing a "__bool__()" method.
The operator "not" yields "True" if its argument is false, "False"
otherwise.
The expression "x and y" first evaluates *x*; if *x* is false, its
value is returned; otherwise, *y* is evaluated and the resulting value
is returned.
The expression "x or y" first evaluates *x*; if *x* is true, its value
is returned; otherwise, *y* is evaluated and the resulting value is
returned.
Note that neither "and" nor "or" restrict the value and type they
return to "False" and "True", but rather return the last evaluated
argument. This is sometimes useful, e.g., if "s" is a string that
should be replaced by a default value if it is empty, the expression
"s or 'foo'" yields the desired value. Because "not" has to create a
new value, it returns a boolean value regardless of the type of its
argument (for example, "not 'foo'" produces "False" rather than "''".)
a$ The "break" statement
*********************
break_stmt ::= "break"
"break" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop.
It terminates the nearest enclosing loop, skipping the optional "else"
clause if the loop has one.
If a "for" loop is terminated by "break", the loop control target
keeps its current value.
When "break" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
loop.
u Emulating callable objects
**************************
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method
is defined, "x(arg1, arg2, ...)" roughly translates to
"type(x).__call__(x, arg1, ...)".
u� Calls
*****
A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:
call ::= primary "(" [argument_list [","] | comprehension] ")"
argument_list ::= positional_arguments ["," starred_and_keywords]
["," keywords_arguments]
| starred_and_keywords ["," keywords_arguments]
| keywords_arguments
positional_arguments ::= positional_item ("," positional_item)*
positional_item ::= assignment_expression | "*" expression
starred_and_keywords ::= ("*" expression | keyword_item)
("," "*" expression | "," keyword_item)*
keywords_arguments ::= (keyword_item | "**" expression)
("," keyword_item | "," "**" expression)*
keyword_item ::= identifier "=" expression
An optional trailing comma may be present after the positional and
keyword arguments but does not affect the semantics.
The primary must evaluate to a callable object (user-defined
functions, built-in functions, methods of built-in objects, class
objects, methods of class instances, and all objects having a
"__call__()" method are callable). All argument expressions are
evaluated before the call is attempted. Please refer to section
Function definitions for the syntax of formal *parameter* lists.
If keyword arguments are present, they are first converted to
positional arguments, as follows. First, a list of unfilled slots is
created for the formal parameters. If there are N positional
arguments, they are placed in the first N slots. Next, for each
keyword argument, the identifier is used to determine the
corresponding slot (if the identifier is the same as the first formal
parameter name, the first slot is used, and so on). If the slot is
already filled, a "TypeError" exception is raised. Otherwise, the
value of the argument is placed in the slot, filling it (even if the
expression is "None", it fills the slot). When all arguments have
been processed, the slots that are still unfilled are filled with the
corresponding default value from the function definition. (Default
values are calculated, once, when the function is defined; thus, a
mutable object such as a list or dictionary used as default value will
be shared by all calls that don’t specify an argument value for the
corresponding slot; this should usually be avoided.) If there are any
unfilled slots for which no default value is specified, a "TypeError"
exception is raised. Otherwise, the list of filled slots is used as
the argument list for the call.
**CPython implementation detail:** An implementation may provide
built-in functions whose positional parameters do not have names, even
if they are ‘named’ for the purpose of documentation, and which
therefore cannot be supplied by keyword. In CPython, this is the case
for functions implemented in C that use "PyArg_ParseTuple()" to parse
their arguments.
If there are more positional arguments than there are formal parameter
slots, a "TypeError" exception is raised, unless a formal parameter
using the syntax "*identifier" is present; in this case, that formal
parameter receives a tuple containing the excess positional arguments
(or an empty tuple if there were no excess positional arguments).
If any keyword argument does not correspond to a formal parameter
name, a "TypeError" exception is raised, unless a formal parameter
using the syntax "**identifier" is present; in this case, that formal
parameter receives a dictionary containing the excess keyword
arguments (using the keywords as keys and the argument values as
corresponding values), or a (new) empty dictionary if there were no
excess keyword arguments.
If the syntax "*expression" appears in the function call, "expression"
must evaluate to an *iterable*. Elements from these iterables are
treated as if they were additional positional arguments. For the call
"f(x1, x2, *y, x3, x4)", if *y* evaluates to a sequence *y1*, …, *yM*,
this is equivalent to a call with M+4 positional arguments *x1*, *x2*,
*y1*, …, *yM*, *x3*, *x4*.
A consequence of this is that although the "*expression" syntax may
appear *after* explicit keyword arguments, it is processed *before*
the keyword arguments (and any "**expression" arguments – see below).
So:
>>> def f(a, b):
... print(a, b)
...
>>> f(b=1, *(2,))
2 1
>>> f(a=1, *(2,))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: f() got multiple values for keyword argument 'a'
>>> f(1, *(2,))
1 2
It is unusual for both keyword arguments and the "*expression" syntax
to be used in the same call, so in practice this confusion does not
arise.
If the syntax "**expression" appears in the function call,
"expression" must evaluate to a *mapping*, the contents of which are
treated as additional keyword arguments. If a keyword is already
present (as an explicit keyword argument, or from another unpacking),
a "TypeError" exception is raised.
Formal parameters using the syntax "*identifier" or "**identifier"
cannot be used as positional argument slots or as keyword argument
names.
Changed in version 3.5: Function calls accept any number of "*" and
"**" unpackings, positional arguments may follow iterable unpackings
("*"), and keyword arguments may follow dictionary unpackings ("**").
Originally proposed by **PEP 448**.
A call always returns some value, possibly "None", unless it raises an
exception. How this value is computed depends on the type of the
callable object.
If it is—
a user-defined function:
The code block for the function is executed, passing it the
argument list. The first thing the code block will do is bind the
formal parameters to the arguments; this is described in section
Function definitions. When the code block executes a "return"
statement, this specifies the return value of the function call.
a built-in function or method:
The result is up to the interpreter; see Built-in Functions for the
descriptions of built-in functions and methods.
a class object:
A new instance of that class is returned.
a class instance method:
The corresponding user-defined function is called, with an argument
list that is one longer than the argument list of the call: the
instance becomes the first argument.
a class instance:
The class must define a "__call__()" method; the effect is then the
same as if that method was called.
u Class definitions
*****************
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [argument_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing. Classes without an inheritance list
inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.) When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [3] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.
The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__". Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.
Class creation can be customized heavily using metaclasses.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result is then bound to the class name.
**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. Descriptors can be used to create instance
variables with different implementation details.
See also:
**PEP 3115** - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the
current syntax, and the semantics for how classes with
metaclasses are constructed.
**PEP 3129** - Class Decorators
The proposal that added class decorators. Function and method
decorators were introduced in **PEP 318**.
u�' Comparisons
***********
Unlike C, all comparison operations in Python have the same priority,
which is lower than that of any arithmetic, shifting or bitwise
operation. Also unlike C, expressions like "a < b < c" have the
interpretation that is conventional in mathematics:
comparison ::= or_expr (comp_operator or_expr)*
comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="
| "is" ["not"] | ["not"] "in"
Comparisons yield boolean values: "True" or "False".
Comparisons can be chained arbitrarily, e.g., "x < y <= z" is
equivalent to "x < y and y <= z", except that "y" is evaluated only
once (but in both cases "z" is not evaluated at all when "x < y" is
found to be false).
Formally, if *a*, *b*, *c*, …, *y*, *z* are expressions and *op1*,
*op2*, …, *opN* are comparison operators, then "a op1 b op2 c ... y
opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except
that each expression is evaluated at most once.
Note that "a op1 b op2 c" doesn’t imply any kind of comparison between
*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though
perhaps not pretty).
Value comparisons
=================
The operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects. The objects do not need to have the same type.
Chapter Objects, values and types states that objects have a value (in
addition to type and identity). The value of an object is a rather
abstract notion in Python: For example, there is no canonical access
method for an object’s value. Also, there is no requirement that the
value of an object should be constructed in a particular way, e.g.
comprised of all its data attributes. Comparison operators implement a
particular notion of what the value of an object is. One can think of
them as defining the value of an object indirectly, by means of their
comparison implementation.
Because all types are (direct or indirect) subtypes of "object", they
inherit the default comparison behavior from "object". Types can
customize their comparison behavior by implementing *rich comparison
methods* like "__lt__()", described in Basic customization.
The default behavior for equality comparison ("==" and "!=") is based
on the identity of the objects. Hence, equality comparison of
instances with the same identity results in equality, and equality
comparison of instances with different identities results in
inequality. A motivation for this default behavior is the desire that
all objects should be reflexive (i.e. "x is y" implies "x == y").
A default order comparison ("<", ">", "<=", and ">=") is not provided;
an attempt raises "TypeError". A motivation for this default behavior
is the lack of a similar invariant as for equality.
The behavior of the default equality comparison, that instances with
different identities are always unequal, may be in contrast to what
types will need that have a sensible definition of object value and
value-based equality. Such types will need to customize their
comparison behavior, and in fact, a number of built-in types have done
that.
The following list describes the comparison behavior of the most
important built-in types.
* Numbers of built-in numeric types (Numeric Types — int, float,
complex) and of the standard library types "fractions.Fraction" and
"decimal.Decimal" can be compared within and across their types,
with the restriction that complex numbers do not support order
comparison. Within the limits of the types involved, they compare
mathematically (algorithmically) correct without loss of precision.
The not-a-number values "float('NaN')" and "decimal.Decimal('NaN')"
are special. Any ordered comparison of a number to a not-a-number
value is false. A counter-intuitive implication is that not-a-number
values are not equal to themselves. For example, if "x =
float('NaN')", "3 < x", "x < 3" and "x == x" are all false, while "x
!= x" is true. This behavior is compliant with IEEE 754.
* "None" and "NotImplemented" are singletons. **PEP 8** advises that
comparisons for singletons should always be done with "is" or "is
not", never the equality operators.
* Binary sequences (instances of "bytes" or "bytearray") can be
compared within and across their types. They compare
lexicographically using the numeric values of their elements.
* Strings (instances of "str") compare lexicographically using the
numerical Unicode code points (the result of the built-in function
"ord()") of their characters. [3]
Strings and binary sequences cannot be directly compared.
* Sequences (instances of "tuple", "list", or "range") can be compared
only within each of their types, with the restriction that ranges do
not support order comparison. Equality comparison across these
types results in inequality, and ordering comparison across these
types raises "TypeError".
Sequences compare lexicographically using comparison of
corresponding elements. The built-in containers typically assume
identical objects are equal to themselves. That lets them bypass
equality tests for identical objects to improve performance and to
maintain their internal invariants.
Lexicographical comparison between built-in collections works as
follows:
* For two collections to compare equal, they must be of the same
type, have the same length, and each pair of corresponding
elements must compare equal (for example, "[1,2] == (1,2)" is
false because the type is not the same).
* Collections that support order comparison are ordered the same as
their first unequal elements (for example, "[1,2,x] <= [1,2,y]"
has the same value as "x <= y"). If a corresponding element does
not exist, the shorter collection is ordered first (for example,
"[1,2] < [1,2,3]" is true).
* Mappings (instances of "dict") compare equal if and only if they
have equal *(key, value)* pairs. Equality comparison of the keys and
values enforces reflexivity.
Order comparisons ("<", ">", "<=", and ">=") raise "TypeError".
* Sets (instances of "set" or "frozenset") can be compared within and
across their types.
They define order comparison operators to mean subset and superset
tests. Those relations do not define total orderings (for example,
the two sets "{1,2}" and "{2,3}" are not equal, nor subsets of one
another, nor supersets of one another). Accordingly, sets are not
appropriate arguments for functions which depend on total ordering
(for example, "min()", "max()", and "sorted()" produce undefined
results given a list of sets as inputs).
Comparison of sets enforces reflexivity of its elements.
* Most other built-in types have no comparison methods implemented, so
they inherit the default comparison behavior.
User-defined classes that customize their comparison behavior should
follow some consistency rules, if possible:
* Equality comparison should be reflexive. In other words, identical
objects should compare equal:
"x is y" implies "x == y"
* Comparison should be symmetric. In other words, the following
expressions should have the same result:
"x == y" and "y == x"
"x != y" and "y != x"
"x < y" and "y > x"
"x <= y" and "y >= x"
* Comparison should be transitive. The following (non-exhaustive)
examples illustrate that:
"x > y and y > z" implies "x > z"
"x < y and y <= z" implies "x < z"
* Inverse comparison should result in the boolean negation. In other
words, the following expressions should have the same result:
"x == y" and "not x != y"
"x < y" and "not x >= y" (for total ordering)
"x > y" and "not x <= y" (for total ordering)
The last two expressions apply to totally ordered collections (e.g.
to sequences, but not to sets or mappings). See also the
"total_ordering()" decorator.
* The "hash()" result should be consistent with equality. Objects that
are equal should either have the same hash value, or be marked as
unhashable.
Python does not enforce these consistency rules. In fact, the
not-a-number values are an example for not following these rules.
Membership test operations
==========================
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z", for which
the expression "x is z or x == z" is true, is produced while iterating
over "y". If an exception is raised during the iteration, it is as if
"in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x is y[i] or x == y[i]", and no
lower integer index raises the "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse truth value of
"in".
Identity comparisons
====================
The operators "is" and "is not" test for an object’s identity: "x is
y" is true if and only if *x* and *y* are the same object. An
Object’s identity is determined using the "id()" function. "x is not
y" yields the inverse truth value. [4]
u�l Compound statements
*******************
Compound statements contain (groups of) other statements; they affect
or control the execution of those other statements in some way. In
general, compound statements span multiple lines, although in simple
incarnations a whole compound statement may be contained in one line.
The "if", "while" and "for" statements implement traditional control
flow constructs. "try" specifies exception handlers and/or cleanup
code for a group of statements, while the "with" statement allows the
execution of initialization and finalization code around a block of
code. Function and class definitions are also syntactically compound
statements.
A compound statement consists of one or more ‘clauses.’ A clause
consists of a header and a ‘suite.’ The clause headers of a
particular compound statement are all at the same indentation level.
Each clause header begins with a uniquely identifying keyword and ends
with a colon. A suite is a group of statements controlled by a
clause. A suite can be one or more semicolon-separated simple
statements on the same line as the header, following the header’s
colon, or it can be one or more indented statements on subsequent
lines. Only the latter form of a suite can contain nested compound
statements; the following is illegal, mostly because it wouldn’t be
clear to which "if" clause a following "else" clause would belong:
if test1: if test2: print(x)
Also note that the semicolon binds tighter than the colon in this
context, so that in the following example, either all or none of the
"print()" calls are executed:
if x < y < z: print(x); print(y); print(z)
Summarizing:
compound_stmt ::= if_stmt
| while_stmt
| for_stmt
| try_stmt
| with_stmt
| funcdef
| classdef
| async_with_stmt
| async_for_stmt
| async_funcdef
suite ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT
statement ::= stmt_list NEWLINE | compound_stmt
stmt_list ::= simple_stmt (";" simple_stmt)* [";"]
Note that statements always end in a "NEWLINE" possibly followed by a
"DEDENT". Also note that optional continuation clauses always begin
with a keyword that cannot start a statement, thus there are no
ambiguities (the ‘dangling "else"’ problem is solved in Python by
requiring nested "if" statements to be indented).
The formatting of the grammar rules in the following sections places
each clause on a separate line for clarity.
The "if" statement
==================
The "if" statement is used for conditional execution:
if_stmt ::= "if" assignment_expression ":" suite
("elif" assignment_expression ":" suite)*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
The "while" statement
=====================
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" assignment_expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
The "for" statement
===================
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator. Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.
The for-loop makes assignments to the variables in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop. Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".
Note:
There is a subtlety when the sequence is being modified by the loop
(this can only occur for mutable sequences, e.g. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
The "try" statement
===================
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object, or a tuple containing an item that is the class or a base
class of the exception object.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed. All except
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred. "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.
The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed. Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a ‘cleanup’ handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return", "break" or "continue" statement, the saved
exception is discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
Changed in version 3.8: Prior to Python 3.8, a "continue" statement
was illegal in the "finally" clause due to a problem with the
implementation.
The "with" statement
====================
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one “item” proceeds as
follows:
1. The context expression (the expression given in the "with_item") is
evaluated to obtain a context manager.
2. The context manager’s "__enter__()" is loaded for later use.
3. The context manager’s "__exit__()" is loaded for later use.
4. The context manager’s "__enter__()" method is invoked.
5. If a target was included in the "with" statement, the return value
from "__enter__()" is assigned to it.
Note:
The "with" statement guarantees that if the "__enter__()" method
returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
6. The suite is executed.
7. The context manager’s "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
The following code:
with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
enter = type(manager).__enter__
exit = type(manager).__exit__
value = enter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not exit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
exit(manager, None, None, None)
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
SUITE
is semantically equivalent to:
with A() as a:
with B() as b:
SUITE
Changed in version 3.1: Support for multiple context expressions.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
Function definitions
====================
A function definition defines a user-defined function object (see
section The standard type hierarchy):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= defparameter ("," defparameter)* "," "/" ["," [parameter_list_no_posonly]]
| parameter_list_no_posonly
parameter_list_no_posonly ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
| parameter_list_starargs
parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
| "**" parameter [","]
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [2]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
except that the original function is not temporarily bound to the name
"func".
When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted. If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.
**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call. This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended. A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from positional arguments, from keyword
arguments, or from default values. If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type. Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed by
keyword arguments. Parameters before “"/"” are positional-only
parameters and may only be passed by positional arguments.
Changed in version 3.8: The "/" function parameter syntax may be used
to indicate positional-only parameters. See **PEP 570** for details.
Parameters may have an *annotation* of the form “": expression"”
following the parameter name. Any parameter may have an annotation,
even those of the form "*identifier" or "**identifier". Functions may
have “return” annotation of the form “"-> expression"” after the
parameter list. These annotations can be any valid Python expression.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object. If the "annotations" import from
"__future__" is used, annotations are preserved as strings at runtime
which enables postponed evaluation. Otherwise, they are evaluated
when the function definition is executed. In this case annotations
may be evaluated in a different order than they appear in the source
code.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.
**Programmer’s note:** Functions are first-class objects. A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around. Free variables used
in the nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
See also:
**PEP 3107** - Function Annotations
The original specification for function annotations.
**PEP 484** - Type Hints
Definition of a standard meaning for annotations: type hints.
**PEP 526** - Syntax for Variable Annotations
Ability to type hint variable declarations, including class
variables and instance variables
**PEP 563** - Postponed Evaluation of Annotations
Support for forward references within annotations by preserving
annotations in a string form at runtime instead of eager
evaluation.
Class definitions
=================
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [argument_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing. Classes without an inheritance list
inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.) When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [3] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.
The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__". Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.
Class creation can be customized heavily using metaclasses.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result is then bound to the class name.
**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. Descriptors can be used to create instance
variables with different implementation details.
See also:
**PEP 3115** - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the
current syntax, and the semantics for how classes with
metaclasses are constructed.
**PEP 3129** - Class Decorators
The proposal that added class decorators. Function and method
decorators were introduced in **PEP 318**.
Coroutines
==========
New in version 3.5.
Coroutine function definition
-----------------------------
async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*). Inside the body of a coroutine function,
"await" and "async" identifiers become reserved keywords; "await"
expressions, "async for" and "async with" can only be used in
coroutine function bodies.
Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.
It is a "SyntaxError" to use a "yield from" expression inside the body
of a coroutine function.
An example of a coroutine function:
async def func(param1, param2):
do_stuff()
await some_coroutine()
The "async for" statement
-------------------------
async_for_stmt ::= "async" for_stmt
An *asynchronous iterable* is able to call asynchronous code in its
*iter* implementation, and *asynchronous iterator* can call
asynchronous code in its *next* method.
The "async for" statement allows convenient iteration over
asynchronous iterators.
The following code:
async for TARGET in ITER:
SUITE
else:
SUITE2
Is semantically equivalent to:
iter = (ITER)
iter = type(iter).__aiter__(iter)
running = True
while running:
try:
TARGET = await type(iter).__anext__(iter)
except StopAsyncIteration:
running = False
else:
SUITE
else:
SUITE2
See also "__aiter__()" and "__anext__()" for details.
It is a "SyntaxError" to use an "async for" statement outside the body
of a coroutine function.
The "async with" statement
--------------------------
async_with_stmt ::= "async" with_stmt
An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.
The following code:
async with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
aexit = type(manager).__aexit__
aenter = type(manager).__aenter__
value = await aenter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not await aexit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
await aexit(manager, None, None, None)
See also "__aenter__()" and "__aexit__()" for details.
It is a "SyntaxError" to use an "async with" statement outside the
body of a coroutine function.
See also:
**PEP 492** - Coroutines with async and await syntax
The proposal that made coroutines a proper standalone concept in
Python, and added supporting syntax.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless there
is a "finally" clause which happens to raise another exception.
That new exception causes the old one to be lost.
[2] A string literal appearing as the first statement in the function
body is transformed into the function’s "__doc__" attribute and
therefore the function’s *docstring*.
[3] A string literal appearing as the first statement in the class
body is transformed into the namespace’s "__doc__" item and
therefore the class’s *docstring*.
u� With Statement Context Managers
*******************************
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method’s return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller’s responsibility.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
a� The "continue" statement
************************
continue_stmt ::= "continue"
"continue" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop. It continues with the next cycle of the nearest enclosing loop.
When "continue" passes control out of a "try" statement with a
"finally" clause, that "finally" clause is executed before really
starting the next loop cycle.
u� Arithmetic conversions
**********************
When a description of an arithmetic operator below uses the phrase
“the numeric arguments are converted to a common type”, this means
that the operator implementation for built-in types works as follows:
* If either argument is a complex number, the other is converted to
complex;
* otherwise, if either argument is a floating point number, the other
is converted to floating point;
* otherwise, both must be integers and no conversion is necessary.
Some additional rules apply for certain operators (e.g., a string as a
left argument to the ‘%’ operator). Extensions must define their own
conversion behavior.
uS5 Basic customization
*******************
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass’s "__new__()" method using
"super().__new__(cls[, ...])" with appropriate arguments and then
modifying the newly-created instance as necessary before returning
it.
If "__new__()" is invoked during object construction and it returns
an instance of *cls*, then the new instance’s "__init__()" method
will be invoked like "__init__(self[, ...])", where *self* is the
new instance and the remaining arguments are the same as were
passed to the object constructor.
If "__new__()" does not return an instance of *cls*, then the new
instance’s "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class’s "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"super().__init__([args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.
It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.
It is not guaranteed that "__del__()" methods are called for
objects that still exist when the interpreter exits.
Note:
"del x" doesn’t directly call "x.__del__()" — the former
decrements the reference count for "x" by one, and the latter is
only called when "x"’s reference count reaches zero.
**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.
See also: Documentation for the "gc" module.
Warning:
Due to the precarious circumstances under which "__del__()"
methods are invoked, exceptions that occur during their execution
are ignored, and a warning is printed to "sys.stderr" instead.
In particular:
* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".
* "__del__()" can be executed during interpreter shutdown. As a
consequence, the global variables it needs to access (including
other modules) may already have been deleted or set to "None".
Python guarantees that globals whose name begins with a single
underscore are deleted from their module before other globals
are deleted; if no other references to such globals exist, this
may help in assuring that imported modules are still available
at the time when the "__del__()" method is called.
object.__repr__(self)
Called by the "repr()" built-in function to compute the “official”
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an “informal”
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the “informal” or nicely printable string
representation of an object. The return value must be a string
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by bytes to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function, and by extension,
evaluation of formatted string literals and the "str.format()"
method, to produce a “formatted” string representation of an
object. The *format_spec* argument is a string that contains a
description of the formatting options desired. The interpretation
of the *format_spec* argument is up to the type implementing
"__format__()", however most classes will either delegate
formatting to one of the built-in types, or use a similar
formatting option syntax.
See Format Specification Mini-Language for a description of the
standard formatting syntax.
The return value must be a string object.
Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.
Changed in version 3.7: "object.__format__(x, '')" is now
equivalent to "str(x)" rather than "format(str(self), '')".
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
By default, "object" implements "__eq__()" by using "is", returning
"NotImplemented" in the case of a false comparison: "True if x is y
else NotImplemented". For "__ne__()", by default it delegates to
"__eq__()" and inverts the result unless it is "NotImplemented".
There are no other implied relationships among the comparison
operators or default implementations; for example, the truth of
"(x<y or x==y)" does not imply "x<=y". To automatically generate
ordering operations from a single root operation, see
"functools.total_ordering()".
See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
Note:
"hash()" truncates the value returned from an object’s custom
"__hash__()" method to the size of a "Py_ssize_t". This is
typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds.
If an object’s "__hash__()" must interoperate on builds of
different bit sizes, be sure to check the width on all supported
builds. An easy way to do this is with "python -c "import sys;
print(sys.hash_info.width)"".
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key’s hash value is immutable (if the object’s hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj,
collections.abc.Hashable)".
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
<ParentClass>.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.abc.Hashable)" call.
Note:
By default, the "__hash__()" values of str and bytes objects are
“salted” with an unpredictable random value. Although they
remain constant within an individual Python process, they are not
predictable between repeated invocations of Python.This is
intended to provide protection against a denial-of-service caused
by carefully-chosen inputs that exploit the worst case
performance of a dict insertion, O(n^2) complexity. See
http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of sets.
Python has never made guarantees about this ordering (and it
typically varies between 32-bit and 64-bit builds).See also
"PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
u�I "pdb" — The Python Debugger
***************************
**Source code:** Lib/pdb.py
======================================================================
The module "pdb" defines an interactive source code debugger for
Python programs. It supports setting (conditional) breakpoints and
single stepping at the source line level, inspection of stack frames,
source code listing, and evaluation of arbitrary Python code in the
context of any stack frame. It also supports post-mortem debugging
and can be called under program control.
The debugger is extensible – it is actually defined as the class
"Pdb". This is currently undocumented but easily understood by reading
the source. The extension interface uses the modules "bdb" and "cmd".
The debugger’s prompt is "(Pdb)". Typical usage to run a program under
control of the debugger is:
>>> import pdb
>>> import mymodule
>>> pdb.run('mymodule.test()')
> <string>(0)?()
(Pdb) continue
> <string>(1)?()
(Pdb) continue
NameError: 'spam'
> <string>(1)?()
(Pdb)
Changed in version 3.3: Tab-completion via the "readline" module is
available for commands and command arguments, e.g. the current global
and local names are offered as arguments of the "p" command.
"pdb.py" can also be invoked as a script to debug other scripts. For
example:
python3 -m pdb myscript.py
When invoked as a script, pdb will automatically enter post-mortem
debugging if the program being debugged exits abnormally. After post-
mortem debugging (or after normal exit of the program), pdb will
restart the program. Automatic restarting preserves pdb’s state (such
as breakpoints) and in most cases is more useful than quitting the
debugger upon program’s exit.
New in version 3.2: "pdb.py" now accepts a "-c" option that executes
commands as if given in a ".pdbrc" file, see Debugger Commands.
New in version 3.7: "pdb.py" now accepts a "-m" option that execute
modules similar to the way "python3 -m" does. As with a script, the
debugger will pause execution just before the first line of the
module.
The typical usage to break into the debugger from a running program is
to insert
import pdb; pdb.set_trace()
at the location you want to break into the debugger. You can then
step through the code following this statement, and continue running
without the debugger using the "continue" command.
New in version 3.7: The built-in "breakpoint()", when called with
defaults, can be used instead of "import pdb; pdb.set_trace()".
The typical usage to inspect a crashed program is:
>>> import pdb
>>> import mymodule
>>> mymodule.test()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "./mymodule.py", line 4, in test
test2()
File "./mymodule.py", line 3, in test2
print(spam)
NameError: spam
>>> pdb.pm()
> ./mymodule.py(3)test2()
-> print(spam)
(Pdb)
The module defines the following functions; each enters the debugger
in a slightly different way:
pdb.run(statement, globals=None, locals=None)
Execute the *statement* (given as a string or a code object) under
debugger control. The debugger prompt appears before any code is
executed; you can set breakpoints and type "continue", or you can
step through the statement using "step" or "next" (all these
commands are explained below). The optional *globals* and *locals*
arguments specify the environment in which the code is executed; by
default the dictionary of the module "__main__" is used. (See the
explanation of the built-in "exec()" or "eval()" functions.)
pdb.runeval(expression, globals=None, locals=None)
Evaluate the *expression* (given as a string or a code object)
under debugger control. When "runeval()" returns, it returns the
value of the expression. Otherwise this function is similar to
"run()".
pdb.runcall(function, *args, **kwds)
Call the *function* (a function or method object, not a string)
with the given arguments. When "runcall()" returns, it returns
whatever the function call returned. The debugger prompt appears
as soon as the function is entered.
pdb.set_trace(*, header=None)
Enter the debugger at the calling stack frame. This is useful to
hard-code a breakpoint at a given point in a program, even if the
code is not otherwise being debugged (e.g. when an assertion
fails). If given, *header* is printed to the console just before
debugging begins.
Changed in version 3.7: The keyword-only argument *header*.
pdb.post_mortem(traceback=None)
Enter post-mortem debugging of the given *traceback* object. If no
*traceback* is given, it uses the one of the exception that is
currently being handled (an exception must be being handled if the
default is to be used).
pdb.pm()
Enter post-mortem debugging of the traceback found in
"sys.last_traceback".
The "run*" functions and "set_trace()" are aliases for instantiating
the "Pdb" class and calling the method of the same name. If you want
to access further features, you have to do this yourself:
class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None, nosigint=False, readrc=True)
"Pdb" is the debugger class.
The *completekey*, *stdin* and *stdout* arguments are passed to the
underlying "cmd.Cmd" class; see the description there.
The *skip* argument, if given, must be an iterable of glob-style
module name patterns. The debugger will not step into frames that
originate in a module that matches one of these patterns. [1]
By default, Pdb sets a handler for the SIGINT signal (which is sent
when the user presses "Ctrl-C" on the console) when you give a
"continue" command. This allows you to break into the debugger
again by pressing "Ctrl-C". If you want Pdb not to touch the
SIGINT handler, set *nosigint* to true.
The *readrc* argument defaults to true and controls whether Pdb
will load .pdbrc files from the filesystem.
Example call to enable tracing with *skip*:
import pdb; pdb.Pdb(skip=['django.*']).set_trace()
Raises an auditing event "pdb.Pdb" with no arguments.
New in version 3.1: The *skip* argument.
New in version 3.2: The *nosigint* argument. Previously, a SIGINT
handler was never set by Pdb.
Changed in version 3.6: The *readrc* argument.
run(statement, globals=None, locals=None)
runeval(expression, globals=None, locals=None)
runcall(function, *args, **kwds)
set_trace()
See the documentation for the functions explained above.
Debugger Commands
=================
The commands recognized by the debugger are listed below. Most
commands can be abbreviated to one or two letters as indicated; e.g.
"h(elp)" means that either "h" or "help" can be used to enter the help
command (but not "he" or "hel", nor "H" or "Help" or "HELP").
Arguments to commands must be separated by whitespace (spaces or
tabs). Optional arguments are enclosed in square brackets ("[]") in
the command syntax; the square brackets must not be typed.
Alternatives in the command syntax are separated by a vertical bar
("|").
Entering a blank line repeats the last command entered. Exception: if
the last command was a "list" command, the next 11 lines are listed.
Commands that the debugger doesn’t recognize are assumed to be Python
statements and are executed in the context of the program being
debugged. Python statements can also be prefixed with an exclamation
point ("!"). This is a powerful way to inspect the program being
debugged; it is even possible to change a variable or call a function.
When an exception occurs in such a statement, the exception name is
printed but the debugger’s state is not changed.
The debugger supports aliases. Aliases can have parameters which
allows one a certain level of adaptability to the context under
examination.
Multiple commands may be entered on a single line, separated by ";;".
(A single ";" is not used as it is the separator for multiple commands
in a line that is passed to the Python parser.) No intelligence is
applied to separating the commands; the input is split at the first
";;" pair, even if it is in the middle of a quoted string.
If a file ".pdbrc" exists in the user’s home directory or in the
current directory, it is read in and executed as if it had been typed
at the debugger prompt. This is particularly useful for aliases. If
both files exist, the one in the home directory is read first and
aliases defined there can be overridden by the local file.
Changed in version 3.2: ".pdbrc" can now contain commands that
continue debugging, such as "continue" or "next". Previously, these
commands had no effect.
h(elp) [command]
Without argument, print the list of available commands. With a
*command* as argument, print help about that command. "help pdb"
displays the full documentation (the docstring of the "pdb"
module). Since the *command* argument must be an identifier, "help
exec" must be entered to get help on the "!" command.
w(here)
Print a stack trace, with the most recent frame at the bottom. An
arrow indicates the current frame, which determines the context of
most commands.
d(own) [count]
Move the current frame *count* (default one) levels down in the
stack trace (to a newer frame).
u(p) [count]
Move the current frame *count* (default one) levels up in the stack
trace (to an older frame).
b(reak) [([filename:]lineno | function) [, condition]]
With a *lineno* argument, set a break there in the current file.
With a *function* argument, set a break at the first executable
statement within that function. The line number may be prefixed
with a filename and a colon, to specify a breakpoint in another
file (probably one that hasn’t been loaded yet). The file is
searched on "sys.path". Note that each breakpoint is assigned a
number to which all the other breakpoint commands refer.
If a second argument is present, it is an expression which must
evaluate to true before the breakpoint is honored.
Without argument, list all breaks, including for each breakpoint,
the number of times that breakpoint has been hit, the current
ignore count, and the associated condition if any.
tbreak [([filename:]lineno | function) [, condition]]
Temporary breakpoint, which is removed automatically when it is
first hit. The arguments are the same as for "break".
cl(ear) [filename:lineno | bpnumber [bpnumber ...]]
With a *filename:lineno* argument, clear all the breakpoints at
this line. With a space separated list of breakpoint numbers, clear
those breakpoints. Without argument, clear all breaks (but first
ask confirmation).
disable [bpnumber [bpnumber ...]]
Disable the breakpoints given as a space separated list of
breakpoint numbers. Disabling a breakpoint means it cannot cause
the program to stop execution, but unlike clearing a breakpoint, it
remains in the list of breakpoints and can be (re-)enabled.
enable [bpnumber [bpnumber ...]]
Enable the breakpoints specified.
ignore bpnumber [count]
Set the ignore count for the given breakpoint number. If count is
omitted, the ignore count is set to 0. A breakpoint becomes active
when the ignore count is zero. When non-zero, the count is
decremented each time the breakpoint is reached and the breakpoint
is not disabled and any associated condition evaluates to true.
condition bpnumber [condition]
Set a new *condition* for the breakpoint, an expression which must
evaluate to true before the breakpoint is honored. If *condition*
is absent, any existing condition is removed; i.e., the breakpoint
is made unconditional.
commands [bpnumber]
Specify a list of commands for breakpoint number *bpnumber*. The
commands themselves appear on the following lines. Type a line
containing just "end" to terminate the commands. An example:
(Pdb) commands 1
(com) p some_variable
(com) end
(Pdb)
To remove all commands from a breakpoint, type "commands" and
follow it immediately with "end"; that is, give no commands.
With no *bpnumber* argument, "commands" refers to the last
breakpoint set.
You can use breakpoint commands to start your program up again.
Simply use the "continue" command, or "step", or any other command
that resumes execution.
Specifying any command resuming execution (currently "continue",
"step", "next", "return", "jump", "quit" and their abbreviations)
terminates the command list (as if that command was immediately
followed by end). This is because any time you resume execution
(even with a simple next or step), you may encounter another
breakpoint—which could have its own command list, leading to
ambiguities about which list to execute.
If you use the ‘silent’ command in the command list, the usual
message about stopping at a breakpoint is not printed. This may be
desirable for breakpoints that are to print a specific message and
then continue. If none of the other commands print anything, you
see no sign that the breakpoint was reached.
s(tep)
Execute the current line, stop at the first possible occasion
(either in a function that is called or on the next line in the
current function).
n(ext)
Continue execution until the next line in the current function is
reached or it returns. (The difference between "next" and "step"
is that "step" stops inside a called function, while "next"
executes called functions at (nearly) full speed, only stopping at
the next line in the current function.)
unt(il) [lineno]
Without argument, continue execution until the line with a number
greater than the current one is reached.
With a line number, continue execution until a line with a number
greater or equal to that is reached. In both cases, also stop when
the current frame returns.
Changed in version 3.2: Allow giving an explicit line number.
r(eturn)
Continue execution until the current function returns.
c(ont(inue))
Continue execution, only stop when a breakpoint is encountered.
j(ump) lineno
Set the next line that will be executed. Only available in the
bottom-most frame. This lets you jump back and execute code again,
or jump forward to skip code that you don’t want to run.
It should be noted that not all jumps are allowed – for instance it
is not possible to jump into the middle of a "for" loop or out of a
"finally" clause.
l(ist) [first[, last]]
List source code for the current file. Without arguments, list 11
lines around the current line or continue the previous listing.
With "." as argument, list 11 lines around the current line. With
one argument, list 11 lines around at that line. With two
arguments, list the given range; if the second argument is less
than the first, it is interpreted as a count.
The current line in the current frame is indicated by "->". If an
exception is being debugged, the line where the exception was
originally raised or propagated is indicated by ">>", if it differs
from the current line.
New in version 3.2: The ">>" marker.
ll | longlist
List all source code for the current function or frame.
Interesting lines are marked as for "list".
New in version 3.2.
a(rgs)
Print the argument list of the current function.
p expression
Evaluate the *expression* in the current context and print its
value.
Note:
"print()" can also be used, but is not a debugger command — this
executes the Python "print()" function.
pp expression
Like the "p" command, except the value of the expression is pretty-
printed using the "pprint" module.
whatis expression
Print the type of the *expression*.
source expression
Try to get source code for the given object and display it.
New in version 3.2.
display [expression]
Display the value of the expression if it changed, each time
execution stops in the current frame.
Without expression, list all display expressions for the current
frame.
New in version 3.2.
undisplay [expression]
Do not display the expression any more in the current frame.
Without expression, clear all display expressions for the current
frame.
New in version 3.2.
interact
Start an interactive interpreter (using the "code" module) whose
global namespace contains all the (global and local) names found in
the current scope.
New in version 3.2.
alias [name [command]]
Create an alias called *name* that executes *command*. The command
must *not* be enclosed in quotes. Replaceable parameters can be
indicated by "%1", "%2", and so on, while "%*" is replaced by all
the parameters. If no command is given, the current alias for
*name* is shown. If no arguments are given, all aliases are listed.
Aliases may be nested and can contain anything that can be legally
typed at the pdb prompt. Note that internal pdb commands *can* be
overridden by aliases. Such a command is then hidden until the
alias is removed. Aliasing is recursively applied to the first
word of the command line; all other words in the line are left
alone.
As an example, here are two useful aliases (especially when placed
in the ".pdbrc" file):
# Print instance variables (usage "pi classInst")
alias pi for k in %1.__dict__.keys(): print("%1.",k,"=",%1.__dict__[k])
# Print instance variables in self
alias ps pi self
unalias name
Delete the specified alias.
! statement
Execute the (one-line) *statement* in the context of the current
stack frame. The exclamation point can be omitted unless the first
word of the statement resembles a debugger command. To set a
global variable, you can prefix the assignment command with a
"global" statement on the same line, e.g.:
(Pdb) global list_options; list_options = ['-l']
(Pdb)
run [args ...]
restart [args ...]
Restart the debugged Python program. If an argument is supplied,
it is split with "shlex" and the result is used as the new
"sys.argv". History, breakpoints, actions and debugger options are
preserved. "restart" is an alias for "run".
q(uit)
Quit from the debugger. The program being executed is aborted.
debug code
Enter a recursive debugger that steps through the code argument
(which is an arbitrary expression or statement to be executed in
the current environment).
retval
Print the return value for the last return of a function.
-[ Footnotes ]-
[1] Whether a frame is considered to originate in a certain module is
determined by the "__name__" in the frame globals.
a� The "del" statement
*******************
del_stmt ::= "del" target_list
Deletion is recursively defined very similar to the way assignment is
defined. Rather than spelling it out in full details, here are some
hints.
Deletion of a target list recursively deletes each target, from left
to right.
Deletion of a name removes the binding of that name from the local or
global namespace, depending on whether the name occurs in a "global"
statement in the same code block. If the name is unbound, a
"NameError" exception will be raised.
Deletion of attribute references, subscriptions and slicings is passed
to the primary object involved; deletion of a slicing is in general
equivalent to assignment of an empty slice of the right type (but even
this is determined by the sliced object).
Changed in version 3.2: Previously it was illegal to delete a name
from the local namespace if it occurs as a free variable in a nested
block.
u Dictionary displays
*******************
A dictionary display is a possibly empty series of key/datum pairs
enclosed in curly braces:
dict_display ::= "{" [key_datum_list | dict_comprehension] "}"
key_datum_list ::= key_datum ("," key_datum)* [","]
key_datum ::= expression ":" expression | "**" or_expr
dict_comprehension ::= expression ":" expression comp_for
A dictionary display yields a new dictionary object.
If a comma-separated sequence of key/datum pairs is given, they are
evaluated from left to right to define the entries of the dictionary:
each key object is used as a key into the dictionary to store the
corresponding datum. This means that you can specify the same key
multiple times in the key/datum list, and the final dictionary’s value
for that key will be the last one given.
A double asterisk "**" denotes *dictionary unpacking*. Its operand
must be a *mapping*. Each mapping item is added to the new
dictionary. Later values replace values already set by earlier
key/datum pairs and earlier dictionary unpackings.
New in version 3.5: Unpacking into dictionary displays, originally
proposed by **PEP 448**.
A dict comprehension, in contrast to list and set comprehensions,
needs two expressions separated with a colon followed by the usual
“for” and “if” clauses. When the comprehension is run, the resulting
key and value elements are inserted in the new dictionary in the order
they are produced.
Restrictions on the types of the key values are listed earlier in
section The standard type hierarchy. (To summarize, the key type
should be *hashable*, which excludes all mutable objects.) Clashes
between duplicate keys are not detected; the last datum (textually
rightmost in the display) stored for a given key value prevails.
Changed in version 3.8: Prior to Python 3.8, in dict comprehensions,
the evaluation order of key and value was not well-defined. In
CPython, the value was evaluated before the key. Starting with 3.8,
the key is evaluated before the value, as proposed by **PEP 572**.
a� Interaction with dynamic features
*********************************
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
aX The "if" statement
******************
The "if" statement is used for conditional execution:
if_stmt ::= "if" assignment_expression ":" suite
("elif" assignment_expression ":" suite)*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
u� Exceptions
**********
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack traceback, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note:
Exception messages are not part of the Python API. Their contents
may change from one version of Python to the next without warning
and should not be relied on by code which will run under multiple
versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by these
operations is not available at the time the module is compiled.
u$ Execution model
***************
Structure of a program
======================
A Python program is constructed from code blocks. A *block* is a piece
of Python program text that is executed as a unit. The following are
blocks: a module, a function body, and a class definition. Each
command typed interactively is a block. A script file (a file given
as standard input to the interpreter or specified as a command line
argument to the interpreter) is a code block. A script command (a
command specified on the interpreter command line with the "-c"
option) is a code block. The string argument passed to the built-in
functions "eval()" and "exec()" is a code block.
A code block is executed in an *execution frame*. A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block’s execution has
completed.
Naming and binding
==================
Binding of names
----------------
*Names* refer to objects. Names are introduced by name binding
operations.
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore. This form
may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global". If a name is bound at the
module level, it is a global variable. (The variables of the module
code block are local and global.) If a variable is used in a code
block but not defined there, it is a *free variable*.
Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.
Resolution of names
-------------------
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block’s *environment*.
When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins". The global namespace is searched first. If
the name is not found there, the builtins namespace is searched. The
"global" statement must precede all uses of the name.
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope. This means that the following
will fail:
class A:
a = 42
b = list(a + i for i in range(10))
Builtins and restricted execution
---------------------------------
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.
Interaction with dynamic features
---------------------------------
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
Exceptions
==========
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack traceback, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note:
Exception messages are not part of the Python API. Their contents
may change from one version of Python to the next without warning
and should not be relied on by code which will run under multiple
versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by these
operations is not available at the time the module is compiled.
uz Expression lists
****************
expression_list ::= expression ("," expression)* [","]
starred_list ::= starred_item ("," starred_item)* [","]
starred_expression ::= expression | (starred_item ",")* [starred_item]
starred_item ::= assignment_expression | "*" or_expr
Except when part of a list or set display, an expression list
containing at least one comma yields a tuple. The length of the tuple
is the number of expressions in the list. The expressions are
evaluated from left to right.
An asterisk "*" denotes *iterable unpacking*. Its operand must be an
*iterable*. The iterable is expanded into a sequence of items, which
are included in the new tuple, list, or set, at the site of the
unpacking.
New in version 3.5: Iterable unpacking in expression lists, originally
proposed by **PEP 448**.
The trailing comma is required only to create a single tuple (a.k.a. a
*singleton*); it is optional in all other cases. A single expression
without a trailing comma doesn’t create a tuple, but rather yields the
value of that expression. (To create an empty tuple, use an empty pair
of parentheses: "()".)
a� Floating point literals
***********************
Floating point literals are described by the following lexical
definitions:
floatnumber ::= pointfloat | exponentfloat
pointfloat ::= [digitpart] fraction | digitpart "."
exponentfloat ::= (digitpart | pointfloat) exponent
digitpart ::= digit (["_"] digit)*
fraction ::= "." digitpart
exponent ::= ("e" | "E") ["+" | "-"] digitpart
Note that the integer and exponent parts are always interpreted using
radix 10. For example, "077e010" is legal, and denotes the same number
as "77e10". The allowed range of floating point literals is
implementation-dependent. As in integer literals, underscores are
supported for digit grouping.
Some examples of floating point literals:
3.14 10. .001 1e100 3.14e-10 0e0 3.14_15_93
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
u�
The "for" statement
*******************
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator. Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.
The for-loop makes assignments to the variables in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop. Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".
Note:
There is a subtlety when the sequence is being modified by the loop
(this can only occur for mutable sequences, e.g. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
u�` Format String Syntax
********************
The "str.format()" method and the "Formatter" class share the same
syntax for format strings (although in the case of "Formatter",
subclasses can define their own format string syntax). The syntax is
related to that of formatted string literals, but it is less
sophisticated and, in particular, does not support arbitrary
expressions.
Format strings contain “replacement fields” surrounded by curly braces
"{}". Anything that is not contained in braces is considered literal
text, which is copied unchanged to the output. If you need to include
a brace character in the literal text, it can be escaped by doubling:
"{{" and "}}".
The grammar for a replacement field is as follows:
replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}"
field_name ::= arg_name ("." attribute_name | "[" element_index "]")*
arg_name ::= [identifier | digit+]
attribute_name ::= identifier
element_index ::= digit+ | index_string
index_string ::= <any source character except "]"> +
conversion ::= "r" | "s" | "a"
format_spec ::= <described in the next section>
In less formal terms, the replacement field can start with a
*field_name* that specifies the object whose value is to be formatted
and inserted into the output instead of the replacement field. The
*field_name* is optionally followed by a *conversion* field, which is
preceded by an exclamation point "'!'", and a *format_spec*, which is
preceded by a colon "':'". These specify a non-default format for the
replacement value.
See also the Format Specification Mini-Language section.
The *field_name* itself begins with an *arg_name* that is either a
number or a keyword. If it’s a number, it refers to a positional
argument, and if it’s a keyword, it refers to a named keyword
argument. If the numerical arg_names in a format string are 0, 1, 2,
… in sequence, they can all be omitted (not just some) and the numbers
0, 1, 2, … will be automatically inserted in that order. Because
*arg_name* is not quote-delimited, it is not possible to specify
arbitrary dictionary keys (e.g., the strings "'10'" or "':-]'") within
a format string. The *arg_name* can be followed by any number of index
or attribute expressions. An expression of the form "'.name'" selects
the named attribute using "getattr()", while an expression of the form
"'[index]'" does an index lookup using "__getitem__()".
Changed in version 3.1: The positional argument specifiers can be
omitted for "str.format()", so "'{} {}'.format(a, b)" is equivalent to
"'{0} {1}'.format(a, b)".
Changed in version 3.4: The positional argument specifiers can be
omitted for "Formatter".
Some simple format string examples:
"First, thou shalt count to {0}" # References first positional argument
"Bring me a {}" # Implicitly references the first positional argument
"From {} to {}" # Same as "From {0} to {1}"
"My quest is {name}" # References keyword argument 'name'
"Weight in tons {0.weight}" # 'weight' attribute of first positional arg
"Units destroyed: {players[0]}" # First element of keyword argument 'players'.
The *conversion* field causes a type coercion before formatting.
Normally, the job of formatting a value is done by the "__format__()"
method of the value itself. However, in some cases it is desirable to
force a type to be formatted as a string, overriding its own
definition of formatting. By converting the value to a string before
calling "__format__()", the normal formatting logic is bypassed.
Three conversion flags are currently supported: "'!s'" which calls
"str()" on the value, "'!r'" which calls "repr()" and "'!a'" which
calls "ascii()".
Some examples:
"Harold's a clever {0!s}" # Calls str() on the argument first
"Bring out the holy {name!r}" # Calls repr() on the argument first
"More {!a}" # Calls ascii() on the argument first
The *format_spec* field contains a specification of how the value
should be presented, including such details as field width, alignment,
padding, decimal precision and so on. Each value type can define its
own “formatting mini-language” or interpretation of the *format_spec*.
Most built-in types support a common formatting mini-language, which
is described in the next section.
A *format_spec* field can also include nested replacement fields
within it. These nested replacement fields may contain a field name,
conversion flag and format specification, but deeper nesting is not
allowed. The replacement fields within the format_spec are
substituted before the *format_spec* string is interpreted. This
allows the formatting of a value to be dynamically specified.
See the Format examples section for some examples.
Format Specification Mini-Language
==================================
“Format specifications” are used within replacement fields contained
within a format string to define how individual values are presented
(see Format String Syntax and Formatted string literals). They can
also be passed directly to the built-in "format()" function. Each
formattable type may define how the format specification is to be
interpreted.
Most built-in types implement the following options for format
specifications, although some of the formatting options are only
supported by the numeric types.
A general convention is that an empty format specification produces
the same result as if you had called "str()" on the value. A non-empty
format specification typically modifies the result.
The general form of a *standard format specifier* is:
format_spec ::= [[fill]align][sign][#][0][width][grouping_option][.precision][type]
fill ::= <any character>
align ::= "<" | ">" | "=" | "^"
sign ::= "+" | "-" | " "
width ::= digit+
grouping_option ::= "_" | ","
precision ::= digit+
type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"
If a valid *align* value is specified, it can be preceded by a *fill*
character that can be any character and defaults to a space if
omitted. It is not possible to use a literal curly brace (”"{"” or
“"}"”) as the *fill* character in a formatted string literal or when
using the "str.format()" method. However, it is possible to insert a
curly brace with a nested replacement field. This limitation doesn’t
affect the "format()" function.
The meaning of the various alignment options is as follows:
+-----------+------------------------------------------------------------+
| Option | Meaning |
|===========|============================================================|
| "'<'" | Forces the field to be left-aligned within the available |
| | space (this is the default for most objects). |
+-----------+------------------------------------------------------------+
| "'>'" | Forces the field to be right-aligned within the available |
| | space (this is the default for numbers). |
+-----------+------------------------------------------------------------+
| "'='" | Forces the padding to be placed after the sign (if any) |
| | but before the digits. This is used for printing fields |
| | in the form ‘+000000120’. This alignment option is only |
| | valid for numeric types. It becomes the default when ‘0’ |
| | immediately precedes the field width. |
+-----------+------------------------------------------------------------+
| "'^'" | Forces the field to be centered within the available |
| | space. |
+-----------+------------------------------------------------------------+
Note that unless a minimum field width is defined, the field width
will always be the same size as the data to fill it, so that the
alignment option has no meaning in this case.
The *sign* option is only valid for number types, and can be one of
the following:
+-----------+------------------------------------------------------------+
| Option | Meaning |
|===========|============================================================|
| "'+'" | indicates that a sign should be used for both positive as |
| | well as negative numbers. |
+-----------+------------------------------------------------------------+
| "'-'" | indicates that a sign should be used only for negative |
| | numbers (this is the default behavior). |
+-----------+------------------------------------------------------------+
| space | indicates that a leading space should be used on positive |
| | numbers, and a minus sign on negative numbers. |
+-----------+------------------------------------------------------------+
The "'#'" option causes the “alternate form” to be used for the
conversion. The alternate form is defined differently for different
types. This option is only valid for integer, float and complex
types. For integers, when binary, octal, or hexadecimal output is
used, this option adds the prefix respective "'0b'", "'0o'", or "'0x'"
to the output value. For float and complex the alternate form causes
the result of the conversion to always contain a decimal-point
character, even if no digits follow it. Normally, a decimal-point
character appears in the result of these conversions only if a digit
follows it. In addition, for "'g'" and "'G'" conversions, trailing
zeros are not removed from the result.
The "','" option signals the use of a comma for a thousands separator.
For a locale aware separator, use the "'n'" integer presentation type
instead.
Changed in version 3.1: Added the "','" option (see also **PEP 378**).
The "'_'" option signals the use of an underscore for a thousands
separator for floating point presentation types and for integer
presentation type "'d'". For integer presentation types "'b'", "'o'",
"'x'", and "'X'", underscores will be inserted every 4 digits. For
other presentation types, specifying this option is an error.
Changed in version 3.6: Added the "'_'" option (see also **PEP 515**).
*width* is a decimal integer defining the minimum total field width,
including any prefixes, separators, and other formatting characters.
If not specified, then the field width will be determined by the
content.
When no explicit alignment is given, preceding the *width* field by a
zero ("'0'") character enables sign-aware zero-padding for numeric
types. This is equivalent to a *fill* character of "'0'" with an
*alignment* type of "'='".
The *precision* is a decimal number indicating how many digits should
be displayed after the decimal point for a floating point value
formatted with "'f'" and "'F'", or before and after the decimal point
for a floating point value formatted with "'g'" or "'G'". For non-
number types the field indicates the maximum field size - in other
words, how many characters will be used from the field content. The
*precision* is not allowed for integer values.
Finally, the *type* determines how the data should be presented.
The available string presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
|===========|============================================================|
| "'s'" | String format. This is the default type for strings and |
| | may be omitted. |
+-----------+------------------------------------------------------------+
| None | The same as "'s'". |
+-----------+------------------------------------------------------------+
The available integer presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
|===========|============================================================|
| "'b'" | Binary format. Outputs the number in base 2. |
+-----------+------------------------------------------------------------+
| "'c'" | Character. Converts the integer to the corresponding |
| | unicode character before printing. |
+-----------+------------------------------------------------------------+
| "'d'" | Decimal Integer. Outputs the number in base 10. |
+-----------+------------------------------------------------------------+
| "'o'" | Octal format. Outputs the number in base 8. |
+-----------+------------------------------------------------------------+
| "'x'" | Hex format. Outputs the number in base 16, using lower- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'X'" | Hex format. Outputs the number in base 16, using upper- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'d'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| None | The same as "'d'". |
+-----------+------------------------------------------------------------+
In addition to the above presentation types, integers can be formatted
with the floating point presentation types listed below (except "'n'"
and "None"). When doing so, "float()" is used to convert the integer
to a floating point number before formatting.
The available presentation types for "float" and "Decimal" values are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
|===========|============================================================|
| "'e'" | Scientific notation. For a given precision "p", formats |
| | the number in scientific notation with the letter ‘e’ |
| | separating the coefficient from the exponent. The |
| | coefficient has one digit before and "p" digits after the |
| | decimal point, for a total of "p + 1" significant digits. |
| | With no precision given, uses a precision of "6" digits |
| | after the decimal point for "float", and shows all |
| | coefficient digits for "Decimal". If no digits follow the |
| | decimal point, the decimal point is also removed unless |
| | the "#" option is used. |
+-----------+------------------------------------------------------------+
| "'E'" | Scientific notation. Same as "'e'" except it uses an upper |
| | case ‘E’ as the separator character. |
+-----------+------------------------------------------------------------+
| "'f'" | Fixed-point notation. For a given precision "p", formats |
| | the number as a decimal number with exactly "p" digits |
| | following the decimal point. With no precision given, uses |
| | a precision of "6" digits after the decimal point for |
| | "float", and uses a precision large enough to show all |
| | coefficient digits for "Decimal". If no digits follow the |
| | decimal point, the decimal point is also removed unless |
| | the "#" option is used. |
+-----------+------------------------------------------------------------+
| "'F'" | Fixed-point notation. Same as "'f'", but converts "nan" to |
| | "NAN" and "inf" to "INF". |
+-----------+------------------------------------------------------------+
| "'g'" | General format. For a given precision "p >= 1", this |
| | rounds the number to "p" significant digits and then |
| | formats the result in either fixed-point format or in |
| | scientific notation, depending on its magnitude. A |
| | precision of "0" is treated as equivalent to a precision |
| | of "1". The precise rules are as follows: suppose that |
| | the result formatted with presentation type "'e'" and |
| | precision "p-1" would have exponent "exp". Then, if "m <= |
| | exp < p", where "m" is -4 for floats and -6 for |
| | "Decimals", the number is formatted with presentation type |
| | "'f'" and precision "p-1-exp". Otherwise, the number is |
| | formatted with presentation type "'e'" and precision |
| | "p-1". In both cases insignificant trailing zeros are |
| | removed from the significand, and the decimal point is |
| | also removed if there are no remaining digits following |
| | it, unless the "'#'" option is used. With no precision |
| | given, uses a precision of "6" significant digits for |
| | "float". For "Decimal", the coefficient of the result is |
| | formed from the coefficient digits of the value; |
| | scientific notation is used for values smaller than "1e-6" |
| | in absolute value and values where the place value of the |
| | least significant digit is larger than 1, and fixed-point |
| | notation is used otherwise. Positive and negative |
| | infinity, positive and negative zero, and nans, are |
| | formatted as "inf", "-inf", "0", "-0" and "nan" |
| | respectively, regardless of the precision. |
+-----------+------------------------------------------------------------+
| "'G'" | General format. Same as "'g'" except switches to "'E'" if |
| | the number gets too large. The representations of infinity |
| | and NaN are uppercased, too. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'g'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| "'%'" | Percentage. Multiplies the number by 100 and displays in |
| | fixed ("'f'") format, followed by a percent sign. |
+-----------+------------------------------------------------------------+
| None | For "float" this is the same as "'g'", except that when |
| | fixed-point notation is used to format the result, it |
| | always includes at least one digit past the decimal point. |
| | The precision used is as large as needed to represent the |
| | given value faithfully. For "Decimal", this is the same |
| | as either "'g'" or "'G'" depending on the value of |
| | "context.capitals" for the current decimal context. The |
| | overall effect is to match the output of "str()" as |
| | altered by the other format modifiers. |
+-----------+------------------------------------------------------------+
Format examples
===============
This section contains examples of the "str.format()" syntax and
comparison with the old "%"-formatting.
In most of the cases the syntax is similar to the old "%"-formatting,
with the addition of the "{}" and with ":" used instead of "%". For
example, "'%03.2f'" can be translated to "'{:03.2f}'".
The new format syntax also supports new and different options, shown
in the following examples.
Accessing arguments by position:
>>> '{0}, {1}, {2}'.format('a', 'b', 'c')
'a, b, c'
>>> '{}, {}, {}'.format('a', 'b', 'c') # 3.1+ only
'a, b, c'
>>> '{2}, {1}, {0}'.format('a', 'b', 'c')
'c, b, a'
>>> '{2}, {1}, {0}'.format(*'abc') # unpacking argument sequence
'c, b, a'
>>> '{0}{1}{0}'.format('abra', 'cad') # arguments' indices can be repeated
'abracadabra'
Accessing arguments by name:
>>> 'Coordinates: {latitude}, {longitude}'.format(latitude='37.24N', longitude='-115.81W')
'Coordinates: 37.24N, -115.81W'
>>> coord = {'latitude': '37.24N', 'longitude': '-115.81W'}
>>> 'Coordinates: {latitude}, {longitude}'.format(**coord)
'Coordinates: 37.24N, -115.81W'
Accessing arguments’ attributes:
>>> c = 3-5j
>>> ('The complex number {0} is formed from the real part {0.real} '
... 'and the imaginary part {0.imag}.').format(c)
'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.'
>>> class Point:
... def __init__(self, x, y):
... self.x, self.y = x, y
... def __str__(self):
... return 'Point({self.x}, {self.y})'.format(self=self)
...
>>> str(Point(4, 2))
'Point(4, 2)'
Accessing arguments’ items:
>>> coord = (3, 5)
>>> 'X: {0[0]}; Y: {0[1]}'.format(coord)
'X: 3; Y: 5'
Replacing "%s" and "%r":
>>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2')
"repr() shows quotes: 'test1'; str() doesn't: test2"
Aligning the text and specifying a width:
>>> '{:<30}'.format('left aligned')
'left aligned '
>>> '{:>30}'.format('right aligned')
' right aligned'
>>> '{:^30}'.format('centered')
' centered '
>>> '{:*^30}'.format('centered') # use '*' as a fill char
'***********centered***********'
Replacing "%+f", "%-f", and "% f" and specifying a sign:
>>> '{:+f}; {:+f}'.format(3.14, -3.14) # show it always
'+3.140000; -3.140000'
>>> '{: f}; {: f}'.format(3.14, -3.14) # show a space for positive numbers
' 3.140000; -3.140000'
>>> '{:-f}; {:-f}'.format(3.14, -3.14) # show only the minus -- same as '{:f}; {:f}'
'3.140000; -3.140000'
Replacing "%x" and "%o" and converting the value to different bases:
>>> # format also supports binary numbers
>>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: {0:b}".format(42)
'int: 42; hex: 2a; oct: 52; bin: 101010'
>>> # with 0x, 0o, or 0b as prefix:
>>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: {0:#b}".format(42)
'int: 42; hex: 0x2a; oct: 0o52; bin: 0b101010'
Using the comma as a thousands separator:
>>> '{:,}'.format(1234567890)
'1,234,567,890'
Expressing a percentage:
>>> points = 19
>>> total = 22
>>> 'Correct answers: {:.2%}'.format(points/total)
'Correct answers: 86.36%'
Using type-specific formatting:
>>> import datetime
>>> d = datetime.datetime(2010, 7, 4, 12, 15, 58)
>>> '{:%Y-%m-%d %H:%M:%S}'.format(d)
'2010-07-04 12:15:58'
Nesting arguments and more complex examples:
>>> for align, text in zip('<^>', ['left', 'center', 'right']):
... '{0:{fill}{align}16}'.format(text, fill=align, align=align)
...
'left<<<<<<<<<<<<'
'^^^^^center^^^^^'
'>>>>>>>>>>>right'
>>>
>>> octets = [192, 168, 0, 1]
>>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets)
'C0A80001'
>>> int(_, 16)
3232235521
>>>
>>> width = 5
>>> for num in range(5,12):
... for base in 'dXob':
... print('{0:{width}{base}}'.format(num, base=base, width=width), end=' ')
... print()
...
5 5 5 101
6 6 6 110
7 7 7 111
8 8 10 1000
9 9 11 1001
10 A 12 1010
11 B 13 1011
u| Function definitions
********************
A function definition defines a user-defined function object (see
section The standard type hierarchy):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= defparameter ("," defparameter)* "," "/" ["," [parameter_list_no_posonly]]
| parameter_list_no_posonly
parameter_list_no_posonly ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
| parameter_list_starargs
parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
| "**" parameter [","]
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [2]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
except that the original function is not temporarily bound to the name
"func".
When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted. If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.
**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call. This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended. A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from positional arguments, from keyword
arguments, or from default values. If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type. Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed by
keyword arguments. Parameters before “"/"” are positional-only
parameters and may only be passed by positional arguments.
Changed in version 3.8: The "/" function parameter syntax may be used
to indicate positional-only parameters. See **PEP 570** for details.
Parameters may have an *annotation* of the form “": expression"”
following the parameter name. Any parameter may have an annotation,
even those of the form "*identifier" or "**identifier". Functions may
have “return” annotation of the form “"-> expression"” after the
parameter list. These annotations can be any valid Python expression.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object. If the "annotations" import from
"__future__" is used, annotations are preserved as strings at runtime
which enables postponed evaluation. Otherwise, they are evaluated
when the function definition is executed. In this case annotations
may be evaluated in a different order than they appear in the source
code.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.
**Programmer’s note:** Functions are first-class objects. A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around. Free variables used
in the nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
See also:
**PEP 3107** - Function Annotations
The original specification for function annotations.
**PEP 484** - Type Hints
Definition of a standard meaning for annotations: type hints.
**PEP 526** - Syntax for Variable Annotations
Ability to type hint variable declarations, including class
variables and instance variables
**PEP 563** - Postponed Evaluation of Annotations
Support for forward references within annotations by preserving
annotations in a string form at runtime instead of eager
evaluation.
u� The "global" statement
**********************
global_stmt ::= "global" identifier ("," identifier)*
The "global" statement is a declaration which holds for the entire
current code block. It means that the listed identifiers are to be
interpreted as globals. It would be impossible to assign to a global
variable without "global", although free variables may refer to
globals without being declared global.
Names listed in a "global" statement must not be used in the same code
block textually preceding that "global" statement.
Names listed in a "global" statement must not be defined as formal
parameters or in a "for" loop control target, "class" definition,
function definition, "import" statement, or variable annotation.
**CPython implementation detail:** The current implementation does not
enforce some of these restrictions, but programs should not abuse this
freedom, as future implementations may enforce them or silently change
the meaning of the program.
**Programmer’s note:** "global" is a directive to the parser. It
applies only to code parsed at the same time as the "global"
statement. In particular, a "global" statement contained in a string
or code object supplied to the built-in "exec()" function does not
affect the code block *containing* the function call, and code
contained in such a string is unaffected by "global" statements in the
code containing the function call. The same applies to the "eval()"
and "compile()" functions.
u� Reserved classes of identifiers
*******************************
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section The import statement.
Note:
The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names, informally known as “dunder” names. These
names are defined by the interpreter and its implementation
(including the standard library). Current system names are
discussed in the Special method names section and elsewhere. More
will likely be defined in future versions of Python. *Any* use of
"__*__" names, in any context, that does not follow explicitly
documented use, is subject to breakage without warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between “private” attributes of base and
derived classes. See section Identifiers (Names).
um Identifiers and keywords
************************
Identifiers (also referred to as *names*) are described by the
following lexical definitions.
The syntax of identifiers in Python is based on the Unicode standard
annex UAX-31, with elaboration and changes as defined below; see also
**PEP 3131** for further details.
Within the ASCII range (U+0001..U+007F), the valid characters for
identifiers are the same as in Python 2.x: the uppercase and lowercase
letters "A" through "Z", the underscore "_" and, except for the first
character, the digits "0" through "9".
Python 3.0 introduces additional characters from outside the ASCII
range (see **PEP 3131**). For these characters, the classification
uses the version of the Unicode Character Database as included in the
"unicodedata" module.
Identifiers are unlimited in length. Case is significant.
identifier ::= xid_start xid_continue*
id_start ::= <all characters in general categories Lu, Ll, Lt, Lm, Lo, Nl, the underscore, and characters with the Other_ID_Start property>
id_continue ::= <all characters in id_start, plus characters in the categories Mn, Mc, Nd, Pc and others with the Other_ID_Continue property>
xid_start ::= <all characters in id_start whose NFKC normalization is in "id_start xid_continue*">
xid_continue ::= <all characters in id_continue whose NFKC normalization is in "id_continue*">
The Unicode category codes mentioned above stand for:
* *Lu* - uppercase letters
* *Ll* - lowercase letters
* *Lt* - titlecase letters
* *Lm* - modifier letters
* *Lo* - other letters
* *Nl* - letter numbers
* *Mn* - nonspacing marks
* *Mc* - spacing combining marks
* *Nd* - decimal numbers
* *Pc* - connector punctuations
* *Other_ID_Start* - explicit list of characters in PropList.txt to
support backwards compatibility
* *Other_ID_Continue* - likewise
All identifiers are converted into the normal form NFKC while parsing;
comparison of identifiers is based on NFKC.
A non-normative HTML file listing all valid identifier characters for
Unicode 4.1 can be found at
https://www.unicode.org/Public/13.0.0/ucd/DerivedCoreProperties.txt
Keywords
========
The following identifiers are used as reserved words, or *keywords* of
the language, and cannot be used as ordinary identifiers. They must
be spelled exactly as written here:
False await else import pass
None break except in raise
True class finally is return
and continue for lambda try
as def from nonlocal while
assert del global not with
async elif if or yield
Reserved classes of identifiers
===============================
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section The import statement.
Note:
The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names, informally known as “dunder” names. These
names are defined by the interpreter and its implementation
(including the standard library). Current system names are
discussed in the Special method names section and elsewhere. More
will likely be defined in future versions of Python. *Any* use of
"__*__" names, in any context, that does not follow explicitly
documented use, is subject to breakage without warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between “private” attributes of base and
derived classes. See section Identifiers (Names).
a5 Imaginary literals
******************
Imaginary literals are described by the following lexical definitions:
imagnumber ::= (floatnumber | digitpart) ("j" | "J")
An imaginary literal yields a complex number with a real part of 0.0.
Complex numbers are represented as a pair of floating point numbers
and have the same restrictions on their range. To create a complex
number with a nonzero real part, add a floating point number to it,
e.g., "(3+4j)". Some examples of imaginary literals:
3.14j 10.j 10j .001j 1e100j 3.14e-10j 3.14_15_93j
u8" The "import" statement
**********************
import_stmt ::= "import" module ["as" identifier] ("," module ["as" identifier])*
| "from" relative_module "import" identifier ["as" identifier]
("," identifier ["as" identifier])*
| "from" relative_module "import" "(" identifier ["as" identifier]
("," identifier ["as" identifier])* [","] ")"
| "from" module "import" "*"
module ::= (identifier ".")* identifier
relative_module ::= "."* module | "."+
The basic import statement (no "from" clause) is executed in two
steps:
1. find a module, loading and initializing it if necessary
2. define a name or names in the local namespace for the scope where
the "import" statement occurs.
When the statement contains multiple clauses (separated by commas) the
two steps are carried out separately for each clause, just as though
the clauses had been separated out into individual import statements.
The details of the first step, finding and loading modules are
described in greater detail in the section on the import system, which
also describes the various types of packages and modules that can be
imported, as well as all the hooks that can be used to customize the
import system. Note that failures in this step may indicate either
that the module could not be located, *or* that an error occurred
while initializing the module, which includes execution of the
module’s code.
If the requested module is retrieved successfully, it will be made
available in the local namespace in one of three ways:
* If the module name is followed by "as", then the name following "as"
is bound directly to the imported module.
* If no other name is specified, and the module being imported is a
top level module, the module’s name is bound in the local namespace
as a reference to the imported module
* If the module being imported is *not* a top level module, then the
name of the top level package that contains the module is bound in
the local namespace as a reference to the top level package. The
imported module must be accessed using its full qualified name
rather than directly
The "from" form uses a slightly more complex process:
1. find the module specified in the "from" clause, loading and
initializing it if necessary;
2. for each of the identifiers specified in the "import" clauses:
1. check if the imported module has an attribute by that name
2. if not, attempt to import a submodule with that name and then
check the imported module again for that attribute
3. if the attribute is not found, "ImportError" is raised.
4. otherwise, a reference to that value is stored in the local
namespace, using the name in the "as" clause if it is present,
otherwise using the attribute name
Examples:
import foo # foo imported and bound locally
import foo.bar.baz # foo.bar.baz imported, foo bound locally
import foo.bar.baz as fbb # foo.bar.baz imported and bound as fbb
from foo.bar import baz # foo.bar.baz imported and bound as baz
from foo import attr # foo imported and foo.attr bound as attr
If the list of identifiers is replaced by a star ("'*'"), all public
names defined in the module are bound in the local namespace for the
scope where the "import" statement occurs.
The *public names* defined by a module are determined by checking the
module’s namespace for a variable named "__all__"; if defined, it must
be a sequence of strings which are names defined or imported by that
module. The names given in "__all__" are all considered public and
are required to exist. If "__all__" is not defined, the set of public
names includes all names found in the module’s namespace which do not
begin with an underscore character ("'_'"). "__all__" should contain
the entire public API. It is intended to avoid accidentally exporting
items that are not part of the API (such as library modules which were
imported and used within the module).
The wild card form of import — "from module import *" — is only
allowed at the module level. Attempting to use it in class or
function definitions will raise a "SyntaxError".
When specifying what module to import you do not have to specify the
absolute name of the module. When a module or package is contained
within another package it is possible to make a relative import within
the same top package without having to mention the package name. By
using leading dots in the specified module or package after "from" you
can specify how high to traverse up the current package hierarchy
without specifying exact names. One leading dot means the current
package where the module making the import exists. Two dots means up
one package level. Three dots is up two levels, etc. So if you execute
"from . import mod" from a module in the "pkg" package then you will
end up importing "pkg.mod". If you execute "from ..subpkg2 import mod"
from within "pkg.subpkg1" you will import "pkg.subpkg2.mod". The
specification for relative imports is contained in the Package
Relative Imports section.
"importlib.import_module()" is provided to support applications that
determine dynamically the modules to be loaded.
Raises an auditing event "import" with arguments "module", "filename",
"sys.path", "sys.meta_path", "sys.path_hooks".
Future statements
=================
A *future statement* is a directive to the compiler that a particular
module should be compiled using syntax or semantics that will be
available in a specified future release of Python where the feature
becomes standard.
The future statement is intended to ease migration to future versions
of Python that introduce incompatible changes to the language. It
allows use of the new features on a per-module basis before the
release in which the feature becomes standard.
future_stmt ::= "from" "__future__" "import" feature ["as" identifier]
("," feature ["as" identifier])*
| "from" "__future__" "import" "(" feature ["as" identifier]
("," feature ["as" identifier])* [","] ")"
feature ::= identifier
A future statement must appear near the top of the module. The only
lines that can appear before a future statement are:
* the module docstring (if any),
* comments,
* blank lines, and
* other future statements.
The only feature that requires using the future statement is
"annotations" (see **PEP 563**).
All historical features enabled by the future statement are still
recognized by Python 3. The list includes "absolute_import",
"division", "generators", "generator_stop", "unicode_literals",
"print_function", "nested_scopes" and "with_statement". They are all
redundant because they are always enabled, and only kept for backwards
compatibility.
A future statement is recognized and treated specially at compile
time: Changes to the semantics of core constructs are often
implemented by generating different code. It may even be the case
that a new feature introduces new incompatible syntax (such as a new
reserved word), in which case the compiler may need to parse the
module differently. Such decisions cannot be pushed off until
runtime.
For any given release, the compiler knows which feature names have
been defined, and raises a compile-time error if a future statement
contains a feature not known to it.
The direct runtime semantics are the same as for any import statement:
there is a standard module "__future__", described later, and it will
be imported in the usual way at the time the future statement is
executed.
The interesting runtime semantics depend on the specific feature
enabled by the future statement.
Note that there is nothing special about the statement:
import __future__ [as name]
That is not a future statement; it’s an ordinary import statement with
no special semantics or syntax restrictions.
Code compiled by calls to the built-in functions "exec()" and
"compile()" that occur in a module "M" containing a future statement
will, by default, use the new syntax or semantics associated with the
future statement. This can be controlled by optional arguments to
"compile()" — see the documentation of that function for details.
A future statement typed at an interactive interpreter prompt will
take effect for the rest of the interpreter session. If an
interpreter is started with the "-i" option, is passed a script name
to execute, and the script includes a future statement, it will be in
effect in the interactive session started after the script is
executed.
See also:
**PEP 236** - Back to the __future__
The original proposal for the __future__ mechanism.
a Membership test operations
**************************
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z", for which
the expression "x is z or x == z" is true, is produced while iterating
over "y". If an exception is raised during the iteration, it is as if
"in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x is y[i] or x == y[i]", and no
lower integer index raises the "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse truth value of
"in".
aV Integer literals
****************
Integer literals are described by the following lexical definitions:
integer ::= decinteger | bininteger | octinteger | hexinteger
decinteger ::= nonzerodigit (["_"] digit)* | "0"+ (["_"] "0")*
bininteger ::= "0" ("b" | "B") (["_"] bindigit)+
octinteger ::= "0" ("o" | "O") (["_"] octdigit)+
hexinteger ::= "0" ("x" | "X") (["_"] hexdigit)+
nonzerodigit ::= "1"..."9"
digit ::= "0"..."9"
bindigit ::= "0" | "1"
octdigit ::= "0"..."7"
hexdigit ::= digit | "a"..."f" | "A"..."F"
There is no limit for the length of integer literals apart from what
can be stored in available memory.
Underscores are ignored for determining the numeric value of the
literal. They can be used to group digits for enhanced readability.
One underscore can occur between digits, and after base specifiers
like "0x".
Note that leading zeros in a non-zero decimal number are not allowed.
This is for disambiguation with C-style octal literals, which Python
used before version 3.0.
Some examples of integer literals:
7 2147483647 0o177 0b100110111
3 79228162514264337593543950336 0o377 0xdeadbeef
100_000_000_000 0b_1110_0101
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
a^ Lambdas
*******
lambda_expr ::= "lambda" [parameter_list] ":" expression
lambda_expr_nocond ::= "lambda" [parameter_list] ":" expression_nocond
Lambda expressions (sometimes called lambda forms) are used to create
anonymous functions. The expression "lambda parameters: expression"
yields a function object. The unnamed object behaves like a function
object defined with:
def <lambda>(parameters):
return expression
See section Function definitions for the syntax of parameter lists.
Note that functions created with lambda expressions cannot contain
statements or annotations.
a/ List displays
*************
A list display is a possibly empty series of expressions enclosed in
square brackets:
list_display ::= "[" [starred_list | comprehension] "]"
A list display yields a new list object, the contents being specified
by either a list of expressions or a comprehension. When a comma-
separated list of expressions is supplied, its elements are evaluated
from left to right and placed into the list object in that order.
When a comprehension is supplied, the list is constructed from the
elements resulting from the comprehension.
u� Naming and binding
******************
Binding of names
================
*Names* refer to objects. Names are introduced by name binding
operations.
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore. This form
may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global". If a name is bound at the
module level, it is a global variable. (The variables of the module
code block are local and global.) If a variable is used in a code
block but not defined there, it is a *free variable*.
Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.
Resolution of names
===================
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block’s *environment*.
When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins". The global namespace is searched first. If
the name is not found there, the builtins namespace is searched. The
"global" statement must precede all uses of the name.
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope. This means that the following
will fail:
class A:
a = 42
b = list(a + i for i in range(10))
Builtins and restricted execution
=================================
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.
Interaction with dynamic features
=================================
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
a� The "nonlocal" statement
************************
nonlocal_stmt ::= "nonlocal" identifier ("," identifier)*
The "nonlocal" statement causes the listed identifiers to refer to
previously bound variables in the nearest enclosing scope excluding
globals. This is important because the default behavior for binding is
to search the local namespace first. The statement allows
encapsulated code to rebind variables outside of the local scope
besides the global (module) scope.
Names listed in a "nonlocal" statement, unlike those listed in a
"global" statement, must refer to pre-existing bindings in an
enclosing scope (the scope in which a new binding should be created
cannot be determined unambiguously).
Names listed in a "nonlocal" statement must not collide with pre-
existing bindings in the local scope.
See also:
**PEP 3104** - Access to Names in Outer Scopes
The specification for the "nonlocal" statement.
u� Numeric literals
****************
There are three types of numeric literals: integers, floating point
numbers, and imaginary numbers. There are no complex literals
(complex numbers can be formed by adding a real number and an
imaginary number).
Note that numeric literals do not include a sign; a phrase like "-1"
is actually an expression composed of the unary operator ‘"-"’ and the
literal "1".
u Emulating numeric types
***********************
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the expression "x + y", where *x* is an instance of a
class that has an "__add__()" method, "x.__add__(y)" is called.
The "__divmod__()" method should be the equivalent to using
"__floordiv__()" and "__mod__()"; it should not be related to
"__truediv__()". Note that "__pow__()" should be defined to accept
an optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other[, modulo])
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)
operands. These functions are only called if the left operand does
not support the corresponding operation [3] and the operands are of
different types. [4] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note:
If the right operand’s type is a subclass of the left operand’s
type and that subclass provides a different implementation of the
reflected method for the operation, this method will be called
before the left operand’s non-reflected method. This behavior
allows subclasses to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (modifying *self*) and return the result
(which could be, but does not have to be, *self*). If a specific
method is not defined, the augmented assignment falls back to the
normal methods. For instance, if *x* is an instance of a class
with an "__iadd__()" method, "x += y" is equivalent to "x =
x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are
considered, as with the evaluation of "x + y". In certain
situations, augmented assignment can result in unexpected errors
(see Why does a_tuple[i] += [‘item’] raise an exception when the
addition works?), but this behavior is in fact part of the data
model.
Note:
Due to a bug in the dispatching mechanism for "**=", a class that
defines "__ipow__()" but returns "NotImplemented" would fail to
fall back to "x.__pow__(y)" and "y.__rpow__(x)". This bug is
fixed in Python 3.10.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()" and
"float()". Should return a value of the appropriate type.
object.__index__(self)
Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.
If "__int__()", "__float__()" and "__complex__()" are not defined
then corresponding built-in functions "int()", "float()" and
"complex()" fall back to "__index__()".
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").
The built-in function "int()" falls back to "__trunc__()" if
neither "__int__()" nor "__index__()" is defined.
u Objects, values and types
*************************
*Objects* are Python’s abstraction for data. All data in a Python
program is represented by objects or by relations between objects. (In
a sense, and in conformance to Von Neumann’s model of a “stored
program computer”, code is also represented by objects.)
Every object has an identity, a type and a value. An object’s
*identity* never changes once it has been created; you may think of it
as the object’s address in memory. The ‘"is"’ operator compares the
identity of two objects; the "id()" function returns an integer
representing its identity.
**CPython implementation detail:** For CPython, "id(x)" is the memory
address where "x" is stored.
An object’s type determines the operations that the object supports
(e.g., “does it have a length?”) and also defines the possible values
for objects of that type. The "type()" function returns an object’s
type (which is an object itself). Like its identity, an object’s
*type* is also unchangeable. [1]
The *value* of some objects can change. Objects whose value can
change are said to be *mutable*; objects whose value is unchangeable
once they are created are called *immutable*. (The value of an
immutable container object that contains a reference to a mutable
object can change when the latter’s value is changed; however the
container is still considered immutable, because the collection of
objects it contains cannot be changed. So, immutability is not
strictly the same as having an unchangeable value, it is more subtle.)
An object’s mutability is determined by its type; for instance,
numbers, strings and tuples are immutable, while dictionaries and
lists are mutable.
Objects are never explicitly destroyed; however, when they become
unreachable they may be garbage-collected. An implementation is
allowed to postpone garbage collection or omit it altogether — it is a
matter of implementation quality how garbage collection is
implemented, as long as no objects are collected that are still
reachable.
**CPython implementation detail:** CPython currently uses a reference-
counting scheme with (optional) delayed detection of cyclically linked
garbage, which collects most objects as soon as they become
unreachable, but is not guaranteed to collect garbage containing
circular references. See the documentation of the "gc" module for
information on controlling the collection of cyclic garbage. Other
implementations act differently and CPython may change. Do not depend
on immediate finalization of objects when they become unreachable (so
you should always close files explicitly).
Note that the use of the implementation’s tracing or debugging
facilities may keep objects alive that would normally be collectable.
Also note that catching an exception with a ‘"try"…"except"’ statement
may keep objects alive.
Some objects contain references to “external” resources such as open
files or windows. It is understood that these resources are freed
when the object is garbage-collected, but since garbage collection is
not guaranteed to happen, such objects also provide an explicit way to
release the external resource, usually a "close()" method. Programs
are strongly recommended to explicitly close such objects. The
‘"try"…"finally"’ statement and the ‘"with"’ statement provide
convenient ways to do this.
Some objects contain references to other objects; these are called
*containers*. Examples of containers are tuples, lists and
dictionaries. The references are part of a container’s value. In
most cases, when we talk about the value of a container, we imply the
values, not the identities of the contained objects; however, when we
talk about the mutability of a container, only the identities of the
immediately contained objects are implied. So, if an immutable
container (like a tuple) contains a reference to a mutable object, its
value changes if that mutable object is changed.
Types affect almost all aspects of object behavior. Even the
importance of object identity is affected in some sense: for immutable
types, operations that compute new values may actually return a
reference to any existing object with the same type and value, while
for mutable objects this is not allowed. E.g., after "a = 1; b = 1",
"a" and "b" may or may not refer to the same object with the value
one, depending on the implementation, but after "c = []; d = []", "c"
and "d" are guaranteed to refer to two different, unique, newly
created empty lists. (Note that "c = d = []" assigns the same object
to both "c" and "d".)
u� Operator precedence
*******************
The following table summarizes the operator precedence in Python, from
lowest precedence (least binding) to highest precedence (most
binding). Operators in the same box have the same precedence. Unless
the syntax is explicitly given, operators are binary. Operators in
the same box group left to right (except for exponentiation, which
groups from right to left).
Note that comparisons, membership tests, and identity tests, all have
the same precedence and have a left-to-right chaining feature as
described in the Comparisons section.
+-------------------------------------------------+---------------------------------------+
| Operator | Description |
|=================================================|=======================================|
| ":=" | Assignment expression |
+-------------------------------------------------+---------------------------------------+
| "lambda" | Lambda expression |
+-------------------------------------------------+---------------------------------------+
| "if" – "else" | Conditional expression |
+-------------------------------------------------+---------------------------------------+
| "or" | Boolean OR |
+-------------------------------------------------+---------------------------------------+
| "and" | Boolean AND |
+-------------------------------------------------+---------------------------------------+
| "not" "x" | Boolean NOT |
+-------------------------------------------------+---------------------------------------+
| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership |
| ">=", "!=", "==" | tests and identity tests |
+-------------------------------------------------+---------------------------------------+
| "|" | Bitwise OR |
+-------------------------------------------------+---------------------------------------+
| "^" | Bitwise XOR |
+-------------------------------------------------+---------------------------------------+
| "&" | Bitwise AND |
+-------------------------------------------------+---------------------------------------+
| "<<", ">>" | Shifts |
+-------------------------------------------------+---------------------------------------+
| "+", "-" | Addition and subtraction |
+-------------------------------------------------+---------------------------------------+
| "*", "@", "/", "//", "%" | Multiplication, matrix |
| | multiplication, division, floor |
| | division, remainder [5] |
+-------------------------------------------------+---------------------------------------+
| "+x", "-x", "~x" | Positive, negative, bitwise NOT |
+-------------------------------------------------+---------------------------------------+
| "**" | Exponentiation [6] |
+-------------------------------------------------+---------------------------------------+
| "await" "x" | Await expression |
+-------------------------------------------------+---------------------------------------+
| "x[index]", "x[index:index]", | Subscription, slicing, call, |
| "x(arguments...)", "x.attribute" | attribute reference |
+-------------------------------------------------+---------------------------------------+
| "(expressions...)", "[expressions...]", "{key: | Binding or parenthesized expression, |
| value...}", "{expressions...}" | list display, dictionary display, set |
| | display |
+-------------------------------------------------+---------------------------------------+
-[ Footnotes ]-
[1] While "abs(x%y) < abs(y)" is true mathematically, for floats it
may not be true numerically due to roundoff. For example, and
assuming a platform on which a Python float is an IEEE 754 double-
precision number, in order that "-1e-100 % 1e100" have the same
sign as "1e100", the computed result is "-1e-100 + 1e100", which
is numerically exactly equal to "1e100". The function
"math.fmod()" returns a result whose sign matches the sign of the
first argument instead, and so returns "-1e-100" in this case.
Which approach is more appropriate depends on the application.
[2] If x is very close to an exact integer multiple of y, it’s
possible for "x//y" to be one larger than "(x-x%y)//y" due to
rounding. In such cases, Python returns the latter result, in
order to preserve that "divmod(x,y)[0] * y + x % y" be very close
to "x".
[3] The Unicode standard distinguishes between *code points* (e.g.
U+0041) and *abstract characters* (e.g. “LATIN CAPITAL LETTER A”).
While most abstract characters in Unicode are only represented
using one code point, there is a number of abstract characters
that can in addition be represented using a sequence of more than
one code point. For example, the abstract character “LATIN
CAPITAL LETTER C WITH CEDILLA” can be represented as a single
*precomposed character* at code position U+00C7, or as a sequence
of a *base character* at code position U+0043 (LATIN CAPITAL
LETTER C), followed by a *combining character* at code position
U+0327 (COMBINING CEDILLA).
The comparison operators on strings compare at the level of
Unicode code points. This may be counter-intuitive to humans. For
example, ""\u00C7" == "\u0043\u0327"" is "False", even though both
strings represent the same abstract character “LATIN CAPITAL
LETTER C WITH CEDILLA”.
To compare strings at the level of abstract characters (that is,
in a way intuitive to humans), use "unicodedata.normalize()".
[4] Due to automatic garbage-collection, free lists, and the dynamic
nature of descriptors, you may notice seemingly unusual behaviour
in certain uses of the "is" operator, like those involving
comparisons between instance methods, or constants. Check their
documentation for more info.
[5] The "%" operator is also used for string formatting; the same
precedence applies.
[6] The power operator "**" binds less tightly than an arithmetic or
bitwise unary operator on its right, that is, "2**-1" is "0.5".
uw The "pass" statement
********************
pass_stmt ::= "pass"
"pass" is a null operation — when it is executed, nothing happens. It
is useful as a placeholder when a statement is required syntactically,
but no code needs to be executed, for example:
def f(arg): pass # a function that does nothing (yet)
class C: pass # a class with no methods (yet)
a� The power operator
******************
The power operator binds more tightly than unary operators on its
left; it binds less tightly than unary operators on its right. The
syntax is:
power ::= (await_expr | primary) ["**" u_expr]
Thus, in an unparenthesized sequence of power and unary operators, the
operators are evaluated from right to left (this does not constrain
the evaluation order for the operands): "-1**2" results in "-1".
The power operator has the same semantics as the built-in "pow()"
function, when called with two arguments: it yields its left argument
raised to the power of its right argument. The numeric arguments are
first converted to a common type, and the result is of that type.
For int operands, the result has the same type as the operands unless
the second argument is negative; in that case, all arguments are
converted to float and a float result is delivered. For example,
"10**2" returns "100", but "10**-2" returns "0.01".
Raising "0.0" to a negative power results in a "ZeroDivisionError".
Raising a negative number to a fractional power results in a "complex"
number. (In earlier versions it raised a "ValueError".)
uJ
The "raise" statement
*********************
raise_stmt ::= "raise" [expression ["from" expression]]
If no expressions are present, "raise" re-raises the last exception
that was active in the current scope. If no exception is active in
the current scope, a "RuntimeError" exception is raised indicating
that this is an error.
Otherwise, "raise" evaluates the first expression as the exception
object. It must be either a subclass or an instance of
"BaseException". If it is a class, the exception instance will be
obtained when needed by instantiating the class with no arguments.
The *type* of the exception is the exception instance’s class, the
*value* is the instance itself.
A traceback object is normally created automatically when an exception
is raised and attached to it as the "__traceback__" attribute, which
is writable. You can create an exception and set your own traceback in
one step using the "with_traceback()" exception method (which returns
the same exception instance, with its traceback set to its argument),
like so:
raise Exception("foo occurred").with_traceback(tracebackobj)
The "from" clause is used for exception chaining: if given, the second
*expression* must be another exception class or instance. If the
second expression is an exception instance, it will be attached to the
raised exception as the "__cause__" attribute (which is writable). If
the expression is an exception class, the class will be instantiated
and the resulting exception instance will be attached to the raised
exception as the "__cause__" attribute. If the raised exception is not
handled, both exceptions will be printed:
>>> try:
... print(1 / 0)
... except Exception as exc:
... raise RuntimeError("Something bad happened") from exc
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
A similar mechanism works implicitly if an exception is raised inside
an exception handler or a "finally" clause: the previous exception is
then attached as the new exception’s "__context__" attribute:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened")
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
Exception chaining can be explicitly suppressed by specifying "None"
in the "from" clause:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened") from None
...
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
Additional information on exceptions can be found in section
Exceptions, and information about handling exceptions is in section
The try statement.
Changed in version 3.3: "None" is now permitted as "Y" in "raise X
from Y".
New in version 3.3: The "__suppress_context__" attribute to suppress
automatic display of the exception context.
a The "return" statement
**********************
return_stmt ::= "return" [expression_list]
"return" may only occur syntactically nested in a function definition,
not within a nested class definition.
If an expression list is present, it is evaluated, else "None" is
substituted.
"return" leaves the current function call with the expression list (or
"None") as return value.
When "return" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
function.
In a generator function, the "return" statement indicates that the
generator is done and will cause "StopIteration" to be raised. The
returned value (if any) is used as an argument to construct
"StopIteration" and becomes the "StopIteration.value" attribute.
In an asynchronous generator function, an empty "return" statement
indicates that the asynchronous generator is done and will cause
"StopAsyncIteration" to be raised. A non-empty "return" statement is
a syntax error in an asynchronous generator function.
u� Emulating container types
*************************
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python’s standard dictionary objects.
The "collections.abc" module provides a "MutableMapping" abstract base
class to help create those methods from a base set of "__getitem__()",
"__setitem__()", "__delitem__()", and "keys()". Mutable sequences
should provide methods "append()", "count()", "index()", "extend()",
"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python
standard list objects. Finally, sequence types should implement
addition (meaning concatenation) and multiplication (meaning
repetition) by defining the methods "__add__()", "__radd__()",
"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described
below; they should not define other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should search the mapping’s keys; for sequences, it
should search through the values. It is further recommended that both
mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should iterate through the object’s keys; for sequences, it should
iterate through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn’t define a "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.
object.__length_hint__(self)
Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. The
return value may also be "NotImplemented", which is treated the
same as if the "__length_hint__" method didn’t exist at all. This
method is purely an optimization and is never required for
correctness.
New in version 3.4.
Note:
Slicing is done exclusively with the following three methods. A
call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note:
"for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container.
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a container. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be iterable.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don’t define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
a� Shifting operations
*******************
The shifting operations have lower priority than the arithmetic
operations:
shift_expr ::= a_expr | shift_expr ("<<" | ">>") a_expr
These operators accept integers as arguments. They shift the first
argument to the left or right by the number of bits given by the
second argument.
A right shift by *n* bits is defined as floor division by "pow(2,n)".
A left shift by *n* bits is defined as multiplication with "pow(2,n)".
a� Slicings
********
A slicing selects a range of items in a sequence object (e.g., a
string, tuple or list). Slicings may be used as expressions or as
targets in assignment or "del" statements. The syntax for a slicing:
slicing ::= primary "[" slice_list "]"
slice_list ::= slice_item ("," slice_item)* [","]
slice_item ::= expression | proper_slice
proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" [stride] ]
lower_bound ::= expression
upper_bound ::= expression
stride ::= expression
There is ambiguity in the formal syntax here: anything that looks like
an expression list also looks like a slice list, so any subscription
can be interpreted as a slicing. Rather than further complicating the
syntax, this is disambiguated by defining that in this case the
interpretation as a subscription takes priority over the
interpretation as a slicing (this is the case if the slice list
contains no proper slice).
The semantics for a slicing are as follows. The primary is indexed
(using the same "__getitem__()" method as normal subscription) with a
key that is constructed from the slice list, as follows. If the slice
list contains at least one comma, the key is a tuple containing the
conversion of the slice items; otherwise, the conversion of the lone
slice item is the key. The conversion of a slice item that is an
expression is that expression. The conversion of a proper slice is a
slice object (see section The standard type hierarchy) whose "start",
"stop" and "step" attributes are the values of the expressions given
as lower bound, upper bound and stride, respectively, substituting
"None" for missing expressions.
u Special Attributes
******************
The implementation adds a few special read-only attributes to several
object types, where they are relevant. Some of these are not reported
by the "dir()" built-in function.
object.__dict__
A dictionary or other mapping object used to store an object’s
(writable) attributes.
instance.__class__
The class to which a class instance belongs.
class.__bases__
The tuple of base classes of a class object.
definition.__name__
The name of the class, function, method, descriptor, or generator
instance.
definition.__qualname__
The *qualified name* of the class, function, method, descriptor, or
generator instance.
New in version 3.3.
class.__mro__
This attribute is a tuple of classes that are considered when
looking for base classes during method resolution.
class.mro()
This method can be overridden by a metaclass to customize the
method resolution order for its instances. It is called at class
instantiation, and its result is stored in "__mro__".
class.__subclasses__()
Each class keeps a list of weak references to its immediate
subclasses. This method returns a list of all those references
still alive. Example:
>>> int.__subclasses__()
[<class 'bool'>]
u�� Special method names
********************
A class can implement certain operations that are invoked by special
syntax (such as arithmetic operations or subscripting and slicing) by
defining methods with special names. This is Python’s approach to
*operator overloading*, allowing classes to define their own behavior
with respect to language operators. For instance, if a class defines
a method named "__getitem__()", and "x" is an instance of this class,
then "x[i]" is roughly equivalent to "type(x).__getitem__(x, i)".
Except where mentioned, attempts to execute an operation raise an
exception when no appropriate method is defined (typically
"AttributeError" or "TypeError").
Setting a special method to "None" indicates that the corresponding
operation is not available. For example, if a class sets "__iter__()"
to "None", the class is not iterable, so calling "iter()" on its
instances will raise a "TypeError" (without falling back to
"__getitem__()"). [2]
When implementing a class that emulates any built-in type, it is
important that the emulation only be implemented to the degree that it
makes sense for the object being modelled. For example, some
sequences may work well with retrieval of individual elements, but
extracting a slice may not make sense. (One example of this is the
"NodeList" interface in the W3C’s Document Object Model.)
Basic customization
===================
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass’s "__new__()" method using
"super().__new__(cls[, ...])" with appropriate arguments and then
modifying the newly-created instance as necessary before returning
it.
If "__new__()" is invoked during object construction and it returns
an instance of *cls*, then the new instance’s "__init__()" method
will be invoked like "__init__(self[, ...])", where *self* is the
new instance and the remaining arguments are the same as were
passed to the object constructor.
If "__new__()" does not return an instance of *cls*, then the new
instance’s "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class’s "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"super().__init__([args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.
It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.
It is not guaranteed that "__del__()" methods are called for
objects that still exist when the interpreter exits.
Note:
"del x" doesn’t directly call "x.__del__()" — the former
decrements the reference count for "x" by one, and the latter is
only called when "x"’s reference count reaches zero.
**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.
See also: Documentation for the "gc" module.
Warning:
Due to the precarious circumstances under which "__del__()"
methods are invoked, exceptions that occur during their execution
are ignored, and a warning is printed to "sys.stderr" instead.
In particular:
* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".
* "__del__()" can be executed during interpreter shutdown. As a
consequence, the global variables it needs to access (including
other modules) may already have been deleted or set to "None".
Python guarantees that globals whose name begins with a single
underscore are deleted from their module before other globals
are deleted; if no other references to such globals exist, this
may help in assuring that imported modules are still available
at the time when the "__del__()" method is called.
object.__repr__(self)
Called by the "repr()" built-in function to compute the “official”
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an “informal”
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the “informal” or nicely printable string
representation of an object. The return value must be a string
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by bytes to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function, and by extension,
evaluation of formatted string literals and the "str.format()"
method, to produce a “formatted” string representation of an
object. The *format_spec* argument is a string that contains a
description of the formatting options desired. The interpretation
of the *format_spec* argument is up to the type implementing
"__format__()", however most classes will either delegate
formatting to one of the built-in types, or use a similar
formatting option syntax.
See Format Specification Mini-Language for a description of the
standard formatting syntax.
The return value must be a string object.
Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.
Changed in version 3.7: "object.__format__(x, '')" is now
equivalent to "str(x)" rather than "format(str(self), '')".
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
By default, "object" implements "__eq__()" by using "is", returning
"NotImplemented" in the case of a false comparison: "True if x is y
else NotImplemented". For "__ne__()", by default it delegates to
"__eq__()" and inverts the result unless it is "NotImplemented".
There are no other implied relationships among the comparison
operators or default implementations; for example, the truth of
"(x<y or x==y)" does not imply "x<=y". To automatically generate
ordering operations from a single root operation, see
"functools.total_ordering()".
See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
Note:
"hash()" truncates the value returned from an object’s custom
"__hash__()" method to the size of a "Py_ssize_t". This is
typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds.
If an object’s "__hash__()" must interoperate on builds of
different bit sizes, be sure to check the width on all supported
builds. An easy way to do this is with "python -c "import sys;
print(sys.hash_info.width)"".
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key’s hash value is immutable (if the object’s hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj,
collections.abc.Hashable)".
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
<ParentClass>.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.abc.Hashable)" call.
Note:
By default, the "__hash__()" values of str and bytes objects are
“salted” with an unpredictable random value. Although they
remain constant within an individual Python process, they are not
predictable between repeated invocations of Python.This is
intended to provide protection against a denial-of-service caused
by carefully-chosen inputs that exploit the worst case
performance of a dict insertion, O(n^2) complexity. See
http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of sets.
Python has never made guarantees about this ordering (and it
typically varies between 32-bit and 64-bit builds).See also
"PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
Customizing attribute access
============================
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
return the (computed) attribute value or raise an "AttributeError"
exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note:
This method may still be bypassed when looking up special methods
as the result of implicit invocation via language syntax or
built-in functions. See Special method lookup.
For certain sensitive attribute accesses, raises an auditing event
"object.__getattr__" with arguments "obj" and "name".
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should call the base class method with the same name, for example,
"object.__setattr__(self, name, value)".
For certain sensitive attribute assignments, raises an auditing
event "object.__setattr__" with arguments "obj", "name", "value".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
For certain sensitive attribute deletions, raises an auditing event
"object.__delattr__" with arguments "obj" and "name".
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
Customizing module attribute access
-----------------------------------
Special names "__getattr__" and "__dir__" can be also used to
customize access to module attributes. The "__getattr__" function at
the module level should accept one argument which is the name of an
attribute and return the computed value or raise an "AttributeError".
If an attribute is not found on a module object through the normal
lookup, i.e. "object.__getattribute__()", then "__getattr__" is
searched in the module "__dict__" before raising an "AttributeError".
If found, it is called with the attribute name and the result is
returned.
The "__dir__" function should accept no arguments, and return a
sequence of strings that represents the names accessible on module. If
present, this function overrides the standard "dir()" search on a
module.
For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
super().__setattr__(attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note:
Defining module "__getattr__" and setting module "__class__" only
affect lookups made using the attribute access syntax – directly
accessing the module globals (whether by code within the module, or
via a reference to the module’s globals dictionary) is unaffected.
Changed in version 3.5: "__class__" module attribute is now writable.
New in version 3.7: "__getattr__" and "__dir__" module attributes.
See also:
**PEP 562** - Module __getattr__ and __dir__
Describes the "__getattr__" and "__dir__" functions on modules.
Implementing Descriptors
------------------------
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents). In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".
object.__get__(self, instance, owner=None)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). The optional *owner* argument is the owner class, while
*instance* is the instance that the attribute was accessed through,
or "None" when the attribute is accessed through the *owner*.
This method should return the computed attribute value or raise an
"AttributeError" exception.
**PEP 252** specifies that "__get__()" is callable with one or two
arguments. Python’s own built-in descriptors support this
specification; however, it is likely that some third-party tools
have descriptors that require both arguments. Python’s own
"__getattribute__()" implementation always passes in both arguments
whether they are required or not.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
Note, adding "__set__()" or "__delete__()" changes the kind of
descriptor to a “data descriptor”. See Invoking Descriptors for
more details.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
object.__set_name__(self, owner, name)
Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.
Note:
"__set_name__()" is only called implicitly as part of the "type"
constructor, so it will need to be called explicitly with the
appropriate parameters when a descriptor is added to a class
after initial creation:
class A:
pass
descr = custom_descriptor()
A.attr = descr
descr.__set_name__(A, 'attr')
See Creating the class object for more details.
New in version 3.6.
The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).
Invoking Descriptors
--------------------
In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a class, "A.x" is transformed into the call:
"A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on which descriptor methods are defined. A descriptor can define any
combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
---------
*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of *__dict__* and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)
The space saved over using *__dict__* can be significant. Attribute
lookup speed can be significantly improved as well.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. *__slots__*
reserves space for the declared variables and prevents the
automatic creation of *__dict__* and *__weakref__* for each
instance.
Notes on using *__slots__*
~~~~~~~~~~~~~~~~~~~~~~~~~~
* When inheriting from a class without *__slots__*, the *__dict__* and
*__weakref__* attribute of the instances will always be accessible.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
* Without a *__weakref__* variable for each instance, classes defining
*__slots__* do not support weak references to its instances. If weak
reference support is needed, then add "'__weakref__'" to the
sequence of strings in the *__slots__* declaration.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is not limited to the class
where it is defined. *__slots__* declared in parents are available
in child classes. However, child subclasses will get a *__dict__*
and *__weakref__* unless they also define *__slots__* (which should
only contain names of any *additional* slots).
* If a class defines a slot also defined in a base class, the instance
variable defined by the base class slot is inaccessible (except by
retrieving its descriptor directly from the base class). This
renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
“variable-length” built-in types such as "int", "bytes" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings may
also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".
* If an iterator is used for *__slots__* then a descriptor is created
for each of the iterator’s values. However, the *__slots__*
attribute will be an empty iterator.
Customizing class creation
==========================
Whenever a class inherits from another class, *__init_subclass__* is
called on that class. This way, it is possible to write classes which
change the behavior of subclasses. This is closely related to class
decorators, but where class decorators only affect the specific class
they’re applied to, "__init_subclass__" solely applies to future
subclasses of the class defining the method.
classmethod object.__init_subclass__(cls)
This method is called whenever the containing class is subclassed.
*cls* is then the new subclass. If defined as a normal instance
method, this method is implicitly converted to a class method.
Keyword arguments which are given to a new class are passed to the
parent’s class "__init_subclass__". For compatibility with other
classes using "__init_subclass__", one should take out the needed
keyword arguments and pass the others over to the base class, as
in:
class Philosopher:
def __init_subclass__(cls, /, default_name, **kwargs):
super().__init_subclass__(**kwargs)
cls.default_name = default_name
class AustralianPhilosopher(Philosopher, default_name="Bruce"):
pass
The default implementation "object.__init_subclass__" does nothing,
but raises an error if it is called with any arguments.
Note:
The metaclass hint "metaclass" is consumed by the rest of the
type machinery, and is never passed to "__init_subclass__"
implementations. The actual metaclass (rather than the explicit
hint) can be accessed as "type(cls)".
New in version 3.6.
Metaclasses
-----------
By default, classes are constructed using "type()". The class body is
executed in a new namespace and the class name is bound locally to the
result of "type(name, bases, namespace)".
The class creation process can be customized by passing the
"metaclass" keyword argument in the class definition line, or by
inheriting from an existing class that included such an argument. In
the following example, both "MyClass" and "MySubclass" are instances
of "Meta":
class Meta(type):
pass
class MyClass(metaclass=Meta):
pass
class MySubclass(MyClass):
pass
Any other keyword arguments that are specified in the class definition
are passed through to all metaclass operations described below.
When a class definition is executed, the following steps occur:
* MRO entries are resolved;
* the appropriate metaclass is determined;
* the class namespace is prepared;
* the class body is executed;
* the class object is created.
Resolving MRO entries
---------------------
If a base that appears in class definition is not an instance of
"type", then an "__mro_entries__" method is searched on it. If found,
it is called with the original bases tuple. This method must return a
tuple of classes that will be used instead of this base. The tuple may
be empty, in such case the original base is ignored.
See also:
**PEP 560** - Core support for typing module and generic types
Determining the appropriate metaclass
-------------------------------------
The appropriate metaclass for a class definition is determined as
follows:
* if no bases and no explicit metaclass are given, then "type()" is
used;
* if an explicit metaclass is given and it is *not* an instance of
"type()", then it is used directly as the metaclass;
* if an instance of "type()" is given as the explicit metaclass, or
bases are defined, then the most derived metaclass is used.
The most derived metaclass is selected from the explicitly specified
metaclass (if any) and the metaclasses (i.e. "type(cls)") of all
specified base classes. The most derived metaclass is one which is a
subtype of *all* of these candidate metaclasses. If none of the
candidate metaclasses meets that criterion, then the class definition
will fail with "TypeError".
Preparing the class namespace
-----------------------------
Once the appropriate metaclass has been identified, then the class
namespace is prepared. If the metaclass has a "__prepare__" attribute,
it is called as "namespace = metaclass.__prepare__(name, bases,
**kwds)" (where the additional keyword arguments, if any, come from
the class definition). The "__prepare__" method should be implemented
as a "classmethod()". The namespace returned by "__prepare__" is
passed in to "__new__", but when the final class object is created the
namespace is copied into a new "dict".
If the metaclass has no "__prepare__" attribute, then the class
namespace is initialised as an empty ordered mapping.
See also:
**PEP 3115** - Metaclasses in Python 3000
Introduced the "__prepare__" namespace hook
Executing the class body
------------------------
The class body is executed (approximately) as "exec(body, globals(),
namespace)". The key difference from a normal call to "exec()" is that
lexical scoping allows the class body (including any methods) to
reference names from the current and outer scopes when the class
definition occurs inside a function.
However, even when the class definition occurs inside the function,
methods defined inside the class still cannot see names defined at the
class scope. Class variables must be accessed through the first
parameter of instance or class methods, or through the implicit
lexically scoped "__class__" reference described in the next section.
Creating the class object
-------------------------
Once the class namespace has been populated by executing the class
body, the class object is created by calling "metaclass(name, bases,
namespace, **kwds)" (the additional keywords passed here are the same
as those passed to "__prepare__").
This class object is the one that will be referenced by the zero-
argument form of "super()". "__class__" is an implicit closure
reference created by the compiler if any methods in a class body refer
to either "__class__" or "super". This allows the zero argument form
of "super()" to correctly identify the class being defined based on
lexical scoping, while the class or instance that was used to make the
current call is identified based on the first argument passed to the
method.
**CPython implementation detail:** In CPython 3.6 and later, the
"__class__" cell is passed to the metaclass as a "__classcell__" entry
in the class namespace. If present, this must be propagated up to the
"type.__new__" call in order for the class to be initialised
correctly. Failing to do so will result in a "RuntimeError" in Python
3.8.
When using the default metaclass "type", or any metaclass that
ultimately calls "type.__new__", the following additional
customisation steps are invoked after creating the class object:
* first, "type.__new__" collects all of the descriptors in the class
namespace that define a "__set_name__()" method;
* second, all of these "__set_name__" methods are called with the
class being defined and the assigned name of that particular
descriptor;
* finally, the "__init_subclass__()" hook is called on the immediate
parent of the new class in its method resolution order.
After the class object is created, it is passed to the class
decorators included in the class definition (if any) and the resulting
object is bound in the local namespace as the defined class.
When a new class is created by "type.__new__", the object provided as
the namespace parameter is copied to a new ordered mapping and the
original object is discarded. The new copy is wrapped in a read-only
proxy, which becomes the "__dict__" attribute of the class object.
See also:
**PEP 3135** - New super
Describes the implicit "__class__" closure reference
Uses for metaclasses
--------------------
The potential uses for metaclasses are boundless. Some ideas that have
been explored include enum, logging, interface checking, automatic
delegation, automatic property creation, proxies, frameworks, and
automatic resource locking/synchronization.
Customizing instance and subclass checks
========================================
The following methods are used to override the default behavior of the
"isinstance()" and "issubclass()" built-in functions.
In particular, the metaclass "abc.ABCMeta" implements these methods in
order to allow the addition of Abstract Base Classes (ABCs) as
“virtual base classes” to any class or type (including built-in
types), including other ABCs.
class.__instancecheck__(self, instance)
Return true if *instance* should be considered a (direct or
indirect) instance of *class*. If defined, called to implement
"isinstance(instance, class)".
class.__subclasscheck__(self, subclass)
Return true if *subclass* should be considered a (direct or
indirect) subclass of *class*. If defined, called to implement
"issubclass(subclass, class)".
Note that these methods are looked up on the type (metaclass) of a
class. They cannot be defined as class methods in the actual class.
This is consistent with the lookup of special methods that are called
on instances, only in this case the instance is itself a class.
See also:
**PEP 3119** - Introducing Abstract Base Classes
Includes the specification for customizing "isinstance()" and
"issubclass()" behavior through "__instancecheck__()" and
"__subclasscheck__()", with motivation for this functionality in
the context of adding Abstract Base Classes (see the "abc"
module) to the language.
Emulating generic types
=======================
One can implement the generic class syntax as specified by **PEP 484**
(for example "List[int]") by defining a special method:
classmethod object.__class_getitem__(cls, key)
Return an object representing the specialization of a generic class
by type arguments found in *key*.
This method is looked up on the class object itself, and when defined
in the class body, this method is implicitly a class method. Note,
this mechanism is primarily reserved for use with static type hints,
other usage is discouraged.
See also:
**PEP 560** - Core support for typing module and generic types
Emulating callable objects
==========================
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method
is defined, "x(arg1, arg2, ...)" roughly translates to
"type(x).__call__(x, arg1, ...)".
Emulating container types
=========================
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python’s standard dictionary objects.
The "collections.abc" module provides a "MutableMapping" abstract base
class to help create those methods from a base set of "__getitem__()",
"__setitem__()", "__delitem__()", and "keys()". Mutable sequences
should provide methods "append()", "count()", "index()", "extend()",
"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python
standard list objects. Finally, sequence types should implement
addition (meaning concatenation) and multiplication (meaning
repetition) by defining the methods "__add__()", "__radd__()",
"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described
below; they should not define other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should search the mapping’s keys; for sequences, it
should search through the values. It is further recommended that both
mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should iterate through the object’s keys; for sequences, it should
iterate through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn’t define a "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.
object.__length_hint__(self)
Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. The
return value may also be "NotImplemented", which is treated the
same as if the "__length_hint__" method didn’t exist at all. This
method is purely an optimization and is never required for
correctness.
New in version 3.4.
Note:
Slicing is done exclusively with the following three methods. A
call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note:
"for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container.
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a container. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be iterable.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don’t define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
Emulating numeric types
=======================
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the expression "x + y", where *x* is an instance of a
class that has an "__add__()" method, "x.__add__(y)" is called.
The "__divmod__()" method should be the equivalent to using
"__floordiv__()" and "__mod__()"; it should not be related to
"__truediv__()". Note that "__pow__()" should be defined to accept
an optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other[, modulo])
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)
operands. These functions are only called if the left operand does
not support the corresponding operation [3] and the operands are of
different types. [4] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note:
If the right operand’s type is a subclass of the left operand’s
type and that subclass provides a different implementation of the
reflected method for the operation, this method will be called
before the left operand’s non-reflected method. This behavior
allows subclasses to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (modifying *self*) and return the result
(which could be, but does not have to be, *self*). If a specific
method is not defined, the augmented assignment falls back to the
normal methods. For instance, if *x* is an instance of a class
with an "__iadd__()" method, "x += y" is equivalent to "x =
x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are
considered, as with the evaluation of "x + y". In certain
situations, augmented assignment can result in unexpected errors
(see Why does a_tuple[i] += [‘item’] raise an exception when the
addition works?), but this behavior is in fact part of the data
model.
Note:
Due to a bug in the dispatching mechanism for "**=", a class that
defines "__ipow__()" but returns "NotImplemented" would fail to
fall back to "x.__pow__(y)" and "y.__rpow__(x)". This bug is
fixed in Python 3.10.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()" and
"float()". Should return a value of the appropriate type.
object.__index__(self)
Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.
If "__int__()", "__float__()" and "__complex__()" are not defined
then corresponding built-in functions "int()", "float()" and
"complex()" fall back to "__index__()".
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").
The built-in function "int()" falls back to "__trunc__()" if
neither "__int__()" nor "__index__()" is defined.
With Statement Context Managers
===============================
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method’s return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller’s responsibility.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
Special method lookup
=====================
For custom classes, implicit invocations of special methods are only
guaranteed to work correctly if defined on an object’s type, not in
the object’s instance dictionary. That behaviour is the reason why
the following code raises an exception:
>>> class C:
... pass
...
>>> c = C()
>>> c.__len__ = lambda: 5
>>> len(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: object of type 'C' has no len()
The rationale behind this behaviour lies with a number of special
methods such as "__hash__()" and "__repr__()" that are implemented by
all objects, including type objects. If the implicit lookup of these
methods used the conventional lookup process, they would fail when
invoked on the type object itself:
>>> 1 .__hash__() == hash(1)
True
>>> int.__hash__() == hash(int)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: descriptor '__hash__' of 'int' object needs an argument
Incorrectly attempting to invoke an unbound method of a class in this
way is sometimes referred to as ‘metaclass confusion’, and is avoided
by bypassing the instance when looking up special methods:
>>> type(1).__hash__(1) == hash(1)
True
>>> type(int).__hash__(int) == hash(int)
True
In addition to bypassing any instance attributes in the interest of
correctness, implicit special method lookup generally also bypasses
the "__getattribute__()" method even of the object’s metaclass:
>>> class Meta(type):
... def __getattribute__(*args):
... print("Metaclass getattribute invoked")
... return type.__getattribute__(*args)
...
>>> class C(object, metaclass=Meta):
... def __len__(self):
... return 10
... def __getattribute__(*args):
... print("Class getattribute invoked")
... return object.__getattribute__(*args)
...
>>> c = C()
>>> c.__len__() # Explicit lookup via instance
Class getattribute invoked
10
>>> type(c).__len__(c) # Explicit lookup via type
Metaclass getattribute invoked
10
>>> len(c) # Implicit lookup
10
Bypassing the "__getattribute__()" machinery in this fashion provides
significant scope for speed optimisations within the interpreter, at
the cost of some flexibility in the handling of special methods (the
special method *must* be set on the class object itself in order to be
consistently invoked by the interpreter).
u�Y String Methods
**************
Strings implement all of the common sequence operations, along with
the additional methods described below.
Strings also support two styles of string formatting, one providing a
large degree of flexibility and customization (see "str.format()",
Format String Syntax and Custom String Formatting) and the other based
on C "printf" style formatting that handles a narrower range of types
and is slightly harder to use correctly, but is often faster for the
cases it can handle (printf-style String Formatting).
The Text Processing Services section of the standard library covers a
number of other modules that provide various text related utilities
(including regular expression support in the "re" module).
str.capitalize()
Return a copy of the string with its first character capitalized
and the rest lowercased.
Changed in version 3.8: The first character is now put into
titlecase rather than uppercase. This means that characters like
digraphs will only have their first letter capitalized, instead of
the full character.
str.casefold()
Return a casefolded copy of the string. Casefolded strings may be
used for caseless matching.
Casefolding is similar to lowercasing but more aggressive because
it is intended to remove all case distinctions in a string. For
example, the German lowercase letter "'ß'" is equivalent to ""ss"".
Since it is already lowercase, "lower()" would do nothing to "'ß'";
"casefold()" converts it to ""ss"".
The casefolding algorithm is described in section 3.13 of the
Unicode Standard.
New in version 3.3.
str.center(width[, fillchar])
Return centered in a string of length *width*. Padding is done
using the specified *fillchar* (default is an ASCII space). The
original string is returned if *width* is less than or equal to
"len(s)".
str.count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring *sub*
in the range [*start*, *end*]. Optional arguments *start* and
*end* are interpreted as in slice notation.
str.encode(encoding="utf-8", errors="strict")
Return an encoded version of the string as a bytes object. Default
encoding is "'utf-8'". *errors* may be given to set a different
error handling scheme. The default for *errors* is "'strict'",
meaning that encoding errors raise a "UnicodeError". Other possible
values are "'ignore'", "'replace'", "'xmlcharrefreplace'",
"'backslashreplace'" and any other name registered via
"codecs.register_error()", see section Error Handlers. For a list
of possible encodings, see section Standard Encodings.
Changed in version 3.1: Support for keyword arguments added.
str.endswith(suffix[, start[, end]])
Return "True" if the string ends with the specified *suffix*,
otherwise return "False". *suffix* can also be a tuple of suffixes
to look for. With optional *start*, test beginning at that
position. With optional *end*, stop comparing at that position.
str.expandtabs(tabsize=8)
Return a copy of the string where all tab characters are replaced
by one or more spaces, depending on the current column and the
given tab size. Tab positions occur every *tabsize* characters
(default is 8, giving tab positions at columns 0, 8, 16 and so on).
To expand the string, the current column is set to zero and the
string is examined character by character. If the character is a
tab ("\t"), one or more space characters are inserted in the result
until the current column is equal to the next tab position. (The
tab character itself is not copied.) If the character is a newline
("\n") or return ("\r"), it is copied and the current column is
reset to zero. Any other character is copied unchanged and the
current column is incremented by one regardless of how the
character is represented when printed.
>>> '01\t012\t0123\t01234'.expandtabs()
'01 012 0123 01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01 012 0123 01234'
str.find(sub[, start[, end]])
Return the lowest index in the string where substring *sub* is
found within the slice "s[start:end]". Optional arguments *start*
and *end* are interpreted as in slice notation. Return "-1" if
*sub* is not found.
Note:
The "find()" method should be used only if you need to know the
position of *sub*. To check if *sub* is a substring or not, use
the "in" operator:
>>> 'Py' in 'Python'
True
str.format(*args, **kwargs)
Perform a string formatting operation. The string on which this
method is called can contain literal text or replacement fields
delimited by braces "{}". Each replacement field contains either
the numeric index of a positional argument, or the name of a
keyword argument. Returns a copy of the string where each
replacement field is replaced with the string value of the
corresponding argument.
>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'
See Format String Syntax for a description of the various
formatting options that can be specified in format strings.
Note:
When formatting a number ("int", "float", "complex",
"decimal.Decimal" and subclasses) with the "n" type (ex:
"'{:n}'.format(1234)"), the function temporarily sets the
"LC_CTYPE" locale to the "LC_NUMERIC" locale to decode
"decimal_point" and "thousands_sep" fields of "localeconv()" if
they are non-ASCII or longer than 1 byte, and the "LC_NUMERIC"
locale is different than the "LC_CTYPE" locale. This temporary
change affects other threads.
Changed in version 3.7: When formatting a number with the "n" type,
the function sets temporarily the "LC_CTYPE" locale to the
"LC_NUMERIC" locale in some cases.
str.format_map(mapping)
Similar to "str.format(**mapping)", except that "mapping" is used
directly and not copied to a "dict". This is useful if for example
"mapping" is a dict subclass:
>>> class Default(dict):
... def __missing__(self, key):
... return key
...
>>> '{name} was born in {country}'.format_map(Default(name='Guido'))
'Guido was born in country'
New in version 3.2.
str.index(sub[, start[, end]])
Like "find()", but raise "ValueError" when the substring is not
found.
str.isalnum()
Return "True" if all characters in the string are alphanumeric and
there is at least one character, "False" otherwise. A character
"c" is alphanumeric if one of the following returns "True":
"c.isalpha()", "c.isdecimal()", "c.isdigit()", or "c.isnumeric()".
str.isalpha()
Return "True" if all characters in the string are alphabetic and
there is at least one character, "False" otherwise. Alphabetic
characters are those characters defined in the Unicode character
database as “Letter”, i.e., those with general category property
being one of “Lm”, “Lt”, “Lu”, “Ll”, or “Lo”. Note that this is
different from the “Alphabetic” property defined in the Unicode
Standard.
str.isascii()
Return "True" if the string is empty or all characters in the
string are ASCII, "False" otherwise. ASCII characters have code
points in the range U+0000-U+007F.
New in version 3.7.
str.isdecimal()
Return "True" if all characters in the string are decimal
characters and there is at least one character, "False" otherwise.
Decimal characters are those that can be used to form numbers in
base 10, e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Formally a decimal
character is a character in the Unicode General Category “Nd”.
str.isdigit()
Return "True" if all characters in the string are digits and there
is at least one character, "False" otherwise. Digits include
decimal characters and digits that need special handling, such as
the compatibility superscript digits. This covers digits which
cannot be used to form numbers in base 10, like the Kharosthi
numbers. Formally, a digit is a character that has the property
value Numeric_Type=Digit or Numeric_Type=Decimal.
str.isidentifier()
Return "True" if the string is a valid identifier according to the
language definition, section Identifiers and keywords.
Call "keyword.iskeyword()" to test whether string "s" is a reserved
identifier, such as "def" and "class".
Example:
>>> from keyword import iskeyword
>>> 'hello'.isidentifier(), iskeyword('hello')
True, False
>>> 'def'.isidentifier(), iskeyword('def')
True, True
str.islower()
Return "True" if all cased characters [4] in the string are
lowercase and there is at least one cased character, "False"
otherwise.
str.isnumeric()
Return "True" if all characters in the string are numeric
characters, and there is at least one character, "False" otherwise.
Numeric characters include digit characters, and all characters
that have the Unicode numeric value property, e.g. U+2155, VULGAR
FRACTION ONE FIFTH. Formally, numeric characters are those with
the property value Numeric_Type=Digit, Numeric_Type=Decimal or
Numeric_Type=Numeric.
str.isprintable()
Return "True" if all characters in the string are printable or the
string is empty, "False" otherwise. Nonprintable characters are
those characters defined in the Unicode character database as
“Other” or “Separator”, excepting the ASCII space (0x20) which is
considered printable. (Note that printable characters in this
context are those which should not be escaped when "repr()" is
invoked on a string. It has no bearing on the handling of strings
written to "sys.stdout" or "sys.stderr".)
str.isspace()
Return "True" if there are only whitespace characters in the string
and there is at least one character, "False" otherwise.
A character is *whitespace* if in the Unicode character database
(see "unicodedata"), either its general category is "Zs"
(“Separator, space”), or its bidirectional class is one of "WS",
"B", or "S".
str.istitle()
Return "True" if the string is a titlecased string and there is at
least one character, for example uppercase characters may only
follow uncased characters and lowercase characters only cased ones.
Return "False" otherwise.
str.isupper()
Return "True" if all cased characters [4] in the string are
uppercase and there is at least one cased character, "False"
otherwise.
str.join(iterable)
Return a string which is the concatenation of the strings in
*iterable*. A "TypeError" will be raised if there are any non-
string values in *iterable*, including "bytes" objects. The
separator between elements is the string providing this method.
str.ljust(width[, fillchar])
Return the string left justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is an ASCII
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.lower()
Return a copy of the string with all the cased characters [4]
converted to lowercase.
The lowercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.lstrip([chars])
Return a copy of the string with leading characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a prefix; rather,
all combinations of its values are stripped:
>>> ' spacious '.lstrip()
'spacious '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'
static str.maketrans(x[, y[, z]])
This static method returns a translation table usable for
"str.translate()".
If there is only one argument, it must be a dictionary mapping
Unicode ordinals (integers) or characters (strings of length 1) to
Unicode ordinals, strings (of arbitrary lengths) or "None".
Character keys will then be converted to ordinals.
If there are two arguments, they must be strings of equal length,
and in the resulting dictionary, each character in x will be mapped
to the character at the same position in y. If there is a third
argument, it must be a string, whose characters will be mapped to
"None" in the result.
str.partition(sep)
Split the string at the first occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing the string itself, followed by
two empty strings.
str.replace(old, new[, count])
Return a copy of the string with all occurrences of substring *old*
replaced by *new*. If the optional argument *count* is given, only
the first *count* occurrences are replaced.
str.rfind(sub[, start[, end]])
Return the highest index in the string where substring *sub* is
found, such that *sub* is contained within "s[start:end]".
Optional arguments *start* and *end* are interpreted as in slice
notation. Return "-1" on failure.
str.rindex(sub[, start[, end]])
Like "rfind()" but raises "ValueError" when the substring *sub* is
not found.
str.rjust(width[, fillchar])
Return the string right justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is an ASCII
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.rpartition(sep)
Split the string at the last occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing two empty strings, followed by
the string itself.
str.rsplit(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit* splits
are done, the *rightmost* ones. If *sep* is not specified or
"None", any whitespace string is a separator. Except for splitting
from the right, "rsplit()" behaves like "split()" which is
described in detail below.
str.rstrip([chars])
Return a copy of the string with trailing characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a suffix; rather,
all combinations of its values are stripped:
>>> ' spacious '.rstrip()
' spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'
str.split(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit*
splits are done (thus, the list will have at most "maxsplit+1"
elements). If *maxsplit* is not specified or "-1", then there is
no limit on the number of splits (all possible splits are made).
If *sep* is given, consecutive delimiters are not grouped together
and are deemed to delimit empty strings (for example,
"'1,,2'.split(',')" returns "['1', '', '2']"). The *sep* argument
may consist of multiple characters (for example,
"'1<>2<>3'.split('<>')" returns "['1', '2', '3']"). Splitting an
empty string with a specified separator returns "['']".
For example:
>>> '1,2,3'.split(',')
['1', '2', '3']
>>> '1,2,3'.split(',', maxsplit=1)
['1', '2,3']
>>> '1,2,,3,'.split(',')
['1', '2', '', '3', '']
If *sep* is not specified or is "None", a different splitting
algorithm is applied: runs of consecutive whitespace are regarded
as a single separator, and the result will contain no empty strings
at the start or end if the string has leading or trailing
whitespace. Consequently, splitting an empty string or a string
consisting of just whitespace with a "None" separator returns "[]".
For example:
>>> '1 2 3'.split()
['1', '2', '3']
>>> '1 2 3'.split(maxsplit=1)
['1', '2 3']
>>> ' 1 2 3 '.split()
['1', '2', '3']
str.splitlines([keepends])
Return a list of the lines in the string, breaking at line
boundaries. Line breaks are not included in the resulting list
unless *keepends* is given and true.
This method splits on the following line boundaries. In
particular, the boundaries are a superset of *universal newlines*.
+-------------------------+-------------------------------+
| Representation | Description |
|=========================|===============================|
| "\n" | Line Feed |
+-------------------------+-------------------------------+
| "\r" | Carriage Return |
+-------------------------+-------------------------------+
| "\r\n" | Carriage Return + Line Feed |
+-------------------------+-------------------------------+
| "\v" or "\x0b" | Line Tabulation |
+-------------------------+-------------------------------+
| "\f" or "\x0c" | Form Feed |
+-------------------------+-------------------------------+
| "\x1c" | File Separator |
+-------------------------+-------------------------------+
| "\x1d" | Group Separator |
+-------------------------+-------------------------------+
| "\x1e" | Record Separator |
+-------------------------+-------------------------------+
| "\x85" | Next Line (C1 Control Code) |
+-------------------------+-------------------------------+
| "\u2028" | Line Separator |
+-------------------------+-------------------------------+
| "\u2029" | Paragraph Separator |
+-------------------------+-------------------------------+
Changed in version 3.2: "\v" and "\f" added to list of line
boundaries.
For example:
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
['ab c', '', 'de fg', 'kl']
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True)
['ab c\n', '\n', 'de fg\r', 'kl\r\n']
Unlike "split()" when a delimiter string *sep* is given, this
method returns an empty list for the empty string, and a terminal
line break does not result in an extra line:
>>> "".splitlines()
[]
>>> "One line\n".splitlines()
['One line']
For comparison, "split('\n')" gives:
>>> ''.split('\n')
['']
>>> 'Two lines\n'.split('\n')
['Two lines', '']
str.startswith(prefix[, start[, end]])
Return "True" if string starts with the *prefix*, otherwise return
"False". *prefix* can also be a tuple of prefixes to look for.
With optional *start*, test string beginning at that position.
With optional *end*, stop comparing string at that position.
str.strip([chars])
Return a copy of the string with the leading and trailing
characters removed. The *chars* argument is a string specifying the
set of characters to be removed. If omitted or "None", the *chars*
argument defaults to removing whitespace. The *chars* argument is
not a prefix or suffix; rather, all combinations of its values are
stripped:
>>> ' spacious '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'
The outermost leading and trailing *chars* argument values are
stripped from the string. Characters are removed from the leading
end until reaching a string character that is not contained in the
set of characters in *chars*. A similar action takes place on the
trailing end. For example:
>>> comment_string = '#....... Section 3.2.1 Issue #32 .......'
>>> comment_string.strip('.#! ')
'Section 3.2.1 Issue #32'
str.swapcase()
Return a copy of the string with uppercase characters converted to
lowercase and vice versa. Note that it is not necessarily true that
"s.swapcase().swapcase() == s".
str.title()
Return a titlecased version of the string where words start with an
uppercase character and the remaining characters are lowercase.
For example:
>>> 'Hello world'.title()
'Hello World'
The algorithm uses a simple language-independent definition of a
word as groups of consecutive letters. The definition works in
many contexts but it means that apostrophes in contractions and
possessives form word boundaries, which may not be the desired
result:
>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"
A workaround for apostrophes can be constructed using regular
expressions:
>>> import re
>>> def titlecase(s):
... return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
... lambda mo: mo.group(0).capitalize(),
... s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."
str.translate(table)
Return a copy of the string in which each character has been mapped
through the given translation table. The table must be an object
that implements indexing via "__getitem__()", typically a *mapping*
or *sequence*. When indexed by a Unicode ordinal (an integer), the
table object can do any of the following: return a Unicode ordinal
or a string, to map the character to one or more other characters;
return "None", to delete the character from the return string; or
raise a "LookupError" exception, to map the character to itself.
You can use "str.maketrans()" to create a translation map from
character-to-character mappings in different formats.
See also the "codecs" module for a more flexible approach to custom
character mappings.
str.upper()
Return a copy of the string with all the cased characters [4]
converted to uppercase. Note that "s.upper().isupper()" might be
"False" if "s" contains uncased characters or if the Unicode
category of the resulting character(s) is not “Lu” (Letter,
uppercase), but e.g. “Lt” (Letter, titlecase).
The uppercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.zfill(width)
Return a copy of the string left filled with ASCII "'0'" digits to
make a string of length *width*. A leading sign prefix
("'+'"/"'-'") is handled by inserting the padding *after* the sign
character rather than before. The original string is returned if
*width* is less than or equal to "len(s)".
For example:
>>> "42".zfill(5)
'00042'
>>> "-42".zfill(5)
'-0042'
u� String and Bytes literals
*************************
String literals are described by the following lexical definitions:
stringliteral ::= [stringprefix](shortstring | longstring)
stringprefix ::= "r" | "u" | "R" | "U" | "f" | "F"
| "fr" | "Fr" | "fR" | "FR" | "rf" | "rF" | "Rf" | "RF"
shortstring ::= "'" shortstringitem* "'" | '"' shortstringitem* '"'
longstring ::= "'''" longstringitem* "'''" | '"""' longstringitem* '"""'
shortstringitem ::= shortstringchar | stringescapeseq
longstringitem ::= longstringchar | stringescapeseq
shortstringchar ::= <any source character except "\" or newline or the quote>
longstringchar ::= <any source character except "\">
stringescapeseq ::= "\" <any source character>
bytesliteral ::= bytesprefix(shortbytes | longbytes)
bytesprefix ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB"
shortbytes ::= "'" shortbytesitem* "'" | '"' shortbytesitem* '"'
longbytes ::= "'''" longbytesitem* "'''" | '"""' longbytesitem* '"""'
shortbytesitem ::= shortbyteschar | bytesescapeseq
longbytesitem ::= longbyteschar | bytesescapeseq
shortbyteschar ::= <any ASCII character except "\" or newline or the quote>
longbyteschar ::= <any ASCII character except "\">
bytesescapeseq ::= "\" <any ASCII character>
One syntactic restriction not indicated by these productions is that
whitespace is not allowed between the "stringprefix" or "bytesprefix"
and the rest of the literal. The source character set is defined by
the encoding declaration; it is UTF-8 if no encoding declaration is
given in the source file; see section Encoding declarations.
In plain English: Both types of literals can be enclosed in matching
single quotes ("'") or double quotes ("""). They can also be enclosed
in matching groups of three single or double quotes (these are
generally referred to as *triple-quoted strings*). The backslash
("\") character is used to escape characters that otherwise have a
special meaning, such as newline, backslash itself, or the quote
character.
Bytes literals are always prefixed with "'b'" or "'B'"; they produce
an instance of the "bytes" type instead of the "str" type. They may
only contain ASCII characters; bytes with a numeric value of 128 or
greater must be expressed with escapes.
Both string and bytes literals may optionally be prefixed with a
letter "'r'" or "'R'"; such strings are called *raw strings* and treat
backslashes as literal characters. As a result, in string literals,
"'\U'" and "'\u'" escapes in raw strings are not treated specially.
Given that Python 2.x’s raw unicode literals behave differently than
Python 3.x’s the "'ur'" syntax is not supported.
New in version 3.3: The "'rb'" prefix of raw bytes literals has been
added as a synonym of "'br'".
New in version 3.3: Support for the unicode legacy literal
("u'value'") was reintroduced to simplify the maintenance of dual
Python 2.x and 3.x codebases. See **PEP 414** for more information.
A string literal with "'f'" or "'F'" in its prefix is a *formatted
string literal*; see Formatted string literals. The "'f'" may be
combined with "'r'", but not with "'b'" or "'u'", therefore raw
formatted strings are possible, but formatted bytes literals are not.
In triple-quoted literals, unescaped newlines and quotes are allowed
(and are retained), except that three unescaped quotes in a row
terminate the literal. (A “quote” is the character used to open the
literal, i.e. either "'" or """.)
Unless an "'r'" or "'R'" prefix is present, escape sequences in string
and bytes literals are interpreted according to rules similar to those
used by Standard C. The recognized escape sequences are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
|===================|===================================|=========|
| "\newline" | Backslash and newline ignored | |
+-------------------+-----------------------------------+---------+
| "\\" | Backslash ("\") | |
+-------------------+-----------------------------------+---------+
| "\'" | Single quote ("'") | |
+-------------------+-----------------------------------+---------+
| "\"" | Double quote (""") | |
+-------------------+-----------------------------------+---------+
| "\a" | ASCII Bell (BEL) | |
+-------------------+-----------------------------------+---------+
| "\b" | ASCII Backspace (BS) | |
+-------------------+-----------------------------------+---------+
| "\f" | ASCII Formfeed (FF) | |
+-------------------+-----------------------------------+---------+
| "\n" | ASCII Linefeed (LF) | |
+-------------------+-----------------------------------+---------+
| "\r" | ASCII Carriage Return (CR) | |
+-------------------+-----------------------------------+---------+
| "\t" | ASCII Horizontal Tab (TAB) | |
+-------------------+-----------------------------------+---------+
| "\v" | ASCII Vertical Tab (VT) | |
+-------------------+-----------------------------------+---------+
| "\ooo" | Character with octal value *ooo* | (1,3) |
+-------------------+-----------------------------------+---------+
| "\xhh" | Character with hex value *hh* | (2,3) |
+-------------------+-----------------------------------+---------+
Escape sequences only recognized in string literals are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
|===================|===================================|=========|
| "\N{name}" | Character named *name* in the | (4) |
| | Unicode database | |
+-------------------+-----------------------------------+---------+
| "\uxxxx" | Character with 16-bit hex value | (5) |
| | *xxxx* | |
+-------------------+-----------------------------------+---------+
| "\Uxxxxxxxx" | Character with 32-bit hex value | (6) |
| | *xxxxxxxx* | |
+-------------------+-----------------------------------+---------+
Notes:
1. As in Standard C, up to three octal digits are accepted.
2. Unlike in Standard C, exactly two hex digits are required.
3. In a bytes literal, hexadecimal and octal escapes denote the byte
with the given value. In a string literal, these escapes denote a
Unicode character with the given value.
4. Changed in version 3.3: Support for name aliases [1] has been
added.
5. Exactly four hex digits are required.
6. Any Unicode character can be encoded this way. Exactly eight hex
digits are required.
Unlike Standard C, all unrecognized escape sequences are left in the
string unchanged, i.e., *the backslash is left in the result*. (This
behavior is useful when debugging: if an escape sequence is mistyped,
the resulting output is more easily recognized as broken.) It is also
important to note that the escape sequences only recognized in string
literals fall into the category of unrecognized escapes for bytes
literals.
Changed in version 3.6: Unrecognized escape sequences produce a
"DeprecationWarning". In a future Python version they will be a
"SyntaxWarning" and eventually a "SyntaxError".
Even in a raw literal, quotes can be escaped with a backslash, but the
backslash remains in the result; for example, "r"\""" is a valid
string literal consisting of two characters: a backslash and a double
quote; "r"\"" is not a valid string literal (even a raw string cannot
end in an odd number of backslashes). Specifically, *a raw literal
cannot end in a single backslash* (since the backslash would escape
the following quote character). Note also that a single backslash
followed by a newline is interpreted as those two characters as part
of the literal, *not* as a line continuation.
uM Subscriptions
*************
A subscription selects an item of a sequence (string, tuple or list)
or mapping (dictionary) object:
subscription ::= primary "[" expression_list "]"
The primary must evaluate to an object that supports subscription
(lists or dictionaries for example). User-defined objects can support
subscription by defining a "__getitem__()" method.
For built-in objects, there are two types of objects that support
subscription:
If the primary is a mapping, the expression list must evaluate to an
object whose value is one of the keys of the mapping, and the
subscription selects the value in the mapping that corresponds to that
key. (The expression list is a tuple except if it has exactly one
item.)
If the primary is a sequence, the expression list must evaluate to an
integer or a slice (as discussed in the following section).
The formal syntax makes no special provision for negative indices in
sequences; however, built-in sequences all provide a "__getitem__()"
method that interprets negative indices by adding the length of the
sequence to the index (so that "x[-1]" selects the last item of "x").
The resulting value must be a nonnegative integer less than the number
of items in the sequence, and the subscription selects the item whose
index is that value (counting from zero). Since the support for
negative indices and slicing occurs in the object’s "__getitem__()"
method, subclasses overriding this method will need to explicitly add
that support.
A string’s items are characters. A character is not a separate data
type but a string of exactly one character.
ax Truth Value Testing
*******************
Any object can be tested for truth value, for use in an "if" or
"while" condition or as operand of the Boolean operations below.
By default, an object is considered true unless its class defines
either a "__bool__()" method that returns "False" or a "__len__()"
method that returns zero, when called with the object. [1] Here are
most of the built-in objects considered false:
* constants defined to be false: "None" and "False".
* zero of any numeric type: "0", "0.0", "0j", "Decimal(0)",
"Fraction(0, 1)"
* empty sequences and collections: "''", "()", "[]", "{}", "set()",
"range(0)"
Operations and built-in functions that have a Boolean result always
return "0" or "False" for false and "1" or "True" for true, unless
otherwise stated. (Important exception: the Boolean operations "or"
and "and" always return one of their operands.)
uR The "try" statement
*******************
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object, or a tuple containing an item that is the class or a base
class of the exception object.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed. All except
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred. "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.
The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed. Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a ‘cleanup’ handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return", "break" or "continue" statement, the saved
exception is discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
Changed in version 3.8: Prior to Python 3.8, a "continue" statement
was illegal in the "finally" clause due to a problem with the
implementation.
ux� The standard type hierarchy
***************************
Below is a list of the types that are built into Python. Extension
modules (written in C, Java, or other languages, depending on the
implementation) can define additional types. Future versions of
Python may add types to the type hierarchy (e.g., rational numbers,
efficiently stored arrays of integers, etc.), although such additions
will often be provided via the standard library instead.
Some of the type descriptions below contain a paragraph listing
‘special attributes.’ These are attributes that provide access to the
implementation and are not intended for general use. Their definition
may change in the future.
None
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name "None". It
is used to signify the absence of a value in many situations, e.g.,
it is returned from functions that don’t explicitly return
anything. Its truth value is false.
NotImplemented
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name
"NotImplemented". Numeric methods and rich comparison methods
should return this value if they do not implement the operation for
the operands provided. (The interpreter will then try the
reflected operation, or some other fallback, depending on the
operator.) Its truth value is true.
See Implementing the arithmetic operations for more details.
Ellipsis
This type has a single value. There is a single object with this
value. This object is accessed through the literal "..." or the
built-in name "Ellipsis". Its truth value is true.
"numbers.Number"
These are created by numeric literals and returned as results by
arithmetic operators and arithmetic built-in functions. Numeric
objects are immutable; once created their value never changes.
Python numbers are of course strongly related to mathematical
numbers, but subject to the limitations of numerical representation
in computers.
The string representations of the numeric classes, computed by
"__repr__()" and "__str__()", have the following properties:
* They are valid numeric literals which, when passed to their class
constructor, produce an object having the value of the original
numeric.
* The representation is in base 10, when possible.
* Leading zeros, possibly excepting a single zero before a decimal
point, are not shown.
* Trailing zeros, possibly excepting a single zero after a decimal
point, are not shown.
* A sign is shown only when the number is negative.
Python distinguishes between integers, floating point numbers, and
complex numbers:
"numbers.Integral"
These represent elements from the mathematical set of integers
(positive and negative).
There are two types of integers:
Integers ("int")
These represent numbers in an unlimited range, subject to
available (virtual) memory only. For the purpose of shift
and mask operations, a binary representation is assumed, and
negative numbers are represented in a variant of 2’s
complement which gives the illusion of an infinite string of
sign bits extending to the left.
Booleans ("bool")
These represent the truth values False and True. The two
objects representing the values "False" and "True" are the
only Boolean objects. The Boolean type is a subtype of the
integer type, and Boolean values behave like the values 0 and
1, respectively, in almost all contexts, the exception being
that when converted to a string, the strings ""False"" or
""True"" are returned, respectively.
The rules for integer representation are intended to give the
most meaningful interpretation of shift and mask operations
involving negative integers.
"numbers.Real" ("float")
These represent machine-level double precision floating point
numbers. You are at the mercy of the underlying machine
architecture (and C or Java implementation) for the accepted
range and handling of overflow. Python does not support single-
precision floating point numbers; the savings in processor and
memory usage that are usually the reason for using these are
dwarfed by the overhead of using objects in Python, so there is
no reason to complicate the language with two kinds of floating
point numbers.
"numbers.Complex" ("complex")
These represent complex numbers as a pair of machine-level
double precision floating point numbers. The same caveats apply
as for floating point numbers. The real and imaginary parts of a
complex number "z" can be retrieved through the read-only
attributes "z.real" and "z.imag".
Sequences
These represent finite ordered sets indexed by non-negative
numbers. The built-in function "len()" returns the number of items
of a sequence. When the length of a sequence is *n*, the index set
contains the numbers 0, 1, …, *n*-1. Item *i* of sequence *a* is
selected by "a[i]".
Sequences also support slicing: "a[i:j]" selects all items with
index *k* such that *i* "<=" *k* "<" *j*. When used as an
expression, a slice is a sequence of the same type. This implies
that the index set is renumbered so that it starts at 0.
Some sequences also support “extended slicing” with a third “step”
parameter: "a[i:j:k]" selects all items of *a* with index *x* where
"x = i + n*k", *n* ">=" "0" and *i* "<=" *x* "<" *j*.
Sequences are distinguished according to their mutability:
Immutable sequences
An object of an immutable sequence type cannot change once it is
created. (If the object contains references to other objects,
these other objects may be mutable and may be changed; however,
the collection of objects directly referenced by an immutable
object cannot change.)
The following types are immutable sequences:
Strings
A string is a sequence of values that represent Unicode code
points. All the code points in the range "U+0000 - U+10FFFF"
can be represented in a string. Python doesn’t have a "char"
type; instead, every code point in the string is represented
as a string object with length "1". The built-in function
"ord()" converts a code point from its string form to an
integer in the range "0 - 10FFFF"; "chr()" converts an
integer in the range "0 - 10FFFF" to the corresponding length
"1" string object. "str.encode()" can be used to convert a
"str" to "bytes" using the given text encoding, and
"bytes.decode()" can be used to achieve the opposite.
Tuples
The items of a tuple are arbitrary Python objects. Tuples of
two or more items are formed by comma-separated lists of
expressions. A tuple of one item (a ‘singleton’) can be
formed by affixing a comma to an expression (an expression by
itself does not create a tuple, since parentheses must be
usable for grouping of expressions). An empty tuple can be
formed by an empty pair of parentheses.
Bytes
A bytes object is an immutable array. The items are 8-bit
bytes, represented by integers in the range 0 <= x < 256.
Bytes literals (like "b'abc'") and the built-in "bytes()"
constructor can be used to create bytes objects. Also, bytes
objects can be decoded to strings via the "decode()" method.
Mutable sequences
Mutable sequences can be changed after they are created. The
subscription and slicing notations can be used as the target of
assignment and "del" (delete) statements.
There are currently two intrinsic mutable sequence types:
Lists
The items of a list are arbitrary Python objects. Lists are
formed by placing a comma-separated list of expressions in
square brackets. (Note that there are no special cases needed
to form lists of length 0 or 1.)
Byte Arrays
A bytearray object is a mutable array. They are created by
the built-in "bytearray()" constructor. Aside from being
mutable (and hence unhashable), byte arrays otherwise provide
the same interface and functionality as immutable "bytes"
objects.
The extension module "array" provides an additional example of a
mutable sequence type, as does the "collections" module.
Set types
These represent unordered, finite sets of unique, immutable
objects. As such, they cannot be indexed by any subscript. However,
they can be iterated over, and the built-in function "len()"
returns the number of items in a set. Common uses for sets are fast
membership testing, removing duplicates from a sequence, and
computing mathematical operations such as intersection, union,
difference, and symmetric difference.
For set elements, the same immutability rules apply as for
dictionary keys. Note that numeric types obey the normal rules for
numeric comparison: if two numbers compare equal (e.g., "1" and
"1.0"), only one of them can be contained in a set.
There are currently two intrinsic set types:
Sets
These represent a mutable set. They are created by the built-in
"set()" constructor and can be modified afterwards by several
methods, such as "add()".
Frozen sets
These represent an immutable set. They are created by the
built-in "frozenset()" constructor. As a frozenset is immutable
and *hashable*, it can be used again as an element of another
set, or as a dictionary key.
Mappings
These represent finite sets of objects indexed by arbitrary index
sets. The subscript notation "a[k]" selects the item indexed by "k"
from the mapping "a"; this can be used in expressions and as the
target of assignments or "del" statements. The built-in function
"len()" returns the number of items in a mapping.
There is currently a single intrinsic mapping type:
Dictionaries
These represent finite sets of objects indexed by nearly
arbitrary values. The only types of values not acceptable as
keys are values containing lists or dictionaries or other
mutable types that are compared by value rather than by object
identity, the reason being that the efficient implementation of
dictionaries requires a key’s hash value to remain constant.
Numeric types used for keys obey the normal rules for numeric
comparison: if two numbers compare equal (e.g., "1" and "1.0")
then they can be used interchangeably to index the same
dictionary entry.
Dictionaries preserve insertion order, meaning that keys will be
produced in the same order they were added sequentially over the
dictionary. Replacing an existing key does not change the order,
however removing a key and re-inserting it will add it to the
end instead of keeping its old place.
Dictionaries are mutable; they can be created by the "{...}"
notation (see section Dictionary displays).
The extension modules "dbm.ndbm" and "dbm.gnu" provide
additional examples of mapping types, as does the "collections"
module.
Changed in version 3.7: Dictionaries did not preserve insertion
order in versions of Python before 3.6. In CPython 3.6,
insertion order was preserved, but it was considered an
implementation detail at that time rather than a language
guarantee.
Callable types
These are the types to which the function call operation (see
section Calls) can be applied:
User-defined functions
A user-defined function object is created by a function
definition (see section Function definitions). It should be
called with an argument list containing the same number of items
as the function’s formal parameter list.
Special attributes:
+---------------------------+---------------------------------+-------------+
| Attribute | Meaning | |
|===========================|=================================|=============|
| "__doc__" | The function’s documentation | Writable |
| | string, or "None" if | |
| | unavailable; not inherited by | |
| | subclasses. | |
+---------------------------+---------------------------------+-------------+
| "__name__" | The function’s name. | Writable |
+---------------------------+---------------------------------+-------------+
| "__qualname__" | The function’s *qualified | Writable |
| | name*. New in version 3.3. | |
+---------------------------+---------------------------------+-------------+
| "__module__" | The name of the module the | Writable |
| | function was defined in, or | |
| | "None" if unavailable. | |
+---------------------------+---------------------------------+-------------+
| "__defaults__" | A tuple containing default | Writable |
| | argument values for those | |
| | arguments that have defaults, | |
| | or "None" if no arguments have | |
| | a default value. | |
+---------------------------+---------------------------------+-------------+
| "__code__" | The code object representing | Writable |
| | the compiled function body. | |
+---------------------------+---------------------------------+-------------+
| "__globals__" | A reference to the dictionary | Read-only |
| | that holds the function’s | |
| | global variables — the global | |
| | namespace of the module in | |
| | which the function was defined. | |
+---------------------------+---------------------------------+-------------+
| "__dict__" | The namespace supporting | Writable |
| | arbitrary function attributes. | |
+---------------------------+---------------------------------+-------------+
| "__closure__" | "None" or a tuple of cells that | Read-only |
| | contain bindings for the | |
| | function’s free variables. See | |
| | below for information on the | |
| | "cell_contents" attribute. | |
+---------------------------+---------------------------------+-------------+
| "__annotations__" | A dict containing annotations | Writable |
| | of parameters. The keys of the | |
| | dict are the parameter names, | |
| | and "'return'" for the return | |
| | annotation, if provided. | |
+---------------------------+---------------------------------+-------------+
| "__kwdefaults__" | A dict containing defaults for | Writable |
| | keyword-only parameters. | |
+---------------------------+---------------------------------+-------------+
Most of the attributes labelled “Writable” check the type of the
assigned value.
Function objects also support getting and setting arbitrary
attributes, which can be used, for example, to attach metadata
to functions. Regular attribute dot-notation is used to get and
set such attributes. *Note that the current implementation only
supports function attributes on user-defined functions. Function
attributes on built-in functions may be supported in the
future.*
A cell object has the attribute "cell_contents". This can be
used to get the value of the cell, as well as set the value.
Additional information about a function’s definition can be
retrieved from its code object; see the description of internal
types below. The "cell" type can be accessed in the "types"
module.
Instance methods
An instance method object combines a class, a class instance and
any callable object (normally a user-defined function).
Special read-only attributes: "__self__" is the class instance
object, "__func__" is the function object; "__doc__" is the
method’s documentation (same as "__func__.__doc__"); "__name__"
is the method name (same as "__func__.__name__"); "__module__"
is the name of the module the method was defined in, or "None"
if unavailable.
Methods also support accessing (but not setting) the arbitrary
function attributes on the underlying function object.
User-defined method objects may be created when getting an
attribute of a class (perhaps via an instance of that class), if
that attribute is a user-defined function object or a class
method object.
When an instance method object is created by retrieving a user-
defined function object from a class via one of its instances,
its "__self__" attribute is the instance, and the method object
is said to be bound. The new method’s "__func__" attribute is
the original function object.
When an instance method object is created by retrieving a class
method object from a class or instance, its "__self__" attribute
is the class itself, and its "__func__" attribute is the
function object underlying the class method.
When an instance method object is called, the underlying
function ("__func__") is called, inserting the class instance
("__self__") in front of the argument list. For instance, when
"C" is a class which contains a definition for a function "f()",
and "x" is an instance of "C", calling "x.f(1)" is equivalent to
calling "C.f(x, 1)".
When an instance method object is derived from a class method
object, the “class instance” stored in "__self__" will actually
be the class itself, so that calling either "x.f(1)" or "C.f(1)"
is equivalent to calling "f(C,1)" where "f" is the underlying
function.
Note that the transformation from function object to instance
method object happens each time the attribute is retrieved from
the instance. In some cases, a fruitful optimization is to
assign the attribute to a local variable and call that local
variable. Also notice that this transformation only happens for
user-defined functions; other callable objects (and all non-
callable objects) are retrieved without transformation. It is
also important to note that user-defined functions which are
attributes of a class instance are not converted to bound
methods; this *only* happens when the function is an attribute
of the class.
Generator functions
A function or method which uses the "yield" statement (see
section The yield statement) is called a *generator function*.
Such a function, when called, always returns an iterator object
which can be used to execute the body of the function: calling
the iterator’s "iterator.__next__()" method will cause the
function to execute until it provides a value using the "yield"
statement. When the function executes a "return" statement or
falls off the end, a "StopIteration" exception is raised and the
iterator will have reached the end of the set of values to be
returned.
Coroutine functions
A function or method which is defined using "async def" is
called a *coroutine function*. Such a function, when called,
returns a *coroutine* object. It may contain "await"
expressions, as well as "async with" and "async for" statements.
See also the Coroutine Objects section.
Asynchronous generator functions
A function or method which is defined using "async def" and
which uses the "yield" statement is called a *asynchronous
generator function*. Such a function, when called, returns an
asynchronous iterator object which can be used in an "async for"
statement to execute the body of the function.
Calling the asynchronous iterator’s "aiterator.__anext__()"
method will return an *awaitable* which when awaited will
execute until it provides a value using the "yield" expression.
When the function executes an empty "return" statement or falls
off the end, a "StopAsyncIteration" exception is raised and the
asynchronous iterator will have reached the end of the set of
values to be yielded.
Built-in functions
A built-in function object is a wrapper around a C function.
Examples of built-in functions are "len()" and "math.sin()"
("math" is a standard built-in module). The number and type of
the arguments are determined by the C function. Special read-
only attributes: "__doc__" is the function’s documentation
string, or "None" if unavailable; "__name__" is the function’s
name; "__self__" is set to "None" (but see the next item);
"__module__" is the name of the module the function was defined
in or "None" if unavailable.
Built-in methods
This is really a different disguise of a built-in function, this
time containing an object passed to the C function as an
implicit extra argument. An example of a built-in method is
"alist.append()", assuming *alist* is a list object. In this
case, the special read-only attribute "__self__" is set to the
object denoted by *alist*.
Classes
Classes are callable. These objects normally act as factories
for new instances of themselves, but variations are possible for
class types that override "__new__()". The arguments of the
call are passed to "__new__()" and, in the typical case, to
"__init__()" to initialize the new instance.
Class Instances
Instances of arbitrary classes can be made callable by defining
a "__call__()" method in their class.
Modules
Modules are a basic organizational unit of Python code, and are
created by the import system as invoked either by the "import"
statement, or by calling functions such as
"importlib.import_module()" and built-in "__import__()". A module
object has a namespace implemented by a dictionary object (this is
the dictionary referenced by the "__globals__" attribute of
functions defined in the module). Attribute references are
translated to lookups in this dictionary, e.g., "m.x" is equivalent
to "m.__dict__["x"]". A module object does not contain the code
object used to initialize the module (since it isn’t needed once
the initialization is done).
Attribute assignment updates the module’s namespace dictionary,
e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".
Predefined (writable) attributes: "__name__" is the module’s name;
"__doc__" is the module’s documentation string, or "None" if
unavailable; "__annotations__" (optional) is a dictionary
containing *variable annotations* collected during module body
execution; "__file__" is the pathname of the file from which the
module was loaded, if it was loaded from a file. The "__file__"
attribute may be missing for certain types of modules, such as C
modules that are statically linked into the interpreter; for
extension modules loaded dynamically from a shared library, it is
the pathname of the shared library file.
Special read-only attribute: "__dict__" is the module’s namespace
as a dictionary object.
**CPython implementation detail:** Because of the way CPython
clears module dictionaries, the module dictionary will be cleared
when the module falls out of scope even if the dictionary still has
live references. To avoid this, copy the dictionary or keep the
module around while using its dictionary directly.
Custom classes
Custom class types are typically created by class definitions (see
section Class definitions). A class has a namespace implemented by
a dictionary object. Class attribute references are translated to
lookups in this dictionary, e.g., "C.x" is translated to
"C.__dict__["x"]" (although there are a number of hooks which allow
for other means of locating attributes). When the attribute name is
not found there, the attribute search continues in the base
classes. This search of the base classes uses the C3 method
resolution order which behaves correctly even in the presence of
‘diamond’ inheritance structures where there are multiple
inheritance paths leading back to a common ancestor. Additional
details on the C3 MRO used by Python can be found in the
documentation accompanying the 2.3 release at
https://www.python.org/download/releases/2.3/mro/.
When a class attribute reference (for class "C", say) would yield a
class method object, it is transformed into an instance method
object whose "__self__" attribute is "C". When it would yield a
static method object, it is transformed into the object wrapped by
the static method object. See section Implementing Descriptors for
another way in which attributes retrieved from a class may differ
from those actually contained in its "__dict__".
Class attribute assignments update the class’s dictionary, never
the dictionary of a base class.
A class object can be called (see above) to yield a class instance
(see below).
Special attributes: "__name__" is the class name; "__module__" is
the module name in which the class was defined; "__dict__" is the
dictionary containing the class’s namespace; "__bases__" is a tuple
containing the base classes, in the order of their occurrence in
the base class list; "__doc__" is the class’s documentation string,
or "None" if undefined; "__annotations__" (optional) is a
dictionary containing *variable annotations* collected during class
body execution.
Class instances
A class instance is created by calling a class object (see above).
A class instance has a namespace implemented as a dictionary which
is the first place in which attribute references are searched.
When an attribute is not found there, and the instance’s class has
an attribute by that name, the search continues with the class
attributes. If a class attribute is found that is a user-defined
function object, it is transformed into an instance method object
whose "__self__" attribute is the instance. Static method and
class method objects are also transformed; see above under
“Classes”. See section Implementing Descriptors for another way in
which attributes of a class retrieved via its instances may differ
from the objects actually stored in the class’s "__dict__". If no
class attribute is found, and the object’s class has a
"__getattr__()" method, that is called to satisfy the lookup.
Attribute assignments and deletions update the instance’s
dictionary, never a class’s dictionary. If the class has a
"__setattr__()" or "__delattr__()" method, this is called instead
of updating the instance dictionary directly.
Class instances can pretend to be numbers, sequences, or mappings
if they have methods with certain special names. See section
Special method names.
Special attributes: "__dict__" is the attribute dictionary;
"__class__" is the instance’s class.
I/O objects (also known as file objects)
A *file object* represents an open file. Various shortcuts are
available to create file objects: the "open()" built-in function,
and also "os.popen()", "os.fdopen()", and the "makefile()" method
of socket objects (and perhaps by other functions or methods
provided by extension modules).
The objects "sys.stdin", "sys.stdout" and "sys.stderr" are
initialized to file objects corresponding to the interpreter’s
standard input, output and error streams; they are all open in text
mode and therefore follow the interface defined by the
"io.TextIOBase" abstract class.
Internal types
A few types used internally by the interpreter are exposed to the
user. Their definitions may change with future versions of the
interpreter, but they are mentioned here for completeness.
Code objects
Code objects represent *byte-compiled* executable Python code,
or *bytecode*. The difference between a code object and a
function object is that the function object contains an explicit
reference to the function’s globals (the module in which it was
defined), while a code object contains no context; also the
default argument values are stored in the function object, not
in the code object (because they represent values calculated at
run-time). Unlike function objects, code objects are immutable
and contain no references (directly or indirectly) to mutable
objects.
Special read-only attributes: "co_name" gives the function name;
"co_argcount" is the total number of positional arguments
(including positional-only arguments and arguments with default
values); "co_posonlyargcount" is the number of positional-only
arguments (including arguments with default values);
"co_kwonlyargcount" is the number of keyword-only arguments
(including arguments with default values); "co_nlocals" is the
number of local variables used by the function (including
arguments); "co_varnames" is a tuple containing the names of the
local variables (starting with the argument names);
"co_cellvars" is a tuple containing the names of local variables
that are referenced by nested functions; "co_freevars" is a
tuple containing the names of free variables; "co_code" is a
string representing the sequence of bytecode instructions;
"co_consts" is a tuple containing the literals used by the
bytecode; "co_names" is a tuple containing the names used by the
bytecode; "co_filename" is the filename from which the code was
compiled; "co_firstlineno" is the first line number of the
function; "co_lnotab" is a string encoding the mapping from
bytecode offsets to line numbers (for details see the source
code of the interpreter); "co_stacksize" is the required stack
size; "co_flags" is an integer encoding a number of flags for
the interpreter.
The following flag bits are defined for "co_flags": bit "0x04"
is set if the function uses the "*arguments" syntax to accept an
arbitrary number of positional arguments; bit "0x08" is set if
the function uses the "**keywords" syntax to accept arbitrary
keyword arguments; bit "0x20" is set if the function is a
generator.
Future feature declarations ("from __future__ import division")
also use bits in "co_flags" to indicate whether a code object
was compiled with a particular feature enabled: bit "0x2000" is
set if the function was compiled with future division enabled;
bits "0x10" and "0x1000" were used in earlier versions of
Python.
Other bits in "co_flags" are reserved for internal use.
If a code object represents a function, the first item in
"co_consts" is the documentation string of the function, or
"None" if undefined.
Frame objects
Frame objects represent execution frames. They may occur in
traceback objects (see below), and are also passed to registered
trace functions.
Special read-only attributes: "f_back" is to the previous stack
frame (towards the caller), or "None" if this is the bottom
stack frame; "f_code" is the code object being executed in this
frame; "f_locals" is the dictionary used to look up local
variables; "f_globals" is used for global variables;
"f_builtins" is used for built-in (intrinsic) names; "f_lasti"
gives the precise instruction (this is an index into the
bytecode string of the code object).
Accessing "f_code" raises an auditing event "object.__getattr__"
with arguments "obj" and ""f_code"".
Special writable attributes: "f_trace", if not "None", is a
function called for various events during code execution (this
is used by the debugger). Normally an event is triggered for
each new source line - this can be disabled by setting
"f_trace_lines" to "False".
Implementations *may* allow per-opcode events to be requested by
setting "f_trace_opcodes" to "True". Note that this may lead to
undefined interpreter behaviour if exceptions raised by the
trace function escape to the function being traced.
"f_lineno" is the current line number of the frame — writing to
this from within a trace function jumps to the given line (only
for the bottom-most frame). A debugger can implement a Jump
command (aka Set Next Statement) by writing to f_lineno.
Frame objects support one method:
frame.clear()
This method clears all references to local variables held by
the frame. Also, if the frame belonged to a generator, the
generator is finalized. This helps break reference cycles
involving frame objects (for example when catching an
exception and storing its traceback for later use).
"RuntimeError" is raised if the frame is currently executing.
New in version 3.4.
Traceback objects
Traceback objects represent a stack trace of an exception. A
traceback object is implicitly created when an exception occurs,
and may also be explicitly created by calling
"types.TracebackType".
For implicitly created tracebacks, when the search for an
exception handler unwinds the execution stack, at each unwound
level a traceback object is inserted in front of the current
traceback. When an exception handler is entered, the stack
trace is made available to the program. (See section The try
statement.) It is accessible as the third item of the tuple
returned by "sys.exc_info()", and as the "__traceback__"
attribute of the caught exception.
When the program contains no suitable handler, the stack trace
is written (nicely formatted) to the standard error stream; if
the interpreter is interactive, it is also made available to the
user as "sys.last_traceback".
For explicitly created tracebacks, it is up to the creator of
the traceback to determine how the "tb_next" attributes should
be linked to form a full stack trace.
Special read-only attributes: "tb_frame" points to the execution
frame of the current level; "tb_lineno" gives the line number
where the exception occurred; "tb_lasti" indicates the precise
instruction. The line number and last instruction in the
traceback may differ from the line number of its frame object if
the exception occurred in a "try" statement with no matching
except clause or with a finally clause.
Accessing "tb_frame" raises an auditing event
"object.__getattr__" with arguments "obj" and ""tb_frame"".
Special writable attribute: "tb_next" is the next level in the
stack trace (towards the frame where the exception occurred), or
"None" if there is no next level.
Changed in version 3.7: Traceback objects can now be explicitly
instantiated from Python code, and the "tb_next" attribute of
existing instances can be updated.
Slice objects
Slice objects are used to represent slices for "__getitem__()"
methods. They are also created by the built-in "slice()"
function.
Special read-only attributes: "start" is the lower bound; "stop"
is the upper bound; "step" is the step value; each is "None" if
omitted. These attributes can have any type.
Slice objects support one method:
slice.indices(self, length)
This method takes a single integer argument *length* and
computes information about the slice that the slice object
would describe if applied to a sequence of *length* items.
It returns a tuple of three integers; respectively these are
the *start* and *stop* indices and the *step* or stride
length of the slice. Missing or out-of-bounds indices are
handled in a manner consistent with regular slices.
Static method objects
Static method objects provide a way of defeating the
transformation of function objects to method objects described
above. A static method object is a wrapper around any other
object, usually a user-defined method object. When a static
method object is retrieved from a class or a class instance, the
object actually returned is the wrapped object, which is not
subject to any further transformation. Static method objects are
not themselves callable, although the objects they wrap usually
are. Static method objects are created by the built-in
"staticmethod()" constructor.
Class method objects
A class method object, like a static method object, is a wrapper
around another object that alters the way in which that object
is retrieved from classes and class instances. The behaviour of
class method objects upon such retrieval is described above,
under “User-defined methods”. Class method objects are created
by the built-in "classmethod()" constructor.
a� Functions
*********
Function objects are created by function definitions. The only
operation on a function object is to call it: "func(argument-list)".
There are really two flavors of function objects: built-in functions
and user-defined functions. Both support the same operation (to call
the function), but the implementation is different, hence the
different object types.
See Function definitions for more information.
u(. Mapping Types — "dict"
**********************
A *mapping* object maps *hashable* values to arbitrary objects.
Mappings are mutable objects. There is currently only one standard
mapping type, the *dictionary*. (For other containers see the built-
in "list", "set", and "tuple" classes, and the "collections" module.)
A dictionary’s keys are *almost* arbitrary values. Values that are
not *hashable*, that is, values containing lists, dictionaries or
other mutable types (that are compared by value rather than by object
identity) may not be used as keys. Numeric types used for keys obey
the normal rules for numeric comparison: if two numbers compare equal
(such as "1" and "1.0") then they can be used interchangeably to index
the same dictionary entry. (Note however, that since computers store
floating-point numbers as approximations it is usually unwise to use
them as dictionary keys.)
Dictionaries can be created by placing a comma-separated list of "key:
value" pairs within braces, for example: "{'jack': 4098, 'sjoerd':
4127}" or "{4098: 'jack', 4127: 'sjoerd'}", or by the "dict"
constructor.
class dict(**kwarg)
class dict(mapping, **kwarg)
class dict(iterable, **kwarg)
Return a new dictionary initialized from an optional positional
argument and a possibly empty set of keyword arguments.
Dictionaries can be created by several means:
* Use a comma-separated list of "key: value" pairs within braces:
"{'jack': 4098, 'sjoerd': 4127}" or "{4098: 'jack', 4127:
'sjoerd'}"
* Use a dict comprehension: "{}", "{x: x ** 2 for x in range(10)}"
* Use the type constructor: "dict()", "dict([('foo', 100), ('bar',
200)])", "dict(foo=100, bar=200)"
If no positional argument is given, an empty dictionary is created.
If a positional argument is given and it is a mapping object, a
dictionary is created with the same key-value pairs as the mapping
object. Otherwise, the positional argument must be an *iterable*
object. Each item in the iterable must itself be an iterable with
exactly two objects. The first object of each item becomes a key
in the new dictionary, and the second object the corresponding
value. If a key occurs more than once, the last value for that key
becomes the corresponding value in the new dictionary.
If keyword arguments are given, the keyword arguments and their
values are added to the dictionary created from the positional
argument. If a key being added is already present, the value from
the keyword argument replaces the value from the positional
argument.
To illustrate, the following examples all return a dictionary equal
to "{"one": 1, "two": 2, "three": 3}":
>>> a = dict(one=1, two=2, three=3)
>>> b = {'one': 1, 'two': 2, 'three': 3}
>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
>>> e = dict({'three': 3, 'one': 1, 'two': 2})
>>> a == b == c == d == e
True
Providing keyword arguments as in the first example only works for
keys that are valid Python identifiers. Otherwise, any valid keys
can be used.
These are the operations that dictionaries support (and therefore,
custom mapping types should support too):
list(d)
Return a list of all the keys used in the dictionary *d*.
len(d)
Return the number of items in the dictionary *d*.
d[key]
Return the item of *d* with key *key*. Raises a "KeyError" if
*key* is not in the map.
If a subclass of dict defines a method "__missing__()" and *key*
is not present, the "d[key]" operation calls that method with
the key *key* as argument. The "d[key]" operation then returns
or raises whatever is returned or raised by the
"__missing__(key)" call. No other operations or methods invoke
"__missing__()". If "__missing__()" is not defined, "KeyError"
is raised. "__missing__()" must be a method; it cannot be an
instance variable:
>>> class Counter(dict):
... def __missing__(self, key):
... return 0
>>> c = Counter()
>>> c['red']
0
>>> c['red'] += 1
>>> c['red']
1
The example above shows part of the implementation of
"collections.Counter". A different "__missing__" method is used
by "collections.defaultdict".
d[key] = value
Set "d[key]" to *value*.
del d[key]
Remove "d[key]" from *d*. Raises a "KeyError" if *key* is not
in the map.
key in d
Return "True" if *d* has a key *key*, else "False".
key not in d
Equivalent to "not key in d".
iter(d)
Return an iterator over the keys of the dictionary. This is a
shortcut for "iter(d.keys())".
clear()
Remove all items from the dictionary.
copy()
Return a shallow copy of the dictionary.
classmethod fromkeys(iterable[, value])
Create a new dictionary with keys from *iterable* and values set
to *value*.
"fromkeys()" is a class method that returns a new dictionary.
*value* defaults to "None". All of the values refer to just a
single instance, so it generally doesn’t make sense for *value*
to be a mutable object such as an empty list. To get distinct
values, use a dict comprehension instead.
get(key[, default])
Return the value for *key* if *key* is in the dictionary, else
*default*. If *default* is not given, it defaults to "None", so
that this method never raises a "KeyError".
items()
Return a new view of the dictionary’s items ("(key, value)"
pairs). See the documentation of view objects.
keys()
Return a new view of the dictionary’s keys. See the
documentation of view objects.
pop(key[, default])
If *key* is in the dictionary, remove it and return its value,
else return *default*. If *default* is not given and *key* is
not in the dictionary, a "KeyError" is raised.
popitem()
Remove and return a "(key, value)" pair from the dictionary.
Pairs are returned in LIFO (last-in, first-out) order.
"popitem()" is useful to destructively iterate over a
dictionary, as often used in set algorithms. If the dictionary
is empty, calling "popitem()" raises a "KeyError".
Changed in version 3.7: LIFO order is now guaranteed. In prior
versions, "popitem()" would return an arbitrary key/value pair.
reversed(d)
Return a reverse iterator over the keys of the dictionary. This
is a shortcut for "reversed(d.keys())".
New in version 3.8.
setdefault(key[, default])
If *key* is in the dictionary, return its value. If not, insert
*key* with a value of *default* and return *default*. *default*
defaults to "None".
update([other])
Update the dictionary with the key/value pairs from *other*,
overwriting existing keys. Return "None".
"update()" accepts either another dictionary object or an
iterable of key/value pairs (as tuples or other iterables of
length two). If keyword arguments are specified, the dictionary
is then updated with those key/value pairs: "d.update(red=1,
blue=2)".
values()
Return a new view of the dictionary’s values. See the
documentation of view objects.
An equality comparison between one "dict.values()" view and
another will always return "False". This also applies when
comparing "dict.values()" to itself:
>>> d = {'a': 1}
>>> d.values() == d.values()
False
Dictionaries compare equal if and only if they have the same "(key,
value)" pairs (regardless of ordering). Order comparisons (‘<’,
‘<=’, ‘>=’, ‘>’) raise "TypeError".
Dictionaries preserve insertion order. Note that updating a key
does not affect the order. Keys added after deletion are inserted
at the end.
>>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}
>>> list(d)
['one', 'two', 'three', 'four']
>>> list(d.values())
[1, 2, 3, 4]
>>> d["one"] = 42
>>> d
{'one': 42, 'two': 2, 'three': 3, 'four': 4}
>>> del d["two"]
>>> d["two"] = None
>>> d
{'one': 42, 'three': 3, 'four': 4, 'two': None}
Changed in version 3.7: Dictionary order is guaranteed to be
insertion order. This behavior was an implementation detail of
CPython from 3.6.
Dictionaries and dictionary views are reversible.
>>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}
>>> list(reversed(d))
['four', 'three', 'two', 'one']
>>> list(reversed(d.values()))
[4, 3, 2, 1]
>>> list(reversed(d.items()))
[('four', 4), ('three', 3), ('two', 2), ('one', 1)]
Changed in version 3.8: Dictionaries are now reversible.
See also:
"types.MappingProxyType" can be used to create a read-only view of a
"dict".
Dictionary view objects
=======================
The objects returned by "dict.keys()", "dict.values()" and
"dict.items()" are *view objects*. They provide a dynamic view on the
dictionary’s entries, which means that when the dictionary changes,
the view reflects these changes.
Dictionary views can be iterated over to yield their respective data,
and support membership tests:
len(dictview)
Return the number of entries in the dictionary.
iter(dictview)
Return an iterator over the keys, values or items (represented as
tuples of "(key, value)") in the dictionary.
Keys and values are iterated over in insertion order. This allows
the creation of "(value, key)" pairs using "zip()": "pairs =
zip(d.values(), d.keys())". Another way to create the same list is
"pairs = [(v, k) for (k, v) in d.items()]".
Iterating views while adding or deleting entries in the dictionary
may raise a "RuntimeError" or fail to iterate over all entries.
Changed in version 3.7: Dictionary order is guaranteed to be
insertion order.
x in dictview
Return "True" if *x* is in the underlying dictionary’s keys, values
or items (in the latter case, *x* should be a "(key, value)"
tuple).
reversed(dictview)
Return a reverse iterator over the keys, values or items of the
dictionary. The view will be iterated in reverse order of the
insertion.
Changed in version 3.8: Dictionary views are now reversible.
Keys views are set-like since their entries are unique and hashable.
If all values are hashable, so that "(key, value)" pairs are unique
and hashable, then the items view is also set-like. (Values views are
not treated as set-like since the entries are generally not unique.)
For set-like views, all of the operations defined for the abstract
base class "collections.abc.Set" are available (for example, "==",
"<", or "^").
An example of dictionary view usage:
>>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500}
>>> keys = dishes.keys()
>>> values = dishes.values()
>>> # iteration
>>> n = 0
>>> for val in values:
... n += val
>>> print(n)
504
>>> # keys and values are iterated over in the same order (insertion order)
>>> list(keys)
['eggs', 'sausage', 'bacon', 'spam']
>>> list(values)
[2, 1, 1, 500]
>>> # view objects are dynamic and reflect dict changes
>>> del dishes['eggs']
>>> del dishes['sausage']
>>> list(keys)
['bacon', 'spam']
>>> # set operations
>>> keys & {'eggs', 'bacon', 'salad'}
{'bacon'}
>>> keys ^ {'sausage', 'juice'}
{'juice', 'sausage', 'bacon', 'spam'}
a� Methods
*******
Methods are functions that are called using the attribute notation.
There are two flavors: built-in methods (such as "append()" on lists)
and class instance methods. Built-in methods are described with the
types that support them.
If you access a method (a function defined in a class namespace)
through an instance, you get a special object: a *bound method* (also
called *instance method*) object. When called, it will add the "self"
argument to the argument list. Bound methods have two special read-
only attributes: "m.__self__" is the object on which the method
operates, and "m.__func__" is the function implementing the method.
Calling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to
calling "m.__func__(m.__self__, arg-1, arg-2, ..., arg-n)".
Like function objects, bound method objects support getting arbitrary
attributes. However, since method attributes are actually stored on
the underlying function object ("meth.__func__"), setting method
attributes on bound methods is disallowed. Attempting to set an
attribute on a method results in an "AttributeError" being raised. In
order to set a method attribute, you need to explicitly set it on the
underlying function object:
>>> class C:
... def method(self):
... pass
...
>>> c = C()
>>> c.method.whoami = 'my name is method' # can't set on the method
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'method' object has no attribute 'whoami'
>>> c.method.__func__.whoami = 'my name is method'
>>> c.method.whoami
'my name is method'
See The standard type hierarchy for more information.
u$ Modules
*******
The only special operation on a module is attribute access: "m.name",
where *m* is a module and *name* accesses a name defined in *m*’s
symbol table. Module attributes can be assigned to. (Note that the
"import" statement is not, strictly speaking, an operation on a module
object; "import foo" does not require a module object named *foo* to
exist, rather it requires an (external) *definition* for a module
named *foo* somewhere.)
A special attribute of every module is "__dict__". This is the
dictionary containing the module’s symbol table. Modifying this
dictionary will actually change the module’s symbol table, but direct
assignment to the "__dict__" attribute is not possible (you can write
"m.__dict__['a'] = 1", which defines "m.a" to be "1", but you can’t
write "m.__dict__ = {}"). Modifying "__dict__" directly is not
recommended.
Modules built into the interpreter are written like this: "<module
'sys' (built-in)>". If loaded from a file, they are written as
"<module 'os' from '/usr/local/lib/pythonX.Y/os.pyc'>".
u�Z Sequence Types — "list", "tuple", "range"
*****************************************
There are three basic sequence types: lists, tuples, and range
objects. Additional sequence types tailored for processing of binary
data and text strings are described in dedicated sections.
Common Sequence Operations
==========================
The operations in the following table are supported by most sequence
types, both mutable and immutable. The "collections.abc.Sequence" ABC
is provided to make it easier to correctly implement these operations
on custom sequence types.
This table lists the sequence operations sorted in ascending priority.
In the table, *s* and *t* are sequences of the same type, *n*, *i*,
*j* and *k* are integers and *x* is an arbitrary object that meets any
type and value restrictions imposed by *s*.
The "in" and "not in" operations have the same priorities as the
comparison operations. The "+" (concatenation) and "*" (repetition)
operations have the same priority as the corresponding numeric
operations. [3]
+----------------------------+----------------------------------+------------+
| Operation | Result | Notes |
|============================|==================================|============|
| "x in s" | "True" if an item of *s* is | (1) |
| | equal to *x*, else "False" | |
+----------------------------+----------------------------------+------------+
| "x not in s" | "False" if an item of *s* is | (1) |
| | equal to *x*, else "True" | |
+----------------------------+----------------------------------+------------+
| "s + t" | the concatenation of *s* and *t* | (6)(7) |
+----------------------------+----------------------------------+------------+
| "s * n" or "n * s" | equivalent to adding *s* to | (2)(7) |
| | itself *n* times | |
+----------------------------+----------------------------------+------------+
| "s[i]" | *i*th item of *s*, origin 0 | (3) |
+----------------------------+----------------------------------+------------+
| "s[i:j]" | slice of *s* from *i* to *j* | (3)(4) |
+----------------------------+----------------------------------+------------+
| "s[i:j:k]" | slice of *s* from *i* to *j* | (3)(5) |
| | with step *k* | |
+----------------------------+----------------------------------+------------+
| "len(s)" | length of *s* | |
+----------------------------+----------------------------------+------------+
| "min(s)" | smallest item of *s* | |
+----------------------------+----------------------------------+------------+
| "max(s)" | largest item of *s* | |
+----------------------------+----------------------------------+------------+
| "s.index(x[, i[, j]])" | index of the first occurrence of | (8) |
| | *x* in *s* (at or after index | |
| | *i* and before index *j*) | |
+----------------------------+----------------------------------+------------+
| "s.count(x)" | total number of occurrences of | |
| | *x* in *s* | |
+----------------------------+----------------------------------+------------+
Sequences of the same type also support comparisons. In particular,
tuples and lists are compared lexicographically by comparing
corresponding elements. This means that to compare equal, every
element must compare equal and the two sequences must be of the same
type and have the same length. (For full details see Comparisons in
the language reference.)
Notes:
1. While the "in" and "not in" operations are used only for simple
containment testing in the general case, some specialised sequences
(such as "str", "bytes" and "bytearray") also use them for
subsequence testing:
>>> "gg" in "eggs"
True
2. Values of *n* less than "0" are treated as "0" (which yields an
empty sequence of the same type as *s*). Note that items in the
sequence *s* are not copied; they are referenced multiple times.
This often haunts new Python programmers; consider:
>>> lists = [[]] * 3
>>> lists
[[], [], []]
>>> lists[0].append(3)
>>> lists
[[3], [3], [3]]
What has happened is that "[[]]" is a one-element list containing
an empty list, so all three elements of "[[]] * 3" are references
to this single empty list. Modifying any of the elements of
"lists" modifies this single list. You can create a list of
different lists this way:
>>> lists = [[] for i in range(3)]
>>> lists[0].append(3)
>>> lists[1].append(5)
>>> lists[2].append(7)
>>> lists
[[3], [5], [7]]
Further explanation is available in the FAQ entry How do I create a
multidimensional list?.
3. If *i* or *j* is negative, the index is relative to the end of
sequence *s*: "len(s) + i" or "len(s) + j" is substituted. But
note that "-0" is still "0".
4. The slice of *s* from *i* to *j* is defined as the sequence of
items with index *k* such that "i <= k < j". If *i* or *j* is
greater than "len(s)", use "len(s)". If *i* is omitted or "None",
use "0". If *j* is omitted or "None", use "len(s)". If *i* is
greater than or equal to *j*, the slice is empty.
5. The slice of *s* from *i* to *j* with step *k* is defined as the
sequence of items with index "x = i + n*k" such that "0 <= n <
(j-i)/k". In other words, the indices are "i", "i+k", "i+2*k",
"i+3*k" and so on, stopping when *j* is reached (but never
including *j*). When *k* is positive, *i* and *j* are reduced to
"len(s)" if they are greater. When *k* is negative, *i* and *j* are
reduced to "len(s) - 1" if they are greater. If *i* or *j* are
omitted or "None", they become “end” values (which end depends on
the sign of *k*). Note, *k* cannot be zero. If *k* is "None", it
is treated like "1".
6. Concatenating immutable sequences always results in a new object.
This means that building up a sequence by repeated concatenation
will have a quadratic runtime cost in the total sequence length.
To get a linear runtime cost, you must switch to one of the
alternatives below:
* if concatenating "str" objects, you can build a list and use
"str.join()" at the end or else write to an "io.StringIO"
instance and retrieve its value when complete
* if concatenating "bytes" objects, you can similarly use
"bytes.join()" or "io.BytesIO", or you can do in-place
concatenation with a "bytearray" object. "bytearray" objects are
mutable and have an efficient overallocation mechanism
* if concatenating "tuple" objects, extend a "list" instead
* for other types, investigate the relevant class documentation
7. Some sequence types (such as "range") only support item sequences
that follow specific patterns, and hence don’t support sequence
concatenation or repetition.
8. "index" raises "ValueError" when *x* is not found in *s*. Not all
implementations support passing the additional arguments *i* and
*j*. These arguments allow efficient searching of subsections of
the sequence. Passing the extra arguments is roughly equivalent to
using "s[i:j].index(x)", only without copying any data and with the
returned index being relative to the start of the sequence rather
than the start of the slice.
Immutable Sequence Types
========================
The only operation that immutable sequence types generally implement
that is not also implemented by mutable sequence types is support for
the "hash()" built-in.
This support allows immutable sequences, such as "tuple" instances, to
be used as "dict" keys and stored in "set" and "frozenset" instances.
Attempting to hash an immutable sequence that contains unhashable
values will result in "TypeError".
Mutable Sequence Types
======================
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
|================================|==================================|=======================|
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | appends *x* to the end of the | |
| | sequence (same as | |
| | "s[len(s):len(s)] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from *s* (same | (5) |
| | as "del s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of *s* | (5) |
| | (same as "s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | extends *s* with the contents of | |
| | *t* (for the most part the same | |
| | as "s[len(s):len(s)] = t") | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (6) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | inserts *x* into *s* at the | |
| | index given by *i* (same as | |
| | "s[i:i] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop()" or "s.pop(i)" | retrieves the item at *i* and | (2) |
| | also removes it from *s* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
| | where "s[i]" is equal to *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default the
last item is removed and returned.
3. "remove()" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for economy
of space when reversing a large sequence. To remind users that it
operates by side effect, it does not return the reversed sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don’t support slicing
operations (such as "dict" and "set"). "copy()" is not part of the
"collections.abc.MutableSequence" ABC, but most concrete mutable
sequence classes provide it.
New in version 3.3: "clear()" and "copy()" methods.
6. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the sequence.
Items in the sequence are not copied; they are referenced multiple
times, as explained for "s * n" under Common Sequence Operations.
Lists
=====
Lists are mutable sequences, typically used to store collections of
homogeneous items (where the precise degree of similarity will vary by
application).
class list([iterable])
Lists may be constructed in several ways:
* Using a pair of square brackets to denote the empty list: "[]"
* Using square brackets, separating items with commas: "[a]", "[a,
b, c]"
* Using a list comprehension: "[x for x in iterable]"
* Using the type constructor: "list()" or "list(iterable)"
The constructor builds a list whose items are the same and in the
same order as *iterable*’s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a list, a copy is made and
returned, similar to "iterable[:]". For example, "list('abc')"
returns "['a', 'b', 'c']" and "list( (1, 2, 3) )" returns "[1, 2,
3]". If no argument is given, the constructor creates a new empty
list, "[]".
Many other operations also produce lists, including the "sorted()"
built-in.
Lists implement all of the common and mutable sequence operations.
Lists also provide the following additional method:
sort(*, key=None, reverse=False)
This method sorts the list in place, using only "<" comparisons
between items. Exceptions are not suppressed - if any comparison
operations fail, the entire sort operation will fail (and the
list will likely be left in a partially modified state).
"sort()" accepts two arguments that can only be passed by
keyword (keyword-only arguments):
*key* specifies a function of one argument that is used to
extract a comparison key from each list element (for example,
"key=str.lower"). The key corresponding to each item in the list
is calculated once and then used for the entire sorting process.
The default value of "None" means that list items are sorted
directly without calculating a separate key value.
The "functools.cmp_to_key()" utility is available to convert a
2.x style *cmp* function to a *key* function.
*reverse* is a boolean value. If set to "True", then the list
elements are sorted as if each comparison were reversed.
This method modifies the sequence in place for economy of space
when sorting a large sequence. To remind users that it operates
by side effect, it does not return the sorted sequence (use
"sorted()" to explicitly request a new sorted list instance).
The "sort()" method is guaranteed to be stable. A sort is
stable if it guarantees not to change the relative order of
elements that compare equal — this is helpful for sorting in
multiple passes (for example, sort by department, then by salary
grade).
For sorting examples and a brief sorting tutorial, see Sorting
HOW TO.
**CPython implementation detail:** While a list is being sorted,
the effect of attempting to mutate, or even inspect, the list is
undefined. The C implementation of Python makes the list appear
empty for the duration, and raises "ValueError" if it can detect
that the list has been mutated during a sort.
Tuples
======
Tuples are immutable sequences, typically used to store collections of
heterogeneous data (such as the 2-tuples produced by the "enumerate()"
built-in). Tuples are also used for cases where an immutable sequence
of homogeneous data is needed (such as allowing storage in a "set" or
"dict" instance).
class tuple([iterable])
Tuples may be constructed in a number of ways:
* Using a pair of parentheses to denote the empty tuple: "()"
* Using a trailing comma for a singleton tuple: "a," or "(a,)"
* Separating items with commas: "a, b, c" or "(a, b, c)"
* Using the "tuple()" built-in: "tuple()" or "tuple(iterable)"
The constructor builds a tuple whose items are the same and in the
same order as *iterable*’s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a tuple, it is returned
unchanged. For example, "tuple('abc')" returns "('a', 'b', 'c')"
and "tuple( [1, 2, 3] )" returns "(1, 2, 3)". If no argument is
given, the constructor creates a new empty tuple, "()".
Note that it is actually the comma which makes a tuple, not the
parentheses. The parentheses are optional, except in the empty
tuple case, or when they are needed to avoid syntactic ambiguity.
For example, "f(a, b, c)" is a function call with three arguments,
while "f((a, b, c))" is a function call with a 3-tuple as the sole
argument.
Tuples implement all of the common sequence operations.
For heterogeneous collections of data where access by name is clearer
than access by index, "collections.namedtuple()" may be a more
appropriate choice than a simple tuple object.
Ranges
======
The "range" type represents an immutable sequence of numbers and is
commonly used for looping a specific number of times in "for" loops.
class range(stop)
class range(start, stop[, step])
The arguments to the range constructor must be integers (either
built-in "int" or any object that implements the "__index__"
special method). If the *step* argument is omitted, it defaults to
"1". If the *start* argument is omitted, it defaults to "0". If
*step* is zero, "ValueError" is raised.
For a positive *step*, the contents of a range "r" are determined
by the formula "r[i] = start + step*i" where "i >= 0" and "r[i] <
stop".
For a negative *step*, the contents of the range are still
determined by the formula "r[i] = start + step*i", but the
constraints are "i >= 0" and "r[i] > stop".
A range object will be empty if "r[0]" does not meet the value
constraint. Ranges do support negative indices, but these are
interpreted as indexing from the end of the sequence determined by
the positive indices.
Ranges containing absolute values larger than "sys.maxsize" are
permitted but some features (such as "len()") may raise
"OverflowError".
Range examples:
>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(range(1, 11))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> list(range(0, 30, 5))
[0, 5, 10, 15, 20, 25]
>>> list(range(0, 10, 3))
[0, 3, 6, 9]
>>> list(range(0, -10, -1))
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
>>> list(range(0))
[]
>>> list(range(1, 0))
[]
Ranges implement all of the common sequence operations except
concatenation and repetition (due to the fact that range objects
can only represent sequences that follow a strict pattern and
repetition and concatenation will usually violate that pattern).
start
The value of the *start* parameter (or "0" if the parameter was
not supplied)
stop
The value of the *stop* parameter
step
The value of the *step* parameter (or "1" if the parameter was
not supplied)
The advantage of the "range" type over a regular "list" or "tuple" is
that a "range" object will always take the same (small) amount of
memory, no matter the size of the range it represents (as it only
stores the "start", "stop" and "step" values, calculating individual
items and subranges as needed).
Range objects implement the "collections.abc.Sequence" ABC, and
provide features such as containment tests, element index lookup,
slicing and support for negative indices (see Sequence Types — list,
tuple, range):
>>> r = range(0, 20, 2)
>>> r
range(0, 20, 2)
>>> 11 in r
False
>>> 10 in r
True
>>> r.index(10)
5
>>> r[5]
10
>>> r[:5]
range(0, 10, 2)
>>> r[-1]
18
Testing range objects for equality with "==" and "!=" compares them as
sequences. That is, two range objects are considered equal if they
represent the same sequence of values. (Note that two range objects
that compare equal might have different "start", "stop" and "step"
attributes, for example "range(0) == range(2, 1, 3)" or "range(0, 3,
2) == range(0, 4, 2)".)
Changed in version 3.2: Implement the Sequence ABC. Support slicing
and negative indices. Test "int" objects for membership in constant
time instead of iterating through all items.
Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range objects
based on the sequence of values they define (instead of comparing
based on object identity).
New in version 3.3: The "start", "stop" and "step" attributes.
See also:
* The linspace recipe shows how to implement a lazy version of range
suitable for floating point applications.
u� Mutable Sequence Types
**********************
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
|================================|==================================|=======================|
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | appends *x* to the end of the | |
| | sequence (same as | |
| | "s[len(s):len(s)] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from *s* (same | (5) |
| | as "del s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of *s* | (5) |
| | (same as "s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | extends *s* with the contents of | |
| | *t* (for the most part the same | |
| | as "s[len(s):len(s)] = t") | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (6) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | inserts *x* into *s* at the | |
| | index given by *i* (same as | |
| | "s[i:i] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop()" or "s.pop(i)" | retrieves the item at *i* and | (2) |
| | also removes it from *s* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
| | where "s[i]" is equal to *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default the
last item is removed and returned.
3. "remove()" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for economy
of space when reversing a large sequence. To remind users that it
operates by side effect, it does not return the reversed sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don’t support slicing
operations (such as "dict" and "set"). "copy()" is not part of the
"collections.abc.MutableSequence" ABC, but most concrete mutable
sequence classes provide it.
New in version 3.3: "clear()" and "copy()" methods.
6. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the sequence.
Items in the sequence are not copied; they are referenced multiple
times, as explained for "s * n" under Common Sequence Operations.
a~ Unary arithmetic and bitwise operations
***************************************
All unary arithmetic and bitwise operations have the same priority:
u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr
The unary "-" (minus) operator yields the negation of its numeric
argument.
The unary "+" (plus) operator yields its numeric argument unchanged.
The unary "~" (invert) operator yields the bitwise inversion of its
integer argument. The bitwise inversion of "x" is defined as
"-(x+1)". It only applies to integral numbers.
In all three cases, if the argument does not have the proper type, a
"TypeError" exception is raised.
u� The "while" statement
*********************
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" assignment_expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
uM The "with" statement
********************
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one “item” proceeds as
follows:
1. The context expression (the expression given in the "with_item") is
evaluated to obtain a context manager.
2. The context manager’s "__enter__()" is loaded for later use.
3. The context manager’s "__exit__()" is loaded for later use.
4. The context manager’s "__enter__()" method is invoked.
5. If a target was included in the "with" statement, the return value
from "__enter__()" is assigned to it.
Note:
The "with" statement guarantees that if the "__enter__()" method
returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
6. The suite is executed.
7. The context manager’s "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
The following code:
with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
enter = type(manager).__enter__
exit = type(manager).__exit__
value = enter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not exit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
exit(manager, None, None, None)
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
SUITE
is semantically equivalent to:
with A() as a:
with B() as b:
SUITE
Changed in version 3.1: Support for multiple context expressions.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
a, The "yield" statement
*********************
yield_stmt ::= yield_expression
A "yield" statement is semantically equivalent to a yield expression.
The yield statement can be used to omit the parentheses that would
otherwise be required in the equivalent yield expression statement.
For example, the yield statements
yield <expr>
yield from <expr>
are equivalent to the yield expression statements
(yield <expr>)
(yield from <expr>)
Yield expressions and statements are only used when defining a
*generator* function, and are only used in the body of the generator
function. Using yield in a function definition is sufficient to cause
that definition to create a generator function instead of a normal
function.
For full details of "yield" semantics, refer to the Yield expressions
section.
)O�assertZ
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customizationZdebugger�del�dictzdynamic-features�else�
exceptionsZ execmodelZ exprlistsZfloating�forZ
formatstringsZfunction�globalz
id-classesZ identifiers�ifZ imaginary�import�inZintegers�lambdaZlistsZnaming�nonlocalZnumbersz
numeric-typesZobjectszoperator-summary�passZpower�raise�returnzsequence-typesZshiftingZslicingsZ specialattrsZ specialnameszstring-methodsZstringsZ
subscriptions�truth�try�typesZtypesfunctionsZ typesmappingZ typesmethodsZ typesmodulesZtypesseqztypesseq-mutableZunary�while�with�yieldN)Ztopics� r r �)/usr/lib64/python3.8/pydoc_data/topics.py�<module> s ' } ( @ X 1 =` b Q> J : 3M D% I
H+1 &. L N 3"o# p3= ^ a i9 ; K9% H h� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �PK a��Z* ;� � ) __pycache__/__init__.cpython-38.opt-1.pycnu �[��� U
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��.e � @ s d S )N� r r r �+/usr/lib64/python3.8/pydoc_data/__init__.py�<module> � PK a��Z �W �~ �~ ' __pycache__/topics.cpython-38.opt-1.pycnu �[��� U
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d d d
ddddddddddddddd d d d d d!d"d#d$d%d&d'd(d d)d*d+d,d-d.d/d0d1d2d3d4d5d6d7d8d9d:d;d<d=d>d?d@dAdBdCdDdEdFdGdHdIdJdKdLdMdN�OZ dOS )Pau The "assert" statement
**********************
Assert statements are a convenient way to insert debugging assertions
into a program:
assert_stmt ::= "assert" expression ["," expression]
The simple form, "assert expression", is equivalent to
if __debug__:
if not expression: raise AssertionError
The extended form, "assert expression1, expression2", is equivalent to
if __debug__:
if not expression1: raise AssertionError(expression2)
These equivalences assume that "__debug__" and "AssertionError" refer
to the built-in variables with those names. In the current
implementation, the built-in variable "__debug__" is "True" under
normal circumstances, "False" when optimization is requested (command
line option "-O"). The current code generator emits no code for an
assert statement when optimization is requested at compile time. Note
that it is unnecessary to include the source code for the expression
that failed in the error message; it will be displayed as part of the
stack trace.
Assignments to "__debug__" are illegal. The value for the built-in
variable is determined when the interpreter starts.
u�, Assignment statements
*********************
Assignment statements are used to (re)bind names to values and to
modify attributes or items of mutable objects:
assignment_stmt ::= (target_list "=")+ (starred_expression | yield_expression)
target_list ::= target ("," target)* [","]
target ::= identifier
| "(" [target_list] ")"
| "[" [target_list] "]"
| attributeref
| subscription
| slicing
| "*" target
(See section Primaries for the syntax definitions for *attributeref*,
*subscription*, and *slicing*.)
An assignment statement evaluates the expression list (remember that
this can be a single expression or a comma-separated list, the latter
yielding a tuple) and assigns the single resulting object to each of
the target lists, from left to right.
Assignment is defined recursively depending on the form of the target
(list). When a target is part of a mutable object (an attribute
reference, subscription or slicing), the mutable object must
ultimately perform the assignment and decide about its validity, and
may raise an exception if the assignment is unacceptable. The rules
observed by various types and the exceptions raised are given with the
definition of the object types (see section The standard type
hierarchy).
Assignment of an object to a target list, optionally enclosed in
parentheses or square brackets, is recursively defined as follows.
* If the target list is a single target with no trailing comma,
optionally in parentheses, the object is assigned to that target.
* Else: The object must be an iterable with the same number of items
as there are targets in the target list, and the items are assigned,
from left to right, to the corresponding targets.
* If the target list contains one target prefixed with an asterisk,
called a “starred” target: The object must be an iterable with at
least as many items as there are targets in the target list, minus
one. The first items of the iterable are assigned, from left to
right, to the targets before the starred target. The final items
of the iterable are assigned to the targets after the starred
target. A list of the remaining items in the iterable is then
assigned to the starred target (the list can be empty).
* Else: The object must be an iterable with the same number of items
as there are targets in the target list, and the items are
assigned, from left to right, to the corresponding targets.
Assignment of an object to a single target is recursively defined as
follows.
* If the target is an identifier (name):
* If the name does not occur in a "global" or "nonlocal" statement
in the current code block: the name is bound to the object in the
current local namespace.
* Otherwise: the name is bound to the object in the global namespace
or the outer namespace determined by "nonlocal", respectively.
The name is rebound if it was already bound. This may cause the
reference count for the object previously bound to the name to reach
zero, causing the object to be deallocated and its destructor (if it
has one) to be called.
* If the target is an attribute reference: The primary expression in
the reference is evaluated. It should yield an object with
assignable attributes; if this is not the case, "TypeError" is
raised. That object is then asked to assign the assigned object to
the given attribute; if it cannot perform the assignment, it raises
an exception (usually but not necessarily "AttributeError").
Note: If the object is a class instance and the attribute reference
occurs on both sides of the assignment operator, the right-hand side
expression, "a.x" can access either an instance attribute or (if no
instance attribute exists) a class attribute. The left-hand side
target "a.x" is always set as an instance attribute, creating it if
necessary. Thus, the two occurrences of "a.x" do not necessarily
refer to the same attribute: if the right-hand side expression
refers to a class attribute, the left-hand side creates a new
instance attribute as the target of the assignment:
class Cls:
x = 3 # class variable
inst = Cls()
inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3
This description does not necessarily apply to descriptor
attributes, such as properties created with "property()".
* If the target is a subscription: The primary expression in the
reference is evaluated. It should yield either a mutable sequence
object (such as a list) or a mapping object (such as a dictionary).
Next, the subscript expression is evaluated.
If the primary is a mutable sequence object (such as a list), the
subscript must yield an integer. If it is negative, the sequence’s
length is added to it. The resulting value must be a nonnegative
integer less than the sequence’s length, and the sequence is asked
to assign the assigned object to its item with that index. If the
index is out of range, "IndexError" is raised (assignment to a
subscripted sequence cannot add new items to a list).
If the primary is a mapping object (such as a dictionary), the
subscript must have a type compatible with the mapping’s key type,
and the mapping is then asked to create a key/datum pair which maps
the subscript to the assigned object. This can either replace an
existing key/value pair with the same key value, or insert a new
key/value pair (if no key with the same value existed).
For user-defined objects, the "__setitem__()" method is called with
appropriate arguments.
* If the target is a slicing: The primary expression in the reference
is evaluated. It should yield a mutable sequence object (such as a
list). The assigned object should be a sequence object of the same
type. Next, the lower and upper bound expressions are evaluated,
insofar they are present; defaults are zero and the sequence’s
length. The bounds should evaluate to integers. If either bound is
negative, the sequence’s length is added to it. The resulting
bounds are clipped to lie between zero and the sequence’s length,
inclusive. Finally, the sequence object is asked to replace the
slice with the items of the assigned sequence. The length of the
slice may be different from the length of the assigned sequence,
thus changing the length of the target sequence, if the target
sequence allows it.
**CPython implementation detail:** In the current implementation, the
syntax for targets is taken to be the same as for expressions, and
invalid syntax is rejected during the code generation phase, causing
less detailed error messages.
Although the definition of assignment implies that overlaps between
the left-hand side and the right-hand side are ‘simultaneous’ (for
example "a, b = b, a" swaps two variables), overlaps *within* the
collection of assigned-to variables occur left-to-right, sometimes
resulting in confusion. For instance, the following program prints
"[0, 2]":
x = [0, 1]
i = 0
i, x[i] = 1, 2 # i is updated, then x[i] is updated
print(x)
See also:
**PEP 3132** - Extended Iterable Unpacking
The specification for the "*target" feature.
Augmented assignment statements
===============================
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions of the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side. For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
Annotated assignment statements
===============================
*Annotation* assignment is the combination, in a single statement, of
a variable or attribute annotation and an optional assignment
statement:
annotated_assignment_stmt ::= augtarget ":" expression
["=" (starred_expression | yield_expression)]
The difference from normal Assignment statements is that only single
target is allowed.
For simple names as assignment targets, if in class or module scope,
the annotations are evaluated and stored in a special class or module
attribute "__annotations__" that is a dictionary mapping from variable
names (mangled if private) to evaluated annotations. This attribute is
writable and is automatically created at the start of class or module
body execution, if annotations are found statically.
For expressions as assignment targets, the annotations are evaluated
if in class or module scope, but not stored.
If a name is annotated in a function scope, then this name is local
for that scope. Annotations are never evaluated and stored in function
scopes.
If the right hand side is present, an annotated assignment performs
the actual assignment before evaluating annotations (where
applicable). If the right hand side is not present for an expression
target, then the interpreter evaluates the target except for the last
"__setitem__()" or "__setattr__()" call.
See also:
**PEP 526** - Syntax for Variable Annotations
The proposal that added syntax for annotating the types of
variables (including class variables and instance variables),
instead of expressing them through comments.
**PEP 484** - Type hints
The proposal that added the "typing" module to provide a standard
syntax for type annotations that can be used in static analysis
tools and IDEs.
Changed in version 3.8: Now annotated assignments allow same
expressions in the right hand side as the regular assignments.
Previously, some expressions (like un-parenthesized tuple expressions)
caused a syntax error.
u>
Coroutines
**********
New in version 3.5.
Coroutine function definition
=============================
async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*). Inside the body of a coroutine function,
"await" and "async" identifiers become reserved keywords; "await"
expressions, "async for" and "async with" can only be used in
coroutine function bodies.
Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.
It is a "SyntaxError" to use a "yield from" expression inside the body
of a coroutine function.
An example of a coroutine function:
async def func(param1, param2):
do_stuff()
await some_coroutine()
The "async for" statement
=========================
async_for_stmt ::= "async" for_stmt
An *asynchronous iterable* is able to call asynchronous code in its
*iter* implementation, and *asynchronous iterator* can call
asynchronous code in its *next* method.
The "async for" statement allows convenient iteration over
asynchronous iterators.
The following code:
async for TARGET in ITER:
SUITE
else:
SUITE2
Is semantically equivalent to:
iter = (ITER)
iter = type(iter).__aiter__(iter)
running = True
while running:
try:
TARGET = await type(iter).__anext__(iter)
except StopAsyncIteration:
running = False
else:
SUITE
else:
SUITE2
See also "__aiter__()" and "__anext__()" for details.
It is a "SyntaxError" to use an "async for" statement outside the body
of a coroutine function.
The "async with" statement
==========================
async_with_stmt ::= "async" with_stmt
An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.
The following code:
async with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
aexit = type(manager).__aexit__
aenter = type(manager).__aenter__
value = await aenter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not await aexit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
await aexit(manager, None, None, None)
See also "__aenter__()" and "__aexit__()" for details.
It is a "SyntaxError" to use an "async with" statement outside the
body of a coroutine function.
See also:
**PEP 492** - Coroutines with async and await syntax
The proposal that made coroutines a proper standalone concept in
Python, and added supporting syntax.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless there
is a "finally" clause which happens to raise another exception.
That new exception causes the old one to be lost.
[2] A string literal appearing as the first statement in the function
body is transformed into the function’s "__doc__" attribute and
therefore the function’s *docstring*.
[3] A string literal appearing as the first statement in the class
body is transformed into the namespace’s "__doc__" item and
therefore the class’s *docstring*.
a� Identifiers (Names)
*******************
An identifier occurring as an atom is a name. See section Identifiers
and keywords for lexical definition and section Naming and binding for
documentation of naming and binding.
When the name is bound to an object, evaluation of the atom yields
that object. When a name is not bound, an attempt to evaluate it
raises a "NameError" exception.
**Private name mangling:** When an identifier that textually occurs in
a class definition begins with two or more underscore characters and
does not end in two or more underscores, it is considered a *private
name* of that class. Private names are transformed to a longer form
before code is generated for them. The transformation inserts the
class name, with leading underscores removed and a single underscore
inserted, in front of the name. For example, the identifier "__spam"
occurring in a class named "Ham" will be transformed to "_Ham__spam".
This transformation is independent of the syntactical context in which
the identifier is used. If the transformed name is extremely long
(longer than 255 characters), implementation defined truncation may
happen. If the class name consists only of underscores, no
transformation is done.
u
Literals
********
Python supports string and bytes literals and various numeric
literals:
literal ::= stringliteral | bytesliteral
| integer | floatnumber | imagnumber
Evaluation of a literal yields an object of the given type (string,
bytes, integer, floating point number, complex number) with the given
value. The value may be approximated in the case of floating point
and imaginary (complex) literals. See section Literals for details.
All literals correspond to immutable data types, and hence the
object’s identity is less important than its value. Multiple
evaluations of literals with the same value (either the same
occurrence in the program text or a different occurrence) may obtain
the same object or a different object with the same value.
uA7 Customizing attribute access
****************************
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
return the (computed) attribute value or raise an "AttributeError"
exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note:
This method may still be bypassed when looking up special methods
as the result of implicit invocation via language syntax or
built-in functions. See Special method lookup.
For certain sensitive attribute accesses, raises an auditing event
"object.__getattr__" with arguments "obj" and "name".
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should call the base class method with the same name, for example,
"object.__setattr__(self, name, value)".
For certain sensitive attribute assignments, raises an auditing
event "object.__setattr__" with arguments "obj", "name", "value".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
For certain sensitive attribute deletions, raises an auditing event
"object.__delattr__" with arguments "obj" and "name".
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
Customizing module attribute access
===================================
Special names "__getattr__" and "__dir__" can be also used to
customize access to module attributes. The "__getattr__" function at
the module level should accept one argument which is the name of an
attribute and return the computed value or raise an "AttributeError".
If an attribute is not found on a module object through the normal
lookup, i.e. "object.__getattribute__()", then "__getattr__" is
searched in the module "__dict__" before raising an "AttributeError".
If found, it is called with the attribute name and the result is
returned.
The "__dir__" function should accept no arguments, and return a
sequence of strings that represents the names accessible on module. If
present, this function overrides the standard "dir()" search on a
module.
For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
super().__setattr__(attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note:
Defining module "__getattr__" and setting module "__class__" only
affect lookups made using the attribute access syntax – directly
accessing the module globals (whether by code within the module, or
via a reference to the module’s globals dictionary) is unaffected.
Changed in version 3.5: "__class__" module attribute is now writable.
New in version 3.7: "__getattr__" and "__dir__" module attributes.
See also:
**PEP 562** - Module __getattr__ and __dir__
Describes the "__getattr__" and "__dir__" functions on modules.
Implementing Descriptors
========================
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents). In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".
object.__get__(self, instance, owner=None)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). The optional *owner* argument is the owner class, while
*instance* is the instance that the attribute was accessed through,
or "None" when the attribute is accessed through the *owner*.
This method should return the computed attribute value or raise an
"AttributeError" exception.
**PEP 252** specifies that "__get__()" is callable with one or two
arguments. Python’s own built-in descriptors support this
specification; however, it is likely that some third-party tools
have descriptors that require both arguments. Python’s own
"__getattribute__()" implementation always passes in both arguments
whether they are required or not.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
Note, adding "__set__()" or "__delete__()" changes the kind of
descriptor to a “data descriptor”. See Invoking Descriptors for
more details.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
object.__set_name__(self, owner, name)
Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.
Note:
"__set_name__()" is only called implicitly as part of the "type"
constructor, so it will need to be called explicitly with the
appropriate parameters when a descriptor is added to a class
after initial creation:
class A:
pass
descr = custom_descriptor()
A.attr = descr
descr.__set_name__(A, 'attr')
See Creating the class object for more details.
New in version 3.6.
The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).
Invoking Descriptors
====================
In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a class, "A.x" is transformed into the call:
"A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on which descriptor methods are defined. A descriptor can define any
combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
=========
*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of *__dict__* and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)
The space saved over using *__dict__* can be significant. Attribute
lookup speed can be significantly improved as well.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. *__slots__*
reserves space for the declared variables and prevents the
automatic creation of *__dict__* and *__weakref__* for each
instance.
Notes on using *__slots__*
--------------------------
* When inheriting from a class without *__slots__*, the *__dict__* and
*__weakref__* attribute of the instances will always be accessible.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
* Without a *__weakref__* variable for each instance, classes defining
*__slots__* do not support weak references to its instances. If weak
reference support is needed, then add "'__weakref__'" to the
sequence of strings in the *__slots__* declaration.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is not limited to the class
where it is defined. *__slots__* declared in parents are available
in child classes. However, child subclasses will get a *__dict__*
and *__weakref__* unless they also define *__slots__* (which should
only contain names of any *additional* slots).
* If a class defines a slot also defined in a base class, the instance
variable defined by the base class slot is inaccessible (except by
retrieving its descriptor directly from the base class). This
renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
“variable-length” built-in types such as "int", "bytes" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings may
also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".
* If an iterator is used for *__slots__* then a descriptor is created
for each of the iterator’s values. However, the *__slots__*
attribute will be an empty iterator.
a� Attribute references
********************
An attribute reference is a primary followed by a period and a name:
attributeref ::= primary "." identifier
The primary must evaluate to an object of a type that supports
attribute references, which most objects do. This object is then
asked to produce the attribute whose name is the identifier. This
production can be customized by overriding the "__getattr__()" method.
If this attribute is not available, the exception "AttributeError" is
raised. Otherwise, the type and value of the object produced is
determined by the object. Multiple evaluations of the same attribute
reference may yield different objects.
a� Augmented assignment statements
*******************************
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions of the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side. For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
z�Await expression
****************
Suspend the execution of *coroutine* on an *awaitable* object. Can
only be used inside a *coroutine function*.
await_expr ::= "await" primary
New in version 3.5.
uj Binary arithmetic operations
****************************
The binary arithmetic operations have the conventional priority
levels. Note that some of these operations also apply to certain non-
numeric types. Apart from the power operator, there are only two
levels, one for multiplicative operators and one for additive
operators:
m_expr ::= u_expr | m_expr "*" u_expr | m_expr "@" m_expr |
m_expr "//" u_expr | m_expr "/" u_expr |
m_expr "%" u_expr
a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr
The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer and the other must be a sequence. In the former case, the
numbers are converted to a common type and then multiplied together.
In the latter case, sequence repetition is performed; a negative
repetition factor yields an empty sequence.
The "@" (at) operator is intended to be used for matrix
multiplication. No builtin Python types implement this operator.
New in version 3.5.
The "/" (division) and "//" (floor division) operators yield the
quotient of their arguments. The numeric arguments are first
converted to a common type. Division of integers yields a float, while
floor division of integers results in an integer; the result is that
of mathematical division with the ‘floor’ function applied to the
result. Division by zero raises the "ZeroDivisionError" exception.
The "%" (modulo) operator yields the remainder from the division of
the first argument by the second. The numeric arguments are first
converted to a common type. A zero right argument raises the
"ZeroDivisionError" exception. The arguments may be floating point
numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 +
0.34".) The modulo operator always yields a result with the same sign
as its second operand (or zero); the absolute value of the result is
strictly smaller than the absolute value of the second operand [1].
The floor division and modulo operators are connected by the following
identity: "x == (x//y)*y + (x%y)". Floor division and modulo are also
connected with the built-in function "divmod()": "divmod(x, y) ==
(x//y, x%y)". [2].
In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string objects to perform old-style
string formatting (also known as interpolation). The syntax for
string formatting is described in the Python Library Reference,
section printf-style String Formatting.
The floor division operator, the modulo operator, and the "divmod()"
function are not defined for complex numbers. Instead, convert to a
floating point number using the "abs()" function if appropriate.
The "+" (addition) operator yields the sum of its arguments. The
arguments must either both be numbers or both be sequences of the same
type. In the former case, the numbers are converted to a common type
and then added together. In the latter case, the sequences are
concatenated.
The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.
a$ Binary bitwise operations
*************************
Each of the three bitwise operations has a different priority level:
and_expr ::= shift_expr | and_expr "&" shift_expr
xor_expr ::= and_expr | xor_expr "^" and_expr
or_expr ::= xor_expr | or_expr "|" xor_expr
The "&" operator yields the bitwise AND of its arguments, which must
be integers.
The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be integers.
The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be integers.
u� Code Objects
************
Code objects are used by the implementation to represent “pseudo-
compiled” executable Python code such as a function body. They differ
from function objects because they don’t contain a reference to their
global execution environment. Code objects are returned by the built-
in "compile()" function and can be extracted from function objects
through their "__code__" attribute. See also the "code" module.
Accessing "__code__" raises an auditing event "object.__getattr__"
with arguments "obj" and ""__code__"".
A code object can be executed or evaluated by passing it (instead of a
source string) to the "exec()" or "eval()" built-in functions.
See The standard type hierarchy for more information.
a. The Ellipsis Object
*******************
This object is commonly used by slicing (see Slicings). It supports
no special operations. There is exactly one ellipsis object, named
"Ellipsis" (a built-in name). "type(Ellipsis)()" produces the
"Ellipsis" singleton.
It is written as "Ellipsis" or "...".
u The Null Object
***************
This object is returned by functions that don’t explicitly return a
value. It supports no special operations. There is exactly one null
object, named "None" (a built-in name). "type(None)()" produces the
same singleton.
It is written as "None".
u5 Type Objects
************
Type objects represent the various object types. An object’s type is
accessed by the built-in function "type()". There are no special
operations on types. The standard module "types" defines names for
all standard built-in types.
Types are written like this: "<class 'int'>".
a� Boolean operations
******************
or_test ::= and_test | or_test "or" and_test
and_test ::= not_test | and_test "and" not_test
not_test ::= comparison | "not" not_test
In the context of Boolean operations, and also when expressions are
used by control flow statements, the following values are interpreted
as false: "False", "None", numeric zero of all types, and empty
strings and containers (including strings, tuples, lists,
dictionaries, sets and frozensets). All other values are interpreted
as true. User-defined objects can customize their truth value by
providing a "__bool__()" method.
The operator "not" yields "True" if its argument is false, "False"
otherwise.
The expression "x and y" first evaluates *x*; if *x* is false, its
value is returned; otherwise, *y* is evaluated and the resulting value
is returned.
The expression "x or y" first evaluates *x*; if *x* is true, its value
is returned; otherwise, *y* is evaluated and the resulting value is
returned.
Note that neither "and" nor "or" restrict the value and type they
return to "False" and "True", but rather return the last evaluated
argument. This is sometimes useful, e.g., if "s" is a string that
should be replaced by a default value if it is empty, the expression
"s or 'foo'" yields the desired value. Because "not" has to create a
new value, it returns a boolean value regardless of the type of its
argument (for example, "not 'foo'" produces "False" rather than "''".)
a$ The "break" statement
*********************
break_stmt ::= "break"
"break" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop.
It terminates the nearest enclosing loop, skipping the optional "else"
clause if the loop has one.
If a "for" loop is terminated by "break", the loop control target
keeps its current value.
When "break" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
loop.
u Emulating callable objects
**************************
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method
is defined, "x(arg1, arg2, ...)" roughly translates to
"type(x).__call__(x, arg1, ...)".
u� Calls
*****
A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:
call ::= primary "(" [argument_list [","] | comprehension] ")"
argument_list ::= positional_arguments ["," starred_and_keywords]
["," keywords_arguments]
| starred_and_keywords ["," keywords_arguments]
| keywords_arguments
positional_arguments ::= positional_item ("," positional_item)*
positional_item ::= assignment_expression | "*" expression
starred_and_keywords ::= ("*" expression | keyword_item)
("," "*" expression | "," keyword_item)*
keywords_arguments ::= (keyword_item | "**" expression)
("," keyword_item | "," "**" expression)*
keyword_item ::= identifier "=" expression
An optional trailing comma may be present after the positional and
keyword arguments but does not affect the semantics.
The primary must evaluate to a callable object (user-defined
functions, built-in functions, methods of built-in objects, class
objects, methods of class instances, and all objects having a
"__call__()" method are callable). All argument expressions are
evaluated before the call is attempted. Please refer to section
Function definitions for the syntax of formal *parameter* lists.
If keyword arguments are present, they are first converted to
positional arguments, as follows. First, a list of unfilled slots is
created for the formal parameters. If there are N positional
arguments, they are placed in the first N slots. Next, for each
keyword argument, the identifier is used to determine the
corresponding slot (if the identifier is the same as the first formal
parameter name, the first slot is used, and so on). If the slot is
already filled, a "TypeError" exception is raised. Otherwise, the
value of the argument is placed in the slot, filling it (even if the
expression is "None", it fills the slot). When all arguments have
been processed, the slots that are still unfilled are filled with the
corresponding default value from the function definition. (Default
values are calculated, once, when the function is defined; thus, a
mutable object such as a list or dictionary used as default value will
be shared by all calls that don’t specify an argument value for the
corresponding slot; this should usually be avoided.) If there are any
unfilled slots for which no default value is specified, a "TypeError"
exception is raised. Otherwise, the list of filled slots is used as
the argument list for the call.
**CPython implementation detail:** An implementation may provide
built-in functions whose positional parameters do not have names, even
if they are ‘named’ for the purpose of documentation, and which
therefore cannot be supplied by keyword. In CPython, this is the case
for functions implemented in C that use "PyArg_ParseTuple()" to parse
their arguments.
If there are more positional arguments than there are formal parameter
slots, a "TypeError" exception is raised, unless a formal parameter
using the syntax "*identifier" is present; in this case, that formal
parameter receives a tuple containing the excess positional arguments
(or an empty tuple if there were no excess positional arguments).
If any keyword argument does not correspond to a formal parameter
name, a "TypeError" exception is raised, unless a formal parameter
using the syntax "**identifier" is present; in this case, that formal
parameter receives a dictionary containing the excess keyword
arguments (using the keywords as keys and the argument values as
corresponding values), or a (new) empty dictionary if there were no
excess keyword arguments.
If the syntax "*expression" appears in the function call, "expression"
must evaluate to an *iterable*. Elements from these iterables are
treated as if they were additional positional arguments. For the call
"f(x1, x2, *y, x3, x4)", if *y* evaluates to a sequence *y1*, …, *yM*,
this is equivalent to a call with M+4 positional arguments *x1*, *x2*,
*y1*, …, *yM*, *x3*, *x4*.
A consequence of this is that although the "*expression" syntax may
appear *after* explicit keyword arguments, it is processed *before*
the keyword arguments (and any "**expression" arguments – see below).
So:
>>> def f(a, b):
... print(a, b)
...
>>> f(b=1, *(2,))
2 1
>>> f(a=1, *(2,))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: f() got multiple values for keyword argument 'a'
>>> f(1, *(2,))
1 2
It is unusual for both keyword arguments and the "*expression" syntax
to be used in the same call, so in practice this confusion does not
arise.
If the syntax "**expression" appears in the function call,
"expression" must evaluate to a *mapping*, the contents of which are
treated as additional keyword arguments. If a keyword is already
present (as an explicit keyword argument, or from another unpacking),
a "TypeError" exception is raised.
Formal parameters using the syntax "*identifier" or "**identifier"
cannot be used as positional argument slots or as keyword argument
names.
Changed in version 3.5: Function calls accept any number of "*" and
"**" unpackings, positional arguments may follow iterable unpackings
("*"), and keyword arguments may follow dictionary unpackings ("**").
Originally proposed by **PEP 448**.
A call always returns some value, possibly "None", unless it raises an
exception. How this value is computed depends on the type of the
callable object.
If it is—
a user-defined function:
The code block for the function is executed, passing it the
argument list. The first thing the code block will do is bind the
formal parameters to the arguments; this is described in section
Function definitions. When the code block executes a "return"
statement, this specifies the return value of the function call.
a built-in function or method:
The result is up to the interpreter; see Built-in Functions for the
descriptions of built-in functions and methods.
a class object:
A new instance of that class is returned.
a class instance method:
The corresponding user-defined function is called, with an argument
list that is one longer than the argument list of the call: the
instance becomes the first argument.
a class instance:
The class must define a "__call__()" method; the effect is then the
same as if that method was called.
u Class definitions
*****************
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [argument_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing. Classes without an inheritance list
inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.) When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [3] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.
The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__". Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.
Class creation can be customized heavily using metaclasses.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result is then bound to the class name.
**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. Descriptors can be used to create instance
variables with different implementation details.
See also:
**PEP 3115** - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the
current syntax, and the semantics for how classes with
metaclasses are constructed.
**PEP 3129** - Class Decorators
The proposal that added class decorators. Function and method
decorators were introduced in **PEP 318**.
u�' Comparisons
***********
Unlike C, all comparison operations in Python have the same priority,
which is lower than that of any arithmetic, shifting or bitwise
operation. Also unlike C, expressions like "a < b < c" have the
interpretation that is conventional in mathematics:
comparison ::= or_expr (comp_operator or_expr)*
comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="
| "is" ["not"] | ["not"] "in"
Comparisons yield boolean values: "True" or "False".
Comparisons can be chained arbitrarily, e.g., "x < y <= z" is
equivalent to "x < y and y <= z", except that "y" is evaluated only
once (but in both cases "z" is not evaluated at all when "x < y" is
found to be false).
Formally, if *a*, *b*, *c*, …, *y*, *z* are expressions and *op1*,
*op2*, …, *opN* are comparison operators, then "a op1 b op2 c ... y
opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except
that each expression is evaluated at most once.
Note that "a op1 b op2 c" doesn’t imply any kind of comparison between
*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though
perhaps not pretty).
Value comparisons
=================
The operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects. The objects do not need to have the same type.
Chapter Objects, values and types states that objects have a value (in
addition to type and identity). The value of an object is a rather
abstract notion in Python: For example, there is no canonical access
method for an object’s value. Also, there is no requirement that the
value of an object should be constructed in a particular way, e.g.
comprised of all its data attributes. Comparison operators implement a
particular notion of what the value of an object is. One can think of
them as defining the value of an object indirectly, by means of their
comparison implementation.
Because all types are (direct or indirect) subtypes of "object", they
inherit the default comparison behavior from "object". Types can
customize their comparison behavior by implementing *rich comparison
methods* like "__lt__()", described in Basic customization.
The default behavior for equality comparison ("==" and "!=") is based
on the identity of the objects. Hence, equality comparison of
instances with the same identity results in equality, and equality
comparison of instances with different identities results in
inequality. A motivation for this default behavior is the desire that
all objects should be reflexive (i.e. "x is y" implies "x == y").
A default order comparison ("<", ">", "<=", and ">=") is not provided;
an attempt raises "TypeError". A motivation for this default behavior
is the lack of a similar invariant as for equality.
The behavior of the default equality comparison, that instances with
different identities are always unequal, may be in contrast to what
types will need that have a sensible definition of object value and
value-based equality. Such types will need to customize their
comparison behavior, and in fact, a number of built-in types have done
that.
The following list describes the comparison behavior of the most
important built-in types.
* Numbers of built-in numeric types (Numeric Types — int, float,
complex) and of the standard library types "fractions.Fraction" and
"decimal.Decimal" can be compared within and across their types,
with the restriction that complex numbers do not support order
comparison. Within the limits of the types involved, they compare
mathematically (algorithmically) correct without loss of precision.
The not-a-number values "float('NaN')" and "decimal.Decimal('NaN')"
are special. Any ordered comparison of a number to a not-a-number
value is false. A counter-intuitive implication is that not-a-number
values are not equal to themselves. For example, if "x =
float('NaN')", "3 < x", "x < 3" and "x == x" are all false, while "x
!= x" is true. This behavior is compliant with IEEE 754.
* "None" and "NotImplemented" are singletons. **PEP 8** advises that
comparisons for singletons should always be done with "is" or "is
not", never the equality operators.
* Binary sequences (instances of "bytes" or "bytearray") can be
compared within and across their types. They compare
lexicographically using the numeric values of their elements.
* Strings (instances of "str") compare lexicographically using the
numerical Unicode code points (the result of the built-in function
"ord()") of their characters. [3]
Strings and binary sequences cannot be directly compared.
* Sequences (instances of "tuple", "list", or "range") can be compared
only within each of their types, with the restriction that ranges do
not support order comparison. Equality comparison across these
types results in inequality, and ordering comparison across these
types raises "TypeError".
Sequences compare lexicographically using comparison of
corresponding elements. The built-in containers typically assume
identical objects are equal to themselves. That lets them bypass
equality tests for identical objects to improve performance and to
maintain their internal invariants.
Lexicographical comparison between built-in collections works as
follows:
* For two collections to compare equal, they must be of the same
type, have the same length, and each pair of corresponding
elements must compare equal (for example, "[1,2] == (1,2)" is
false because the type is not the same).
* Collections that support order comparison are ordered the same as
their first unequal elements (for example, "[1,2,x] <= [1,2,y]"
has the same value as "x <= y"). If a corresponding element does
not exist, the shorter collection is ordered first (for example,
"[1,2] < [1,2,3]" is true).
* Mappings (instances of "dict") compare equal if and only if they
have equal *(key, value)* pairs. Equality comparison of the keys and
values enforces reflexivity.
Order comparisons ("<", ">", "<=", and ">=") raise "TypeError".
* Sets (instances of "set" or "frozenset") can be compared within and
across their types.
They define order comparison operators to mean subset and superset
tests. Those relations do not define total orderings (for example,
the two sets "{1,2}" and "{2,3}" are not equal, nor subsets of one
another, nor supersets of one another). Accordingly, sets are not
appropriate arguments for functions which depend on total ordering
(for example, "min()", "max()", and "sorted()" produce undefined
results given a list of sets as inputs).
Comparison of sets enforces reflexivity of its elements.
* Most other built-in types have no comparison methods implemented, so
they inherit the default comparison behavior.
User-defined classes that customize their comparison behavior should
follow some consistency rules, if possible:
* Equality comparison should be reflexive. In other words, identical
objects should compare equal:
"x is y" implies "x == y"
* Comparison should be symmetric. In other words, the following
expressions should have the same result:
"x == y" and "y == x"
"x != y" and "y != x"
"x < y" and "y > x"
"x <= y" and "y >= x"
* Comparison should be transitive. The following (non-exhaustive)
examples illustrate that:
"x > y and y > z" implies "x > z"
"x < y and y <= z" implies "x < z"
* Inverse comparison should result in the boolean negation. In other
words, the following expressions should have the same result:
"x == y" and "not x != y"
"x < y" and "not x >= y" (for total ordering)
"x > y" and "not x <= y" (for total ordering)
The last two expressions apply to totally ordered collections (e.g.
to sequences, but not to sets or mappings). See also the
"total_ordering()" decorator.
* The "hash()" result should be consistent with equality. Objects that
are equal should either have the same hash value, or be marked as
unhashable.
Python does not enforce these consistency rules. In fact, the
not-a-number values are an example for not following these rules.
Membership test operations
==========================
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z", for which
the expression "x is z or x == z" is true, is produced while iterating
over "y". If an exception is raised during the iteration, it is as if
"in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x is y[i] or x == y[i]", and no
lower integer index raises the "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse truth value of
"in".
Identity comparisons
====================
The operators "is" and "is not" test for an object’s identity: "x is
y" is true if and only if *x* and *y* are the same object. An
Object’s identity is determined using the "id()" function. "x is not
y" yields the inverse truth value. [4]
u�l Compound statements
*******************
Compound statements contain (groups of) other statements; they affect
or control the execution of those other statements in some way. In
general, compound statements span multiple lines, although in simple
incarnations a whole compound statement may be contained in one line.
The "if", "while" and "for" statements implement traditional control
flow constructs. "try" specifies exception handlers and/or cleanup
code for a group of statements, while the "with" statement allows the
execution of initialization and finalization code around a block of
code. Function and class definitions are also syntactically compound
statements.
A compound statement consists of one or more ‘clauses.’ A clause
consists of a header and a ‘suite.’ The clause headers of a
particular compound statement are all at the same indentation level.
Each clause header begins with a uniquely identifying keyword and ends
with a colon. A suite is a group of statements controlled by a
clause. A suite can be one or more semicolon-separated simple
statements on the same line as the header, following the header’s
colon, or it can be one or more indented statements on subsequent
lines. Only the latter form of a suite can contain nested compound
statements; the following is illegal, mostly because it wouldn’t be
clear to which "if" clause a following "else" clause would belong:
if test1: if test2: print(x)
Also note that the semicolon binds tighter than the colon in this
context, so that in the following example, either all or none of the
"print()" calls are executed:
if x < y < z: print(x); print(y); print(z)
Summarizing:
compound_stmt ::= if_stmt
| while_stmt
| for_stmt
| try_stmt
| with_stmt
| funcdef
| classdef
| async_with_stmt
| async_for_stmt
| async_funcdef
suite ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT
statement ::= stmt_list NEWLINE | compound_stmt
stmt_list ::= simple_stmt (";" simple_stmt)* [";"]
Note that statements always end in a "NEWLINE" possibly followed by a
"DEDENT". Also note that optional continuation clauses always begin
with a keyword that cannot start a statement, thus there are no
ambiguities (the ‘dangling "else"’ problem is solved in Python by
requiring nested "if" statements to be indented).
The formatting of the grammar rules in the following sections places
each clause on a separate line for clarity.
The "if" statement
==================
The "if" statement is used for conditional execution:
if_stmt ::= "if" assignment_expression ":" suite
("elif" assignment_expression ":" suite)*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
The "while" statement
=====================
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" assignment_expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
The "for" statement
===================
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator. Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.
The for-loop makes assignments to the variables in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop. Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".
Note:
There is a subtlety when the sequence is being modified by the loop
(this can only occur for mutable sequences, e.g. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
The "try" statement
===================
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object, or a tuple containing an item that is the class or a base
class of the exception object.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed. All except
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred. "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.
The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed. Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a ‘cleanup’ handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return", "break" or "continue" statement, the saved
exception is discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
Changed in version 3.8: Prior to Python 3.8, a "continue" statement
was illegal in the "finally" clause due to a problem with the
implementation.
The "with" statement
====================
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one “item” proceeds as
follows:
1. The context expression (the expression given in the "with_item") is
evaluated to obtain a context manager.
2. The context manager’s "__enter__()" is loaded for later use.
3. The context manager’s "__exit__()" is loaded for later use.
4. The context manager’s "__enter__()" method is invoked.
5. If a target was included in the "with" statement, the return value
from "__enter__()" is assigned to it.
Note:
The "with" statement guarantees that if the "__enter__()" method
returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
6. The suite is executed.
7. The context manager’s "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
The following code:
with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
enter = type(manager).__enter__
exit = type(manager).__exit__
value = enter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not exit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
exit(manager, None, None, None)
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
SUITE
is semantically equivalent to:
with A() as a:
with B() as b:
SUITE
Changed in version 3.1: Support for multiple context expressions.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
Function definitions
====================
A function definition defines a user-defined function object (see
section The standard type hierarchy):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= defparameter ("," defparameter)* "," "/" ["," [parameter_list_no_posonly]]
| parameter_list_no_posonly
parameter_list_no_posonly ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
| parameter_list_starargs
parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
| "**" parameter [","]
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [2]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
except that the original function is not temporarily bound to the name
"func".
When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted. If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.
**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call. This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended. A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from positional arguments, from keyword
arguments, or from default values. If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type. Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed by
keyword arguments. Parameters before “"/"” are positional-only
parameters and may only be passed by positional arguments.
Changed in version 3.8: The "/" function parameter syntax may be used
to indicate positional-only parameters. See **PEP 570** for details.
Parameters may have an *annotation* of the form “": expression"”
following the parameter name. Any parameter may have an annotation,
even those of the form "*identifier" or "**identifier". Functions may
have “return” annotation of the form “"-> expression"” after the
parameter list. These annotations can be any valid Python expression.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object. If the "annotations" import from
"__future__" is used, annotations are preserved as strings at runtime
which enables postponed evaluation. Otherwise, they are evaluated
when the function definition is executed. In this case annotations
may be evaluated in a different order than they appear in the source
code.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.
**Programmer’s note:** Functions are first-class objects. A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around. Free variables used
in the nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
See also:
**PEP 3107** - Function Annotations
The original specification for function annotations.
**PEP 484** - Type Hints
Definition of a standard meaning for annotations: type hints.
**PEP 526** - Syntax for Variable Annotations
Ability to type hint variable declarations, including class
variables and instance variables
**PEP 563** - Postponed Evaluation of Annotations
Support for forward references within annotations by preserving
annotations in a string form at runtime instead of eager
evaluation.
Class definitions
=================
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [argument_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing. Classes without an inheritance list
inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.) When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [3] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.
The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__". Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.
Class creation can be customized heavily using metaclasses.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result is then bound to the class name.
**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. Descriptors can be used to create instance
variables with different implementation details.
See also:
**PEP 3115** - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the
current syntax, and the semantics for how classes with
metaclasses are constructed.
**PEP 3129** - Class Decorators
The proposal that added class decorators. Function and method
decorators were introduced in **PEP 318**.
Coroutines
==========
New in version 3.5.
Coroutine function definition
-----------------------------
async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*). Inside the body of a coroutine function,
"await" and "async" identifiers become reserved keywords; "await"
expressions, "async for" and "async with" can only be used in
coroutine function bodies.
Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.
It is a "SyntaxError" to use a "yield from" expression inside the body
of a coroutine function.
An example of a coroutine function:
async def func(param1, param2):
do_stuff()
await some_coroutine()
The "async for" statement
-------------------------
async_for_stmt ::= "async" for_stmt
An *asynchronous iterable* is able to call asynchronous code in its
*iter* implementation, and *asynchronous iterator* can call
asynchronous code in its *next* method.
The "async for" statement allows convenient iteration over
asynchronous iterators.
The following code:
async for TARGET in ITER:
SUITE
else:
SUITE2
Is semantically equivalent to:
iter = (ITER)
iter = type(iter).__aiter__(iter)
running = True
while running:
try:
TARGET = await type(iter).__anext__(iter)
except StopAsyncIteration:
running = False
else:
SUITE
else:
SUITE2
See also "__aiter__()" and "__anext__()" for details.
It is a "SyntaxError" to use an "async for" statement outside the body
of a coroutine function.
The "async with" statement
--------------------------
async_with_stmt ::= "async" with_stmt
An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.
The following code:
async with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
aexit = type(manager).__aexit__
aenter = type(manager).__aenter__
value = await aenter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not await aexit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
await aexit(manager, None, None, None)
See also "__aenter__()" and "__aexit__()" for details.
It is a "SyntaxError" to use an "async with" statement outside the
body of a coroutine function.
See also:
**PEP 492** - Coroutines with async and await syntax
The proposal that made coroutines a proper standalone concept in
Python, and added supporting syntax.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless there
is a "finally" clause which happens to raise another exception.
That new exception causes the old one to be lost.
[2] A string literal appearing as the first statement in the function
body is transformed into the function’s "__doc__" attribute and
therefore the function’s *docstring*.
[3] A string literal appearing as the first statement in the class
body is transformed into the namespace’s "__doc__" item and
therefore the class’s *docstring*.
u� With Statement Context Managers
*******************************
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method’s return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller’s responsibility.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
a� The "continue" statement
************************
continue_stmt ::= "continue"
"continue" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop. It continues with the next cycle of the nearest enclosing loop.
When "continue" passes control out of a "try" statement with a
"finally" clause, that "finally" clause is executed before really
starting the next loop cycle.
u� Arithmetic conversions
**********************
When a description of an arithmetic operator below uses the phrase
“the numeric arguments are converted to a common type”, this means
that the operator implementation for built-in types works as follows:
* If either argument is a complex number, the other is converted to
complex;
* otherwise, if either argument is a floating point number, the other
is converted to floating point;
* otherwise, both must be integers and no conversion is necessary.
Some additional rules apply for certain operators (e.g., a string as a
left argument to the ‘%’ operator). Extensions must define their own
conversion behavior.
uS5 Basic customization
*******************
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass’s "__new__()" method using
"super().__new__(cls[, ...])" with appropriate arguments and then
modifying the newly-created instance as necessary before returning
it.
If "__new__()" is invoked during object construction and it returns
an instance of *cls*, then the new instance’s "__init__()" method
will be invoked like "__init__(self[, ...])", where *self* is the
new instance and the remaining arguments are the same as were
passed to the object constructor.
If "__new__()" does not return an instance of *cls*, then the new
instance’s "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class’s "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"super().__init__([args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.
It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.
It is not guaranteed that "__del__()" methods are called for
objects that still exist when the interpreter exits.
Note:
"del x" doesn’t directly call "x.__del__()" — the former
decrements the reference count for "x" by one, and the latter is
only called when "x"’s reference count reaches zero.
**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.
See also: Documentation for the "gc" module.
Warning:
Due to the precarious circumstances under which "__del__()"
methods are invoked, exceptions that occur during their execution
are ignored, and a warning is printed to "sys.stderr" instead.
In particular:
* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".
* "__del__()" can be executed during interpreter shutdown. As a
consequence, the global variables it needs to access (including
other modules) may already have been deleted or set to "None".
Python guarantees that globals whose name begins with a single
underscore are deleted from their module before other globals
are deleted; if no other references to such globals exist, this
may help in assuring that imported modules are still available
at the time when the "__del__()" method is called.
object.__repr__(self)
Called by the "repr()" built-in function to compute the “official”
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an “informal”
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the “informal” or nicely printable string
representation of an object. The return value must be a string
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by bytes to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function, and by extension,
evaluation of formatted string literals and the "str.format()"
method, to produce a “formatted” string representation of an
object. The *format_spec* argument is a string that contains a
description of the formatting options desired. The interpretation
of the *format_spec* argument is up to the type implementing
"__format__()", however most classes will either delegate
formatting to one of the built-in types, or use a similar
formatting option syntax.
See Format Specification Mini-Language for a description of the
standard formatting syntax.
The return value must be a string object.
Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.
Changed in version 3.7: "object.__format__(x, '')" is now
equivalent to "str(x)" rather than "format(str(self), '')".
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
By default, "object" implements "__eq__()" by using "is", returning
"NotImplemented" in the case of a false comparison: "True if x is y
else NotImplemented". For "__ne__()", by default it delegates to
"__eq__()" and inverts the result unless it is "NotImplemented".
There are no other implied relationships among the comparison
operators or default implementations; for example, the truth of
"(x<y or x==y)" does not imply "x<=y". To automatically generate
ordering operations from a single root operation, see
"functools.total_ordering()".
See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
Note:
"hash()" truncates the value returned from an object’s custom
"__hash__()" method to the size of a "Py_ssize_t". This is
typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds.
If an object’s "__hash__()" must interoperate on builds of
different bit sizes, be sure to check the width on all supported
builds. An easy way to do this is with "python -c "import sys;
print(sys.hash_info.width)"".
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key’s hash value is immutable (if the object’s hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj,
collections.abc.Hashable)".
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
<ParentClass>.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.abc.Hashable)" call.
Note:
By default, the "__hash__()" values of str and bytes objects are
“salted” with an unpredictable random value. Although they
remain constant within an individual Python process, they are not
predictable between repeated invocations of Python.This is
intended to provide protection against a denial-of-service caused
by carefully-chosen inputs that exploit the worst case
performance of a dict insertion, O(n^2) complexity. See
http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of sets.
Python has never made guarantees about this ordering (and it
typically varies between 32-bit and 64-bit builds).See also
"PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
u�I "pdb" — The Python Debugger
***************************
**Source code:** Lib/pdb.py
======================================================================
The module "pdb" defines an interactive source code debugger for
Python programs. It supports setting (conditional) breakpoints and
single stepping at the source line level, inspection of stack frames,
source code listing, and evaluation of arbitrary Python code in the
context of any stack frame. It also supports post-mortem debugging
and can be called under program control.
The debugger is extensible – it is actually defined as the class
"Pdb". This is currently undocumented but easily understood by reading
the source. The extension interface uses the modules "bdb" and "cmd".
The debugger’s prompt is "(Pdb)". Typical usage to run a program under
control of the debugger is:
>>> import pdb
>>> import mymodule
>>> pdb.run('mymodule.test()')
> <string>(0)?()
(Pdb) continue
> <string>(1)?()
(Pdb) continue
NameError: 'spam'
> <string>(1)?()
(Pdb)
Changed in version 3.3: Tab-completion via the "readline" module is
available for commands and command arguments, e.g. the current global
and local names are offered as arguments of the "p" command.
"pdb.py" can also be invoked as a script to debug other scripts. For
example:
python3 -m pdb myscript.py
When invoked as a script, pdb will automatically enter post-mortem
debugging if the program being debugged exits abnormally. After post-
mortem debugging (or after normal exit of the program), pdb will
restart the program. Automatic restarting preserves pdb’s state (such
as breakpoints) and in most cases is more useful than quitting the
debugger upon program’s exit.
New in version 3.2: "pdb.py" now accepts a "-c" option that executes
commands as if given in a ".pdbrc" file, see Debugger Commands.
New in version 3.7: "pdb.py" now accepts a "-m" option that execute
modules similar to the way "python3 -m" does. As with a script, the
debugger will pause execution just before the first line of the
module.
The typical usage to break into the debugger from a running program is
to insert
import pdb; pdb.set_trace()
at the location you want to break into the debugger. You can then
step through the code following this statement, and continue running
without the debugger using the "continue" command.
New in version 3.7: The built-in "breakpoint()", when called with
defaults, can be used instead of "import pdb; pdb.set_trace()".
The typical usage to inspect a crashed program is:
>>> import pdb
>>> import mymodule
>>> mymodule.test()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "./mymodule.py", line 4, in test
test2()
File "./mymodule.py", line 3, in test2
print(spam)
NameError: spam
>>> pdb.pm()
> ./mymodule.py(3)test2()
-> print(spam)
(Pdb)
The module defines the following functions; each enters the debugger
in a slightly different way:
pdb.run(statement, globals=None, locals=None)
Execute the *statement* (given as a string or a code object) under
debugger control. The debugger prompt appears before any code is
executed; you can set breakpoints and type "continue", or you can
step through the statement using "step" or "next" (all these
commands are explained below). The optional *globals* and *locals*
arguments specify the environment in which the code is executed; by
default the dictionary of the module "__main__" is used. (See the
explanation of the built-in "exec()" or "eval()" functions.)
pdb.runeval(expression, globals=None, locals=None)
Evaluate the *expression* (given as a string or a code object)
under debugger control. When "runeval()" returns, it returns the
value of the expression. Otherwise this function is similar to
"run()".
pdb.runcall(function, *args, **kwds)
Call the *function* (a function or method object, not a string)
with the given arguments. When "runcall()" returns, it returns
whatever the function call returned. The debugger prompt appears
as soon as the function is entered.
pdb.set_trace(*, header=None)
Enter the debugger at the calling stack frame. This is useful to
hard-code a breakpoint at a given point in a program, even if the
code is not otherwise being debugged (e.g. when an assertion
fails). If given, *header* is printed to the console just before
debugging begins.
Changed in version 3.7: The keyword-only argument *header*.
pdb.post_mortem(traceback=None)
Enter post-mortem debugging of the given *traceback* object. If no
*traceback* is given, it uses the one of the exception that is
currently being handled (an exception must be being handled if the
default is to be used).
pdb.pm()
Enter post-mortem debugging of the traceback found in
"sys.last_traceback".
The "run*" functions and "set_trace()" are aliases for instantiating
the "Pdb" class and calling the method of the same name. If you want
to access further features, you have to do this yourself:
class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None, nosigint=False, readrc=True)
"Pdb" is the debugger class.
The *completekey*, *stdin* and *stdout* arguments are passed to the
underlying "cmd.Cmd" class; see the description there.
The *skip* argument, if given, must be an iterable of glob-style
module name patterns. The debugger will not step into frames that
originate in a module that matches one of these patterns. [1]
By default, Pdb sets a handler for the SIGINT signal (which is sent
when the user presses "Ctrl-C" on the console) when you give a
"continue" command. This allows you to break into the debugger
again by pressing "Ctrl-C". If you want Pdb not to touch the
SIGINT handler, set *nosigint* to true.
The *readrc* argument defaults to true and controls whether Pdb
will load .pdbrc files from the filesystem.
Example call to enable tracing with *skip*:
import pdb; pdb.Pdb(skip=['django.*']).set_trace()
Raises an auditing event "pdb.Pdb" with no arguments.
New in version 3.1: The *skip* argument.
New in version 3.2: The *nosigint* argument. Previously, a SIGINT
handler was never set by Pdb.
Changed in version 3.6: The *readrc* argument.
run(statement, globals=None, locals=None)
runeval(expression, globals=None, locals=None)
runcall(function, *args, **kwds)
set_trace()
See the documentation for the functions explained above.
Debugger Commands
=================
The commands recognized by the debugger are listed below. Most
commands can be abbreviated to one or two letters as indicated; e.g.
"h(elp)" means that either "h" or "help" can be used to enter the help
command (but not "he" or "hel", nor "H" or "Help" or "HELP").
Arguments to commands must be separated by whitespace (spaces or
tabs). Optional arguments are enclosed in square brackets ("[]") in
the command syntax; the square brackets must not be typed.
Alternatives in the command syntax are separated by a vertical bar
("|").
Entering a blank line repeats the last command entered. Exception: if
the last command was a "list" command, the next 11 lines are listed.
Commands that the debugger doesn’t recognize are assumed to be Python
statements and are executed in the context of the program being
debugged. Python statements can also be prefixed with an exclamation
point ("!"). This is a powerful way to inspect the program being
debugged; it is even possible to change a variable or call a function.
When an exception occurs in such a statement, the exception name is
printed but the debugger’s state is not changed.
The debugger supports aliases. Aliases can have parameters which
allows one a certain level of adaptability to the context under
examination.
Multiple commands may be entered on a single line, separated by ";;".
(A single ";" is not used as it is the separator for multiple commands
in a line that is passed to the Python parser.) No intelligence is
applied to separating the commands; the input is split at the first
";;" pair, even if it is in the middle of a quoted string.
If a file ".pdbrc" exists in the user’s home directory or in the
current directory, it is read in and executed as if it had been typed
at the debugger prompt. This is particularly useful for aliases. If
both files exist, the one in the home directory is read first and
aliases defined there can be overridden by the local file.
Changed in version 3.2: ".pdbrc" can now contain commands that
continue debugging, such as "continue" or "next". Previously, these
commands had no effect.
h(elp) [command]
Without argument, print the list of available commands. With a
*command* as argument, print help about that command. "help pdb"
displays the full documentation (the docstring of the "pdb"
module). Since the *command* argument must be an identifier, "help
exec" must be entered to get help on the "!" command.
w(here)
Print a stack trace, with the most recent frame at the bottom. An
arrow indicates the current frame, which determines the context of
most commands.
d(own) [count]
Move the current frame *count* (default one) levels down in the
stack trace (to a newer frame).
u(p) [count]
Move the current frame *count* (default one) levels up in the stack
trace (to an older frame).
b(reak) [([filename:]lineno | function) [, condition]]
With a *lineno* argument, set a break there in the current file.
With a *function* argument, set a break at the first executable
statement within that function. The line number may be prefixed
with a filename and a colon, to specify a breakpoint in another
file (probably one that hasn’t been loaded yet). The file is
searched on "sys.path". Note that each breakpoint is assigned a
number to which all the other breakpoint commands refer.
If a second argument is present, it is an expression which must
evaluate to true before the breakpoint is honored.
Without argument, list all breaks, including for each breakpoint,
the number of times that breakpoint has been hit, the current
ignore count, and the associated condition if any.
tbreak [([filename:]lineno | function) [, condition]]
Temporary breakpoint, which is removed automatically when it is
first hit. The arguments are the same as for "break".
cl(ear) [filename:lineno | bpnumber [bpnumber ...]]
With a *filename:lineno* argument, clear all the breakpoints at
this line. With a space separated list of breakpoint numbers, clear
those breakpoints. Without argument, clear all breaks (but first
ask confirmation).
disable [bpnumber [bpnumber ...]]
Disable the breakpoints given as a space separated list of
breakpoint numbers. Disabling a breakpoint means it cannot cause
the program to stop execution, but unlike clearing a breakpoint, it
remains in the list of breakpoints and can be (re-)enabled.
enable [bpnumber [bpnumber ...]]
Enable the breakpoints specified.
ignore bpnumber [count]
Set the ignore count for the given breakpoint number. If count is
omitted, the ignore count is set to 0. A breakpoint becomes active
when the ignore count is zero. When non-zero, the count is
decremented each time the breakpoint is reached and the breakpoint
is not disabled and any associated condition evaluates to true.
condition bpnumber [condition]
Set a new *condition* for the breakpoint, an expression which must
evaluate to true before the breakpoint is honored. If *condition*
is absent, any existing condition is removed; i.e., the breakpoint
is made unconditional.
commands [bpnumber]
Specify a list of commands for breakpoint number *bpnumber*. The
commands themselves appear on the following lines. Type a line
containing just "end" to terminate the commands. An example:
(Pdb) commands 1
(com) p some_variable
(com) end
(Pdb)
To remove all commands from a breakpoint, type "commands" and
follow it immediately with "end"; that is, give no commands.
With no *bpnumber* argument, "commands" refers to the last
breakpoint set.
You can use breakpoint commands to start your program up again.
Simply use the "continue" command, or "step", or any other command
that resumes execution.
Specifying any command resuming execution (currently "continue",
"step", "next", "return", "jump", "quit" and their abbreviations)
terminates the command list (as if that command was immediately
followed by end). This is because any time you resume execution
(even with a simple next or step), you may encounter another
breakpoint—which could have its own command list, leading to
ambiguities about which list to execute.
If you use the ‘silent’ command in the command list, the usual
message about stopping at a breakpoint is not printed. This may be
desirable for breakpoints that are to print a specific message and
then continue. If none of the other commands print anything, you
see no sign that the breakpoint was reached.
s(tep)
Execute the current line, stop at the first possible occasion
(either in a function that is called or on the next line in the
current function).
n(ext)
Continue execution until the next line in the current function is
reached or it returns. (The difference between "next" and "step"
is that "step" stops inside a called function, while "next"
executes called functions at (nearly) full speed, only stopping at
the next line in the current function.)
unt(il) [lineno]
Without argument, continue execution until the line with a number
greater than the current one is reached.
With a line number, continue execution until a line with a number
greater or equal to that is reached. In both cases, also stop when
the current frame returns.
Changed in version 3.2: Allow giving an explicit line number.
r(eturn)
Continue execution until the current function returns.
c(ont(inue))
Continue execution, only stop when a breakpoint is encountered.
j(ump) lineno
Set the next line that will be executed. Only available in the
bottom-most frame. This lets you jump back and execute code again,
or jump forward to skip code that you don’t want to run.
It should be noted that not all jumps are allowed – for instance it
is not possible to jump into the middle of a "for" loop or out of a
"finally" clause.
l(ist) [first[, last]]
List source code for the current file. Without arguments, list 11
lines around the current line or continue the previous listing.
With "." as argument, list 11 lines around the current line. With
one argument, list 11 lines around at that line. With two
arguments, list the given range; if the second argument is less
than the first, it is interpreted as a count.
The current line in the current frame is indicated by "->". If an
exception is being debugged, the line where the exception was
originally raised or propagated is indicated by ">>", if it differs
from the current line.
New in version 3.2: The ">>" marker.
ll | longlist
List all source code for the current function or frame.
Interesting lines are marked as for "list".
New in version 3.2.
a(rgs)
Print the argument list of the current function.
p expression
Evaluate the *expression* in the current context and print its
value.
Note:
"print()" can also be used, but is not a debugger command — this
executes the Python "print()" function.
pp expression
Like the "p" command, except the value of the expression is pretty-
printed using the "pprint" module.
whatis expression
Print the type of the *expression*.
source expression
Try to get source code for the given object and display it.
New in version 3.2.
display [expression]
Display the value of the expression if it changed, each time
execution stops in the current frame.
Without expression, list all display expressions for the current
frame.
New in version 3.2.
undisplay [expression]
Do not display the expression any more in the current frame.
Without expression, clear all display expressions for the current
frame.
New in version 3.2.
interact
Start an interactive interpreter (using the "code" module) whose
global namespace contains all the (global and local) names found in
the current scope.
New in version 3.2.
alias [name [command]]
Create an alias called *name* that executes *command*. The command
must *not* be enclosed in quotes. Replaceable parameters can be
indicated by "%1", "%2", and so on, while "%*" is replaced by all
the parameters. If no command is given, the current alias for
*name* is shown. If no arguments are given, all aliases are listed.
Aliases may be nested and can contain anything that can be legally
typed at the pdb prompt. Note that internal pdb commands *can* be
overridden by aliases. Such a command is then hidden until the
alias is removed. Aliasing is recursively applied to the first
word of the command line; all other words in the line are left
alone.
As an example, here are two useful aliases (especially when placed
in the ".pdbrc" file):
# Print instance variables (usage "pi classInst")
alias pi for k in %1.__dict__.keys(): print("%1.",k,"=",%1.__dict__[k])
# Print instance variables in self
alias ps pi self
unalias name
Delete the specified alias.
! statement
Execute the (one-line) *statement* in the context of the current
stack frame. The exclamation point can be omitted unless the first
word of the statement resembles a debugger command. To set a
global variable, you can prefix the assignment command with a
"global" statement on the same line, e.g.:
(Pdb) global list_options; list_options = ['-l']
(Pdb)
run [args ...]
restart [args ...]
Restart the debugged Python program. If an argument is supplied,
it is split with "shlex" and the result is used as the new
"sys.argv". History, breakpoints, actions and debugger options are
preserved. "restart" is an alias for "run".
q(uit)
Quit from the debugger. The program being executed is aborted.
debug code
Enter a recursive debugger that steps through the code argument
(which is an arbitrary expression or statement to be executed in
the current environment).
retval
Print the return value for the last return of a function.
-[ Footnotes ]-
[1] Whether a frame is considered to originate in a certain module is
determined by the "__name__" in the frame globals.
a� The "del" statement
*******************
del_stmt ::= "del" target_list
Deletion is recursively defined very similar to the way assignment is
defined. Rather than spelling it out in full details, here are some
hints.
Deletion of a target list recursively deletes each target, from left
to right.
Deletion of a name removes the binding of that name from the local or
global namespace, depending on whether the name occurs in a "global"
statement in the same code block. If the name is unbound, a
"NameError" exception will be raised.
Deletion of attribute references, subscriptions and slicings is passed
to the primary object involved; deletion of a slicing is in general
equivalent to assignment of an empty slice of the right type (but even
this is determined by the sliced object).
Changed in version 3.2: Previously it was illegal to delete a name
from the local namespace if it occurs as a free variable in a nested
block.
u Dictionary displays
*******************
A dictionary display is a possibly empty series of key/datum pairs
enclosed in curly braces:
dict_display ::= "{" [key_datum_list | dict_comprehension] "}"
key_datum_list ::= key_datum ("," key_datum)* [","]
key_datum ::= expression ":" expression | "**" or_expr
dict_comprehension ::= expression ":" expression comp_for
A dictionary display yields a new dictionary object.
If a comma-separated sequence of key/datum pairs is given, they are
evaluated from left to right to define the entries of the dictionary:
each key object is used as a key into the dictionary to store the
corresponding datum. This means that you can specify the same key
multiple times in the key/datum list, and the final dictionary’s value
for that key will be the last one given.
A double asterisk "**" denotes *dictionary unpacking*. Its operand
must be a *mapping*. Each mapping item is added to the new
dictionary. Later values replace values already set by earlier
key/datum pairs and earlier dictionary unpackings.
New in version 3.5: Unpacking into dictionary displays, originally
proposed by **PEP 448**.
A dict comprehension, in contrast to list and set comprehensions,
needs two expressions separated with a colon followed by the usual
“for” and “if” clauses. When the comprehension is run, the resulting
key and value elements are inserted in the new dictionary in the order
they are produced.
Restrictions on the types of the key values are listed earlier in
section The standard type hierarchy. (To summarize, the key type
should be *hashable*, which excludes all mutable objects.) Clashes
between duplicate keys are not detected; the last datum (textually
rightmost in the display) stored for a given key value prevails.
Changed in version 3.8: Prior to Python 3.8, in dict comprehensions,
the evaluation order of key and value was not well-defined. In
CPython, the value was evaluated before the key. Starting with 3.8,
the key is evaluated before the value, as proposed by **PEP 572**.
a� Interaction with dynamic features
*********************************
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
aX The "if" statement
******************
The "if" statement is used for conditional execution:
if_stmt ::= "if" assignment_expression ":" suite
("elif" assignment_expression ":" suite)*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
u� Exceptions
**********
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack traceback, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note:
Exception messages are not part of the Python API. Their contents
may change from one version of Python to the next without warning
and should not be relied on by code which will run under multiple
versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by these
operations is not available at the time the module is compiled.
u$ Execution model
***************
Structure of a program
======================
A Python program is constructed from code blocks. A *block* is a piece
of Python program text that is executed as a unit. The following are
blocks: a module, a function body, and a class definition. Each
command typed interactively is a block. A script file (a file given
as standard input to the interpreter or specified as a command line
argument to the interpreter) is a code block. A script command (a
command specified on the interpreter command line with the "-c"
option) is a code block. The string argument passed to the built-in
functions "eval()" and "exec()" is a code block.
A code block is executed in an *execution frame*. A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block’s execution has
completed.
Naming and binding
==================
Binding of names
----------------
*Names* refer to objects. Names are introduced by name binding
operations.
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore. This form
may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global". If a name is bound at the
module level, it is a global variable. (The variables of the module
code block are local and global.) If a variable is used in a code
block but not defined there, it is a *free variable*.
Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.
Resolution of names
-------------------
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block’s *environment*.
When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins". The global namespace is searched first. If
the name is not found there, the builtins namespace is searched. The
"global" statement must precede all uses of the name.
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope. This means that the following
will fail:
class A:
a = 42
b = list(a + i for i in range(10))
Builtins and restricted execution
---------------------------------
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.
Interaction with dynamic features
---------------------------------
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
Exceptions
==========
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack traceback, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note:
Exception messages are not part of the Python API. Their contents
may change from one version of Python to the next without warning
and should not be relied on by code which will run under multiple
versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by these
operations is not available at the time the module is compiled.
uz Expression lists
****************
expression_list ::= expression ("," expression)* [","]
starred_list ::= starred_item ("," starred_item)* [","]
starred_expression ::= expression | (starred_item ",")* [starred_item]
starred_item ::= assignment_expression | "*" or_expr
Except when part of a list or set display, an expression list
containing at least one comma yields a tuple. The length of the tuple
is the number of expressions in the list. The expressions are
evaluated from left to right.
An asterisk "*" denotes *iterable unpacking*. Its operand must be an
*iterable*. The iterable is expanded into a sequence of items, which
are included in the new tuple, list, or set, at the site of the
unpacking.
New in version 3.5: Iterable unpacking in expression lists, originally
proposed by **PEP 448**.
The trailing comma is required only to create a single tuple (a.k.a. a
*singleton*); it is optional in all other cases. A single expression
without a trailing comma doesn’t create a tuple, but rather yields the
value of that expression. (To create an empty tuple, use an empty pair
of parentheses: "()".)
a� Floating point literals
***********************
Floating point literals are described by the following lexical
definitions:
floatnumber ::= pointfloat | exponentfloat
pointfloat ::= [digitpart] fraction | digitpart "."
exponentfloat ::= (digitpart | pointfloat) exponent
digitpart ::= digit (["_"] digit)*
fraction ::= "." digitpart
exponent ::= ("e" | "E") ["+" | "-"] digitpart
Note that the integer and exponent parts are always interpreted using
radix 10. For example, "077e010" is legal, and denotes the same number
as "77e10". The allowed range of floating point literals is
implementation-dependent. As in integer literals, underscores are
supported for digit grouping.
Some examples of floating point literals:
3.14 10. .001 1e100 3.14e-10 0e0 3.14_15_93
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
u�
The "for" statement
*******************
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator. Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.
The for-loop makes assignments to the variables in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop. Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".
Note:
There is a subtlety when the sequence is being modified by the loop
(this can only occur for mutable sequences, e.g. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
u�` Format String Syntax
********************
The "str.format()" method and the "Formatter" class share the same
syntax for format strings (although in the case of "Formatter",
subclasses can define their own format string syntax). The syntax is
related to that of formatted string literals, but it is less
sophisticated and, in particular, does not support arbitrary
expressions.
Format strings contain “replacement fields” surrounded by curly braces
"{}". Anything that is not contained in braces is considered literal
text, which is copied unchanged to the output. If you need to include
a brace character in the literal text, it can be escaped by doubling:
"{{" and "}}".
The grammar for a replacement field is as follows:
replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}"
field_name ::= arg_name ("." attribute_name | "[" element_index "]")*
arg_name ::= [identifier | digit+]
attribute_name ::= identifier
element_index ::= digit+ | index_string
index_string ::= <any source character except "]"> +
conversion ::= "r" | "s" | "a"
format_spec ::= <described in the next section>
In less formal terms, the replacement field can start with a
*field_name* that specifies the object whose value is to be formatted
and inserted into the output instead of the replacement field. The
*field_name* is optionally followed by a *conversion* field, which is
preceded by an exclamation point "'!'", and a *format_spec*, which is
preceded by a colon "':'". These specify a non-default format for the
replacement value.
See also the Format Specification Mini-Language section.
The *field_name* itself begins with an *arg_name* that is either a
number or a keyword. If it’s a number, it refers to a positional
argument, and if it’s a keyword, it refers to a named keyword
argument. If the numerical arg_names in a format string are 0, 1, 2,
… in sequence, they can all be omitted (not just some) and the numbers
0, 1, 2, … will be automatically inserted in that order. Because
*arg_name* is not quote-delimited, it is not possible to specify
arbitrary dictionary keys (e.g., the strings "'10'" or "':-]'") within
a format string. The *arg_name* can be followed by any number of index
or attribute expressions. An expression of the form "'.name'" selects
the named attribute using "getattr()", while an expression of the form
"'[index]'" does an index lookup using "__getitem__()".
Changed in version 3.1: The positional argument specifiers can be
omitted for "str.format()", so "'{} {}'.format(a, b)" is equivalent to
"'{0} {1}'.format(a, b)".
Changed in version 3.4: The positional argument specifiers can be
omitted for "Formatter".
Some simple format string examples:
"First, thou shalt count to {0}" # References first positional argument
"Bring me a {}" # Implicitly references the first positional argument
"From {} to {}" # Same as "From {0} to {1}"
"My quest is {name}" # References keyword argument 'name'
"Weight in tons {0.weight}" # 'weight' attribute of first positional arg
"Units destroyed: {players[0]}" # First element of keyword argument 'players'.
The *conversion* field causes a type coercion before formatting.
Normally, the job of formatting a value is done by the "__format__()"
method of the value itself. However, in some cases it is desirable to
force a type to be formatted as a string, overriding its own
definition of formatting. By converting the value to a string before
calling "__format__()", the normal formatting logic is bypassed.
Three conversion flags are currently supported: "'!s'" which calls
"str()" on the value, "'!r'" which calls "repr()" and "'!a'" which
calls "ascii()".
Some examples:
"Harold's a clever {0!s}" # Calls str() on the argument first
"Bring out the holy {name!r}" # Calls repr() on the argument first
"More {!a}" # Calls ascii() on the argument first
The *format_spec* field contains a specification of how the value
should be presented, including such details as field width, alignment,
padding, decimal precision and so on. Each value type can define its
own “formatting mini-language” or interpretation of the *format_spec*.
Most built-in types support a common formatting mini-language, which
is described in the next section.
A *format_spec* field can also include nested replacement fields
within it. These nested replacement fields may contain a field name,
conversion flag and format specification, but deeper nesting is not
allowed. The replacement fields within the format_spec are
substituted before the *format_spec* string is interpreted. This
allows the formatting of a value to be dynamically specified.
See the Format examples section for some examples.
Format Specification Mini-Language
==================================
“Format specifications” are used within replacement fields contained
within a format string to define how individual values are presented
(see Format String Syntax and Formatted string literals). They can
also be passed directly to the built-in "format()" function. Each
formattable type may define how the format specification is to be
interpreted.
Most built-in types implement the following options for format
specifications, although some of the formatting options are only
supported by the numeric types.
A general convention is that an empty format specification produces
the same result as if you had called "str()" on the value. A non-empty
format specification typically modifies the result.
The general form of a *standard format specifier* is:
format_spec ::= [[fill]align][sign][#][0][width][grouping_option][.precision][type]
fill ::= <any character>
align ::= "<" | ">" | "=" | "^"
sign ::= "+" | "-" | " "
width ::= digit+
grouping_option ::= "_" | ","
precision ::= digit+
type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"
If a valid *align* value is specified, it can be preceded by a *fill*
character that can be any character and defaults to a space if
omitted. It is not possible to use a literal curly brace (”"{"” or
“"}"”) as the *fill* character in a formatted string literal or when
using the "str.format()" method. However, it is possible to insert a
curly brace with a nested replacement field. This limitation doesn’t
affect the "format()" function.
The meaning of the various alignment options is as follows:
+-----------+------------------------------------------------------------+
| Option | Meaning |
|===========|============================================================|
| "'<'" | Forces the field to be left-aligned within the available |
| | space (this is the default for most objects). |
+-----------+------------------------------------------------------------+
| "'>'" | Forces the field to be right-aligned within the available |
| | space (this is the default for numbers). |
+-----------+------------------------------------------------------------+
| "'='" | Forces the padding to be placed after the sign (if any) |
| | but before the digits. This is used for printing fields |
| | in the form ‘+000000120’. This alignment option is only |
| | valid for numeric types. It becomes the default when ‘0’ |
| | immediately precedes the field width. |
+-----------+------------------------------------------------------------+
| "'^'" | Forces the field to be centered within the available |
| | space. |
+-----------+------------------------------------------------------------+
Note that unless a minimum field width is defined, the field width
will always be the same size as the data to fill it, so that the
alignment option has no meaning in this case.
The *sign* option is only valid for number types, and can be one of
the following:
+-----------+------------------------------------------------------------+
| Option | Meaning |
|===========|============================================================|
| "'+'" | indicates that a sign should be used for both positive as |
| | well as negative numbers. |
+-----------+------------------------------------------------------------+
| "'-'" | indicates that a sign should be used only for negative |
| | numbers (this is the default behavior). |
+-----------+------------------------------------------------------------+
| space | indicates that a leading space should be used on positive |
| | numbers, and a minus sign on negative numbers. |
+-----------+------------------------------------------------------------+
The "'#'" option causes the “alternate form” to be used for the
conversion. The alternate form is defined differently for different
types. This option is only valid for integer, float and complex
types. For integers, when binary, octal, or hexadecimal output is
used, this option adds the prefix respective "'0b'", "'0o'", or "'0x'"
to the output value. For float and complex the alternate form causes
the result of the conversion to always contain a decimal-point
character, even if no digits follow it. Normally, a decimal-point
character appears in the result of these conversions only if a digit
follows it. In addition, for "'g'" and "'G'" conversions, trailing
zeros are not removed from the result.
The "','" option signals the use of a comma for a thousands separator.
For a locale aware separator, use the "'n'" integer presentation type
instead.
Changed in version 3.1: Added the "','" option (see also **PEP 378**).
The "'_'" option signals the use of an underscore for a thousands
separator for floating point presentation types and for integer
presentation type "'d'". For integer presentation types "'b'", "'o'",
"'x'", and "'X'", underscores will be inserted every 4 digits. For
other presentation types, specifying this option is an error.
Changed in version 3.6: Added the "'_'" option (see also **PEP 515**).
*width* is a decimal integer defining the minimum total field width,
including any prefixes, separators, and other formatting characters.
If not specified, then the field width will be determined by the
content.
When no explicit alignment is given, preceding the *width* field by a
zero ("'0'") character enables sign-aware zero-padding for numeric
types. This is equivalent to a *fill* character of "'0'" with an
*alignment* type of "'='".
The *precision* is a decimal number indicating how many digits should
be displayed after the decimal point for a floating point value
formatted with "'f'" and "'F'", or before and after the decimal point
for a floating point value formatted with "'g'" or "'G'". For non-
number types the field indicates the maximum field size - in other
words, how many characters will be used from the field content. The
*precision* is not allowed for integer values.
Finally, the *type* determines how the data should be presented.
The available string presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
|===========|============================================================|
| "'s'" | String format. This is the default type for strings and |
| | may be omitted. |
+-----------+------------------------------------------------------------+
| None | The same as "'s'". |
+-----------+------------------------------------------------------------+
The available integer presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
|===========|============================================================|
| "'b'" | Binary format. Outputs the number in base 2. |
+-----------+------------------------------------------------------------+
| "'c'" | Character. Converts the integer to the corresponding |
| | unicode character before printing. |
+-----------+------------------------------------------------------------+
| "'d'" | Decimal Integer. Outputs the number in base 10. |
+-----------+------------------------------------------------------------+
| "'o'" | Octal format. Outputs the number in base 8. |
+-----------+------------------------------------------------------------+
| "'x'" | Hex format. Outputs the number in base 16, using lower- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'X'" | Hex format. Outputs the number in base 16, using upper- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'d'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| None | The same as "'d'". |
+-----------+------------------------------------------------------------+
In addition to the above presentation types, integers can be formatted
with the floating point presentation types listed below (except "'n'"
and "None"). When doing so, "float()" is used to convert the integer
to a floating point number before formatting.
The available presentation types for "float" and "Decimal" values are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
|===========|============================================================|
| "'e'" | Scientific notation. For a given precision "p", formats |
| | the number in scientific notation with the letter ‘e’ |
| | separating the coefficient from the exponent. The |
| | coefficient has one digit before and "p" digits after the |
| | decimal point, for a total of "p + 1" significant digits. |
| | With no precision given, uses a precision of "6" digits |
| | after the decimal point for "float", and shows all |
| | coefficient digits for "Decimal". If no digits follow the |
| | decimal point, the decimal point is also removed unless |
| | the "#" option is used. |
+-----------+------------------------------------------------------------+
| "'E'" | Scientific notation. Same as "'e'" except it uses an upper |
| | case ‘E’ as the separator character. |
+-----------+------------------------------------------------------------+
| "'f'" | Fixed-point notation. For a given precision "p", formats |
| | the number as a decimal number with exactly "p" digits |
| | following the decimal point. With no precision given, uses |
| | a precision of "6" digits after the decimal point for |
| | "float", and uses a precision large enough to show all |
| | coefficient digits for "Decimal". If no digits follow the |
| | decimal point, the decimal point is also removed unless |
| | the "#" option is used. |
+-----------+------------------------------------------------------------+
| "'F'" | Fixed-point notation. Same as "'f'", but converts "nan" to |
| | "NAN" and "inf" to "INF". |
+-----------+------------------------------------------------------------+
| "'g'" | General format. For a given precision "p >= 1", this |
| | rounds the number to "p" significant digits and then |
| | formats the result in either fixed-point format or in |
| | scientific notation, depending on its magnitude. A |
| | precision of "0" is treated as equivalent to a precision |
| | of "1". The precise rules are as follows: suppose that |
| | the result formatted with presentation type "'e'" and |
| | precision "p-1" would have exponent "exp". Then, if "m <= |
| | exp < p", where "m" is -4 for floats and -6 for |
| | "Decimals", the number is formatted with presentation type |
| | "'f'" and precision "p-1-exp". Otherwise, the number is |
| | formatted with presentation type "'e'" and precision |
| | "p-1". In both cases insignificant trailing zeros are |
| | removed from the significand, and the decimal point is |
| | also removed if there are no remaining digits following |
| | it, unless the "'#'" option is used. With no precision |
| | given, uses a precision of "6" significant digits for |
| | "float". For "Decimal", the coefficient of the result is |
| | formed from the coefficient digits of the value; |
| | scientific notation is used for values smaller than "1e-6" |
| | in absolute value and values where the place value of the |
| | least significant digit is larger than 1, and fixed-point |
| | notation is used otherwise. Positive and negative |
| | infinity, positive and negative zero, and nans, are |
| | formatted as "inf", "-inf", "0", "-0" and "nan" |
| | respectively, regardless of the precision. |
+-----------+------------------------------------------------------------+
| "'G'" | General format. Same as "'g'" except switches to "'E'" if |
| | the number gets too large. The representations of infinity |
| | and NaN are uppercased, too. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'g'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| "'%'" | Percentage. Multiplies the number by 100 and displays in |
| | fixed ("'f'") format, followed by a percent sign. |
+-----------+------------------------------------------------------------+
| None | For "float" this is the same as "'g'", except that when |
| | fixed-point notation is used to format the result, it |
| | always includes at least one digit past the decimal point. |
| | The precision used is as large as needed to represent the |
| | given value faithfully. For "Decimal", this is the same |
| | as either "'g'" or "'G'" depending on the value of |
| | "context.capitals" for the current decimal context. The |
| | overall effect is to match the output of "str()" as |
| | altered by the other format modifiers. |
+-----------+------------------------------------------------------------+
Format examples
===============
This section contains examples of the "str.format()" syntax and
comparison with the old "%"-formatting.
In most of the cases the syntax is similar to the old "%"-formatting,
with the addition of the "{}" and with ":" used instead of "%". For
example, "'%03.2f'" can be translated to "'{:03.2f}'".
The new format syntax also supports new and different options, shown
in the following examples.
Accessing arguments by position:
>>> '{0}, {1}, {2}'.format('a', 'b', 'c')
'a, b, c'
>>> '{}, {}, {}'.format('a', 'b', 'c') # 3.1+ only
'a, b, c'
>>> '{2}, {1}, {0}'.format('a', 'b', 'c')
'c, b, a'
>>> '{2}, {1}, {0}'.format(*'abc') # unpacking argument sequence
'c, b, a'
>>> '{0}{1}{0}'.format('abra', 'cad') # arguments' indices can be repeated
'abracadabra'
Accessing arguments by name:
>>> 'Coordinates: {latitude}, {longitude}'.format(latitude='37.24N', longitude='-115.81W')
'Coordinates: 37.24N, -115.81W'
>>> coord = {'latitude': '37.24N', 'longitude': '-115.81W'}
>>> 'Coordinates: {latitude}, {longitude}'.format(**coord)
'Coordinates: 37.24N, -115.81W'
Accessing arguments’ attributes:
>>> c = 3-5j
>>> ('The complex number {0} is formed from the real part {0.real} '
... 'and the imaginary part {0.imag}.').format(c)
'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.'
>>> class Point:
... def __init__(self, x, y):
... self.x, self.y = x, y
... def __str__(self):
... return 'Point({self.x}, {self.y})'.format(self=self)
...
>>> str(Point(4, 2))
'Point(4, 2)'
Accessing arguments’ items:
>>> coord = (3, 5)
>>> 'X: {0[0]}; Y: {0[1]}'.format(coord)
'X: 3; Y: 5'
Replacing "%s" and "%r":
>>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2')
"repr() shows quotes: 'test1'; str() doesn't: test2"
Aligning the text and specifying a width:
>>> '{:<30}'.format('left aligned')
'left aligned '
>>> '{:>30}'.format('right aligned')
' right aligned'
>>> '{:^30}'.format('centered')
' centered '
>>> '{:*^30}'.format('centered') # use '*' as a fill char
'***********centered***********'
Replacing "%+f", "%-f", and "% f" and specifying a sign:
>>> '{:+f}; {:+f}'.format(3.14, -3.14) # show it always
'+3.140000; -3.140000'
>>> '{: f}; {: f}'.format(3.14, -3.14) # show a space for positive numbers
' 3.140000; -3.140000'
>>> '{:-f}; {:-f}'.format(3.14, -3.14) # show only the minus -- same as '{:f}; {:f}'
'3.140000; -3.140000'
Replacing "%x" and "%o" and converting the value to different bases:
>>> # format also supports binary numbers
>>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: {0:b}".format(42)
'int: 42; hex: 2a; oct: 52; bin: 101010'
>>> # with 0x, 0o, or 0b as prefix:
>>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: {0:#b}".format(42)
'int: 42; hex: 0x2a; oct: 0o52; bin: 0b101010'
Using the comma as a thousands separator:
>>> '{:,}'.format(1234567890)
'1,234,567,890'
Expressing a percentage:
>>> points = 19
>>> total = 22
>>> 'Correct answers: {:.2%}'.format(points/total)
'Correct answers: 86.36%'
Using type-specific formatting:
>>> import datetime
>>> d = datetime.datetime(2010, 7, 4, 12, 15, 58)
>>> '{:%Y-%m-%d %H:%M:%S}'.format(d)
'2010-07-04 12:15:58'
Nesting arguments and more complex examples:
>>> for align, text in zip('<^>', ['left', 'center', 'right']):
... '{0:{fill}{align}16}'.format(text, fill=align, align=align)
...
'left<<<<<<<<<<<<'
'^^^^^center^^^^^'
'>>>>>>>>>>>right'
>>>
>>> octets = [192, 168, 0, 1]
>>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets)
'C0A80001'
>>> int(_, 16)
3232235521
>>>
>>> width = 5
>>> for num in range(5,12):
... for base in 'dXob':
... print('{0:{width}{base}}'.format(num, base=base, width=width), end=' ')
... print()
...
5 5 5 101
6 6 6 110
7 7 7 111
8 8 10 1000
9 9 11 1001
10 A 12 1010
11 B 13 1011
u| Function definitions
********************
A function definition defines a user-defined function object (see
section The standard type hierarchy):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= defparameter ("," defparameter)* "," "/" ["," [parameter_list_no_posonly]]
| parameter_list_no_posonly
parameter_list_no_posonly ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
| parameter_list_starargs
parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
| "**" parameter [","]
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [2]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
except that the original function is not temporarily bound to the name
"func".
When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted. If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.
**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call. This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended. A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from positional arguments, from keyword
arguments, or from default values. If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type. Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed by
keyword arguments. Parameters before “"/"” are positional-only
parameters and may only be passed by positional arguments.
Changed in version 3.8: The "/" function parameter syntax may be used
to indicate positional-only parameters. See **PEP 570** for details.
Parameters may have an *annotation* of the form “": expression"”
following the parameter name. Any parameter may have an annotation,
even those of the form "*identifier" or "**identifier". Functions may
have “return” annotation of the form “"-> expression"” after the
parameter list. These annotations can be any valid Python expression.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object. If the "annotations" import from
"__future__" is used, annotations are preserved as strings at runtime
which enables postponed evaluation. Otherwise, they are evaluated
when the function definition is executed. In this case annotations
may be evaluated in a different order than they appear in the source
code.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.
**Programmer’s note:** Functions are first-class objects. A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around. Free variables used
in the nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
See also:
**PEP 3107** - Function Annotations
The original specification for function annotations.
**PEP 484** - Type Hints
Definition of a standard meaning for annotations: type hints.
**PEP 526** - Syntax for Variable Annotations
Ability to type hint variable declarations, including class
variables and instance variables
**PEP 563** - Postponed Evaluation of Annotations
Support for forward references within annotations by preserving
annotations in a string form at runtime instead of eager
evaluation.
u� The "global" statement
**********************
global_stmt ::= "global" identifier ("," identifier)*
The "global" statement is a declaration which holds for the entire
current code block. It means that the listed identifiers are to be
interpreted as globals. It would be impossible to assign to a global
variable without "global", although free variables may refer to
globals without being declared global.
Names listed in a "global" statement must not be used in the same code
block textually preceding that "global" statement.
Names listed in a "global" statement must not be defined as formal
parameters or in a "for" loop control target, "class" definition,
function definition, "import" statement, or variable annotation.
**CPython implementation detail:** The current implementation does not
enforce some of these restrictions, but programs should not abuse this
freedom, as future implementations may enforce them or silently change
the meaning of the program.
**Programmer’s note:** "global" is a directive to the parser. It
applies only to code parsed at the same time as the "global"
statement. In particular, a "global" statement contained in a string
or code object supplied to the built-in "exec()" function does not
affect the code block *containing* the function call, and code
contained in such a string is unaffected by "global" statements in the
code containing the function call. The same applies to the "eval()"
and "compile()" functions.
u� Reserved classes of identifiers
*******************************
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section The import statement.
Note:
The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names, informally known as “dunder” names. These
names are defined by the interpreter and its implementation
(including the standard library). Current system names are
discussed in the Special method names section and elsewhere. More
will likely be defined in future versions of Python. *Any* use of
"__*__" names, in any context, that does not follow explicitly
documented use, is subject to breakage without warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between “private” attributes of base and
derived classes. See section Identifiers (Names).
um Identifiers and keywords
************************
Identifiers (also referred to as *names*) are described by the
following lexical definitions.
The syntax of identifiers in Python is based on the Unicode standard
annex UAX-31, with elaboration and changes as defined below; see also
**PEP 3131** for further details.
Within the ASCII range (U+0001..U+007F), the valid characters for
identifiers are the same as in Python 2.x: the uppercase and lowercase
letters "A" through "Z", the underscore "_" and, except for the first
character, the digits "0" through "9".
Python 3.0 introduces additional characters from outside the ASCII
range (see **PEP 3131**). For these characters, the classification
uses the version of the Unicode Character Database as included in the
"unicodedata" module.
Identifiers are unlimited in length. Case is significant.
identifier ::= xid_start xid_continue*
id_start ::= <all characters in general categories Lu, Ll, Lt, Lm, Lo, Nl, the underscore, and characters with the Other_ID_Start property>
id_continue ::= <all characters in id_start, plus characters in the categories Mn, Mc, Nd, Pc and others with the Other_ID_Continue property>
xid_start ::= <all characters in id_start whose NFKC normalization is in "id_start xid_continue*">
xid_continue ::= <all characters in id_continue whose NFKC normalization is in "id_continue*">
The Unicode category codes mentioned above stand for:
* *Lu* - uppercase letters
* *Ll* - lowercase letters
* *Lt* - titlecase letters
* *Lm* - modifier letters
* *Lo* - other letters
* *Nl* - letter numbers
* *Mn* - nonspacing marks
* *Mc* - spacing combining marks
* *Nd* - decimal numbers
* *Pc* - connector punctuations
* *Other_ID_Start* - explicit list of characters in PropList.txt to
support backwards compatibility
* *Other_ID_Continue* - likewise
All identifiers are converted into the normal form NFKC while parsing;
comparison of identifiers is based on NFKC.
A non-normative HTML file listing all valid identifier characters for
Unicode 4.1 can be found at
https://www.unicode.org/Public/13.0.0/ucd/DerivedCoreProperties.txt
Keywords
========
The following identifiers are used as reserved words, or *keywords* of
the language, and cannot be used as ordinary identifiers. They must
be spelled exactly as written here:
False await else import pass
None break except in raise
True class finally is return
and continue for lambda try
as def from nonlocal while
assert del global not with
async elif if or yield
Reserved classes of identifiers
===============================
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section The import statement.
Note:
The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names, informally known as “dunder” names. These
names are defined by the interpreter and its implementation
(including the standard library). Current system names are
discussed in the Special method names section and elsewhere. More
will likely be defined in future versions of Python. *Any* use of
"__*__" names, in any context, that does not follow explicitly
documented use, is subject to breakage without warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between “private” attributes of base and
derived classes. See section Identifiers (Names).
a5 Imaginary literals
******************
Imaginary literals are described by the following lexical definitions:
imagnumber ::= (floatnumber | digitpart) ("j" | "J")
An imaginary literal yields a complex number with a real part of 0.0.
Complex numbers are represented as a pair of floating point numbers
and have the same restrictions on their range. To create a complex
number with a nonzero real part, add a floating point number to it,
e.g., "(3+4j)". Some examples of imaginary literals:
3.14j 10.j 10j .001j 1e100j 3.14e-10j 3.14_15_93j
u8" The "import" statement
**********************
import_stmt ::= "import" module ["as" identifier] ("," module ["as" identifier])*
| "from" relative_module "import" identifier ["as" identifier]
("," identifier ["as" identifier])*
| "from" relative_module "import" "(" identifier ["as" identifier]
("," identifier ["as" identifier])* [","] ")"
| "from" module "import" "*"
module ::= (identifier ".")* identifier
relative_module ::= "."* module | "."+
The basic import statement (no "from" clause) is executed in two
steps:
1. find a module, loading and initializing it if necessary
2. define a name or names in the local namespace for the scope where
the "import" statement occurs.
When the statement contains multiple clauses (separated by commas) the
two steps are carried out separately for each clause, just as though
the clauses had been separated out into individual import statements.
The details of the first step, finding and loading modules are
described in greater detail in the section on the import system, which
also describes the various types of packages and modules that can be
imported, as well as all the hooks that can be used to customize the
import system. Note that failures in this step may indicate either
that the module could not be located, *or* that an error occurred
while initializing the module, which includes execution of the
module’s code.
If the requested module is retrieved successfully, it will be made
available in the local namespace in one of three ways:
* If the module name is followed by "as", then the name following "as"
is bound directly to the imported module.
* If no other name is specified, and the module being imported is a
top level module, the module’s name is bound in the local namespace
as a reference to the imported module
* If the module being imported is *not* a top level module, then the
name of the top level package that contains the module is bound in
the local namespace as a reference to the top level package. The
imported module must be accessed using its full qualified name
rather than directly
The "from" form uses a slightly more complex process:
1. find the module specified in the "from" clause, loading and
initializing it if necessary;
2. for each of the identifiers specified in the "import" clauses:
1. check if the imported module has an attribute by that name
2. if not, attempt to import a submodule with that name and then
check the imported module again for that attribute
3. if the attribute is not found, "ImportError" is raised.
4. otherwise, a reference to that value is stored in the local
namespace, using the name in the "as" clause if it is present,
otherwise using the attribute name
Examples:
import foo # foo imported and bound locally
import foo.bar.baz # foo.bar.baz imported, foo bound locally
import foo.bar.baz as fbb # foo.bar.baz imported and bound as fbb
from foo.bar import baz # foo.bar.baz imported and bound as baz
from foo import attr # foo imported and foo.attr bound as attr
If the list of identifiers is replaced by a star ("'*'"), all public
names defined in the module are bound in the local namespace for the
scope where the "import" statement occurs.
The *public names* defined by a module are determined by checking the
module’s namespace for a variable named "__all__"; if defined, it must
be a sequence of strings which are names defined or imported by that
module. The names given in "__all__" are all considered public and
are required to exist. If "__all__" is not defined, the set of public
names includes all names found in the module’s namespace which do not
begin with an underscore character ("'_'"). "__all__" should contain
the entire public API. It is intended to avoid accidentally exporting
items that are not part of the API (such as library modules which were
imported and used within the module).
The wild card form of import — "from module import *" — is only
allowed at the module level. Attempting to use it in class or
function definitions will raise a "SyntaxError".
When specifying what module to import you do not have to specify the
absolute name of the module. When a module or package is contained
within another package it is possible to make a relative import within
the same top package without having to mention the package name. By
using leading dots in the specified module or package after "from" you
can specify how high to traverse up the current package hierarchy
without specifying exact names. One leading dot means the current
package where the module making the import exists. Two dots means up
one package level. Three dots is up two levels, etc. So if you execute
"from . import mod" from a module in the "pkg" package then you will
end up importing "pkg.mod". If you execute "from ..subpkg2 import mod"
from within "pkg.subpkg1" you will import "pkg.subpkg2.mod". The
specification for relative imports is contained in the Package
Relative Imports section.
"importlib.import_module()" is provided to support applications that
determine dynamically the modules to be loaded.
Raises an auditing event "import" with arguments "module", "filename",
"sys.path", "sys.meta_path", "sys.path_hooks".
Future statements
=================
A *future statement* is a directive to the compiler that a particular
module should be compiled using syntax or semantics that will be
available in a specified future release of Python where the feature
becomes standard.
The future statement is intended to ease migration to future versions
of Python that introduce incompatible changes to the language. It
allows use of the new features on a per-module basis before the
release in which the feature becomes standard.
future_stmt ::= "from" "__future__" "import" feature ["as" identifier]
("," feature ["as" identifier])*
| "from" "__future__" "import" "(" feature ["as" identifier]
("," feature ["as" identifier])* [","] ")"
feature ::= identifier
A future statement must appear near the top of the module. The only
lines that can appear before a future statement are:
* the module docstring (if any),
* comments,
* blank lines, and
* other future statements.
The only feature that requires using the future statement is
"annotations" (see **PEP 563**).
All historical features enabled by the future statement are still
recognized by Python 3. The list includes "absolute_import",
"division", "generators", "generator_stop", "unicode_literals",
"print_function", "nested_scopes" and "with_statement". They are all
redundant because they are always enabled, and only kept for backwards
compatibility.
A future statement is recognized and treated specially at compile
time: Changes to the semantics of core constructs are often
implemented by generating different code. It may even be the case
that a new feature introduces new incompatible syntax (such as a new
reserved word), in which case the compiler may need to parse the
module differently. Such decisions cannot be pushed off until
runtime.
For any given release, the compiler knows which feature names have
been defined, and raises a compile-time error if a future statement
contains a feature not known to it.
The direct runtime semantics are the same as for any import statement:
there is a standard module "__future__", described later, and it will
be imported in the usual way at the time the future statement is
executed.
The interesting runtime semantics depend on the specific feature
enabled by the future statement.
Note that there is nothing special about the statement:
import __future__ [as name]
That is not a future statement; it’s an ordinary import statement with
no special semantics or syntax restrictions.
Code compiled by calls to the built-in functions "exec()" and
"compile()" that occur in a module "M" containing a future statement
will, by default, use the new syntax or semantics associated with the
future statement. This can be controlled by optional arguments to
"compile()" — see the documentation of that function for details.
A future statement typed at an interactive interpreter prompt will
take effect for the rest of the interpreter session. If an
interpreter is started with the "-i" option, is passed a script name
to execute, and the script includes a future statement, it will be in
effect in the interactive session started after the script is
executed.
See also:
**PEP 236** - Back to the __future__
The original proposal for the __future__ mechanism.
a Membership test operations
**************************
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z", for which
the expression "x is z or x == z" is true, is produced while iterating
over "y". If an exception is raised during the iteration, it is as if
"in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x is y[i] or x == y[i]", and no
lower integer index raises the "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse truth value of
"in".
aV Integer literals
****************
Integer literals are described by the following lexical definitions:
integer ::= decinteger | bininteger | octinteger | hexinteger
decinteger ::= nonzerodigit (["_"] digit)* | "0"+ (["_"] "0")*
bininteger ::= "0" ("b" | "B") (["_"] bindigit)+
octinteger ::= "0" ("o" | "O") (["_"] octdigit)+
hexinteger ::= "0" ("x" | "X") (["_"] hexdigit)+
nonzerodigit ::= "1"..."9"
digit ::= "0"..."9"
bindigit ::= "0" | "1"
octdigit ::= "0"..."7"
hexdigit ::= digit | "a"..."f" | "A"..."F"
There is no limit for the length of integer literals apart from what
can be stored in available memory.
Underscores are ignored for determining the numeric value of the
literal. They can be used to group digits for enhanced readability.
One underscore can occur between digits, and after base specifiers
like "0x".
Note that leading zeros in a non-zero decimal number are not allowed.
This is for disambiguation with C-style octal literals, which Python
used before version 3.0.
Some examples of integer literals:
7 2147483647 0o177 0b100110111
3 79228162514264337593543950336 0o377 0xdeadbeef
100_000_000_000 0b_1110_0101
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
a^ Lambdas
*******
lambda_expr ::= "lambda" [parameter_list] ":" expression
lambda_expr_nocond ::= "lambda" [parameter_list] ":" expression_nocond
Lambda expressions (sometimes called lambda forms) are used to create
anonymous functions. The expression "lambda parameters: expression"
yields a function object. The unnamed object behaves like a function
object defined with:
def <lambda>(parameters):
return expression
See section Function definitions for the syntax of parameter lists.
Note that functions created with lambda expressions cannot contain
statements or annotations.
a/ List displays
*************
A list display is a possibly empty series of expressions enclosed in
square brackets:
list_display ::= "[" [starred_list | comprehension] "]"
A list display yields a new list object, the contents being specified
by either a list of expressions or a comprehension. When a comma-
separated list of expressions is supplied, its elements are evaluated
from left to right and placed into the list object in that order.
When a comprehension is supplied, the list is constructed from the
elements resulting from the comprehension.
u� Naming and binding
******************
Binding of names
================
*Names* refer to objects. Names are introduced by name binding
operations.
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore. This form
may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global". If a name is bound at the
module level, it is a global variable. (The variables of the module
code block are local and global.) If a variable is used in a code
block but not defined there, it is a *free variable*.
Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.
Resolution of names
===================
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block’s *environment*.
When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins". The global namespace is searched first. If
the name is not found there, the builtins namespace is searched. The
"global" statement must precede all uses of the name.
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope. This means that the following
will fail:
class A:
a = 42
b = list(a + i for i in range(10))
Builtins and restricted execution
=================================
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.
Interaction with dynamic features
=================================
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
a� The "nonlocal" statement
************************
nonlocal_stmt ::= "nonlocal" identifier ("," identifier)*
The "nonlocal" statement causes the listed identifiers to refer to
previously bound variables in the nearest enclosing scope excluding
globals. This is important because the default behavior for binding is
to search the local namespace first. The statement allows
encapsulated code to rebind variables outside of the local scope
besides the global (module) scope.
Names listed in a "nonlocal" statement, unlike those listed in a
"global" statement, must refer to pre-existing bindings in an
enclosing scope (the scope in which a new binding should be created
cannot be determined unambiguously).
Names listed in a "nonlocal" statement must not collide with pre-
existing bindings in the local scope.
See also:
**PEP 3104** - Access to Names in Outer Scopes
The specification for the "nonlocal" statement.
u� Numeric literals
****************
There are three types of numeric literals: integers, floating point
numbers, and imaginary numbers. There are no complex literals
(complex numbers can be formed by adding a real number and an
imaginary number).
Note that numeric literals do not include a sign; a phrase like "-1"
is actually an expression composed of the unary operator ‘"-"’ and the
literal "1".
u Emulating numeric types
***********************
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the expression "x + y", where *x* is an instance of a
class that has an "__add__()" method, "x.__add__(y)" is called.
The "__divmod__()" method should be the equivalent to using
"__floordiv__()" and "__mod__()"; it should not be related to
"__truediv__()". Note that "__pow__()" should be defined to accept
an optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other[, modulo])
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)
operands. These functions are only called if the left operand does
not support the corresponding operation [3] and the operands are of
different types. [4] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note:
If the right operand’s type is a subclass of the left operand’s
type and that subclass provides a different implementation of the
reflected method for the operation, this method will be called
before the left operand’s non-reflected method. This behavior
allows subclasses to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (modifying *self*) and return the result
(which could be, but does not have to be, *self*). If a specific
method is not defined, the augmented assignment falls back to the
normal methods. For instance, if *x* is an instance of a class
with an "__iadd__()" method, "x += y" is equivalent to "x =
x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are
considered, as with the evaluation of "x + y". In certain
situations, augmented assignment can result in unexpected errors
(see Why does a_tuple[i] += [‘item’] raise an exception when the
addition works?), but this behavior is in fact part of the data
model.
Note:
Due to a bug in the dispatching mechanism for "**=", a class that
defines "__ipow__()" but returns "NotImplemented" would fail to
fall back to "x.__pow__(y)" and "y.__rpow__(x)". This bug is
fixed in Python 3.10.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()" and
"float()". Should return a value of the appropriate type.
object.__index__(self)
Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.
If "__int__()", "__float__()" and "__complex__()" are not defined
then corresponding built-in functions "int()", "float()" and
"complex()" fall back to "__index__()".
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").
The built-in function "int()" falls back to "__trunc__()" if
neither "__int__()" nor "__index__()" is defined.
u Objects, values and types
*************************
*Objects* are Python’s abstraction for data. All data in a Python
program is represented by objects or by relations between objects. (In
a sense, and in conformance to Von Neumann’s model of a “stored
program computer”, code is also represented by objects.)
Every object has an identity, a type and a value. An object’s
*identity* never changes once it has been created; you may think of it
as the object’s address in memory. The ‘"is"’ operator compares the
identity of two objects; the "id()" function returns an integer
representing its identity.
**CPython implementation detail:** For CPython, "id(x)" is the memory
address where "x" is stored.
An object’s type determines the operations that the object supports
(e.g., “does it have a length?”) and also defines the possible values
for objects of that type. The "type()" function returns an object’s
type (which is an object itself). Like its identity, an object’s
*type* is also unchangeable. [1]
The *value* of some objects can change. Objects whose value can
change are said to be *mutable*; objects whose value is unchangeable
once they are created are called *immutable*. (The value of an
immutable container object that contains a reference to a mutable
object can change when the latter’s value is changed; however the
container is still considered immutable, because the collection of
objects it contains cannot be changed. So, immutability is not
strictly the same as having an unchangeable value, it is more subtle.)
An object’s mutability is determined by its type; for instance,
numbers, strings and tuples are immutable, while dictionaries and
lists are mutable.
Objects are never explicitly destroyed; however, when they become
unreachable they may be garbage-collected. An implementation is
allowed to postpone garbage collection or omit it altogether — it is a
matter of implementation quality how garbage collection is
implemented, as long as no objects are collected that are still
reachable.
**CPython implementation detail:** CPython currently uses a reference-
counting scheme with (optional) delayed detection of cyclically linked
garbage, which collects most objects as soon as they become
unreachable, but is not guaranteed to collect garbage containing
circular references. See the documentation of the "gc" module for
information on controlling the collection of cyclic garbage. Other
implementations act differently and CPython may change. Do not depend
on immediate finalization of objects when they become unreachable (so
you should always close files explicitly).
Note that the use of the implementation’s tracing or debugging
facilities may keep objects alive that would normally be collectable.
Also note that catching an exception with a ‘"try"…"except"’ statement
may keep objects alive.
Some objects contain references to “external” resources such as open
files or windows. It is understood that these resources are freed
when the object is garbage-collected, but since garbage collection is
not guaranteed to happen, such objects also provide an explicit way to
release the external resource, usually a "close()" method. Programs
are strongly recommended to explicitly close such objects. The
‘"try"…"finally"’ statement and the ‘"with"’ statement provide
convenient ways to do this.
Some objects contain references to other objects; these are called
*containers*. Examples of containers are tuples, lists and
dictionaries. The references are part of a container’s value. In
most cases, when we talk about the value of a container, we imply the
values, not the identities of the contained objects; however, when we
talk about the mutability of a container, only the identities of the
immediately contained objects are implied. So, if an immutable
container (like a tuple) contains a reference to a mutable object, its
value changes if that mutable object is changed.
Types affect almost all aspects of object behavior. Even the
importance of object identity is affected in some sense: for immutable
types, operations that compute new values may actually return a
reference to any existing object with the same type and value, while
for mutable objects this is not allowed. E.g., after "a = 1; b = 1",
"a" and "b" may or may not refer to the same object with the value
one, depending on the implementation, but after "c = []; d = []", "c"
and "d" are guaranteed to refer to two different, unique, newly
created empty lists. (Note that "c = d = []" assigns the same object
to both "c" and "d".)
u� Operator precedence
*******************
The following table summarizes the operator precedence in Python, from
lowest precedence (least binding) to highest precedence (most
binding). Operators in the same box have the same precedence. Unless
the syntax is explicitly given, operators are binary. Operators in
the same box group left to right (except for exponentiation, which
groups from right to left).
Note that comparisons, membership tests, and identity tests, all have
the same precedence and have a left-to-right chaining feature as
described in the Comparisons section.
+-------------------------------------------------+---------------------------------------+
| Operator | Description |
|=================================================|=======================================|
| ":=" | Assignment expression |
+-------------------------------------------------+---------------------------------------+
| "lambda" | Lambda expression |
+-------------------------------------------------+---------------------------------------+
| "if" – "else" | Conditional expression |
+-------------------------------------------------+---------------------------------------+
| "or" | Boolean OR |
+-------------------------------------------------+---------------------------------------+
| "and" | Boolean AND |
+-------------------------------------------------+---------------------------------------+
| "not" "x" | Boolean NOT |
+-------------------------------------------------+---------------------------------------+
| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership |
| ">=", "!=", "==" | tests and identity tests |
+-------------------------------------------------+---------------------------------------+
| "|" | Bitwise OR |
+-------------------------------------------------+---------------------------------------+
| "^" | Bitwise XOR |
+-------------------------------------------------+---------------------------------------+
| "&" | Bitwise AND |
+-------------------------------------------------+---------------------------------------+
| "<<", ">>" | Shifts |
+-------------------------------------------------+---------------------------------------+
| "+", "-" | Addition and subtraction |
+-------------------------------------------------+---------------------------------------+
| "*", "@", "/", "//", "%" | Multiplication, matrix |
| | multiplication, division, floor |
| | division, remainder [5] |
+-------------------------------------------------+---------------------------------------+
| "+x", "-x", "~x" | Positive, negative, bitwise NOT |
+-------------------------------------------------+---------------------------------------+
| "**" | Exponentiation [6] |
+-------------------------------------------------+---------------------------------------+
| "await" "x" | Await expression |
+-------------------------------------------------+---------------------------------------+
| "x[index]", "x[index:index]", | Subscription, slicing, call, |
| "x(arguments...)", "x.attribute" | attribute reference |
+-------------------------------------------------+---------------------------------------+
| "(expressions...)", "[expressions...]", "{key: | Binding or parenthesized expression, |
| value...}", "{expressions...}" | list display, dictionary display, set |
| | display |
+-------------------------------------------------+---------------------------------------+
-[ Footnotes ]-
[1] While "abs(x%y) < abs(y)" is true mathematically, for floats it
may not be true numerically due to roundoff. For example, and
assuming a platform on which a Python float is an IEEE 754 double-
precision number, in order that "-1e-100 % 1e100" have the same
sign as "1e100", the computed result is "-1e-100 + 1e100", which
is numerically exactly equal to "1e100". The function
"math.fmod()" returns a result whose sign matches the sign of the
first argument instead, and so returns "-1e-100" in this case.
Which approach is more appropriate depends on the application.
[2] If x is very close to an exact integer multiple of y, it’s
possible for "x//y" to be one larger than "(x-x%y)//y" due to
rounding. In such cases, Python returns the latter result, in
order to preserve that "divmod(x,y)[0] * y + x % y" be very close
to "x".
[3] The Unicode standard distinguishes between *code points* (e.g.
U+0041) and *abstract characters* (e.g. “LATIN CAPITAL LETTER A”).
While most abstract characters in Unicode are only represented
using one code point, there is a number of abstract characters
that can in addition be represented using a sequence of more than
one code point. For example, the abstract character “LATIN
CAPITAL LETTER C WITH CEDILLA” can be represented as a single
*precomposed character* at code position U+00C7, or as a sequence
of a *base character* at code position U+0043 (LATIN CAPITAL
LETTER C), followed by a *combining character* at code position
U+0327 (COMBINING CEDILLA).
The comparison operators on strings compare at the level of
Unicode code points. This may be counter-intuitive to humans. For
example, ""\u00C7" == "\u0043\u0327"" is "False", even though both
strings represent the same abstract character “LATIN CAPITAL
LETTER C WITH CEDILLA”.
To compare strings at the level of abstract characters (that is,
in a way intuitive to humans), use "unicodedata.normalize()".
[4] Due to automatic garbage-collection, free lists, and the dynamic
nature of descriptors, you may notice seemingly unusual behaviour
in certain uses of the "is" operator, like those involving
comparisons between instance methods, or constants. Check their
documentation for more info.
[5] The "%" operator is also used for string formatting; the same
precedence applies.
[6] The power operator "**" binds less tightly than an arithmetic or
bitwise unary operator on its right, that is, "2**-1" is "0.5".
uw The "pass" statement
********************
pass_stmt ::= "pass"
"pass" is a null operation — when it is executed, nothing happens. It
is useful as a placeholder when a statement is required syntactically,
but no code needs to be executed, for example:
def f(arg): pass # a function that does nothing (yet)
class C: pass # a class with no methods (yet)
a� The power operator
******************
The power operator binds more tightly than unary operators on its
left; it binds less tightly than unary operators on its right. The
syntax is:
power ::= (await_expr | primary) ["**" u_expr]
Thus, in an unparenthesized sequence of power and unary operators, the
operators are evaluated from right to left (this does not constrain
the evaluation order for the operands): "-1**2" results in "-1".
The power operator has the same semantics as the built-in "pow()"
function, when called with two arguments: it yields its left argument
raised to the power of its right argument. The numeric arguments are
first converted to a common type, and the result is of that type.
For int operands, the result has the same type as the operands unless
the second argument is negative; in that case, all arguments are
converted to float and a float result is delivered. For example,
"10**2" returns "100", but "10**-2" returns "0.01".
Raising "0.0" to a negative power results in a "ZeroDivisionError".
Raising a negative number to a fractional power results in a "complex"
number. (In earlier versions it raised a "ValueError".)
uJ
The "raise" statement
*********************
raise_stmt ::= "raise" [expression ["from" expression]]
If no expressions are present, "raise" re-raises the last exception
that was active in the current scope. If no exception is active in
the current scope, a "RuntimeError" exception is raised indicating
that this is an error.
Otherwise, "raise" evaluates the first expression as the exception
object. It must be either a subclass or an instance of
"BaseException". If it is a class, the exception instance will be
obtained when needed by instantiating the class with no arguments.
The *type* of the exception is the exception instance’s class, the
*value* is the instance itself.
A traceback object is normally created automatically when an exception
is raised and attached to it as the "__traceback__" attribute, which
is writable. You can create an exception and set your own traceback in
one step using the "with_traceback()" exception method (which returns
the same exception instance, with its traceback set to its argument),
like so:
raise Exception("foo occurred").with_traceback(tracebackobj)
The "from" clause is used for exception chaining: if given, the second
*expression* must be another exception class or instance. If the
second expression is an exception instance, it will be attached to the
raised exception as the "__cause__" attribute (which is writable). If
the expression is an exception class, the class will be instantiated
and the resulting exception instance will be attached to the raised
exception as the "__cause__" attribute. If the raised exception is not
handled, both exceptions will be printed:
>>> try:
... print(1 / 0)
... except Exception as exc:
... raise RuntimeError("Something bad happened") from exc
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
A similar mechanism works implicitly if an exception is raised inside
an exception handler or a "finally" clause: the previous exception is
then attached as the new exception’s "__context__" attribute:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened")
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
Exception chaining can be explicitly suppressed by specifying "None"
in the "from" clause:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened") from None
...
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
Additional information on exceptions can be found in section
Exceptions, and information about handling exceptions is in section
The try statement.
Changed in version 3.3: "None" is now permitted as "Y" in "raise X
from Y".
New in version 3.3: The "__suppress_context__" attribute to suppress
automatic display of the exception context.
a The "return" statement
**********************
return_stmt ::= "return" [expression_list]
"return" may only occur syntactically nested in a function definition,
not within a nested class definition.
If an expression list is present, it is evaluated, else "None" is
substituted.
"return" leaves the current function call with the expression list (or
"None") as return value.
When "return" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
function.
In a generator function, the "return" statement indicates that the
generator is done and will cause "StopIteration" to be raised. The
returned value (if any) is used as an argument to construct
"StopIteration" and becomes the "StopIteration.value" attribute.
In an asynchronous generator function, an empty "return" statement
indicates that the asynchronous generator is done and will cause
"StopAsyncIteration" to be raised. A non-empty "return" statement is
a syntax error in an asynchronous generator function.
u� Emulating container types
*************************
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python’s standard dictionary objects.
The "collections.abc" module provides a "MutableMapping" abstract base
class to help create those methods from a base set of "__getitem__()",
"__setitem__()", "__delitem__()", and "keys()". Mutable sequences
should provide methods "append()", "count()", "index()", "extend()",
"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python
standard list objects. Finally, sequence types should implement
addition (meaning concatenation) and multiplication (meaning
repetition) by defining the methods "__add__()", "__radd__()",
"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described
below; they should not define other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should search the mapping’s keys; for sequences, it
should search through the values. It is further recommended that both
mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should iterate through the object’s keys; for sequences, it should
iterate through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn’t define a "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.
object.__length_hint__(self)
Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. The
return value may also be "NotImplemented", which is treated the
same as if the "__length_hint__" method didn’t exist at all. This
method is purely an optimization and is never required for
correctness.
New in version 3.4.
Note:
Slicing is done exclusively with the following three methods. A
call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note:
"for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container.
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a container. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be iterable.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don’t define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
a� Shifting operations
*******************
The shifting operations have lower priority than the arithmetic
operations:
shift_expr ::= a_expr | shift_expr ("<<" | ">>") a_expr
These operators accept integers as arguments. They shift the first
argument to the left or right by the number of bits given by the
second argument.
A right shift by *n* bits is defined as floor division by "pow(2,n)".
A left shift by *n* bits is defined as multiplication with "pow(2,n)".
a� Slicings
********
A slicing selects a range of items in a sequence object (e.g., a
string, tuple or list). Slicings may be used as expressions or as
targets in assignment or "del" statements. The syntax for a slicing:
slicing ::= primary "[" slice_list "]"
slice_list ::= slice_item ("," slice_item)* [","]
slice_item ::= expression | proper_slice
proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" [stride] ]
lower_bound ::= expression
upper_bound ::= expression
stride ::= expression
There is ambiguity in the formal syntax here: anything that looks like
an expression list also looks like a slice list, so any subscription
can be interpreted as a slicing. Rather than further complicating the
syntax, this is disambiguated by defining that in this case the
interpretation as a subscription takes priority over the
interpretation as a slicing (this is the case if the slice list
contains no proper slice).
The semantics for a slicing are as follows. The primary is indexed
(using the same "__getitem__()" method as normal subscription) with a
key that is constructed from the slice list, as follows. If the slice
list contains at least one comma, the key is a tuple containing the
conversion of the slice items; otherwise, the conversion of the lone
slice item is the key. The conversion of a slice item that is an
expression is that expression. The conversion of a proper slice is a
slice object (see section The standard type hierarchy) whose "start",
"stop" and "step" attributes are the values of the expressions given
as lower bound, upper bound and stride, respectively, substituting
"None" for missing expressions.
u Special Attributes
******************
The implementation adds a few special read-only attributes to several
object types, where they are relevant. Some of these are not reported
by the "dir()" built-in function.
object.__dict__
A dictionary or other mapping object used to store an object’s
(writable) attributes.
instance.__class__
The class to which a class instance belongs.
class.__bases__
The tuple of base classes of a class object.
definition.__name__
The name of the class, function, method, descriptor, or generator
instance.
definition.__qualname__
The *qualified name* of the class, function, method, descriptor, or
generator instance.
New in version 3.3.
class.__mro__
This attribute is a tuple of classes that are considered when
looking for base classes during method resolution.
class.mro()
This method can be overridden by a metaclass to customize the
method resolution order for its instances. It is called at class
instantiation, and its result is stored in "__mro__".
class.__subclasses__()
Each class keeps a list of weak references to its immediate
subclasses. This method returns a list of all those references
still alive. Example:
>>> int.__subclasses__()
[<class 'bool'>]
u�� Special method names
********************
A class can implement certain operations that are invoked by special
syntax (such as arithmetic operations or subscripting and slicing) by
defining methods with special names. This is Python’s approach to
*operator overloading*, allowing classes to define their own behavior
with respect to language operators. For instance, if a class defines
a method named "__getitem__()", and "x" is an instance of this class,
then "x[i]" is roughly equivalent to "type(x).__getitem__(x, i)".
Except where mentioned, attempts to execute an operation raise an
exception when no appropriate method is defined (typically
"AttributeError" or "TypeError").
Setting a special method to "None" indicates that the corresponding
operation is not available. For example, if a class sets "__iter__()"
to "None", the class is not iterable, so calling "iter()" on its
instances will raise a "TypeError" (without falling back to
"__getitem__()"). [2]
When implementing a class that emulates any built-in type, it is
important that the emulation only be implemented to the degree that it
makes sense for the object being modelled. For example, some
sequences may work well with retrieval of individual elements, but
extracting a slice may not make sense. (One example of this is the
"NodeList" interface in the W3C’s Document Object Model.)
Basic customization
===================
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass’s "__new__()" method using
"super().__new__(cls[, ...])" with appropriate arguments and then
modifying the newly-created instance as necessary before returning
it.
If "__new__()" is invoked during object construction and it returns
an instance of *cls*, then the new instance’s "__init__()" method
will be invoked like "__init__(self[, ...])", where *self* is the
new instance and the remaining arguments are the same as were
passed to the object constructor.
If "__new__()" does not return an instance of *cls*, then the new
instance’s "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class’s "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"super().__init__([args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.
It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.
It is not guaranteed that "__del__()" methods are called for
objects that still exist when the interpreter exits.
Note:
"del x" doesn’t directly call "x.__del__()" — the former
decrements the reference count for "x" by one, and the latter is
only called when "x"’s reference count reaches zero.
**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.
See also: Documentation for the "gc" module.
Warning:
Due to the precarious circumstances under which "__del__()"
methods are invoked, exceptions that occur during their execution
are ignored, and a warning is printed to "sys.stderr" instead.
In particular:
* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".
* "__del__()" can be executed during interpreter shutdown. As a
consequence, the global variables it needs to access (including
other modules) may already have been deleted or set to "None".
Python guarantees that globals whose name begins with a single
underscore are deleted from their module before other globals
are deleted; if no other references to such globals exist, this
may help in assuring that imported modules are still available
at the time when the "__del__()" method is called.
object.__repr__(self)
Called by the "repr()" built-in function to compute the “official”
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an “informal”
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the “informal” or nicely printable string
representation of an object. The return value must be a string
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by bytes to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function, and by extension,
evaluation of formatted string literals and the "str.format()"
method, to produce a “formatted” string representation of an
object. The *format_spec* argument is a string that contains a
description of the formatting options desired. The interpretation
of the *format_spec* argument is up to the type implementing
"__format__()", however most classes will either delegate
formatting to one of the built-in types, or use a similar
formatting option syntax.
See Format Specification Mini-Language for a description of the
standard formatting syntax.
The return value must be a string object.
Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.
Changed in version 3.7: "object.__format__(x, '')" is now
equivalent to "str(x)" rather than "format(str(self), '')".
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
By default, "object" implements "__eq__()" by using "is", returning
"NotImplemented" in the case of a false comparison: "True if x is y
else NotImplemented". For "__ne__()", by default it delegates to
"__eq__()" and inverts the result unless it is "NotImplemented".
There are no other implied relationships among the comparison
operators or default implementations; for example, the truth of
"(x<y or x==y)" does not imply "x<=y". To automatically generate
ordering operations from a single root operation, see
"functools.total_ordering()".
See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
Note:
"hash()" truncates the value returned from an object’s custom
"__hash__()" method to the size of a "Py_ssize_t". This is
typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds.
If an object’s "__hash__()" must interoperate on builds of
different bit sizes, be sure to check the width on all supported
builds. An easy way to do this is with "python -c "import sys;
print(sys.hash_info.width)"".
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key’s hash value is immutable (if the object’s hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj,
collections.abc.Hashable)".
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
<ParentClass>.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.abc.Hashable)" call.
Note:
By default, the "__hash__()" values of str and bytes objects are
“salted” with an unpredictable random value. Although they
remain constant within an individual Python process, they are not
predictable between repeated invocations of Python.This is
intended to provide protection against a denial-of-service caused
by carefully-chosen inputs that exploit the worst case
performance of a dict insertion, O(n^2) complexity. See
http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of sets.
Python has never made guarantees about this ordering (and it
typically varies between 32-bit and 64-bit builds).See also
"PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
Customizing attribute access
============================
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
return the (computed) attribute value or raise an "AttributeError"
exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note:
This method may still be bypassed when looking up special methods
as the result of implicit invocation via language syntax or
built-in functions. See Special method lookup.
For certain sensitive attribute accesses, raises an auditing event
"object.__getattr__" with arguments "obj" and "name".
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should call the base class method with the same name, for example,
"object.__setattr__(self, name, value)".
For certain sensitive attribute assignments, raises an auditing
event "object.__setattr__" with arguments "obj", "name", "value".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
For certain sensitive attribute deletions, raises an auditing event
"object.__delattr__" with arguments "obj" and "name".
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
Customizing module attribute access
-----------------------------------
Special names "__getattr__" and "__dir__" can be also used to
customize access to module attributes. The "__getattr__" function at
the module level should accept one argument which is the name of an
attribute and return the computed value or raise an "AttributeError".
If an attribute is not found on a module object through the normal
lookup, i.e. "object.__getattribute__()", then "__getattr__" is
searched in the module "__dict__" before raising an "AttributeError".
If found, it is called with the attribute name and the result is
returned.
The "__dir__" function should accept no arguments, and return a
sequence of strings that represents the names accessible on module. If
present, this function overrides the standard "dir()" search on a
module.
For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
super().__setattr__(attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note:
Defining module "__getattr__" and setting module "__class__" only
affect lookups made using the attribute access syntax – directly
accessing the module globals (whether by code within the module, or
via a reference to the module’s globals dictionary) is unaffected.
Changed in version 3.5: "__class__" module attribute is now writable.
New in version 3.7: "__getattr__" and "__dir__" module attributes.
See also:
**PEP 562** - Module __getattr__ and __dir__
Describes the "__getattr__" and "__dir__" functions on modules.
Implementing Descriptors
------------------------
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents). In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".
object.__get__(self, instance, owner=None)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). The optional *owner* argument is the owner class, while
*instance* is the instance that the attribute was accessed through,
or "None" when the attribute is accessed through the *owner*.
This method should return the computed attribute value or raise an
"AttributeError" exception.
**PEP 252** specifies that "__get__()" is callable with one or two
arguments. Python’s own built-in descriptors support this
specification; however, it is likely that some third-party tools
have descriptors that require both arguments. Python’s own
"__getattribute__()" implementation always passes in both arguments
whether they are required or not.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
Note, adding "__set__()" or "__delete__()" changes the kind of
descriptor to a “data descriptor”. See Invoking Descriptors for
more details.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
object.__set_name__(self, owner, name)
Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.
Note:
"__set_name__()" is only called implicitly as part of the "type"
constructor, so it will need to be called explicitly with the
appropriate parameters when a descriptor is added to a class
after initial creation:
class A:
pass
descr = custom_descriptor()
A.attr = descr
descr.__set_name__(A, 'attr')
See Creating the class object for more details.
New in version 3.6.
The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).
Invoking Descriptors
--------------------
In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a class, "A.x" is transformed into the call:
"A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on which descriptor methods are defined. A descriptor can define any
combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
---------
*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of *__dict__* and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)
The space saved over using *__dict__* can be significant. Attribute
lookup speed can be significantly improved as well.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. *__slots__*
reserves space for the declared variables and prevents the
automatic creation of *__dict__* and *__weakref__* for each
instance.
Notes on using *__slots__*
~~~~~~~~~~~~~~~~~~~~~~~~~~
* When inheriting from a class without *__slots__*, the *__dict__* and
*__weakref__* attribute of the instances will always be accessible.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
* Without a *__weakref__* variable for each instance, classes defining
*__slots__* do not support weak references to its instances. If weak
reference support is needed, then add "'__weakref__'" to the
sequence of strings in the *__slots__* declaration.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is not limited to the class
where it is defined. *__slots__* declared in parents are available
in child classes. However, child subclasses will get a *__dict__*
and *__weakref__* unless they also define *__slots__* (which should
only contain names of any *additional* slots).
* If a class defines a slot also defined in a base class, the instance
variable defined by the base class slot is inaccessible (except by
retrieving its descriptor directly from the base class). This
renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
“variable-length” built-in types such as "int", "bytes" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings may
also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".
* If an iterator is used for *__slots__* then a descriptor is created
for each of the iterator’s values. However, the *__slots__*
attribute will be an empty iterator.
Customizing class creation
==========================
Whenever a class inherits from another class, *__init_subclass__* is
called on that class. This way, it is possible to write classes which
change the behavior of subclasses. This is closely related to class
decorators, but where class decorators only affect the specific class
they’re applied to, "__init_subclass__" solely applies to future
subclasses of the class defining the method.
classmethod object.__init_subclass__(cls)
This method is called whenever the containing class is subclassed.
*cls* is then the new subclass. If defined as a normal instance
method, this method is implicitly converted to a class method.
Keyword arguments which are given to a new class are passed to the
parent’s class "__init_subclass__". For compatibility with other
classes using "__init_subclass__", one should take out the needed
keyword arguments and pass the others over to the base class, as
in:
class Philosopher:
def __init_subclass__(cls, /, default_name, **kwargs):
super().__init_subclass__(**kwargs)
cls.default_name = default_name
class AustralianPhilosopher(Philosopher, default_name="Bruce"):
pass
The default implementation "object.__init_subclass__" does nothing,
but raises an error if it is called with any arguments.
Note:
The metaclass hint "metaclass" is consumed by the rest of the
type machinery, and is never passed to "__init_subclass__"
implementations. The actual metaclass (rather than the explicit
hint) can be accessed as "type(cls)".
New in version 3.6.
Metaclasses
-----------
By default, classes are constructed using "type()". The class body is
executed in a new namespace and the class name is bound locally to the
result of "type(name, bases, namespace)".
The class creation process can be customized by passing the
"metaclass" keyword argument in the class definition line, or by
inheriting from an existing class that included such an argument. In
the following example, both "MyClass" and "MySubclass" are instances
of "Meta":
class Meta(type):
pass
class MyClass(metaclass=Meta):
pass
class MySubclass(MyClass):
pass
Any other keyword arguments that are specified in the class definition
are passed through to all metaclass operations described below.
When a class definition is executed, the following steps occur:
* MRO entries are resolved;
* the appropriate metaclass is determined;
* the class namespace is prepared;
* the class body is executed;
* the class object is created.
Resolving MRO entries
---------------------
If a base that appears in class definition is not an instance of
"type", then an "__mro_entries__" method is searched on it. If found,
it is called with the original bases tuple. This method must return a
tuple of classes that will be used instead of this base. The tuple may
be empty, in such case the original base is ignored.
See also:
**PEP 560** - Core support for typing module and generic types
Determining the appropriate metaclass
-------------------------------------
The appropriate metaclass for a class definition is determined as
follows:
* if no bases and no explicit metaclass are given, then "type()" is
used;
* if an explicit metaclass is given and it is *not* an instance of
"type()", then it is used directly as the metaclass;
* if an instance of "type()" is given as the explicit metaclass, or
bases are defined, then the most derived metaclass is used.
The most derived metaclass is selected from the explicitly specified
metaclass (if any) and the metaclasses (i.e. "type(cls)") of all
specified base classes. The most derived metaclass is one which is a
subtype of *all* of these candidate metaclasses. If none of the
candidate metaclasses meets that criterion, then the class definition
will fail with "TypeError".
Preparing the class namespace
-----------------------------
Once the appropriate metaclass has been identified, then the class
namespace is prepared. If the metaclass has a "__prepare__" attribute,
it is called as "namespace = metaclass.__prepare__(name, bases,
**kwds)" (where the additional keyword arguments, if any, come from
the class definition). The "__prepare__" method should be implemented
as a "classmethod()". The namespace returned by "__prepare__" is
passed in to "__new__", but when the final class object is created the
namespace is copied into a new "dict".
If the metaclass has no "__prepare__" attribute, then the class
namespace is initialised as an empty ordered mapping.
See also:
**PEP 3115** - Metaclasses in Python 3000
Introduced the "__prepare__" namespace hook
Executing the class body
------------------------
The class body is executed (approximately) as "exec(body, globals(),
namespace)". The key difference from a normal call to "exec()" is that
lexical scoping allows the class body (including any methods) to
reference names from the current and outer scopes when the class
definition occurs inside a function.
However, even when the class definition occurs inside the function,
methods defined inside the class still cannot see names defined at the
class scope. Class variables must be accessed through the first
parameter of instance or class methods, or through the implicit
lexically scoped "__class__" reference described in the next section.
Creating the class object
-------------------------
Once the class namespace has been populated by executing the class
body, the class object is created by calling "metaclass(name, bases,
namespace, **kwds)" (the additional keywords passed here are the same
as those passed to "__prepare__").
This class object is the one that will be referenced by the zero-
argument form of "super()". "__class__" is an implicit closure
reference created by the compiler if any methods in a class body refer
to either "__class__" or "super". This allows the zero argument form
of "super()" to correctly identify the class being defined based on
lexical scoping, while the class or instance that was used to make the
current call is identified based on the first argument passed to the
method.
**CPython implementation detail:** In CPython 3.6 and later, the
"__class__" cell is passed to the metaclass as a "__classcell__" entry
in the class namespace. If present, this must be propagated up to the
"type.__new__" call in order for the class to be initialised
correctly. Failing to do so will result in a "RuntimeError" in Python
3.8.
When using the default metaclass "type", or any metaclass that
ultimately calls "type.__new__", the following additional
customisation steps are invoked after creating the class object:
* first, "type.__new__" collects all of the descriptors in the class
namespace that define a "__set_name__()" method;
* second, all of these "__set_name__" methods are called with the
class being defined and the assigned name of that particular
descriptor;
* finally, the "__init_subclass__()" hook is called on the immediate
parent of the new class in its method resolution order.
After the class object is created, it is passed to the class
decorators included in the class definition (if any) and the resulting
object is bound in the local namespace as the defined class.
When a new class is created by "type.__new__", the object provided as
the namespace parameter is copied to a new ordered mapping and the
original object is discarded. The new copy is wrapped in a read-only
proxy, which becomes the "__dict__" attribute of the class object.
See also:
**PEP 3135** - New super
Describes the implicit "__class__" closure reference
Uses for metaclasses
--------------------
The potential uses for metaclasses are boundless. Some ideas that have
been explored include enum, logging, interface checking, automatic
delegation, automatic property creation, proxies, frameworks, and
automatic resource locking/synchronization.
Customizing instance and subclass checks
========================================
The following methods are used to override the default behavior of the
"isinstance()" and "issubclass()" built-in functions.
In particular, the metaclass "abc.ABCMeta" implements these methods in
order to allow the addition of Abstract Base Classes (ABCs) as
“virtual base classes” to any class or type (including built-in
types), including other ABCs.
class.__instancecheck__(self, instance)
Return true if *instance* should be considered a (direct or
indirect) instance of *class*. If defined, called to implement
"isinstance(instance, class)".
class.__subclasscheck__(self, subclass)
Return true if *subclass* should be considered a (direct or
indirect) subclass of *class*. If defined, called to implement
"issubclass(subclass, class)".
Note that these methods are looked up on the type (metaclass) of a
class. They cannot be defined as class methods in the actual class.
This is consistent with the lookup of special methods that are called
on instances, only in this case the instance is itself a class.
See also:
**PEP 3119** - Introducing Abstract Base Classes
Includes the specification for customizing "isinstance()" and
"issubclass()" behavior through "__instancecheck__()" and
"__subclasscheck__()", with motivation for this functionality in
the context of adding Abstract Base Classes (see the "abc"
module) to the language.
Emulating generic types
=======================
One can implement the generic class syntax as specified by **PEP 484**
(for example "List[int]") by defining a special method:
classmethod object.__class_getitem__(cls, key)
Return an object representing the specialization of a generic class
by type arguments found in *key*.
This method is looked up on the class object itself, and when defined
in the class body, this method is implicitly a class method. Note,
this mechanism is primarily reserved for use with static type hints,
other usage is discouraged.
See also:
**PEP 560** - Core support for typing module and generic types
Emulating callable objects
==========================
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method
is defined, "x(arg1, arg2, ...)" roughly translates to
"type(x).__call__(x, arg1, ...)".
Emulating container types
=========================
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python’s standard dictionary objects.
The "collections.abc" module provides a "MutableMapping" abstract base
class to help create those methods from a base set of "__getitem__()",
"__setitem__()", "__delitem__()", and "keys()". Mutable sequences
should provide methods "append()", "count()", "index()", "extend()",
"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python
standard list objects. Finally, sequence types should implement
addition (meaning concatenation) and multiplication (meaning
repetition) by defining the methods "__add__()", "__radd__()",
"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described
below; they should not define other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should search the mapping’s keys; for sequences, it
should search through the values. It is further recommended that both
mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should iterate through the object’s keys; for sequences, it should
iterate through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn’t define a "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.
object.__length_hint__(self)
Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. The
return value may also be "NotImplemented", which is treated the
same as if the "__length_hint__" method didn’t exist at all. This
method is purely an optimization and is never required for
correctness.
New in version 3.4.
Note:
Slicing is done exclusively with the following three methods. A
call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note:
"for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container.
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a container. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be iterable.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don’t define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
Emulating numeric types
=======================
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the expression "x + y", where *x* is an instance of a
class that has an "__add__()" method, "x.__add__(y)" is called.
The "__divmod__()" method should be the equivalent to using
"__floordiv__()" and "__mod__()"; it should not be related to
"__truediv__()". Note that "__pow__()" should be defined to accept
an optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other[, modulo])
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)
operands. These functions are only called if the left operand does
not support the corresponding operation [3] and the operands are of
different types. [4] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note:
If the right operand’s type is a subclass of the left operand’s
type and that subclass provides a different implementation of the
reflected method for the operation, this method will be called
before the left operand’s non-reflected method. This behavior
allows subclasses to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (modifying *self*) and return the result
(which could be, but does not have to be, *self*). If a specific
method is not defined, the augmented assignment falls back to the
normal methods. For instance, if *x* is an instance of a class
with an "__iadd__()" method, "x += y" is equivalent to "x =
x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are
considered, as with the evaluation of "x + y". In certain
situations, augmented assignment can result in unexpected errors
(see Why does a_tuple[i] += [‘item’] raise an exception when the
addition works?), but this behavior is in fact part of the data
model.
Note:
Due to a bug in the dispatching mechanism for "**=", a class that
defines "__ipow__()" but returns "NotImplemented" would fail to
fall back to "x.__pow__(y)" and "y.__rpow__(x)". This bug is
fixed in Python 3.10.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()" and
"float()". Should return a value of the appropriate type.
object.__index__(self)
Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.
If "__int__()", "__float__()" and "__complex__()" are not defined
then corresponding built-in functions "int()", "float()" and
"complex()" fall back to "__index__()".
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").
The built-in function "int()" falls back to "__trunc__()" if
neither "__int__()" nor "__index__()" is defined.
With Statement Context Managers
===============================
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method’s return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller’s responsibility.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
Special method lookup
=====================
For custom classes, implicit invocations of special methods are only
guaranteed to work correctly if defined on an object’s type, not in
the object’s instance dictionary. That behaviour is the reason why
the following code raises an exception:
>>> class C:
... pass
...
>>> c = C()
>>> c.__len__ = lambda: 5
>>> len(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: object of type 'C' has no len()
The rationale behind this behaviour lies with a number of special
methods such as "__hash__()" and "__repr__()" that are implemented by
all objects, including type objects. If the implicit lookup of these
methods used the conventional lookup process, they would fail when
invoked on the type object itself:
>>> 1 .__hash__() == hash(1)
True
>>> int.__hash__() == hash(int)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: descriptor '__hash__' of 'int' object needs an argument
Incorrectly attempting to invoke an unbound method of a class in this
way is sometimes referred to as ‘metaclass confusion’, and is avoided
by bypassing the instance when looking up special methods:
>>> type(1).__hash__(1) == hash(1)
True
>>> type(int).__hash__(int) == hash(int)
True
In addition to bypassing any instance attributes in the interest of
correctness, implicit special method lookup generally also bypasses
the "__getattribute__()" method even of the object’s metaclass:
>>> class Meta(type):
... def __getattribute__(*args):
... print("Metaclass getattribute invoked")
... return type.__getattribute__(*args)
...
>>> class C(object, metaclass=Meta):
... def __len__(self):
... return 10
... def __getattribute__(*args):
... print("Class getattribute invoked")
... return object.__getattribute__(*args)
...
>>> c = C()
>>> c.__len__() # Explicit lookup via instance
Class getattribute invoked
10
>>> type(c).__len__(c) # Explicit lookup via type
Metaclass getattribute invoked
10
>>> len(c) # Implicit lookup
10
Bypassing the "__getattribute__()" machinery in this fashion provides
significant scope for speed optimisations within the interpreter, at
the cost of some flexibility in the handling of special methods (the
special method *must* be set on the class object itself in order to be
consistently invoked by the interpreter).
u�Y String Methods
**************
Strings implement all of the common sequence operations, along with
the additional methods described below.
Strings also support two styles of string formatting, one providing a
large degree of flexibility and customization (see "str.format()",
Format String Syntax and Custom String Formatting) and the other based
on C "printf" style formatting that handles a narrower range of types
and is slightly harder to use correctly, but is often faster for the
cases it can handle (printf-style String Formatting).
The Text Processing Services section of the standard library covers a
number of other modules that provide various text related utilities
(including regular expression support in the "re" module).
str.capitalize()
Return a copy of the string with its first character capitalized
and the rest lowercased.
Changed in version 3.8: The first character is now put into
titlecase rather than uppercase. This means that characters like
digraphs will only have their first letter capitalized, instead of
the full character.
str.casefold()
Return a casefolded copy of the string. Casefolded strings may be
used for caseless matching.
Casefolding is similar to lowercasing but more aggressive because
it is intended to remove all case distinctions in a string. For
example, the German lowercase letter "'ß'" is equivalent to ""ss"".
Since it is already lowercase, "lower()" would do nothing to "'ß'";
"casefold()" converts it to ""ss"".
The casefolding algorithm is described in section 3.13 of the
Unicode Standard.
New in version 3.3.
str.center(width[, fillchar])
Return centered in a string of length *width*. Padding is done
using the specified *fillchar* (default is an ASCII space). The
original string is returned if *width* is less than or equal to
"len(s)".
str.count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring *sub*
in the range [*start*, *end*]. Optional arguments *start* and
*end* are interpreted as in slice notation.
str.encode(encoding="utf-8", errors="strict")
Return an encoded version of the string as a bytes object. Default
encoding is "'utf-8'". *errors* may be given to set a different
error handling scheme. The default for *errors* is "'strict'",
meaning that encoding errors raise a "UnicodeError". Other possible
values are "'ignore'", "'replace'", "'xmlcharrefreplace'",
"'backslashreplace'" and any other name registered via
"codecs.register_error()", see section Error Handlers. For a list
of possible encodings, see section Standard Encodings.
Changed in version 3.1: Support for keyword arguments added.
str.endswith(suffix[, start[, end]])
Return "True" if the string ends with the specified *suffix*,
otherwise return "False". *suffix* can also be a tuple of suffixes
to look for. With optional *start*, test beginning at that
position. With optional *end*, stop comparing at that position.
str.expandtabs(tabsize=8)
Return a copy of the string where all tab characters are replaced
by one or more spaces, depending on the current column and the
given tab size. Tab positions occur every *tabsize* characters
(default is 8, giving tab positions at columns 0, 8, 16 and so on).
To expand the string, the current column is set to zero and the
string is examined character by character. If the character is a
tab ("\t"), one or more space characters are inserted in the result
until the current column is equal to the next tab position. (The
tab character itself is not copied.) If the character is a newline
("\n") or return ("\r"), it is copied and the current column is
reset to zero. Any other character is copied unchanged and the
current column is incremented by one regardless of how the
character is represented when printed.
>>> '01\t012\t0123\t01234'.expandtabs()
'01 012 0123 01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01 012 0123 01234'
str.find(sub[, start[, end]])
Return the lowest index in the string where substring *sub* is
found within the slice "s[start:end]". Optional arguments *start*
and *end* are interpreted as in slice notation. Return "-1" if
*sub* is not found.
Note:
The "find()" method should be used only if you need to know the
position of *sub*. To check if *sub* is a substring or not, use
the "in" operator:
>>> 'Py' in 'Python'
True
str.format(*args, **kwargs)
Perform a string formatting operation. The string on which this
method is called can contain literal text or replacement fields
delimited by braces "{}". Each replacement field contains either
the numeric index of a positional argument, or the name of a
keyword argument. Returns a copy of the string where each
replacement field is replaced with the string value of the
corresponding argument.
>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'
See Format String Syntax for a description of the various
formatting options that can be specified in format strings.
Note:
When formatting a number ("int", "float", "complex",
"decimal.Decimal" and subclasses) with the "n" type (ex:
"'{:n}'.format(1234)"), the function temporarily sets the
"LC_CTYPE" locale to the "LC_NUMERIC" locale to decode
"decimal_point" and "thousands_sep" fields of "localeconv()" if
they are non-ASCII or longer than 1 byte, and the "LC_NUMERIC"
locale is different than the "LC_CTYPE" locale. This temporary
change affects other threads.
Changed in version 3.7: When formatting a number with the "n" type,
the function sets temporarily the "LC_CTYPE" locale to the
"LC_NUMERIC" locale in some cases.
str.format_map(mapping)
Similar to "str.format(**mapping)", except that "mapping" is used
directly and not copied to a "dict". This is useful if for example
"mapping" is a dict subclass:
>>> class Default(dict):
... def __missing__(self, key):
... return key
...
>>> '{name} was born in {country}'.format_map(Default(name='Guido'))
'Guido was born in country'
New in version 3.2.
str.index(sub[, start[, end]])
Like "find()", but raise "ValueError" when the substring is not
found.
str.isalnum()
Return "True" if all characters in the string are alphanumeric and
there is at least one character, "False" otherwise. A character
"c" is alphanumeric if one of the following returns "True":
"c.isalpha()", "c.isdecimal()", "c.isdigit()", or "c.isnumeric()".
str.isalpha()
Return "True" if all characters in the string are alphabetic and
there is at least one character, "False" otherwise. Alphabetic
characters are those characters defined in the Unicode character
database as “Letter”, i.e., those with general category property
being one of “Lm”, “Lt”, “Lu”, “Ll”, or “Lo”. Note that this is
different from the “Alphabetic” property defined in the Unicode
Standard.
str.isascii()
Return "True" if the string is empty or all characters in the
string are ASCII, "False" otherwise. ASCII characters have code
points in the range U+0000-U+007F.
New in version 3.7.
str.isdecimal()
Return "True" if all characters in the string are decimal
characters and there is at least one character, "False" otherwise.
Decimal characters are those that can be used to form numbers in
base 10, e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Formally a decimal
character is a character in the Unicode General Category “Nd”.
str.isdigit()
Return "True" if all characters in the string are digits and there
is at least one character, "False" otherwise. Digits include
decimal characters and digits that need special handling, such as
the compatibility superscript digits. This covers digits which
cannot be used to form numbers in base 10, like the Kharosthi
numbers. Formally, a digit is a character that has the property
value Numeric_Type=Digit or Numeric_Type=Decimal.
str.isidentifier()
Return "True" if the string is a valid identifier according to the
language definition, section Identifiers and keywords.
Call "keyword.iskeyword()" to test whether string "s" is a reserved
identifier, such as "def" and "class".
Example:
>>> from keyword import iskeyword
>>> 'hello'.isidentifier(), iskeyword('hello')
True, False
>>> 'def'.isidentifier(), iskeyword('def')
True, True
str.islower()
Return "True" if all cased characters [4] in the string are
lowercase and there is at least one cased character, "False"
otherwise.
str.isnumeric()
Return "True" if all characters in the string are numeric
characters, and there is at least one character, "False" otherwise.
Numeric characters include digit characters, and all characters
that have the Unicode numeric value property, e.g. U+2155, VULGAR
FRACTION ONE FIFTH. Formally, numeric characters are those with
the property value Numeric_Type=Digit, Numeric_Type=Decimal or
Numeric_Type=Numeric.
str.isprintable()
Return "True" if all characters in the string are printable or the
string is empty, "False" otherwise. Nonprintable characters are
those characters defined in the Unicode character database as
“Other” or “Separator”, excepting the ASCII space (0x20) which is
considered printable. (Note that printable characters in this
context are those which should not be escaped when "repr()" is
invoked on a string. It has no bearing on the handling of strings
written to "sys.stdout" or "sys.stderr".)
str.isspace()
Return "True" if there are only whitespace characters in the string
and there is at least one character, "False" otherwise.
A character is *whitespace* if in the Unicode character database
(see "unicodedata"), either its general category is "Zs"
(“Separator, space”), or its bidirectional class is one of "WS",
"B", or "S".
str.istitle()
Return "True" if the string is a titlecased string and there is at
least one character, for example uppercase characters may only
follow uncased characters and lowercase characters only cased ones.
Return "False" otherwise.
str.isupper()
Return "True" if all cased characters [4] in the string are
uppercase and there is at least one cased character, "False"
otherwise.
str.join(iterable)
Return a string which is the concatenation of the strings in
*iterable*. A "TypeError" will be raised if there are any non-
string values in *iterable*, including "bytes" objects. The
separator between elements is the string providing this method.
str.ljust(width[, fillchar])
Return the string left justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is an ASCII
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.lower()
Return a copy of the string with all the cased characters [4]
converted to lowercase.
The lowercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.lstrip([chars])
Return a copy of the string with leading characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a prefix; rather,
all combinations of its values are stripped:
>>> ' spacious '.lstrip()
'spacious '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'
static str.maketrans(x[, y[, z]])
This static method returns a translation table usable for
"str.translate()".
If there is only one argument, it must be a dictionary mapping
Unicode ordinals (integers) or characters (strings of length 1) to
Unicode ordinals, strings (of arbitrary lengths) or "None".
Character keys will then be converted to ordinals.
If there are two arguments, they must be strings of equal length,
and in the resulting dictionary, each character in x will be mapped
to the character at the same position in y. If there is a third
argument, it must be a string, whose characters will be mapped to
"None" in the result.
str.partition(sep)
Split the string at the first occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing the string itself, followed by
two empty strings.
str.replace(old, new[, count])
Return a copy of the string with all occurrences of substring *old*
replaced by *new*. If the optional argument *count* is given, only
the first *count* occurrences are replaced.
str.rfind(sub[, start[, end]])
Return the highest index in the string where substring *sub* is
found, such that *sub* is contained within "s[start:end]".
Optional arguments *start* and *end* are interpreted as in slice
notation. Return "-1" on failure.
str.rindex(sub[, start[, end]])
Like "rfind()" but raises "ValueError" when the substring *sub* is
not found.
str.rjust(width[, fillchar])
Return the string right justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is an ASCII
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.rpartition(sep)
Split the string at the last occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing two empty strings, followed by
the string itself.
str.rsplit(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit* splits
are done, the *rightmost* ones. If *sep* is not specified or
"None", any whitespace string is a separator. Except for splitting
from the right, "rsplit()" behaves like "split()" which is
described in detail below.
str.rstrip([chars])
Return a copy of the string with trailing characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a suffix; rather,
all combinations of its values are stripped:
>>> ' spacious '.rstrip()
' spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'
str.split(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit*
splits are done (thus, the list will have at most "maxsplit+1"
elements). If *maxsplit* is not specified or "-1", then there is
no limit on the number of splits (all possible splits are made).
If *sep* is given, consecutive delimiters are not grouped together
and are deemed to delimit empty strings (for example,
"'1,,2'.split(',')" returns "['1', '', '2']"). The *sep* argument
may consist of multiple characters (for example,
"'1<>2<>3'.split('<>')" returns "['1', '2', '3']"). Splitting an
empty string with a specified separator returns "['']".
For example:
>>> '1,2,3'.split(',')
['1', '2', '3']
>>> '1,2,3'.split(',', maxsplit=1)
['1', '2,3']
>>> '1,2,,3,'.split(',')
['1', '2', '', '3', '']
If *sep* is not specified or is "None", a different splitting
algorithm is applied: runs of consecutive whitespace are regarded
as a single separator, and the result will contain no empty strings
at the start or end if the string has leading or trailing
whitespace. Consequently, splitting an empty string or a string
consisting of just whitespace with a "None" separator returns "[]".
For example:
>>> '1 2 3'.split()
['1', '2', '3']
>>> '1 2 3'.split(maxsplit=1)
['1', '2 3']
>>> ' 1 2 3 '.split()
['1', '2', '3']
str.splitlines([keepends])
Return a list of the lines in the string, breaking at line
boundaries. Line breaks are not included in the resulting list
unless *keepends* is given and true.
This method splits on the following line boundaries. In
particular, the boundaries are a superset of *universal newlines*.
+-------------------------+-------------------------------+
| Representation | Description |
|=========================|===============================|
| "\n" | Line Feed |
+-------------------------+-------------------------------+
| "\r" | Carriage Return |
+-------------------------+-------------------------------+
| "\r\n" | Carriage Return + Line Feed |
+-------------------------+-------------------------------+
| "\v" or "\x0b" | Line Tabulation |
+-------------------------+-------------------------------+
| "\f" or "\x0c" | Form Feed |
+-------------------------+-------------------------------+
| "\x1c" | File Separator |
+-------------------------+-------------------------------+
| "\x1d" | Group Separator |
+-------------------------+-------------------------------+
| "\x1e" | Record Separator |
+-------------------------+-------------------------------+
| "\x85" | Next Line (C1 Control Code) |
+-------------------------+-------------------------------+
| "\u2028" | Line Separator |
+-------------------------+-------------------------------+
| "\u2029" | Paragraph Separator |
+-------------------------+-------------------------------+
Changed in version 3.2: "\v" and "\f" added to list of line
boundaries.
For example:
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
['ab c', '', 'de fg', 'kl']
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True)
['ab c\n', '\n', 'de fg\r', 'kl\r\n']
Unlike "split()" when a delimiter string *sep* is given, this
method returns an empty list for the empty string, and a terminal
line break does not result in an extra line:
>>> "".splitlines()
[]
>>> "One line\n".splitlines()
['One line']
For comparison, "split('\n')" gives:
>>> ''.split('\n')
['']
>>> 'Two lines\n'.split('\n')
['Two lines', '']
str.startswith(prefix[, start[, end]])
Return "True" if string starts with the *prefix*, otherwise return
"False". *prefix* can also be a tuple of prefixes to look for.
With optional *start*, test string beginning at that position.
With optional *end*, stop comparing string at that position.
str.strip([chars])
Return a copy of the string with the leading and trailing
characters removed. The *chars* argument is a string specifying the
set of characters to be removed. If omitted or "None", the *chars*
argument defaults to removing whitespace. The *chars* argument is
not a prefix or suffix; rather, all combinations of its values are
stripped:
>>> ' spacious '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'
The outermost leading and trailing *chars* argument values are
stripped from the string. Characters are removed from the leading
end until reaching a string character that is not contained in the
set of characters in *chars*. A similar action takes place on the
trailing end. For example:
>>> comment_string = '#....... Section 3.2.1 Issue #32 .......'
>>> comment_string.strip('.#! ')
'Section 3.2.1 Issue #32'
str.swapcase()
Return a copy of the string with uppercase characters converted to
lowercase and vice versa. Note that it is not necessarily true that
"s.swapcase().swapcase() == s".
str.title()
Return a titlecased version of the string where words start with an
uppercase character and the remaining characters are lowercase.
For example:
>>> 'Hello world'.title()
'Hello World'
The algorithm uses a simple language-independent definition of a
word as groups of consecutive letters. The definition works in
many contexts but it means that apostrophes in contractions and
possessives form word boundaries, which may not be the desired
result:
>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"
A workaround for apostrophes can be constructed using regular
expressions:
>>> import re
>>> def titlecase(s):
... return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
... lambda mo: mo.group(0).capitalize(),
... s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."
str.translate(table)
Return a copy of the string in which each character has been mapped
through the given translation table. The table must be an object
that implements indexing via "__getitem__()", typically a *mapping*
or *sequence*. When indexed by a Unicode ordinal (an integer), the
table object can do any of the following: return a Unicode ordinal
or a string, to map the character to one or more other characters;
return "None", to delete the character from the return string; or
raise a "LookupError" exception, to map the character to itself.
You can use "str.maketrans()" to create a translation map from
character-to-character mappings in different formats.
See also the "codecs" module for a more flexible approach to custom
character mappings.
str.upper()
Return a copy of the string with all the cased characters [4]
converted to uppercase. Note that "s.upper().isupper()" might be
"False" if "s" contains uncased characters or if the Unicode
category of the resulting character(s) is not “Lu” (Letter,
uppercase), but e.g. “Lt” (Letter, titlecase).
The uppercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.zfill(width)
Return a copy of the string left filled with ASCII "'0'" digits to
make a string of length *width*. A leading sign prefix
("'+'"/"'-'") is handled by inserting the padding *after* the sign
character rather than before. The original string is returned if
*width* is less than or equal to "len(s)".
For example:
>>> "42".zfill(5)
'00042'
>>> "-42".zfill(5)
'-0042'
u� String and Bytes literals
*************************
String literals are described by the following lexical definitions:
stringliteral ::= [stringprefix](shortstring | longstring)
stringprefix ::= "r" | "u" | "R" | "U" | "f" | "F"
| "fr" | "Fr" | "fR" | "FR" | "rf" | "rF" | "Rf" | "RF"
shortstring ::= "'" shortstringitem* "'" | '"' shortstringitem* '"'
longstring ::= "'''" longstringitem* "'''" | '"""' longstringitem* '"""'
shortstringitem ::= shortstringchar | stringescapeseq
longstringitem ::= longstringchar | stringescapeseq
shortstringchar ::= <any source character except "\" or newline or the quote>
longstringchar ::= <any source character except "\">
stringescapeseq ::= "\" <any source character>
bytesliteral ::= bytesprefix(shortbytes | longbytes)
bytesprefix ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB"
shortbytes ::= "'" shortbytesitem* "'" | '"' shortbytesitem* '"'
longbytes ::= "'''" longbytesitem* "'''" | '"""' longbytesitem* '"""'
shortbytesitem ::= shortbyteschar | bytesescapeseq
longbytesitem ::= longbyteschar | bytesescapeseq
shortbyteschar ::= <any ASCII character except "\" or newline or the quote>
longbyteschar ::= <any ASCII character except "\">
bytesescapeseq ::= "\" <any ASCII character>
One syntactic restriction not indicated by these productions is that
whitespace is not allowed between the "stringprefix" or "bytesprefix"
and the rest of the literal. The source character set is defined by
the encoding declaration; it is UTF-8 if no encoding declaration is
given in the source file; see section Encoding declarations.
In plain English: Both types of literals can be enclosed in matching
single quotes ("'") or double quotes ("""). They can also be enclosed
in matching groups of three single or double quotes (these are
generally referred to as *triple-quoted strings*). The backslash
("\") character is used to escape characters that otherwise have a
special meaning, such as newline, backslash itself, or the quote
character.
Bytes literals are always prefixed with "'b'" or "'B'"; they produce
an instance of the "bytes" type instead of the "str" type. They may
only contain ASCII characters; bytes with a numeric value of 128 or
greater must be expressed with escapes.
Both string and bytes literals may optionally be prefixed with a
letter "'r'" or "'R'"; such strings are called *raw strings* and treat
backslashes as literal characters. As a result, in string literals,
"'\U'" and "'\u'" escapes in raw strings are not treated specially.
Given that Python 2.x’s raw unicode literals behave differently than
Python 3.x’s the "'ur'" syntax is not supported.
New in version 3.3: The "'rb'" prefix of raw bytes literals has been
added as a synonym of "'br'".
New in version 3.3: Support for the unicode legacy literal
("u'value'") was reintroduced to simplify the maintenance of dual
Python 2.x and 3.x codebases. See **PEP 414** for more information.
A string literal with "'f'" or "'F'" in its prefix is a *formatted
string literal*; see Formatted string literals. The "'f'" may be
combined with "'r'", but not with "'b'" or "'u'", therefore raw
formatted strings are possible, but formatted bytes literals are not.
In triple-quoted literals, unescaped newlines and quotes are allowed
(and are retained), except that three unescaped quotes in a row
terminate the literal. (A “quote” is the character used to open the
literal, i.e. either "'" or """.)
Unless an "'r'" or "'R'" prefix is present, escape sequences in string
and bytes literals are interpreted according to rules similar to those
used by Standard C. The recognized escape sequences are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
|===================|===================================|=========|
| "\newline" | Backslash and newline ignored | |
+-------------------+-----------------------------------+---------+
| "\\" | Backslash ("\") | |
+-------------------+-----------------------------------+---------+
| "\'" | Single quote ("'") | |
+-------------------+-----------------------------------+---------+
| "\"" | Double quote (""") | |
+-------------------+-----------------------------------+---------+
| "\a" | ASCII Bell (BEL) | |
+-------------------+-----------------------------------+---------+
| "\b" | ASCII Backspace (BS) | |
+-------------------+-----------------------------------+---------+
| "\f" | ASCII Formfeed (FF) | |
+-------------------+-----------------------------------+---------+
| "\n" | ASCII Linefeed (LF) | |
+-------------------+-----------------------------------+---------+
| "\r" | ASCII Carriage Return (CR) | |
+-------------------+-----------------------------------+---------+
| "\t" | ASCII Horizontal Tab (TAB) | |
+-------------------+-----------------------------------+---------+
| "\v" | ASCII Vertical Tab (VT) | |
+-------------------+-----------------------------------+---------+
| "\ooo" | Character with octal value *ooo* | (1,3) |
+-------------------+-----------------------------------+---------+
| "\xhh" | Character with hex value *hh* | (2,3) |
+-------------------+-----------------------------------+---------+
Escape sequences only recognized in string literals are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
|===================|===================================|=========|
| "\N{name}" | Character named *name* in the | (4) |
| | Unicode database | |
+-------------------+-----------------------------------+---------+
| "\uxxxx" | Character with 16-bit hex value | (5) |
| | *xxxx* | |
+-------------------+-----------------------------------+---------+
| "\Uxxxxxxxx" | Character with 32-bit hex value | (6) |
| | *xxxxxxxx* | |
+-------------------+-----------------------------------+---------+
Notes:
1. As in Standard C, up to three octal digits are accepted.
2. Unlike in Standard C, exactly two hex digits are required.
3. In a bytes literal, hexadecimal and octal escapes denote the byte
with the given value. In a string literal, these escapes denote a
Unicode character with the given value.
4. Changed in version 3.3: Support for name aliases [1] has been
added.
5. Exactly four hex digits are required.
6. Any Unicode character can be encoded this way. Exactly eight hex
digits are required.
Unlike Standard C, all unrecognized escape sequences are left in the
string unchanged, i.e., *the backslash is left in the result*. (This
behavior is useful when debugging: if an escape sequence is mistyped,
the resulting output is more easily recognized as broken.) It is also
important to note that the escape sequences only recognized in string
literals fall into the category of unrecognized escapes for bytes
literals.
Changed in version 3.6: Unrecognized escape sequences produce a
"DeprecationWarning". In a future Python version they will be a
"SyntaxWarning" and eventually a "SyntaxError".
Even in a raw literal, quotes can be escaped with a backslash, but the
backslash remains in the result; for example, "r"\""" is a valid
string literal consisting of two characters: a backslash and a double
quote; "r"\"" is not a valid string literal (even a raw string cannot
end in an odd number of backslashes). Specifically, *a raw literal
cannot end in a single backslash* (since the backslash would escape
the following quote character). Note also that a single backslash
followed by a newline is interpreted as those two characters as part
of the literal, *not* as a line continuation.
uM Subscriptions
*************
A subscription selects an item of a sequence (string, tuple or list)
or mapping (dictionary) object:
subscription ::= primary "[" expression_list "]"
The primary must evaluate to an object that supports subscription
(lists or dictionaries for example). User-defined objects can support
subscription by defining a "__getitem__()" method.
For built-in objects, there are two types of objects that support
subscription:
If the primary is a mapping, the expression list must evaluate to an
object whose value is one of the keys of the mapping, and the
subscription selects the value in the mapping that corresponds to that
key. (The expression list is a tuple except if it has exactly one
item.)
If the primary is a sequence, the expression list must evaluate to an
integer or a slice (as discussed in the following section).
The formal syntax makes no special provision for negative indices in
sequences; however, built-in sequences all provide a "__getitem__()"
method that interprets negative indices by adding the length of the
sequence to the index (so that "x[-1]" selects the last item of "x").
The resulting value must be a nonnegative integer less than the number
of items in the sequence, and the subscription selects the item whose
index is that value (counting from zero). Since the support for
negative indices and slicing occurs in the object’s "__getitem__()"
method, subclasses overriding this method will need to explicitly add
that support.
A string’s items are characters. A character is not a separate data
type but a string of exactly one character.
ax Truth Value Testing
*******************
Any object can be tested for truth value, for use in an "if" or
"while" condition or as operand of the Boolean operations below.
By default, an object is considered true unless its class defines
either a "__bool__()" method that returns "False" or a "__len__()"
method that returns zero, when called with the object. [1] Here are
most of the built-in objects considered false:
* constants defined to be false: "None" and "False".
* zero of any numeric type: "0", "0.0", "0j", "Decimal(0)",
"Fraction(0, 1)"
* empty sequences and collections: "''", "()", "[]", "{}", "set()",
"range(0)"
Operations and built-in functions that have a Boolean result always
return "0" or "False" for false and "1" or "True" for true, unless
otherwise stated. (Important exception: the Boolean operations "or"
and "and" always return one of their operands.)
uR The "try" statement
*******************
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object, or a tuple containing an item that is the class or a base
class of the exception object.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed. All except
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred. "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.
The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed. Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a ‘cleanup’ handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return", "break" or "continue" statement, the saved
exception is discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
Changed in version 3.8: Prior to Python 3.8, a "continue" statement
was illegal in the "finally" clause due to a problem with the
implementation.
ux� The standard type hierarchy
***************************
Below is a list of the types that are built into Python. Extension
modules (written in C, Java, or other languages, depending on the
implementation) can define additional types. Future versions of
Python may add types to the type hierarchy (e.g., rational numbers,
efficiently stored arrays of integers, etc.), although such additions
will often be provided via the standard library instead.
Some of the type descriptions below contain a paragraph listing
‘special attributes.’ These are attributes that provide access to the
implementation and are not intended for general use. Their definition
may change in the future.
None
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name "None". It
is used to signify the absence of a value in many situations, e.g.,
it is returned from functions that don’t explicitly return
anything. Its truth value is false.
NotImplemented
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name
"NotImplemented". Numeric methods and rich comparison methods
should return this value if they do not implement the operation for
the operands provided. (The interpreter will then try the
reflected operation, or some other fallback, depending on the
operator.) Its truth value is true.
See Implementing the arithmetic operations for more details.
Ellipsis
This type has a single value. There is a single object with this
value. This object is accessed through the literal "..." or the
built-in name "Ellipsis". Its truth value is true.
"numbers.Number"
These are created by numeric literals and returned as results by
arithmetic operators and arithmetic built-in functions. Numeric
objects are immutable; once created their value never changes.
Python numbers are of course strongly related to mathematical
numbers, but subject to the limitations of numerical representation
in computers.
The string representations of the numeric classes, computed by
"__repr__()" and "__str__()", have the following properties:
* They are valid numeric literals which, when passed to their class
constructor, produce an object having the value of the original
numeric.
* The representation is in base 10, when possible.
* Leading zeros, possibly excepting a single zero before a decimal
point, are not shown.
* Trailing zeros, possibly excepting a single zero after a decimal
point, are not shown.
* A sign is shown only when the number is negative.
Python distinguishes between integers, floating point numbers, and
complex numbers:
"numbers.Integral"
These represent elements from the mathematical set of integers
(positive and negative).
There are two types of integers:
Integers ("int")
These represent numbers in an unlimited range, subject to
available (virtual) memory only. For the purpose of shift
and mask operations, a binary representation is assumed, and
negative numbers are represented in a variant of 2’s
complement which gives the illusion of an infinite string of
sign bits extending to the left.
Booleans ("bool")
These represent the truth values False and True. The two
objects representing the values "False" and "True" are the
only Boolean objects. The Boolean type is a subtype of the
integer type, and Boolean values behave like the values 0 and
1, respectively, in almost all contexts, the exception being
that when converted to a string, the strings ""False"" or
""True"" are returned, respectively.
The rules for integer representation are intended to give the
most meaningful interpretation of shift and mask operations
involving negative integers.
"numbers.Real" ("float")
These represent machine-level double precision floating point
numbers. You are at the mercy of the underlying machine
architecture (and C or Java implementation) for the accepted
range and handling of overflow. Python does not support single-
precision floating point numbers; the savings in processor and
memory usage that are usually the reason for using these are
dwarfed by the overhead of using objects in Python, so there is
no reason to complicate the language with two kinds of floating
point numbers.
"numbers.Complex" ("complex")
These represent complex numbers as a pair of machine-level
double precision floating point numbers. The same caveats apply
as for floating point numbers. The real and imaginary parts of a
complex number "z" can be retrieved through the read-only
attributes "z.real" and "z.imag".
Sequences
These represent finite ordered sets indexed by non-negative
numbers. The built-in function "len()" returns the number of items
of a sequence. When the length of a sequence is *n*, the index set
contains the numbers 0, 1, …, *n*-1. Item *i* of sequence *a* is
selected by "a[i]".
Sequences also support slicing: "a[i:j]" selects all items with
index *k* such that *i* "<=" *k* "<" *j*. When used as an
expression, a slice is a sequence of the same type. This implies
that the index set is renumbered so that it starts at 0.
Some sequences also support “extended slicing” with a third “step”
parameter: "a[i:j:k]" selects all items of *a* with index *x* where
"x = i + n*k", *n* ">=" "0" and *i* "<=" *x* "<" *j*.
Sequences are distinguished according to their mutability:
Immutable sequences
An object of an immutable sequence type cannot change once it is
created. (If the object contains references to other objects,
these other objects may be mutable and may be changed; however,
the collection of objects directly referenced by an immutable
object cannot change.)
The following types are immutable sequences:
Strings
A string is a sequence of values that represent Unicode code
points. All the code points in the range "U+0000 - U+10FFFF"
can be represented in a string. Python doesn’t have a "char"
type; instead, every code point in the string is represented
as a string object with length "1". The built-in function
"ord()" converts a code point from its string form to an
integer in the range "0 - 10FFFF"; "chr()" converts an
integer in the range "0 - 10FFFF" to the corresponding length
"1" string object. "str.encode()" can be used to convert a
"str" to "bytes" using the given text encoding, and
"bytes.decode()" can be used to achieve the opposite.
Tuples
The items of a tuple are arbitrary Python objects. Tuples of
two or more items are formed by comma-separated lists of
expressions. A tuple of one item (a ‘singleton’) can be
formed by affixing a comma to an expression (an expression by
itself does not create a tuple, since parentheses must be
usable for grouping of expressions). An empty tuple can be
formed by an empty pair of parentheses.
Bytes
A bytes object is an immutable array. The items are 8-bit
bytes, represented by integers in the range 0 <= x < 256.
Bytes literals (like "b'abc'") and the built-in "bytes()"
constructor can be used to create bytes objects. Also, bytes
objects can be decoded to strings via the "decode()" method.
Mutable sequences
Mutable sequences can be changed after they are created. The
subscription and slicing notations can be used as the target of
assignment and "del" (delete) statements.
There are currently two intrinsic mutable sequence types:
Lists
The items of a list are arbitrary Python objects. Lists are
formed by placing a comma-separated list of expressions in
square brackets. (Note that there are no special cases needed
to form lists of length 0 or 1.)
Byte Arrays
A bytearray object is a mutable array. They are created by
the built-in "bytearray()" constructor. Aside from being
mutable (and hence unhashable), byte arrays otherwise provide
the same interface and functionality as immutable "bytes"
objects.
The extension module "array" provides an additional example of a
mutable sequence type, as does the "collections" module.
Set types
These represent unordered, finite sets of unique, immutable
objects. As such, they cannot be indexed by any subscript. However,
they can be iterated over, and the built-in function "len()"
returns the number of items in a set. Common uses for sets are fast
membership testing, removing duplicates from a sequence, and
computing mathematical operations such as intersection, union,
difference, and symmetric difference.
For set elements, the same immutability rules apply as for
dictionary keys. Note that numeric types obey the normal rules for
numeric comparison: if two numbers compare equal (e.g., "1" and
"1.0"), only one of them can be contained in a set.
There are currently two intrinsic set types:
Sets
These represent a mutable set. They are created by the built-in
"set()" constructor and can be modified afterwards by several
methods, such as "add()".
Frozen sets
These represent an immutable set. They are created by the
built-in "frozenset()" constructor. As a frozenset is immutable
and *hashable*, it can be used again as an element of another
set, or as a dictionary key.
Mappings
These represent finite sets of objects indexed by arbitrary index
sets. The subscript notation "a[k]" selects the item indexed by "k"
from the mapping "a"; this can be used in expressions and as the
target of assignments or "del" statements. The built-in function
"len()" returns the number of items in a mapping.
There is currently a single intrinsic mapping type:
Dictionaries
These represent finite sets of objects indexed by nearly
arbitrary values. The only types of values not acceptable as
keys are values containing lists or dictionaries or other
mutable types that are compared by value rather than by object
identity, the reason being that the efficient implementation of
dictionaries requires a key’s hash value to remain constant.
Numeric types used for keys obey the normal rules for numeric
comparison: if two numbers compare equal (e.g., "1" and "1.0")
then they can be used interchangeably to index the same
dictionary entry.
Dictionaries preserve insertion order, meaning that keys will be
produced in the same order they were added sequentially over the
dictionary. Replacing an existing key does not change the order,
however removing a key and re-inserting it will add it to the
end instead of keeping its old place.
Dictionaries are mutable; they can be created by the "{...}"
notation (see section Dictionary displays).
The extension modules "dbm.ndbm" and "dbm.gnu" provide
additional examples of mapping types, as does the "collections"
module.
Changed in version 3.7: Dictionaries did not preserve insertion
order in versions of Python before 3.6. In CPython 3.6,
insertion order was preserved, but it was considered an
implementation detail at that time rather than a language
guarantee.
Callable types
These are the types to which the function call operation (see
section Calls) can be applied:
User-defined functions
A user-defined function object is created by a function
definition (see section Function definitions). It should be
called with an argument list containing the same number of items
as the function’s formal parameter list.
Special attributes:
+---------------------------+---------------------------------+-------------+
| Attribute | Meaning | |
|===========================|=================================|=============|
| "__doc__" | The function’s documentation | Writable |
| | string, or "None" if | |
| | unavailable; not inherited by | |
| | subclasses. | |
+---------------------------+---------------------------------+-------------+
| "__name__" | The function’s name. | Writable |
+---------------------------+---------------------------------+-------------+
| "__qualname__" | The function’s *qualified | Writable |
| | name*. New in version 3.3. | |
+---------------------------+---------------------------------+-------------+
| "__module__" | The name of the module the | Writable |
| | function was defined in, or | |
| | "None" if unavailable. | |
+---------------------------+---------------------------------+-------------+
| "__defaults__" | A tuple containing default | Writable |
| | argument values for those | |
| | arguments that have defaults, | |
| | or "None" if no arguments have | |
| | a default value. | |
+---------------------------+---------------------------------+-------------+
| "__code__" | The code object representing | Writable |
| | the compiled function body. | |
+---------------------------+---------------------------------+-------------+
| "__globals__" | A reference to the dictionary | Read-only |
| | that holds the function’s | |
| | global variables — the global | |
| | namespace of the module in | |
| | which the function was defined. | |
+---------------------------+---------------------------------+-------------+
| "__dict__" | The namespace supporting | Writable |
| | arbitrary function attributes. | |
+---------------------------+---------------------------------+-------------+
| "__closure__" | "None" or a tuple of cells that | Read-only |
| | contain bindings for the | |
| | function’s free variables. See | |
| | below for information on the | |
| | "cell_contents" attribute. | |
+---------------------------+---------------------------------+-------------+
| "__annotations__" | A dict containing annotations | Writable |
| | of parameters. The keys of the | |
| | dict are the parameter names, | |
| | and "'return'" for the return | |
| | annotation, if provided. | |
+---------------------------+---------------------------------+-------------+
| "__kwdefaults__" | A dict containing defaults for | Writable |
| | keyword-only parameters. | |
+---------------------------+---------------------------------+-------------+
Most of the attributes labelled “Writable” check the type of the
assigned value.
Function objects also support getting and setting arbitrary
attributes, which can be used, for example, to attach metadata
to functions. Regular attribute dot-notation is used to get and
set such attributes. *Note that the current implementation only
supports function attributes on user-defined functions. Function
attributes on built-in functions may be supported in the
future.*
A cell object has the attribute "cell_contents". This can be
used to get the value of the cell, as well as set the value.
Additional information about a function’s definition can be
retrieved from its code object; see the description of internal
types below. The "cell" type can be accessed in the "types"
module.
Instance methods
An instance method object combines a class, a class instance and
any callable object (normally a user-defined function).
Special read-only attributes: "__self__" is the class instance
object, "__func__" is the function object; "__doc__" is the
method’s documentation (same as "__func__.__doc__"); "__name__"
is the method name (same as "__func__.__name__"); "__module__"
is the name of the module the method was defined in, or "None"
if unavailable.
Methods also support accessing (but not setting) the arbitrary
function attributes on the underlying function object.
User-defined method objects may be created when getting an
attribute of a class (perhaps via an instance of that class), if
that attribute is a user-defined function object or a class
method object.
When an instance method object is created by retrieving a user-
defined function object from a class via one of its instances,
its "__self__" attribute is the instance, and the method object
is said to be bound. The new method’s "__func__" attribute is
the original function object.
When an instance method object is created by retrieving a class
method object from a class or instance, its "__self__" attribute
is the class itself, and its "__func__" attribute is the
function object underlying the class method.
When an instance method object is called, the underlying
function ("__func__") is called, inserting the class instance
("__self__") in front of the argument list. For instance, when
"C" is a class which contains a definition for a function "f()",
and "x" is an instance of "C", calling "x.f(1)" is equivalent to
calling "C.f(x, 1)".
When an instance method object is derived from a class method
object, the “class instance” stored in "__self__" will actually
be the class itself, so that calling either "x.f(1)" or "C.f(1)"
is equivalent to calling "f(C,1)" where "f" is the underlying
function.
Note that the transformation from function object to instance
method object happens each time the attribute is retrieved from
the instance. In some cases, a fruitful optimization is to
assign the attribute to a local variable and call that local
variable. Also notice that this transformation only happens for
user-defined functions; other callable objects (and all non-
callable objects) are retrieved without transformation. It is
also important to note that user-defined functions which are
attributes of a class instance are not converted to bound
methods; this *only* happens when the function is an attribute
of the class.
Generator functions
A function or method which uses the "yield" statement (see
section The yield statement) is called a *generator function*.
Such a function, when called, always returns an iterator object
which can be used to execute the body of the function: calling
the iterator’s "iterator.__next__()" method will cause the
function to execute until it provides a value using the "yield"
statement. When the function executes a "return" statement or
falls off the end, a "StopIteration" exception is raised and the
iterator will have reached the end of the set of values to be
returned.
Coroutine functions
A function or method which is defined using "async def" is
called a *coroutine function*. Such a function, when called,
returns a *coroutine* object. It may contain "await"
expressions, as well as "async with" and "async for" statements.
See also the Coroutine Objects section.
Asynchronous generator functions
A function or method which is defined using "async def" and
which uses the "yield" statement is called a *asynchronous
generator function*. Such a function, when called, returns an
asynchronous iterator object which can be used in an "async for"
statement to execute the body of the function.
Calling the asynchronous iterator’s "aiterator.__anext__()"
method will return an *awaitable* which when awaited will
execute until it provides a value using the "yield" expression.
When the function executes an empty "return" statement or falls
off the end, a "StopAsyncIteration" exception is raised and the
asynchronous iterator will have reached the end of the set of
values to be yielded.
Built-in functions
A built-in function object is a wrapper around a C function.
Examples of built-in functions are "len()" and "math.sin()"
("math" is a standard built-in module). The number and type of
the arguments are determined by the C function. Special read-
only attributes: "__doc__" is the function’s documentation
string, or "None" if unavailable; "__name__" is the function’s
name; "__self__" is set to "None" (but see the next item);
"__module__" is the name of the module the function was defined
in or "None" if unavailable.
Built-in methods
This is really a different disguise of a built-in function, this
time containing an object passed to the C function as an
implicit extra argument. An example of a built-in method is
"alist.append()", assuming *alist* is a list object. In this
case, the special read-only attribute "__self__" is set to the
object denoted by *alist*.
Classes
Classes are callable. These objects normally act as factories
for new instances of themselves, but variations are possible for
class types that override "__new__()". The arguments of the
call are passed to "__new__()" and, in the typical case, to
"__init__()" to initialize the new instance.
Class Instances
Instances of arbitrary classes can be made callable by defining
a "__call__()" method in their class.
Modules
Modules are a basic organizational unit of Python code, and are
created by the import system as invoked either by the "import"
statement, or by calling functions such as
"importlib.import_module()" and built-in "__import__()". A module
object has a namespace implemented by a dictionary object (this is
the dictionary referenced by the "__globals__" attribute of
functions defined in the module). Attribute references are
translated to lookups in this dictionary, e.g., "m.x" is equivalent
to "m.__dict__["x"]". A module object does not contain the code
object used to initialize the module (since it isn’t needed once
the initialization is done).
Attribute assignment updates the module’s namespace dictionary,
e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".
Predefined (writable) attributes: "__name__" is the module’s name;
"__doc__" is the module’s documentation string, or "None" if
unavailable; "__annotations__" (optional) is a dictionary
containing *variable annotations* collected during module body
execution; "__file__" is the pathname of the file from which the
module was loaded, if it was loaded from a file. The "__file__"
attribute may be missing for certain types of modules, such as C
modules that are statically linked into the interpreter; for
extension modules loaded dynamically from a shared library, it is
the pathname of the shared library file.
Special read-only attribute: "__dict__" is the module’s namespace
as a dictionary object.
**CPython implementation detail:** Because of the way CPython
clears module dictionaries, the module dictionary will be cleared
when the module falls out of scope even if the dictionary still has
live references. To avoid this, copy the dictionary or keep the
module around while using its dictionary directly.
Custom classes
Custom class types are typically created by class definitions (see
section Class definitions). A class has a namespace implemented by
a dictionary object. Class attribute references are translated to
lookups in this dictionary, e.g., "C.x" is translated to
"C.__dict__["x"]" (although there are a number of hooks which allow
for other means of locating attributes). When the attribute name is
not found there, the attribute search continues in the base
classes. This search of the base classes uses the C3 method
resolution order which behaves correctly even in the presence of
‘diamond’ inheritance structures where there are multiple
inheritance paths leading back to a common ancestor. Additional
details on the C3 MRO used by Python can be found in the
documentation accompanying the 2.3 release at
https://www.python.org/download/releases/2.3/mro/.
When a class attribute reference (for class "C", say) would yield a
class method object, it is transformed into an instance method
object whose "__self__" attribute is "C". When it would yield a
static method object, it is transformed into the object wrapped by
the static method object. See section Implementing Descriptors for
another way in which attributes retrieved from a class may differ
from those actually contained in its "__dict__".
Class attribute assignments update the class’s dictionary, never
the dictionary of a base class.
A class object can be called (see above) to yield a class instance
(see below).
Special attributes: "__name__" is the class name; "__module__" is
the module name in which the class was defined; "__dict__" is the
dictionary containing the class’s namespace; "__bases__" is a tuple
containing the base classes, in the order of their occurrence in
the base class list; "__doc__" is the class’s documentation string,
or "None" if undefined; "__annotations__" (optional) is a
dictionary containing *variable annotations* collected during class
body execution.
Class instances
A class instance is created by calling a class object (see above).
A class instance has a namespace implemented as a dictionary which
is the first place in which attribute references are searched.
When an attribute is not found there, and the instance’s class has
an attribute by that name, the search continues with the class
attributes. If a class attribute is found that is a user-defined
function object, it is transformed into an instance method object
whose "__self__" attribute is the instance. Static method and
class method objects are also transformed; see above under
“Classes”. See section Implementing Descriptors for another way in
which attributes of a class retrieved via its instances may differ
from the objects actually stored in the class’s "__dict__". If no
class attribute is found, and the object’s class has a
"__getattr__()" method, that is called to satisfy the lookup.
Attribute assignments and deletions update the instance’s
dictionary, never a class’s dictionary. If the class has a
"__setattr__()" or "__delattr__()" method, this is called instead
of updating the instance dictionary directly.
Class instances can pretend to be numbers, sequences, or mappings
if they have methods with certain special names. See section
Special method names.
Special attributes: "__dict__" is the attribute dictionary;
"__class__" is the instance’s class.
I/O objects (also known as file objects)
A *file object* represents an open file. Various shortcuts are
available to create file objects: the "open()" built-in function,
and also "os.popen()", "os.fdopen()", and the "makefile()" method
of socket objects (and perhaps by other functions or methods
provided by extension modules).
The objects "sys.stdin", "sys.stdout" and "sys.stderr" are
initialized to file objects corresponding to the interpreter’s
standard input, output and error streams; they are all open in text
mode and therefore follow the interface defined by the
"io.TextIOBase" abstract class.
Internal types
A few types used internally by the interpreter are exposed to the
user. Their definitions may change with future versions of the
interpreter, but they are mentioned here for completeness.
Code objects
Code objects represent *byte-compiled* executable Python code,
or *bytecode*. The difference between a code object and a
function object is that the function object contains an explicit
reference to the function’s globals (the module in which it was
defined), while a code object contains no context; also the
default argument values are stored in the function object, not
in the code object (because they represent values calculated at
run-time). Unlike function objects, code objects are immutable
and contain no references (directly or indirectly) to mutable
objects.
Special read-only attributes: "co_name" gives the function name;
"co_argcount" is the total number of positional arguments
(including positional-only arguments and arguments with default
values); "co_posonlyargcount" is the number of positional-only
arguments (including arguments with default values);
"co_kwonlyargcount" is the number of keyword-only arguments
(including arguments with default values); "co_nlocals" is the
number of local variables used by the function (including
arguments); "co_varnames" is a tuple containing the names of the
local variables (starting with the argument names);
"co_cellvars" is a tuple containing the names of local variables
that are referenced by nested functions; "co_freevars" is a
tuple containing the names of free variables; "co_code" is a
string representing the sequence of bytecode instructions;
"co_consts" is a tuple containing the literals used by the
bytecode; "co_names" is a tuple containing the names used by the
bytecode; "co_filename" is the filename from which the code was
compiled; "co_firstlineno" is the first line number of the
function; "co_lnotab" is a string encoding the mapping from
bytecode offsets to line numbers (for details see the source
code of the interpreter); "co_stacksize" is the required stack
size; "co_flags" is an integer encoding a number of flags for
the interpreter.
The following flag bits are defined for "co_flags": bit "0x04"
is set if the function uses the "*arguments" syntax to accept an
arbitrary number of positional arguments; bit "0x08" is set if
the function uses the "**keywords" syntax to accept arbitrary
keyword arguments; bit "0x20" is set if the function is a
generator.
Future feature declarations ("from __future__ import division")
also use bits in "co_flags" to indicate whether a code object
was compiled with a particular feature enabled: bit "0x2000" is
set if the function was compiled with future division enabled;
bits "0x10" and "0x1000" were used in earlier versions of
Python.
Other bits in "co_flags" are reserved for internal use.
If a code object represents a function, the first item in
"co_consts" is the documentation string of the function, or
"None" if undefined.
Frame objects
Frame objects represent execution frames. They may occur in
traceback objects (see below), and are also passed to registered
trace functions.
Special read-only attributes: "f_back" is to the previous stack
frame (towards the caller), or "None" if this is the bottom
stack frame; "f_code" is the code object being executed in this
frame; "f_locals" is the dictionary used to look up local
variables; "f_globals" is used for global variables;
"f_builtins" is used for built-in (intrinsic) names; "f_lasti"
gives the precise instruction (this is an index into the
bytecode string of the code object).
Accessing "f_code" raises an auditing event "object.__getattr__"
with arguments "obj" and ""f_code"".
Special writable attributes: "f_trace", if not "None", is a
function called for various events during code execution (this
is used by the debugger). Normally an event is triggered for
each new source line - this can be disabled by setting
"f_trace_lines" to "False".
Implementations *may* allow per-opcode events to be requested by
setting "f_trace_opcodes" to "True". Note that this may lead to
undefined interpreter behaviour if exceptions raised by the
trace function escape to the function being traced.
"f_lineno" is the current line number of the frame — writing to
this from within a trace function jumps to the given line (only
for the bottom-most frame). A debugger can implement a Jump
command (aka Set Next Statement) by writing to f_lineno.
Frame objects support one method:
frame.clear()
This method clears all references to local variables held by
the frame. Also, if the frame belonged to a generator, the
generator is finalized. This helps break reference cycles
involving frame objects (for example when catching an
exception and storing its traceback for later use).
"RuntimeError" is raised if the frame is currently executing.
New in version 3.4.
Traceback objects
Traceback objects represent a stack trace of an exception. A
traceback object is implicitly created when an exception occurs,
and may also be explicitly created by calling
"types.TracebackType".
For implicitly created tracebacks, when the search for an
exception handler unwinds the execution stack, at each unwound
level a traceback object is inserted in front of the current
traceback. When an exception handler is entered, the stack
trace is made available to the program. (See section The try
statement.) It is accessible as the third item of the tuple
returned by "sys.exc_info()", and as the "__traceback__"
attribute of the caught exception.
When the program contains no suitable handler, the stack trace
is written (nicely formatted) to the standard error stream; if
the interpreter is interactive, it is also made available to the
user as "sys.last_traceback".
For explicitly created tracebacks, it is up to the creator of
the traceback to determine how the "tb_next" attributes should
be linked to form a full stack trace.
Special read-only attributes: "tb_frame" points to the execution
frame of the current level; "tb_lineno" gives the line number
where the exception occurred; "tb_lasti" indicates the precise
instruction. The line number and last instruction in the
traceback may differ from the line number of its frame object if
the exception occurred in a "try" statement with no matching
except clause or with a finally clause.
Accessing "tb_frame" raises an auditing event
"object.__getattr__" with arguments "obj" and ""tb_frame"".
Special writable attribute: "tb_next" is the next level in the
stack trace (towards the frame where the exception occurred), or
"None" if there is no next level.
Changed in version 3.7: Traceback objects can now be explicitly
instantiated from Python code, and the "tb_next" attribute of
existing instances can be updated.
Slice objects
Slice objects are used to represent slices for "__getitem__()"
methods. They are also created by the built-in "slice()"
function.
Special read-only attributes: "start" is the lower bound; "stop"
is the upper bound; "step" is the step value; each is "None" if
omitted. These attributes can have any type.
Slice objects support one method:
slice.indices(self, length)
This method takes a single integer argument *length* and
computes information about the slice that the slice object
would describe if applied to a sequence of *length* items.
It returns a tuple of three integers; respectively these are
the *start* and *stop* indices and the *step* or stride
length of the slice. Missing or out-of-bounds indices are
handled in a manner consistent with regular slices.
Static method objects
Static method objects provide a way of defeating the
transformation of function objects to method objects described
above. A static method object is a wrapper around any other
object, usually a user-defined method object. When a static
method object is retrieved from a class or a class instance, the
object actually returned is the wrapped object, which is not
subject to any further transformation. Static method objects are
not themselves callable, although the objects they wrap usually
are. Static method objects are created by the built-in
"staticmethod()" constructor.
Class method objects
A class method object, like a static method object, is a wrapper
around another object that alters the way in which that object
is retrieved from classes and class instances. The behaviour of
class method objects upon such retrieval is described above,
under “User-defined methods”. Class method objects are created
by the built-in "classmethod()" constructor.
a� Functions
*********
Function objects are created by function definitions. The only
operation on a function object is to call it: "func(argument-list)".
There are really two flavors of function objects: built-in functions
and user-defined functions. Both support the same operation (to call
the function), but the implementation is different, hence the
different object types.
See Function definitions for more information.
u(. Mapping Types — "dict"
**********************
A *mapping* object maps *hashable* values to arbitrary objects.
Mappings are mutable objects. There is currently only one standard
mapping type, the *dictionary*. (For other containers see the built-
in "list", "set", and "tuple" classes, and the "collections" module.)
A dictionary’s keys are *almost* arbitrary values. Values that are
not *hashable*, that is, values containing lists, dictionaries or
other mutable types (that are compared by value rather than by object
identity) may not be used as keys. Numeric types used for keys obey
the normal rules for numeric comparison: if two numbers compare equal
(such as "1" and "1.0") then they can be used interchangeably to index
the same dictionary entry. (Note however, that since computers store
floating-point numbers as approximations it is usually unwise to use
them as dictionary keys.)
Dictionaries can be created by placing a comma-separated list of "key:
value" pairs within braces, for example: "{'jack': 4098, 'sjoerd':
4127}" or "{4098: 'jack', 4127: 'sjoerd'}", or by the "dict"
constructor.
class dict(**kwarg)
class dict(mapping, **kwarg)
class dict(iterable, **kwarg)
Return a new dictionary initialized from an optional positional
argument and a possibly empty set of keyword arguments.
Dictionaries can be created by several means:
* Use a comma-separated list of "key: value" pairs within braces:
"{'jack': 4098, 'sjoerd': 4127}" or "{4098: 'jack', 4127:
'sjoerd'}"
* Use a dict comprehension: "{}", "{x: x ** 2 for x in range(10)}"
* Use the type constructor: "dict()", "dict([('foo', 100), ('bar',
200)])", "dict(foo=100, bar=200)"
If no positional argument is given, an empty dictionary is created.
If a positional argument is given and it is a mapping object, a
dictionary is created with the same key-value pairs as the mapping
object. Otherwise, the positional argument must be an *iterable*
object. Each item in the iterable must itself be an iterable with
exactly two objects. The first object of each item becomes a key
in the new dictionary, and the second object the corresponding
value. If a key occurs more than once, the last value for that key
becomes the corresponding value in the new dictionary.
If keyword arguments are given, the keyword arguments and their
values are added to the dictionary created from the positional
argument. If a key being added is already present, the value from
the keyword argument replaces the value from the positional
argument.
To illustrate, the following examples all return a dictionary equal
to "{"one": 1, "two": 2, "three": 3}":
>>> a = dict(one=1, two=2, three=3)
>>> b = {'one': 1, 'two': 2, 'three': 3}
>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
>>> e = dict({'three': 3, 'one': 1, 'two': 2})
>>> a == b == c == d == e
True
Providing keyword arguments as in the first example only works for
keys that are valid Python identifiers. Otherwise, any valid keys
can be used.
These are the operations that dictionaries support (and therefore,
custom mapping types should support too):
list(d)
Return a list of all the keys used in the dictionary *d*.
len(d)
Return the number of items in the dictionary *d*.
d[key]
Return the item of *d* with key *key*. Raises a "KeyError" if
*key* is not in the map.
If a subclass of dict defines a method "__missing__()" and *key*
is not present, the "d[key]" operation calls that method with
the key *key* as argument. The "d[key]" operation then returns
or raises whatever is returned or raised by the
"__missing__(key)" call. No other operations or methods invoke
"__missing__()". If "__missing__()" is not defined, "KeyError"
is raised. "__missing__()" must be a method; it cannot be an
instance variable:
>>> class Counter(dict):
... def __missing__(self, key):
... return 0
>>> c = Counter()
>>> c['red']
0
>>> c['red'] += 1
>>> c['red']
1
The example above shows part of the implementation of
"collections.Counter". A different "__missing__" method is used
by "collections.defaultdict".
d[key] = value
Set "d[key]" to *value*.
del d[key]
Remove "d[key]" from *d*. Raises a "KeyError" if *key* is not
in the map.
key in d
Return "True" if *d* has a key *key*, else "False".
key not in d
Equivalent to "not key in d".
iter(d)
Return an iterator over the keys of the dictionary. This is a
shortcut for "iter(d.keys())".
clear()
Remove all items from the dictionary.
copy()
Return a shallow copy of the dictionary.
classmethod fromkeys(iterable[, value])
Create a new dictionary with keys from *iterable* and values set
to *value*.
"fromkeys()" is a class method that returns a new dictionary.
*value* defaults to "None". All of the values refer to just a
single instance, so it generally doesn’t make sense for *value*
to be a mutable object such as an empty list. To get distinct
values, use a dict comprehension instead.
get(key[, default])
Return the value for *key* if *key* is in the dictionary, else
*default*. If *default* is not given, it defaults to "None", so
that this method never raises a "KeyError".
items()
Return a new view of the dictionary’s items ("(key, value)"
pairs). See the documentation of view objects.
keys()
Return a new view of the dictionary’s keys. See the
documentation of view objects.
pop(key[, default])
If *key* is in the dictionary, remove it and return its value,
else return *default*. If *default* is not given and *key* is
not in the dictionary, a "KeyError" is raised.
popitem()
Remove and return a "(key, value)" pair from the dictionary.
Pairs are returned in LIFO (last-in, first-out) order.
"popitem()" is useful to destructively iterate over a
dictionary, as often used in set algorithms. If the dictionary
is empty, calling "popitem()" raises a "KeyError".
Changed in version 3.7: LIFO order is now guaranteed. In prior
versions, "popitem()" would return an arbitrary key/value pair.
reversed(d)
Return a reverse iterator over the keys of the dictionary. This
is a shortcut for "reversed(d.keys())".
New in version 3.8.
setdefault(key[, default])
If *key* is in the dictionary, return its value. If not, insert
*key* with a value of *default* and return *default*. *default*
defaults to "None".
update([other])
Update the dictionary with the key/value pairs from *other*,
overwriting existing keys. Return "None".
"update()" accepts either another dictionary object or an
iterable of key/value pairs (as tuples or other iterables of
length two). If keyword arguments are specified, the dictionary
is then updated with those key/value pairs: "d.update(red=1,
blue=2)".
values()
Return a new view of the dictionary’s values. See the
documentation of view objects.
An equality comparison between one "dict.values()" view and
another will always return "False". This also applies when
comparing "dict.values()" to itself:
>>> d = {'a': 1}
>>> d.values() == d.values()
False
Dictionaries compare equal if and only if they have the same "(key,
value)" pairs (regardless of ordering). Order comparisons (‘<’,
‘<=’, ‘>=’, ‘>’) raise "TypeError".
Dictionaries preserve insertion order. Note that updating a key
does not affect the order. Keys added after deletion are inserted
at the end.
>>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}
>>> list(d)
['one', 'two', 'three', 'four']
>>> list(d.values())
[1, 2, 3, 4]
>>> d["one"] = 42
>>> d
{'one': 42, 'two': 2, 'three': 3, 'four': 4}
>>> del d["two"]
>>> d["two"] = None
>>> d
{'one': 42, 'three': 3, 'four': 4, 'two': None}
Changed in version 3.7: Dictionary order is guaranteed to be
insertion order. This behavior was an implementation detail of
CPython from 3.6.
Dictionaries and dictionary views are reversible.
>>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}
>>> list(reversed(d))
['four', 'three', 'two', 'one']
>>> list(reversed(d.values()))
[4, 3, 2, 1]
>>> list(reversed(d.items()))
[('four', 4), ('three', 3), ('two', 2), ('one', 1)]
Changed in version 3.8: Dictionaries are now reversible.
See also:
"types.MappingProxyType" can be used to create a read-only view of a
"dict".
Dictionary view objects
=======================
The objects returned by "dict.keys()", "dict.values()" and
"dict.items()" are *view objects*. They provide a dynamic view on the
dictionary’s entries, which means that when the dictionary changes,
the view reflects these changes.
Dictionary views can be iterated over to yield their respective data,
and support membership tests:
len(dictview)
Return the number of entries in the dictionary.
iter(dictview)
Return an iterator over the keys, values or items (represented as
tuples of "(key, value)") in the dictionary.
Keys and values are iterated over in insertion order. This allows
the creation of "(value, key)" pairs using "zip()": "pairs =
zip(d.values(), d.keys())". Another way to create the same list is
"pairs = [(v, k) for (k, v) in d.items()]".
Iterating views while adding or deleting entries in the dictionary
may raise a "RuntimeError" or fail to iterate over all entries.
Changed in version 3.7: Dictionary order is guaranteed to be
insertion order.
x in dictview
Return "True" if *x* is in the underlying dictionary’s keys, values
or items (in the latter case, *x* should be a "(key, value)"
tuple).
reversed(dictview)
Return a reverse iterator over the keys, values or items of the
dictionary. The view will be iterated in reverse order of the
insertion.
Changed in version 3.8: Dictionary views are now reversible.
Keys views are set-like since their entries are unique and hashable.
If all values are hashable, so that "(key, value)" pairs are unique
and hashable, then the items view is also set-like. (Values views are
not treated as set-like since the entries are generally not unique.)
For set-like views, all of the operations defined for the abstract
base class "collections.abc.Set" are available (for example, "==",
"<", or "^").
An example of dictionary view usage:
>>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500}
>>> keys = dishes.keys()
>>> values = dishes.values()
>>> # iteration
>>> n = 0
>>> for val in values:
... n += val
>>> print(n)
504
>>> # keys and values are iterated over in the same order (insertion order)
>>> list(keys)
['eggs', 'sausage', 'bacon', 'spam']
>>> list(values)
[2, 1, 1, 500]
>>> # view objects are dynamic and reflect dict changes
>>> del dishes['eggs']
>>> del dishes['sausage']
>>> list(keys)
['bacon', 'spam']
>>> # set operations
>>> keys & {'eggs', 'bacon', 'salad'}
{'bacon'}
>>> keys ^ {'sausage', 'juice'}
{'juice', 'sausage', 'bacon', 'spam'}
a� Methods
*******
Methods are functions that are called using the attribute notation.
There are two flavors: built-in methods (such as "append()" on lists)
and class instance methods. Built-in methods are described with the
types that support them.
If you access a method (a function defined in a class namespace)
through an instance, you get a special object: a *bound method* (also
called *instance method*) object. When called, it will add the "self"
argument to the argument list. Bound methods have two special read-
only attributes: "m.__self__" is the object on which the method
operates, and "m.__func__" is the function implementing the method.
Calling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to
calling "m.__func__(m.__self__, arg-1, arg-2, ..., arg-n)".
Like function objects, bound method objects support getting arbitrary
attributes. However, since method attributes are actually stored on
the underlying function object ("meth.__func__"), setting method
attributes on bound methods is disallowed. Attempting to set an
attribute on a method results in an "AttributeError" being raised. In
order to set a method attribute, you need to explicitly set it on the
underlying function object:
>>> class C:
... def method(self):
... pass
...
>>> c = C()
>>> c.method.whoami = 'my name is method' # can't set on the method
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'method' object has no attribute 'whoami'
>>> c.method.__func__.whoami = 'my name is method'
>>> c.method.whoami
'my name is method'
See The standard type hierarchy for more information.
u$ Modules
*******
The only special operation on a module is attribute access: "m.name",
where *m* is a module and *name* accesses a name defined in *m*’s
symbol table. Module attributes can be assigned to. (Note that the
"import" statement is not, strictly speaking, an operation on a module
object; "import foo" does not require a module object named *foo* to
exist, rather it requires an (external) *definition* for a module
named *foo* somewhere.)
A special attribute of every module is "__dict__". This is the
dictionary containing the module’s symbol table. Modifying this
dictionary will actually change the module’s symbol table, but direct
assignment to the "__dict__" attribute is not possible (you can write
"m.__dict__['a'] = 1", which defines "m.a" to be "1", but you can’t
write "m.__dict__ = {}"). Modifying "__dict__" directly is not
recommended.
Modules built into the interpreter are written like this: "<module
'sys' (built-in)>". If loaded from a file, they are written as
"<module 'os' from '/usr/local/lib/pythonX.Y/os.pyc'>".
u�Z Sequence Types — "list", "tuple", "range"
*****************************************
There are three basic sequence types: lists, tuples, and range
objects. Additional sequence types tailored for processing of binary
data and text strings are described in dedicated sections.
Common Sequence Operations
==========================
The operations in the following table are supported by most sequence
types, both mutable and immutable. The "collections.abc.Sequence" ABC
is provided to make it easier to correctly implement these operations
on custom sequence types.
This table lists the sequence operations sorted in ascending priority.
In the table, *s* and *t* are sequences of the same type, *n*, *i*,
*j* and *k* are integers and *x* is an arbitrary object that meets any
type and value restrictions imposed by *s*.
The "in" and "not in" operations have the same priorities as the
comparison operations. The "+" (concatenation) and "*" (repetition)
operations have the same priority as the corresponding numeric
operations. [3]
+----------------------------+----------------------------------+------------+
| Operation | Result | Notes |
|============================|==================================|============|
| "x in s" | "True" if an item of *s* is | (1) |
| | equal to *x*, else "False" | |
+----------------------------+----------------------------------+------------+
| "x not in s" | "False" if an item of *s* is | (1) |
| | equal to *x*, else "True" | |
+----------------------------+----------------------------------+------------+
| "s + t" | the concatenation of *s* and *t* | (6)(7) |
+----------------------------+----------------------------------+------------+
| "s * n" or "n * s" | equivalent to adding *s* to | (2)(7) |
| | itself *n* times | |
+----------------------------+----------------------------------+------------+
| "s[i]" | *i*th item of *s*, origin 0 | (3) |
+----------------------------+----------------------------------+------------+
| "s[i:j]" | slice of *s* from *i* to *j* | (3)(4) |
+----------------------------+----------------------------------+------------+
| "s[i:j:k]" | slice of *s* from *i* to *j* | (3)(5) |
| | with step *k* | |
+----------------------------+----------------------------------+------------+
| "len(s)" | length of *s* | |
+----------------------------+----------------------------------+------------+
| "min(s)" | smallest item of *s* | |
+----------------------------+----------------------------------+------------+
| "max(s)" | largest item of *s* | |
+----------------------------+----------------------------------+------------+
| "s.index(x[, i[, j]])" | index of the first occurrence of | (8) |
| | *x* in *s* (at or after index | |
| | *i* and before index *j*) | |
+----------------------------+----------------------------------+------------+
| "s.count(x)" | total number of occurrences of | |
| | *x* in *s* | |
+----------------------------+----------------------------------+------------+
Sequences of the same type also support comparisons. In particular,
tuples and lists are compared lexicographically by comparing
corresponding elements. This means that to compare equal, every
element must compare equal and the two sequences must be of the same
type and have the same length. (For full details see Comparisons in
the language reference.)
Notes:
1. While the "in" and "not in" operations are used only for simple
containment testing in the general case, some specialised sequences
(such as "str", "bytes" and "bytearray") also use them for
subsequence testing:
>>> "gg" in "eggs"
True
2. Values of *n* less than "0" are treated as "0" (which yields an
empty sequence of the same type as *s*). Note that items in the
sequence *s* are not copied; they are referenced multiple times.
This often haunts new Python programmers; consider:
>>> lists = [[]] * 3
>>> lists
[[], [], []]
>>> lists[0].append(3)
>>> lists
[[3], [3], [3]]
What has happened is that "[[]]" is a one-element list containing
an empty list, so all three elements of "[[]] * 3" are references
to this single empty list. Modifying any of the elements of
"lists" modifies this single list. You can create a list of
different lists this way:
>>> lists = [[] for i in range(3)]
>>> lists[0].append(3)
>>> lists[1].append(5)
>>> lists[2].append(7)
>>> lists
[[3], [5], [7]]
Further explanation is available in the FAQ entry How do I create a
multidimensional list?.
3. If *i* or *j* is negative, the index is relative to the end of
sequence *s*: "len(s) + i" or "len(s) + j" is substituted. But
note that "-0" is still "0".
4. The slice of *s* from *i* to *j* is defined as the sequence of
items with index *k* such that "i <= k < j". If *i* or *j* is
greater than "len(s)", use "len(s)". If *i* is omitted or "None",
use "0". If *j* is omitted or "None", use "len(s)". If *i* is
greater than or equal to *j*, the slice is empty.
5. The slice of *s* from *i* to *j* with step *k* is defined as the
sequence of items with index "x = i + n*k" such that "0 <= n <
(j-i)/k". In other words, the indices are "i", "i+k", "i+2*k",
"i+3*k" and so on, stopping when *j* is reached (but never
including *j*). When *k* is positive, *i* and *j* are reduced to
"len(s)" if they are greater. When *k* is negative, *i* and *j* are
reduced to "len(s) - 1" if they are greater. If *i* or *j* are
omitted or "None", they become “end” values (which end depends on
the sign of *k*). Note, *k* cannot be zero. If *k* is "None", it
is treated like "1".
6. Concatenating immutable sequences always results in a new object.
This means that building up a sequence by repeated concatenation
will have a quadratic runtime cost in the total sequence length.
To get a linear runtime cost, you must switch to one of the
alternatives below:
* if concatenating "str" objects, you can build a list and use
"str.join()" at the end or else write to an "io.StringIO"
instance and retrieve its value when complete
* if concatenating "bytes" objects, you can similarly use
"bytes.join()" or "io.BytesIO", or you can do in-place
concatenation with a "bytearray" object. "bytearray" objects are
mutable and have an efficient overallocation mechanism
* if concatenating "tuple" objects, extend a "list" instead
* for other types, investigate the relevant class documentation
7. Some sequence types (such as "range") only support item sequences
that follow specific patterns, and hence don’t support sequence
concatenation or repetition.
8. "index" raises "ValueError" when *x* is not found in *s*. Not all
implementations support passing the additional arguments *i* and
*j*. These arguments allow efficient searching of subsections of
the sequence. Passing the extra arguments is roughly equivalent to
using "s[i:j].index(x)", only without copying any data and with the
returned index being relative to the start of the sequence rather
than the start of the slice.
Immutable Sequence Types
========================
The only operation that immutable sequence types generally implement
that is not also implemented by mutable sequence types is support for
the "hash()" built-in.
This support allows immutable sequences, such as "tuple" instances, to
be used as "dict" keys and stored in "set" and "frozenset" instances.
Attempting to hash an immutable sequence that contains unhashable
values will result in "TypeError".
Mutable Sequence Types
======================
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
|================================|==================================|=======================|
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | appends *x* to the end of the | |
| | sequence (same as | |
| | "s[len(s):len(s)] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from *s* (same | (5) |
| | as "del s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of *s* | (5) |
| | (same as "s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | extends *s* with the contents of | |
| | *t* (for the most part the same | |
| | as "s[len(s):len(s)] = t") | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (6) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | inserts *x* into *s* at the | |
| | index given by *i* (same as | |
| | "s[i:i] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop()" or "s.pop(i)" | retrieves the item at *i* and | (2) |
| | also removes it from *s* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
| | where "s[i]" is equal to *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default the
last item is removed and returned.
3. "remove()" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for economy
of space when reversing a large sequence. To remind users that it
operates by side effect, it does not return the reversed sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don’t support slicing
operations (such as "dict" and "set"). "copy()" is not part of the
"collections.abc.MutableSequence" ABC, but most concrete mutable
sequence classes provide it.
New in version 3.3: "clear()" and "copy()" methods.
6. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the sequence.
Items in the sequence are not copied; they are referenced multiple
times, as explained for "s * n" under Common Sequence Operations.
Lists
=====
Lists are mutable sequences, typically used to store collections of
homogeneous items (where the precise degree of similarity will vary by
application).
class list([iterable])
Lists may be constructed in several ways:
* Using a pair of square brackets to denote the empty list: "[]"
* Using square brackets, separating items with commas: "[a]", "[a,
b, c]"
* Using a list comprehension: "[x for x in iterable]"
* Using the type constructor: "list()" or "list(iterable)"
The constructor builds a list whose items are the same and in the
same order as *iterable*’s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a list, a copy is made and
returned, similar to "iterable[:]". For example, "list('abc')"
returns "['a', 'b', 'c']" and "list( (1, 2, 3) )" returns "[1, 2,
3]". If no argument is given, the constructor creates a new empty
list, "[]".
Many other operations also produce lists, including the "sorted()"
built-in.
Lists implement all of the common and mutable sequence operations.
Lists also provide the following additional method:
sort(*, key=None, reverse=False)
This method sorts the list in place, using only "<" comparisons
between items. Exceptions are not suppressed - if any comparison
operations fail, the entire sort operation will fail (and the
list will likely be left in a partially modified state).
"sort()" accepts two arguments that can only be passed by
keyword (keyword-only arguments):
*key* specifies a function of one argument that is used to
extract a comparison key from each list element (for example,
"key=str.lower"). The key corresponding to each item in the list
is calculated once and then used for the entire sorting process.
The default value of "None" means that list items are sorted
directly without calculating a separate key value.
The "functools.cmp_to_key()" utility is available to convert a
2.x style *cmp* function to a *key* function.
*reverse* is a boolean value. If set to "True", then the list
elements are sorted as if each comparison were reversed.
This method modifies the sequence in place for economy of space
when sorting a large sequence. To remind users that it operates
by side effect, it does not return the sorted sequence (use
"sorted()" to explicitly request a new sorted list instance).
The "sort()" method is guaranteed to be stable. A sort is
stable if it guarantees not to change the relative order of
elements that compare equal — this is helpful for sorting in
multiple passes (for example, sort by department, then by salary
grade).
For sorting examples and a brief sorting tutorial, see Sorting
HOW TO.
**CPython implementation detail:** While a list is being sorted,
the effect of attempting to mutate, or even inspect, the list is
undefined. The C implementation of Python makes the list appear
empty for the duration, and raises "ValueError" if it can detect
that the list has been mutated during a sort.
Tuples
======
Tuples are immutable sequences, typically used to store collections of
heterogeneous data (such as the 2-tuples produced by the "enumerate()"
built-in). Tuples are also used for cases where an immutable sequence
of homogeneous data is needed (such as allowing storage in a "set" or
"dict" instance).
class tuple([iterable])
Tuples may be constructed in a number of ways:
* Using a pair of parentheses to denote the empty tuple: "()"
* Using a trailing comma for a singleton tuple: "a," or "(a,)"
* Separating items with commas: "a, b, c" or "(a, b, c)"
* Using the "tuple()" built-in: "tuple()" or "tuple(iterable)"
The constructor builds a tuple whose items are the same and in the
same order as *iterable*’s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a tuple, it is returned
unchanged. For example, "tuple('abc')" returns "('a', 'b', 'c')"
and "tuple( [1, 2, 3] )" returns "(1, 2, 3)". If no argument is
given, the constructor creates a new empty tuple, "()".
Note that it is actually the comma which makes a tuple, not the
parentheses. The parentheses are optional, except in the empty
tuple case, or when they are needed to avoid syntactic ambiguity.
For example, "f(a, b, c)" is a function call with three arguments,
while "f((a, b, c))" is a function call with a 3-tuple as the sole
argument.
Tuples implement all of the common sequence operations.
For heterogeneous collections of data where access by name is clearer
than access by index, "collections.namedtuple()" may be a more
appropriate choice than a simple tuple object.
Ranges
======
The "range" type represents an immutable sequence of numbers and is
commonly used for looping a specific number of times in "for" loops.
class range(stop)
class range(start, stop[, step])
The arguments to the range constructor must be integers (either
built-in "int" or any object that implements the "__index__"
special method). If the *step* argument is omitted, it defaults to
"1". If the *start* argument is omitted, it defaults to "0". If
*step* is zero, "ValueError" is raised.
For a positive *step*, the contents of a range "r" are determined
by the formula "r[i] = start + step*i" where "i >= 0" and "r[i] <
stop".
For a negative *step*, the contents of the range are still
determined by the formula "r[i] = start + step*i", but the
constraints are "i >= 0" and "r[i] > stop".
A range object will be empty if "r[0]" does not meet the value
constraint. Ranges do support negative indices, but these are
interpreted as indexing from the end of the sequence determined by
the positive indices.
Ranges containing absolute values larger than "sys.maxsize" are
permitted but some features (such as "len()") may raise
"OverflowError".
Range examples:
>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(range(1, 11))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> list(range(0, 30, 5))
[0, 5, 10, 15, 20, 25]
>>> list(range(0, 10, 3))
[0, 3, 6, 9]
>>> list(range(0, -10, -1))
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
>>> list(range(0))
[]
>>> list(range(1, 0))
[]
Ranges implement all of the common sequence operations except
concatenation and repetition (due to the fact that range objects
can only represent sequences that follow a strict pattern and
repetition and concatenation will usually violate that pattern).
start
The value of the *start* parameter (or "0" if the parameter was
not supplied)
stop
The value of the *stop* parameter
step
The value of the *step* parameter (or "1" if the parameter was
not supplied)
The advantage of the "range" type over a regular "list" or "tuple" is
that a "range" object will always take the same (small) amount of
memory, no matter the size of the range it represents (as it only
stores the "start", "stop" and "step" values, calculating individual
items and subranges as needed).
Range objects implement the "collections.abc.Sequence" ABC, and
provide features such as containment tests, element index lookup,
slicing and support for negative indices (see Sequence Types — list,
tuple, range):
>>> r = range(0, 20, 2)
>>> r
range(0, 20, 2)
>>> 11 in r
False
>>> 10 in r
True
>>> r.index(10)
5
>>> r[5]
10
>>> r[:5]
range(0, 10, 2)
>>> r[-1]
18
Testing range objects for equality with "==" and "!=" compares them as
sequences. That is, two range objects are considered equal if they
represent the same sequence of values. (Note that two range objects
that compare equal might have different "start", "stop" and "step"
attributes, for example "range(0) == range(2, 1, 3)" or "range(0, 3,
2) == range(0, 4, 2)".)
Changed in version 3.2: Implement the Sequence ABC. Support slicing
and negative indices. Test "int" objects for membership in constant
time instead of iterating through all items.
Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range objects
based on the sequence of values they define (instead of comparing
based on object identity).
New in version 3.3: The "start", "stop" and "step" attributes.
See also:
* The linspace recipe shows how to implement a lazy version of range
suitable for floating point applications.
u� Mutable Sequence Types
**********************
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
|================================|==================================|=======================|
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | appends *x* to the end of the | |
| | sequence (same as | |
| | "s[len(s):len(s)] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from *s* (same | (5) |
| | as "del s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of *s* | (5) |
| | (same as "s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | extends *s* with the contents of | |
| | *t* (for the most part the same | |
| | as "s[len(s):len(s)] = t") | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (6) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | inserts *x* into *s* at the | |
| | index given by *i* (same as | |
| | "s[i:i] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop()" or "s.pop(i)" | retrieves the item at *i* and | (2) |
| | also removes it from *s* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
| | where "s[i]" is equal to *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default the
last item is removed and returned.
3. "remove()" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for economy
of space when reversing a large sequence. To remind users that it
operates by side effect, it does not return the reversed sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don’t support slicing
operations (such as "dict" and "set"). "copy()" is not part of the
"collections.abc.MutableSequence" ABC, but most concrete mutable
sequence classes provide it.
New in version 3.3: "clear()" and "copy()" methods.
6. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the sequence.
Items in the sequence are not copied; they are referenced multiple
times, as explained for "s * n" under Common Sequence Operations.
a~ Unary arithmetic and bitwise operations
***************************************
All unary arithmetic and bitwise operations have the same priority:
u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr
The unary "-" (minus) operator yields the negation of its numeric
argument.
The unary "+" (plus) operator yields its numeric argument unchanged.
The unary "~" (invert) operator yields the bitwise inversion of its
integer argument. The bitwise inversion of "x" is defined as
"-(x+1)". It only applies to integral numbers.
In all three cases, if the argument does not have the proper type, a
"TypeError" exception is raised.
u� The "while" statement
*********************
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" assignment_expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
uM The "with" statement
********************
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one “item” proceeds as
follows:
1. The context expression (the expression given in the "with_item") is
evaluated to obtain a context manager.
2. The context manager’s "__enter__()" is loaded for later use.
3. The context manager’s "__exit__()" is loaded for later use.
4. The context manager’s "__enter__()" method is invoked.
5. If a target was included in the "with" statement, the return value
from "__enter__()" is assigned to it.
Note:
The "with" statement guarantees that if the "__enter__()" method
returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
6. The suite is executed.
7. The context manager’s "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
The following code:
with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
enter = type(manager).__enter__
exit = type(manager).__exit__
value = enter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not exit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
exit(manager, None, None, None)
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
SUITE
is semantically equivalent to:
with A() as a:
with B() as b:
SUITE
Changed in version 3.1: Support for multiple context expressions.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
a, The "yield" statement
*********************
yield_stmt ::= yield_expression
A "yield" statement is semantically equivalent to a yield expression.
The yield statement can be used to omit the parentheses that would
otherwise be required in the equivalent yield expression statement.
For example, the yield statements
yield <expr>
yield from <expr>
are equivalent to the yield expression statements
(yield <expr>)
(yield from <expr>)
Yield expressions and statements are only used when defining a
*generator* function, and are only used in the body of the generator
function. Using yield in a function definition is sufficient to cause
that definition to create a generator function instead of a normal
function.
For full details of "yield" semantics, refer to the Yield expressions
section.
)O�assertZ
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id-classesZ identifiers�ifZ imaginary�import�inZintegers�lambdaZlistsZnaming�nonlocalZnumbersz
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ddddddddddddddd d d d d d!d"d#d$d%d&d'd(d d)d*d+d,d-d.d/d0d1d2d3d4d5d6d7d8d9d:d;d<d=d>d?d@dAdBdCdDdEdFdGdHdIdJdKdLdMdN�OZ dOS )Pau The "assert" statement
**********************
Assert statements are a convenient way to insert debugging assertions
into a program:
assert_stmt ::= "assert" expression ["," expression]
The simple form, "assert expression", is equivalent to
if __debug__:
if not expression: raise AssertionError
The extended form, "assert expression1, expression2", is equivalent to
if __debug__:
if not expression1: raise AssertionError(expression2)
These equivalences assume that "__debug__" and "AssertionError" refer
to the built-in variables with those names. In the current
implementation, the built-in variable "__debug__" is "True" under
normal circumstances, "False" when optimization is requested (command
line option "-O"). The current code generator emits no code for an
assert statement when optimization is requested at compile time. Note
that it is unnecessary to include the source code for the expression
that failed in the error message; it will be displayed as part of the
stack trace.
Assignments to "__debug__" are illegal. The value for the built-in
variable is determined when the interpreter starts.
u�, Assignment statements
*********************
Assignment statements are used to (re)bind names to values and to
modify attributes or items of mutable objects:
assignment_stmt ::= (target_list "=")+ (starred_expression | yield_expression)
target_list ::= target ("," target)* [","]
target ::= identifier
| "(" [target_list] ")"
| "[" [target_list] "]"
| attributeref
| subscription
| slicing
| "*" target
(See section Primaries for the syntax definitions for *attributeref*,
*subscription*, and *slicing*.)
An assignment statement evaluates the expression list (remember that
this can be a single expression or a comma-separated list, the latter
yielding a tuple) and assigns the single resulting object to each of
the target lists, from left to right.
Assignment is defined recursively depending on the form of the target
(list). When a target is part of a mutable object (an attribute
reference, subscription or slicing), the mutable object must
ultimately perform the assignment and decide about its validity, and
may raise an exception if the assignment is unacceptable. The rules
observed by various types and the exceptions raised are given with the
definition of the object types (see section The standard type
hierarchy).
Assignment of an object to a target list, optionally enclosed in
parentheses or square brackets, is recursively defined as follows.
* If the target list is a single target with no trailing comma,
optionally in parentheses, the object is assigned to that target.
* Else: The object must be an iterable with the same number of items
as there are targets in the target list, and the items are assigned,
from left to right, to the corresponding targets.
* If the target list contains one target prefixed with an asterisk,
called a “starred” target: The object must be an iterable with at
least as many items as there are targets in the target list, minus
one. The first items of the iterable are assigned, from left to
right, to the targets before the starred target. The final items
of the iterable are assigned to the targets after the starred
target. A list of the remaining items in the iterable is then
assigned to the starred target (the list can be empty).
* Else: The object must be an iterable with the same number of items
as there are targets in the target list, and the items are
assigned, from left to right, to the corresponding targets.
Assignment of an object to a single target is recursively defined as
follows.
* If the target is an identifier (name):
* If the name does not occur in a "global" or "nonlocal" statement
in the current code block: the name is bound to the object in the
current local namespace.
* Otherwise: the name is bound to the object in the global namespace
or the outer namespace determined by "nonlocal", respectively.
The name is rebound if it was already bound. This may cause the
reference count for the object previously bound to the name to reach
zero, causing the object to be deallocated and its destructor (if it
has one) to be called.
* If the target is an attribute reference: The primary expression in
the reference is evaluated. It should yield an object with
assignable attributes; if this is not the case, "TypeError" is
raised. That object is then asked to assign the assigned object to
the given attribute; if it cannot perform the assignment, it raises
an exception (usually but not necessarily "AttributeError").
Note: If the object is a class instance and the attribute reference
occurs on both sides of the assignment operator, the right-hand side
expression, "a.x" can access either an instance attribute or (if no
instance attribute exists) a class attribute. The left-hand side
target "a.x" is always set as an instance attribute, creating it if
necessary. Thus, the two occurrences of "a.x" do not necessarily
refer to the same attribute: if the right-hand side expression
refers to a class attribute, the left-hand side creates a new
instance attribute as the target of the assignment:
class Cls:
x = 3 # class variable
inst = Cls()
inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3
This description does not necessarily apply to descriptor
attributes, such as properties created with "property()".
* If the target is a subscription: The primary expression in the
reference is evaluated. It should yield either a mutable sequence
object (such as a list) or a mapping object (such as a dictionary).
Next, the subscript expression is evaluated.
If the primary is a mutable sequence object (such as a list), the
subscript must yield an integer. If it is negative, the sequence’s
length is added to it. The resulting value must be a nonnegative
integer less than the sequence’s length, and the sequence is asked
to assign the assigned object to its item with that index. If the
index is out of range, "IndexError" is raised (assignment to a
subscripted sequence cannot add new items to a list).
If the primary is a mapping object (such as a dictionary), the
subscript must have a type compatible with the mapping’s key type,
and the mapping is then asked to create a key/datum pair which maps
the subscript to the assigned object. This can either replace an
existing key/value pair with the same key value, or insert a new
key/value pair (if no key with the same value existed).
For user-defined objects, the "__setitem__()" method is called with
appropriate arguments.
* If the target is a slicing: The primary expression in the reference
is evaluated. It should yield a mutable sequence object (such as a
list). The assigned object should be a sequence object of the same
type. Next, the lower and upper bound expressions are evaluated,
insofar they are present; defaults are zero and the sequence’s
length. The bounds should evaluate to integers. If either bound is
negative, the sequence’s length is added to it. The resulting
bounds are clipped to lie between zero and the sequence’s length,
inclusive. Finally, the sequence object is asked to replace the
slice with the items of the assigned sequence. The length of the
slice may be different from the length of the assigned sequence,
thus changing the length of the target sequence, if the target
sequence allows it.
**CPython implementation detail:** In the current implementation, the
syntax for targets is taken to be the same as for expressions, and
invalid syntax is rejected during the code generation phase, causing
less detailed error messages.
Although the definition of assignment implies that overlaps between
the left-hand side and the right-hand side are ‘simultaneous’ (for
example "a, b = b, a" swaps two variables), overlaps *within* the
collection of assigned-to variables occur left-to-right, sometimes
resulting in confusion. For instance, the following program prints
"[0, 2]":
x = [0, 1]
i = 0
i, x[i] = 1, 2 # i is updated, then x[i] is updated
print(x)
See also:
**PEP 3132** - Extended Iterable Unpacking
The specification for the "*target" feature.
Augmented assignment statements
===============================
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions of the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side. For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
Annotated assignment statements
===============================
*Annotation* assignment is the combination, in a single statement, of
a variable or attribute annotation and an optional assignment
statement:
annotated_assignment_stmt ::= augtarget ":" expression
["=" (starred_expression | yield_expression)]
The difference from normal Assignment statements is that only single
target is allowed.
For simple names as assignment targets, if in class or module scope,
the annotations are evaluated and stored in a special class or module
attribute "__annotations__" that is a dictionary mapping from variable
names (mangled if private) to evaluated annotations. This attribute is
writable and is automatically created at the start of class or module
body execution, if annotations are found statically.
For expressions as assignment targets, the annotations are evaluated
if in class or module scope, but not stored.
If a name is annotated in a function scope, then this name is local
for that scope. Annotations are never evaluated and stored in function
scopes.
If the right hand side is present, an annotated assignment performs
the actual assignment before evaluating annotations (where
applicable). If the right hand side is not present for an expression
target, then the interpreter evaluates the target except for the last
"__setitem__()" or "__setattr__()" call.
See also:
**PEP 526** - Syntax for Variable Annotations
The proposal that added syntax for annotating the types of
variables (including class variables and instance variables),
instead of expressing them through comments.
**PEP 484** - Type hints
The proposal that added the "typing" module to provide a standard
syntax for type annotations that can be used in static analysis
tools and IDEs.
Changed in version 3.8: Now annotated assignments allow same
expressions in the right hand side as the regular assignments.
Previously, some expressions (like un-parenthesized tuple expressions)
caused a syntax error.
u>
Coroutines
**********
New in version 3.5.
Coroutine function definition
=============================
async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*). Inside the body of a coroutine function,
"await" and "async" identifiers become reserved keywords; "await"
expressions, "async for" and "async with" can only be used in
coroutine function bodies.
Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.
It is a "SyntaxError" to use a "yield from" expression inside the body
of a coroutine function.
An example of a coroutine function:
async def func(param1, param2):
do_stuff()
await some_coroutine()
The "async for" statement
=========================
async_for_stmt ::= "async" for_stmt
An *asynchronous iterable* is able to call asynchronous code in its
*iter* implementation, and *asynchronous iterator* can call
asynchronous code in its *next* method.
The "async for" statement allows convenient iteration over
asynchronous iterators.
The following code:
async for TARGET in ITER:
SUITE
else:
SUITE2
Is semantically equivalent to:
iter = (ITER)
iter = type(iter).__aiter__(iter)
running = True
while running:
try:
TARGET = await type(iter).__anext__(iter)
except StopAsyncIteration:
running = False
else:
SUITE
else:
SUITE2
See also "__aiter__()" and "__anext__()" for details.
It is a "SyntaxError" to use an "async for" statement outside the body
of a coroutine function.
The "async with" statement
==========================
async_with_stmt ::= "async" with_stmt
An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.
The following code:
async with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
aexit = type(manager).__aexit__
aenter = type(manager).__aenter__
value = await aenter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not await aexit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
await aexit(manager, None, None, None)
See also "__aenter__()" and "__aexit__()" for details.
It is a "SyntaxError" to use an "async with" statement outside the
body of a coroutine function.
See also:
**PEP 492** - Coroutines with async and await syntax
The proposal that made coroutines a proper standalone concept in
Python, and added supporting syntax.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless there
is a "finally" clause which happens to raise another exception.
That new exception causes the old one to be lost.
[2] A string literal appearing as the first statement in the function
body is transformed into the function’s "__doc__" attribute and
therefore the function’s *docstring*.
[3] A string literal appearing as the first statement in the class
body is transformed into the namespace’s "__doc__" item and
therefore the class’s *docstring*.
a� Identifiers (Names)
*******************
An identifier occurring as an atom is a name. See section Identifiers
and keywords for lexical definition and section Naming and binding for
documentation of naming and binding.
When the name is bound to an object, evaluation of the atom yields
that object. When a name is not bound, an attempt to evaluate it
raises a "NameError" exception.
**Private name mangling:** When an identifier that textually occurs in
a class definition begins with two or more underscore characters and
does not end in two or more underscores, it is considered a *private
name* of that class. Private names are transformed to a longer form
before code is generated for them. The transformation inserts the
class name, with leading underscores removed and a single underscore
inserted, in front of the name. For example, the identifier "__spam"
occurring in a class named "Ham" will be transformed to "_Ham__spam".
This transformation is independent of the syntactical context in which
the identifier is used. If the transformed name is extremely long
(longer than 255 characters), implementation defined truncation may
happen. If the class name consists only of underscores, no
transformation is done.
u
Literals
********
Python supports string and bytes literals and various numeric
literals:
literal ::= stringliteral | bytesliteral
| integer | floatnumber | imagnumber
Evaluation of a literal yields an object of the given type (string,
bytes, integer, floating point number, complex number) with the given
value. The value may be approximated in the case of floating point
and imaginary (complex) literals. See section Literals for details.
All literals correspond to immutable data types, and hence the
object’s identity is less important than its value. Multiple
evaluations of literals with the same value (either the same
occurrence in the program text or a different occurrence) may obtain
the same object or a different object with the same value.
uA7 Customizing attribute access
****************************
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
return the (computed) attribute value or raise an "AttributeError"
exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note:
This method may still be bypassed when looking up special methods
as the result of implicit invocation via language syntax or
built-in functions. See Special method lookup.
For certain sensitive attribute accesses, raises an auditing event
"object.__getattr__" with arguments "obj" and "name".
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should call the base class method with the same name, for example,
"object.__setattr__(self, name, value)".
For certain sensitive attribute assignments, raises an auditing
event "object.__setattr__" with arguments "obj", "name", "value".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
For certain sensitive attribute deletions, raises an auditing event
"object.__delattr__" with arguments "obj" and "name".
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
Customizing module attribute access
===================================
Special names "__getattr__" and "__dir__" can be also used to
customize access to module attributes. The "__getattr__" function at
the module level should accept one argument which is the name of an
attribute and return the computed value or raise an "AttributeError".
If an attribute is not found on a module object through the normal
lookup, i.e. "object.__getattribute__()", then "__getattr__" is
searched in the module "__dict__" before raising an "AttributeError".
If found, it is called with the attribute name and the result is
returned.
The "__dir__" function should accept no arguments, and return a
sequence of strings that represents the names accessible on module. If
present, this function overrides the standard "dir()" search on a
module.
For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
super().__setattr__(attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note:
Defining module "__getattr__" and setting module "__class__" only
affect lookups made using the attribute access syntax – directly
accessing the module globals (whether by code within the module, or
via a reference to the module’s globals dictionary) is unaffected.
Changed in version 3.5: "__class__" module attribute is now writable.
New in version 3.7: "__getattr__" and "__dir__" module attributes.
See also:
**PEP 562** - Module __getattr__ and __dir__
Describes the "__getattr__" and "__dir__" functions on modules.
Implementing Descriptors
========================
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents). In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".
object.__get__(self, instance, owner=None)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). The optional *owner* argument is the owner class, while
*instance* is the instance that the attribute was accessed through,
or "None" when the attribute is accessed through the *owner*.
This method should return the computed attribute value or raise an
"AttributeError" exception.
**PEP 252** specifies that "__get__()" is callable with one or two
arguments. Python’s own built-in descriptors support this
specification; however, it is likely that some third-party tools
have descriptors that require both arguments. Python’s own
"__getattribute__()" implementation always passes in both arguments
whether they are required or not.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
Note, adding "__set__()" or "__delete__()" changes the kind of
descriptor to a “data descriptor”. See Invoking Descriptors for
more details.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
object.__set_name__(self, owner, name)
Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.
Note:
"__set_name__()" is only called implicitly as part of the "type"
constructor, so it will need to be called explicitly with the
appropriate parameters when a descriptor is added to a class
after initial creation:
class A:
pass
descr = custom_descriptor()
A.attr = descr
descr.__set_name__(A, 'attr')
See Creating the class object for more details.
New in version 3.6.
The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).
Invoking Descriptors
====================
In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a class, "A.x" is transformed into the call:
"A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on which descriptor methods are defined. A descriptor can define any
combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
=========
*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of *__dict__* and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)
The space saved over using *__dict__* can be significant. Attribute
lookup speed can be significantly improved as well.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. *__slots__*
reserves space for the declared variables and prevents the
automatic creation of *__dict__* and *__weakref__* for each
instance.
Notes on using *__slots__*
--------------------------
* When inheriting from a class without *__slots__*, the *__dict__* and
*__weakref__* attribute of the instances will always be accessible.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
* Without a *__weakref__* variable for each instance, classes defining
*__slots__* do not support weak references to its instances. If weak
reference support is needed, then add "'__weakref__'" to the
sequence of strings in the *__slots__* declaration.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is not limited to the class
where it is defined. *__slots__* declared in parents are available
in child classes. However, child subclasses will get a *__dict__*
and *__weakref__* unless they also define *__slots__* (which should
only contain names of any *additional* slots).
* If a class defines a slot also defined in a base class, the instance
variable defined by the base class slot is inaccessible (except by
retrieving its descriptor directly from the base class). This
renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
“variable-length” built-in types such as "int", "bytes" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings may
also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".
* If an iterator is used for *__slots__* then a descriptor is created
for each of the iterator’s values. However, the *__slots__*
attribute will be an empty iterator.
a� Attribute references
********************
An attribute reference is a primary followed by a period and a name:
attributeref ::= primary "." identifier
The primary must evaluate to an object of a type that supports
attribute references, which most objects do. This object is then
asked to produce the attribute whose name is the identifier. This
production can be customized by overriding the "__getattr__()" method.
If this attribute is not available, the exception "AttributeError" is
raised. Otherwise, the type and value of the object produced is
determined by the object. Multiple evaluations of the same attribute
reference may yield different objects.
a� Augmented assignment statements
*******************************
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions of the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side. For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
z�Await expression
****************
Suspend the execution of *coroutine* on an *awaitable* object. Can
only be used inside a *coroutine function*.
await_expr ::= "await" primary
New in version 3.5.
uj Binary arithmetic operations
****************************
The binary arithmetic operations have the conventional priority
levels. Note that some of these operations also apply to certain non-
numeric types. Apart from the power operator, there are only two
levels, one for multiplicative operators and one for additive
operators:
m_expr ::= u_expr | m_expr "*" u_expr | m_expr "@" m_expr |
m_expr "//" u_expr | m_expr "/" u_expr |
m_expr "%" u_expr
a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr
The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer and the other must be a sequence. In the former case, the
numbers are converted to a common type and then multiplied together.
In the latter case, sequence repetition is performed; a negative
repetition factor yields an empty sequence.
The "@" (at) operator is intended to be used for matrix
multiplication. No builtin Python types implement this operator.
New in version 3.5.
The "/" (division) and "//" (floor division) operators yield the
quotient of their arguments. The numeric arguments are first
converted to a common type. Division of integers yields a float, while
floor division of integers results in an integer; the result is that
of mathematical division with the ‘floor’ function applied to the
result. Division by zero raises the "ZeroDivisionError" exception.
The "%" (modulo) operator yields the remainder from the division of
the first argument by the second. The numeric arguments are first
converted to a common type. A zero right argument raises the
"ZeroDivisionError" exception. The arguments may be floating point
numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 +
0.34".) The modulo operator always yields a result with the same sign
as its second operand (or zero); the absolute value of the result is
strictly smaller than the absolute value of the second operand [1].
The floor division and modulo operators are connected by the following
identity: "x == (x//y)*y + (x%y)". Floor division and modulo are also
connected with the built-in function "divmod()": "divmod(x, y) ==
(x//y, x%y)". [2].
In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string objects to perform old-style
string formatting (also known as interpolation). The syntax for
string formatting is described in the Python Library Reference,
section printf-style String Formatting.
The floor division operator, the modulo operator, and the "divmod()"
function are not defined for complex numbers. Instead, convert to a
floating point number using the "abs()" function if appropriate.
The "+" (addition) operator yields the sum of its arguments. The
arguments must either both be numbers or both be sequences of the same
type. In the former case, the numbers are converted to a common type
and then added together. In the latter case, the sequences are
concatenated.
The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.
a$ Binary bitwise operations
*************************
Each of the three bitwise operations has a different priority level:
and_expr ::= shift_expr | and_expr "&" shift_expr
xor_expr ::= and_expr | xor_expr "^" and_expr
or_expr ::= xor_expr | or_expr "|" xor_expr
The "&" operator yields the bitwise AND of its arguments, which must
be integers.
The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be integers.
The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be integers.
u� Code Objects
************
Code objects are used by the implementation to represent “pseudo-
compiled” executable Python code such as a function body. They differ
from function objects because they don’t contain a reference to their
global execution environment. Code objects are returned by the built-
in "compile()" function and can be extracted from function objects
through their "__code__" attribute. See also the "code" module.
Accessing "__code__" raises an auditing event "object.__getattr__"
with arguments "obj" and ""__code__"".
A code object can be executed or evaluated by passing it (instead of a
source string) to the "exec()" or "eval()" built-in functions.
See The standard type hierarchy for more information.
a. The Ellipsis Object
*******************
This object is commonly used by slicing (see Slicings). It supports
no special operations. There is exactly one ellipsis object, named
"Ellipsis" (a built-in name). "type(Ellipsis)()" produces the
"Ellipsis" singleton.
It is written as "Ellipsis" or "...".
u The Null Object
***************
This object is returned by functions that don’t explicitly return a
value. It supports no special operations. There is exactly one null
object, named "None" (a built-in name). "type(None)()" produces the
same singleton.
It is written as "None".
u5 Type Objects
************
Type objects represent the various object types. An object’s type is
accessed by the built-in function "type()". There are no special
operations on types. The standard module "types" defines names for
all standard built-in types.
Types are written like this: "<class 'int'>".
a� Boolean operations
******************
or_test ::= and_test | or_test "or" and_test
and_test ::= not_test | and_test "and" not_test
not_test ::= comparison | "not" not_test
In the context of Boolean operations, and also when expressions are
used by control flow statements, the following values are interpreted
as false: "False", "None", numeric zero of all types, and empty
strings and containers (including strings, tuples, lists,
dictionaries, sets and frozensets). All other values are interpreted
as true. User-defined objects can customize their truth value by
providing a "__bool__()" method.
The operator "not" yields "True" if its argument is false, "False"
otherwise.
The expression "x and y" first evaluates *x*; if *x* is false, its
value is returned; otherwise, *y* is evaluated and the resulting value
is returned.
The expression "x or y" first evaluates *x*; if *x* is true, its value
is returned; otherwise, *y* is evaluated and the resulting value is
returned.
Note that neither "and" nor "or" restrict the value and type they
return to "False" and "True", but rather return the last evaluated
argument. This is sometimes useful, e.g., if "s" is a string that
should be replaced by a default value if it is empty, the expression
"s or 'foo'" yields the desired value. Because "not" has to create a
new value, it returns a boolean value regardless of the type of its
argument (for example, "not 'foo'" produces "False" rather than "''".)
a$ The "break" statement
*********************
break_stmt ::= "break"
"break" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop.
It terminates the nearest enclosing loop, skipping the optional "else"
clause if the loop has one.
If a "for" loop is terminated by "break", the loop control target
keeps its current value.
When "break" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
loop.
u Emulating callable objects
**************************
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method
is defined, "x(arg1, arg2, ...)" roughly translates to
"type(x).__call__(x, arg1, ...)".
u� Calls
*****
A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:
call ::= primary "(" [argument_list [","] | comprehension] ")"
argument_list ::= positional_arguments ["," starred_and_keywords]
["," keywords_arguments]
| starred_and_keywords ["," keywords_arguments]
| keywords_arguments
positional_arguments ::= positional_item ("," positional_item)*
positional_item ::= assignment_expression | "*" expression
starred_and_keywords ::= ("*" expression | keyword_item)
("," "*" expression | "," keyword_item)*
keywords_arguments ::= (keyword_item | "**" expression)
("," keyword_item | "," "**" expression)*
keyword_item ::= identifier "=" expression
An optional trailing comma may be present after the positional and
keyword arguments but does not affect the semantics.
The primary must evaluate to a callable object (user-defined
functions, built-in functions, methods of built-in objects, class
objects, methods of class instances, and all objects having a
"__call__()" method are callable). All argument expressions are
evaluated before the call is attempted. Please refer to section
Function definitions for the syntax of formal *parameter* lists.
If keyword arguments are present, they are first converted to
positional arguments, as follows. First, a list of unfilled slots is
created for the formal parameters. If there are N positional
arguments, they are placed in the first N slots. Next, for each
keyword argument, the identifier is used to determine the
corresponding slot (if the identifier is the same as the first formal
parameter name, the first slot is used, and so on). If the slot is
already filled, a "TypeError" exception is raised. Otherwise, the
value of the argument is placed in the slot, filling it (even if the
expression is "None", it fills the slot). When all arguments have
been processed, the slots that are still unfilled are filled with the
corresponding default value from the function definition. (Default
values are calculated, once, when the function is defined; thus, a
mutable object such as a list or dictionary used as default value will
be shared by all calls that don’t specify an argument value for the
corresponding slot; this should usually be avoided.) If there are any
unfilled slots for which no default value is specified, a "TypeError"
exception is raised. Otherwise, the list of filled slots is used as
the argument list for the call.
**CPython implementation detail:** An implementation may provide
built-in functions whose positional parameters do not have names, even
if they are ‘named’ for the purpose of documentation, and which
therefore cannot be supplied by keyword. In CPython, this is the case
for functions implemented in C that use "PyArg_ParseTuple()" to parse
their arguments.
If there are more positional arguments than there are formal parameter
slots, a "TypeError" exception is raised, unless a formal parameter
using the syntax "*identifier" is present; in this case, that formal
parameter receives a tuple containing the excess positional arguments
(or an empty tuple if there were no excess positional arguments).
If any keyword argument does not correspond to a formal parameter
name, a "TypeError" exception is raised, unless a formal parameter
using the syntax "**identifier" is present; in this case, that formal
parameter receives a dictionary containing the excess keyword
arguments (using the keywords as keys and the argument values as
corresponding values), or a (new) empty dictionary if there were no
excess keyword arguments.
If the syntax "*expression" appears in the function call, "expression"
must evaluate to an *iterable*. Elements from these iterables are
treated as if they were additional positional arguments. For the call
"f(x1, x2, *y, x3, x4)", if *y* evaluates to a sequence *y1*, …, *yM*,
this is equivalent to a call with M+4 positional arguments *x1*, *x2*,
*y1*, …, *yM*, *x3*, *x4*.
A consequence of this is that although the "*expression" syntax may
appear *after* explicit keyword arguments, it is processed *before*
the keyword arguments (and any "**expression" arguments – see below).
So:
>>> def f(a, b):
... print(a, b)
...
>>> f(b=1, *(2,))
2 1
>>> f(a=1, *(2,))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: f() got multiple values for keyword argument 'a'
>>> f(1, *(2,))
1 2
It is unusual for both keyword arguments and the "*expression" syntax
to be used in the same call, so in practice this confusion does not
arise.
If the syntax "**expression" appears in the function call,
"expression" must evaluate to a *mapping*, the contents of which are
treated as additional keyword arguments. If a keyword is already
present (as an explicit keyword argument, or from another unpacking),
a "TypeError" exception is raised.
Formal parameters using the syntax "*identifier" or "**identifier"
cannot be used as positional argument slots or as keyword argument
names.
Changed in version 3.5: Function calls accept any number of "*" and
"**" unpackings, positional arguments may follow iterable unpackings
("*"), and keyword arguments may follow dictionary unpackings ("**").
Originally proposed by **PEP 448**.
A call always returns some value, possibly "None", unless it raises an
exception. How this value is computed depends on the type of the
callable object.
If it is—
a user-defined function:
The code block for the function is executed, passing it the
argument list. The first thing the code block will do is bind the
formal parameters to the arguments; this is described in section
Function definitions. When the code block executes a "return"
statement, this specifies the return value of the function call.
a built-in function or method:
The result is up to the interpreter; see Built-in Functions for the
descriptions of built-in functions and methods.
a class object:
A new instance of that class is returned.
a class instance method:
The corresponding user-defined function is called, with an argument
list that is one longer than the argument list of the call: the
instance becomes the first argument.
a class instance:
The class must define a "__call__()" method; the effect is then the
same as if that method was called.
u Class definitions
*****************
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [argument_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing. Classes without an inheritance list
inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.) When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [3] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.
The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__". Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.
Class creation can be customized heavily using metaclasses.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result is then bound to the class name.
**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. Descriptors can be used to create instance
variables with different implementation details.
See also:
**PEP 3115** - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the
current syntax, and the semantics for how classes with
metaclasses are constructed.
**PEP 3129** - Class Decorators
The proposal that added class decorators. Function and method
decorators were introduced in **PEP 318**.
u�' Comparisons
***********
Unlike C, all comparison operations in Python have the same priority,
which is lower than that of any arithmetic, shifting or bitwise
operation. Also unlike C, expressions like "a < b < c" have the
interpretation that is conventional in mathematics:
comparison ::= or_expr (comp_operator or_expr)*
comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="
| "is" ["not"] | ["not"] "in"
Comparisons yield boolean values: "True" or "False".
Comparisons can be chained arbitrarily, e.g., "x < y <= z" is
equivalent to "x < y and y <= z", except that "y" is evaluated only
once (but in both cases "z" is not evaluated at all when "x < y" is
found to be false).
Formally, if *a*, *b*, *c*, …, *y*, *z* are expressions and *op1*,
*op2*, …, *opN* are comparison operators, then "a op1 b op2 c ... y
opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except
that each expression is evaluated at most once.
Note that "a op1 b op2 c" doesn’t imply any kind of comparison between
*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though
perhaps not pretty).
Value comparisons
=================
The operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects. The objects do not need to have the same type.
Chapter Objects, values and types states that objects have a value (in
addition to type and identity). The value of an object is a rather
abstract notion in Python: For example, there is no canonical access
method for an object’s value. Also, there is no requirement that the
value of an object should be constructed in a particular way, e.g.
comprised of all its data attributes. Comparison operators implement a
particular notion of what the value of an object is. One can think of
them as defining the value of an object indirectly, by means of their
comparison implementation.
Because all types are (direct or indirect) subtypes of "object", they
inherit the default comparison behavior from "object". Types can
customize their comparison behavior by implementing *rich comparison
methods* like "__lt__()", described in Basic customization.
The default behavior for equality comparison ("==" and "!=") is based
on the identity of the objects. Hence, equality comparison of
instances with the same identity results in equality, and equality
comparison of instances with different identities results in
inequality. A motivation for this default behavior is the desire that
all objects should be reflexive (i.e. "x is y" implies "x == y").
A default order comparison ("<", ">", "<=", and ">=") is not provided;
an attempt raises "TypeError". A motivation for this default behavior
is the lack of a similar invariant as for equality.
The behavior of the default equality comparison, that instances with
different identities are always unequal, may be in contrast to what
types will need that have a sensible definition of object value and
value-based equality. Such types will need to customize their
comparison behavior, and in fact, a number of built-in types have done
that.
The following list describes the comparison behavior of the most
important built-in types.
* Numbers of built-in numeric types (Numeric Types — int, float,
complex) and of the standard library types "fractions.Fraction" and
"decimal.Decimal" can be compared within and across their types,
with the restriction that complex numbers do not support order
comparison. Within the limits of the types involved, they compare
mathematically (algorithmically) correct without loss of precision.
The not-a-number values "float('NaN')" and "decimal.Decimal('NaN')"
are special. Any ordered comparison of a number to a not-a-number
value is false. A counter-intuitive implication is that not-a-number
values are not equal to themselves. For example, if "x =
float('NaN')", "3 < x", "x < 3" and "x == x" are all false, while "x
!= x" is true. This behavior is compliant with IEEE 754.
* "None" and "NotImplemented" are singletons. **PEP 8** advises that
comparisons for singletons should always be done with "is" or "is
not", never the equality operators.
* Binary sequences (instances of "bytes" or "bytearray") can be
compared within and across their types. They compare
lexicographically using the numeric values of their elements.
* Strings (instances of "str") compare lexicographically using the
numerical Unicode code points (the result of the built-in function
"ord()") of their characters. [3]
Strings and binary sequences cannot be directly compared.
* Sequences (instances of "tuple", "list", or "range") can be compared
only within each of their types, with the restriction that ranges do
not support order comparison. Equality comparison across these
types results in inequality, and ordering comparison across these
types raises "TypeError".
Sequences compare lexicographically using comparison of
corresponding elements. The built-in containers typically assume
identical objects are equal to themselves. That lets them bypass
equality tests for identical objects to improve performance and to
maintain their internal invariants.
Lexicographical comparison between built-in collections works as
follows:
* For two collections to compare equal, they must be of the same
type, have the same length, and each pair of corresponding
elements must compare equal (for example, "[1,2] == (1,2)" is
false because the type is not the same).
* Collections that support order comparison are ordered the same as
their first unequal elements (for example, "[1,2,x] <= [1,2,y]"
has the same value as "x <= y"). If a corresponding element does
not exist, the shorter collection is ordered first (for example,
"[1,2] < [1,2,3]" is true).
* Mappings (instances of "dict") compare equal if and only if they
have equal *(key, value)* pairs. Equality comparison of the keys and
values enforces reflexivity.
Order comparisons ("<", ">", "<=", and ">=") raise "TypeError".
* Sets (instances of "set" or "frozenset") can be compared within and
across their types.
They define order comparison operators to mean subset and superset
tests. Those relations do not define total orderings (for example,
the two sets "{1,2}" and "{2,3}" are not equal, nor subsets of one
another, nor supersets of one another). Accordingly, sets are not
appropriate arguments for functions which depend on total ordering
(for example, "min()", "max()", and "sorted()" produce undefined
results given a list of sets as inputs).
Comparison of sets enforces reflexivity of its elements.
* Most other built-in types have no comparison methods implemented, so
they inherit the default comparison behavior.
User-defined classes that customize their comparison behavior should
follow some consistency rules, if possible:
* Equality comparison should be reflexive. In other words, identical
objects should compare equal:
"x is y" implies "x == y"
* Comparison should be symmetric. In other words, the following
expressions should have the same result:
"x == y" and "y == x"
"x != y" and "y != x"
"x < y" and "y > x"
"x <= y" and "y >= x"
* Comparison should be transitive. The following (non-exhaustive)
examples illustrate that:
"x > y and y > z" implies "x > z"
"x < y and y <= z" implies "x < z"
* Inverse comparison should result in the boolean negation. In other
words, the following expressions should have the same result:
"x == y" and "not x != y"
"x < y" and "not x >= y" (for total ordering)
"x > y" and "not x <= y" (for total ordering)
The last two expressions apply to totally ordered collections (e.g.
to sequences, but not to sets or mappings). See also the
"total_ordering()" decorator.
* The "hash()" result should be consistent with equality. Objects that
are equal should either have the same hash value, or be marked as
unhashable.
Python does not enforce these consistency rules. In fact, the
not-a-number values are an example for not following these rules.
Membership test operations
==========================
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z", for which
the expression "x is z or x == z" is true, is produced while iterating
over "y". If an exception is raised during the iteration, it is as if
"in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x is y[i] or x == y[i]", and no
lower integer index raises the "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse truth value of
"in".
Identity comparisons
====================
The operators "is" and "is not" test for an object’s identity: "x is
y" is true if and only if *x* and *y* are the same object. An
Object’s identity is determined using the "id()" function. "x is not
y" yields the inverse truth value. [4]
u�l Compound statements
*******************
Compound statements contain (groups of) other statements; they affect
or control the execution of those other statements in some way. In
general, compound statements span multiple lines, although in simple
incarnations a whole compound statement may be contained in one line.
The "if", "while" and "for" statements implement traditional control
flow constructs. "try" specifies exception handlers and/or cleanup
code for a group of statements, while the "with" statement allows the
execution of initialization and finalization code around a block of
code. Function and class definitions are also syntactically compound
statements.
A compound statement consists of one or more ‘clauses.’ A clause
consists of a header and a ‘suite.’ The clause headers of a
particular compound statement are all at the same indentation level.
Each clause header begins with a uniquely identifying keyword and ends
with a colon. A suite is a group of statements controlled by a
clause. A suite can be one or more semicolon-separated simple
statements on the same line as the header, following the header’s
colon, or it can be one or more indented statements on subsequent
lines. Only the latter form of a suite can contain nested compound
statements; the following is illegal, mostly because it wouldn’t be
clear to which "if" clause a following "else" clause would belong:
if test1: if test2: print(x)
Also note that the semicolon binds tighter than the colon in this
context, so that in the following example, either all or none of the
"print()" calls are executed:
if x < y < z: print(x); print(y); print(z)
Summarizing:
compound_stmt ::= if_stmt
| while_stmt
| for_stmt
| try_stmt
| with_stmt
| funcdef
| classdef
| async_with_stmt
| async_for_stmt
| async_funcdef
suite ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT
statement ::= stmt_list NEWLINE | compound_stmt
stmt_list ::= simple_stmt (";" simple_stmt)* [";"]
Note that statements always end in a "NEWLINE" possibly followed by a
"DEDENT". Also note that optional continuation clauses always begin
with a keyword that cannot start a statement, thus there are no
ambiguities (the ‘dangling "else"’ problem is solved in Python by
requiring nested "if" statements to be indented).
The formatting of the grammar rules in the following sections places
each clause on a separate line for clarity.
The "if" statement
==================
The "if" statement is used for conditional execution:
if_stmt ::= "if" assignment_expression ":" suite
("elif" assignment_expression ":" suite)*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
The "while" statement
=====================
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" assignment_expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
The "for" statement
===================
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator. Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.
The for-loop makes assignments to the variables in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop. Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".
Note:
There is a subtlety when the sequence is being modified by the loop
(this can only occur for mutable sequences, e.g. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
The "try" statement
===================
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object, or a tuple containing an item that is the class or a base
class of the exception object.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed. All except
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred. "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.
The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed. Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a ‘cleanup’ handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return", "break" or "continue" statement, the saved
exception is discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
Changed in version 3.8: Prior to Python 3.8, a "continue" statement
was illegal in the "finally" clause due to a problem with the
implementation.
The "with" statement
====================
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one “item” proceeds as
follows:
1. The context expression (the expression given in the "with_item") is
evaluated to obtain a context manager.
2. The context manager’s "__enter__()" is loaded for later use.
3. The context manager’s "__exit__()" is loaded for later use.
4. The context manager’s "__enter__()" method is invoked.
5. If a target was included in the "with" statement, the return value
from "__enter__()" is assigned to it.
Note:
The "with" statement guarantees that if the "__enter__()" method
returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
6. The suite is executed.
7. The context manager’s "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
The following code:
with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
enter = type(manager).__enter__
exit = type(manager).__exit__
value = enter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not exit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
exit(manager, None, None, None)
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
SUITE
is semantically equivalent to:
with A() as a:
with B() as b:
SUITE
Changed in version 3.1: Support for multiple context expressions.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
Function definitions
====================
A function definition defines a user-defined function object (see
section The standard type hierarchy):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= defparameter ("," defparameter)* "," "/" ["," [parameter_list_no_posonly]]
| parameter_list_no_posonly
parameter_list_no_posonly ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
| parameter_list_starargs
parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
| "**" parameter [","]
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [2]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
except that the original function is not temporarily bound to the name
"func".
When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted. If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.
**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call. This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended. A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from positional arguments, from keyword
arguments, or from default values. If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type. Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed by
keyword arguments. Parameters before “"/"” are positional-only
parameters and may only be passed by positional arguments.
Changed in version 3.8: The "/" function parameter syntax may be used
to indicate positional-only parameters. See **PEP 570** for details.
Parameters may have an *annotation* of the form “": expression"”
following the parameter name. Any parameter may have an annotation,
even those of the form "*identifier" or "**identifier". Functions may
have “return” annotation of the form “"-> expression"” after the
parameter list. These annotations can be any valid Python expression.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object. If the "annotations" import from
"__future__" is used, annotations are preserved as strings at runtime
which enables postponed evaluation. Otherwise, they are evaluated
when the function definition is executed. In this case annotations
may be evaluated in a different order than they appear in the source
code.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.
**Programmer’s note:** Functions are first-class objects. A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around. Free variables used
in the nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
See also:
**PEP 3107** - Function Annotations
The original specification for function annotations.
**PEP 484** - Type Hints
Definition of a standard meaning for annotations: type hints.
**PEP 526** - Syntax for Variable Annotations
Ability to type hint variable declarations, including class
variables and instance variables
**PEP 563** - Postponed Evaluation of Annotations
Support for forward references within annotations by preserving
annotations in a string form at runtime instead of eager
evaluation.
Class definitions
=================
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [argument_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing. Classes without an inheritance list
inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.) When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [3] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.
The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__". Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.
Class creation can be customized heavily using metaclasses.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result is then bound to the class name.
**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. Descriptors can be used to create instance
variables with different implementation details.
See also:
**PEP 3115** - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the
current syntax, and the semantics for how classes with
metaclasses are constructed.
**PEP 3129** - Class Decorators
The proposal that added class decorators. Function and method
decorators were introduced in **PEP 318**.
Coroutines
==========
New in version 3.5.
Coroutine function definition
-----------------------------
async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*). Inside the body of a coroutine function,
"await" and "async" identifiers become reserved keywords; "await"
expressions, "async for" and "async with" can only be used in
coroutine function bodies.
Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.
It is a "SyntaxError" to use a "yield from" expression inside the body
of a coroutine function.
An example of a coroutine function:
async def func(param1, param2):
do_stuff()
await some_coroutine()
The "async for" statement
-------------------------
async_for_stmt ::= "async" for_stmt
An *asynchronous iterable* is able to call asynchronous code in its
*iter* implementation, and *asynchronous iterator* can call
asynchronous code in its *next* method.
The "async for" statement allows convenient iteration over
asynchronous iterators.
The following code:
async for TARGET in ITER:
SUITE
else:
SUITE2
Is semantically equivalent to:
iter = (ITER)
iter = type(iter).__aiter__(iter)
running = True
while running:
try:
TARGET = await type(iter).__anext__(iter)
except StopAsyncIteration:
running = False
else:
SUITE
else:
SUITE2
See also "__aiter__()" and "__anext__()" for details.
It is a "SyntaxError" to use an "async for" statement outside the body
of a coroutine function.
The "async with" statement
--------------------------
async_with_stmt ::= "async" with_stmt
An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.
The following code:
async with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
aexit = type(manager).__aexit__
aenter = type(manager).__aenter__
value = await aenter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not await aexit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
await aexit(manager, None, None, None)
See also "__aenter__()" and "__aexit__()" for details.
It is a "SyntaxError" to use an "async with" statement outside the
body of a coroutine function.
See also:
**PEP 492** - Coroutines with async and await syntax
The proposal that made coroutines a proper standalone concept in
Python, and added supporting syntax.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless there
is a "finally" clause which happens to raise another exception.
That new exception causes the old one to be lost.
[2] A string literal appearing as the first statement in the function
body is transformed into the function’s "__doc__" attribute and
therefore the function’s *docstring*.
[3] A string literal appearing as the first statement in the class
body is transformed into the namespace’s "__doc__" item and
therefore the class’s *docstring*.
u� With Statement Context Managers
*******************************
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method’s return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller’s responsibility.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
a� The "continue" statement
************************
continue_stmt ::= "continue"
"continue" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop. It continues with the next cycle of the nearest enclosing loop.
When "continue" passes control out of a "try" statement with a
"finally" clause, that "finally" clause is executed before really
starting the next loop cycle.
u� Arithmetic conversions
**********************
When a description of an arithmetic operator below uses the phrase
“the numeric arguments are converted to a common type”, this means
that the operator implementation for built-in types works as follows:
* If either argument is a complex number, the other is converted to
complex;
* otherwise, if either argument is a floating point number, the other
is converted to floating point;
* otherwise, both must be integers and no conversion is necessary.
Some additional rules apply for certain operators (e.g., a string as a
left argument to the ‘%’ operator). Extensions must define their own
conversion behavior.
uS5 Basic customization
*******************
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass’s "__new__()" method using
"super().__new__(cls[, ...])" with appropriate arguments and then
modifying the newly-created instance as necessary before returning
it.
If "__new__()" is invoked during object construction and it returns
an instance of *cls*, then the new instance’s "__init__()" method
will be invoked like "__init__(self[, ...])", where *self* is the
new instance and the remaining arguments are the same as were
passed to the object constructor.
If "__new__()" does not return an instance of *cls*, then the new
instance’s "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class’s "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"super().__init__([args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.
It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.
It is not guaranteed that "__del__()" methods are called for
objects that still exist when the interpreter exits.
Note:
"del x" doesn’t directly call "x.__del__()" — the former
decrements the reference count for "x" by one, and the latter is
only called when "x"’s reference count reaches zero.
**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.
See also: Documentation for the "gc" module.
Warning:
Due to the precarious circumstances under which "__del__()"
methods are invoked, exceptions that occur during their execution
are ignored, and a warning is printed to "sys.stderr" instead.
In particular:
* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".
* "__del__()" can be executed during interpreter shutdown. As a
consequence, the global variables it needs to access (including
other modules) may already have been deleted or set to "None".
Python guarantees that globals whose name begins with a single
underscore are deleted from their module before other globals
are deleted; if no other references to such globals exist, this
may help in assuring that imported modules are still available
at the time when the "__del__()" method is called.
object.__repr__(self)
Called by the "repr()" built-in function to compute the “official”
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an “informal”
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the “informal” or nicely printable string
representation of an object. The return value must be a string
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by bytes to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function, and by extension,
evaluation of formatted string literals and the "str.format()"
method, to produce a “formatted” string representation of an
object. The *format_spec* argument is a string that contains a
description of the formatting options desired. The interpretation
of the *format_spec* argument is up to the type implementing
"__format__()", however most classes will either delegate
formatting to one of the built-in types, or use a similar
formatting option syntax.
See Format Specification Mini-Language for a description of the
standard formatting syntax.
The return value must be a string object.
Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.
Changed in version 3.7: "object.__format__(x, '')" is now
equivalent to "str(x)" rather than "format(str(self), '')".
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
By default, "object" implements "__eq__()" by using "is", returning
"NotImplemented" in the case of a false comparison: "True if x is y
else NotImplemented". For "__ne__()", by default it delegates to
"__eq__()" and inverts the result unless it is "NotImplemented".
There are no other implied relationships among the comparison
operators or default implementations; for example, the truth of
"(x<y or x==y)" does not imply "x<=y". To automatically generate
ordering operations from a single root operation, see
"functools.total_ordering()".
See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
Note:
"hash()" truncates the value returned from an object’s custom
"__hash__()" method to the size of a "Py_ssize_t". This is
typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds.
If an object’s "__hash__()" must interoperate on builds of
different bit sizes, be sure to check the width on all supported
builds. An easy way to do this is with "python -c "import sys;
print(sys.hash_info.width)"".
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key’s hash value is immutable (if the object’s hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj,
collections.abc.Hashable)".
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
<ParentClass>.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.abc.Hashable)" call.
Note:
By default, the "__hash__()" values of str and bytes objects are
“salted” with an unpredictable random value. Although they
remain constant within an individual Python process, they are not
predictable between repeated invocations of Python.This is
intended to provide protection against a denial-of-service caused
by carefully-chosen inputs that exploit the worst case
performance of a dict insertion, O(n^2) complexity. See
http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of sets.
Python has never made guarantees about this ordering (and it
typically varies between 32-bit and 64-bit builds).See also
"PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
u�I "pdb" — The Python Debugger
***************************
**Source code:** Lib/pdb.py
======================================================================
The module "pdb" defines an interactive source code debugger for
Python programs. It supports setting (conditional) breakpoints and
single stepping at the source line level, inspection of stack frames,
source code listing, and evaluation of arbitrary Python code in the
context of any stack frame. It also supports post-mortem debugging
and can be called under program control.
The debugger is extensible – it is actually defined as the class
"Pdb". This is currently undocumented but easily understood by reading
the source. The extension interface uses the modules "bdb" and "cmd".
The debugger’s prompt is "(Pdb)". Typical usage to run a program under
control of the debugger is:
>>> import pdb
>>> import mymodule
>>> pdb.run('mymodule.test()')
> <string>(0)?()
(Pdb) continue
> <string>(1)?()
(Pdb) continue
NameError: 'spam'
> <string>(1)?()
(Pdb)
Changed in version 3.3: Tab-completion via the "readline" module is
available for commands and command arguments, e.g. the current global
and local names are offered as arguments of the "p" command.
"pdb.py" can also be invoked as a script to debug other scripts. For
example:
python3 -m pdb myscript.py
When invoked as a script, pdb will automatically enter post-mortem
debugging if the program being debugged exits abnormally. After post-
mortem debugging (or after normal exit of the program), pdb will
restart the program. Automatic restarting preserves pdb’s state (such
as breakpoints) and in most cases is more useful than quitting the
debugger upon program’s exit.
New in version 3.2: "pdb.py" now accepts a "-c" option that executes
commands as if given in a ".pdbrc" file, see Debugger Commands.
New in version 3.7: "pdb.py" now accepts a "-m" option that execute
modules similar to the way "python3 -m" does. As with a script, the
debugger will pause execution just before the first line of the
module.
The typical usage to break into the debugger from a running program is
to insert
import pdb; pdb.set_trace()
at the location you want to break into the debugger. You can then
step through the code following this statement, and continue running
without the debugger using the "continue" command.
New in version 3.7: The built-in "breakpoint()", when called with
defaults, can be used instead of "import pdb; pdb.set_trace()".
The typical usage to inspect a crashed program is:
>>> import pdb
>>> import mymodule
>>> mymodule.test()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "./mymodule.py", line 4, in test
test2()
File "./mymodule.py", line 3, in test2
print(spam)
NameError: spam
>>> pdb.pm()
> ./mymodule.py(3)test2()
-> print(spam)
(Pdb)
The module defines the following functions; each enters the debugger
in a slightly different way:
pdb.run(statement, globals=None, locals=None)
Execute the *statement* (given as a string or a code object) under
debugger control. The debugger prompt appears before any code is
executed; you can set breakpoints and type "continue", or you can
step through the statement using "step" or "next" (all these
commands are explained below). The optional *globals* and *locals*
arguments specify the environment in which the code is executed; by
default the dictionary of the module "__main__" is used. (See the
explanation of the built-in "exec()" or "eval()" functions.)
pdb.runeval(expression, globals=None, locals=None)
Evaluate the *expression* (given as a string or a code object)
under debugger control. When "runeval()" returns, it returns the
value of the expression. Otherwise this function is similar to
"run()".
pdb.runcall(function, *args, **kwds)
Call the *function* (a function or method object, not a string)
with the given arguments. When "runcall()" returns, it returns
whatever the function call returned. The debugger prompt appears
as soon as the function is entered.
pdb.set_trace(*, header=None)
Enter the debugger at the calling stack frame. This is useful to
hard-code a breakpoint at a given point in a program, even if the
code is not otherwise being debugged (e.g. when an assertion
fails). If given, *header* is printed to the console just before
debugging begins.
Changed in version 3.7: The keyword-only argument *header*.
pdb.post_mortem(traceback=None)
Enter post-mortem debugging of the given *traceback* object. If no
*traceback* is given, it uses the one of the exception that is
currently being handled (an exception must be being handled if the
default is to be used).
pdb.pm()
Enter post-mortem debugging of the traceback found in
"sys.last_traceback".
The "run*" functions and "set_trace()" are aliases for instantiating
the "Pdb" class and calling the method of the same name. If you want
to access further features, you have to do this yourself:
class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None, nosigint=False, readrc=True)
"Pdb" is the debugger class.
The *completekey*, *stdin* and *stdout* arguments are passed to the
underlying "cmd.Cmd" class; see the description there.
The *skip* argument, if given, must be an iterable of glob-style
module name patterns. The debugger will not step into frames that
originate in a module that matches one of these patterns. [1]
By default, Pdb sets a handler for the SIGINT signal (which is sent
when the user presses "Ctrl-C" on the console) when you give a
"continue" command. This allows you to break into the debugger
again by pressing "Ctrl-C". If you want Pdb not to touch the
SIGINT handler, set *nosigint* to true.
The *readrc* argument defaults to true and controls whether Pdb
will load .pdbrc files from the filesystem.
Example call to enable tracing with *skip*:
import pdb; pdb.Pdb(skip=['django.*']).set_trace()
Raises an auditing event "pdb.Pdb" with no arguments.
New in version 3.1: The *skip* argument.
New in version 3.2: The *nosigint* argument. Previously, a SIGINT
handler was never set by Pdb.
Changed in version 3.6: The *readrc* argument.
run(statement, globals=None, locals=None)
runeval(expression, globals=None, locals=None)
runcall(function, *args, **kwds)
set_trace()
See the documentation for the functions explained above.
Debugger Commands
=================
The commands recognized by the debugger are listed below. Most
commands can be abbreviated to one or two letters as indicated; e.g.
"h(elp)" means that either "h" or "help" can be used to enter the help
command (but not "he" or "hel", nor "H" or "Help" or "HELP").
Arguments to commands must be separated by whitespace (spaces or
tabs). Optional arguments are enclosed in square brackets ("[]") in
the command syntax; the square brackets must not be typed.
Alternatives in the command syntax are separated by a vertical bar
("|").
Entering a blank line repeats the last command entered. Exception: if
the last command was a "list" command, the next 11 lines are listed.
Commands that the debugger doesn’t recognize are assumed to be Python
statements and are executed in the context of the program being
debugged. Python statements can also be prefixed with an exclamation
point ("!"). This is a powerful way to inspect the program being
debugged; it is even possible to change a variable or call a function.
When an exception occurs in such a statement, the exception name is
printed but the debugger’s state is not changed.
The debugger supports aliases. Aliases can have parameters which
allows one a certain level of adaptability to the context under
examination.
Multiple commands may be entered on a single line, separated by ";;".
(A single ";" is not used as it is the separator for multiple commands
in a line that is passed to the Python parser.) No intelligence is
applied to separating the commands; the input is split at the first
";;" pair, even if it is in the middle of a quoted string.
If a file ".pdbrc" exists in the user’s home directory or in the
current directory, it is read in and executed as if it had been typed
at the debugger prompt. This is particularly useful for aliases. If
both files exist, the one in the home directory is read first and
aliases defined there can be overridden by the local file.
Changed in version 3.2: ".pdbrc" can now contain commands that
continue debugging, such as "continue" or "next". Previously, these
commands had no effect.
h(elp) [command]
Without argument, print the list of available commands. With a
*command* as argument, print help about that command. "help pdb"
displays the full documentation (the docstring of the "pdb"
module). Since the *command* argument must be an identifier, "help
exec" must be entered to get help on the "!" command.
w(here)
Print a stack trace, with the most recent frame at the bottom. An
arrow indicates the current frame, which determines the context of
most commands.
d(own) [count]
Move the current frame *count* (default one) levels down in the
stack trace (to a newer frame).
u(p) [count]
Move the current frame *count* (default one) levels up in the stack
trace (to an older frame).
b(reak) [([filename:]lineno | function) [, condition]]
With a *lineno* argument, set a break there in the current file.
With a *function* argument, set a break at the first executable
statement within that function. The line number may be prefixed
with a filename and a colon, to specify a breakpoint in another
file (probably one that hasn’t been loaded yet). The file is
searched on "sys.path". Note that each breakpoint is assigned a
number to which all the other breakpoint commands refer.
If a second argument is present, it is an expression which must
evaluate to true before the breakpoint is honored.
Without argument, list all breaks, including for each breakpoint,
the number of times that breakpoint has been hit, the current
ignore count, and the associated condition if any.
tbreak [([filename:]lineno | function) [, condition]]
Temporary breakpoint, which is removed automatically when it is
first hit. The arguments are the same as for "break".
cl(ear) [filename:lineno | bpnumber [bpnumber ...]]
With a *filename:lineno* argument, clear all the breakpoints at
this line. With a space separated list of breakpoint numbers, clear
those breakpoints. Without argument, clear all breaks (but first
ask confirmation).
disable [bpnumber [bpnumber ...]]
Disable the breakpoints given as a space separated list of
breakpoint numbers. Disabling a breakpoint means it cannot cause
the program to stop execution, but unlike clearing a breakpoint, it
remains in the list of breakpoints and can be (re-)enabled.
enable [bpnumber [bpnumber ...]]
Enable the breakpoints specified.
ignore bpnumber [count]
Set the ignore count for the given breakpoint number. If count is
omitted, the ignore count is set to 0. A breakpoint becomes active
when the ignore count is zero. When non-zero, the count is
decremented each time the breakpoint is reached and the breakpoint
is not disabled and any associated condition evaluates to true.
condition bpnumber [condition]
Set a new *condition* for the breakpoint, an expression which must
evaluate to true before the breakpoint is honored. If *condition*
is absent, any existing condition is removed; i.e., the breakpoint
is made unconditional.
commands [bpnumber]
Specify a list of commands for breakpoint number *bpnumber*. The
commands themselves appear on the following lines. Type a line
containing just "end" to terminate the commands. An example:
(Pdb) commands 1
(com) p some_variable
(com) end
(Pdb)
To remove all commands from a breakpoint, type "commands" and
follow it immediately with "end"; that is, give no commands.
With no *bpnumber* argument, "commands" refers to the last
breakpoint set.
You can use breakpoint commands to start your program up again.
Simply use the "continue" command, or "step", or any other command
that resumes execution.
Specifying any command resuming execution (currently "continue",
"step", "next", "return", "jump", "quit" and their abbreviations)
terminates the command list (as if that command was immediately
followed by end). This is because any time you resume execution
(even with a simple next or step), you may encounter another
breakpoint—which could have its own command list, leading to
ambiguities about which list to execute.
If you use the ‘silent’ command in the command list, the usual
message about stopping at a breakpoint is not printed. This may be
desirable for breakpoints that are to print a specific message and
then continue. If none of the other commands print anything, you
see no sign that the breakpoint was reached.
s(tep)
Execute the current line, stop at the first possible occasion
(either in a function that is called or on the next line in the
current function).
n(ext)
Continue execution until the next line in the current function is
reached or it returns. (The difference between "next" and "step"
is that "step" stops inside a called function, while "next"
executes called functions at (nearly) full speed, only stopping at
the next line in the current function.)
unt(il) [lineno]
Without argument, continue execution until the line with a number
greater than the current one is reached.
With a line number, continue execution until a line with a number
greater or equal to that is reached. In both cases, also stop when
the current frame returns.
Changed in version 3.2: Allow giving an explicit line number.
r(eturn)
Continue execution until the current function returns.
c(ont(inue))
Continue execution, only stop when a breakpoint is encountered.
j(ump) lineno
Set the next line that will be executed. Only available in the
bottom-most frame. This lets you jump back and execute code again,
or jump forward to skip code that you don’t want to run.
It should be noted that not all jumps are allowed – for instance it
is not possible to jump into the middle of a "for" loop or out of a
"finally" clause.
l(ist) [first[, last]]
List source code for the current file. Without arguments, list 11
lines around the current line or continue the previous listing.
With "." as argument, list 11 lines around the current line. With
one argument, list 11 lines around at that line. With two
arguments, list the given range; if the second argument is less
than the first, it is interpreted as a count.
The current line in the current frame is indicated by "->". If an
exception is being debugged, the line where the exception was
originally raised or propagated is indicated by ">>", if it differs
from the current line.
New in version 3.2: The ">>" marker.
ll | longlist
List all source code for the current function or frame.
Interesting lines are marked as for "list".
New in version 3.2.
a(rgs)
Print the argument list of the current function.
p expression
Evaluate the *expression* in the current context and print its
value.
Note:
"print()" can also be used, but is not a debugger command — this
executes the Python "print()" function.
pp expression
Like the "p" command, except the value of the expression is pretty-
printed using the "pprint" module.
whatis expression
Print the type of the *expression*.
source expression
Try to get source code for the given object and display it.
New in version 3.2.
display [expression]
Display the value of the expression if it changed, each time
execution stops in the current frame.
Without expression, list all display expressions for the current
frame.
New in version 3.2.
undisplay [expression]
Do not display the expression any more in the current frame.
Without expression, clear all display expressions for the current
frame.
New in version 3.2.
interact
Start an interactive interpreter (using the "code" module) whose
global namespace contains all the (global and local) names found in
the current scope.
New in version 3.2.
alias [name [command]]
Create an alias called *name* that executes *command*. The command
must *not* be enclosed in quotes. Replaceable parameters can be
indicated by "%1", "%2", and so on, while "%*" is replaced by all
the parameters. If no command is given, the current alias for
*name* is shown. If no arguments are given, all aliases are listed.
Aliases may be nested and can contain anything that can be legally
typed at the pdb prompt. Note that internal pdb commands *can* be
overridden by aliases. Such a command is then hidden until the
alias is removed. Aliasing is recursively applied to the first
word of the command line; all other words in the line are left
alone.
As an example, here are two useful aliases (especially when placed
in the ".pdbrc" file):
# Print instance variables (usage "pi classInst")
alias pi for k in %1.__dict__.keys(): print("%1.",k,"=",%1.__dict__[k])
# Print instance variables in self
alias ps pi self
unalias name
Delete the specified alias.
! statement
Execute the (one-line) *statement* in the context of the current
stack frame. The exclamation point can be omitted unless the first
word of the statement resembles a debugger command. To set a
global variable, you can prefix the assignment command with a
"global" statement on the same line, e.g.:
(Pdb) global list_options; list_options = ['-l']
(Pdb)
run [args ...]
restart [args ...]
Restart the debugged Python program. If an argument is supplied,
it is split with "shlex" and the result is used as the new
"sys.argv". History, breakpoints, actions and debugger options are
preserved. "restart" is an alias for "run".
q(uit)
Quit from the debugger. The program being executed is aborted.
debug code
Enter a recursive debugger that steps through the code argument
(which is an arbitrary expression or statement to be executed in
the current environment).
retval
Print the return value for the last return of a function.
-[ Footnotes ]-
[1] Whether a frame is considered to originate in a certain module is
determined by the "__name__" in the frame globals.
a� The "del" statement
*******************
del_stmt ::= "del" target_list
Deletion is recursively defined very similar to the way assignment is
defined. Rather than spelling it out in full details, here are some
hints.
Deletion of a target list recursively deletes each target, from left
to right.
Deletion of a name removes the binding of that name from the local or
global namespace, depending on whether the name occurs in a "global"
statement in the same code block. If the name is unbound, a
"NameError" exception will be raised.
Deletion of attribute references, subscriptions and slicings is passed
to the primary object involved; deletion of a slicing is in general
equivalent to assignment of an empty slice of the right type (but even
this is determined by the sliced object).
Changed in version 3.2: Previously it was illegal to delete a name
from the local namespace if it occurs as a free variable in a nested
block.
u Dictionary displays
*******************
A dictionary display is a possibly empty series of key/datum pairs
enclosed in curly braces:
dict_display ::= "{" [key_datum_list | dict_comprehension] "}"
key_datum_list ::= key_datum ("," key_datum)* [","]
key_datum ::= expression ":" expression | "**" or_expr
dict_comprehension ::= expression ":" expression comp_for
A dictionary display yields a new dictionary object.
If a comma-separated sequence of key/datum pairs is given, they are
evaluated from left to right to define the entries of the dictionary:
each key object is used as a key into the dictionary to store the
corresponding datum. This means that you can specify the same key
multiple times in the key/datum list, and the final dictionary’s value
for that key will be the last one given.
A double asterisk "**" denotes *dictionary unpacking*. Its operand
must be a *mapping*. Each mapping item is added to the new
dictionary. Later values replace values already set by earlier
key/datum pairs and earlier dictionary unpackings.
New in version 3.5: Unpacking into dictionary displays, originally
proposed by **PEP 448**.
A dict comprehension, in contrast to list and set comprehensions,
needs two expressions separated with a colon followed by the usual
“for” and “if” clauses. When the comprehension is run, the resulting
key and value elements are inserted in the new dictionary in the order
they are produced.
Restrictions on the types of the key values are listed earlier in
section The standard type hierarchy. (To summarize, the key type
should be *hashable*, which excludes all mutable objects.) Clashes
between duplicate keys are not detected; the last datum (textually
rightmost in the display) stored for a given key value prevails.
Changed in version 3.8: Prior to Python 3.8, in dict comprehensions,
the evaluation order of key and value was not well-defined. In
CPython, the value was evaluated before the key. Starting with 3.8,
the key is evaluated before the value, as proposed by **PEP 572**.
a� Interaction with dynamic features
*********************************
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
aX The "if" statement
******************
The "if" statement is used for conditional execution:
if_stmt ::= "if" assignment_expression ":" suite
("elif" assignment_expression ":" suite)*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
u� Exceptions
**********
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack traceback, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note:
Exception messages are not part of the Python API. Their contents
may change from one version of Python to the next without warning
and should not be relied on by code which will run under multiple
versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by these
operations is not available at the time the module is compiled.
u$ Execution model
***************
Structure of a program
======================
A Python program is constructed from code blocks. A *block* is a piece
of Python program text that is executed as a unit. The following are
blocks: a module, a function body, and a class definition. Each
command typed interactively is a block. A script file (a file given
as standard input to the interpreter or specified as a command line
argument to the interpreter) is a code block. A script command (a
command specified on the interpreter command line with the "-c"
option) is a code block. The string argument passed to the built-in
functions "eval()" and "exec()" is a code block.
A code block is executed in an *execution frame*. A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block’s execution has
completed.
Naming and binding
==================
Binding of names
----------------
*Names* refer to objects. Names are introduced by name binding
operations.
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore. This form
may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global". If a name is bound at the
module level, it is a global variable. (The variables of the module
code block are local and global.) If a variable is used in a code
block but not defined there, it is a *free variable*.
Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.
Resolution of names
-------------------
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block’s *environment*.
When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins". The global namespace is searched first. If
the name is not found there, the builtins namespace is searched. The
"global" statement must precede all uses of the name.
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope. This means that the following
will fail:
class A:
a = 42
b = list(a + i for i in range(10))
Builtins and restricted execution
---------------------------------
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.
Interaction with dynamic features
---------------------------------
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
Exceptions
==========
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack traceback, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note:
Exception messages are not part of the Python API. Their contents
may change from one version of Python to the next without warning
and should not be relied on by code which will run under multiple
versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by these
operations is not available at the time the module is compiled.
uz Expression lists
****************
expression_list ::= expression ("," expression)* [","]
starred_list ::= starred_item ("," starred_item)* [","]
starred_expression ::= expression | (starred_item ",")* [starred_item]
starred_item ::= assignment_expression | "*" or_expr
Except when part of a list or set display, an expression list
containing at least one comma yields a tuple. The length of the tuple
is the number of expressions in the list. The expressions are
evaluated from left to right.
An asterisk "*" denotes *iterable unpacking*. Its operand must be an
*iterable*. The iterable is expanded into a sequence of items, which
are included in the new tuple, list, or set, at the site of the
unpacking.
New in version 3.5: Iterable unpacking in expression lists, originally
proposed by **PEP 448**.
The trailing comma is required only to create a single tuple (a.k.a. a
*singleton*); it is optional in all other cases. A single expression
without a trailing comma doesn’t create a tuple, but rather yields the
value of that expression. (To create an empty tuple, use an empty pair
of parentheses: "()".)
a� Floating point literals
***********************
Floating point literals are described by the following lexical
definitions:
floatnumber ::= pointfloat | exponentfloat
pointfloat ::= [digitpart] fraction | digitpart "."
exponentfloat ::= (digitpart | pointfloat) exponent
digitpart ::= digit (["_"] digit)*
fraction ::= "." digitpart
exponent ::= ("e" | "E") ["+" | "-"] digitpart
Note that the integer and exponent parts are always interpreted using
radix 10. For example, "077e010" is legal, and denotes the same number
as "77e10". The allowed range of floating point literals is
implementation-dependent. As in integer literals, underscores are
supported for digit grouping.
Some examples of floating point literals:
3.14 10. .001 1e100 3.14e-10 0e0 3.14_15_93
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
u�
The "for" statement
*******************
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator. Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.
The for-loop makes assignments to the variables in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop. Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".
Note:
There is a subtlety when the sequence is being modified by the loop
(this can only occur for mutable sequences, e.g. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
u�` Format String Syntax
********************
The "str.format()" method and the "Formatter" class share the same
syntax for format strings (although in the case of "Formatter",
subclasses can define their own format string syntax). The syntax is
related to that of formatted string literals, but it is less
sophisticated and, in particular, does not support arbitrary
expressions.
Format strings contain “replacement fields” surrounded by curly braces
"{}". Anything that is not contained in braces is considered literal
text, which is copied unchanged to the output. If you need to include
a brace character in the literal text, it can be escaped by doubling:
"{{" and "}}".
The grammar for a replacement field is as follows:
replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}"
field_name ::= arg_name ("." attribute_name | "[" element_index "]")*
arg_name ::= [identifier | digit+]
attribute_name ::= identifier
element_index ::= digit+ | index_string
index_string ::= <any source character except "]"> +
conversion ::= "r" | "s" | "a"
format_spec ::= <described in the next section>
In less formal terms, the replacement field can start with a
*field_name* that specifies the object whose value is to be formatted
and inserted into the output instead of the replacement field. The
*field_name* is optionally followed by a *conversion* field, which is
preceded by an exclamation point "'!'", and a *format_spec*, which is
preceded by a colon "':'". These specify a non-default format for the
replacement value.
See also the Format Specification Mini-Language section.
The *field_name* itself begins with an *arg_name* that is either a
number or a keyword. If it’s a number, it refers to a positional
argument, and if it’s a keyword, it refers to a named keyword
argument. If the numerical arg_names in a format string are 0, 1, 2,
… in sequence, they can all be omitted (not just some) and the numbers
0, 1, 2, … will be automatically inserted in that order. Because
*arg_name* is not quote-delimited, it is not possible to specify
arbitrary dictionary keys (e.g., the strings "'10'" or "':-]'") within
a format string. The *arg_name* can be followed by any number of index
or attribute expressions. An expression of the form "'.name'" selects
the named attribute using "getattr()", while an expression of the form
"'[index]'" does an index lookup using "__getitem__()".
Changed in version 3.1: The positional argument specifiers can be
omitted for "str.format()", so "'{} {}'.format(a, b)" is equivalent to
"'{0} {1}'.format(a, b)".
Changed in version 3.4: The positional argument specifiers can be
omitted for "Formatter".
Some simple format string examples:
"First, thou shalt count to {0}" # References first positional argument
"Bring me a {}" # Implicitly references the first positional argument
"From {} to {}" # Same as "From {0} to {1}"
"My quest is {name}" # References keyword argument 'name'
"Weight in tons {0.weight}" # 'weight' attribute of first positional arg
"Units destroyed: {players[0]}" # First element of keyword argument 'players'.
The *conversion* field causes a type coercion before formatting.
Normally, the job of formatting a value is done by the "__format__()"
method of the value itself. However, in some cases it is desirable to
force a type to be formatted as a string, overriding its own
definition of formatting. By converting the value to a string before
calling "__format__()", the normal formatting logic is bypassed.
Three conversion flags are currently supported: "'!s'" which calls
"str()" on the value, "'!r'" which calls "repr()" and "'!a'" which
calls "ascii()".
Some examples:
"Harold's a clever {0!s}" # Calls str() on the argument first
"Bring out the holy {name!r}" # Calls repr() on the argument first
"More {!a}" # Calls ascii() on the argument first
The *format_spec* field contains a specification of how the value
should be presented, including such details as field width, alignment,
padding, decimal precision and so on. Each value type can define its
own “formatting mini-language” or interpretation of the *format_spec*.
Most built-in types support a common formatting mini-language, which
is described in the next section.
A *format_spec* field can also include nested replacement fields
within it. These nested replacement fields may contain a field name,
conversion flag and format specification, but deeper nesting is not
allowed. The replacement fields within the format_spec are
substituted before the *format_spec* string is interpreted. This
allows the formatting of a value to be dynamically specified.
See the Format examples section for some examples.
Format Specification Mini-Language
==================================
“Format specifications” are used within replacement fields contained
within a format string to define how individual values are presented
(see Format String Syntax and Formatted string literals). They can
also be passed directly to the built-in "format()" function. Each
formattable type may define how the format specification is to be
interpreted.
Most built-in types implement the following options for format
specifications, although some of the formatting options are only
supported by the numeric types.
A general convention is that an empty format specification produces
the same result as if you had called "str()" on the value. A non-empty
format specification typically modifies the result.
The general form of a *standard format specifier* is:
format_spec ::= [[fill]align][sign][#][0][width][grouping_option][.precision][type]
fill ::= <any character>
align ::= "<" | ">" | "=" | "^"
sign ::= "+" | "-" | " "
width ::= digit+
grouping_option ::= "_" | ","
precision ::= digit+
type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"
If a valid *align* value is specified, it can be preceded by a *fill*
character that can be any character and defaults to a space if
omitted. It is not possible to use a literal curly brace (”"{"” or
“"}"”) as the *fill* character in a formatted string literal or when
using the "str.format()" method. However, it is possible to insert a
curly brace with a nested replacement field. This limitation doesn’t
affect the "format()" function.
The meaning of the various alignment options is as follows:
+-----------+------------------------------------------------------------+
| Option | Meaning |
|===========|============================================================|
| "'<'" | Forces the field to be left-aligned within the available |
| | space (this is the default for most objects). |
+-----------+------------------------------------------------------------+
| "'>'" | Forces the field to be right-aligned within the available |
| | space (this is the default for numbers). |
+-----------+------------------------------------------------------------+
| "'='" | Forces the padding to be placed after the sign (if any) |
| | but before the digits. This is used for printing fields |
| | in the form ‘+000000120’. This alignment option is only |
| | valid for numeric types. It becomes the default when ‘0’ |
| | immediately precedes the field width. |
+-----------+------------------------------------------------------------+
| "'^'" | Forces the field to be centered within the available |
| | space. |
+-----------+------------------------------------------------------------+
Note that unless a minimum field width is defined, the field width
will always be the same size as the data to fill it, so that the
alignment option has no meaning in this case.
The *sign* option is only valid for number types, and can be one of
the following:
+-----------+------------------------------------------------------------+
| Option | Meaning |
|===========|============================================================|
| "'+'" | indicates that a sign should be used for both positive as |
| | well as negative numbers. |
+-----------+------------------------------------------------------------+
| "'-'" | indicates that a sign should be used only for negative |
| | numbers (this is the default behavior). |
+-----------+------------------------------------------------------------+
| space | indicates that a leading space should be used on positive |
| | numbers, and a minus sign on negative numbers. |
+-----------+------------------------------------------------------------+
The "'#'" option causes the “alternate form” to be used for the
conversion. The alternate form is defined differently for different
types. This option is only valid for integer, float and complex
types. For integers, when binary, octal, or hexadecimal output is
used, this option adds the prefix respective "'0b'", "'0o'", or "'0x'"
to the output value. For float and complex the alternate form causes
the result of the conversion to always contain a decimal-point
character, even if no digits follow it. Normally, a decimal-point
character appears in the result of these conversions only if a digit
follows it. In addition, for "'g'" and "'G'" conversions, trailing
zeros are not removed from the result.
The "','" option signals the use of a comma for a thousands separator.
For a locale aware separator, use the "'n'" integer presentation type
instead.
Changed in version 3.1: Added the "','" option (see also **PEP 378**).
The "'_'" option signals the use of an underscore for a thousands
separator for floating point presentation types and for integer
presentation type "'d'". For integer presentation types "'b'", "'o'",
"'x'", and "'X'", underscores will be inserted every 4 digits. For
other presentation types, specifying this option is an error.
Changed in version 3.6: Added the "'_'" option (see also **PEP 515**).
*width* is a decimal integer defining the minimum total field width,
including any prefixes, separators, and other formatting characters.
If not specified, then the field width will be determined by the
content.
When no explicit alignment is given, preceding the *width* field by a
zero ("'0'") character enables sign-aware zero-padding for numeric
types. This is equivalent to a *fill* character of "'0'" with an
*alignment* type of "'='".
The *precision* is a decimal number indicating how many digits should
be displayed after the decimal point for a floating point value
formatted with "'f'" and "'F'", or before and after the decimal point
for a floating point value formatted with "'g'" or "'G'". For non-
number types the field indicates the maximum field size - in other
words, how many characters will be used from the field content. The
*precision* is not allowed for integer values.
Finally, the *type* determines how the data should be presented.
The available string presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
|===========|============================================================|
| "'s'" | String format. This is the default type for strings and |
| | may be omitted. |
+-----------+------------------------------------------------------------+
| None | The same as "'s'". |
+-----------+------------------------------------------------------------+
The available integer presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
|===========|============================================================|
| "'b'" | Binary format. Outputs the number in base 2. |
+-----------+------------------------------------------------------------+
| "'c'" | Character. Converts the integer to the corresponding |
| | unicode character before printing. |
+-----------+------------------------------------------------------------+
| "'d'" | Decimal Integer. Outputs the number in base 10. |
+-----------+------------------------------------------------------------+
| "'o'" | Octal format. Outputs the number in base 8. |
+-----------+------------------------------------------------------------+
| "'x'" | Hex format. Outputs the number in base 16, using lower- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'X'" | Hex format. Outputs the number in base 16, using upper- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'d'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| None | The same as "'d'". |
+-----------+------------------------------------------------------------+
In addition to the above presentation types, integers can be formatted
with the floating point presentation types listed below (except "'n'"
and "None"). When doing so, "float()" is used to convert the integer
to a floating point number before formatting.
The available presentation types for "float" and "Decimal" values are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
|===========|============================================================|
| "'e'" | Scientific notation. For a given precision "p", formats |
| | the number in scientific notation with the letter ‘e’ |
| | separating the coefficient from the exponent. The |
| | coefficient has one digit before and "p" digits after the |
| | decimal point, for a total of "p + 1" significant digits. |
| | With no precision given, uses a precision of "6" digits |
| | after the decimal point for "float", and shows all |
| | coefficient digits for "Decimal". If no digits follow the |
| | decimal point, the decimal point is also removed unless |
| | the "#" option is used. |
+-----------+------------------------------------------------------------+
| "'E'" | Scientific notation. Same as "'e'" except it uses an upper |
| | case ‘E’ as the separator character. |
+-----------+------------------------------------------------------------+
| "'f'" | Fixed-point notation. For a given precision "p", formats |
| | the number as a decimal number with exactly "p" digits |
| | following the decimal point. With no precision given, uses |
| | a precision of "6" digits after the decimal point for |
| | "float", and uses a precision large enough to show all |
| | coefficient digits for "Decimal". If no digits follow the |
| | decimal point, the decimal point is also removed unless |
| | the "#" option is used. |
+-----------+------------------------------------------------------------+
| "'F'" | Fixed-point notation. Same as "'f'", but converts "nan" to |
| | "NAN" and "inf" to "INF". |
+-----------+------------------------------------------------------------+
| "'g'" | General format. For a given precision "p >= 1", this |
| | rounds the number to "p" significant digits and then |
| | formats the result in either fixed-point format or in |
| | scientific notation, depending on its magnitude. A |
| | precision of "0" is treated as equivalent to a precision |
| | of "1". The precise rules are as follows: suppose that |
| | the result formatted with presentation type "'e'" and |
| | precision "p-1" would have exponent "exp". Then, if "m <= |
| | exp < p", where "m" is -4 for floats and -6 for |
| | "Decimals", the number is formatted with presentation type |
| | "'f'" and precision "p-1-exp". Otherwise, the number is |
| | formatted with presentation type "'e'" and precision |
| | "p-1". In both cases insignificant trailing zeros are |
| | removed from the significand, and the decimal point is |
| | also removed if there are no remaining digits following |
| | it, unless the "'#'" option is used. With no precision |
| | given, uses a precision of "6" significant digits for |
| | "float". For "Decimal", the coefficient of the result is |
| | formed from the coefficient digits of the value; |
| | scientific notation is used for values smaller than "1e-6" |
| | in absolute value and values where the place value of the |
| | least significant digit is larger than 1, and fixed-point |
| | notation is used otherwise. Positive and negative |
| | infinity, positive and negative zero, and nans, are |
| | formatted as "inf", "-inf", "0", "-0" and "nan" |
| | respectively, regardless of the precision. |
+-----------+------------------------------------------------------------+
| "'G'" | General format. Same as "'g'" except switches to "'E'" if |
| | the number gets too large. The representations of infinity |
| | and NaN are uppercased, too. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'g'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| "'%'" | Percentage. Multiplies the number by 100 and displays in |
| | fixed ("'f'") format, followed by a percent sign. |
+-----------+------------------------------------------------------------+
| None | For "float" this is the same as "'g'", except that when |
| | fixed-point notation is used to format the result, it |
| | always includes at least one digit past the decimal point. |
| | The precision used is as large as needed to represent the |
| | given value faithfully. For "Decimal", this is the same |
| | as either "'g'" or "'G'" depending on the value of |
| | "context.capitals" for the current decimal context. The |
| | overall effect is to match the output of "str()" as |
| | altered by the other format modifiers. |
+-----------+------------------------------------------------------------+
Format examples
===============
This section contains examples of the "str.format()" syntax and
comparison with the old "%"-formatting.
In most of the cases the syntax is similar to the old "%"-formatting,
with the addition of the "{}" and with ":" used instead of "%". For
example, "'%03.2f'" can be translated to "'{:03.2f}'".
The new format syntax also supports new and different options, shown
in the following examples.
Accessing arguments by position:
>>> '{0}, {1}, {2}'.format('a', 'b', 'c')
'a, b, c'
>>> '{}, {}, {}'.format('a', 'b', 'c') # 3.1+ only
'a, b, c'
>>> '{2}, {1}, {0}'.format('a', 'b', 'c')
'c, b, a'
>>> '{2}, {1}, {0}'.format(*'abc') # unpacking argument sequence
'c, b, a'
>>> '{0}{1}{0}'.format('abra', 'cad') # arguments' indices can be repeated
'abracadabra'
Accessing arguments by name:
>>> 'Coordinates: {latitude}, {longitude}'.format(latitude='37.24N', longitude='-115.81W')
'Coordinates: 37.24N, -115.81W'
>>> coord = {'latitude': '37.24N', 'longitude': '-115.81W'}
>>> 'Coordinates: {latitude}, {longitude}'.format(**coord)
'Coordinates: 37.24N, -115.81W'
Accessing arguments’ attributes:
>>> c = 3-5j
>>> ('The complex number {0} is formed from the real part {0.real} '
... 'and the imaginary part {0.imag}.').format(c)
'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.'
>>> class Point:
... def __init__(self, x, y):
... self.x, self.y = x, y
... def __str__(self):
... return 'Point({self.x}, {self.y})'.format(self=self)
...
>>> str(Point(4, 2))
'Point(4, 2)'
Accessing arguments’ items:
>>> coord = (3, 5)
>>> 'X: {0[0]}; Y: {0[1]}'.format(coord)
'X: 3; Y: 5'
Replacing "%s" and "%r":
>>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2')
"repr() shows quotes: 'test1'; str() doesn't: test2"
Aligning the text and specifying a width:
>>> '{:<30}'.format('left aligned')
'left aligned '
>>> '{:>30}'.format('right aligned')
' right aligned'
>>> '{:^30}'.format('centered')
' centered '
>>> '{:*^30}'.format('centered') # use '*' as a fill char
'***********centered***********'
Replacing "%+f", "%-f", and "% f" and specifying a sign:
>>> '{:+f}; {:+f}'.format(3.14, -3.14) # show it always
'+3.140000; -3.140000'
>>> '{: f}; {: f}'.format(3.14, -3.14) # show a space for positive numbers
' 3.140000; -3.140000'
>>> '{:-f}; {:-f}'.format(3.14, -3.14) # show only the minus -- same as '{:f}; {:f}'
'3.140000; -3.140000'
Replacing "%x" and "%o" and converting the value to different bases:
>>> # format also supports binary numbers
>>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: {0:b}".format(42)
'int: 42; hex: 2a; oct: 52; bin: 101010'
>>> # with 0x, 0o, or 0b as prefix:
>>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: {0:#b}".format(42)
'int: 42; hex: 0x2a; oct: 0o52; bin: 0b101010'
Using the comma as a thousands separator:
>>> '{:,}'.format(1234567890)
'1,234,567,890'
Expressing a percentage:
>>> points = 19
>>> total = 22
>>> 'Correct answers: {:.2%}'.format(points/total)
'Correct answers: 86.36%'
Using type-specific formatting:
>>> import datetime
>>> d = datetime.datetime(2010, 7, 4, 12, 15, 58)
>>> '{:%Y-%m-%d %H:%M:%S}'.format(d)
'2010-07-04 12:15:58'
Nesting arguments and more complex examples:
>>> for align, text in zip('<^>', ['left', 'center', 'right']):
... '{0:{fill}{align}16}'.format(text, fill=align, align=align)
...
'left<<<<<<<<<<<<'
'^^^^^center^^^^^'
'>>>>>>>>>>>right'
>>>
>>> octets = [192, 168, 0, 1]
>>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets)
'C0A80001'
>>> int(_, 16)
3232235521
>>>
>>> width = 5
>>> for num in range(5,12):
... for base in 'dXob':
... print('{0:{width}{base}}'.format(num, base=base, width=width), end=' ')
... print()
...
5 5 5 101
6 6 6 110
7 7 7 111
8 8 10 1000
9 9 11 1001
10 A 12 1010
11 B 13 1011
u| Function definitions
********************
A function definition defines a user-defined function object (see
section The standard type hierarchy):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= defparameter ("," defparameter)* "," "/" ["," [parameter_list_no_posonly]]
| parameter_list_no_posonly
parameter_list_no_posonly ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
| parameter_list_starargs
parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
| "**" parameter [","]
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [2]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
except that the original function is not temporarily bound to the name
"func".
When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted. If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.
**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call. This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended. A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from positional arguments, from keyword
arguments, or from default values. If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type. Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed by
keyword arguments. Parameters before “"/"” are positional-only
parameters and may only be passed by positional arguments.
Changed in version 3.8: The "/" function parameter syntax may be used
to indicate positional-only parameters. See **PEP 570** for details.
Parameters may have an *annotation* of the form “": expression"”
following the parameter name. Any parameter may have an annotation,
even those of the form "*identifier" or "**identifier". Functions may
have “return” annotation of the form “"-> expression"” after the
parameter list. These annotations can be any valid Python expression.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object. If the "annotations" import from
"__future__" is used, annotations are preserved as strings at runtime
which enables postponed evaluation. Otherwise, they are evaluated
when the function definition is executed. In this case annotations
may be evaluated in a different order than they appear in the source
code.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.
**Programmer’s note:** Functions are first-class objects. A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around. Free variables used
in the nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
See also:
**PEP 3107** - Function Annotations
The original specification for function annotations.
**PEP 484** - Type Hints
Definition of a standard meaning for annotations: type hints.
**PEP 526** - Syntax for Variable Annotations
Ability to type hint variable declarations, including class
variables and instance variables
**PEP 563** - Postponed Evaluation of Annotations
Support for forward references within annotations by preserving
annotations in a string form at runtime instead of eager
evaluation.
u� The "global" statement
**********************
global_stmt ::= "global" identifier ("," identifier)*
The "global" statement is a declaration which holds for the entire
current code block. It means that the listed identifiers are to be
interpreted as globals. It would be impossible to assign to a global
variable without "global", although free variables may refer to
globals without being declared global.
Names listed in a "global" statement must not be used in the same code
block textually preceding that "global" statement.
Names listed in a "global" statement must not be defined as formal
parameters or in a "for" loop control target, "class" definition,
function definition, "import" statement, or variable annotation.
**CPython implementation detail:** The current implementation does not
enforce some of these restrictions, but programs should not abuse this
freedom, as future implementations may enforce them or silently change
the meaning of the program.
**Programmer’s note:** "global" is a directive to the parser. It
applies only to code parsed at the same time as the "global"
statement. In particular, a "global" statement contained in a string
or code object supplied to the built-in "exec()" function does not
affect the code block *containing* the function call, and code
contained in such a string is unaffected by "global" statements in the
code containing the function call. The same applies to the "eval()"
and "compile()" functions.
u� Reserved classes of identifiers
*******************************
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section The import statement.
Note:
The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names, informally known as “dunder” names. These
names are defined by the interpreter and its implementation
(including the standard library). Current system names are
discussed in the Special method names section and elsewhere. More
will likely be defined in future versions of Python. *Any* use of
"__*__" names, in any context, that does not follow explicitly
documented use, is subject to breakage without warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between “private” attributes of base and
derived classes. See section Identifiers (Names).
um Identifiers and keywords
************************
Identifiers (also referred to as *names*) are described by the
following lexical definitions.
The syntax of identifiers in Python is based on the Unicode standard
annex UAX-31, with elaboration and changes as defined below; see also
**PEP 3131** for further details.
Within the ASCII range (U+0001..U+007F), the valid characters for
identifiers are the same as in Python 2.x: the uppercase and lowercase
letters "A" through "Z", the underscore "_" and, except for the first
character, the digits "0" through "9".
Python 3.0 introduces additional characters from outside the ASCII
range (see **PEP 3131**). For these characters, the classification
uses the version of the Unicode Character Database as included in the
"unicodedata" module.
Identifiers are unlimited in length. Case is significant.
identifier ::= xid_start xid_continue*
id_start ::= <all characters in general categories Lu, Ll, Lt, Lm, Lo, Nl, the underscore, and characters with the Other_ID_Start property>
id_continue ::= <all characters in id_start, plus characters in the categories Mn, Mc, Nd, Pc and others with the Other_ID_Continue property>
xid_start ::= <all characters in id_start whose NFKC normalization is in "id_start xid_continue*">
xid_continue ::= <all characters in id_continue whose NFKC normalization is in "id_continue*">
The Unicode category codes mentioned above stand for:
* *Lu* - uppercase letters
* *Ll* - lowercase letters
* *Lt* - titlecase letters
* *Lm* - modifier letters
* *Lo* - other letters
* *Nl* - letter numbers
* *Mn* - nonspacing marks
* *Mc* - spacing combining marks
* *Nd* - decimal numbers
* *Pc* - connector punctuations
* *Other_ID_Start* - explicit list of characters in PropList.txt to
support backwards compatibility
* *Other_ID_Continue* - likewise
All identifiers are converted into the normal form NFKC while parsing;
comparison of identifiers is based on NFKC.
A non-normative HTML file listing all valid identifier characters for
Unicode 4.1 can be found at
https://www.unicode.org/Public/13.0.0/ucd/DerivedCoreProperties.txt
Keywords
========
The following identifiers are used as reserved words, or *keywords* of
the language, and cannot be used as ordinary identifiers. They must
be spelled exactly as written here:
False await else import pass
None break except in raise
True class finally is return
and continue for lambda try
as def from nonlocal while
assert del global not with
async elif if or yield
Reserved classes of identifiers
===============================
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section The import statement.
Note:
The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names, informally known as “dunder” names. These
names are defined by the interpreter and its implementation
(including the standard library). Current system names are
discussed in the Special method names section and elsewhere. More
will likely be defined in future versions of Python. *Any* use of
"__*__" names, in any context, that does not follow explicitly
documented use, is subject to breakage without warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between “private” attributes of base and
derived classes. See section Identifiers (Names).
a5 Imaginary literals
******************
Imaginary literals are described by the following lexical definitions:
imagnumber ::= (floatnumber | digitpart) ("j" | "J")
An imaginary literal yields a complex number with a real part of 0.0.
Complex numbers are represented as a pair of floating point numbers
and have the same restrictions on their range. To create a complex
number with a nonzero real part, add a floating point number to it,
e.g., "(3+4j)". Some examples of imaginary literals:
3.14j 10.j 10j .001j 1e100j 3.14e-10j 3.14_15_93j
u8" The "import" statement
**********************
import_stmt ::= "import" module ["as" identifier] ("," module ["as" identifier])*
| "from" relative_module "import" identifier ["as" identifier]
("," identifier ["as" identifier])*
| "from" relative_module "import" "(" identifier ["as" identifier]
("," identifier ["as" identifier])* [","] ")"
| "from" module "import" "*"
module ::= (identifier ".")* identifier
relative_module ::= "."* module | "."+
The basic import statement (no "from" clause) is executed in two
steps:
1. find a module, loading and initializing it if necessary
2. define a name or names in the local namespace for the scope where
the "import" statement occurs.
When the statement contains multiple clauses (separated by commas) the
two steps are carried out separately for each clause, just as though
the clauses had been separated out into individual import statements.
The details of the first step, finding and loading modules are
described in greater detail in the section on the import system, which
also describes the various types of packages and modules that can be
imported, as well as all the hooks that can be used to customize the
import system. Note that failures in this step may indicate either
that the module could not be located, *or* that an error occurred
while initializing the module, which includes execution of the
module’s code.
If the requested module is retrieved successfully, it will be made
available in the local namespace in one of three ways:
* If the module name is followed by "as", then the name following "as"
is bound directly to the imported module.
* If no other name is specified, and the module being imported is a
top level module, the module’s name is bound in the local namespace
as a reference to the imported module
* If the module being imported is *not* a top level module, then the
name of the top level package that contains the module is bound in
the local namespace as a reference to the top level package. The
imported module must be accessed using its full qualified name
rather than directly
The "from" form uses a slightly more complex process:
1. find the module specified in the "from" clause, loading and
initializing it if necessary;
2. for each of the identifiers specified in the "import" clauses:
1. check if the imported module has an attribute by that name
2. if not, attempt to import a submodule with that name and then
check the imported module again for that attribute
3. if the attribute is not found, "ImportError" is raised.
4. otherwise, a reference to that value is stored in the local
namespace, using the name in the "as" clause if it is present,
otherwise using the attribute name
Examples:
import foo # foo imported and bound locally
import foo.bar.baz # foo.bar.baz imported, foo bound locally
import foo.bar.baz as fbb # foo.bar.baz imported and bound as fbb
from foo.bar import baz # foo.bar.baz imported and bound as baz
from foo import attr # foo imported and foo.attr bound as attr
If the list of identifiers is replaced by a star ("'*'"), all public
names defined in the module are bound in the local namespace for the
scope where the "import" statement occurs.
The *public names* defined by a module are determined by checking the
module’s namespace for a variable named "__all__"; if defined, it must
be a sequence of strings which are names defined or imported by that
module. The names given in "__all__" are all considered public and
are required to exist. If "__all__" is not defined, the set of public
names includes all names found in the module’s namespace which do not
begin with an underscore character ("'_'"). "__all__" should contain
the entire public API. It is intended to avoid accidentally exporting
items that are not part of the API (such as library modules which were
imported and used within the module).
The wild card form of import — "from module import *" — is only
allowed at the module level. Attempting to use it in class or
function definitions will raise a "SyntaxError".
When specifying what module to import you do not have to specify the
absolute name of the module. When a module or package is contained
within another package it is possible to make a relative import within
the same top package without having to mention the package name. By
using leading dots in the specified module or package after "from" you
can specify how high to traverse up the current package hierarchy
without specifying exact names. One leading dot means the current
package where the module making the import exists. Two dots means up
one package level. Three dots is up two levels, etc. So if you execute
"from . import mod" from a module in the "pkg" package then you will
end up importing "pkg.mod". If you execute "from ..subpkg2 import mod"
from within "pkg.subpkg1" you will import "pkg.subpkg2.mod". The
specification for relative imports is contained in the Package
Relative Imports section.
"importlib.import_module()" is provided to support applications that
determine dynamically the modules to be loaded.
Raises an auditing event "import" with arguments "module", "filename",
"sys.path", "sys.meta_path", "sys.path_hooks".
Future statements
=================
A *future statement* is a directive to the compiler that a particular
module should be compiled using syntax or semantics that will be
available in a specified future release of Python where the feature
becomes standard.
The future statement is intended to ease migration to future versions
of Python that introduce incompatible changes to the language. It
allows use of the new features on a per-module basis before the
release in which the feature becomes standard.
future_stmt ::= "from" "__future__" "import" feature ["as" identifier]
("," feature ["as" identifier])*
| "from" "__future__" "import" "(" feature ["as" identifier]
("," feature ["as" identifier])* [","] ")"
feature ::= identifier
A future statement must appear near the top of the module. The only
lines that can appear before a future statement are:
* the module docstring (if any),
* comments,
* blank lines, and
* other future statements.
The only feature that requires using the future statement is
"annotations" (see **PEP 563**).
All historical features enabled by the future statement are still
recognized by Python 3. The list includes "absolute_import",
"division", "generators", "generator_stop", "unicode_literals",
"print_function", "nested_scopes" and "with_statement". They are all
redundant because they are always enabled, and only kept for backwards
compatibility.
A future statement is recognized and treated specially at compile
time: Changes to the semantics of core constructs are often
implemented by generating different code. It may even be the case
that a new feature introduces new incompatible syntax (such as a new
reserved word), in which case the compiler may need to parse the
module differently. Such decisions cannot be pushed off until
runtime.
For any given release, the compiler knows which feature names have
been defined, and raises a compile-time error if a future statement
contains a feature not known to it.
The direct runtime semantics are the same as for any import statement:
there is a standard module "__future__", described later, and it will
be imported in the usual way at the time the future statement is
executed.
The interesting runtime semantics depend on the specific feature
enabled by the future statement.
Note that there is nothing special about the statement:
import __future__ [as name]
That is not a future statement; it’s an ordinary import statement with
no special semantics or syntax restrictions.
Code compiled by calls to the built-in functions "exec()" and
"compile()" that occur in a module "M" containing a future statement
will, by default, use the new syntax or semantics associated with the
future statement. This can be controlled by optional arguments to
"compile()" — see the documentation of that function for details.
A future statement typed at an interactive interpreter prompt will
take effect for the rest of the interpreter session. If an
interpreter is started with the "-i" option, is passed a script name
to execute, and the script includes a future statement, it will be in
effect in the interactive session started after the script is
executed.
See also:
**PEP 236** - Back to the __future__
The original proposal for the __future__ mechanism.
a Membership test operations
**************************
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z", for which
the expression "x is z or x == z" is true, is produced while iterating
over "y". If an exception is raised during the iteration, it is as if
"in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x is y[i] or x == y[i]", and no
lower integer index raises the "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse truth value of
"in".
aV Integer literals
****************
Integer literals are described by the following lexical definitions:
integer ::= decinteger | bininteger | octinteger | hexinteger
decinteger ::= nonzerodigit (["_"] digit)* | "0"+ (["_"] "0")*
bininteger ::= "0" ("b" | "B") (["_"] bindigit)+
octinteger ::= "0" ("o" | "O") (["_"] octdigit)+
hexinteger ::= "0" ("x" | "X") (["_"] hexdigit)+
nonzerodigit ::= "1"..."9"
digit ::= "0"..."9"
bindigit ::= "0" | "1"
octdigit ::= "0"..."7"
hexdigit ::= digit | "a"..."f" | "A"..."F"
There is no limit for the length of integer literals apart from what
can be stored in available memory.
Underscores are ignored for determining the numeric value of the
literal. They can be used to group digits for enhanced readability.
One underscore can occur between digits, and after base specifiers
like "0x".
Note that leading zeros in a non-zero decimal number are not allowed.
This is for disambiguation with C-style octal literals, which Python
used before version 3.0.
Some examples of integer literals:
7 2147483647 0o177 0b100110111
3 79228162514264337593543950336 0o377 0xdeadbeef
100_000_000_000 0b_1110_0101
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
a^ Lambdas
*******
lambda_expr ::= "lambda" [parameter_list] ":" expression
lambda_expr_nocond ::= "lambda" [parameter_list] ":" expression_nocond
Lambda expressions (sometimes called lambda forms) are used to create
anonymous functions. The expression "lambda parameters: expression"
yields a function object. The unnamed object behaves like a function
object defined with:
def <lambda>(parameters):
return expression
See section Function definitions for the syntax of parameter lists.
Note that functions created with lambda expressions cannot contain
statements or annotations.
a/ List displays
*************
A list display is a possibly empty series of expressions enclosed in
square brackets:
list_display ::= "[" [starred_list | comprehension] "]"
A list display yields a new list object, the contents being specified
by either a list of expressions or a comprehension. When a comma-
separated list of expressions is supplied, its elements are evaluated
from left to right and placed into the list object in that order.
When a comprehension is supplied, the list is constructed from the
elements resulting from the comprehension.
u� Naming and binding
******************
Binding of names
================
*Names* refer to objects. Names are introduced by name binding
operations.
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore. This form
may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global". If a name is bound at the
module level, it is a global variable. (The variables of the module
code block are local and global.) If a variable is used in a code
block but not defined there, it is a *free variable*.
Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.
Resolution of names
===================
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block’s *environment*.
When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins". The global namespace is searched first. If
the name is not found there, the builtins namespace is searched. The
"global" statement must precede all uses of the name.
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope. This means that the following
will fail:
class A:
a = 42
b = list(a + i for i in range(10))
Builtins and restricted execution
=================================
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.
Interaction with dynamic features
=================================
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
a� The "nonlocal" statement
************************
nonlocal_stmt ::= "nonlocal" identifier ("," identifier)*
The "nonlocal" statement causes the listed identifiers to refer to
previously bound variables in the nearest enclosing scope excluding
globals. This is important because the default behavior for binding is
to search the local namespace first. The statement allows
encapsulated code to rebind variables outside of the local scope
besides the global (module) scope.
Names listed in a "nonlocal" statement, unlike those listed in a
"global" statement, must refer to pre-existing bindings in an
enclosing scope (the scope in which a new binding should be created
cannot be determined unambiguously).
Names listed in a "nonlocal" statement must not collide with pre-
existing bindings in the local scope.
See also:
**PEP 3104** - Access to Names in Outer Scopes
The specification for the "nonlocal" statement.
u� Numeric literals
****************
There are three types of numeric literals: integers, floating point
numbers, and imaginary numbers. There are no complex literals
(complex numbers can be formed by adding a real number and an
imaginary number).
Note that numeric literals do not include a sign; a phrase like "-1"
is actually an expression composed of the unary operator ‘"-"’ and the
literal "1".
u Emulating numeric types
***********************
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the expression "x + y", where *x* is an instance of a
class that has an "__add__()" method, "x.__add__(y)" is called.
The "__divmod__()" method should be the equivalent to using
"__floordiv__()" and "__mod__()"; it should not be related to
"__truediv__()". Note that "__pow__()" should be defined to accept
an optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other[, modulo])
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)
operands. These functions are only called if the left operand does
not support the corresponding operation [3] and the operands are of
different types. [4] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note:
If the right operand’s type is a subclass of the left operand’s
type and that subclass provides a different implementation of the
reflected method for the operation, this method will be called
before the left operand’s non-reflected method. This behavior
allows subclasses to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (modifying *self*) and return the result
(which could be, but does not have to be, *self*). If a specific
method is not defined, the augmented assignment falls back to the
normal methods. For instance, if *x* is an instance of a class
with an "__iadd__()" method, "x += y" is equivalent to "x =
x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are
considered, as with the evaluation of "x + y". In certain
situations, augmented assignment can result in unexpected errors
(see Why does a_tuple[i] += [‘item’] raise an exception when the
addition works?), but this behavior is in fact part of the data
model.
Note:
Due to a bug in the dispatching mechanism for "**=", a class that
defines "__ipow__()" but returns "NotImplemented" would fail to
fall back to "x.__pow__(y)" and "y.__rpow__(x)". This bug is
fixed in Python 3.10.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()" and
"float()". Should return a value of the appropriate type.
object.__index__(self)
Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.
If "__int__()", "__float__()" and "__complex__()" are not defined
then corresponding built-in functions "int()", "float()" and
"complex()" fall back to "__index__()".
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").
The built-in function "int()" falls back to "__trunc__()" if
neither "__int__()" nor "__index__()" is defined.
u Objects, values and types
*************************
*Objects* are Python’s abstraction for data. All data in a Python
program is represented by objects or by relations between objects. (In
a sense, and in conformance to Von Neumann’s model of a “stored
program computer”, code is also represented by objects.)
Every object has an identity, a type and a value. An object’s
*identity* never changes once it has been created; you may think of it
as the object’s address in memory. The ‘"is"’ operator compares the
identity of two objects; the "id()" function returns an integer
representing its identity.
**CPython implementation detail:** For CPython, "id(x)" is the memory
address where "x" is stored.
An object’s type determines the operations that the object supports
(e.g., “does it have a length?”) and also defines the possible values
for objects of that type. The "type()" function returns an object’s
type (which is an object itself). Like its identity, an object’s
*type* is also unchangeable. [1]
The *value* of some objects can change. Objects whose value can
change are said to be *mutable*; objects whose value is unchangeable
once they are created are called *immutable*. (The value of an
immutable container object that contains a reference to a mutable
object can change when the latter’s value is changed; however the
container is still considered immutable, because the collection of
objects it contains cannot be changed. So, immutability is not
strictly the same as having an unchangeable value, it is more subtle.)
An object’s mutability is determined by its type; for instance,
numbers, strings and tuples are immutable, while dictionaries and
lists are mutable.
Objects are never explicitly destroyed; however, when they become
unreachable they may be garbage-collected. An implementation is
allowed to postpone garbage collection or omit it altogether — it is a
matter of implementation quality how garbage collection is
implemented, as long as no objects are collected that are still
reachable.
**CPython implementation detail:** CPython currently uses a reference-
counting scheme with (optional) delayed detection of cyclically linked
garbage, which collects most objects as soon as they become
unreachable, but is not guaranteed to collect garbage containing
circular references. See the documentation of the "gc" module for
information on controlling the collection of cyclic garbage. Other
implementations act differently and CPython may change. Do not depend
on immediate finalization of objects when they become unreachable (so
you should always close files explicitly).
Note that the use of the implementation’s tracing or debugging
facilities may keep objects alive that would normally be collectable.
Also note that catching an exception with a ‘"try"…"except"’ statement
may keep objects alive.
Some objects contain references to “external” resources such as open
files or windows. It is understood that these resources are freed
when the object is garbage-collected, but since garbage collection is
not guaranteed to happen, such objects also provide an explicit way to
release the external resource, usually a "close()" method. Programs
are strongly recommended to explicitly close such objects. The
‘"try"…"finally"’ statement and the ‘"with"’ statement provide
convenient ways to do this.
Some objects contain references to other objects; these are called
*containers*. Examples of containers are tuples, lists and
dictionaries. The references are part of a container’s value. In
most cases, when we talk about the value of a container, we imply the
values, not the identities of the contained objects; however, when we
talk about the mutability of a container, only the identities of the
immediately contained objects are implied. So, if an immutable
container (like a tuple) contains a reference to a mutable object, its
value changes if that mutable object is changed.
Types affect almost all aspects of object behavior. Even the
importance of object identity is affected in some sense: for immutable
types, operations that compute new values may actually return a
reference to any existing object with the same type and value, while
for mutable objects this is not allowed. E.g., after "a = 1; b = 1",
"a" and "b" may or may not refer to the same object with the value
one, depending on the implementation, but after "c = []; d = []", "c"
and "d" are guaranteed to refer to two different, unique, newly
created empty lists. (Note that "c = d = []" assigns the same object
to both "c" and "d".)
u� Operator precedence
*******************
The following table summarizes the operator precedence in Python, from
lowest precedence (least binding) to highest precedence (most
binding). Operators in the same box have the same precedence. Unless
the syntax is explicitly given, operators are binary. Operators in
the same box group left to right (except for exponentiation, which
groups from right to left).
Note that comparisons, membership tests, and identity tests, all have
the same precedence and have a left-to-right chaining feature as
described in the Comparisons section.
+-------------------------------------------------+---------------------------------------+
| Operator | Description |
|=================================================|=======================================|
| ":=" | Assignment expression |
+-------------------------------------------------+---------------------------------------+
| "lambda" | Lambda expression |
+-------------------------------------------------+---------------------------------------+
| "if" – "else" | Conditional expression |
+-------------------------------------------------+---------------------------------------+
| "or" | Boolean OR |
+-------------------------------------------------+---------------------------------------+
| "and" | Boolean AND |
+-------------------------------------------------+---------------------------------------+
| "not" "x" | Boolean NOT |
+-------------------------------------------------+---------------------------------------+
| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership |
| ">=", "!=", "==" | tests and identity tests |
+-------------------------------------------------+---------------------------------------+
| "|" | Bitwise OR |
+-------------------------------------------------+---------------------------------------+
| "^" | Bitwise XOR |
+-------------------------------------------------+---------------------------------------+
| "&" | Bitwise AND |
+-------------------------------------------------+---------------------------------------+
| "<<", ">>" | Shifts |
+-------------------------------------------------+---------------------------------------+
| "+", "-" | Addition and subtraction |
+-------------------------------------------------+---------------------------------------+
| "*", "@", "/", "//", "%" | Multiplication, matrix |
| | multiplication, division, floor |
| | division, remainder [5] |
+-------------------------------------------------+---------------------------------------+
| "+x", "-x", "~x" | Positive, negative, bitwise NOT |
+-------------------------------------------------+---------------------------------------+
| "**" | Exponentiation [6] |
+-------------------------------------------------+---------------------------------------+
| "await" "x" | Await expression |
+-------------------------------------------------+---------------------------------------+
| "x[index]", "x[index:index]", | Subscription, slicing, call, |
| "x(arguments...)", "x.attribute" | attribute reference |
+-------------------------------------------------+---------------------------------------+
| "(expressions...)", "[expressions...]", "{key: | Binding or parenthesized expression, |
| value...}", "{expressions...}" | list display, dictionary display, set |
| | display |
+-------------------------------------------------+---------------------------------------+
-[ Footnotes ]-
[1] While "abs(x%y) < abs(y)" is true mathematically, for floats it
may not be true numerically due to roundoff. For example, and
assuming a platform on which a Python float is an IEEE 754 double-
precision number, in order that "-1e-100 % 1e100" have the same
sign as "1e100", the computed result is "-1e-100 + 1e100", which
is numerically exactly equal to "1e100". The function
"math.fmod()" returns a result whose sign matches the sign of the
first argument instead, and so returns "-1e-100" in this case.
Which approach is more appropriate depends on the application.
[2] If x is very close to an exact integer multiple of y, it’s
possible for "x//y" to be one larger than "(x-x%y)//y" due to
rounding. In such cases, Python returns the latter result, in
order to preserve that "divmod(x,y)[0] * y + x % y" be very close
to "x".
[3] The Unicode standard distinguishes between *code points* (e.g.
U+0041) and *abstract characters* (e.g. “LATIN CAPITAL LETTER A”).
While most abstract characters in Unicode are only represented
using one code point, there is a number of abstract characters
that can in addition be represented using a sequence of more than
one code point. For example, the abstract character “LATIN
CAPITAL LETTER C WITH CEDILLA” can be represented as a single
*precomposed character* at code position U+00C7, or as a sequence
of a *base character* at code position U+0043 (LATIN CAPITAL
LETTER C), followed by a *combining character* at code position
U+0327 (COMBINING CEDILLA).
The comparison operators on strings compare at the level of
Unicode code points. This may be counter-intuitive to humans. For
example, ""\u00C7" == "\u0043\u0327"" is "False", even though both
strings represent the same abstract character “LATIN CAPITAL
LETTER C WITH CEDILLA”.
To compare strings at the level of abstract characters (that is,
in a way intuitive to humans), use "unicodedata.normalize()".
[4] Due to automatic garbage-collection, free lists, and the dynamic
nature of descriptors, you may notice seemingly unusual behaviour
in certain uses of the "is" operator, like those involving
comparisons between instance methods, or constants. Check their
documentation for more info.
[5] The "%" operator is also used for string formatting; the same
precedence applies.
[6] The power operator "**" binds less tightly than an arithmetic or
bitwise unary operator on its right, that is, "2**-1" is "0.5".
uw The "pass" statement
********************
pass_stmt ::= "pass"
"pass" is a null operation — when it is executed, nothing happens. It
is useful as a placeholder when a statement is required syntactically,
but no code needs to be executed, for example:
def f(arg): pass # a function that does nothing (yet)
class C: pass # a class with no methods (yet)
a� The power operator
******************
The power operator binds more tightly than unary operators on its
left; it binds less tightly than unary operators on its right. The
syntax is:
power ::= (await_expr | primary) ["**" u_expr]
Thus, in an unparenthesized sequence of power and unary operators, the
operators are evaluated from right to left (this does not constrain
the evaluation order for the operands): "-1**2" results in "-1".
The power operator has the same semantics as the built-in "pow()"
function, when called with two arguments: it yields its left argument
raised to the power of its right argument. The numeric arguments are
first converted to a common type, and the result is of that type.
For int operands, the result has the same type as the operands unless
the second argument is negative; in that case, all arguments are
converted to float and a float result is delivered. For example,
"10**2" returns "100", but "10**-2" returns "0.01".
Raising "0.0" to a negative power results in a "ZeroDivisionError".
Raising a negative number to a fractional power results in a "complex"
number. (In earlier versions it raised a "ValueError".)
uJ
The "raise" statement
*********************
raise_stmt ::= "raise" [expression ["from" expression]]
If no expressions are present, "raise" re-raises the last exception
that was active in the current scope. If no exception is active in
the current scope, a "RuntimeError" exception is raised indicating
that this is an error.
Otherwise, "raise" evaluates the first expression as the exception
object. It must be either a subclass or an instance of
"BaseException". If it is a class, the exception instance will be
obtained when needed by instantiating the class with no arguments.
The *type* of the exception is the exception instance’s class, the
*value* is the instance itself.
A traceback object is normally created automatically when an exception
is raised and attached to it as the "__traceback__" attribute, which
is writable. You can create an exception and set your own traceback in
one step using the "with_traceback()" exception method (which returns
the same exception instance, with its traceback set to its argument),
like so:
raise Exception("foo occurred").with_traceback(tracebackobj)
The "from" clause is used for exception chaining: if given, the second
*expression* must be another exception class or instance. If the
second expression is an exception instance, it will be attached to the
raised exception as the "__cause__" attribute (which is writable). If
the expression is an exception class, the class will be instantiated
and the resulting exception instance will be attached to the raised
exception as the "__cause__" attribute. If the raised exception is not
handled, both exceptions will be printed:
>>> try:
... print(1 / 0)
... except Exception as exc:
... raise RuntimeError("Something bad happened") from exc
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
A similar mechanism works implicitly if an exception is raised inside
an exception handler or a "finally" clause: the previous exception is
then attached as the new exception’s "__context__" attribute:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened")
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
Exception chaining can be explicitly suppressed by specifying "None"
in the "from" clause:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened") from None
...
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
Additional information on exceptions can be found in section
Exceptions, and information about handling exceptions is in section
The try statement.
Changed in version 3.3: "None" is now permitted as "Y" in "raise X
from Y".
New in version 3.3: The "__suppress_context__" attribute to suppress
automatic display of the exception context.
a The "return" statement
**********************
return_stmt ::= "return" [expression_list]
"return" may only occur syntactically nested in a function definition,
not within a nested class definition.
If an expression list is present, it is evaluated, else "None" is
substituted.
"return" leaves the current function call with the expression list (or
"None") as return value.
When "return" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
function.
In a generator function, the "return" statement indicates that the
generator is done and will cause "StopIteration" to be raised. The
returned value (if any) is used as an argument to construct
"StopIteration" and becomes the "StopIteration.value" attribute.
In an asynchronous generator function, an empty "return" statement
indicates that the asynchronous generator is done and will cause
"StopAsyncIteration" to be raised. A non-empty "return" statement is
a syntax error in an asynchronous generator function.
u� Emulating container types
*************************
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python’s standard dictionary objects.
The "collections.abc" module provides a "MutableMapping" abstract base
class to help create those methods from a base set of "__getitem__()",
"__setitem__()", "__delitem__()", and "keys()". Mutable sequences
should provide methods "append()", "count()", "index()", "extend()",
"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python
standard list objects. Finally, sequence types should implement
addition (meaning concatenation) and multiplication (meaning
repetition) by defining the methods "__add__()", "__radd__()",
"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described
below; they should not define other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should search the mapping’s keys; for sequences, it
should search through the values. It is further recommended that both
mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should iterate through the object’s keys; for sequences, it should
iterate through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn’t define a "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.
object.__length_hint__(self)
Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. The
return value may also be "NotImplemented", which is treated the
same as if the "__length_hint__" method didn’t exist at all. This
method is purely an optimization and is never required for
correctness.
New in version 3.4.
Note:
Slicing is done exclusively with the following three methods. A
call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note:
"for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container.
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a container. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be iterable.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don’t define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
a� Shifting operations
*******************
The shifting operations have lower priority than the arithmetic
operations:
shift_expr ::= a_expr | shift_expr ("<<" | ">>") a_expr
These operators accept integers as arguments. They shift the first
argument to the left or right by the number of bits given by the
second argument.
A right shift by *n* bits is defined as floor division by "pow(2,n)".
A left shift by *n* bits is defined as multiplication with "pow(2,n)".
a� Slicings
********
A slicing selects a range of items in a sequence object (e.g., a
string, tuple or list). Slicings may be used as expressions or as
targets in assignment or "del" statements. The syntax for a slicing:
slicing ::= primary "[" slice_list "]"
slice_list ::= slice_item ("," slice_item)* [","]
slice_item ::= expression | proper_slice
proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" [stride] ]
lower_bound ::= expression
upper_bound ::= expression
stride ::= expression
There is ambiguity in the formal syntax here: anything that looks like
an expression list also looks like a slice list, so any subscription
can be interpreted as a slicing. Rather than further complicating the
syntax, this is disambiguated by defining that in this case the
interpretation as a subscription takes priority over the
interpretation as a slicing (this is the case if the slice list
contains no proper slice).
The semantics for a slicing are as follows. The primary is indexed
(using the same "__getitem__()" method as normal subscription) with a
key that is constructed from the slice list, as follows. If the slice
list contains at least one comma, the key is a tuple containing the
conversion of the slice items; otherwise, the conversion of the lone
slice item is the key. The conversion of a slice item that is an
expression is that expression. The conversion of a proper slice is a
slice object (see section The standard type hierarchy) whose "start",
"stop" and "step" attributes are the values of the expressions given
as lower bound, upper bound and stride, respectively, substituting
"None" for missing expressions.
u Special Attributes
******************
The implementation adds a few special read-only attributes to several
object types, where they are relevant. Some of these are not reported
by the "dir()" built-in function.
object.__dict__
A dictionary or other mapping object used to store an object’s
(writable) attributes.
instance.__class__
The class to which a class instance belongs.
class.__bases__
The tuple of base classes of a class object.
definition.__name__
The name of the class, function, method, descriptor, or generator
instance.
definition.__qualname__
The *qualified name* of the class, function, method, descriptor, or
generator instance.
New in version 3.3.
class.__mro__
This attribute is a tuple of classes that are considered when
looking for base classes during method resolution.
class.mro()
This method can be overridden by a metaclass to customize the
method resolution order for its instances. It is called at class
instantiation, and its result is stored in "__mro__".
class.__subclasses__()
Each class keeps a list of weak references to its immediate
subclasses. This method returns a list of all those references
still alive. Example:
>>> int.__subclasses__()
[<class 'bool'>]
u�� Special method names
********************
A class can implement certain operations that are invoked by special
syntax (such as arithmetic operations or subscripting and slicing) by
defining methods with special names. This is Python’s approach to
*operator overloading*, allowing classes to define their own behavior
with respect to language operators. For instance, if a class defines
a method named "__getitem__()", and "x" is an instance of this class,
then "x[i]" is roughly equivalent to "type(x).__getitem__(x, i)".
Except where mentioned, attempts to execute an operation raise an
exception when no appropriate method is defined (typically
"AttributeError" or "TypeError").
Setting a special method to "None" indicates that the corresponding
operation is not available. For example, if a class sets "__iter__()"
to "None", the class is not iterable, so calling "iter()" on its
instances will raise a "TypeError" (without falling back to
"__getitem__()"). [2]
When implementing a class that emulates any built-in type, it is
important that the emulation only be implemented to the degree that it
makes sense for the object being modelled. For example, some
sequences may work well with retrieval of individual elements, but
extracting a slice may not make sense. (One example of this is the
"NodeList" interface in the W3C’s Document Object Model.)
Basic customization
===================
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass’s "__new__()" method using
"super().__new__(cls[, ...])" with appropriate arguments and then
modifying the newly-created instance as necessary before returning
it.
If "__new__()" is invoked during object construction and it returns
an instance of *cls*, then the new instance’s "__init__()" method
will be invoked like "__init__(self[, ...])", where *self* is the
new instance and the remaining arguments are the same as were
passed to the object constructor.
If "__new__()" does not return an instance of *cls*, then the new
instance’s "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class’s "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"super().__init__([args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.
It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.
It is not guaranteed that "__del__()" methods are called for
objects that still exist when the interpreter exits.
Note:
"del x" doesn’t directly call "x.__del__()" — the former
decrements the reference count for "x" by one, and the latter is
only called when "x"’s reference count reaches zero.
**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.
See also: Documentation for the "gc" module.
Warning:
Due to the precarious circumstances under which "__del__()"
methods are invoked, exceptions that occur during their execution
are ignored, and a warning is printed to "sys.stderr" instead.
In particular:
* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".
* "__del__()" can be executed during interpreter shutdown. As a
consequence, the global variables it needs to access (including
other modules) may already have been deleted or set to "None".
Python guarantees that globals whose name begins with a single
underscore are deleted from their module before other globals
are deleted; if no other references to such globals exist, this
may help in assuring that imported modules are still available
at the time when the "__del__()" method is called.
object.__repr__(self)
Called by the "repr()" built-in function to compute the “official”
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an “informal”
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the “informal” or nicely printable string
representation of an object. The return value must be a string
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by bytes to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function, and by extension,
evaluation of formatted string literals and the "str.format()"
method, to produce a “formatted” string representation of an
object. The *format_spec* argument is a string that contains a
description of the formatting options desired. The interpretation
of the *format_spec* argument is up to the type implementing
"__format__()", however most classes will either delegate
formatting to one of the built-in types, or use a similar
formatting option syntax.
See Format Specification Mini-Language for a description of the
standard formatting syntax.
The return value must be a string object.
Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.
Changed in version 3.7: "object.__format__(x, '')" is now
equivalent to "str(x)" rather than "format(str(self), '')".
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
By default, "object" implements "__eq__()" by using "is", returning
"NotImplemented" in the case of a false comparison: "True if x is y
else NotImplemented". For "__ne__()", by default it delegates to
"__eq__()" and inverts the result unless it is "NotImplemented".
There are no other implied relationships among the comparison
operators or default implementations; for example, the truth of
"(x<y or x==y)" does not imply "x<=y". To automatically generate
ordering operations from a single root operation, see
"functools.total_ordering()".
See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
Note:
"hash()" truncates the value returned from an object’s custom
"__hash__()" method to the size of a "Py_ssize_t". This is
typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds.
If an object’s "__hash__()" must interoperate on builds of
different bit sizes, be sure to check the width on all supported
builds. An easy way to do this is with "python -c "import sys;
print(sys.hash_info.width)"".
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key’s hash value is immutable (if the object’s hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj,
collections.abc.Hashable)".
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
<ParentClass>.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.abc.Hashable)" call.
Note:
By default, the "__hash__()" values of str and bytes objects are
“salted” with an unpredictable random value. Although they
remain constant within an individual Python process, they are not
predictable between repeated invocations of Python.This is
intended to provide protection against a denial-of-service caused
by carefully-chosen inputs that exploit the worst case
performance of a dict insertion, O(n^2) complexity. See
http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of sets.
Python has never made guarantees about this ordering (and it
typically varies between 32-bit and 64-bit builds).See also
"PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
Customizing attribute access
============================
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
return the (computed) attribute value or raise an "AttributeError"
exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note:
This method may still be bypassed when looking up special methods
as the result of implicit invocation via language syntax or
built-in functions. See Special method lookup.
For certain sensitive attribute accesses, raises an auditing event
"object.__getattr__" with arguments "obj" and "name".
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should call the base class method with the same name, for example,
"object.__setattr__(self, name, value)".
For certain sensitive attribute assignments, raises an auditing
event "object.__setattr__" with arguments "obj", "name", "value".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
For certain sensitive attribute deletions, raises an auditing event
"object.__delattr__" with arguments "obj" and "name".
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
Customizing module attribute access
-----------------------------------
Special names "__getattr__" and "__dir__" can be also used to
customize access to module attributes. The "__getattr__" function at
the module level should accept one argument which is the name of an
attribute and return the computed value or raise an "AttributeError".
If an attribute is not found on a module object through the normal
lookup, i.e. "object.__getattribute__()", then "__getattr__" is
searched in the module "__dict__" before raising an "AttributeError".
If found, it is called with the attribute name and the result is
returned.
The "__dir__" function should accept no arguments, and return a
sequence of strings that represents the names accessible on module. If
present, this function overrides the standard "dir()" search on a
module.
For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
super().__setattr__(attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note:
Defining module "__getattr__" and setting module "__class__" only
affect lookups made using the attribute access syntax – directly
accessing the module globals (whether by code within the module, or
via a reference to the module’s globals dictionary) is unaffected.
Changed in version 3.5: "__class__" module attribute is now writable.
New in version 3.7: "__getattr__" and "__dir__" module attributes.
See also:
**PEP 562** - Module __getattr__ and __dir__
Describes the "__getattr__" and "__dir__" functions on modules.
Implementing Descriptors
------------------------
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents). In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".
object.__get__(self, instance, owner=None)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). The optional *owner* argument is the owner class, while
*instance* is the instance that the attribute was accessed through,
or "None" when the attribute is accessed through the *owner*.
This method should return the computed attribute value or raise an
"AttributeError" exception.
**PEP 252** specifies that "__get__()" is callable with one or two
arguments. Python’s own built-in descriptors support this
specification; however, it is likely that some third-party tools
have descriptors that require both arguments. Python’s own
"__getattribute__()" implementation always passes in both arguments
whether they are required or not.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
Note, adding "__set__()" or "__delete__()" changes the kind of
descriptor to a “data descriptor”. See Invoking Descriptors for
more details.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
object.__set_name__(self, owner, name)
Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.
Note:
"__set_name__()" is only called implicitly as part of the "type"
constructor, so it will need to be called explicitly with the
appropriate parameters when a descriptor is added to a class
after initial creation:
class A:
pass
descr = custom_descriptor()
A.attr = descr
descr.__set_name__(A, 'attr')
See Creating the class object for more details.
New in version 3.6.
The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).
Invoking Descriptors
--------------------
In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a class, "A.x" is transformed into the call:
"A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on which descriptor methods are defined. A descriptor can define any
combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
---------
*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of *__dict__* and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)
The space saved over using *__dict__* can be significant. Attribute
lookup speed can be significantly improved as well.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. *__slots__*
reserves space for the declared variables and prevents the
automatic creation of *__dict__* and *__weakref__* for each
instance.
Notes on using *__slots__*
~~~~~~~~~~~~~~~~~~~~~~~~~~
* When inheriting from a class without *__slots__*, the *__dict__* and
*__weakref__* attribute of the instances will always be accessible.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
* Without a *__weakref__* variable for each instance, classes defining
*__slots__* do not support weak references to its instances. If weak
reference support is needed, then add "'__weakref__'" to the
sequence of strings in the *__slots__* declaration.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is not limited to the class
where it is defined. *__slots__* declared in parents are available
in child classes. However, child subclasses will get a *__dict__*
and *__weakref__* unless they also define *__slots__* (which should
only contain names of any *additional* slots).
* If a class defines a slot also defined in a base class, the instance
variable defined by the base class slot is inaccessible (except by
retrieving its descriptor directly from the base class). This
renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
“variable-length” built-in types such as "int", "bytes" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings may
also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".
* If an iterator is used for *__slots__* then a descriptor is created
for each of the iterator’s values. However, the *__slots__*
attribute will be an empty iterator.
Customizing class creation
==========================
Whenever a class inherits from another class, *__init_subclass__* is
called on that class. This way, it is possible to write classes which
change the behavior of subclasses. This is closely related to class
decorators, but where class decorators only affect the specific class
they’re applied to, "__init_subclass__" solely applies to future
subclasses of the class defining the method.
classmethod object.__init_subclass__(cls)
This method is called whenever the containing class is subclassed.
*cls* is then the new subclass. If defined as a normal instance
method, this method is implicitly converted to a class method.
Keyword arguments which are given to a new class are passed to the
parent’s class "__init_subclass__". For compatibility with other
classes using "__init_subclass__", one should take out the needed
keyword arguments and pass the others over to the base class, as
in:
class Philosopher:
def __init_subclass__(cls, /, default_name, **kwargs):
super().__init_subclass__(**kwargs)
cls.default_name = default_name
class AustralianPhilosopher(Philosopher, default_name="Bruce"):
pass
The default implementation "object.__init_subclass__" does nothing,
but raises an error if it is called with any arguments.
Note:
The metaclass hint "metaclass" is consumed by the rest of the
type machinery, and is never passed to "__init_subclass__"
implementations. The actual metaclass (rather than the explicit
hint) can be accessed as "type(cls)".
New in version 3.6.
Metaclasses
-----------
By default, classes are constructed using "type()". The class body is
executed in a new namespace and the class name is bound locally to the
result of "type(name, bases, namespace)".
The class creation process can be customized by passing the
"metaclass" keyword argument in the class definition line, or by
inheriting from an existing class that included such an argument. In
the following example, both "MyClass" and "MySubclass" are instances
of "Meta":
class Meta(type):
pass
class MyClass(metaclass=Meta):
pass
class MySubclass(MyClass):
pass
Any other keyword arguments that are specified in the class definition
are passed through to all metaclass operations described below.
When a class definition is executed, the following steps occur:
* MRO entries are resolved;
* the appropriate metaclass is determined;
* the class namespace is prepared;
* the class body is executed;
* the class object is created.
Resolving MRO entries
---------------------
If a base that appears in class definition is not an instance of
"type", then an "__mro_entries__" method is searched on it. If found,
it is called with the original bases tuple. This method must return a
tuple of classes that will be used instead of this base. The tuple may
be empty, in such case the original base is ignored.
See also:
**PEP 560** - Core support for typing module and generic types
Determining the appropriate metaclass
-------------------------------------
The appropriate metaclass for a class definition is determined as
follows:
* if no bases and no explicit metaclass are given, then "type()" is
used;
* if an explicit metaclass is given and it is *not* an instance of
"type()", then it is used directly as the metaclass;
* if an instance of "type()" is given as the explicit metaclass, or
bases are defined, then the most derived metaclass is used.
The most derived metaclass is selected from the explicitly specified
metaclass (if any) and the metaclasses (i.e. "type(cls)") of all
specified base classes. The most derived metaclass is one which is a
subtype of *all* of these candidate metaclasses. If none of the
candidate metaclasses meets that criterion, then the class definition
will fail with "TypeError".
Preparing the class namespace
-----------------------------
Once the appropriate metaclass has been identified, then the class
namespace is prepared. If the metaclass has a "__prepare__" attribute,
it is called as "namespace = metaclass.__prepare__(name, bases,
**kwds)" (where the additional keyword arguments, if any, come from
the class definition). The "__prepare__" method should be implemented
as a "classmethod()". The namespace returned by "__prepare__" is
passed in to "__new__", but when the final class object is created the
namespace is copied into a new "dict".
If the metaclass has no "__prepare__" attribute, then the class
namespace is initialised as an empty ordered mapping.
See also:
**PEP 3115** - Metaclasses in Python 3000
Introduced the "__prepare__" namespace hook
Executing the class body
------------------------
The class body is executed (approximately) as "exec(body, globals(),
namespace)". The key difference from a normal call to "exec()" is that
lexical scoping allows the class body (including any methods) to
reference names from the current and outer scopes when the class
definition occurs inside a function.
However, even when the class definition occurs inside the function,
methods defined inside the class still cannot see names defined at the
class scope. Class variables must be accessed through the first
parameter of instance or class methods, or through the implicit
lexically scoped "__class__" reference described in the next section.
Creating the class object
-------------------------
Once the class namespace has been populated by executing the class
body, the class object is created by calling "metaclass(name, bases,
namespace, **kwds)" (the additional keywords passed here are the same
as those passed to "__prepare__").
This class object is the one that will be referenced by the zero-
argument form of "super()". "__class__" is an implicit closure
reference created by the compiler if any methods in a class body refer
to either "__class__" or "super". This allows the zero argument form
of "super()" to correctly identify the class being defined based on
lexical scoping, while the class or instance that was used to make the
current call is identified based on the first argument passed to the
method.
**CPython implementation detail:** In CPython 3.6 and later, the
"__class__" cell is passed to the metaclass as a "__classcell__" entry
in the class namespace. If present, this must be propagated up to the
"type.__new__" call in order for the class to be initialised
correctly. Failing to do so will result in a "RuntimeError" in Python
3.8.
When using the default metaclass "type", or any metaclass that
ultimately calls "type.__new__", the following additional
customisation steps are invoked after creating the class object:
* first, "type.__new__" collects all of the descriptors in the class
namespace that define a "__set_name__()" method;
* second, all of these "__set_name__" methods are called with the
class being defined and the assigned name of that particular
descriptor;
* finally, the "__init_subclass__()" hook is called on the immediate
parent of the new class in its method resolution order.
After the class object is created, it is passed to the class
decorators included in the class definition (if any) and the resulting
object is bound in the local namespace as the defined class.
When a new class is created by "type.__new__", the object provided as
the namespace parameter is copied to a new ordered mapping and the
original object is discarded. The new copy is wrapped in a read-only
proxy, which becomes the "__dict__" attribute of the class object.
See also:
**PEP 3135** - New super
Describes the implicit "__class__" closure reference
Uses for metaclasses
--------------------
The potential uses for metaclasses are boundless. Some ideas that have
been explored include enum, logging, interface checking, automatic
delegation, automatic property creation, proxies, frameworks, and
automatic resource locking/synchronization.
Customizing instance and subclass checks
========================================
The following methods are used to override the default behavior of the
"isinstance()" and "issubclass()" built-in functions.
In particular, the metaclass "abc.ABCMeta" implements these methods in
order to allow the addition of Abstract Base Classes (ABCs) as
“virtual base classes” to any class or type (including built-in
types), including other ABCs.
class.__instancecheck__(self, instance)
Return true if *instance* should be considered a (direct or
indirect) instance of *class*. If defined, called to implement
"isinstance(instance, class)".
class.__subclasscheck__(self, subclass)
Return true if *subclass* should be considered a (direct or
indirect) subclass of *class*. If defined, called to implement
"issubclass(subclass, class)".
Note that these methods are looked up on the type (metaclass) of a
class. They cannot be defined as class methods in the actual class.
This is consistent with the lookup of special methods that are called
on instances, only in this case the instance is itself a class.
See also:
**PEP 3119** - Introducing Abstract Base Classes
Includes the specification for customizing "isinstance()" and
"issubclass()" behavior through "__instancecheck__()" and
"__subclasscheck__()", with motivation for this functionality in
the context of adding Abstract Base Classes (see the "abc"
module) to the language.
Emulating generic types
=======================
One can implement the generic class syntax as specified by **PEP 484**
(for example "List[int]") by defining a special method:
classmethod object.__class_getitem__(cls, key)
Return an object representing the specialization of a generic class
by type arguments found in *key*.
This method is looked up on the class object itself, and when defined
in the class body, this method is implicitly a class method. Note,
this mechanism is primarily reserved for use with static type hints,
other usage is discouraged.
See also:
**PEP 560** - Core support for typing module and generic types
Emulating callable objects
==========================
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method
is defined, "x(arg1, arg2, ...)" roughly translates to
"type(x).__call__(x, arg1, ...)".
Emulating container types
=========================
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python’s standard dictionary objects.
The "collections.abc" module provides a "MutableMapping" abstract base
class to help create those methods from a base set of "__getitem__()",
"__setitem__()", "__delitem__()", and "keys()". Mutable sequences
should provide methods "append()", "count()", "index()", "extend()",
"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python
standard list objects. Finally, sequence types should implement
addition (meaning concatenation) and multiplication (meaning
repetition) by defining the methods "__add__()", "__radd__()",
"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described
below; they should not define other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should search the mapping’s keys; for sequences, it
should search through the values. It is further recommended that both
mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should iterate through the object’s keys; for sequences, it should
iterate through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn’t define a "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.
object.__length_hint__(self)
Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. The
return value may also be "NotImplemented", which is treated the
same as if the "__length_hint__" method didn’t exist at all. This
method is purely an optimization and is never required for
correctness.
New in version 3.4.
Note:
Slicing is done exclusively with the following three methods. A
call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note:
"for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container.
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a container. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be iterable.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don’t define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
Emulating numeric types
=======================
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the expression "x + y", where *x* is an instance of a
class that has an "__add__()" method, "x.__add__(y)" is called.
The "__divmod__()" method should be the equivalent to using
"__floordiv__()" and "__mod__()"; it should not be related to
"__truediv__()". Note that "__pow__()" should be defined to accept
an optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other[, modulo])
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)
operands. These functions are only called if the left operand does
not support the corresponding operation [3] and the operands are of
different types. [4] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note:
If the right operand’s type is a subclass of the left operand’s
type and that subclass provides a different implementation of the
reflected method for the operation, this method will be called
before the left operand’s non-reflected method. This behavior
allows subclasses to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (modifying *self*) and return the result
(which could be, but does not have to be, *self*). If a specific
method is not defined, the augmented assignment falls back to the
normal methods. For instance, if *x* is an instance of a class
with an "__iadd__()" method, "x += y" is equivalent to "x =
x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are
considered, as with the evaluation of "x + y". In certain
situations, augmented assignment can result in unexpected errors
(see Why does a_tuple[i] += [‘item’] raise an exception when the
addition works?), but this behavior is in fact part of the data
model.
Note:
Due to a bug in the dispatching mechanism for "**=", a class that
defines "__ipow__()" but returns "NotImplemented" would fail to
fall back to "x.__pow__(y)" and "y.__rpow__(x)". This bug is
fixed in Python 3.10.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()" and
"float()". Should return a value of the appropriate type.
object.__index__(self)
Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.
If "__int__()", "__float__()" and "__complex__()" are not defined
then corresponding built-in functions "int()", "float()" and
"complex()" fall back to "__index__()".
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").
The built-in function "int()" falls back to "__trunc__()" if
neither "__int__()" nor "__index__()" is defined.
With Statement Context Managers
===============================
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method’s return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller’s responsibility.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
Special method lookup
=====================
For custom classes, implicit invocations of special methods are only
guaranteed to work correctly if defined on an object’s type, not in
the object’s instance dictionary. That behaviour is the reason why
the following code raises an exception:
>>> class C:
... pass
...
>>> c = C()
>>> c.__len__ = lambda: 5
>>> len(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: object of type 'C' has no len()
The rationale behind this behaviour lies with a number of special
methods such as "__hash__()" and "__repr__()" that are implemented by
all objects, including type objects. If the implicit lookup of these
methods used the conventional lookup process, they would fail when
invoked on the type object itself:
>>> 1 .__hash__() == hash(1)
True
>>> int.__hash__() == hash(int)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: descriptor '__hash__' of 'int' object needs an argument
Incorrectly attempting to invoke an unbound method of a class in this
way is sometimes referred to as ‘metaclass confusion’, and is avoided
by bypassing the instance when looking up special methods:
>>> type(1).__hash__(1) == hash(1)
True
>>> type(int).__hash__(int) == hash(int)
True
In addition to bypassing any instance attributes in the interest of
correctness, implicit special method lookup generally also bypasses
the "__getattribute__()" method even of the object’s metaclass:
>>> class Meta(type):
... def __getattribute__(*args):
... print("Metaclass getattribute invoked")
... return type.__getattribute__(*args)
...
>>> class C(object, metaclass=Meta):
... def __len__(self):
... return 10
... def __getattribute__(*args):
... print("Class getattribute invoked")
... return object.__getattribute__(*args)
...
>>> c = C()
>>> c.__len__() # Explicit lookup via instance
Class getattribute invoked
10
>>> type(c).__len__(c) # Explicit lookup via type
Metaclass getattribute invoked
10
>>> len(c) # Implicit lookup
10
Bypassing the "__getattribute__()" machinery in this fashion provides
significant scope for speed optimisations within the interpreter, at
the cost of some flexibility in the handling of special methods (the
special method *must* be set on the class object itself in order to be
consistently invoked by the interpreter).
u�Y String Methods
**************
Strings implement all of the common sequence operations, along with
the additional methods described below.
Strings also support two styles of string formatting, one providing a
large degree of flexibility and customization (see "str.format()",
Format String Syntax and Custom String Formatting) and the other based
on C "printf" style formatting that handles a narrower range of types
and is slightly harder to use correctly, but is often faster for the
cases it can handle (printf-style String Formatting).
The Text Processing Services section of the standard library covers a
number of other modules that provide various text related utilities
(including regular expression support in the "re" module).
str.capitalize()
Return a copy of the string with its first character capitalized
and the rest lowercased.
Changed in version 3.8: The first character is now put into
titlecase rather than uppercase. This means that characters like
digraphs will only have their first letter capitalized, instead of
the full character.
str.casefold()
Return a casefolded copy of the string. Casefolded strings may be
used for caseless matching.
Casefolding is similar to lowercasing but more aggressive because
it is intended to remove all case distinctions in a string. For
example, the German lowercase letter "'ß'" is equivalent to ""ss"".
Since it is already lowercase, "lower()" would do nothing to "'ß'";
"casefold()" converts it to ""ss"".
The casefolding algorithm is described in section 3.13 of the
Unicode Standard.
New in version 3.3.
str.center(width[, fillchar])
Return centered in a string of length *width*. Padding is done
using the specified *fillchar* (default is an ASCII space). The
original string is returned if *width* is less than or equal to
"len(s)".
str.count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring *sub*
in the range [*start*, *end*]. Optional arguments *start* and
*end* are interpreted as in slice notation.
str.encode(encoding="utf-8", errors="strict")
Return an encoded version of the string as a bytes object. Default
encoding is "'utf-8'". *errors* may be given to set a different
error handling scheme. The default for *errors* is "'strict'",
meaning that encoding errors raise a "UnicodeError". Other possible
values are "'ignore'", "'replace'", "'xmlcharrefreplace'",
"'backslashreplace'" and any other name registered via
"codecs.register_error()", see section Error Handlers. For a list
of possible encodings, see section Standard Encodings.
Changed in version 3.1: Support for keyword arguments added.
str.endswith(suffix[, start[, end]])
Return "True" if the string ends with the specified *suffix*,
otherwise return "False". *suffix* can also be a tuple of suffixes
to look for. With optional *start*, test beginning at that
position. With optional *end*, stop comparing at that position.
str.expandtabs(tabsize=8)
Return a copy of the string where all tab characters are replaced
by one or more spaces, depending on the current column and the
given tab size. Tab positions occur every *tabsize* characters
(default is 8, giving tab positions at columns 0, 8, 16 and so on).
To expand the string, the current column is set to zero and the
string is examined character by character. If the character is a
tab ("\t"), one or more space characters are inserted in the result
until the current column is equal to the next tab position. (The
tab character itself is not copied.) If the character is a newline
("\n") or return ("\r"), it is copied and the current column is
reset to zero. Any other character is copied unchanged and the
current column is incremented by one regardless of how the
character is represented when printed.
>>> '01\t012\t0123\t01234'.expandtabs()
'01 012 0123 01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01 012 0123 01234'
str.find(sub[, start[, end]])
Return the lowest index in the string where substring *sub* is
found within the slice "s[start:end]". Optional arguments *start*
and *end* are interpreted as in slice notation. Return "-1" if
*sub* is not found.
Note:
The "find()" method should be used only if you need to know the
position of *sub*. To check if *sub* is a substring or not, use
the "in" operator:
>>> 'Py' in 'Python'
True
str.format(*args, **kwargs)
Perform a string formatting operation. The string on which this
method is called can contain literal text or replacement fields
delimited by braces "{}". Each replacement field contains either
the numeric index of a positional argument, or the name of a
keyword argument. Returns a copy of the string where each
replacement field is replaced with the string value of the
corresponding argument.
>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'
See Format String Syntax for a description of the various
formatting options that can be specified in format strings.
Note:
When formatting a number ("int", "float", "complex",
"decimal.Decimal" and subclasses) with the "n" type (ex:
"'{:n}'.format(1234)"), the function temporarily sets the
"LC_CTYPE" locale to the "LC_NUMERIC" locale to decode
"decimal_point" and "thousands_sep" fields of "localeconv()" if
they are non-ASCII or longer than 1 byte, and the "LC_NUMERIC"
locale is different than the "LC_CTYPE" locale. This temporary
change affects other threads.
Changed in version 3.7: When formatting a number with the "n" type,
the function sets temporarily the "LC_CTYPE" locale to the
"LC_NUMERIC" locale in some cases.
str.format_map(mapping)
Similar to "str.format(**mapping)", except that "mapping" is used
directly and not copied to a "dict". This is useful if for example
"mapping" is a dict subclass:
>>> class Default(dict):
... def __missing__(self, key):
... return key
...
>>> '{name} was born in {country}'.format_map(Default(name='Guido'))
'Guido was born in country'
New in version 3.2.
str.index(sub[, start[, end]])
Like "find()", but raise "ValueError" when the substring is not
found.
str.isalnum()
Return "True" if all characters in the string are alphanumeric and
there is at least one character, "False" otherwise. A character
"c" is alphanumeric if one of the following returns "True":
"c.isalpha()", "c.isdecimal()", "c.isdigit()", or "c.isnumeric()".
str.isalpha()
Return "True" if all characters in the string are alphabetic and
there is at least one character, "False" otherwise. Alphabetic
characters are those characters defined in the Unicode character
database as “Letter”, i.e., those with general category property
being one of “Lm”, “Lt”, “Lu”, “Ll”, or “Lo”. Note that this is
different from the “Alphabetic” property defined in the Unicode
Standard.
str.isascii()
Return "True" if the string is empty or all characters in the
string are ASCII, "False" otherwise. ASCII characters have code
points in the range U+0000-U+007F.
New in version 3.7.
str.isdecimal()
Return "True" if all characters in the string are decimal
characters and there is at least one character, "False" otherwise.
Decimal characters are those that can be used to form numbers in
base 10, e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Formally a decimal
character is a character in the Unicode General Category “Nd”.
str.isdigit()
Return "True" if all characters in the string are digits and there
is at least one character, "False" otherwise. Digits include
decimal characters and digits that need special handling, such as
the compatibility superscript digits. This covers digits which
cannot be used to form numbers in base 10, like the Kharosthi
numbers. Formally, a digit is a character that has the property
value Numeric_Type=Digit or Numeric_Type=Decimal.
str.isidentifier()
Return "True" if the string is a valid identifier according to the
language definition, section Identifiers and keywords.
Call "keyword.iskeyword()" to test whether string "s" is a reserved
identifier, such as "def" and "class".
Example:
>>> from keyword import iskeyword
>>> 'hello'.isidentifier(), iskeyword('hello')
True, False
>>> 'def'.isidentifier(), iskeyword('def')
True, True
str.islower()
Return "True" if all cased characters [4] in the string are
lowercase and there is at least one cased character, "False"
otherwise.
str.isnumeric()
Return "True" if all characters in the string are numeric
characters, and there is at least one character, "False" otherwise.
Numeric characters include digit characters, and all characters
that have the Unicode numeric value property, e.g. U+2155, VULGAR
FRACTION ONE FIFTH. Formally, numeric characters are those with
the property value Numeric_Type=Digit, Numeric_Type=Decimal or
Numeric_Type=Numeric.
str.isprintable()
Return "True" if all characters in the string are printable or the
string is empty, "False" otherwise. Nonprintable characters are
those characters defined in the Unicode character database as
“Other” or “Separator”, excepting the ASCII space (0x20) which is
considered printable. (Note that printable characters in this
context are those which should not be escaped when "repr()" is
invoked on a string. It has no bearing on the handling of strings
written to "sys.stdout" or "sys.stderr".)
str.isspace()
Return "True" if there are only whitespace characters in the string
and there is at least one character, "False" otherwise.
A character is *whitespace* if in the Unicode character database
(see "unicodedata"), either its general category is "Zs"
(“Separator, space”), or its bidirectional class is one of "WS",
"B", or "S".
str.istitle()
Return "True" if the string is a titlecased string and there is at
least one character, for example uppercase characters may only
follow uncased characters and lowercase characters only cased ones.
Return "False" otherwise.
str.isupper()
Return "True" if all cased characters [4] in the string are
uppercase and there is at least one cased character, "False"
otherwise.
str.join(iterable)
Return a string which is the concatenation of the strings in
*iterable*. A "TypeError" will be raised if there are any non-
string values in *iterable*, including "bytes" objects. The
separator between elements is the string providing this method.
str.ljust(width[, fillchar])
Return the string left justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is an ASCII
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.lower()
Return a copy of the string with all the cased characters [4]
converted to lowercase.
The lowercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.lstrip([chars])
Return a copy of the string with leading characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a prefix; rather,
all combinations of its values are stripped:
>>> ' spacious '.lstrip()
'spacious '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'
static str.maketrans(x[, y[, z]])
This static method returns a translation table usable for
"str.translate()".
If there is only one argument, it must be a dictionary mapping
Unicode ordinals (integers) or characters (strings of length 1) to
Unicode ordinals, strings (of arbitrary lengths) or "None".
Character keys will then be converted to ordinals.
If there are two arguments, they must be strings of equal length,
and in the resulting dictionary, each character in x will be mapped
to the character at the same position in y. If there is a third
argument, it must be a string, whose characters will be mapped to
"None" in the result.
str.partition(sep)
Split the string at the first occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing the string itself, followed by
two empty strings.
str.replace(old, new[, count])
Return a copy of the string with all occurrences of substring *old*
replaced by *new*. If the optional argument *count* is given, only
the first *count* occurrences are replaced.
str.rfind(sub[, start[, end]])
Return the highest index in the string where substring *sub* is
found, such that *sub* is contained within "s[start:end]".
Optional arguments *start* and *end* are interpreted as in slice
notation. Return "-1" on failure.
str.rindex(sub[, start[, end]])
Like "rfind()" but raises "ValueError" when the substring *sub* is
not found.
str.rjust(width[, fillchar])
Return the string right justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is an ASCII
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.rpartition(sep)
Split the string at the last occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing two empty strings, followed by
the string itself.
str.rsplit(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit* splits
are done, the *rightmost* ones. If *sep* is not specified or
"None", any whitespace string is a separator. Except for splitting
from the right, "rsplit()" behaves like "split()" which is
described in detail below.
str.rstrip([chars])
Return a copy of the string with trailing characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a suffix; rather,
all combinations of its values are stripped:
>>> ' spacious '.rstrip()
' spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'
str.split(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit*
splits are done (thus, the list will have at most "maxsplit+1"
elements). If *maxsplit* is not specified or "-1", then there is
no limit on the number of splits (all possible splits are made).
If *sep* is given, consecutive delimiters are not grouped together
and are deemed to delimit empty strings (for example,
"'1,,2'.split(',')" returns "['1', '', '2']"). The *sep* argument
may consist of multiple characters (for example,
"'1<>2<>3'.split('<>')" returns "['1', '2', '3']"). Splitting an
empty string with a specified separator returns "['']".
For example:
>>> '1,2,3'.split(',')
['1', '2', '3']
>>> '1,2,3'.split(',', maxsplit=1)
['1', '2,3']
>>> '1,2,,3,'.split(',')
['1', '2', '', '3', '']
If *sep* is not specified or is "None", a different splitting
algorithm is applied: runs of consecutive whitespace are regarded
as a single separator, and the result will contain no empty strings
at the start or end if the string has leading or trailing
whitespace. Consequently, splitting an empty string or a string
consisting of just whitespace with a "None" separator returns "[]".
For example:
>>> '1 2 3'.split()
['1', '2', '3']
>>> '1 2 3'.split(maxsplit=1)
['1', '2 3']
>>> ' 1 2 3 '.split()
['1', '2', '3']
str.splitlines([keepends])
Return a list of the lines in the string, breaking at line
boundaries. Line breaks are not included in the resulting list
unless *keepends* is given and true.
This method splits on the following line boundaries. In
particular, the boundaries are a superset of *universal newlines*.
+-------------------------+-------------------------------+
| Representation | Description |
|=========================|===============================|
| "\n" | Line Feed |
+-------------------------+-------------------------------+
| "\r" | Carriage Return |
+-------------------------+-------------------------------+
| "\r\n" | Carriage Return + Line Feed |
+-------------------------+-------------------------------+
| "\v" or "\x0b" | Line Tabulation |
+-------------------------+-------------------------------+
| "\f" or "\x0c" | Form Feed |
+-------------------------+-------------------------------+
| "\x1c" | File Separator |
+-------------------------+-------------------------------+
| "\x1d" | Group Separator |
+-------------------------+-------------------------------+
| "\x1e" | Record Separator |
+-------------------------+-------------------------------+
| "\x85" | Next Line (C1 Control Code) |
+-------------------------+-------------------------------+
| "\u2028" | Line Separator |
+-------------------------+-------------------------------+
| "\u2029" | Paragraph Separator |
+-------------------------+-------------------------------+
Changed in version 3.2: "\v" and "\f" added to list of line
boundaries.
For example:
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
['ab c', '', 'de fg', 'kl']
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True)
['ab c\n', '\n', 'de fg\r', 'kl\r\n']
Unlike "split()" when a delimiter string *sep* is given, this
method returns an empty list for the empty string, and a terminal
line break does not result in an extra line:
>>> "".splitlines()
[]
>>> "One line\n".splitlines()
['One line']
For comparison, "split('\n')" gives:
>>> ''.split('\n')
['']
>>> 'Two lines\n'.split('\n')
['Two lines', '']
str.startswith(prefix[, start[, end]])
Return "True" if string starts with the *prefix*, otherwise return
"False". *prefix* can also be a tuple of prefixes to look for.
With optional *start*, test string beginning at that position.
With optional *end*, stop comparing string at that position.
str.strip([chars])
Return a copy of the string with the leading and trailing
characters removed. The *chars* argument is a string specifying the
set of characters to be removed. If omitted or "None", the *chars*
argument defaults to removing whitespace. The *chars* argument is
not a prefix or suffix; rather, all combinations of its values are
stripped:
>>> ' spacious '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'
The outermost leading and trailing *chars* argument values are
stripped from the string. Characters are removed from the leading
end until reaching a string character that is not contained in the
set of characters in *chars*. A similar action takes place on the
trailing end. For example:
>>> comment_string = '#....... Section 3.2.1 Issue #32 .......'
>>> comment_string.strip('.#! ')
'Section 3.2.1 Issue #32'
str.swapcase()
Return a copy of the string with uppercase characters converted to
lowercase and vice versa. Note that it is not necessarily true that
"s.swapcase().swapcase() == s".
str.title()
Return a titlecased version of the string where words start with an
uppercase character and the remaining characters are lowercase.
For example:
>>> 'Hello world'.title()
'Hello World'
The algorithm uses a simple language-independent definition of a
word as groups of consecutive letters. The definition works in
many contexts but it means that apostrophes in contractions and
possessives form word boundaries, which may not be the desired
result:
>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"
A workaround for apostrophes can be constructed using regular
expressions:
>>> import re
>>> def titlecase(s):
... return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
... lambda mo: mo.group(0).capitalize(),
... s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."
str.translate(table)
Return a copy of the string in which each character has been mapped
through the given translation table. The table must be an object
that implements indexing via "__getitem__()", typically a *mapping*
or *sequence*. When indexed by a Unicode ordinal (an integer), the
table object can do any of the following: return a Unicode ordinal
or a string, to map the character to one or more other characters;
return "None", to delete the character from the return string; or
raise a "LookupError" exception, to map the character to itself.
You can use "str.maketrans()" to create a translation map from
character-to-character mappings in different formats.
See also the "codecs" module for a more flexible approach to custom
character mappings.
str.upper()
Return a copy of the string with all the cased characters [4]
converted to uppercase. Note that "s.upper().isupper()" might be
"False" if "s" contains uncased characters or if the Unicode
category of the resulting character(s) is not “Lu” (Letter,
uppercase), but e.g. “Lt” (Letter, titlecase).
The uppercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.zfill(width)
Return a copy of the string left filled with ASCII "'0'" digits to
make a string of length *width*. A leading sign prefix
("'+'"/"'-'") is handled by inserting the padding *after* the sign
character rather than before. The original string is returned if
*width* is less than or equal to "len(s)".
For example:
>>> "42".zfill(5)
'00042'
>>> "-42".zfill(5)
'-0042'
u� String and Bytes literals
*************************
String literals are described by the following lexical definitions:
stringliteral ::= [stringprefix](shortstring | longstring)
stringprefix ::= "r" | "u" | "R" | "U" | "f" | "F"
| "fr" | "Fr" | "fR" | "FR" | "rf" | "rF" | "Rf" | "RF"
shortstring ::= "'" shortstringitem* "'" | '"' shortstringitem* '"'
longstring ::= "'''" longstringitem* "'''" | '"""' longstringitem* '"""'
shortstringitem ::= shortstringchar | stringescapeseq
longstringitem ::= longstringchar | stringescapeseq
shortstringchar ::= <any source character except "\" or newline or the quote>
longstringchar ::= <any source character except "\">
stringescapeseq ::= "\" <any source character>
bytesliteral ::= bytesprefix(shortbytes | longbytes)
bytesprefix ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB"
shortbytes ::= "'" shortbytesitem* "'" | '"' shortbytesitem* '"'
longbytes ::= "'''" longbytesitem* "'''" | '"""' longbytesitem* '"""'
shortbytesitem ::= shortbyteschar | bytesescapeseq
longbytesitem ::= longbyteschar | bytesescapeseq
shortbyteschar ::= <any ASCII character except "\" or newline or the quote>
longbyteschar ::= <any ASCII character except "\">
bytesescapeseq ::= "\" <any ASCII character>
One syntactic restriction not indicated by these productions is that
whitespace is not allowed between the "stringprefix" or "bytesprefix"
and the rest of the literal. The source character set is defined by
the encoding declaration; it is UTF-8 if no encoding declaration is
given in the source file; see section Encoding declarations.
In plain English: Both types of literals can be enclosed in matching
single quotes ("'") or double quotes ("""). They can also be enclosed
in matching groups of three single or double quotes (these are
generally referred to as *triple-quoted strings*). The backslash
("\") character is used to escape characters that otherwise have a
special meaning, such as newline, backslash itself, or the quote
character.
Bytes literals are always prefixed with "'b'" or "'B'"; they produce
an instance of the "bytes" type instead of the "str" type. They may
only contain ASCII characters; bytes with a numeric value of 128 or
greater must be expressed with escapes.
Both string and bytes literals may optionally be prefixed with a
letter "'r'" or "'R'"; such strings are called *raw strings* and treat
backslashes as literal characters. As a result, in string literals,
"'\U'" and "'\u'" escapes in raw strings are not treated specially.
Given that Python 2.x’s raw unicode literals behave differently than
Python 3.x’s the "'ur'" syntax is not supported.
New in version 3.3: The "'rb'" prefix of raw bytes literals has been
added as a synonym of "'br'".
New in version 3.3: Support for the unicode legacy literal
("u'value'") was reintroduced to simplify the maintenance of dual
Python 2.x and 3.x codebases. See **PEP 414** for more information.
A string literal with "'f'" or "'F'" in its prefix is a *formatted
string literal*; see Formatted string literals. The "'f'" may be
combined with "'r'", but not with "'b'" or "'u'", therefore raw
formatted strings are possible, but formatted bytes literals are not.
In triple-quoted literals, unescaped newlines and quotes are allowed
(and are retained), except that three unescaped quotes in a row
terminate the literal. (A “quote” is the character used to open the
literal, i.e. either "'" or """.)
Unless an "'r'" or "'R'" prefix is present, escape sequences in string
and bytes literals are interpreted according to rules similar to those
used by Standard C. The recognized escape sequences are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
|===================|===================================|=========|
| "\newline" | Backslash and newline ignored | |
+-------------------+-----------------------------------+---------+
| "\\" | Backslash ("\") | |
+-------------------+-----------------------------------+---------+
| "\'" | Single quote ("'") | |
+-------------------+-----------------------------------+---------+
| "\"" | Double quote (""") | |
+-------------------+-----------------------------------+---------+
| "\a" | ASCII Bell (BEL) | |
+-------------------+-----------------------------------+---------+
| "\b" | ASCII Backspace (BS) | |
+-------------------+-----------------------------------+---------+
| "\f" | ASCII Formfeed (FF) | |
+-------------------+-----------------------------------+---------+
| "\n" | ASCII Linefeed (LF) | |
+-------------------+-----------------------------------+---------+
| "\r" | ASCII Carriage Return (CR) | |
+-------------------+-----------------------------------+---------+
| "\t" | ASCII Horizontal Tab (TAB) | |
+-------------------+-----------------------------------+---------+
| "\v" | ASCII Vertical Tab (VT) | |
+-------------------+-----------------------------------+---------+
| "\ooo" | Character with octal value *ooo* | (1,3) |
+-------------------+-----------------------------------+---------+
| "\xhh" | Character with hex value *hh* | (2,3) |
+-------------------+-----------------------------------+---------+
Escape sequences only recognized in string literals are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
|===================|===================================|=========|
| "\N{name}" | Character named *name* in the | (4) |
| | Unicode database | |
+-------------------+-----------------------------------+---------+
| "\uxxxx" | Character with 16-bit hex value | (5) |
| | *xxxx* | |
+-------------------+-----------------------------------+---------+
| "\Uxxxxxxxx" | Character with 32-bit hex value | (6) |
| | *xxxxxxxx* | |
+-------------------+-----------------------------------+---------+
Notes:
1. As in Standard C, up to three octal digits are accepted.
2. Unlike in Standard C, exactly two hex digits are required.
3. In a bytes literal, hexadecimal and octal escapes denote the byte
with the given value. In a string literal, these escapes denote a
Unicode character with the given value.
4. Changed in version 3.3: Support for name aliases [1] has been
added.
5. Exactly four hex digits are required.
6. Any Unicode character can be encoded this way. Exactly eight hex
digits are required.
Unlike Standard C, all unrecognized escape sequences are left in the
string unchanged, i.e., *the backslash is left in the result*. (This
behavior is useful when debugging: if an escape sequence is mistyped,
the resulting output is more easily recognized as broken.) It is also
important to note that the escape sequences only recognized in string
literals fall into the category of unrecognized escapes for bytes
literals.
Changed in version 3.6: Unrecognized escape sequences produce a
"DeprecationWarning". In a future Python version they will be a
"SyntaxWarning" and eventually a "SyntaxError".
Even in a raw literal, quotes can be escaped with a backslash, but the
backslash remains in the result; for example, "r"\""" is a valid
string literal consisting of two characters: a backslash and a double
quote; "r"\"" is not a valid string literal (even a raw string cannot
end in an odd number of backslashes). Specifically, *a raw literal
cannot end in a single backslash* (since the backslash would escape
the following quote character). Note also that a single backslash
followed by a newline is interpreted as those two characters as part
of the literal, *not* as a line continuation.
uM Subscriptions
*************
A subscription selects an item of a sequence (string, tuple or list)
or mapping (dictionary) object:
subscription ::= primary "[" expression_list "]"
The primary must evaluate to an object that supports subscription
(lists or dictionaries for example). User-defined objects can support
subscription by defining a "__getitem__()" method.
For built-in objects, there are two types of objects that support
subscription:
If the primary is a mapping, the expression list must evaluate to an
object whose value is one of the keys of the mapping, and the
subscription selects the value in the mapping that corresponds to that
key. (The expression list is a tuple except if it has exactly one
item.)
If the primary is a sequence, the expression list must evaluate to an
integer or a slice (as discussed in the following section).
The formal syntax makes no special provision for negative indices in
sequences; however, built-in sequences all provide a "__getitem__()"
method that interprets negative indices by adding the length of the
sequence to the index (so that "x[-1]" selects the last item of "x").
The resulting value must be a nonnegative integer less than the number
of items in the sequence, and the subscription selects the item whose
index is that value (counting from zero). Since the support for
negative indices and slicing occurs in the object’s "__getitem__()"
method, subclasses overriding this method will need to explicitly add
that support.
A string’s items are characters. A character is not a separate data
type but a string of exactly one character.
ax Truth Value Testing
*******************
Any object can be tested for truth value, for use in an "if" or
"while" condition or as operand of the Boolean operations below.
By default, an object is considered true unless its class defines
either a "__bool__()" method that returns "False" or a "__len__()"
method that returns zero, when called with the object. [1] Here are
most of the built-in objects considered false:
* constants defined to be false: "None" and "False".
* zero of any numeric type: "0", "0.0", "0j", "Decimal(0)",
"Fraction(0, 1)"
* empty sequences and collections: "''", "()", "[]", "{}", "set()",
"range(0)"
Operations and built-in functions that have a Boolean result always
return "0" or "False" for false and "1" or "True" for true, unless
otherwise stated. (Important exception: the Boolean operations "or"
and "and" always return one of their operands.)
uR The "try" statement
*******************
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object, or a tuple containing an item that is the class or a base
class of the exception object.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed. All except
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred. "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.
The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed. Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a ‘cleanup’ handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return", "break" or "continue" statement, the saved
exception is discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
Changed in version 3.8: Prior to Python 3.8, a "continue" statement
was illegal in the "finally" clause due to a problem with the
implementation.
ux� The standard type hierarchy
***************************
Below is a list of the types that are built into Python. Extension
modules (written in C, Java, or other languages, depending on the
implementation) can define additional types. Future versions of
Python may add types to the type hierarchy (e.g., rational numbers,
efficiently stored arrays of integers, etc.), although such additions
will often be provided via the standard library instead.
Some of the type descriptions below contain a paragraph listing
‘special attributes.’ These are attributes that provide access to the
implementation and are not intended for general use. Their definition
may change in the future.
None
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name "None". It
is used to signify the absence of a value in many situations, e.g.,
it is returned from functions that don’t explicitly return
anything. Its truth value is false.
NotImplemented
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name
"NotImplemented". Numeric methods and rich comparison methods
should return this value if they do not implement the operation for
the operands provided. (The interpreter will then try the
reflected operation, or some other fallback, depending on the
operator.) Its truth value is true.
See Implementing the arithmetic operations for more details.
Ellipsis
This type has a single value. There is a single object with this
value. This object is accessed through the literal "..." or the
built-in name "Ellipsis". Its truth value is true.
"numbers.Number"
These are created by numeric literals and returned as results by
arithmetic operators and arithmetic built-in functions. Numeric
objects are immutable; once created their value never changes.
Python numbers are of course strongly related to mathematical
numbers, but subject to the limitations of numerical representation
in computers.
The string representations of the numeric classes, computed by
"__repr__()" and "__str__()", have the following properties:
* They are valid numeric literals which, when passed to their class
constructor, produce an object having the value of the original
numeric.
* The representation is in base 10, when possible.
* Leading zeros, possibly excepting a single zero before a decimal
point, are not shown.
* Trailing zeros, possibly excepting a single zero after a decimal
point, are not shown.
* A sign is shown only when the number is negative.
Python distinguishes between integers, floating point numbers, and
complex numbers:
"numbers.Integral"
These represent elements from the mathematical set of integers
(positive and negative).
There are two types of integers:
Integers ("int")
These represent numbers in an unlimited range, subject to
available (virtual) memory only. For the purpose of shift
and mask operations, a binary representation is assumed, and
negative numbers are represented in a variant of 2’s
complement which gives the illusion of an infinite string of
sign bits extending to the left.
Booleans ("bool")
These represent the truth values False and True. The two
objects representing the values "False" and "True" are the
only Boolean objects. The Boolean type is a subtype of the
integer type, and Boolean values behave like the values 0 and
1, respectively, in almost all contexts, the exception being
that when converted to a string, the strings ""False"" or
""True"" are returned, respectively.
The rules for integer representation are intended to give the
most meaningful interpretation of shift and mask operations
involving negative integers.
"numbers.Real" ("float")
These represent machine-level double precision floating point
numbers. You are at the mercy of the underlying machine
architecture (and C or Java implementation) for the accepted
range and handling of overflow. Python does not support single-
precision floating point numbers; the savings in processor and
memory usage that are usually the reason for using these are
dwarfed by the overhead of using objects in Python, so there is
no reason to complicate the language with two kinds of floating
point numbers.
"numbers.Complex" ("complex")
These represent complex numbers as a pair of machine-level
double precision floating point numbers. The same caveats apply
as for floating point numbers. The real and imaginary parts of a
complex number "z" can be retrieved through the read-only
attributes "z.real" and "z.imag".
Sequences
These represent finite ordered sets indexed by non-negative
numbers. The built-in function "len()" returns the number of items
of a sequence. When the length of a sequence is *n*, the index set
contains the numbers 0, 1, …, *n*-1. Item *i* of sequence *a* is
selected by "a[i]".
Sequences also support slicing: "a[i:j]" selects all items with
index *k* such that *i* "<=" *k* "<" *j*. When used as an
expression, a slice is a sequence of the same type. This implies
that the index set is renumbered so that it starts at 0.
Some sequences also support “extended slicing” with a third “step”
parameter: "a[i:j:k]" selects all items of *a* with index *x* where
"x = i + n*k", *n* ">=" "0" and *i* "<=" *x* "<" *j*.
Sequences are distinguished according to their mutability:
Immutable sequences
An object of an immutable sequence type cannot change once it is
created. (If the object contains references to other objects,
these other objects may be mutable and may be changed; however,
the collection of objects directly referenced by an immutable
object cannot change.)
The following types are immutable sequences:
Strings
A string is a sequence of values that represent Unicode code
points. All the code points in the range "U+0000 - U+10FFFF"
can be represented in a string. Python doesn’t have a "char"
type; instead, every code point in the string is represented
as a string object with length "1". The built-in function
"ord()" converts a code point from its string form to an
integer in the range "0 - 10FFFF"; "chr()" converts an
integer in the range "0 - 10FFFF" to the corresponding length
"1" string object. "str.encode()" can be used to convert a
"str" to "bytes" using the given text encoding, and
"bytes.decode()" can be used to achieve the opposite.
Tuples
The items of a tuple are arbitrary Python objects. Tuples of
two or more items are formed by comma-separated lists of
expressions. A tuple of one item (a ‘singleton’) can be
formed by affixing a comma to an expression (an expression by
itself does not create a tuple, since parentheses must be
usable for grouping of expressions). An empty tuple can be
formed by an empty pair of parentheses.
Bytes
A bytes object is an immutable array. The items are 8-bit
bytes, represented by integers in the range 0 <= x < 256.
Bytes literals (like "b'abc'") and the built-in "bytes()"
constructor can be used to create bytes objects. Also, bytes
objects can be decoded to strings via the "decode()" method.
Mutable sequences
Mutable sequences can be changed after they are created. The
subscription and slicing notations can be used as the target of
assignment and "del" (delete) statements.
There are currently two intrinsic mutable sequence types:
Lists
The items of a list are arbitrary Python objects. Lists are
formed by placing a comma-separated list of expressions in
square brackets. (Note that there are no special cases needed
to form lists of length 0 or 1.)
Byte Arrays
A bytearray object is a mutable array. They are created by
the built-in "bytearray()" constructor. Aside from being
mutable (and hence unhashable), byte arrays otherwise provide
the same interface and functionality as immutable "bytes"
objects.
The extension module "array" provides an additional example of a
mutable sequence type, as does the "collections" module.
Set types
These represent unordered, finite sets of unique, immutable
objects. As such, they cannot be indexed by any subscript. However,
they can be iterated over, and the built-in function "len()"
returns the number of items in a set. Common uses for sets are fast
membership testing, removing duplicates from a sequence, and
computing mathematical operations such as intersection, union,
difference, and symmetric difference.
For set elements, the same immutability rules apply as for
dictionary keys. Note that numeric types obey the normal rules for
numeric comparison: if two numbers compare equal (e.g., "1" and
"1.0"), only one of them can be contained in a set.
There are currently two intrinsic set types:
Sets
These represent a mutable set. They are created by the built-in
"set()" constructor and can be modified afterwards by several
methods, such as "add()".
Frozen sets
These represent an immutable set. They are created by the
built-in "frozenset()" constructor. As a frozenset is immutable
and *hashable*, it can be used again as an element of another
set, or as a dictionary key.
Mappings
These represent finite sets of objects indexed by arbitrary index
sets. The subscript notation "a[k]" selects the item indexed by "k"
from the mapping "a"; this can be used in expressions and as the
target of assignments or "del" statements. The built-in function
"len()" returns the number of items in a mapping.
There is currently a single intrinsic mapping type:
Dictionaries
These represent finite sets of objects indexed by nearly
arbitrary values. The only types of values not acceptable as
keys are values containing lists or dictionaries or other
mutable types that are compared by value rather than by object
identity, the reason being that the efficient implementation of
dictionaries requires a key’s hash value to remain constant.
Numeric types used for keys obey the normal rules for numeric
comparison: if two numbers compare equal (e.g., "1" and "1.0")
then they can be used interchangeably to index the same
dictionary entry.
Dictionaries preserve insertion order, meaning that keys will be
produced in the same order they were added sequentially over the
dictionary. Replacing an existing key does not change the order,
however removing a key and re-inserting it will add it to the
end instead of keeping its old place.
Dictionaries are mutable; they can be created by the "{...}"
notation (see section Dictionary displays).
The extension modules "dbm.ndbm" and "dbm.gnu" provide
additional examples of mapping types, as does the "collections"
module.
Changed in version 3.7: Dictionaries did not preserve insertion
order in versions of Python before 3.6. In CPython 3.6,
insertion order was preserved, but it was considered an
implementation detail at that time rather than a language
guarantee.
Callable types
These are the types to which the function call operation (see
section Calls) can be applied:
User-defined functions
A user-defined function object is created by a function
definition (see section Function definitions). It should be
called with an argument list containing the same number of items
as the function’s formal parameter list.
Special attributes:
+---------------------------+---------------------------------+-------------+
| Attribute | Meaning | |
|===========================|=================================|=============|
| "__doc__" | The function’s documentation | Writable |
| | string, or "None" if | |
| | unavailable; not inherited by | |
| | subclasses. | |
+---------------------------+---------------------------------+-------------+
| "__name__" | The function’s name. | Writable |
+---------------------------+---------------------------------+-------------+
| "__qualname__" | The function’s *qualified | Writable |
| | name*. New in version 3.3. | |
+---------------------------+---------------------------------+-------------+
| "__module__" | The name of the module the | Writable |
| | function was defined in, or | |
| | "None" if unavailable. | |
+---------------------------+---------------------------------+-------------+
| "__defaults__" | A tuple containing default | Writable |
| | argument values for those | |
| | arguments that have defaults, | |
| | or "None" if no arguments have | |
| | a default value. | |
+---------------------------+---------------------------------+-------------+
| "__code__" | The code object representing | Writable |
| | the compiled function body. | |
+---------------------------+---------------------------------+-------------+
| "__globals__" | A reference to the dictionary | Read-only |
| | that holds the function’s | |
| | global variables — the global | |
| | namespace of the module in | |
| | which the function was defined. | |
+---------------------------+---------------------------------+-------------+
| "__dict__" | The namespace supporting | Writable |
| | arbitrary function attributes. | |
+---------------------------+---------------------------------+-------------+
| "__closure__" | "None" or a tuple of cells that | Read-only |
| | contain bindings for the | |
| | function’s free variables. See | |
| | below for information on the | |
| | "cell_contents" attribute. | |
+---------------------------+---------------------------------+-------------+
| "__annotations__" | A dict containing annotations | Writable |
| | of parameters. The keys of the | |
| | dict are the parameter names, | |
| | and "'return'" for the return | |
| | annotation, if provided. | |
+---------------------------+---------------------------------+-------------+
| "__kwdefaults__" | A dict containing defaults for | Writable |
| | keyword-only parameters. | |
+---------------------------+---------------------------------+-------------+
Most of the attributes labelled “Writable” check the type of the
assigned value.
Function objects also support getting and setting arbitrary
attributes, which can be used, for example, to attach metadata
to functions. Regular attribute dot-notation is used to get and
set such attributes. *Note that the current implementation only
supports function attributes on user-defined functions. Function
attributes on built-in functions may be supported in the
future.*
A cell object has the attribute "cell_contents". This can be
used to get the value of the cell, as well as set the value.
Additional information about a function’s definition can be
retrieved from its code object; see the description of internal
types below. The "cell" type can be accessed in the "types"
module.
Instance methods
An instance method object combines a class, a class instance and
any callable object (normally a user-defined function).
Special read-only attributes: "__self__" is the class instance
object, "__func__" is the function object; "__doc__" is the
method’s documentation (same as "__func__.__doc__"); "__name__"
is the method name (same as "__func__.__name__"); "__module__"
is the name of the module the method was defined in, or "None"
if unavailable.
Methods also support accessing (but not setting) the arbitrary
function attributes on the underlying function object.
User-defined method objects may be created when getting an
attribute of a class (perhaps via an instance of that class), if
that attribute is a user-defined function object or a class
method object.
When an instance method object is created by retrieving a user-
defined function object from a class via one of its instances,
its "__self__" attribute is the instance, and the method object
is said to be bound. The new method’s "__func__" attribute is
the original function object.
When an instance method object is created by retrieving a class
method object from a class or instance, its "__self__" attribute
is the class itself, and its "__func__" attribute is the
function object underlying the class method.
When an instance method object is called, the underlying
function ("__func__") is called, inserting the class instance
("__self__") in front of the argument list. For instance, when
"C" is a class which contains a definition for a function "f()",
and "x" is an instance of "C", calling "x.f(1)" is equivalent to
calling "C.f(x, 1)".
When an instance method object is derived from a class method
object, the “class instance” stored in "__self__" will actually
be the class itself, so that calling either "x.f(1)" or "C.f(1)"
is equivalent to calling "f(C,1)" where "f" is the underlying
function.
Note that the transformation from function object to instance
method object happens each time the attribute is retrieved from
the instance. In some cases, a fruitful optimization is to
assign the attribute to a local variable and call that local
variable. Also notice that this transformation only happens for
user-defined functions; other callable objects (and all non-
callable objects) are retrieved without transformation. It is
also important to note that user-defined functions which are
attributes of a class instance are not converted to bound
methods; this *only* happens when the function is an attribute
of the class.
Generator functions
A function or method which uses the "yield" statement (see
section The yield statement) is called a *generator function*.
Such a function, when called, always returns an iterator object
which can be used to execute the body of the function: calling
the iterator’s "iterator.__next__()" method will cause the
function to execute until it provides a value using the "yield"
statement. When the function executes a "return" statement or
falls off the end, a "StopIteration" exception is raised and the
iterator will have reached the end of the set of values to be
returned.
Coroutine functions
A function or method which is defined using "async def" is
called a *coroutine function*. Such a function, when called,
returns a *coroutine* object. It may contain "await"
expressions, as well as "async with" and "async for" statements.
See also the Coroutine Objects section.
Asynchronous generator functions
A function or method which is defined using "async def" and
which uses the "yield" statement is called a *asynchronous
generator function*. Such a function, when called, returns an
asynchronous iterator object which can be used in an "async for"
statement to execute the body of the function.
Calling the asynchronous iterator’s "aiterator.__anext__()"
method will return an *awaitable* which when awaited will
execute until it provides a value using the "yield" expression.
When the function executes an empty "return" statement or falls
off the end, a "StopAsyncIteration" exception is raised and the
asynchronous iterator will have reached the end of the set of
values to be yielded.
Built-in functions
A built-in function object is a wrapper around a C function.
Examples of built-in functions are "len()" and "math.sin()"
("math" is a standard built-in module). The number and type of
the arguments are determined by the C function. Special read-
only attributes: "__doc__" is the function’s documentation
string, or "None" if unavailable; "__name__" is the function’s
name; "__self__" is set to "None" (but see the next item);
"__module__" is the name of the module the function was defined
in or "None" if unavailable.
Built-in methods
This is really a different disguise of a built-in function, this
time containing an object passed to the C function as an
implicit extra argument. An example of a built-in method is
"alist.append()", assuming *alist* is a list object. In this
case, the special read-only attribute "__self__" is set to the
object denoted by *alist*.
Classes
Classes are callable. These objects normally act as factories
for new instances of themselves, but variations are possible for
class types that override "__new__()". The arguments of the
call are passed to "__new__()" and, in the typical case, to
"__init__()" to initialize the new instance.
Class Instances
Instances of arbitrary classes can be made callable by defining
a "__call__()" method in their class.
Modules
Modules are a basic organizational unit of Python code, and are
created by the import system as invoked either by the "import"
statement, or by calling functions such as
"importlib.import_module()" and built-in "__import__()". A module
object has a namespace implemented by a dictionary object (this is
the dictionary referenced by the "__globals__" attribute of
functions defined in the module). Attribute references are
translated to lookups in this dictionary, e.g., "m.x" is equivalent
to "m.__dict__["x"]". A module object does not contain the code
object used to initialize the module (since it isn’t needed once
the initialization is done).
Attribute assignment updates the module’s namespace dictionary,
e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".
Predefined (writable) attributes: "__name__" is the module’s name;
"__doc__" is the module’s documentation string, or "None" if
unavailable; "__annotations__" (optional) is a dictionary
containing *variable annotations* collected during module body
execution; "__file__" is the pathname of the file from which the
module was loaded, if it was loaded from a file. The "__file__"
attribute may be missing for certain types of modules, such as C
modules that are statically linked into the interpreter; for
extension modules loaded dynamically from a shared library, it is
the pathname of the shared library file.
Special read-only attribute: "__dict__" is the module’s namespace
as a dictionary object.
**CPython implementation detail:** Because of the way CPython
clears module dictionaries, the module dictionary will be cleared
when the module falls out of scope even if the dictionary still has
live references. To avoid this, copy the dictionary or keep the
module around while using its dictionary directly.
Custom classes
Custom class types are typically created by class definitions (see
section Class definitions). A class has a namespace implemented by
a dictionary object. Class attribute references are translated to
lookups in this dictionary, e.g., "C.x" is translated to
"C.__dict__["x"]" (although there are a number of hooks which allow
for other means of locating attributes). When the attribute name is
not found there, the attribute search continues in the base
classes. This search of the base classes uses the C3 method
resolution order which behaves correctly even in the presence of
‘diamond’ inheritance structures where there are multiple
inheritance paths leading back to a common ancestor. Additional
details on the C3 MRO used by Python can be found in the
documentation accompanying the 2.3 release at
https://www.python.org/download/releases/2.3/mro/.
When a class attribute reference (for class "C", say) would yield a
class method object, it is transformed into an instance method
object whose "__self__" attribute is "C". When it would yield a
static method object, it is transformed into the object wrapped by
the static method object. See section Implementing Descriptors for
another way in which attributes retrieved from a class may differ
from those actually contained in its "__dict__".
Class attribute assignments update the class’s dictionary, never
the dictionary of a base class.
A class object can be called (see above) to yield a class instance
(see below).
Special attributes: "__name__" is the class name; "__module__" is
the module name in which the class was defined; "__dict__" is the
dictionary containing the class’s namespace; "__bases__" is a tuple
containing the base classes, in the order of their occurrence in
the base class list; "__doc__" is the class’s documentation string,
or "None" if undefined; "__annotations__" (optional) is a
dictionary containing *variable annotations* collected during class
body execution.
Class instances
A class instance is created by calling a class object (see above).
A class instance has a namespace implemented as a dictionary which
is the first place in which attribute references are searched.
When an attribute is not found there, and the instance’s class has
an attribute by that name, the search continues with the class
attributes. If a class attribute is found that is a user-defined
function object, it is transformed into an instance method object
whose "__self__" attribute is the instance. Static method and
class method objects are also transformed; see above under
“Classes”. See section Implementing Descriptors for another way in
which attributes of a class retrieved via its instances may differ
from the objects actually stored in the class’s "__dict__". If no
class attribute is found, and the object’s class has a
"__getattr__()" method, that is called to satisfy the lookup.
Attribute assignments and deletions update the instance’s
dictionary, never a class’s dictionary. If the class has a
"__setattr__()" or "__delattr__()" method, this is called instead
of updating the instance dictionary directly.
Class instances can pretend to be numbers, sequences, or mappings
if they have methods with certain special names. See section
Special method names.
Special attributes: "__dict__" is the attribute dictionary;
"__class__" is the instance’s class.
I/O objects (also known as file objects)
A *file object* represents an open file. Various shortcuts are
available to create file objects: the "open()" built-in function,
and also "os.popen()", "os.fdopen()", and the "makefile()" method
of socket objects (and perhaps by other functions or methods
provided by extension modules).
The objects "sys.stdin", "sys.stdout" and "sys.stderr" are
initialized to file objects corresponding to the interpreter’s
standard input, output and error streams; they are all open in text
mode and therefore follow the interface defined by the
"io.TextIOBase" abstract class.
Internal types
A few types used internally by the interpreter are exposed to the
user. Their definitions may change with future versions of the
interpreter, but they are mentioned here for completeness.
Code objects
Code objects represent *byte-compiled* executable Python code,
or *bytecode*. The difference between a code object and a
function object is that the function object contains an explicit
reference to the function’s globals (the module in which it was
defined), while a code object contains no context; also the
default argument values are stored in the function object, not
in the code object (because they represent values calculated at
run-time). Unlike function objects, code objects are immutable
and contain no references (directly or indirectly) to mutable
objects.
Special read-only attributes: "co_name" gives the function name;
"co_argcount" is the total number of positional arguments
(including positional-only arguments and arguments with default
values); "co_posonlyargcount" is the number of positional-only
arguments (including arguments with default values);
"co_kwonlyargcount" is the number of keyword-only arguments
(including arguments with default values); "co_nlocals" is the
number of local variables used by the function (including
arguments); "co_varnames" is a tuple containing the names of the
local variables (starting with the argument names);
"co_cellvars" is a tuple containing the names of local variables
that are referenced by nested functions; "co_freevars" is a
tuple containing the names of free variables; "co_code" is a
string representing the sequence of bytecode instructions;
"co_consts" is a tuple containing the literals used by the
bytecode; "co_names" is a tuple containing the names used by the
bytecode; "co_filename" is the filename from which the code was
compiled; "co_firstlineno" is the first line number of the
function; "co_lnotab" is a string encoding the mapping from
bytecode offsets to line numbers (for details see the source
code of the interpreter); "co_stacksize" is the required stack
size; "co_flags" is an integer encoding a number of flags for
the interpreter.
The following flag bits are defined for "co_flags": bit "0x04"
is set if the function uses the "*arguments" syntax to accept an
arbitrary number of positional arguments; bit "0x08" is set if
the function uses the "**keywords" syntax to accept arbitrary
keyword arguments; bit "0x20" is set if the function is a
generator.
Future feature declarations ("from __future__ import division")
also use bits in "co_flags" to indicate whether a code object
was compiled with a particular feature enabled: bit "0x2000" is
set if the function was compiled with future division enabled;
bits "0x10" and "0x1000" were used in earlier versions of
Python.
Other bits in "co_flags" are reserved for internal use.
If a code object represents a function, the first item in
"co_consts" is the documentation string of the function, or
"None" if undefined.
Frame objects
Frame objects represent execution frames. They may occur in
traceback objects (see below), and are also passed to registered
trace functions.
Special read-only attributes: "f_back" is to the previous stack
frame (towards the caller), or "None" if this is the bottom
stack frame; "f_code" is the code object being executed in this
frame; "f_locals" is the dictionary used to look up local
variables; "f_globals" is used for global variables;
"f_builtins" is used for built-in (intrinsic) names; "f_lasti"
gives the precise instruction (this is an index into the
bytecode string of the code object).
Accessing "f_code" raises an auditing event "object.__getattr__"
with arguments "obj" and ""f_code"".
Special writable attributes: "f_trace", if not "None", is a
function called for various events during code execution (this
is used by the debugger). Normally an event is triggered for
each new source line - this can be disabled by setting
"f_trace_lines" to "False".
Implementations *may* allow per-opcode events to be requested by
setting "f_trace_opcodes" to "True". Note that this may lead to
undefined interpreter behaviour if exceptions raised by the
trace function escape to the function being traced.
"f_lineno" is the current line number of the frame — writing to
this from within a trace function jumps to the given line (only
for the bottom-most frame). A debugger can implement a Jump
command (aka Set Next Statement) by writing to f_lineno.
Frame objects support one method:
frame.clear()
This method clears all references to local variables held by
the frame. Also, if the frame belonged to a generator, the
generator is finalized. This helps break reference cycles
involving frame objects (for example when catching an
exception and storing its traceback for later use).
"RuntimeError" is raised if the frame is currently executing.
New in version 3.4.
Traceback objects
Traceback objects represent a stack trace of an exception. A
traceback object is implicitly created when an exception occurs,
and may also be explicitly created by calling
"types.TracebackType".
For implicitly created tracebacks, when the search for an
exception handler unwinds the execution stack, at each unwound
level a traceback object is inserted in front of the current
traceback. When an exception handler is entered, the stack
trace is made available to the program. (See section The try
statement.) It is accessible as the third item of the tuple
returned by "sys.exc_info()", and as the "__traceback__"
attribute of the caught exception.
When the program contains no suitable handler, the stack trace
is written (nicely formatted) to the standard error stream; if
the interpreter is interactive, it is also made available to the
user as "sys.last_traceback".
For explicitly created tracebacks, it is up to the creator of
the traceback to determine how the "tb_next" attributes should
be linked to form a full stack trace.
Special read-only attributes: "tb_frame" points to the execution
frame of the current level; "tb_lineno" gives the line number
where the exception occurred; "tb_lasti" indicates the precise
instruction. The line number and last instruction in the
traceback may differ from the line number of its frame object if
the exception occurred in a "try" statement with no matching
except clause or with a finally clause.
Accessing "tb_frame" raises an auditing event
"object.__getattr__" with arguments "obj" and ""tb_frame"".
Special writable attribute: "tb_next" is the next level in the
stack trace (towards the frame where the exception occurred), or
"None" if there is no next level.
Changed in version 3.7: Traceback objects can now be explicitly
instantiated from Python code, and the "tb_next" attribute of
existing instances can be updated.
Slice objects
Slice objects are used to represent slices for "__getitem__()"
methods. They are also created by the built-in "slice()"
function.
Special read-only attributes: "start" is the lower bound; "stop"
is the upper bound; "step" is the step value; each is "None" if
omitted. These attributes can have any type.
Slice objects support one method:
slice.indices(self, length)
This method takes a single integer argument *length* and
computes information about the slice that the slice object
would describe if applied to a sequence of *length* items.
It returns a tuple of three integers; respectively these are
the *start* and *stop* indices and the *step* or stride
length of the slice. Missing or out-of-bounds indices are
handled in a manner consistent with regular slices.
Static method objects
Static method objects provide a way of defeating the
transformation of function objects to method objects described
above. A static method object is a wrapper around any other
object, usually a user-defined method object. When a static
method object is retrieved from a class or a class instance, the
object actually returned is the wrapped object, which is not
subject to any further transformation. Static method objects are
not themselves callable, although the objects they wrap usually
are. Static method objects are created by the built-in
"staticmethod()" constructor.
Class method objects
A class method object, like a static method object, is a wrapper
around another object that alters the way in which that object
is retrieved from classes and class instances. The behaviour of
class method objects upon such retrieval is described above,
under “User-defined methods”. Class method objects are created
by the built-in "classmethod()" constructor.
a� Functions
*********
Function objects are created by function definitions. The only
operation on a function object is to call it: "func(argument-list)".
There are really two flavors of function objects: built-in functions
and user-defined functions. Both support the same operation (to call
the function), but the implementation is different, hence the
different object types.
See Function definitions for more information.
u(. Mapping Types — "dict"
**********************
A *mapping* object maps *hashable* values to arbitrary objects.
Mappings are mutable objects. There is currently only one standard
mapping type, the *dictionary*. (For other containers see the built-
in "list", "set", and "tuple" classes, and the "collections" module.)
A dictionary’s keys are *almost* arbitrary values. Values that are
not *hashable*, that is, values containing lists, dictionaries or
other mutable types (that are compared by value rather than by object
identity) may not be used as keys. Numeric types used for keys obey
the normal rules for numeric comparison: if two numbers compare equal
(such as "1" and "1.0") then they can be used interchangeably to index
the same dictionary entry. (Note however, that since computers store
floating-point numbers as approximations it is usually unwise to use
them as dictionary keys.)
Dictionaries can be created by placing a comma-separated list of "key:
value" pairs within braces, for example: "{'jack': 4098, 'sjoerd':
4127}" or "{4098: 'jack', 4127: 'sjoerd'}", or by the "dict"
constructor.
class dict(**kwarg)
class dict(mapping, **kwarg)
class dict(iterable, **kwarg)
Return a new dictionary initialized from an optional positional
argument and a possibly empty set of keyword arguments.
Dictionaries can be created by several means:
* Use a comma-separated list of "key: value" pairs within braces:
"{'jack': 4098, 'sjoerd': 4127}" or "{4098: 'jack', 4127:
'sjoerd'}"
* Use a dict comprehension: "{}", "{x: x ** 2 for x in range(10)}"
* Use the type constructor: "dict()", "dict([('foo', 100), ('bar',
200)])", "dict(foo=100, bar=200)"
If no positional argument is given, an empty dictionary is created.
If a positional argument is given and it is a mapping object, a
dictionary is created with the same key-value pairs as the mapping
object. Otherwise, the positional argument must be an *iterable*
object. Each item in the iterable must itself be an iterable with
exactly two objects. The first object of each item becomes a key
in the new dictionary, and the second object the corresponding
value. If a key occurs more than once, the last value for that key
becomes the corresponding value in the new dictionary.
If keyword arguments are given, the keyword arguments and their
values are added to the dictionary created from the positional
argument. If a key being added is already present, the value from
the keyword argument replaces the value from the positional
argument.
To illustrate, the following examples all return a dictionary equal
to "{"one": 1, "two": 2, "three": 3}":
>>> a = dict(one=1, two=2, three=3)
>>> b = {'one': 1, 'two': 2, 'three': 3}
>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
>>> e = dict({'three': 3, 'one': 1, 'two': 2})
>>> a == b == c == d == e
True
Providing keyword arguments as in the first example only works for
keys that are valid Python identifiers. Otherwise, any valid keys
can be used.
These are the operations that dictionaries support (and therefore,
custom mapping types should support too):
list(d)
Return a list of all the keys used in the dictionary *d*.
len(d)
Return the number of items in the dictionary *d*.
d[key]
Return the item of *d* with key *key*. Raises a "KeyError" if
*key* is not in the map.
If a subclass of dict defines a method "__missing__()" and *key*
is not present, the "d[key]" operation calls that method with
the key *key* as argument. The "d[key]" operation then returns
or raises whatever is returned or raised by the
"__missing__(key)" call. No other operations or methods invoke
"__missing__()". If "__missing__()" is not defined, "KeyError"
is raised. "__missing__()" must be a method; it cannot be an
instance variable:
>>> class Counter(dict):
... def __missing__(self, key):
... return 0
>>> c = Counter()
>>> c['red']
0
>>> c['red'] += 1
>>> c['red']
1
The example above shows part of the implementation of
"collections.Counter". A different "__missing__" method is used
by "collections.defaultdict".
d[key] = value
Set "d[key]" to *value*.
del d[key]
Remove "d[key]" from *d*. Raises a "KeyError" if *key* is not
in the map.
key in d
Return "True" if *d* has a key *key*, else "False".
key not in d
Equivalent to "not key in d".
iter(d)
Return an iterator over the keys of the dictionary. This is a
shortcut for "iter(d.keys())".
clear()
Remove all items from the dictionary.
copy()
Return a shallow copy of the dictionary.
classmethod fromkeys(iterable[, value])
Create a new dictionary with keys from *iterable* and values set
to *value*.
"fromkeys()" is a class method that returns a new dictionary.
*value* defaults to "None". All of the values refer to just a
single instance, so it generally doesn’t make sense for *value*
to be a mutable object such as an empty list. To get distinct
values, use a dict comprehension instead.
get(key[, default])
Return the value for *key* if *key* is in the dictionary, else
*default*. If *default* is not given, it defaults to "None", so
that this method never raises a "KeyError".
items()
Return a new view of the dictionary’s items ("(key, value)"
pairs). See the documentation of view objects.
keys()
Return a new view of the dictionary’s keys. See the
documentation of view objects.
pop(key[, default])
If *key* is in the dictionary, remove it and return its value,
else return *default*. If *default* is not given and *key* is
not in the dictionary, a "KeyError" is raised.
popitem()
Remove and return a "(key, value)" pair from the dictionary.
Pairs are returned in LIFO (last-in, first-out) order.
"popitem()" is useful to destructively iterate over a
dictionary, as often used in set algorithms. If the dictionary
is empty, calling "popitem()" raises a "KeyError".
Changed in version 3.7: LIFO order is now guaranteed. In prior
versions, "popitem()" would return an arbitrary key/value pair.
reversed(d)
Return a reverse iterator over the keys of the dictionary. This
is a shortcut for "reversed(d.keys())".
New in version 3.8.
setdefault(key[, default])
If *key* is in the dictionary, return its value. If not, insert
*key* with a value of *default* and return *default*. *default*
defaults to "None".
update([other])
Update the dictionary with the key/value pairs from *other*,
overwriting existing keys. Return "None".
"update()" accepts either another dictionary object or an
iterable of key/value pairs (as tuples or other iterables of
length two). If keyword arguments are specified, the dictionary
is then updated with those key/value pairs: "d.update(red=1,
blue=2)".
values()
Return a new view of the dictionary’s values. See the
documentation of view objects.
An equality comparison between one "dict.values()" view and
another will always return "False". This also applies when
comparing "dict.values()" to itself:
>>> d = {'a': 1}
>>> d.values() == d.values()
False
Dictionaries compare equal if and only if they have the same "(key,
value)" pairs (regardless of ordering). Order comparisons (‘<’,
‘<=’, ‘>=’, ‘>’) raise "TypeError".
Dictionaries preserve insertion order. Note that updating a key
does not affect the order. Keys added after deletion are inserted
at the end.
>>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}
>>> list(d)
['one', 'two', 'three', 'four']
>>> list(d.values())
[1, 2, 3, 4]
>>> d["one"] = 42
>>> d
{'one': 42, 'two': 2, 'three': 3, 'four': 4}
>>> del d["two"]
>>> d["two"] = None
>>> d
{'one': 42, 'three': 3, 'four': 4, 'two': None}
Changed in version 3.7: Dictionary order is guaranteed to be
insertion order. This behavior was an implementation detail of
CPython from 3.6.
Dictionaries and dictionary views are reversible.
>>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}
>>> list(reversed(d))
['four', 'three', 'two', 'one']
>>> list(reversed(d.values()))
[4, 3, 2, 1]
>>> list(reversed(d.items()))
[('four', 4), ('three', 3), ('two', 2), ('one', 1)]
Changed in version 3.8: Dictionaries are now reversible.
See also:
"types.MappingProxyType" can be used to create a read-only view of a
"dict".
Dictionary view objects
=======================
The objects returned by "dict.keys()", "dict.values()" and
"dict.items()" are *view objects*. They provide a dynamic view on the
dictionary’s entries, which means that when the dictionary changes,
the view reflects these changes.
Dictionary views can be iterated over to yield their respective data,
and support membership tests:
len(dictview)
Return the number of entries in the dictionary.
iter(dictview)
Return an iterator over the keys, values or items (represented as
tuples of "(key, value)") in the dictionary.
Keys and values are iterated over in insertion order. This allows
the creation of "(value, key)" pairs using "zip()": "pairs =
zip(d.values(), d.keys())". Another way to create the same list is
"pairs = [(v, k) for (k, v) in d.items()]".
Iterating views while adding or deleting entries in the dictionary
may raise a "RuntimeError" or fail to iterate over all entries.
Changed in version 3.7: Dictionary order is guaranteed to be
insertion order.
x in dictview
Return "True" if *x* is in the underlying dictionary’s keys, values
or items (in the latter case, *x* should be a "(key, value)"
tuple).
reversed(dictview)
Return a reverse iterator over the keys, values or items of the
dictionary. The view will be iterated in reverse order of the
insertion.
Changed in version 3.8: Dictionary views are now reversible.
Keys views are set-like since their entries are unique and hashable.
If all values are hashable, so that "(key, value)" pairs are unique
and hashable, then the items view is also set-like. (Values views are
not treated as set-like since the entries are generally not unique.)
For set-like views, all of the operations defined for the abstract
base class "collections.abc.Set" are available (for example, "==",
"<", or "^").
An example of dictionary view usage:
>>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500}
>>> keys = dishes.keys()
>>> values = dishes.values()
>>> # iteration
>>> n = 0
>>> for val in values:
... n += val
>>> print(n)
504
>>> # keys and values are iterated over in the same order (insertion order)
>>> list(keys)
['eggs', 'sausage', 'bacon', 'spam']
>>> list(values)
[2, 1, 1, 500]
>>> # view objects are dynamic and reflect dict changes
>>> del dishes['eggs']
>>> del dishes['sausage']
>>> list(keys)
['bacon', 'spam']
>>> # set operations
>>> keys & {'eggs', 'bacon', 'salad'}
{'bacon'}
>>> keys ^ {'sausage', 'juice'}
{'juice', 'sausage', 'bacon', 'spam'}
a� Methods
*******
Methods are functions that are called using the attribute notation.
There are two flavors: built-in methods (such as "append()" on lists)
and class instance methods. Built-in methods are described with the
types that support them.
If you access a method (a function defined in a class namespace)
through an instance, you get a special object: a *bound method* (also
called *instance method*) object. When called, it will add the "self"
argument to the argument list. Bound methods have two special read-
only attributes: "m.__self__" is the object on which the method
operates, and "m.__func__" is the function implementing the method.
Calling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to
calling "m.__func__(m.__self__, arg-1, arg-2, ..., arg-n)".
Like function objects, bound method objects support getting arbitrary
attributes. However, since method attributes are actually stored on
the underlying function object ("meth.__func__"), setting method
attributes on bound methods is disallowed. Attempting to set an
attribute on a method results in an "AttributeError" being raised. In
order to set a method attribute, you need to explicitly set it on the
underlying function object:
>>> class C:
... def method(self):
... pass
...
>>> c = C()
>>> c.method.whoami = 'my name is method' # can't set on the method
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'method' object has no attribute 'whoami'
>>> c.method.__func__.whoami = 'my name is method'
>>> c.method.whoami
'my name is method'
See The standard type hierarchy for more information.
u$ Modules
*******
The only special operation on a module is attribute access: "m.name",
where *m* is a module and *name* accesses a name defined in *m*’s
symbol table. Module attributes can be assigned to. (Note that the
"import" statement is not, strictly speaking, an operation on a module
object; "import foo" does not require a module object named *foo* to
exist, rather it requires an (external) *definition* for a module
named *foo* somewhere.)
A special attribute of every module is "__dict__". This is the
dictionary containing the module’s symbol table. Modifying this
dictionary will actually change the module’s symbol table, but direct
assignment to the "__dict__" attribute is not possible (you can write
"m.__dict__['a'] = 1", which defines "m.a" to be "1", but you can’t
write "m.__dict__ = {}"). Modifying "__dict__" directly is not
recommended.
Modules built into the interpreter are written like this: "<module
'sys' (built-in)>". If loaded from a file, they are written as
"<module 'os' from '/usr/local/lib/pythonX.Y/os.pyc'>".
u�Z Sequence Types — "list", "tuple", "range"
*****************************************
There are three basic sequence types: lists, tuples, and range
objects. Additional sequence types tailored for processing of binary
data and text strings are described in dedicated sections.
Common Sequence Operations
==========================
The operations in the following table are supported by most sequence
types, both mutable and immutable. The "collections.abc.Sequence" ABC
is provided to make it easier to correctly implement these operations
on custom sequence types.
This table lists the sequence operations sorted in ascending priority.
In the table, *s* and *t* are sequences of the same type, *n*, *i*,
*j* and *k* are integers and *x* is an arbitrary object that meets any
type and value restrictions imposed by *s*.
The "in" and "not in" operations have the same priorities as the
comparison operations. The "+" (concatenation) and "*" (repetition)
operations have the same priority as the corresponding numeric
operations. [3]
+----------------------------+----------------------------------+------------+
| Operation | Result | Notes |
|============================|==================================|============|
| "x in s" | "True" if an item of *s* is | (1) |
| | equal to *x*, else "False" | |
+----------------------------+----------------------------------+------------+
| "x not in s" | "False" if an item of *s* is | (1) |
| | equal to *x*, else "True" | |
+----------------------------+----------------------------------+------------+
| "s + t" | the concatenation of *s* and *t* | (6)(7) |
+----------------------------+----------------------------------+------------+
| "s * n" or "n * s" | equivalent to adding *s* to | (2)(7) |
| | itself *n* times | |
+----------------------------+----------------------------------+------------+
| "s[i]" | *i*th item of *s*, origin 0 | (3) |
+----------------------------+----------------------------------+------------+
| "s[i:j]" | slice of *s* from *i* to *j* | (3)(4) |
+----------------------------+----------------------------------+------------+
| "s[i:j:k]" | slice of *s* from *i* to *j* | (3)(5) |
| | with step *k* | |
+----------------------------+----------------------------------+------------+
| "len(s)" | length of *s* | |
+----------------------------+----------------------------------+------------+
| "min(s)" | smallest item of *s* | |
+----------------------------+----------------------------------+------------+
| "max(s)" | largest item of *s* | |
+----------------------------+----------------------------------+------------+
| "s.index(x[, i[, j]])" | index of the first occurrence of | (8) |
| | *x* in *s* (at or after index | |
| | *i* and before index *j*) | |
+----------------------------+----------------------------------+------------+
| "s.count(x)" | total number of occurrences of | |
| | *x* in *s* | |
+----------------------------+----------------------------------+------------+
Sequences of the same type also support comparisons. In particular,
tuples and lists are compared lexicographically by comparing
corresponding elements. This means that to compare equal, every
element must compare equal and the two sequences must be of the same
type and have the same length. (For full details see Comparisons in
the language reference.)
Notes:
1. While the "in" and "not in" operations are used only for simple
containment testing in the general case, some specialised sequences
(such as "str", "bytes" and "bytearray") also use them for
subsequence testing:
>>> "gg" in "eggs"
True
2. Values of *n* less than "0" are treated as "0" (which yields an
empty sequence of the same type as *s*). Note that items in the
sequence *s* are not copied; they are referenced multiple times.
This often haunts new Python programmers; consider:
>>> lists = [[]] * 3
>>> lists
[[], [], []]
>>> lists[0].append(3)
>>> lists
[[3], [3], [3]]
What has happened is that "[[]]" is a one-element list containing
an empty list, so all three elements of "[[]] * 3" are references
to this single empty list. Modifying any of the elements of
"lists" modifies this single list. You can create a list of
different lists this way:
>>> lists = [[] for i in range(3)]
>>> lists[0].append(3)
>>> lists[1].append(5)
>>> lists[2].append(7)
>>> lists
[[3], [5], [7]]
Further explanation is available in the FAQ entry How do I create a
multidimensional list?.
3. If *i* or *j* is negative, the index is relative to the end of
sequence *s*: "len(s) + i" or "len(s) + j" is substituted. But
note that "-0" is still "0".
4. The slice of *s* from *i* to *j* is defined as the sequence of
items with index *k* such that "i <= k < j". If *i* or *j* is
greater than "len(s)", use "len(s)". If *i* is omitted or "None",
use "0". If *j* is omitted or "None", use "len(s)". If *i* is
greater than or equal to *j*, the slice is empty.
5. The slice of *s* from *i* to *j* with step *k* is defined as the
sequence of items with index "x = i + n*k" such that "0 <= n <
(j-i)/k". In other words, the indices are "i", "i+k", "i+2*k",
"i+3*k" and so on, stopping when *j* is reached (but never
including *j*). When *k* is positive, *i* and *j* are reduced to
"len(s)" if they are greater. When *k* is negative, *i* and *j* are
reduced to "len(s) - 1" if they are greater. If *i* or *j* are
omitted or "None", they become “end” values (which end depends on
the sign of *k*). Note, *k* cannot be zero. If *k* is "None", it
is treated like "1".
6. Concatenating immutable sequences always results in a new object.
This means that building up a sequence by repeated concatenation
will have a quadratic runtime cost in the total sequence length.
To get a linear runtime cost, you must switch to one of the
alternatives below:
* if concatenating "str" objects, you can build a list and use
"str.join()" at the end or else write to an "io.StringIO"
instance and retrieve its value when complete
* if concatenating "bytes" objects, you can similarly use
"bytes.join()" or "io.BytesIO", or you can do in-place
concatenation with a "bytearray" object. "bytearray" objects are
mutable and have an efficient overallocation mechanism
* if concatenating "tuple" objects, extend a "list" instead
* for other types, investigate the relevant class documentation
7. Some sequence types (such as "range") only support item sequences
that follow specific patterns, and hence don’t support sequence
concatenation or repetition.
8. "index" raises "ValueError" when *x* is not found in *s*. Not all
implementations support passing the additional arguments *i* and
*j*. These arguments allow efficient searching of subsections of
the sequence. Passing the extra arguments is roughly equivalent to
using "s[i:j].index(x)", only without copying any data and with the
returned index being relative to the start of the sequence rather
than the start of the slice.
Immutable Sequence Types
========================
The only operation that immutable sequence types generally implement
that is not also implemented by mutable sequence types is support for
the "hash()" built-in.
This support allows immutable sequences, such as "tuple" instances, to
be used as "dict" keys and stored in "set" and "frozenset" instances.
Attempting to hash an immutable sequence that contains unhashable
values will result in "TypeError".
Mutable Sequence Types
======================
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
|================================|==================================|=======================|
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | appends *x* to the end of the | |
| | sequence (same as | |
| | "s[len(s):len(s)] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from *s* (same | (5) |
| | as "del s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of *s* | (5) |
| | (same as "s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | extends *s* with the contents of | |
| | *t* (for the most part the same | |
| | as "s[len(s):len(s)] = t") | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (6) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | inserts *x* into *s* at the | |
| | index given by *i* (same as | |
| | "s[i:i] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop()" or "s.pop(i)" | retrieves the item at *i* and | (2) |
| | also removes it from *s* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
| | where "s[i]" is equal to *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default the
last item is removed and returned.
3. "remove()" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for economy
of space when reversing a large sequence. To remind users that it
operates by side effect, it does not return the reversed sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don’t support slicing
operations (such as "dict" and "set"). "copy()" is not part of the
"collections.abc.MutableSequence" ABC, but most concrete mutable
sequence classes provide it.
New in version 3.3: "clear()" and "copy()" methods.
6. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the sequence.
Items in the sequence are not copied; they are referenced multiple
times, as explained for "s * n" under Common Sequence Operations.
Lists
=====
Lists are mutable sequences, typically used to store collections of
homogeneous items (where the precise degree of similarity will vary by
application).
class list([iterable])
Lists may be constructed in several ways:
* Using a pair of square brackets to denote the empty list: "[]"
* Using square brackets, separating items with commas: "[a]", "[a,
b, c]"
* Using a list comprehension: "[x for x in iterable]"
* Using the type constructor: "list()" or "list(iterable)"
The constructor builds a list whose items are the same and in the
same order as *iterable*’s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a list, a copy is made and
returned, similar to "iterable[:]". For example, "list('abc')"
returns "['a', 'b', 'c']" and "list( (1, 2, 3) )" returns "[1, 2,
3]". If no argument is given, the constructor creates a new empty
list, "[]".
Many other operations also produce lists, including the "sorted()"
built-in.
Lists implement all of the common and mutable sequence operations.
Lists also provide the following additional method:
sort(*, key=None, reverse=False)
This method sorts the list in place, using only "<" comparisons
between items. Exceptions are not suppressed - if any comparison
operations fail, the entire sort operation will fail (and the
list will likely be left in a partially modified state).
"sort()" accepts two arguments that can only be passed by
keyword (keyword-only arguments):
*key* specifies a function of one argument that is used to
extract a comparison key from each list element (for example,
"key=str.lower"). The key corresponding to each item in the list
is calculated once and then used for the entire sorting process.
The default value of "None" means that list items are sorted
directly without calculating a separate key value.
The "functools.cmp_to_key()" utility is available to convert a
2.x style *cmp* function to a *key* function.
*reverse* is a boolean value. If set to "True", then the list
elements are sorted as if each comparison were reversed.
This method modifies the sequence in place for economy of space
when sorting a large sequence. To remind users that it operates
by side effect, it does not return the sorted sequence (use
"sorted()" to explicitly request a new sorted list instance).
The "sort()" method is guaranteed to be stable. A sort is
stable if it guarantees not to change the relative order of
elements that compare equal — this is helpful for sorting in
multiple passes (for example, sort by department, then by salary
grade).
For sorting examples and a brief sorting tutorial, see Sorting
HOW TO.
**CPython implementation detail:** While a list is being sorted,
the effect of attempting to mutate, or even inspect, the list is
undefined. The C implementation of Python makes the list appear
empty for the duration, and raises "ValueError" if it can detect
that the list has been mutated during a sort.
Tuples
======
Tuples are immutable sequences, typically used to store collections of
heterogeneous data (such as the 2-tuples produced by the "enumerate()"
built-in). Tuples are also used for cases where an immutable sequence
of homogeneous data is needed (such as allowing storage in a "set" or
"dict" instance).
class tuple([iterable])
Tuples may be constructed in a number of ways:
* Using a pair of parentheses to denote the empty tuple: "()"
* Using a trailing comma for a singleton tuple: "a," or "(a,)"
* Separating items with commas: "a, b, c" or "(a, b, c)"
* Using the "tuple()" built-in: "tuple()" or "tuple(iterable)"
The constructor builds a tuple whose items are the same and in the
same order as *iterable*’s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a tuple, it is returned
unchanged. For example, "tuple('abc')" returns "('a', 'b', 'c')"
and "tuple( [1, 2, 3] )" returns "(1, 2, 3)". If no argument is
given, the constructor creates a new empty tuple, "()".
Note that it is actually the comma which makes a tuple, not the
parentheses. The parentheses are optional, except in the empty
tuple case, or when they are needed to avoid syntactic ambiguity.
For example, "f(a, b, c)" is a function call with three arguments,
while "f((a, b, c))" is a function call with a 3-tuple as the sole
argument.
Tuples implement all of the common sequence operations.
For heterogeneous collections of data where access by name is clearer
than access by index, "collections.namedtuple()" may be a more
appropriate choice than a simple tuple object.
Ranges
======
The "range" type represents an immutable sequence of numbers and is
commonly used for looping a specific number of times in "for" loops.
class range(stop)
class range(start, stop[, step])
The arguments to the range constructor must be integers (either
built-in "int" or any object that implements the "__index__"
special method). If the *step* argument is omitted, it defaults to
"1". If the *start* argument is omitted, it defaults to "0". If
*step* is zero, "ValueError" is raised.
For a positive *step*, the contents of a range "r" are determined
by the formula "r[i] = start + step*i" where "i >= 0" and "r[i] <
stop".
For a negative *step*, the contents of the range are still
determined by the formula "r[i] = start + step*i", but the
constraints are "i >= 0" and "r[i] > stop".
A range object will be empty if "r[0]" does not meet the value
constraint. Ranges do support negative indices, but these are
interpreted as indexing from the end of the sequence determined by
the positive indices.
Ranges containing absolute values larger than "sys.maxsize" are
permitted but some features (such as "len()") may raise
"OverflowError".
Range examples:
>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(range(1, 11))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> list(range(0, 30, 5))
[0, 5, 10, 15, 20, 25]
>>> list(range(0, 10, 3))
[0, 3, 6, 9]
>>> list(range(0, -10, -1))
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
>>> list(range(0))
[]
>>> list(range(1, 0))
[]
Ranges implement all of the common sequence operations except
concatenation and repetition (due to the fact that range objects
can only represent sequences that follow a strict pattern and
repetition and concatenation will usually violate that pattern).
start
The value of the *start* parameter (or "0" if the parameter was
not supplied)
stop
The value of the *stop* parameter
step
The value of the *step* parameter (or "1" if the parameter was
not supplied)
The advantage of the "range" type over a regular "list" or "tuple" is
that a "range" object will always take the same (small) amount of
memory, no matter the size of the range it represents (as it only
stores the "start", "stop" and "step" values, calculating individual
items and subranges as needed).
Range objects implement the "collections.abc.Sequence" ABC, and
provide features such as containment tests, element index lookup,
slicing and support for negative indices (see Sequence Types — list,
tuple, range):
>>> r = range(0, 20, 2)
>>> r
range(0, 20, 2)
>>> 11 in r
False
>>> 10 in r
True
>>> r.index(10)
5
>>> r[5]
10
>>> r[:5]
range(0, 10, 2)
>>> r[-1]
18
Testing range objects for equality with "==" and "!=" compares them as
sequences. That is, two range objects are considered equal if they
represent the same sequence of values. (Note that two range objects
that compare equal might have different "start", "stop" and "step"
attributes, for example "range(0) == range(2, 1, 3)" or "range(0, 3,
2) == range(0, 4, 2)".)
Changed in version 3.2: Implement the Sequence ABC. Support slicing
and negative indices. Test "int" objects for membership in constant
time instead of iterating through all items.
Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range objects
based on the sequence of values they define (instead of comparing
based on object identity).
New in version 3.3: The "start", "stop" and "step" attributes.
See also:
* The linspace recipe shows how to implement a lazy version of range
suitable for floating point applications.
u� Mutable Sequence Types
**********************
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
|================================|==================================|=======================|
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | appends *x* to the end of the | |
| | sequence (same as | |
| | "s[len(s):len(s)] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from *s* (same | (5) |
| | as "del s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of *s* | (5) |
| | (same as "s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | extends *s* with the contents of | |
| | *t* (for the most part the same | |
| | as "s[len(s):len(s)] = t") | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (6) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | inserts *x* into *s* at the | |
| | index given by *i* (same as | |
| | "s[i:i] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop()" or "s.pop(i)" | retrieves the item at *i* and | (2) |
| | also removes it from *s* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
| | where "s[i]" is equal to *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default the
last item is removed and returned.
3. "remove()" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for economy
of space when reversing a large sequence. To remind users that it
operates by side effect, it does not return the reversed sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don’t support slicing
operations (such as "dict" and "set"). "copy()" is not part of the
"collections.abc.MutableSequence" ABC, but most concrete mutable
sequence classes provide it.
New in version 3.3: "clear()" and "copy()" methods.
6. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the sequence.
Items in the sequence are not copied; they are referenced multiple
times, as explained for "s * n" under Common Sequence Operations.
a~ Unary arithmetic and bitwise operations
***************************************
All unary arithmetic and bitwise operations have the same priority:
u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr
The unary "-" (minus) operator yields the negation of its numeric
argument.
The unary "+" (plus) operator yields its numeric argument unchanged.
The unary "~" (invert) operator yields the bitwise inversion of its
integer argument. The bitwise inversion of "x" is defined as
"-(x+1)". It only applies to integral numbers.
In all three cases, if the argument does not have the proper type, a
"TypeError" exception is raised.
u� The "while" statement
*********************
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" assignment_expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
uM The "with" statement
********************
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one “item” proceeds as
follows:
1. The context expression (the expression given in the "with_item") is
evaluated to obtain a context manager.
2. The context manager’s "__enter__()" is loaded for later use.
3. The context manager’s "__exit__()" is loaded for later use.
4. The context manager’s "__enter__()" method is invoked.
5. If a target was included in the "with" statement, the return value
from "__enter__()" is assigned to it.
Note:
The "with" statement guarantees that if the "__enter__()" method
returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
6. The suite is executed.
7. The context manager’s "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
The following code:
with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
enter = type(manager).__enter__
exit = type(manager).__exit__
value = enter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not exit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
exit(manager, None, None, None)
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
SUITE
is semantically equivalent to:
with A() as a:
with B() as b:
SUITE
Changed in version 3.1: Support for multiple context expressions.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
a, The "yield" statement
*********************
yield_stmt ::= yield_expression
A "yield" statement is semantically equivalent to a yield expression.
The yield statement can be used to omit the parentheses that would
otherwise be required in the equivalent yield expression statement.
For example, the yield statements
yield <expr>
yield from <expr>
are equivalent to the yield expression statements
(yield <expr>)
(yield from <expr>)
Yield expressions and statements are only used when defining a
*generator* function, and are only used in the body of the generator
function. Using yield in a function definition is sufficient to cause
that definition to create a generator function instead of a normal
function.
For full details of "yield" semantics, refer to the Yield expressions
section.
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ddddddddddddddd d d d d d!d"d#d$d%d&d d'd(d)d*d+d,d-d.d/d0d1d2d3d4d5d6d7d8d9d:d;d<d=d>d?d@dAdBdCdDdEdFdGdHdIdJdKdL�MZ dMS )Nau The "assert" statement
**********************
Assert statements are a convenient way to insert debugging assertions
into a program:
assert_stmt ::= "assert" expression ["," expression]
The simple form, "assert expression", is equivalent to
if __debug__:
if not expression: raise AssertionError
The extended form, "assert expression1, expression2", is equivalent to
if __debug__:
if not expression1: raise AssertionError(expression2)
These equivalences assume that "__debug__" and "AssertionError" refer
to the built-in variables with those names. In the current
implementation, the built-in variable "__debug__" is "True" under
normal circumstances, "False" when optimization is requested (command
line option "-O"). The current code generator emits no code for an
assert statement when optimization is requested at compile time. Note
that it is unnecessary to include the source code for the expression
that failed in the error message; it will be displayed as part of the
stack trace.
Assignments to "__debug__" are illegal. The value for the built-in
variable is determined when the interpreter starts.
us+ Assignment statements
*********************
Assignment statements are used to (re)bind names to values and to
modify attributes or items of mutable objects:
assignment_stmt ::= (target_list "=")+ (starred_expression | yield_expression)
target_list ::= target ("," target)* [","]
target ::= identifier
| "(" [target_list] ")"
| "[" [target_list] "]"
| attributeref
| subscription
| slicing
| "*" target
(See section Primaries for the syntax definitions for *attributeref*,
*subscription*, and *slicing*.)
An assignment statement evaluates the expression list (remember that
this can be a single expression or a comma-separated list, the latter
yielding a tuple) and assigns the single resulting object to each of
the target lists, from left to right.
Assignment is defined recursively depending on the form of the target
(list). When a target is part of a mutable object (an attribute
reference, subscription or slicing), the mutable object must
ultimately perform the assignment and decide about its validity, and
may raise an exception if the assignment is unacceptable. The rules
observed by various types and the exceptions raised are given with the
definition of the object types (see section The standard type
hierarchy).
Assignment of an object to a target list, optionally enclosed in
parentheses or square brackets, is recursively defined as follows.
* If the target list is a single target with no trailing comma,
optionally in parentheses, the object is assigned to that target.
* Else: The object must be an iterable with the same number of items
as there are targets in the target list, and the items are assigned,
from left to right, to the corresponding targets.
* If the target list contains one target prefixed with an
asterisk, called a “starred” target: The object must be an
iterable with at least as many items as there are targets in the
target list, minus one. The first items of the iterable are
assigned, from left to right, to the targets before the starred
target. The final items of the iterable are assigned to the
targets after the starred target. A list of the remaining items
in the iterable is then assigned to the starred target (the list
can be empty).
* Else: The object must be an iterable with the same number of
items as there are targets in the target list, and the items are
assigned, from left to right, to the corresponding targets.
Assignment of an object to a single target is recursively defined as
follows.
* If the target is an identifier (name):
* If the name does not occur in a "global" or "nonlocal" statement
in the current code block: the name is bound to the object in the
current local namespace.
* Otherwise: the name is bound to the object in the global
namespace or the outer namespace determined by "nonlocal",
respectively.
The name is rebound if it was already bound. This may cause the
reference count for the object previously bound to the name to reach
zero, causing the object to be deallocated and its destructor (if it
has one) to be called.
* If the target is an attribute reference: The primary expression in
the reference is evaluated. It should yield an object with
assignable attributes; if this is not the case, "TypeError" is
raised. That object is then asked to assign the assigned object to
the given attribute; if it cannot perform the assignment, it raises
an exception (usually but not necessarily "AttributeError").
Note: If the object is a class instance and the attribute reference
occurs on both sides of the assignment operator, the RHS expression,
"a.x" can access either an instance attribute or (if no instance
attribute exists) a class attribute. The LHS target "a.x" is always
set as an instance attribute, creating it if necessary. Thus, the
two occurrences of "a.x" do not necessarily refer to the same
attribute: if the RHS expression refers to a class attribute, the
LHS creates a new instance attribute as the target of the
assignment:
class Cls:
x = 3 # class variable
inst = Cls()
inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3
This description does not necessarily apply to descriptor
attributes, such as properties created with "property()".
* If the target is a subscription: The primary expression in the
reference is evaluated. It should yield either a mutable sequence
object (such as a list) or a mapping object (such as a dictionary).
Next, the subscript expression is evaluated.
If the primary is a mutable sequence object (such as a list), the
subscript must yield an integer. If it is negative, the sequence’s
length is added to it. The resulting value must be a nonnegative
integer less than the sequence’s length, and the sequence is asked
to assign the assigned object to its item with that index. If the
index is out of range, "IndexError" is raised (assignment to a
subscripted sequence cannot add new items to a list).
If the primary is a mapping object (such as a dictionary), the
subscript must have a type compatible with the mapping’s key type,
and the mapping is then asked to create a key/datum pair which maps
the subscript to the assigned object. This can either replace an
existing key/value pair with the same key value, or insert a new
key/value pair (if no key with the same value existed).
For user-defined objects, the "__setitem__()" method is called with
appropriate arguments.
* If the target is a slicing: The primary expression in the
reference is evaluated. It should yield a mutable sequence object
(such as a list). The assigned object should be a sequence object
of the same type. Next, the lower and upper bound expressions are
evaluated, insofar they are present; defaults are zero and the
sequence’s length. The bounds should evaluate to integers. If
either bound is negative, the sequence’s length is added to it. The
resulting bounds are clipped to lie between zero and the sequence’s
length, inclusive. Finally, the sequence object is asked to replace
the slice with the items of the assigned sequence. The length of
the slice may be different from the length of the assigned sequence,
thus changing the length of the target sequence, if the target
sequence allows it.
**CPython implementation detail:** In the current implementation, the
syntax for targets is taken to be the same as for expressions, and
invalid syntax is rejected during the code generation phase, causing
less detailed error messages.
Although the definition of assignment implies that overlaps between
the left-hand side and the right-hand side are ‘simultaneous’ (for
example "a, b = b, a" swaps two variables), overlaps *within* the
collection of assigned-to variables occur left-to-right, sometimes
resulting in confusion. For instance, the following program prints
"[0, 2]":
x = [0, 1]
i = 0
i, x[i] = 1, 2 # i is updated, then x[i] is updated
print(x)
See also:
**PEP 3132** - Extended Iterable Unpacking
The specification for the "*target" feature.
Augmented assignment statements
===============================
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions of the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side. For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
Annotated assignment statements
===============================
Annotation assignment is the combination, in a single statement, of a
variable or attribute annotation and an optional assignment statement:
annotated_assignment_stmt ::= augtarget ":" expression ["=" expression]
The difference from normal Assignment statements is that only single
target and only single right hand side value is allowed.
For simple names as assignment targets, if in class or module scope,
the annotations are evaluated and stored in a special class or module
attribute "__annotations__" that is a dictionary mapping from variable
names (mangled if private) to evaluated annotations. This attribute is
writable and is automatically created at the start of class or module
body execution, if annotations are found statically.
For expressions as assignment targets, the annotations are evaluated
if in class or module scope, but not stored.
If a name is annotated in a function scope, then this name is local
for that scope. Annotations are never evaluated and stored in function
scopes.
If the right hand side is present, an annotated assignment performs
the actual assignment before evaluating annotations (where
applicable). If the right hand side is not present for an expression
target, then the interpreter evaluates the target except for the last
"__setitem__()" or "__setattr__()" call.
See also:
**PEP 526** - Syntax for Variable Annotations
The proposal that added syntax for annotating the types of
variables (including class variables and instance variables),
instead of expressing them through comments.
**PEP 484** - Type hints
The proposal that added the "typing" module to provide a standard
syntax for type annotations that can be used in static analysis
tools and IDEs.
a� Identifiers (Names)
*******************
An identifier occurring as an atom is a name. See section Identifiers
and keywords for lexical definition and section Naming and binding for
documentation of naming and binding.
When the name is bound to an object, evaluation of the atom yields
that object. When a name is not bound, an attempt to evaluate it
raises a "NameError" exception.
**Private name mangling:** When an identifier that textually occurs in
a class definition begins with two or more underscore characters and
does not end in two or more underscores, it is considered a *private
name* of that class. Private names are transformed to a longer form
before code is generated for them. The transformation inserts the
class name, with leading underscores removed and a single underscore
inserted, in front of the name. For example, the identifier "__spam"
occurring in a class named "Ham" will be transformed to "_Ham__spam".
This transformation is independent of the syntactical context in which
the identifier is used. If the transformed name is extremely long
(longer than 255 characters), implementation defined truncation may
happen. If the class name consists only of underscores, no
transformation is done.
u
Literals
********
Python supports string and bytes literals and various numeric
literals:
literal ::= stringliteral | bytesliteral
| integer | floatnumber | imagnumber
Evaluation of a literal yields an object of the given type (string,
bytes, integer, floating point number, complex number) with the given
value. The value may be approximated in the case of floating point
and imaginary (complex) literals. See section Literals for details.
All literals correspond to immutable data types, and hence the
object’s identity is less important than its value. Multiple
evaluations of literals with the same value (either the same
occurrence in the program text or a different occurrence) may obtain
the same object or a different object with the same value.
u- Customizing attribute access
****************************
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
return the (computed) attribute value or raise an "AttributeError"
exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note: This method may still be bypassed when looking up special
methods as the result of implicit invocation via language syntax
or built-in functions. See Special method lookup.
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should call the base class method with the same name, for example,
"object.__setattr__(self, name, value)".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
Customizing module attribute access
===================================
For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
setattr(self, attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note: Setting module "__class__" only affects lookups made using the
attribute access syntax – directly accessing the module globals
(whether by code within the module, or via a reference to the
module’s globals dictionary) is unaffected.
Changed in version 3.5: "__class__" module attribute is now writable.
Implementing Descriptors
========================
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents). In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".
object.__get__(self, instance, owner)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). *owner* is always the owner class, while *instance* is the
instance that the attribute was accessed through, or "None" when
the attribute is accessed through the *owner*. This method should
return the (computed) attribute value or raise an "AttributeError"
exception.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
object.__set_name__(self, owner, name)
Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.
New in version 3.6.
The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).
Invoking Descriptors
====================
In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a class, "A.x" is transformed into the call:
"A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined. A descriptor can define
any combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
=========
*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of *__dict__* and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)
The space saved over using *__dict__* can be significant.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. *__slots__*
reserves space for the declared variables and prevents the
automatic creation of *__dict__* and *__weakref__* for each
instance.
Notes on using *__slots__*
--------------------------
* When inheriting from a class without *__slots__*, the *__dict__*
and *__weakref__* attribute of the instances will always be
accessible.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
* Without a *__weakref__* variable for each instance, classes
defining *__slots__* do not support weak references to its
instances. If weak reference support is needed, then add
"'__weakref__'" to the sequence of strings in the *__slots__*
declaration.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is not limited to the
class where it is defined. *__slots__* declared in parents are
available in child classes. However, child subclasses will get a
*__dict__* and *__weakref__* unless they also define *__slots__*
(which should only contain names of any *additional* slots).
* If a class defines a slot also defined in a base class, the
instance variable defined by the base class slot is inaccessible
(except by retrieving its descriptor directly from the base class).
This renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
“variable-length” built-in types such as "int", "bytes" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings
may also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".
a� Attribute references
********************
An attribute reference is a primary followed by a period and a name:
attributeref ::= primary "." identifier
The primary must evaluate to an object of a type that supports
attribute references, which most objects do. This object is then
asked to produce the attribute whose name is the identifier. This
production can be customized by overriding the "__getattr__()" method.
If this attribute is not available, the exception "AttributeError" is
raised. Otherwise, the type and value of the object produced is
determined by the object. Multiple evaluations of the same attribute
reference may yield different objects.
a� Augmented assignment statements
*******************************
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions of the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side. For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
uj Binary arithmetic operations
****************************
The binary arithmetic operations have the conventional priority
levels. Note that some of these operations also apply to certain non-
numeric types. Apart from the power operator, there are only two
levels, one for multiplicative operators and one for additive
operators:
m_expr ::= u_expr | m_expr "*" u_expr | m_expr "@" m_expr |
m_expr "//" u_expr | m_expr "/" u_expr |
m_expr "%" u_expr
a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr
The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer and the other must be a sequence. In the former case, the
numbers are converted to a common type and then multiplied together.
In the latter case, sequence repetition is performed; a negative
repetition factor yields an empty sequence.
The "@" (at) operator is intended to be used for matrix
multiplication. No builtin Python types implement this operator.
New in version 3.5.
The "/" (division) and "//" (floor division) operators yield the
quotient of their arguments. The numeric arguments are first
converted to a common type. Division of integers yields a float, while
floor division of integers results in an integer; the result is that
of mathematical division with the ‘floor’ function applied to the
result. Division by zero raises the "ZeroDivisionError" exception.
The "%" (modulo) operator yields the remainder from the division of
the first argument by the second. The numeric arguments are first
converted to a common type. A zero right argument raises the
"ZeroDivisionError" exception. The arguments may be floating point
numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 +
0.34".) The modulo operator always yields a result with the same sign
as its second operand (or zero); the absolute value of the result is
strictly smaller than the absolute value of the second operand [1].
The floor division and modulo operators are connected by the following
identity: "x == (x//y)*y + (x%y)". Floor division and modulo are also
connected with the built-in function "divmod()": "divmod(x, y) ==
(x//y, x%y)". [2].
In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string objects to perform old-style
string formatting (also known as interpolation). The syntax for
string formatting is described in the Python Library Reference,
section printf-style String Formatting.
The floor division operator, the modulo operator, and the "divmod()"
function are not defined for complex numbers. Instead, convert to a
floating point number using the "abs()" function if appropriate.
The "+" (addition) operator yields the sum of its arguments. The
arguments must either both be numbers or both be sequences of the same
type. In the former case, the numbers are converted to a common type
and then added together. In the latter case, the sequences are
concatenated.
The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.
a$ Binary bitwise operations
*************************
Each of the three bitwise operations has a different priority level:
and_expr ::= shift_expr | and_expr "&" shift_expr
xor_expr ::= and_expr | xor_expr "^" and_expr
or_expr ::= xor_expr | or_expr "|" xor_expr
The "&" operator yields the bitwise AND of its arguments, which must
be integers.
The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be integers.
The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be integers.
ux Code Objects
************
Code objects are used by the implementation to represent “pseudo-
compiled” executable Python code such as a function body. They differ
from function objects because they don’t contain a reference to their
global execution environment. Code objects are returned by the built-
in "compile()" function and can be extracted from function objects
through their "__code__" attribute. See also the "code" module.
A code object can be executed or evaluated by passing it (instead of a
source string) to the "exec()" or "eval()" built-in functions.
See The standard type hierarchy for more information.
a. The Ellipsis Object
*******************
This object is commonly used by slicing (see Slicings). It supports
no special operations. There is exactly one ellipsis object, named
"Ellipsis" (a built-in name). "type(Ellipsis)()" produces the
"Ellipsis" singleton.
It is written as "Ellipsis" or "...".
u The Null Object
***************
This object is returned by functions that don’t explicitly return a
value. It supports no special operations. There is exactly one null
object, named "None" (a built-in name). "type(None)()" produces the
same singleton.
It is written as "None".
u5 Type Objects
************
Type objects represent the various object types. An object’s type is
accessed by the built-in function "type()". There are no special
operations on types. The standard module "types" defines names for
all standard built-in types.
Types are written like this: "<class 'int'>".
a� Boolean operations
******************
or_test ::= and_test | or_test "or" and_test
and_test ::= not_test | and_test "and" not_test
not_test ::= comparison | "not" not_test
In the context of Boolean operations, and also when expressions are
used by control flow statements, the following values are interpreted
as false: "False", "None", numeric zero of all types, and empty
strings and containers (including strings, tuples, lists,
dictionaries, sets and frozensets). All other values are interpreted
as true. User-defined objects can customize their truth value by
providing a "__bool__()" method.
The operator "not" yields "True" if its argument is false, "False"
otherwise.
The expression "x and y" first evaluates *x*; if *x* is false, its
value is returned; otherwise, *y* is evaluated and the resulting value
is returned.
The expression "x or y" first evaluates *x*; if *x* is true, its value
is returned; otherwise, *y* is evaluated and the resulting value is
returned.
Note that neither "and" nor "or" restrict the value and type they
return to "False" and "True", but rather return the last evaluated
argument. This is sometimes useful, e.g., if "s" is a string that
should be replaced by a default value if it is empty, the expression
"s or 'foo'" yields the desired value. Because "not" has to create a
new value, it returns a boolean value regardless of the type of its
argument (for example, "not 'foo'" produces "False" rather than "''".)
a$ The "break" statement
*********************
break_stmt ::= "break"
"break" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop.
It terminates the nearest enclosing loop, skipping the optional "else"
clause if the loop has one.
If a "for" loop is terminated by "break", the loop control target
keeps its current value.
When "break" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
loop.
u� Emulating callable objects
**************************
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method
is defined, "x(arg1, arg2, ...)" is a shorthand for
"x.__call__(arg1, arg2, ...)".
uC Calls
*****
A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:
call ::= primary "(" [argument_list [","] | comprehension] ")"
argument_list ::= positional_arguments ["," starred_and_keywords]
["," keywords_arguments]
| starred_and_keywords ["," keywords_arguments]
| keywords_arguments
positional_arguments ::= ["*"] expression ("," ["*"] expression)*
starred_and_keywords ::= ("*" expression | keyword_item)
("," "*" expression | "," keyword_item)*
keywords_arguments ::= (keyword_item | "**" expression)
("," keyword_item | "," "**" expression)*
keyword_item ::= identifier "=" expression
An optional trailing comma may be present after the positional and
keyword arguments but does not affect the semantics.
The primary must evaluate to a callable object (user-defined
functions, built-in functions, methods of built-in objects, class
objects, methods of class instances, and all objects having a
"__call__()" method are callable). All argument expressions are
evaluated before the call is attempted. Please refer to section
Function definitions for the syntax of formal *parameter* lists.
If keyword arguments are present, they are first converted to
positional arguments, as follows. First, a list of unfilled slots is
created for the formal parameters. If there are N positional
arguments, they are placed in the first N slots. Next, for each
keyword argument, the identifier is used to determine the
corresponding slot (if the identifier is the same as the first formal
parameter name, the first slot is used, and so on). If the slot is
already filled, a "TypeError" exception is raised. Otherwise, the
value of the argument is placed in the slot, filling it (even if the
expression is "None", it fills the slot). When all arguments have
been processed, the slots that are still unfilled are filled with the
corresponding default value from the function definition. (Default
values are calculated, once, when the function is defined; thus, a
mutable object such as a list or dictionary used as default value will
be shared by all calls that don’t specify an argument value for the
corresponding slot; this should usually be avoided.) If there are any
unfilled slots for which no default value is specified, a "TypeError"
exception is raised. Otherwise, the list of filled slots is used as
the argument list for the call.
**CPython implementation detail:** An implementation may provide
built-in functions whose positional parameters do not have names, even
if they are ‘named’ for the purpose of documentation, and which
therefore cannot be supplied by keyword. In CPython, this is the case
for functions implemented in C that use "PyArg_ParseTuple()" to parse
their arguments.
If there are more positional arguments than there are formal parameter
slots, a "TypeError" exception is raised, unless a formal parameter
using the syntax "*identifier" is present; in this case, that formal
parameter receives a tuple containing the excess positional arguments
(or an empty tuple if there were no excess positional arguments).
If any keyword argument does not correspond to a formal parameter
name, a "TypeError" exception is raised, unless a formal parameter
using the syntax "**identifier" is present; in this case, that formal
parameter receives a dictionary containing the excess keyword
arguments (using the keywords as keys and the argument values as
corresponding values), or a (new) empty dictionary if there were no
excess keyword arguments.
If the syntax "*expression" appears in the function call, "expression"
must evaluate to an *iterable*. Elements from these iterables are
treated as if they were additional positional arguments. For the call
"f(x1, x2, *y, x3, x4)", if *y* evaluates to a sequence *y1*, …, *yM*,
this is equivalent to a call with M+4 positional arguments *x1*, *x2*,
*y1*, …, *yM*, *x3*, *x4*.
A consequence of this is that although the "*expression" syntax may
appear *after* explicit keyword arguments, it is processed *before*
the keyword arguments (and any "**expression" arguments – see below).
So:
>>> def f(a, b):
... print(a, b)
...
>>> f(b=1, *(2,))
2 1
>>> f(a=1, *(2,))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: f() got multiple values for keyword argument 'a'
>>> f(1, *(2,))
1 2
It is unusual for both keyword arguments and the "*expression" syntax
to be used in the same call, so in practice this confusion does not
arise.
If the syntax "**expression" appears in the function call,
"expression" must evaluate to a *mapping*, the contents of which are
treated as additional keyword arguments. If a keyword is already
present (as an explicit keyword argument, or from another unpacking),
a "TypeError" exception is raised.
Formal parameters using the syntax "*identifier" or "**identifier"
cannot be used as positional argument slots or as keyword argument
names.
Changed in version 3.5: Function calls accept any number of "*" and
"**" unpackings, positional arguments may follow iterable unpackings
("*"), and keyword arguments may follow dictionary unpackings ("**").
Originally proposed by **PEP 448**.
A call always returns some value, possibly "None", unless it raises an
exception. How this value is computed depends on the type of the
callable object.
If it is—
a user-defined function:
The code block for the function is executed, passing it the
argument list. The first thing the code block will do is bind the
formal parameters to the arguments; this is described in section
Function definitions. When the code block executes a "return"
statement, this specifies the return value of the function call.
a built-in function or method:
The result is up to the interpreter; see Built-in Functions for the
descriptions of built-in functions and methods.
a class object:
A new instance of that class is returned.
a class instance method:
The corresponding user-defined function is called, with an argument
list that is one longer than the argument list of the call: the
instance becomes the first argument.
a class instance:
The class must define a "__call__()" method; the effect is then the
same as if that method was called.
u Class definitions
*****************
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [argument_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing. Classes without an inheritance list
inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.) When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [3] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.
The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__". Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.
Class creation can be customized heavily using metaclasses.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result is then bound to the class name.
**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. Descriptors can be used to create instance
variables with different implementation details.
See also:
**PEP 3115** - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the
current syntax, and the semantics for how classes with
metaclasses are constructed.
**PEP 3129** - Class Decorators
The proposal that added class decorators. Function and method
decorators were introduced in **PEP 318**.
u4) Comparisons
***********
Unlike C, all comparison operations in Python have the same priority,
which is lower than that of any arithmetic, shifting or bitwise
operation. Also unlike C, expressions like "a < b < c" have the
interpretation that is conventional in mathematics:
comparison ::= or_expr (comp_operator or_expr)*
comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="
| "is" ["not"] | ["not"] "in"
Comparisons yield boolean values: "True" or "False".
Comparisons can be chained arbitrarily, e.g., "x < y <= z" is
equivalent to "x < y and y <= z", except that "y" is evaluated only
once (but in both cases "z" is not evaluated at all when "x < y" is
found to be false).
Formally, if *a*, *b*, *c*, …, *y*, *z* are expressions and *op1*,
*op2*, …, *opN* are comparison operators, then "a op1 b op2 c ... y
opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except
that each expression is evaluated at most once.
Note that "a op1 b op2 c" doesn’t imply any kind of comparison between
*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though
perhaps not pretty).
Value comparisons
=================
The operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects. The objects do not need to have the same type.
Chapter Objects, values and types states that objects have a value (in
addition to type and identity). The value of an object is a rather
abstract notion in Python: For example, there is no canonical access
method for an object’s value. Also, there is no requirement that the
value of an object should be constructed in a particular way, e.g.
comprised of all its data attributes. Comparison operators implement a
particular notion of what the value of an object is. One can think of
them as defining the value of an object indirectly, by means of their
comparison implementation.
Because all types are (direct or indirect) subtypes of "object", they
inherit the default comparison behavior from "object". Types can
customize their comparison behavior by implementing *rich comparison
methods* like "__lt__()", described in Basic customization.
The default behavior for equality comparison ("==" and "!=") is based
on the identity of the objects. Hence, equality comparison of
instances with the same identity results in equality, and equality
comparison of instances with different identities results in
inequality. A motivation for this default behavior is the desire that
all objects should be reflexive (i.e. "x is y" implies "x == y").
A default order comparison ("<", ">", "<=", and ">=") is not provided;
an attempt raises "TypeError". A motivation for this default behavior
is the lack of a similar invariant as for equality.
The behavior of the default equality comparison, that instances with
different identities are always unequal, may be in contrast to what
types will need that have a sensible definition of object value and
value-based equality. Such types will need to customize their
comparison behavior, and in fact, a number of built-in types have done
that.
The following list describes the comparison behavior of the most
important built-in types.
* Numbers of built-in numeric types (Numeric Types — int, float,
complex) and of the standard library types "fractions.Fraction" and
"decimal.Decimal" can be compared within and across their types,
with the restriction that complex numbers do not support order
comparison. Within the limits of the types involved, they compare
mathematically (algorithmically) correct without loss of precision.
The not-a-number values "float('NaN')" and "Decimal('NaN')" are
special. They are identical to themselves ("x is x" is true) but
are not equal to themselves ("x == x" is false). Additionally,
comparing any number to a not-a-number value will return "False".
For example, both "3 < float('NaN')" and "float('NaN') < 3" will
return "False".
* Binary sequences (instances of "bytes" or "bytearray") can be
compared within and across their types. They compare
lexicographically using the numeric values of their elements.
* Strings (instances of "str") compare lexicographically using the
numerical Unicode code points (the result of the built-in function
"ord()") of their characters. [3]
Strings and binary sequences cannot be directly compared.
* Sequences (instances of "tuple", "list", or "range") can be
compared only within each of their types, with the restriction that
ranges do not support order comparison. Equality comparison across
these types results in inequality, and ordering comparison across
these types raises "TypeError".
Sequences compare lexicographically using comparison of
corresponding elements, whereby reflexivity of the elements is
enforced.
In enforcing reflexivity of elements, the comparison of collections
assumes that for a collection element "x", "x == x" is always true.
Based on that assumption, element identity is compared first, and
element comparison is performed only for distinct elements. This
approach yields the same result as a strict element comparison
would, if the compared elements are reflexive. For non-reflexive
elements, the result is different than for strict element
comparison, and may be surprising: The non-reflexive not-a-number
values for example result in the following comparison behavior when
used in a list:
>>> nan = float('NaN')
>>> nan is nan
True
>>> nan == nan
False <-- the defined non-reflexive behavior of NaN
>>> [nan] == [nan]
True <-- list enforces reflexivity and tests identity first
Lexicographical comparison between built-in collections works as
follows:
* For two collections to compare equal, they must be of the same
type, have the same length, and each pair of corresponding
elements must compare equal (for example, "[1,2] == (1,2)" is
false because the type is not the same).
* Collections that support order comparison are ordered the same
as their first unequal elements (for example, "[1,2,x] <= [1,2,y]"
has the same value as "x <= y"). If a corresponding element does
not exist, the shorter collection is ordered first (for example,
"[1,2] < [1,2,3]" is true).
* Mappings (instances of "dict") compare equal if and only if they
have equal *(key, value)* pairs. Equality comparison of the keys and
values enforces reflexivity.
Order comparisons ("<", ">", "<=", and ">=") raise "TypeError".
* Sets (instances of "set" or "frozenset") can be compared within
and across their types.
They define order comparison operators to mean subset and superset
tests. Those relations do not define total orderings (for example,
the two sets "{1,2}" and "{2,3}" are not equal, nor subsets of one
another, nor supersets of one another). Accordingly, sets are not
appropriate arguments for functions which depend on total ordering
(for example, "min()", "max()", and "sorted()" produce undefined
results given a list of sets as inputs).
Comparison of sets enforces reflexivity of its elements.
* Most other built-in types have no comparison methods implemented,
so they inherit the default comparison behavior.
User-defined classes that customize their comparison behavior should
follow some consistency rules, if possible:
* Equality comparison should be reflexive. In other words, identical
objects should compare equal:
"x is y" implies "x == y"
* Comparison should be symmetric. In other words, the following
expressions should have the same result:
"x == y" and "y == x"
"x != y" and "y != x"
"x < y" and "y > x"
"x <= y" and "y >= x"
* Comparison should be transitive. The following (non-exhaustive)
examples illustrate that:
"x > y and y > z" implies "x > z"
"x < y and y <= z" implies "x < z"
* Inverse comparison should result in the boolean negation. In other
words, the following expressions should have the same result:
"x == y" and "not x != y"
"x < y" and "not x >= y" (for total ordering)
"x > y" and "not x <= y" (for total ordering)
The last two expressions apply to totally ordered collections (e.g.
to sequences, but not to sets or mappings). See also the
"total_ordering()" decorator.
* The "hash()" result should be consistent with equality. Objects
that are equal should either have the same hash value, or be marked
as unhashable.
Python does not enforce these consistency rules. In fact, the
not-a-number values are an example for not following these rules.
Membership test operations
==========================
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y". If an exception is raised
during the iteration, it is as if "in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse true value of
"in".
Identity comparisons
====================
The operators "is" and "is not" test for object identity: "x is y" is
true if and only if *x* and *y* are the same object. Object identity
is determined using the "id()" function. "x is not y" yields the
inverse truth value. [4]
uxe Compound statements
*******************
Compound statements contain (groups of) other statements; they affect
or control the execution of those other statements in some way. In
general, compound statements span multiple lines, although in simple
incarnations a whole compound statement may be contained in one line.
The "if", "while" and "for" statements implement traditional control
flow constructs. "try" specifies exception handlers and/or cleanup
code for a group of statements, while the "with" statement allows the
execution of initialization and finalization code around a block of
code. Function and class definitions are also syntactically compound
statements.
A compound statement consists of one or more ‘clauses.’ A clause
consists of a header and a ‘suite.’ The clause headers of a
particular compound statement are all at the same indentation level.
Each clause header begins with a uniquely identifying keyword and ends
with a colon. A suite is a group of statements controlled by a
clause. A suite can be one or more semicolon-separated simple
statements on the same line as the header, following the header’s
colon, or it can be one or more indented statements on subsequent
lines. Only the latter form of a suite can contain nested compound
statements; the following is illegal, mostly because it wouldn’t be
clear to which "if" clause a following "else" clause would belong:
if test1: if test2: print(x)
Also note that the semicolon binds tighter than the colon in this
context, so that in the following example, either all or none of the
"print()" calls are executed:
if x < y < z: print(x); print(y); print(z)
Summarizing:
compound_stmt ::= if_stmt
| while_stmt
| for_stmt
| try_stmt
| with_stmt
| funcdef
| classdef
| async_with_stmt
| async_for_stmt
| async_funcdef
suite ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT
statement ::= stmt_list NEWLINE | compound_stmt
stmt_list ::= simple_stmt (";" simple_stmt)* [";"]
Note that statements always end in a "NEWLINE" possibly followed by a
"DEDENT". Also note that optional continuation clauses always begin
with a keyword that cannot start a statement, thus there are no
ambiguities (the ‘dangling "else"’ problem is solved in Python by
requiring nested "if" statements to be indented).
The formatting of the grammar rules in the following sections places
each clause on a separate line for clarity.
The "if" statement
==================
The "if" statement is used for conditional execution:
if_stmt ::= "if" expression ":" suite
("elif" expression ":" suite)*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
The "while" statement
=====================
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
The "for" statement
===================
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator. Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.
The for-loop makes assignments to the variables(s) in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop. Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".
Note: There is a subtlety when the sequence is being modified by the
loop (this can only occur for mutable sequences, e.g. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
The "try" statement
===================
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object or a tuple containing an item compatible with the exception.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed. All except
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred. "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.
The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed. Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a ‘cleanup’ handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return" or "break" statement, the saved exception
is discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’ A "continue" statement is illegal in
the "finally" clause. (The reason is a problem with the current
implementation — this restriction may be lifted in the future).
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
The "with" statement
====================
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one “item” proceeds as
follows:
1. The context expression (the expression given in the "with_item")
is evaluated to obtain a context manager.
2. The context manager’s "__exit__()" is loaded for later use.
3. The context manager’s "__enter__()" method is invoked.
4. If a target was included in the "with" statement, the return
value from "__enter__()" is assigned to it.
Note: The "with" statement guarantees that if the "__enter__()"
method returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
5. The suite is executed.
6. The context manager’s "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
suite
is equivalent to
with A() as a:
with B() as b:
suite
Changed in version 3.1: Support for multiple context expressions.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
Function definitions
====================
A function definition defines a user-defined function object (see
section The standard type hierarchy):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
| parameter_list_starargs
parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
| "**" parameter [","]
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [2]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
except that the original function is not temporarily bound to the name
"func".
When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted. If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.
**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call. This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended. A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from position arguments, from keyword
arguments, or from default values. If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type. Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed
used keyword arguments.
Parameters may have annotations of the form “": expression"” following
the parameter name. Any parameter may have an annotation even those
of the form "*identifier" or "**identifier". Functions may have
“return” annotation of the form “"-> expression"” after the parameter
list. These annotations can be any valid Python expression and are
evaluated when the function definition is executed. Annotations may
be evaluated in a different order than they appear in the source code.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.
**Programmer’s note:** Functions are first-class objects. A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around. Free variables used
in the nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
See also:
**PEP 3107** - Function Annotations
The original specification for function annotations.
Class definitions
=================
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [argument_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing. Classes without an inheritance list
inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.) When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [3] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.
The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__". Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.
Class creation can be customized heavily using metaclasses.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result is then bound to the class name.
**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. Descriptors can be used to create instance
variables with different implementation details.
See also:
**PEP 3115** - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the
current syntax, and the semantics for how classes with
metaclasses are constructed.
**PEP 3129** - Class Decorators
The proposal that added class decorators. Function and method
decorators were introduced in **PEP 318**.
Coroutines
==========
New in version 3.5.
Coroutine function definition
-----------------------------
async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*). In the body of a coroutine, any "await" and
"async" identifiers become reserved keywords; "await" expressions,
"async for" and "async with" can only be used in coroutine bodies.
Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.
It is a "SyntaxError" to use "yield from" expressions in "async def"
coroutines.
An example of a coroutine function:
async def func(param1, param2):
do_stuff()
await some_coroutine()
The "async for" statement
-------------------------
async_for_stmt ::= "async" for_stmt
An *asynchronous iterable* is able to call asynchronous code in its
*iter* implementation, and *asynchronous iterator* can call
asynchronous code in its *next* method.
The "async for" statement allows convenient iteration over
asynchronous iterators.
The following code:
async for TARGET in ITER:
BLOCK
else:
BLOCK2
Is semantically equivalent to:
iter = (ITER)
iter = type(iter).__aiter__(iter)
running = True
while running:
try:
TARGET = await type(iter).__anext__(iter)
except StopAsyncIteration:
running = False
else:
BLOCK
else:
BLOCK2
See also "__aiter__()" and "__anext__()" for details.
It is a "SyntaxError" to use "async for" statement outside of an
"async def" function.
The "async with" statement
--------------------------
async_with_stmt ::= "async" with_stmt
An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.
The following code:
async with EXPR as VAR:
BLOCK
Is semantically equivalent to:
mgr = (EXPR)
aexit = type(mgr).__aexit__
aenter = type(mgr).__aenter__(mgr)
VAR = await aenter
try:
BLOCK
except:
if not await aexit(mgr, *sys.exc_info()):
raise
else:
await aexit(mgr, None, None, None)
See also "__aenter__()" and "__aexit__()" for details.
It is a "SyntaxError" to use "async with" statement outside of an
"async def" function.
See also:
**PEP 492** - Coroutines with async and await syntax
The proposal that made coroutines a proper standalone concept in
Python, and added supporting syntax.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless
there is a "finally" clause which happens to raise another
exception. That new exception causes the old one to be lost.
[2] A string literal appearing as the first statement in the
function body is transformed into the function’s "__doc__"
attribute and therefore the function’s *docstring*.
[3] A string literal appearing as the first statement in the class
body is transformed into the namespace’s "__doc__" item and
therefore the class’s *docstring*.
u� With Statement Context Managers
*******************************
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method’s return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller’s responsibility.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
a� The "continue" statement
************************
continue_stmt ::= "continue"
"continue" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition or "finally"
clause within that loop. It continues with the next cycle of the
nearest enclosing loop.
When "continue" passes control out of a "try" statement with a
"finally" clause, that "finally" clause is executed before really
starting the next loop cycle.
u� Arithmetic conversions
**********************
When a description of an arithmetic operator below uses the phrase
“the numeric arguments are converted to a common type,” this means
that the operator implementation for built-in types works as follows:
* If either argument is a complex number, the other is converted to
complex;
* otherwise, if either argument is a floating point number, the
other is converted to floating point;
* otherwise, both must be integers and no conversion is necessary.
Some additional rules apply for certain operators (e.g., a string as a
left argument to the ‘%’ operator). Extensions must define their own
conversion behavior.
u�3 Basic customization
*******************
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass’s "__new__()" method using
"super().__new__(cls[, ...])" with appropriate arguments and then
modifying the newly-created instance as necessary before returning
it.
If "__new__()" returns an instance of *cls*, then the new
instance’s "__init__()" method will be invoked like
"__init__(self[, ...])", where *self* is the new instance and the
remaining arguments are the same as were passed to "__new__()".
If "__new__()" does not return an instance of *cls*, then the new
instance’s "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class’s "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"super().__init__([args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.
It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.
It is not guaranteed that "__del__()" methods are called for
objects that still exist when the interpreter exits.
Note: "del x" doesn’t directly call "x.__del__()" — the former
decrements the reference count for "x" by one, and the latter is
only called when "x"’s reference count reaches zero.
**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.
See also: Documentation for the "gc" module.
Warning: Due to the precarious circumstances under which
"__del__()" methods are invoked, exceptions that occur during
their execution are ignored, and a warning is printed to
"sys.stderr" instead. In particular:
* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".
* "__del__()" can be executed during interpreter shutdown. As
a consequence, the global variables it needs to access
(including other modules) may already have been deleted or set
to "None". Python guarantees that globals whose name begins
with a single underscore are deleted from their module before
other globals are deleted; if no other references to such
globals exist, this may help in assuring that imported modules
are still available at the time when the "__del__()" method is
called.
object.__repr__(self)
Called by the "repr()" built-in function to compute the “official”
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an “informal”
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the “informal” or nicely printable string
representation of an object. The return value must be a string
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by bytes to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function, and by extension,
evaluation of formatted string literals and the "str.format()"
method, to produce a “formatted” string representation of an
object. The "format_spec" argument is a string that contains a
description of the formatting options desired. The interpretation
of the "format_spec" argument is up to the type implementing
"__format__()", however most classes will either delegate
formatting to one of the built-in types, or use a similar
formatting option syntax.
See Format Specification Mini-Language for a description of the
standard formatting syntax.
The return value must be a string object.
Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
By default, "__ne__()" delegates to "__eq__()" and inverts the
result unless it is "NotImplemented". There are no other implied
relationships among the comparison operators, for example, the
truth of "(x<y or x==y)" does not imply "x<=y". To automatically
generate ordering operations from a single root operation, see
"functools.total_ordering()".
See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
Note: "hash()" truncates the value returned from an object’s
custom "__hash__()" method to the size of a "Py_ssize_t". This
is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit
builds. If an object’s "__hash__()" must interoperate on builds
of different bit sizes, be sure to check the width on all
supported builds. An easy way to do this is with "python -c
"import sys; print(sys.hash_info.width)"".
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key’s hash value is immutable (if the object’s hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj, collections.Hashable)".
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
<ParentClass>.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.Hashable)" call.
Note: By default, the "__hash__()" values of str, bytes and
datetime objects are “salted” with an unpredictable random value.
Although they remain constant within an individual Python
process, they are not predictable between repeated invocations of
Python.This is intended to provide protection against a denial-
of-service caused by carefully-chosen inputs that exploit the
worst case performance of a dict insertion, O(n^2) complexity.
See http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of
dicts, sets and other mappings. Python has never made guarantees
about this ordering (and it typically varies between 32-bit and
64-bit builds).See also "PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
uiF "pdb" — The Python Debugger
***************************
**Source code:** Lib/pdb.py
======================================================================
The module "pdb" defines an interactive source code debugger for
Python programs. It supports setting (conditional) breakpoints and
single stepping at the source line level, inspection of stack frames,
source code listing, and evaluation of arbitrary Python code in the
context of any stack frame. It also supports post-mortem debugging
and can be called under program control.
The debugger is extensible – it is actually defined as the class
"Pdb". This is currently undocumented but easily understood by reading
the source. The extension interface uses the modules "bdb" and "cmd".
The debugger’s prompt is "(Pdb)". Typical usage to run a program under
control of the debugger is:
>>> import pdb
>>> import mymodule
>>> pdb.run('mymodule.test()')
> <string>(0)?()
(Pdb) continue
> <string>(1)?()
(Pdb) continue
NameError: 'spam'
> <string>(1)?()
(Pdb)
Changed in version 3.3: Tab-completion via the "readline" module is
available for commands and command arguments, e.g. the current global
and local names are offered as arguments of the "p" command.
"pdb.py" can also be invoked as a script to debug other scripts. For
example:
python3 -m pdb myscript.py
When invoked as a script, pdb will automatically enter post-mortem
debugging if the program being debugged exits abnormally. After post-
mortem debugging (or after normal exit of the program), pdb will
restart the program. Automatic restarting preserves pdb’s state (such
as breakpoints) and in most cases is more useful than quitting the
debugger upon program’s exit.
New in version 3.2: "pdb.py" now accepts a "-c" option that executes
commands as if given in a ".pdbrc" file, see Debugger Commands.
The typical usage to break into the debugger from a running program is
to insert
import pdb; pdb.set_trace()
at the location you want to break into the debugger. You can then
step through the code following this statement, and continue running
without the debugger using the "continue" command.
The typical usage to inspect a crashed program is:
>>> import pdb
>>> import mymodule
>>> mymodule.test()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "./mymodule.py", line 4, in test
test2()
File "./mymodule.py", line 3, in test2
print(spam)
NameError: spam
>>> pdb.pm()
> ./mymodule.py(3)test2()
-> print(spam)
(Pdb)
The module defines the following functions; each enters the debugger
in a slightly different way:
pdb.run(statement, globals=None, locals=None)
Execute the *statement* (given as a string or a code object) under
debugger control. The debugger prompt appears before any code is
executed; you can set breakpoints and type "continue", or you can
step through the statement using "step" or "next" (all these
commands are explained below). The optional *globals* and *locals*
arguments specify the environment in which the code is executed; by
default the dictionary of the module "__main__" is used. (See the
explanation of the built-in "exec()" or "eval()" functions.)
pdb.runeval(expression, globals=None, locals=None)
Evaluate the *expression* (given as a string or a code object)
under debugger control. When "runeval()" returns, it returns the
value of the expression. Otherwise this function is similar to
"run()".
pdb.runcall(function, *args, **kwds)
Call the *function* (a function or method object, not a string)
with the given arguments. When "runcall()" returns, it returns
whatever the function call returned. The debugger prompt appears
as soon as the function is entered.
pdb.set_trace()
Enter the debugger at the calling stack frame. This is useful to
hard-code a breakpoint at a given point in a program, even if the
code is not otherwise being debugged (e.g. when an assertion
fails).
pdb.post_mortem(traceback=None)
Enter post-mortem debugging of the given *traceback* object. If no
*traceback* is given, it uses the one of the exception that is
currently being handled (an exception must be being handled if the
default is to be used).
pdb.pm()
Enter post-mortem debugging of the traceback found in
"sys.last_traceback".
The "run*" functions and "set_trace()" are aliases for instantiating
the "Pdb" class and calling the method of the same name. If you want
to access further features, you have to do this yourself:
class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None, nosigint=False, readrc=True)
"Pdb" is the debugger class.
The *completekey*, *stdin* and *stdout* arguments are passed to the
underlying "cmd.Cmd" class; see the description there.
The *skip* argument, if given, must be an iterable of glob-style
module name patterns. The debugger will not step into frames that
originate in a module that matches one of these patterns. [1]
By default, Pdb sets a handler for the SIGINT signal (which is sent
when the user presses "Ctrl-C" on the console) when you give a
"continue" command. This allows you to break into the debugger
again by pressing "Ctrl-C". If you want Pdb not to touch the
SIGINT handler, set *nosigint* to true.
The *readrc* argument defaults to true and controls whether Pdb
will load .pdbrc files from the filesystem.
Example call to enable tracing with *skip*:
import pdb; pdb.Pdb(skip=['django.*']).set_trace()
New in version 3.1: The *skip* argument.
New in version 3.2: The *nosigint* argument. Previously, a SIGINT
handler was never set by Pdb.
Changed in version 3.6: The *readrc* argument.
run(statement, globals=None, locals=None)
runeval(expression, globals=None, locals=None)
runcall(function, *args, **kwds)
set_trace()
See the documentation for the functions explained above.
Debugger Commands
=================
The commands recognized by the debugger are listed below. Most
commands can be abbreviated to one or two letters as indicated; e.g.
"h(elp)" means that either "h" or "help" can be used to enter the help
command (but not "he" or "hel", nor "H" or "Help" or "HELP").
Arguments to commands must be separated by whitespace (spaces or
tabs). Optional arguments are enclosed in square brackets ("[]") in
the command syntax; the square brackets must not be typed.
Alternatives in the command syntax are separated by a vertical bar
("|").
Entering a blank line repeats the last command entered. Exception: if
the last command was a "list" command, the next 11 lines are listed.
Commands that the debugger doesn’t recognize are assumed to be Python
statements and are executed in the context of the program being
debugged. Python statements can also be prefixed with an exclamation
point ("!"). This is a powerful way to inspect the program being
debugged; it is even possible to change a variable or call a function.
When an exception occurs in such a statement, the exception name is
printed but the debugger’s state is not changed.
The debugger supports aliases. Aliases can have parameters which
allows one a certain level of adaptability to the context under
examination.
Multiple commands may be entered on a single line, separated by ";;".
(A single ";" is not used as it is the separator for multiple commands
in a line that is passed to the Python parser.) No intelligence is
applied to separating the commands; the input is split at the first
";;" pair, even if it is in the middle of a quoted string.
If a file ".pdbrc" exists in the user’s home directory or in the
current directory, it is read in and executed as if it had been typed
at the debugger prompt. This is particularly useful for aliases. If
both files exist, the one in the home directory is read first and
aliases defined there can be overridden by the local file.
Changed in version 3.2: ".pdbrc" can now contain commands that
continue debugging, such as "continue" or "next". Previously, these
commands had no effect.
h(elp) [command]
Without argument, print the list of available commands. With a
*command* as argument, print help about that command. "help pdb"
displays the full documentation (the docstring of the "pdb"
module). Since the *command* argument must be an identifier, "help
exec" must be entered to get help on the "!" command.
w(here)
Print a stack trace, with the most recent frame at the bottom. An
arrow indicates the current frame, which determines the context of
most commands.
d(own) [count]
Move the current frame *count* (default one) levels down in the
stack trace (to a newer frame).
u(p) [count]
Move the current frame *count* (default one) levels up in the stack
trace (to an older frame).
b(reak) [([filename:]lineno | function) [, condition]]
With a *lineno* argument, set a break there in the current file.
With a *function* argument, set a break at the first executable
statement within that function. The line number may be prefixed
with a filename and a colon, to specify a breakpoint in another
file (probably one that hasn’t been loaded yet). The file is
searched on "sys.path". Note that each breakpoint is assigned a
number to which all the other breakpoint commands refer.
If a second argument is present, it is an expression which must
evaluate to true before the breakpoint is honored.
Without argument, list all breaks, including for each breakpoint,
the number of times that breakpoint has been hit, the current
ignore count, and the associated condition if any.
tbreak [([filename:]lineno | function) [, condition]]
Temporary breakpoint, which is removed automatically when it is
first hit. The arguments are the same as for "break".
cl(ear) [filename:lineno | bpnumber [bpnumber ...]]
With a *filename:lineno* argument, clear all the breakpoints at
this line. With a space separated list of breakpoint numbers, clear
those breakpoints. Without argument, clear all breaks (but first
ask confirmation).
disable [bpnumber [bpnumber ...]]
Disable the breakpoints given as a space separated list of
breakpoint numbers. Disabling a breakpoint means it cannot cause
the program to stop execution, but unlike clearing a breakpoint, it
remains in the list of breakpoints and can be (re-)enabled.
enable [bpnumber [bpnumber ...]]
Enable the breakpoints specified.
ignore bpnumber [count]
Set the ignore count for the given breakpoint number. If count is
omitted, the ignore count is set to 0. A breakpoint becomes active
when the ignore count is zero. When non-zero, the count is
decremented each time the breakpoint is reached and the breakpoint
is not disabled and any associated condition evaluates to true.
condition bpnumber [condition]
Set a new *condition* for the breakpoint, an expression which must
evaluate to true before the breakpoint is honored. If *condition*
is absent, any existing condition is removed; i.e., the breakpoint
is made unconditional.
commands [bpnumber]
Specify a list of commands for breakpoint number *bpnumber*. The
commands themselves appear on the following lines. Type a line
containing just "end" to terminate the commands. An example:
(Pdb) commands 1
(com) p some_variable
(com) end
(Pdb)
To remove all commands from a breakpoint, type commands and follow
it immediately with "end"; that is, give no commands.
With no *bpnumber* argument, commands refers to the last breakpoint
set.
You can use breakpoint commands to start your program up again.
Simply use the continue command, or step, or any other command that
resumes execution.
Specifying any command resuming execution (currently continue,
step, next, return, jump, quit and their abbreviations) terminates
the command list (as if that command was immediately followed by
end). This is because any time you resume execution (even with a
simple next or step), you may encounter another breakpoint—which
could have its own command list, leading to ambiguities about which
list to execute.
If you use the ‘silent’ command in the command list, the usual
message about stopping at a breakpoint is not printed. This may be
desirable for breakpoints that are to print a specific message and
then continue. If none of the other commands print anything, you
see no sign that the breakpoint was reached.
s(tep)
Execute the current line, stop at the first possible occasion
(either in a function that is called or on the next line in the
current function).
n(ext)
Continue execution until the next line in the current function is
reached or it returns. (The difference between "next" and "step"
is that "step" stops inside a called function, while "next"
executes called functions at (nearly) full speed, only stopping at
the next line in the current function.)
unt(il) [lineno]
Without argument, continue execution until the line with a number
greater than the current one is reached.
With a line number, continue execution until a line with a number
greater or equal to that is reached. In both cases, also stop when
the current frame returns.
Changed in version 3.2: Allow giving an explicit line number.
r(eturn)
Continue execution until the current function returns.
c(ont(inue))
Continue execution, only stop when a breakpoint is encountered.
j(ump) lineno
Set the next line that will be executed. Only available in the
bottom-most frame. This lets you jump back and execute code again,
or jump forward to skip code that you don’t want to run.
It should be noted that not all jumps are allowed – for instance it
is not possible to jump into the middle of a "for" loop or out of a
"finally" clause.
l(ist) [first[, last]]
List source code for the current file. Without arguments, list 11
lines around the current line or continue the previous listing.
With "." as argument, list 11 lines around the current line. With
one argument, list 11 lines around at that line. With two
arguments, list the given range; if the second argument is less
than the first, it is interpreted as a count.
The current line in the current frame is indicated by "->". If an
exception is being debugged, the line where the exception was
originally raised or propagated is indicated by ">>", if it differs
from the current line.
New in version 3.2: The ">>" marker.
ll | longlist
List all source code for the current function or frame.
Interesting lines are marked as for "list".
New in version 3.2.
a(rgs)
Print the argument list of the current function.
p expression
Evaluate the *expression* in the current context and print its
value.
Note: "print()" can also be used, but is not a debugger command —
this executes the Python "print()" function.
pp expression
Like the "p" command, except the value of the expression is pretty-
printed using the "pprint" module.
whatis expression
Print the type of the *expression*.
source expression
Try to get source code for the given object and display it.
New in version 3.2.
display [expression]
Display the value of the expression if it changed, each time
execution stops in the current frame.
Without expression, list all display expressions for the current
frame.
New in version 3.2.
undisplay [expression]
Do not display the expression any more in the current frame.
Without expression, clear all display expressions for the current
frame.
New in version 3.2.
interact
Start an interactive interpreter (using the "code" module) whose
global namespace contains all the (global and local) names found in
the current scope.
New in version 3.2.
alias [name [command]]
Create an alias called *name* that executes *command*. The command
must *not* be enclosed in quotes. Replaceable parameters can be
indicated by "%1", "%2", and so on, while "%*" is replaced by all
the parameters. If no command is given, the current alias for
*name* is shown. If no arguments are given, all aliases are listed.
Aliases may be nested and can contain anything that can be legally
typed at the pdb prompt. Note that internal pdb commands *can* be
overridden by aliases. Such a command is then hidden until the
alias is removed. Aliasing is recursively applied to the first
word of the command line; all other words in the line are left
alone.
As an example, here are two useful aliases (especially when placed
in the ".pdbrc" file):
# Print instance variables (usage "pi classInst")
alias pi for k in %1.__dict__.keys(): print("%1.",k,"=",%1.__dict__[k])
# Print instance variables in self
alias ps pi self
unalias name
Delete the specified alias.
! statement
Execute the (one-line) *statement* in the context of the current
stack frame. The exclamation point can be omitted unless the first
word of the statement resembles a debugger command. To set a
global variable, you can prefix the assignment command with a
"global" statement on the same line, e.g.:
(Pdb) global list_options; list_options = ['-l']
(Pdb)
run [args ...]
restart [args ...]
Restart the debugged Python program. If an argument is supplied,
it is split with "shlex" and the result is used as the new
"sys.argv". History, breakpoints, actions and debugger options are
preserved. "restart" is an alias for "run".
q(uit)
Quit from the debugger. The program being executed is aborted.
-[ Footnotes ]-
[1] Whether a frame is considered to originate in a certain module
is determined by the "__name__" in the frame globals.
a� The "del" statement
*******************
del_stmt ::= "del" target_list
Deletion is recursively defined very similar to the way assignment is
defined. Rather than spelling it out in full details, here are some
hints.
Deletion of a target list recursively deletes each target, from left
to right.
Deletion of a name removes the binding of that name from the local or
global namespace, depending on whether the name occurs in a "global"
statement in the same code block. If the name is unbound, a
"NameError" exception will be raised.
Deletion of attribute references, subscriptions and slicings is passed
to the primary object involved; deletion of a slicing is in general
equivalent to assignment of an empty slice of the right type (but even
this is determined by the sliced object).
Changed in version 3.2: Previously it was illegal to delete a name
from the local namespace if it occurs as a free variable in a nested
block.
u Dictionary displays
*******************
A dictionary display is a possibly empty series of key/datum pairs
enclosed in curly braces:
dict_display ::= "{" [key_datum_list | dict_comprehension] "}"
key_datum_list ::= key_datum ("," key_datum)* [","]
key_datum ::= expression ":" expression | "**" or_expr
dict_comprehension ::= expression ":" expression comp_for
A dictionary display yields a new dictionary object.
If a comma-separated sequence of key/datum pairs is given, they are
evaluated from left to right to define the entries of the dictionary:
each key object is used as a key into the dictionary to store the
corresponding datum. This means that you can specify the same key
multiple times in the key/datum list, and the final dictionary’s value
for that key will be the last one given.
A double asterisk "**" denotes *dictionary unpacking*. Its operand
must be a *mapping*. Each mapping item is added to the new
dictionary. Later values replace values already set by earlier
key/datum pairs and earlier dictionary unpackings.
New in version 3.5: Unpacking into dictionary displays, originally
proposed by **PEP 448**.
A dict comprehension, in contrast to list and set comprehensions,
needs two expressions separated with a colon followed by the usual
“for” and “if” clauses. When the comprehension is run, the resulting
key and value elements are inserted in the new dictionary in the order
they are produced.
Restrictions on the types of the key values are listed earlier in
section The standard type hierarchy. (To summarize, the key type
should be *hashable*, which excludes all mutable objects.) Clashes
between duplicate keys are not detected; the last datum (textually
rightmost in the display) stored for a given key value prevails.
a� Interaction with dynamic features
*********************************
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
aB The "if" statement
******************
The "if" statement is used for conditional execution:
if_stmt ::= "if" expression ":" suite
("elif" expression ":" suite)*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
u� Exceptions
**********
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note: Exception messages are not part of the Python API. Their
contents may change from one version of Python to the next without
warning and should not be relied on by code which will run under
multiple versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by
these operations is not available at the time the module is
compiled.
u$ Execution model
***************
Structure of a program
======================
A Python program is constructed from code blocks. A *block* is a piece
of Python program text that is executed as a unit. The following are
blocks: a module, a function body, and a class definition. Each
command typed interactively is a block. A script file (a file given
as standard input to the interpreter or specified as a command line
argument to the interpreter) is a code block. A script command (a
command specified on the interpreter command line with the "-c"
option) is a code block. The string argument passed to the built-in
functions "eval()" and "exec()" is a code block.
A code block is executed in an *execution frame*. A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block’s execution has
completed.
Naming and binding
==================
Binding of names
----------------
*Names* refer to objects. Names are introduced by name binding
operations.
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore. This form
may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global". If a name is bound at the
module level, it is a global variable. (The variables of the module
code block are local and global.) If a variable is used in a code
block but not defined there, it is a *free variable*.
Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.
Resolution of names
-------------------
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block’s *environment*.
When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins". The global namespace is searched first. If
the name is not found there, the builtins namespace is searched. The
"global" statement must precede all uses of the name.
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope. This means that the following
will fail:
class A:
a = 42
b = list(a + i for i in range(10))
Builtins and restricted execution
---------------------------------
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.
Interaction with dynamic features
---------------------------------
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
Exceptions
==========
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note: Exception messages are not part of the Python API. Their
contents may change from one version of Python to the next without
warning and should not be relied on by code which will run under
multiple versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by
these operations is not available at the time the module is
compiled.
uo Expression lists
****************
expression_list ::= expression ("," expression)* [","]
starred_list ::= starred_item ("," starred_item)* [","]
starred_expression ::= expression | (starred_item ",")* [starred_item]
starred_item ::= expression | "*" or_expr
Except when part of a list or set display, an expression list
containing at least one comma yields a tuple. The length of the tuple
is the number of expressions in the list. The expressions are
evaluated from left to right.
An asterisk "*" denotes *iterable unpacking*. Its operand must be an
*iterable*. The iterable is expanded into a sequence of items, which
are included in the new tuple, list, or set, at the site of the
unpacking.
New in version 3.5: Iterable unpacking in expression lists, originally
proposed by **PEP 448**.
The trailing comma is required only to create a single tuple (a.k.a. a
*singleton*); it is optional in all other cases. A single expression
without a trailing comma doesn’t create a tuple, but rather yields the
value of that expression. (To create an empty tuple, use an empty pair
of parentheses: "()".)
a� Floating point literals
***********************
Floating point literals are described by the following lexical
definitions:
floatnumber ::= pointfloat | exponentfloat
pointfloat ::= [digitpart] fraction | digitpart "."
exponentfloat ::= (digitpart | pointfloat) exponent
digitpart ::= digit (["_"] digit)*
fraction ::= "." digitpart
exponent ::= ("e" | "E") ["+" | "-"] digitpart
Note that the integer and exponent parts are always interpreted using
radix 10. For example, "077e010" is legal, and denotes the same number
as "77e10". The allowed range of floating point literals is
implementation-dependent. As in integer literals, underscores are
supported for digit grouping.
Some examples of floating point literals:
3.14 10. .001 1e100 3.14e-10 0e0 3.14_15_93
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
u�
The "for" statement
*******************
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator. Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.
The for-loop makes assignments to the variables(s) in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop. Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".
Note: There is a subtlety when the sequence is being modified by the
loop (this can only occur for mutable sequences, e.g. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
u Y Format String Syntax
********************
The "str.format()" method and the "Formatter" class share the same
syntax for format strings (although in the case of "Formatter",
subclasses can define their own format string syntax). The syntax is
related to that of formatted string literals, but there are
differences.
Format strings contain “replacement fields” surrounded by curly braces
"{}". Anything that is not contained in braces is considered literal
text, which is copied unchanged to the output. If you need to include
a brace character in the literal text, it can be escaped by doubling:
"{{" and "}}".
The grammar for a replacement field is as follows:
replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}"
field_name ::= arg_name ("." attribute_name | "[" element_index "]")*
arg_name ::= [identifier | digit+]
attribute_name ::= identifier
element_index ::= digit+ | index_string
index_string ::= <any source character except "]"> +
conversion ::= "r" | "s" | "a"
format_spec ::= <described in the next section>
In less formal terms, the replacement field can start with a
*field_name* that specifies the object whose value is to be formatted
and inserted into the output instead of the replacement field. The
*field_name* is optionally followed by a *conversion* field, which is
preceded by an exclamation point "'!'", and a *format_spec*, which is
preceded by a colon "':'". These specify a non-default format for the
replacement value.
See also the Format Specification Mini-Language section.
The *field_name* itself begins with an *arg_name* that is either a
number or a keyword. If it’s a number, it refers to a positional
argument, and if it’s a keyword, it refers to a named keyword
argument. If the numerical arg_names in a format string are 0, 1, 2,
… in sequence, they can all be omitted (not just some) and the numbers
0, 1, 2, … will be automatically inserted in that order. Because
*arg_name* is not quote-delimited, it is not possible to specify
arbitrary dictionary keys (e.g., the strings "'10'" or "':-]'") within
a format string. The *arg_name* can be followed by any number of index
or attribute expressions. An expression of the form "'.name'" selects
the named attribute using "getattr()", while an expression of the form
"'[index]'" does an index lookup using "__getitem__()".
Changed in version 3.1: The positional argument specifiers can be
omitted for "str.format()", so "'{} {}'.format(a, b)" is equivalent to
"'{0} {1}'.format(a, b)".
Changed in version 3.4: The positional argument specifiers can be
omitted for "Formatter".
Some simple format string examples:
"First, thou shalt count to {0}" # References first positional argument
"Bring me a {}" # Implicitly references the first positional argument
"From {} to {}" # Same as "From {0} to {1}"
"My quest is {name}" # References keyword argument 'name'
"Weight in tons {0.weight}" # 'weight' attribute of first positional arg
"Units destroyed: {players[0]}" # First element of keyword argument 'players'.
The *conversion* field causes a type coercion before formatting.
Normally, the job of formatting a value is done by the "__format__()"
method of the value itself. However, in some cases it is desirable to
force a type to be formatted as a string, overriding its own
definition of formatting. By converting the value to a string before
calling "__format__()", the normal formatting logic is bypassed.
Three conversion flags are currently supported: "'!s'" which calls
"str()" on the value, "'!r'" which calls "repr()" and "'!a'" which
calls "ascii()".
Some examples:
"Harold's a clever {0!s}" # Calls str() on the argument first
"Bring out the holy {name!r}" # Calls repr() on the argument first
"More {!a}" # Calls ascii() on the argument first
The *format_spec* field contains a specification of how the value
should be presented, including such details as field width, alignment,
padding, decimal precision and so on. Each value type can define its
own “formatting mini-language” or interpretation of the *format_spec*.
Most built-in types support a common formatting mini-language, which
is described in the next section.
A *format_spec* field can also include nested replacement fields
within it. These nested replacement fields may contain a field name,
conversion flag and format specification, but deeper nesting is not
allowed. The replacement fields within the format_spec are
substituted before the *format_spec* string is interpreted. This
allows the formatting of a value to be dynamically specified.
See the Format examples section for some examples.
Format Specification Mini-Language
==================================
“Format specifications” are used within replacement fields contained
within a format string to define how individual values are presented
(see Format String Syntax and Formatted string literals). They can
also be passed directly to the built-in "format()" function. Each
formattable type may define how the format specification is to be
interpreted.
Most built-in types implement the following options for format
specifications, although some of the formatting options are only
supported by the numeric types.
A general convention is that an empty format string ("""") produces
the same result as if you had called "str()" on the value. A non-empty
format string typically modifies the result.
The general form of a *standard format specifier* is:
format_spec ::= [[fill]align][sign][#][0][width][grouping_option][.precision][type]
fill ::= <any character>
align ::= "<" | ">" | "=" | "^"
sign ::= "+" | "-" | " "
width ::= digit+
grouping_option ::= "_" | ","
precision ::= digit+
type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"
If a valid *align* value is specified, it can be preceded by a *fill*
character that can be any character and defaults to a space if
omitted. It is not possible to use a literal curly brace (“"{"” or
“"}"”) as the *fill* character in a formatted string literal or when
using the "str.format()" method. However, it is possible to insert a
curly brace with a nested replacement field. This limitation doesn’t
affect the "format()" function.
The meaning of the various alignment options is as follows:
+-----------+------------------------------------------------------------+
| Option | Meaning |
+===========+============================================================+
| "'<'" | Forces the field to be left-aligned within the available |
| | space (this is the default for most objects). |
+-----------+------------------------------------------------------------+
| "'>'" | Forces the field to be right-aligned within the available |
| | space (this is the default for numbers). |
+-----------+------------------------------------------------------------+
| "'='" | Forces the padding to be placed after the sign (if any) |
| | but before the digits. This is used for printing fields |
| | in the form ‘+000000120’. This alignment option is only |
| | valid for numeric types. It becomes the default when ‘0’ |
| | immediately precedes the field width. |
+-----------+------------------------------------------------------------+
| "'^'" | Forces the field to be centered within the available |
| | space. |
+-----------+------------------------------------------------------------+
Note that unless a minimum field width is defined, the field width
will always be the same size as the data to fill it, so that the
alignment option has no meaning in this case.
The *sign* option is only valid for number types, and can be one of
the following:
+-----------+------------------------------------------------------------+
| Option | Meaning |
+===========+============================================================+
| "'+'" | indicates that a sign should be used for both positive as |
| | well as negative numbers. |
+-----------+------------------------------------------------------------+
| "'-'" | indicates that a sign should be used only for negative |
| | numbers (this is the default behavior). |
+-----------+------------------------------------------------------------+
| space | indicates that a leading space should be used on positive |
| | numbers, and a minus sign on negative numbers. |
+-----------+------------------------------------------------------------+
The "'#'" option causes the “alternate form” to be used for the
conversion. The alternate form is defined differently for different
types. This option is only valid for integer, float, complex and
Decimal types. For integers, when binary, octal, or hexadecimal output
is used, this option adds the prefix respective "'0b'", "'0o'", or
"'0x'" to the output value. For floats, complex and Decimal the
alternate form causes the result of the conversion to always contain a
decimal-point character, even if no digits follow it. Normally, a
decimal-point character appears in the result of these conversions
only if a digit follows it. In addition, for "'g'" and "'G'"
conversions, trailing zeros are not removed from the result.
The "','" option signals the use of a comma for a thousands separator.
For a locale aware separator, use the "'n'" integer presentation type
instead.
Changed in version 3.1: Added the "','" option (see also **PEP 378**).
The "'_'" option signals the use of an underscore for a thousands
separator for floating point presentation types and for integer
presentation type "'d'". For integer presentation types "'b'", "'o'",
"'x'", and "'X'", underscores will be inserted every 4 digits. For
other presentation types, specifying this option is an error.
Changed in version 3.6: Added the "'_'" option (see also **PEP 515**).
*width* is a decimal integer defining the minimum field width. If not
specified, then the field width will be determined by the content.
When no explicit alignment is given, preceding the *width* field by a
zero ("'0'") character enables sign-aware zero-padding for numeric
types. This is equivalent to a *fill* character of "'0'" with an
*alignment* type of "'='".
The *precision* is a decimal number indicating how many digits should
be displayed after the decimal point for a floating point value
formatted with "'f'" and "'F'", or before and after the decimal point
for a floating point value formatted with "'g'" or "'G'". For non-
number types the field indicates the maximum field size - in other
words, how many characters will be used from the field content. The
*precision* is not allowed for integer values.
Finally, the *type* determines how the data should be presented.
The available string presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'s'" | String format. This is the default type for strings and |
| | may be omitted. |
+-----------+------------------------------------------------------------+
| None | The same as "'s'". |
+-----------+------------------------------------------------------------+
The available integer presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'b'" | Binary format. Outputs the number in base 2. |
+-----------+------------------------------------------------------------+
| "'c'" | Character. Converts the integer to the corresponding |
| | unicode character before printing. |
+-----------+------------------------------------------------------------+
| "'d'" | Decimal Integer. Outputs the number in base 10. |
+-----------+------------------------------------------------------------+
| "'o'" | Octal format. Outputs the number in base 8. |
+-----------+------------------------------------------------------------+
| "'x'" | Hex format. Outputs the number in base 16, using lower- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'X'" | Hex format. Outputs the number in base 16, using upper- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'d'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| None | The same as "'d'". |
+-----------+------------------------------------------------------------+
In addition to the above presentation types, integers can be formatted
with the floating point presentation types listed below (except "'n'"
and "None"). When doing so, "float()" is used to convert the integer
to a floating point number before formatting.
The available presentation types for floating point and decimal values
are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'e'" | Exponent notation. Prints the number in scientific |
| | notation using the letter ‘e’ to indicate the exponent. |
| | The default precision is "6". |
+-----------+------------------------------------------------------------+
| "'E'" | Exponent notation. Same as "'e'" except it uses an upper |
| | case ‘E’ as the separator character. |
+-----------+------------------------------------------------------------+
| "'f'" | Fixed-point notation. Displays the number as a fixed-point |
| | number. The default precision is "6". |
+-----------+------------------------------------------------------------+
| "'F'" | Fixed-point notation. Same as "'f'", but converts "nan" to |
| | "NAN" and "inf" to "INF". |
+-----------+------------------------------------------------------------+
| "'g'" | General format. For a given precision "p >= 1", this |
| | rounds the number to "p" significant digits and then |
| | formats the result in either fixed-point format or in |
| | scientific notation, depending on its magnitude. The |
| | precise rules are as follows: suppose that the result |
| | formatted with presentation type "'e'" and precision "p-1" |
| | would have exponent "exp". Then if "-4 <= exp < p", the |
| | number is formatted with presentation type "'f'" and |
| | precision "p-1-exp". Otherwise, the number is formatted |
| | with presentation type "'e'" and precision "p-1". In both |
| | cases insignificant trailing zeros are removed from the |
| | significand, and the decimal point is also removed if |
| | there are no remaining digits following it. Positive and |
| | negative infinity, positive and negative zero, and nans, |
| | are formatted as "inf", "-inf", "0", "-0" and "nan" |
| | respectively, regardless of the precision. A precision of |
| | "0" is treated as equivalent to a precision of "1". The |
| | default precision is "6". |
+-----------+------------------------------------------------------------+
| "'G'" | General format. Same as "'g'" except switches to "'E'" if |
| | the number gets too large. The representations of infinity |
| | and NaN are uppercased, too. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'g'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| "'%'" | Percentage. Multiplies the number by 100 and displays in |
| | fixed ("'f'") format, followed by a percent sign. |
+-----------+------------------------------------------------------------+
| None | Similar to "'g'", except that fixed-point notation, when |
| | used, has at least one digit past the decimal point. The |
| | default precision is as high as needed to represent the |
| | particular value. The overall effect is to match the |
| | output of "str()" as altered by the other format |
| | modifiers. |
+-----------+------------------------------------------------------------+
Format examples
===============
This section contains examples of the "str.format()" syntax and
comparison with the old "%"-formatting.
In most of the cases the syntax is similar to the old "%"-formatting,
with the addition of the "{}" and with ":" used instead of "%". For
example, "'%03.2f'" can be translated to "'{:03.2f}'".
The new format syntax also supports new and different options, shown
in the following examples.
Accessing arguments by position:
>>> '{0}, {1}, {2}'.format('a', 'b', 'c')
'a, b, c'
>>> '{}, {}, {}'.format('a', 'b', 'c') # 3.1+ only
'a, b, c'
>>> '{2}, {1}, {0}'.format('a', 'b', 'c')
'c, b, a'
>>> '{2}, {1}, {0}'.format(*'abc') # unpacking argument sequence
'c, b, a'
>>> '{0}{1}{0}'.format('abra', 'cad') # arguments' indices can be repeated
'abracadabra'
Accessing arguments by name:
>>> 'Coordinates: {latitude}, {longitude}'.format(latitude='37.24N', longitude='-115.81W')
'Coordinates: 37.24N, -115.81W'
>>> coord = {'latitude': '37.24N', 'longitude': '-115.81W'}
>>> 'Coordinates: {latitude}, {longitude}'.format(**coord)
'Coordinates: 37.24N, -115.81W'
Accessing arguments’ attributes:
>>> c = 3-5j
>>> ('The complex number {0} is formed from the real part {0.real} '
... 'and the imaginary part {0.imag}.').format(c)
'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.'
>>> class Point:
... def __init__(self, x, y):
... self.x, self.y = x, y
... def __str__(self):
... return 'Point({self.x}, {self.y})'.format(self=self)
...
>>> str(Point(4, 2))
'Point(4, 2)'
Accessing arguments’ items:
>>> coord = (3, 5)
>>> 'X: {0[0]}; Y: {0[1]}'.format(coord)
'X: 3; Y: 5'
Replacing "%s" and "%r":
>>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2')
"repr() shows quotes: 'test1'; str() doesn't: test2"
Aligning the text and specifying a width:
>>> '{:<30}'.format('left aligned')
'left aligned '
>>> '{:>30}'.format('right aligned')
' right aligned'
>>> '{:^30}'.format('centered')
' centered '
>>> '{:*^30}'.format('centered') # use '*' as a fill char
'***********centered***********'
Replacing "%+f", "%-f", and "% f" and specifying a sign:
>>> '{:+f}; {:+f}'.format(3.14, -3.14) # show it always
'+3.140000; -3.140000'
>>> '{: f}; {: f}'.format(3.14, -3.14) # show a space for positive numbers
' 3.140000; -3.140000'
>>> '{:-f}; {:-f}'.format(3.14, -3.14) # show only the minus -- same as '{:f}; {:f}'
'3.140000; -3.140000'
Replacing "%x" and "%o" and converting the value to different bases:
>>> # format also supports binary numbers
>>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: {0:b}".format(42)
'int: 42; hex: 2a; oct: 52; bin: 101010'
>>> # with 0x, 0o, or 0b as prefix:
>>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: {0:#b}".format(42)
'int: 42; hex: 0x2a; oct: 0o52; bin: 0b101010'
Using the comma as a thousands separator:
>>> '{:,}'.format(1234567890)
'1,234,567,890'
Expressing a percentage:
>>> points = 19
>>> total = 22
>>> 'Correct answers: {:.2%}'.format(points/total)
'Correct answers: 86.36%'
Using type-specific formatting:
>>> import datetime
>>> d = datetime.datetime(2010, 7, 4, 12, 15, 58)
>>> '{:%Y-%m-%d %H:%M:%S}'.format(d)
'2010-07-04 12:15:58'
Nesting arguments and more complex examples:
>>> for align, text in zip('<^>', ['left', 'center', 'right']):
... '{0:{fill}{align}16}'.format(text, fill=align, align=align)
...
'left<<<<<<<<<<<<'
'^^^^^center^^^^^'
'>>>>>>>>>>>right'
>>>
>>> octets = [192, 168, 0, 1]
>>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets)
'C0A80001'
>>> int(_, 16)
3232235521
>>>
>>> width = 5
>>> for num in range(5,12):
... for base in 'dXob':
... print('{0:{width}{base}}'.format(num, base=base, width=width), end=' ')
... print()
...
5 5 5 101
6 6 6 110
7 7 7 111
8 8 10 1000
9 9 11 1001
10 A 12 1010
11 B 13 1011
u[ Function definitions
********************
A function definition defines a user-defined function object (see
section The standard type hierarchy):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
| parameter_list_starargs
parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
| "**" parameter [","]
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [2]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
except that the original function is not temporarily bound to the name
"func".
When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted. If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.
**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call. This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended. A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from position arguments, from keyword
arguments, or from default values. If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type. Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed
used keyword arguments.
Parameters may have annotations of the form “": expression"” following
the parameter name. Any parameter may have an annotation even those
of the form "*identifier" or "**identifier". Functions may have
“return” annotation of the form “"-> expression"” after the parameter
list. These annotations can be any valid Python expression and are
evaluated when the function definition is executed. Annotations may
be evaluated in a different order than they appear in the source code.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.
**Programmer’s note:** Functions are first-class objects. A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around. Free variables used
in the nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
See also:
**PEP 3107** - Function Annotations
The original specification for function annotations.
u� The "global" statement
**********************
global_stmt ::= "global" identifier ("," identifier)*
The "global" statement is a declaration which holds for the entire
current code block. It means that the listed identifiers are to be
interpreted as globals. It would be impossible to assign to a global
variable without "global", although free variables may refer to
globals without being declared global.
Names listed in a "global" statement must not be used in the same code
block textually preceding that "global" statement.
Names listed in a "global" statement must not be defined as formal
parameters or in a "for" loop control target, "class" definition,
function definition, "import" statement, or variable annotation.
**CPython implementation detail:** The current implementation does not
enforce some of these restrictions, but programs should not abuse this
freedom, as future implementations may enforce them or silently change
the meaning of the program.
**Programmer’s note:** "global" is a directive to the parser. It
applies only to code parsed at the same time as the "global"
statement. In particular, a "global" statement contained in a string
or code object supplied to the built-in "exec()" function does not
affect the code block *containing* the function call, and code
contained in such a string is unaffected by "global" statements in the
code containing the function call. The same applies to the "eval()"
and "compile()" functions.
u� Reserved classes of identifiers
*******************************
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section The import statement.
Note: The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names. These names are defined by the interpreter
and its implementation (including the standard library). Current
system names are discussed in the Special method names section and
elsewhere. More will likely be defined in future versions of
Python. *Any* use of "__*__" names, in any context, that does not
follow explicitly documented use, is subject to breakage without
warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between “private” attributes of base and
derived classes. See section Identifiers (Names).
u( Identifiers and keywords
************************
Identifiers (also referred to as *names*) are described by the
following lexical definitions.
The syntax of identifiers in Python is based on the Unicode standard
annex UAX-31, with elaboration and changes as defined below; see also
**PEP 3131** for further details.
Within the ASCII range (U+0001..U+007F), the valid characters for
identifiers are the same as in Python 2.x: the uppercase and lowercase
letters "A" through "Z", the underscore "_" and, except for the first
character, the digits "0" through "9".
Python 3.0 introduces additional characters from outside the ASCII
range (see **PEP 3131**). For these characters, the classification
uses the version of the Unicode Character Database as included in the
"unicodedata" module.
Identifiers are unlimited in length. Case is significant.
identifier ::= xid_start xid_continue*
id_start ::= <all characters in general categories Lu, Ll, Lt, Lm, Lo, Nl, the underscore, and characters with the Other_ID_Start property>
id_continue ::= <all characters in id_start, plus characters in the categories Mn, Mc, Nd, Pc and others with the Other_ID_Continue property>
xid_start ::= <all characters in id_start whose NFKC normalization is in "id_start xid_continue*">
xid_continue ::= <all characters in id_continue whose NFKC normalization is in "id_continue*">
The Unicode category codes mentioned above stand for:
* *Lu* - uppercase letters
* *Ll* - lowercase letters
* *Lt* - titlecase letters
* *Lm* - modifier letters
* *Lo* - other letters
* *Nl* - letter numbers
* *Mn* - nonspacing marks
* *Mc* - spacing combining marks
* *Nd* - decimal numbers
* *Pc* - connector punctuations
* *Other_ID_Start* - explicit list of characters in PropList.txt to
support backwards compatibility
* *Other_ID_Continue* - likewise
All identifiers are converted into the normal form NFKC while parsing;
comparison of identifiers is based on NFKC.
A non-normative HTML file listing all valid identifier characters for
Unicode 4.1 can be found at https://www.dcl.hpi.uni-
potsdam.de/home/loewis/table-3131.html.
Keywords
========
The following identifiers are used as reserved words, or *keywords* of
the language, and cannot be used as ordinary identifiers. They must
be spelled exactly as written here:
False class finally is return
None continue for lambda try
True def from nonlocal while
and del global not with
as elif if or yield
assert else import pass
break except in raise
Reserved classes of identifiers
===============================
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section The import statement.
Note: The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names. These names are defined by the interpreter
and its implementation (including the standard library). Current
system names are discussed in the Special method names section and
elsewhere. More will likely be defined in future versions of
Python. *Any* use of "__*__" names, in any context, that does not
follow explicitly documented use, is subject to breakage without
warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between “private” attributes of base and
derived classes. See section Identifiers (Names).
a5 Imaginary literals
******************
Imaginary literals are described by the following lexical definitions:
imagnumber ::= (floatnumber | digitpart) ("j" | "J")
An imaginary literal yields a complex number with a real part of 0.0.
Complex numbers are represented as a pair of floating point numbers
and have the same restrictions on their range. To create a complex
number with a nonzero real part, add a floating point number to it,
e.g., "(3+4j)". Some examples of imaginary literals:
3.14j 10.j 10j .001j 1e100j 3.14e-10j 3.14_15_93j
u� The "import" statement
**********************
import_stmt ::= "import" module ["as" identifier] ("," module ["as" identifier])*
| "from" relative_module "import" identifier ["as" identifier]
("," identifier ["as" identifier])*
| "from" relative_module "import" "(" identifier ["as" identifier]
("," identifier ["as" identifier])* [","] ")"
| "from" module "import" "*"
module ::= (identifier ".")* identifier
relative_module ::= "."* module | "."+
The basic import statement (no "from" clause) is executed in two
steps:
1. find a module, loading and initializing it if necessary
2. define a name or names in the local namespace for the scope
where the "import" statement occurs.
When the statement contains multiple clauses (separated by commas) the
two steps are carried out separately for each clause, just as though
the clauses had been separated out into individual import statements.
The details of the first step, finding and loading modules are
described in greater detail in the section on the import system, which
also describes the various types of packages and modules that can be
imported, as well as all the hooks that can be used to customize the
import system. Note that failures in this step may indicate either
that the module could not be located, *or* that an error occurred
while initializing the module, which includes execution of the
module’s code.
If the requested module is retrieved successfully, it will be made
available in the local namespace in one of three ways:
* If the module name is followed by "as", then the name following
"as" is bound directly to the imported module.
* If no other name is specified, and the module being imported is a
top level module, the module’s name is bound in the local namespace
as a reference to the imported module
* If the module being imported is *not* a top level module, then the
name of the top level package that contains the module is bound in
the local namespace as a reference to the top level package. The
imported module must be accessed using its full qualified name
rather than directly
The "from" form uses a slightly more complex process:
1. find the module specified in the "from" clause, loading and
initializing it if necessary;
2. for each of the identifiers specified in the "import" clauses:
1. check if the imported module has an attribute by that name
2. if not, attempt to import a submodule with that name and then
check the imported module again for that attribute
3. if the attribute is not found, "ImportError" is raised.
4. otherwise, a reference to that value is stored in the local
namespace, using the name in the "as" clause if it is present,
otherwise using the attribute name
Examples:
import foo # foo imported and bound locally
import foo.bar.baz # foo.bar.baz imported, foo bound locally
import foo.bar.baz as fbb # foo.bar.baz imported and bound as fbb
from foo.bar import baz # foo.bar.baz imported and bound as baz
from foo import attr # foo imported and foo.attr bound as attr
If the list of identifiers is replaced by a star ("'*'"), all public
names defined in the module are bound in the local namespace for the
scope where the "import" statement occurs.
The *public names* defined by a module are determined by checking the
module’s namespace for a variable named "__all__"; if defined, it must
be a sequence of strings which are names defined or imported by that
module. The names given in "__all__" are all considered public and
are required to exist. If "__all__" is not defined, the set of public
names includes all names found in the module’s namespace which do not
begin with an underscore character ("'_'"). "__all__" should contain
the entire public API. It is intended to avoid accidentally exporting
items that are not part of the API (such as library modules which were
imported and used within the module).
The wild card form of import — "from module import *" — is only
allowed at the module level. Attempting to use it in class or
function definitions will raise a "SyntaxError".
When specifying what module to import you do not have to specify the
absolute name of the module. When a module or package is contained
within another package it is possible to make a relative import within
the same top package without having to mention the package name. By
using leading dots in the specified module or package after "from" you
can specify how high to traverse up the current package hierarchy
without specifying exact names. One leading dot means the current
package where the module making the import exists. Two dots means up
one package level. Three dots is up two levels, etc. So if you execute
"from . import mod" from a module in the "pkg" package then you will
end up importing "pkg.mod". If you execute "from ..subpkg2 import mod"
from within "pkg.subpkg1" you will import "pkg.subpkg2.mod". The
specification for relative imports is contained within **PEP 328**.
"importlib.import_module()" is provided to support applications that
determine dynamically the modules to be loaded.
Future statements
=================
A *future statement* is a directive to the compiler that a particular
module should be compiled using syntax or semantics that will be
available in a specified future release of Python where the feature
becomes standard.
The future statement is intended to ease migration to future versions
of Python that introduce incompatible changes to the language. It
allows use of the new features on a per-module basis before the
release in which the feature becomes standard.
future_stmt ::= "from" "__future__" "import" feature ["as" identifier]
("," feature ["as" identifier])*
| "from" "__future__" "import" "(" feature ["as" identifier]
("," feature ["as" identifier])* [","] ")"
feature ::= identifier
A future statement must appear near the top of the module. The only
lines that can appear before a future statement are:
* the module docstring (if any),
* comments,
* blank lines, and
* other future statements.
The features recognized by Python 3.0 are "absolute_import",
"division", "generators", "unicode_literals", "print_function",
"nested_scopes" and "with_statement". They are all redundant because
they are always enabled, and only kept for backwards compatibility.
A future statement is recognized and treated specially at compile
time: Changes to the semantics of core constructs are often
implemented by generating different code. It may even be the case
that a new feature introduces new incompatible syntax (such as a new
reserved word), in which case the compiler may need to parse the
module differently. Such decisions cannot be pushed off until
runtime.
For any given release, the compiler knows which feature names have
been defined, and raises a compile-time error if a future statement
contains a feature not known to it.
The direct runtime semantics are the same as for any import statement:
there is a standard module "__future__", described later, and it will
be imported in the usual way at the time the future statement is
executed.
The interesting runtime semantics depend on the specific feature
enabled by the future statement.
Note that there is nothing special about the statement:
import __future__ [as name]
That is not a future statement; it’s an ordinary import statement with
no special semantics or syntax restrictions.
Code compiled by calls to the built-in functions "exec()" and
"compile()" that occur in a module "M" containing a future statement
will, by default, use the new syntax or semantics associated with the
future statement. This can be controlled by optional arguments to
"compile()" — see the documentation of that function for details.
A future statement typed at an interactive interpreter prompt will
take effect for the rest of the interpreter session. If an
interpreter is started with the "-i" option, is passed a script name
to execute, and the script includes a future statement, it will be in
effect in the interactive session started after the script is
executed.
See also:
**PEP 236** - Back to the __future__
The original proposal for the __future__ mechanism.
aO Membership test operations
**************************
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y". If an exception is raised
during the iteration, it is as if "in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse true value of
"in".
aV Integer literals
****************
Integer literals are described by the following lexical definitions:
integer ::= decinteger | bininteger | octinteger | hexinteger
decinteger ::= nonzerodigit (["_"] digit)* | "0"+ (["_"] "0")*
bininteger ::= "0" ("b" | "B") (["_"] bindigit)+
octinteger ::= "0" ("o" | "O") (["_"] octdigit)+
hexinteger ::= "0" ("x" | "X") (["_"] hexdigit)+
nonzerodigit ::= "1"..."9"
digit ::= "0"..."9"
bindigit ::= "0" | "1"
octdigit ::= "0"..."7"
hexdigit ::= digit | "a"..."f" | "A"..."F"
There is no limit for the length of integer literals apart from what
can be stored in available memory.
Underscores are ignored for determining the numeric value of the
literal. They can be used to group digits for enhanced readability.
One underscore can occur between digits, and after base specifiers
like "0x".
Note that leading zeros in a non-zero decimal number are not allowed.
This is for disambiguation with C-style octal literals, which Python
used before version 3.0.
Some examples of integer literals:
7 2147483647 0o177 0b100110111
3 79228162514264337593543950336 0o377 0xdeadbeef
100_000_000_000 0b_1110_0101
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
a^ Lambdas
*******
lambda_expr ::= "lambda" [parameter_list] ":" expression
lambda_expr_nocond ::= "lambda" [parameter_list] ":" expression_nocond
Lambda expressions (sometimes called lambda forms) are used to create
anonymous functions. The expression "lambda parameters: expression"
yields a function object. The unnamed object behaves like a function
object defined with:
def <lambda>(parameters):
return expression
See section Function definitions for the syntax of parameter lists.
Note that functions created with lambda expressions cannot contain
statements or annotations.
a/ List displays
*************
A list display is a possibly empty series of expressions enclosed in
square brackets:
list_display ::= "[" [starred_list | comprehension] "]"
A list display yields a new list object, the contents being specified
by either a list of expressions or a comprehension. When a comma-
separated list of expressions is supplied, its elements are evaluated
from left to right and placed into the list object in that order.
When a comprehension is supplied, the list is constructed from the
elements resulting from the comprehension.
u� Naming and binding
******************
Binding of names
================
*Names* refer to objects. Names are introduced by name binding
operations.
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore. This form
may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global". If a name is bound at the
module level, it is a global variable. (The variables of the module
code block are local and global.) If a variable is used in a code
block but not defined there, it is a *free variable*.
Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.
Resolution of names
===================
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block’s *environment*.
When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins". The global namespace is searched first. If
the name is not found there, the builtins namespace is searched. The
"global" statement must precede all uses of the name.
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope. This means that the following
will fail:
class A:
a = 42
b = list(a + i for i in range(10))
Builtins and restricted execution
=================================
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.
Interaction with dynamic features
=================================
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
a� The "nonlocal" statement
************************
nonlocal_stmt ::= "nonlocal" identifier ("," identifier)*
The "nonlocal" statement causes the listed identifiers to refer to
previously bound variables in the nearest enclosing scope excluding
globals. This is important because the default behavior for binding is
to search the local namespace first. The statement allows
encapsulated code to rebind variables outside of the local scope
besides the global (module) scope.
Names listed in a "nonlocal" statement, unlike those listed in a
"global" statement, must refer to pre-existing bindings in an
enclosing scope (the scope in which a new binding should be created
cannot be determined unambiguously).
Names listed in a "nonlocal" statement must not collide with pre-
existing bindings in the local scope.
See also:
**PEP 3104** - Access to Names in Outer Scopes
The specification for the "nonlocal" statement.
u� Numeric literals
****************
There are three types of numeric literals: integers, floating point
numbers, and imaginary numbers. There are no complex literals
(complex numbers can be formed by adding a real number and an
imaginary number).
Note that numeric literals do not include a sign; a phrase like "-1"
is actually an expression composed of the unary operator ‘"-"‘ and the
literal "1".
u� Emulating numeric types
***********************
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the expression "x + y", where *x* is an instance of a
class that has an "__add__()" method, "x.__add__(y)" is called.
The "__divmod__()" method should be the equivalent to using
"__floordiv__()" and "__mod__()"; it should not be related to
"__truediv__()". Note that "__pow__()" should be defined to accept
an optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other)
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)
operands. These functions are only called if the left operand does
not support the corresponding operation [3] and the operands are of
different types. [4] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note: If the right operand’s type is a subclass of the left
operand’s type and that subclass provides the reflected method
for the operation, this method will be called before the left
operand’s non-reflected method. This behavior allows subclasses
to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (modifying *self*) and return the result
(which could be, but does not have to be, *self*). If a specific
method is not defined, the augmented assignment falls back to the
normal methods. For instance, if *x* is an instance of a class
with an "__iadd__()" method, "x += y" is equivalent to "x =
x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are
considered, as with the evaluation of "x + y". In certain
situations, augmented assignment can result in unexpected errors
(see Why does a_tuple[i] += [‘item’] raise an exception when the
addition works?), but this behavior is in fact part of the data
model.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()" and
"float()". Should return a value of the appropriate type.
object.__index__(self)
Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.
Note: In order to have a coherent integer type class, when
"__index__()" is defined "__int__()" should also be defined, and
both should return the same value.
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").
If "__int__()" is not defined then the built-in function "int()"
falls back to "__trunc__()".
u Objects, values and types
*************************
*Objects* are Python’s abstraction for data. All data in a Python
program is represented by objects or by relations between objects. (In
a sense, and in conformance to Von Neumann’s model of a “stored
program computer,” code is also represented by objects.)
Every object has an identity, a type and a value. An object’s
*identity* never changes once it has been created; you may think of it
as the object’s address in memory. The ‘"is"’ operator compares the
identity of two objects; the "id()" function returns an integer
representing its identity.
**CPython implementation detail:** For CPython, "id(x)" is the memory
address where "x" is stored.
An object’s type determines the operations that the object supports
(e.g., “does it have a length?”) and also defines the possible values
for objects of that type. The "type()" function returns an object’s
type (which is an object itself). Like its identity, an object’s
*type* is also unchangeable. [1]
The *value* of some objects can change. Objects whose value can
change are said to be *mutable*; objects whose value is unchangeable
once they are created are called *immutable*. (The value of an
immutable container object that contains a reference to a mutable
object can change when the latter’s value is changed; however the
container is still considered immutable, because the collection of
objects it contains cannot be changed. So, immutability is not
strictly the same as having an unchangeable value, it is more subtle.)
An object’s mutability is determined by its type; for instance,
numbers, strings and tuples are immutable, while dictionaries and
lists are mutable.
Objects are never explicitly destroyed; however, when they become
unreachable they may be garbage-collected. An implementation is
allowed to postpone garbage collection or omit it altogether — it is a
matter of implementation quality how garbage collection is
implemented, as long as no objects are collected that are still
reachable.
**CPython implementation detail:** CPython currently uses a reference-
counting scheme with (optional) delayed detection of cyclically linked
garbage, which collects most objects as soon as they become
unreachable, but is not guaranteed to collect garbage containing
circular references. See the documentation of the "gc" module for
information on controlling the collection of cyclic garbage. Other
implementations act differently and CPython may change. Do not depend
on immediate finalization of objects when they become unreachable (so
you should always close files explicitly).
Note that the use of the implementation’s tracing or debugging
facilities may keep objects alive that would normally be collectable.
Also note that catching an exception with a ‘"try"…"except"’ statement
may keep objects alive.
Some objects contain references to “external” resources such as open
files or windows. It is understood that these resources are freed
when the object is garbage-collected, but since garbage collection is
not guaranteed to happen, such objects also provide an explicit way to
release the external resource, usually a "close()" method. Programs
are strongly recommended to explicitly close such objects. The
‘"try"…"finally"’ statement and the ‘"with"’ statement provide
convenient ways to do this.
Some objects contain references to other objects; these are called
*containers*. Examples of containers are tuples, lists and
dictionaries. The references are part of a container’s value. In
most cases, when we talk about the value of a container, we imply the
values, not the identities of the contained objects; however, when we
talk about the mutability of a container, only the identities of the
immediately contained objects are implied. So, if an immutable
container (like a tuple) contains a reference to a mutable object, its
value changes if that mutable object is changed.
Types affect almost all aspects of object behavior. Even the
importance of object identity is affected in some sense: for immutable
types, operations that compute new values may actually return a
reference to any existing object with the same type and value, while
for mutable objects this is not allowed. E.g., after "a = 1; b = 1",
"a" and "b" may or may not refer to the same object with the value
one, depending on the implementation, but after "c = []; d = []", "c"
and "d" are guaranteed to refer to two different, unique, newly
created empty lists. (Note that "c = d = []" assigns the same object
to both "c" and "d".)
u� Operator precedence
*******************
The following table summarizes the operator precedence in Python, from
lowest precedence (least binding) to highest precedence (most
binding). Operators in the same box have the same precedence. Unless
the syntax is explicitly given, operators are binary. Operators in
the same box group left to right (except for exponentiation, which
groups from right to left).
Note that comparisons, membership tests, and identity tests, all have
the same precedence and have a left-to-right chaining feature as
described in the Comparisons section.
+-------------------------------------------------+---------------------------------------+
| Operator | Description |
+=================================================+=======================================+
| "lambda" | Lambda expression |
+-------------------------------------------------+---------------------------------------+
| "if" – "else" | Conditional expression |
+-------------------------------------------------+---------------------------------------+
| "or" | Boolean OR |
+-------------------------------------------------+---------------------------------------+
| "and" | Boolean AND |
+-------------------------------------------------+---------------------------------------+
| "not" "x" | Boolean NOT |
+-------------------------------------------------+---------------------------------------+
| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership |
| ">=", "!=", "==" | tests and identity tests |
+-------------------------------------------------+---------------------------------------+
| "|" | Bitwise OR |
+-------------------------------------------------+---------------------------------------+
| "^" | Bitwise XOR |
+-------------------------------------------------+---------------------------------------+
| "&" | Bitwise AND |
+-------------------------------------------------+---------------------------------------+
| "<<", ">>" | Shifts |
+-------------------------------------------------+---------------------------------------+
| "+", "-" | Addition and subtraction |
+-------------------------------------------------+---------------------------------------+
| "*", "@", "/", "//", "%" | Multiplication, matrix |
| | multiplication, division, floor |
| | division, remainder [5] |
+-------------------------------------------------+---------------------------------------+
| "+x", "-x", "~x" | Positive, negative, bitwise NOT |
+-------------------------------------------------+---------------------------------------+
| "**" | Exponentiation [6] |
+-------------------------------------------------+---------------------------------------+
| "await" "x" | Await expression |
+-------------------------------------------------+---------------------------------------+
| "x[index]", "x[index:index]", | Subscription, slicing, call, |
| "x(arguments...)", "x.attribute" | attribute reference |
+-------------------------------------------------+---------------------------------------+
| "(expressions...)", "[expressions...]", "{key: | Binding or tuple display, list |
| value...}", "{expressions...}" | display, dictionary display, set |
| | display |
+-------------------------------------------------+---------------------------------------+
-[ Footnotes ]-
[1] While "abs(x%y) < abs(y)" is true mathematically, for floats
it may not be true numerically due to roundoff. For example, and
assuming a platform on which a Python float is an IEEE 754 double-
precision number, in order that "-1e-100 % 1e100" have the same
sign as "1e100", the computed result is "-1e-100 + 1e100", which
is numerically exactly equal to "1e100". The function
"math.fmod()" returns a result whose sign matches the sign of the
first argument instead, and so returns "-1e-100" in this case.
Which approach is more appropriate depends on the application.
[2] If x is very close to an exact integer multiple of y, it’s
possible for "x//y" to be one larger than "(x-x%y)//y" due to
rounding. In such cases, Python returns the latter result, in
order to preserve that "divmod(x,y)[0] * y + x % y" be very close
to "x".
[3] The Unicode standard distinguishes between *code points* (e.g.
U+0041) and *abstract characters* (e.g. “LATIN CAPITAL LETTER A”).
While most abstract characters in Unicode are only represented
using one code point, there is a number of abstract characters
that can in addition be represented using a sequence of more than
one code point. For example, the abstract character “LATIN
CAPITAL LETTER C WITH CEDILLA” can be represented as a single
*precomposed character* at code position U+00C7, or as a sequence
of a *base character* at code position U+0043 (LATIN CAPITAL
LETTER C), followed by a *combining character* at code position
U+0327 (COMBINING CEDILLA).
The comparison operators on strings compare at the level of
Unicode code points. This may be counter-intuitive to humans. For
example, ""\u00C7" == "\u0043\u0327"" is "False", even though both
strings represent the same abstract character “LATIN CAPITAL
LETTER C WITH CEDILLA”.
To compare strings at the level of abstract characters (that is,
in a way intuitive to humans), use "unicodedata.normalize()".
[4] Due to automatic garbage-collection, free lists, and the
dynamic nature of descriptors, you may notice seemingly unusual
behaviour in certain uses of the "is" operator, like those
involving comparisons between instance methods, or constants.
Check their documentation for more info.
[5] The "%" operator is also used for string formatting; the same
precedence applies.
[6] The power operator "**" binds less tightly than an arithmetic
or bitwise unary operator on its right, that is, "2**-1" is "0.5".
uw The "pass" statement
********************
pass_stmt ::= "pass"
"pass" is a null operation — when it is executed, nothing happens. It
is useful as a placeholder when a statement is required syntactically,
but no code needs to be executed, for example:
def f(arg): pass # a function that does nothing (yet)
class C: pass # a class with no methods (yet)
a� The power operator
******************
The power operator binds more tightly than unary operators on its
left; it binds less tightly than unary operators on its right. The
syntax is:
power ::= (await_expr | primary) ["**" u_expr]
Thus, in an unparenthesized sequence of power and unary operators, the
operators are evaluated from right to left (this does not constrain
the evaluation order for the operands): "-1**2" results in "-1".
The power operator has the same semantics as the built-in "pow()"
function, when called with two arguments: it yields its left argument
raised to the power of its right argument. The numeric arguments are
first converted to a common type, and the result is of that type.
For int operands, the result has the same type as the operands unless
the second argument is negative; in that case, all arguments are
converted to float and a float result is delivered. For example,
"10**2" returns "100", but "10**-2" returns "0.01".
Raising "0.0" to a negative power results in a "ZeroDivisionError".
Raising a negative number to a fractional power results in a "complex"
number. (In earlier versions it raised a "ValueError".)
ul The "raise" statement
*********************
raise_stmt ::= "raise" [expression ["from" expression]]
If no expressions are present, "raise" re-raises the last exception
that was active in the current scope. If no exception is active in
the current scope, a "RuntimeError" exception is raised indicating
that this is an error.
Otherwise, "raise" evaluates the first expression as the exception
object. It must be either a subclass or an instance of
"BaseException". If it is a class, the exception instance will be
obtained when needed by instantiating the class with no arguments.
The *type* of the exception is the exception instance’s class, the
*value* is the instance itself.
A traceback object is normally created automatically when an exception
is raised and attached to it as the "__traceback__" attribute, which
is writable. You can create an exception and set your own traceback in
one step using the "with_traceback()" exception method (which returns
the same exception instance, with its traceback set to its argument),
like so:
raise Exception("foo occurred").with_traceback(tracebackobj)
The "from" clause is used for exception chaining: if given, the second
*expression* must be another exception class or instance, which will
then be attached to the raised exception as the "__cause__" attribute
(which is writable). If the raised exception is not handled, both
exceptions will be printed:
>>> try:
... print(1 / 0)
... except Exception as exc:
... raise RuntimeError("Something bad happened") from exc
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
A similar mechanism works implicitly if an exception is raised inside
an exception handler or a "finally" clause: the previous exception is
then attached as the new exception’s "__context__" attribute:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened")
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
Exception chaining can be explicitly suppressed by specifying "None"
in the "from" clause:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened") from None
...
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
Additional information on exceptions can be found in section
Exceptions, and information about handling exceptions is in section
The try statement.
Changed in version 3.3: "None" is now permitted as "Y" in "raise X
from Y".
New in version 3.3: The "__suppress_context__" attribute to suppress
automatic display of the exception context.
a The "return" statement
**********************
return_stmt ::= "return" [expression_list]
"return" may only occur syntactically nested in a function definition,
not within a nested class definition.
If an expression list is present, it is evaluated, else "None" is
substituted.
"return" leaves the current function call with the expression list (or
"None") as return value.
When "return" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
function.
In a generator function, the "return" statement indicates that the
generator is done and will cause "StopIteration" to be raised. The
returned value (if any) is used as an argument to construct
"StopIteration" and becomes the "StopIteration.value" attribute.
In an asynchronous generator function, an empty "return" statement
indicates that the asynchronous generator is done and will cause
"StopAsyncIteration" to be raised. A non-empty "return" statement is
a syntax error in an asynchronous generator function.
u� Emulating container types
*************************
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python’s standard dictionary objects.
The "collections" module provides a "MutableMapping" abstract base
class to help create those methods from a base set of "__getitem__()",
"__setitem__()", "__delitem__()", and "keys()". Mutable sequences
should provide methods "append()", "count()", "index()", "extend()",
"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python
standard list objects. Finally, sequence types should implement
addition (meaning concatenation) and multiplication (meaning
repetition) by defining the methods "__add__()", "__radd__()",
"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described
below; they should not define other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should search the mapping’s keys; for sequences, it
should search through the values. It is further recommended that both
mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should be the same as "keys()"; for sequences, it should iterate
through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn’t define a "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.
object.__length_hint__(self)
Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. This
method is purely an optimization and is never required for
correctness.
New in version 3.4.
Note: Slicing is done exclusively with the following three methods.
A call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note: "for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container.
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a sequence. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don’t define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
a� Shifting operations
*******************
The shifting operations have lower priority than the arithmetic
operations:
shift_expr ::= a_expr | shift_expr ("<<" | ">>") a_expr
These operators accept integers as arguments. They shift the first
argument to the left or right by the number of bits given by the
second argument.
A right shift by *n* bits is defined as floor division by "pow(2,n)".
A left shift by *n* bits is defined as multiplication with "pow(2,n)".
Note: In the current implementation, the right-hand operand is
required to be at most "sys.maxsize". If the right-hand operand is
larger than "sys.maxsize" an "OverflowError" exception is raised.
a� Slicings
********
A slicing selects a range of items in a sequence object (e.g., a
string, tuple or list). Slicings may be used as expressions or as
targets in assignment or "del" statements. The syntax for a slicing:
slicing ::= primary "[" slice_list "]"
slice_list ::= slice_item ("," slice_item)* [","]
slice_item ::= expression | proper_slice
proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" [stride] ]
lower_bound ::= expression
upper_bound ::= expression
stride ::= expression
There is ambiguity in the formal syntax here: anything that looks like
an expression list also looks like a slice list, so any subscription
can be interpreted as a slicing. Rather than further complicating the
syntax, this is disambiguated by defining that in this case the
interpretation as a subscription takes priority over the
interpretation as a slicing (this is the case if the slice list
contains no proper slice).
The semantics for a slicing are as follows. The primary is indexed
(using the same "__getitem__()" method as normal subscription) with a
key that is constructed from the slice list, as follows. If the slice
list contains at least one comma, the key is a tuple containing the
conversion of the slice items; otherwise, the conversion of the lone
slice item is the key. The conversion of a slice item that is an
expression is that expression. The conversion of a proper slice is a
slice object (see section The standard type hierarchy) whose "start",
"stop" and "step" attributes are the values of the expressions given
as lower bound, upper bound and stride, respectively, substituting
"None" for missing expressions.
u~ Special Attributes
******************
The implementation adds a few special read-only attributes to several
object types, where they are relevant. Some of these are not reported
by the "dir()" built-in function.
object.__dict__
A dictionary or other mapping object used to store an object’s
(writable) attributes.
instance.__class__
The class to which a class instance belongs.
class.__bases__
The tuple of base classes of a class object.
definition.__name__
The name of the class, function, method, descriptor, or generator
instance.
definition.__qualname__
The *qualified name* of the class, function, method, descriptor, or
generator instance.
New in version 3.3.
class.__mro__
This attribute is a tuple of classes that are considered when
looking for base classes during method resolution.
class.mro()
This method can be overridden by a metaclass to customize the
method resolution order for its instances. It is called at class
instantiation, and its result is stored in "__mro__".
class.__subclasses__()
Each class keeps a list of weak references to its immediate
subclasses. This method returns a list of all those references
still alive. Example:
>>> int.__subclasses__()
[<class 'bool'>]
-[ Footnotes ]-
[1] Additional information on these special methods may be found
in the Python Reference Manual (Basic customization).
[2] As a consequence, the list "[1, 2]" is considered equal to
"[1.0, 2.0]", and similarly for tuples.
[3] They must have since the parser can’t tell the type of the
operands.
[4] Cased characters are those with general category property
being one of “Lu” (Letter, uppercase), “Ll” (Letter, lowercase),
or “Lt” (Letter, titlecase).
[5] To format only a tuple you should therefore provide a
singleton tuple whose only element is the tuple to be formatted.
u.� Special method names
********************
A class can implement certain operations that are invoked by special
syntax (such as arithmetic operations or subscripting and slicing) by
defining methods with special names. This is Python’s approach to
*operator overloading*, allowing classes to define their own behavior
with respect to language operators. For instance, if a class defines
a method named "__getitem__()", and "x" is an instance of this class,
then "x[i]" is roughly equivalent to "type(x).__getitem__(x, i)".
Except where mentioned, attempts to execute an operation raise an
exception when no appropriate method is defined (typically
"AttributeError" or "TypeError").
Setting a special method to "None" indicates that the corresponding
operation is not available. For example, if a class sets "__iter__()"
to "None", the class is not iterable, so calling "iter()" on its
instances will raise a "TypeError" (without falling back to
"__getitem__()"). [2]
When implementing a class that emulates any built-in type, it is
important that the emulation only be implemented to the degree that it
makes sense for the object being modelled. For example, some
sequences may work well with retrieval of individual elements, but
extracting a slice may not make sense. (One example of this is the
"NodeList" interface in the W3C’s Document Object Model.)
Basic customization
===================
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass’s "__new__()" method using
"super().__new__(cls[, ...])" with appropriate arguments and then
modifying the newly-created instance as necessary before returning
it.
If "__new__()" returns an instance of *cls*, then the new
instance’s "__init__()" method will be invoked like
"__init__(self[, ...])", where *self* is the new instance and the
remaining arguments are the same as were passed to "__new__()".
If "__new__()" does not return an instance of *cls*, then the new
instance’s "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class’s "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"super().__init__([args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.
It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.
It is not guaranteed that "__del__()" methods are called for
objects that still exist when the interpreter exits.
Note: "del x" doesn’t directly call "x.__del__()" — the former
decrements the reference count for "x" by one, and the latter is
only called when "x"’s reference count reaches zero.
**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.
See also: Documentation for the "gc" module.
Warning: Due to the precarious circumstances under which
"__del__()" methods are invoked, exceptions that occur during
their execution are ignored, and a warning is printed to
"sys.stderr" instead. In particular:
* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".
* "__del__()" can be executed during interpreter shutdown. As
a consequence, the global variables it needs to access
(including other modules) may already have been deleted or set
to "None". Python guarantees that globals whose name begins
with a single underscore are deleted from their module before
other globals are deleted; if no other references to such
globals exist, this may help in assuring that imported modules
are still available at the time when the "__del__()" method is
called.
object.__repr__(self)
Called by the "repr()" built-in function to compute the “official”
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an “informal”
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the “informal” or nicely printable string
representation of an object. The return value must be a string
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by bytes to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function, and by extension,
evaluation of formatted string literals and the "str.format()"
method, to produce a “formatted” string representation of an
object. The "format_spec" argument is a string that contains a
description of the formatting options desired. The interpretation
of the "format_spec" argument is up to the type implementing
"__format__()", however most classes will either delegate
formatting to one of the built-in types, or use a similar
formatting option syntax.
See Format Specification Mini-Language for a description of the
standard formatting syntax.
The return value must be a string object.
Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
By default, "__ne__()" delegates to "__eq__()" and inverts the
result unless it is "NotImplemented". There are no other implied
relationships among the comparison operators, for example, the
truth of "(x<y or x==y)" does not imply "x<=y". To automatically
generate ordering operations from a single root operation, see
"functools.total_ordering()".
See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
Note: "hash()" truncates the value returned from an object’s
custom "__hash__()" method to the size of a "Py_ssize_t". This
is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit
builds. If an object’s "__hash__()" must interoperate on builds
of different bit sizes, be sure to check the width on all
supported builds. An easy way to do this is with "python -c
"import sys; print(sys.hash_info.width)"".
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key’s hash value is immutable (if the object’s hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj, collections.Hashable)".
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
<ParentClass>.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.Hashable)" call.
Note: By default, the "__hash__()" values of str, bytes and
datetime objects are “salted” with an unpredictable random value.
Although they remain constant within an individual Python
process, they are not predictable between repeated invocations of
Python.This is intended to provide protection against a denial-
of-service caused by carefully-chosen inputs that exploit the
worst case performance of a dict insertion, O(n^2) complexity.
See http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of
dicts, sets and other mappings. Python has never made guarantees
about this ordering (and it typically varies between 32-bit and
64-bit builds).See also "PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
Customizing attribute access
============================
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
return the (computed) attribute value or raise an "AttributeError"
exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note: This method may still be bypassed when looking up special
methods as the result of implicit invocation via language syntax
or built-in functions. See Special method lookup.
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should call the base class method with the same name, for example,
"object.__setattr__(self, name, value)".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
Customizing module attribute access
-----------------------------------
For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
setattr(self, attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note: Setting module "__class__" only affects lookups made using the
attribute access syntax – directly accessing the module globals
(whether by code within the module, or via a reference to the
module’s globals dictionary) is unaffected.
Changed in version 3.5: "__class__" module attribute is now writable.
Implementing Descriptors
------------------------
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents). In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".
object.__get__(self, instance, owner)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). *owner* is always the owner class, while *instance* is the
instance that the attribute was accessed through, or "None" when
the attribute is accessed through the *owner*. This method should
return the (computed) attribute value or raise an "AttributeError"
exception.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
object.__set_name__(self, owner, name)
Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.
New in version 3.6.
The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).
Invoking Descriptors
--------------------
In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a class, "A.x" is transformed into the call:
"A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined. A descriptor can define
any combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
---------
*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of *__dict__* and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)
The space saved over using *__dict__* can be significant.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. *__slots__*
reserves space for the declared variables and prevents the
automatic creation of *__dict__* and *__weakref__* for each
instance.
Notes on using *__slots__*
~~~~~~~~~~~~~~~~~~~~~~~~~~
* When inheriting from a class without *__slots__*, the *__dict__*
and *__weakref__* attribute of the instances will always be
accessible.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
* Without a *__weakref__* variable for each instance, classes
defining *__slots__* do not support weak references to its
instances. If weak reference support is needed, then add
"'__weakref__'" to the sequence of strings in the *__slots__*
declaration.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is not limited to the
class where it is defined. *__slots__* declared in parents are
available in child classes. However, child subclasses will get a
*__dict__* and *__weakref__* unless they also define *__slots__*
(which should only contain names of any *additional* slots).
* If a class defines a slot also defined in a base class, the
instance variable defined by the base class slot is inaccessible
(except by retrieving its descriptor directly from the base class).
This renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
“variable-length” built-in types such as "int", "bytes" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings
may also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".
Customizing class creation
==========================
Whenever a class inherits from another class, *__init_subclass__* is
called on that class. This way, it is possible to write classes which
change the behavior of subclasses. This is closely related to class
decorators, but where class decorators only affect the specific class
they’re applied to, "__init_subclass__" solely applies to future
subclasses of the class defining the method.
classmethod object.__init_subclass__(cls)
This method is called whenever the containing class is subclassed.
*cls* is then the new subclass. If defined as a normal instance
method, this method is implicitly converted to a class method.
Keyword arguments which are given to a new class are passed to the
parent’s class "__init_subclass__". For compatibility with other
classes using "__init_subclass__", one should take out the needed
keyword arguments and pass the others over to the base class, as
in:
class Philosopher:
def __init_subclass__(cls, default_name, **kwargs):
super().__init_subclass__(**kwargs)
cls.default_name = default_name
class AustralianPhilosopher(Philosopher, default_name="Bruce"):
pass
The default implementation "object.__init_subclass__" does nothing,
but raises an error if it is called with any arguments.
Note: The metaclass hint "metaclass" is consumed by the rest of
the type machinery, and is never passed to "__init_subclass__"
implementations. The actual metaclass (rather than the explicit
hint) can be accessed as "type(cls)".
New in version 3.6.
Metaclasses
-----------
By default, classes are constructed using "type()". The class body is
executed in a new namespace and the class name is bound locally to the
result of "type(name, bases, namespace)".
The class creation process can be customized by passing the
"metaclass" keyword argument in the class definition line, or by
inheriting from an existing class that included such an argument. In
the following example, both "MyClass" and "MySubclass" are instances
of "Meta":
class Meta(type):
pass
class MyClass(metaclass=Meta):
pass
class MySubclass(MyClass):
pass
Any other keyword arguments that are specified in the class definition
are passed through to all metaclass operations described below.
When a class definition is executed, the following steps occur:
* the appropriate metaclass is determined
* the class namespace is prepared
* the class body is executed
* the class object is created
Determining the appropriate metaclass
-------------------------------------
The appropriate metaclass for a class definition is determined as
follows:
* if no bases and no explicit metaclass are given, then "type()" is
used
* if an explicit metaclass is given and it is *not* an instance of
"type()", then it is used directly as the metaclass
* if an instance of "type()" is given as the explicit metaclass, or
bases are defined, then the most derived metaclass is used
The most derived metaclass is selected from the explicitly specified
metaclass (if any) and the metaclasses (i.e. "type(cls)") of all
specified base classes. The most derived metaclass is one which is a
subtype of *all* of these candidate metaclasses. If none of the
candidate metaclasses meets that criterion, then the class definition
will fail with "TypeError".
Preparing the class namespace
-----------------------------
Once the appropriate metaclass has been identified, then the class
namespace is prepared. If the metaclass has a "__prepare__" attribute,
it is called as "namespace = metaclass.__prepare__(name, bases,
**kwds)" (where the additional keyword arguments, if any, come from
the class definition).
If the metaclass has no "__prepare__" attribute, then the class
namespace is initialised as an empty ordered mapping.
See also:
**PEP 3115** - Metaclasses in Python 3000
Introduced the "__prepare__" namespace hook
Executing the class body
------------------------
The class body is executed (approximately) as "exec(body, globals(),
namespace)". The key difference from a normal call to "exec()" is that
lexical scoping allows the class body (including any methods) to
reference names from the current and outer scopes when the class
definition occurs inside a function.
However, even when the class definition occurs inside the function,
methods defined inside the class still cannot see names defined at the
class scope. Class variables must be accessed through the first
parameter of instance or class methods, or through the implicit
lexically scoped "__class__" reference described in the next section.
Creating the class object
-------------------------
Once the class namespace has been populated by executing the class
body, the class object is created by calling "metaclass(name, bases,
namespace, **kwds)" (the additional keywords passed here are the same
as those passed to "__prepare__").
This class object is the one that will be referenced by the zero-
argument form of "super()". "__class__" is an implicit closure
reference created by the compiler if any methods in a class body refer
to either "__class__" or "super". This allows the zero argument form
of "super()" to correctly identify the class being defined based on
lexical scoping, while the class or instance that was used to make the
current call is identified based on the first argument passed to the
method.
**CPython implementation detail:** In CPython 3.6 and later, the
"__class__" cell is passed to the metaclass as a "__classcell__" entry
in the class namespace. If present, this must be propagated up to the
"type.__new__" call in order for the class to be initialised
correctly. Failing to do so will result in a "DeprecationWarning" in
Python 3.6, and a "RuntimeError" in Python 3.8.
When using the default metaclass "type", or any metaclass that
ultimately calls "type.__new__", the following additional
customisation steps are invoked after creating the class object:
* first, "type.__new__" collects all of the descriptors in the class
namespace that define a "__set_name__()" method;
* second, all of these "__set_name__" methods are called with the
class being defined and the assigned name of that particular
descriptor; and
* finally, the "__init_subclass__()" hook is called on the immediate
parent of the new class in its method resolution order.
After the class object is created, it is passed to the class
decorators included in the class definition (if any) and the resulting
object is bound in the local namespace as the defined class.
When a new class is created by "type.__new__", the object provided as
the namespace parameter is copied to a new ordered mapping and the
original object is discarded. The new copy is wrapped in a read-only
proxy, which becomes the "__dict__" attribute of the class object.
See also:
**PEP 3135** - New super
Describes the implicit "__class__" closure reference
Uses for metaclasses
--------------------
The potential uses for metaclasses are boundless. Some ideas that have
been explored include enum, logging, interface checking, automatic
delegation, automatic property creation, proxies, frameworks, and
automatic resource locking/synchronization.
Customizing instance and subclass checks
========================================
The following methods are used to override the default behavior of the
"isinstance()" and "issubclass()" built-in functions.
In particular, the metaclass "abc.ABCMeta" implements these methods in
order to allow the addition of Abstract Base Classes (ABCs) as
“virtual base classes” to any class or type (including built-in
types), including other ABCs.
class.__instancecheck__(self, instance)
Return true if *instance* should be considered a (direct or
indirect) instance of *class*. If defined, called to implement
"isinstance(instance, class)".
class.__subclasscheck__(self, subclass)
Return true if *subclass* should be considered a (direct or
indirect) subclass of *class*. If defined, called to implement
"issubclass(subclass, class)".
Note that these methods are looked up on the type (metaclass) of a
class. They cannot be defined as class methods in the actual class.
This is consistent with the lookup of special methods that are called
on instances, only in this case the instance is itself a class.
See also:
**PEP 3119** - Introducing Abstract Base Classes
Includes the specification for customizing "isinstance()" and
"issubclass()" behavior through "__instancecheck__()" and
"__subclasscheck__()", with motivation for this functionality in
the context of adding Abstract Base Classes (see the "abc"
module) to the language.
Emulating callable objects
==========================
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method
is defined, "x(arg1, arg2, ...)" is a shorthand for
"x.__call__(arg1, arg2, ...)".
Emulating container types
=========================
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python’s standard dictionary objects.
The "collections" module provides a "MutableMapping" abstract base
class to help create those methods from a base set of "__getitem__()",
"__setitem__()", "__delitem__()", and "keys()". Mutable sequences
should provide methods "append()", "count()", "index()", "extend()",
"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python
standard list objects. Finally, sequence types should implement
addition (meaning concatenation) and multiplication (meaning
repetition) by defining the methods "__add__()", "__radd__()",
"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described
below; they should not define other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should search the mapping’s keys; for sequences, it
should search through the values. It is further recommended that both
mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should be the same as "keys()"; for sequences, it should iterate
through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn’t define a "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.
object.__length_hint__(self)
Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. This
method is purely an optimization and is never required for
correctness.
New in version 3.4.
Note: Slicing is done exclusively with the following three methods.
A call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note: "for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container.
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a sequence. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don’t define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
Emulating numeric types
=======================
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the expression "x + y", where *x* is an instance of a
class that has an "__add__()" method, "x.__add__(y)" is called.
The "__divmod__()" method should be the equivalent to using
"__floordiv__()" and "__mod__()"; it should not be related to
"__truediv__()". Note that "__pow__()" should be defined to accept
an optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other)
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)
operands. These functions are only called if the left operand does
not support the corresponding operation [3] and the operands are of
different types. [4] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note: If the right operand’s type is a subclass of the left
operand’s type and that subclass provides the reflected method
for the operation, this method will be called before the left
operand’s non-reflected method. This behavior allows subclasses
to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (modifying *self*) and return the result
(which could be, but does not have to be, *self*). If a specific
method is not defined, the augmented assignment falls back to the
normal methods. For instance, if *x* is an instance of a class
with an "__iadd__()" method, "x += y" is equivalent to "x =
x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are
considered, as with the evaluation of "x + y". In certain
situations, augmented assignment can result in unexpected errors
(see Why does a_tuple[i] += [‘item’] raise an exception when the
addition works?), but this behavior is in fact part of the data
model.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()" and
"float()". Should return a value of the appropriate type.
object.__index__(self)
Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.
Note: In order to have a coherent integer type class, when
"__index__()" is defined "__int__()" should also be defined, and
both should return the same value.
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").
If "__int__()" is not defined then the built-in function "int()"
falls back to "__trunc__()".
With Statement Context Managers
===============================
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method’s return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller’s responsibility.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
Special method lookup
=====================
For custom classes, implicit invocations of special methods are only
guaranteed to work correctly if defined on an object’s type, not in
the object’s instance dictionary. That behaviour is the reason why
the following code raises an exception:
>>> class C:
... pass
...
>>> c = C()
>>> c.__len__ = lambda: 5
>>> len(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: object of type 'C' has no len()
The rationale behind this behaviour lies with a number of special
methods such as "__hash__()" and "__repr__()" that are implemented by
all objects, including type objects. If the implicit lookup of these
methods used the conventional lookup process, they would fail when
invoked on the type object itself:
>>> 1 .__hash__() == hash(1)
True
>>> int.__hash__() == hash(int)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: descriptor '__hash__' of 'int' object needs an argument
Incorrectly attempting to invoke an unbound method of a class in this
way is sometimes referred to as ‘metaclass confusion’, and is avoided
by bypassing the instance when looking up special methods:
>>> type(1).__hash__(1) == hash(1)
True
>>> type(int).__hash__(int) == hash(int)
True
In addition to bypassing any instance attributes in the interest of
correctness, implicit special method lookup generally also bypasses
the "__getattribute__()" method even of the object’s metaclass:
>>> class Meta(type):
... def __getattribute__(*args):
... print("Metaclass getattribute invoked")
... return type.__getattribute__(*args)
...
>>> class C(object, metaclass=Meta):
... def __len__(self):
... return 10
... def __getattribute__(*args):
... print("Class getattribute invoked")
... return object.__getattribute__(*args)
...
>>> c = C()
>>> c.__len__() # Explicit lookup via instance
Class getattribute invoked
10
>>> type(c).__len__(c) # Explicit lookup via type
Metaclass getattribute invoked
10
>>> len(c) # Implicit lookup
10
Bypassing the "__getattribute__()" machinery in this fashion provides
significant scope for speed optimisations within the interpreter, at
the cost of some flexibility in the handling of special methods (the
special method *must* be set on the class object itself in order to be
consistently invoked by the interpreter).
u=W String Methods
**************
Strings implement all of the common sequence operations, along with
the additional methods described below.
Strings also support two styles of string formatting, one providing a
large degree of flexibility and customization (see "str.format()",
Format String Syntax and Custom String Formatting) and the other based
on C "printf" style formatting that handles a narrower range of types
and is slightly harder to use correctly, but is often faster for the
cases it can handle (printf-style String Formatting).
The Text Processing Services section of the standard library covers a
number of other modules that provide various text related utilities
(including regular expression support in the "re" module).
str.capitalize()
Return a copy of the string with its first character capitalized
and the rest lowercased.
str.casefold()
Return a casefolded copy of the string. Casefolded strings may be
used for caseless matching.
Casefolding is similar to lowercasing but more aggressive because
it is intended to remove all case distinctions in a string. For
example, the German lowercase letter "'ß'" is equivalent to ""ss"".
Since it is already lowercase, "lower()" would do nothing to "'ß'";
"casefold()" converts it to ""ss"".
The casefolding algorithm is described in section 3.13 of the
Unicode Standard.
New in version 3.3.
str.center(width[, fillchar])
Return centered in a string of length *width*. Padding is done
using the specified *fillchar* (default is an ASCII space). The
original string is returned if *width* is less than or equal to
"len(s)".
str.count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring *sub*
in the range [*start*, *end*]. Optional arguments *start* and
*end* are interpreted as in slice notation.
str.encode(encoding="utf-8", errors="strict")
Return an encoded version of the string as a bytes object. Default
encoding is "'utf-8'". *errors* may be given to set a different
error handling scheme. The default for *errors* is "'strict'",
meaning that encoding errors raise a "UnicodeError". Other possible
values are "'ignore'", "'replace'", "'xmlcharrefreplace'",
"'backslashreplace'" and any other name registered via
"codecs.register_error()", see section Error Handlers. For a list
of possible encodings, see section Standard Encodings.
Changed in version 3.1: Support for keyword arguments added.
str.endswith(suffix[, start[, end]])
Return "True" if the string ends with the specified *suffix*,
otherwise return "False". *suffix* can also be a tuple of suffixes
to look for. With optional *start*, test beginning at that
position. With optional *end*, stop comparing at that position.
str.expandtabs(tabsize=8)
Return a copy of the string where all tab characters are replaced
by one or more spaces, depending on the current column and the
given tab size. Tab positions occur every *tabsize* characters
(default is 8, giving tab positions at columns 0, 8, 16 and so on).
To expand the string, the current column is set to zero and the
string is examined character by character. If the character is a
tab ("\t"), one or more space characters are inserted in the result
until the current column is equal to the next tab position. (The
tab character itself is not copied.) If the character is a newline
("\n") or return ("\r"), it is copied and the current column is
reset to zero. Any other character is copied unchanged and the
current column is incremented by one regardless of how the
character is represented when printed.
>>> '01\t012\t0123\t01234'.expandtabs()
'01 012 0123 01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01 012 0123 01234'
str.find(sub[, start[, end]])
Return the lowest index in the string where substring *sub* is
found within the slice "s[start:end]". Optional arguments *start*
and *end* are interpreted as in slice notation. Return "-1" if
*sub* is not found.
Note: The "find()" method should be used only if you need to know
the position of *sub*. To check if *sub* is a substring or not,
use the "in" operator:
>>> 'Py' in 'Python'
True
str.format(*args, **kwargs)
Perform a string formatting operation. The string on which this
method is called can contain literal text or replacement fields
delimited by braces "{}". Each replacement field contains either
the numeric index of a positional argument, or the name of a
keyword argument. Returns a copy of the string where each
replacement field is replaced with the string value of the
corresponding argument.
>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'
See Format String Syntax for a description of the various
formatting options that can be specified in format strings.
Note: When formatting a number ("int", "float", "complex",
"decimal.Decimal" and subclasses) with the "n" type (ex:
"'{:n}'.format(1234)"), the function temporarily sets the
"LC_CTYPE" locale to the "LC_NUMERIC" locale to decode
"decimal_point" and "thousands_sep" fields of "localeconv()" if
they are non-ASCII or longer than 1 byte, and the "LC_NUMERIC"
locale is different than the "LC_CTYPE" locale. This temporary
change affects other threads.
Changed in version 3.6.5: When formatting a number with the "n"
type, the function sets temporarily the "LC_CTYPE" locale to the
"LC_NUMERIC" locale in some cases.
str.format_map(mapping)
Similar to "str.format(**mapping)", except that "mapping" is used
directly and not copied to a "dict". This is useful if for example
"mapping" is a dict subclass:
>>> class Default(dict):
... def __missing__(self, key):
... return key
...
>>> '{name} was born in {country}'.format_map(Default(name='Guido'))
'Guido was born in country'
New in version 3.2.
str.index(sub[, start[, end]])
Like "find()", but raise "ValueError" when the substring is not
found.
str.isalnum()
Return true if all characters in the string are alphanumeric and
there is at least one character, false otherwise. A character "c"
is alphanumeric if one of the following returns "True":
"c.isalpha()", "c.isdecimal()", "c.isdigit()", or "c.isnumeric()".
str.isalpha()
Return true if all characters in the string are alphabetic and
there is at least one character, false otherwise. Alphabetic
characters are those characters defined in the Unicode character
database as “Letter”, i.e., those with general category property
being one of “Lm”, “Lt”, “Lu”, “Ll”, or “Lo”. Note that this is
different from the “Alphabetic” property defined in the Unicode
Standard.
str.isdecimal()
Return true if all characters in the string are decimal characters
and there is at least one character, false otherwise. Decimal
characters are those that can be used to form numbers in base 10,
e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Formally a decimal character
is a character in the Unicode General Category “Nd”.
str.isdigit()
Return true if all characters in the string are digits and there is
at least one character, false otherwise. Digits include decimal
characters and digits that need special handling, such as the
compatibility superscript digits. This covers digits which cannot
be used to form numbers in base 10, like the Kharosthi numbers.
Formally, a digit is a character that has the property value
Numeric_Type=Digit or Numeric_Type=Decimal.
str.isidentifier()
Return true if the string is a valid identifier according to the
language definition, section Identifiers and keywords.
Use "keyword.iskeyword()" to test for reserved identifiers such as
"def" and "class".
str.islower()
Return true if all cased characters [4] in the string are lowercase
and there is at least one cased character, false otherwise.
str.isnumeric()
Return true if all characters in the string are numeric characters,
and there is at least one character, false otherwise. Numeric
characters include digit characters, and all characters that have
the Unicode numeric value property, e.g. U+2155, VULGAR FRACTION
ONE FIFTH. Formally, numeric characters are those with the
property value Numeric_Type=Digit, Numeric_Type=Decimal or
Numeric_Type=Numeric.
str.isprintable()
Return true if all characters in the string are printable or the
string is empty, false otherwise. Nonprintable characters are
those characters defined in the Unicode character database as
“Other” or “Separator”, excepting the ASCII space (0x20) which is
considered printable. (Note that printable characters in this
context are those which should not be escaped when "repr()" is
invoked on a string. It has no bearing on the handling of strings
written to "sys.stdout" or "sys.stderr".)
str.isspace()
Return true if there are only whitespace characters in the string
and there is at least one character, false otherwise. Whitespace
characters are those characters defined in the Unicode character
database as “Other” or “Separator” and those with bidirectional
property being one of “WS”, “B”, or “S”.
str.istitle()
Return true if the string is a titlecased string and there is at
least one character, for example uppercase characters may only
follow uncased characters and lowercase characters only cased ones.
Return false otherwise.
str.isupper()
Return true if all cased characters [4] in the string are uppercase
and there is at least one cased character, false otherwise.
str.join(iterable)
Return a string which is the concatenation of the strings in
*iterable*. A "TypeError" will be raised if there are any non-
string values in *iterable*, including "bytes" objects. The
separator between elements is the string providing this method.
str.ljust(width[, fillchar])
Return the string left justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is an ASCII
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.lower()
Return a copy of the string with all the cased characters [4]
converted to lowercase.
The lowercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.lstrip([chars])
Return a copy of the string with leading characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a prefix; rather,
all combinations of its values are stripped:
>>> ' spacious '.lstrip()
'spacious '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'
static str.maketrans(x[, y[, z]])
This static method returns a translation table usable for
"str.translate()".
If there is only one argument, it must be a dictionary mapping
Unicode ordinals (integers) or characters (strings of length 1) to
Unicode ordinals, strings (of arbitrary lengths) or "None".
Character keys will then be converted to ordinals.
If there are two arguments, they must be strings of equal length,
and in the resulting dictionary, each character in x will be mapped
to the character at the same position in y. If there is a third
argument, it must be a string, whose characters will be mapped to
"None" in the result.
str.partition(sep)
Split the string at the first occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing the string itself, followed by
two empty strings.
str.replace(old, new[, count])
Return a copy of the string with all occurrences of substring *old*
replaced by *new*. If the optional argument *count* is given, only
the first *count* occurrences are replaced.
str.rfind(sub[, start[, end]])
Return the highest index in the string where substring *sub* is
found, such that *sub* is contained within "s[start:end]".
Optional arguments *start* and *end* are interpreted as in slice
notation. Return "-1" on failure.
str.rindex(sub[, start[, end]])
Like "rfind()" but raises "ValueError" when the substring *sub* is
not found.
str.rjust(width[, fillchar])
Return the string right justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is an ASCII
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.rpartition(sep)
Split the string at the last occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing two empty strings, followed by
the string itself.
str.rsplit(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit* splits
are done, the *rightmost* ones. If *sep* is not specified or
"None", any whitespace string is a separator. Except for splitting
from the right, "rsplit()" behaves like "split()" which is
described in detail below.
str.rstrip([chars])
Return a copy of the string with trailing characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a suffix; rather,
all combinations of its values are stripped:
>>> ' spacious '.rstrip()
' spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'
str.split(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit*
splits are done (thus, the list will have at most "maxsplit+1"
elements). If *maxsplit* is not specified or "-1", then there is
no limit on the number of splits (all possible splits are made).
If *sep* is given, consecutive delimiters are not grouped together
and are deemed to delimit empty strings (for example,
"'1,,2'.split(',')" returns "['1', '', '2']"). The *sep* argument
may consist of multiple characters (for example,
"'1<>2<>3'.split('<>')" returns "['1', '2', '3']"). Splitting an
empty string with a specified separator returns "['']".
For example:
>>> '1,2,3'.split(',')
['1', '2', '3']
>>> '1,2,3'.split(',', maxsplit=1)
['1', '2,3']
>>> '1,2,,3,'.split(',')
['1', '2', '', '3', '']
If *sep* is not specified or is "None", a different splitting
algorithm is applied: runs of consecutive whitespace are regarded
as a single separator, and the result will contain no empty strings
at the start or end if the string has leading or trailing
whitespace. Consequently, splitting an empty string or a string
consisting of just whitespace with a "None" separator returns "[]".
For example:
>>> '1 2 3'.split()
['1', '2', '3']
>>> '1 2 3'.split(maxsplit=1)
['1', '2 3']
>>> ' 1 2 3 '.split()
['1', '2', '3']
str.splitlines([keepends])
Return a list of the lines in the string, breaking at line
boundaries. Line breaks are not included in the resulting list
unless *keepends* is given and true.
This method splits on the following line boundaries. In
particular, the boundaries are a superset of *universal newlines*.
+-------------------------+-------------------------------+
| Representation | Description |
+=========================+===============================+
| "\n" | Line Feed |
+-------------------------+-------------------------------+
| "\r" | Carriage Return |
+-------------------------+-------------------------------+
| "\r\n" | Carriage Return + Line Feed |
+-------------------------+-------------------------------+
| "\v" or "\x0b" | Line Tabulation |
+-------------------------+-------------------------------+
| "\f" or "\x0c" | Form Feed |
+-------------------------+-------------------------------+
| "\x1c" | File Separator |
+-------------------------+-------------------------------+
| "\x1d" | Group Separator |
+-------------------------+-------------------------------+
| "\x1e" | Record Separator |
+-------------------------+-------------------------------+
| "\x85" | Next Line (C1 Control Code) |
+-------------------------+-------------------------------+
| "\u2028" | Line Separator |
+-------------------------+-------------------------------+
| "\u2029" | Paragraph Separator |
+-------------------------+-------------------------------+
Changed in version 3.2: "\v" and "\f" added to list of line
boundaries.
For example:
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
['ab c', '', 'de fg', 'kl']
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True)
['ab c\n', '\n', 'de fg\r', 'kl\r\n']
Unlike "split()" when a delimiter string *sep* is given, this
method returns an empty list for the empty string, and a terminal
line break does not result in an extra line:
>>> "".splitlines()
[]
>>> "One line\n".splitlines()
['One line']
For comparison, "split('\n')" gives:
>>> ''.split('\n')
['']
>>> 'Two lines\n'.split('\n')
['Two lines', '']
str.startswith(prefix[, start[, end]])
Return "True" if string starts with the *prefix*, otherwise return
"False". *prefix* can also be a tuple of prefixes to look for.
With optional *start*, test string beginning at that position.
With optional *end*, stop comparing string at that position.
str.strip([chars])
Return a copy of the string with the leading and trailing
characters removed. The *chars* argument is a string specifying the
set of characters to be removed. If omitted or "None", the *chars*
argument defaults to removing whitespace. The *chars* argument is
not a prefix or suffix; rather, all combinations of its values are
stripped:
>>> ' spacious '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'
The outermost leading and trailing *chars* argument values are
stripped from the string. Characters are removed from the leading
end until reaching a string character that is not contained in the
set of characters in *chars*. A similar action takes place on the
trailing end. For example:
>>> comment_string = '#....... Section 3.2.1 Issue #32 .......'
>>> comment_string.strip('.#! ')
'Section 3.2.1 Issue #32'
str.swapcase()
Return a copy of the string with uppercase characters converted to
lowercase and vice versa. Note that it is not necessarily true that
"s.swapcase().swapcase() == s".
str.title()
Return a titlecased version of the string where words start with an
uppercase character and the remaining characters are lowercase.
For example:
>>> 'Hello world'.title()
'Hello World'
The algorithm uses a simple language-independent definition of a
word as groups of consecutive letters. The definition works in
many contexts but it means that apostrophes in contractions and
possessives form word boundaries, which may not be the desired
result:
>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"
A workaround for apostrophes can be constructed using regular
expressions:
>>> import re
>>> def titlecase(s):
... return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
... lambda mo: mo.group(0)[0].upper() +
... mo.group(0)[1:].lower(),
... s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."
str.translate(table)
Return a copy of the string in which each character has been mapped
through the given translation table. The table must be an object
that implements indexing via "__getitem__()", typically a *mapping*
or *sequence*. When indexed by a Unicode ordinal (an integer), the
table object can do any of the following: return a Unicode ordinal
or a string, to map the character to one or more other characters;
return "None", to delete the character from the return string; or
raise a "LookupError" exception, to map the character to itself.
You can use "str.maketrans()" to create a translation map from
character-to-character mappings in different formats.
See also the "codecs" module for a more flexible approach to custom
character mappings.
str.upper()
Return a copy of the string with all the cased characters [4]
converted to uppercase. Note that "s.upper().isupper()" might be
"False" if "s" contains uncased characters or if the Unicode
category of the resulting character(s) is not “Lu” (Letter,
uppercase), but e.g. “Lt” (Letter, titlecase).
The uppercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.zfill(width)
Return a copy of the string left filled with ASCII "'0'" digits to
make a string of length *width*. A leading sign prefix
("'+'"/"'-'") is handled by inserting the padding *after* the sign
character rather than before. The original string is returned if
*width* is less than or equal to "len(s)".
For example:
>>> "42".zfill(5)
'00042'
>>> "-42".zfill(5)
'-0042'
uw String and Bytes literals
*************************
String literals are described by the following lexical definitions:
stringliteral ::= [stringprefix](shortstring | longstring)
stringprefix ::= "r" | "u" | "R" | "U" | "f" | "F"
| "fr" | "Fr" | "fR" | "FR" | "rf" | "rF" | "Rf" | "RF"
shortstring ::= "'" shortstringitem* "'" | '"' shortstringitem* '"'
longstring ::= "'''" longstringitem* "'''" | '"""' longstringitem* '"""'
shortstringitem ::= shortstringchar | stringescapeseq
longstringitem ::= longstringchar | stringescapeseq
shortstringchar ::= <any source character except "\" or newline or the quote>
longstringchar ::= <any source character except "\">
stringescapeseq ::= "\" <any source character>
bytesliteral ::= bytesprefix(shortbytes | longbytes)
bytesprefix ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB"
shortbytes ::= "'" shortbytesitem* "'" | '"' shortbytesitem* '"'
longbytes ::= "'''" longbytesitem* "'''" | '"""' longbytesitem* '"""'
shortbytesitem ::= shortbyteschar | bytesescapeseq
longbytesitem ::= longbyteschar | bytesescapeseq
shortbyteschar ::= <any ASCII character except "\" or newline or the quote>
longbyteschar ::= <any ASCII character except "\">
bytesescapeseq ::= "\" <any ASCII character>
One syntactic restriction not indicated by these productions is that
whitespace is not allowed between the "stringprefix" or "bytesprefix"
and the rest of the literal. The source character set is defined by
the encoding declaration; it is UTF-8 if no encoding declaration is
given in the source file; see section Encoding declarations.
In plain English: Both types of literals can be enclosed in matching
single quotes ("'") or double quotes ("""). They can also be enclosed
in matching groups of three single or double quotes (these are
generally referred to as *triple-quoted strings*). The backslash
("\") character is used to escape characters that otherwise have a
special meaning, such as newline, backslash itself, or the quote
character.
Bytes literals are always prefixed with "'b'" or "'B'"; they produce
an instance of the "bytes" type instead of the "str" type. They may
only contain ASCII characters; bytes with a numeric value of 128 or
greater must be expressed with escapes.
Both string and bytes literals may optionally be prefixed with a
letter "'r'" or "'R'"; such strings are called *raw strings* and treat
backslashes as literal characters. As a result, in string literals,
"'\U'" and "'\u'" escapes in raw strings are not treated specially.
Given that Python 2.x’s raw unicode literals behave differently than
Python 3.x’s the "'ur'" syntax is not supported.
New in version 3.3: The "'rb'" prefix of raw bytes literals has been
added as a synonym of "'br'".
New in version 3.3: Support for the unicode legacy literal
("u'value'") was reintroduced to simplify the maintenance of dual
Python 2.x and 3.x codebases. See **PEP 414** for more information.
A string literal with "'f'" or "'F'" in its prefix is a *formatted
string literal*; see Formatted string literals. The "'f'" may be
combined with "'r'", but not with "'b'" or "'u'", therefore raw
formatted strings are possible, but formatted bytes literals are not.
In triple-quoted literals, unescaped newlines and quotes are allowed
(and are retained), except that three unescaped quotes in a row
terminate the literal. (A “quote” is the character used to open the
literal, i.e. either "'" or """.)
Unless an "'r'" or "'R'" prefix is present, escape sequences in string
and bytes literals are interpreted according to rules similar to those
used by Standard C. The recognized escape sequences are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
+===================+===================================+=========+
| "\newline" | Backslash and newline ignored | |
+-------------------+-----------------------------------+---------+
| "\\" | Backslash ("\") | |
+-------------------+-----------------------------------+---------+
| "\'" | Single quote ("'") | |
+-------------------+-----------------------------------+---------+
| "\"" | Double quote (""") | |
+-------------------+-----------------------------------+---------+
| "\a" | ASCII Bell (BEL) | |
+-------------------+-----------------------------------+---------+
| "\b" | ASCII Backspace (BS) | |
+-------------------+-----------------------------------+---------+
| "\f" | ASCII Formfeed (FF) | |
+-------------------+-----------------------------------+---------+
| "\n" | ASCII Linefeed (LF) | |
+-------------------+-----------------------------------+---------+
| "\r" | ASCII Carriage Return (CR) | |
+-------------------+-----------------------------------+---------+
| "\t" | ASCII Horizontal Tab (TAB) | |
+-------------------+-----------------------------------+---------+
| "\v" | ASCII Vertical Tab (VT) | |
+-------------------+-----------------------------------+---------+
| "\ooo" | Character with octal value *ooo* | (1,3) |
+-------------------+-----------------------------------+---------+
| "\xhh" | Character with hex value *hh* | (2,3) |
+-------------------+-----------------------------------+---------+
Escape sequences only recognized in string literals are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
+===================+===================================+=========+
| "\N{name}" | Character named *name* in the | (4) |
| | Unicode database | |
+-------------------+-----------------------------------+---------+
| "\uxxxx" | Character with 16-bit hex value | (5) |
| | *xxxx* | |
+-------------------+-----------------------------------+---------+
| "\Uxxxxxxxx" | Character with 32-bit hex value | (6) |
| | *xxxxxxxx* | |
+-------------------+-----------------------------------+---------+
Notes:
1. As in Standard C, up to three octal digits are accepted.
2. Unlike in Standard C, exactly two hex digits are required.
3. In a bytes literal, hexadecimal and octal escapes denote the
byte with the given value. In a string literal, these escapes
denote a Unicode character with the given value.
4. Changed in version 3.3: Support for name aliases [1] has been
added.
5. Exactly four hex digits are required.
6. Any Unicode character can be encoded this way. Exactly eight
hex digits are required.
Unlike Standard C, all unrecognized escape sequences are left in the
string unchanged, i.e., *the backslash is left in the result*. (This
behavior is useful when debugging: if an escape sequence is mistyped,
the resulting output is more easily recognized as broken.) It is also
important to note that the escape sequences only recognized in string
literals fall into the category of unrecognized escapes for bytes
literals.
Changed in version 3.6: Unrecognized escape sequences produce a
DeprecationWarning. In some future version of Python they will be
a SyntaxError.
Even in a raw literal, quotes can be escaped with a backslash, but the
backslash remains in the result; for example, "r"\""" is a valid
string literal consisting of two characters: a backslash and a double
quote; "r"\"" is not a valid string literal (even a raw string cannot
end in an odd number of backslashes). Specifically, *a raw literal
cannot end in a single backslash* (since the backslash would escape
the following quote character). Note also that a single backslash
followed by a newline is interpreted as those two characters as part
of the literal, *not* as a line continuation.
uM Subscriptions
*************
A subscription selects an item of a sequence (string, tuple or list)
or mapping (dictionary) object:
subscription ::= primary "[" expression_list "]"
The primary must evaluate to an object that supports subscription
(lists or dictionaries for example). User-defined objects can support
subscription by defining a "__getitem__()" method.
For built-in objects, there are two types of objects that support
subscription:
If the primary is a mapping, the expression list must evaluate to an
object whose value is one of the keys of the mapping, and the
subscription selects the value in the mapping that corresponds to that
key. (The expression list is a tuple except if it has exactly one
item.)
If the primary is a sequence, the expression list must evaluate to an
integer or a slice (as discussed in the following section).
The formal syntax makes no special provision for negative indices in
sequences; however, built-in sequences all provide a "__getitem__()"
method that interprets negative indices by adding the length of the
sequence to the index (so that "x[-1]" selects the last item of "x").
The resulting value must be a nonnegative integer less than the number
of items in the sequence, and the subscription selects the item whose
index is that value (counting from zero). Since the support for
negative indices and slicing occurs in the object’s "__getitem__()"
method, subclasses overriding this method will need to explicitly add
that support.
A string’s items are characters. A character is not a separate data
type but a string of exactly one character.
ax Truth Value Testing
*******************
Any object can be tested for truth value, for use in an "if" or
"while" condition or as operand of the Boolean operations below.
By default, an object is considered true unless its class defines
either a "__bool__()" method that returns "False" or a "__len__()"
method that returns zero, when called with the object. [1] Here are
most of the built-in objects considered false:
* constants defined to be false: "None" and "False".
* zero of any numeric type: "0", "0.0", "0j", "Decimal(0)",
"Fraction(0, 1)"
* empty sequences and collections: "''", "()", "[]", "{}", "set()",
"range(0)"
Operations and built-in functions that have a Boolean result always
return "0" or "False" for false and "1" or "True" for true, unless
otherwise stated. (Important exception: the Boolean operations "or"
and "and" always return one of their operands.)
u= The "try" statement
*******************
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object or a tuple containing an item compatible with the exception.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed. All except
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred. "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.
The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed. Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a ‘cleanup’ handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return" or "break" statement, the saved exception
is discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’ A "continue" statement is illegal in
the "finally" clause. (The reason is a problem with the current
implementation — this restriction may be lifted in the future).
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
u�� The standard type hierarchy
***************************
Below is a list of the types that are built into Python. Extension
modules (written in C, Java, or other languages, depending on the
implementation) can define additional types. Future versions of
Python may add types to the type hierarchy (e.g., rational numbers,
efficiently stored arrays of integers, etc.), although such additions
will often be provided via the standard library instead.
Some of the type descriptions below contain a paragraph listing
‘special attributes.’ These are attributes that provide access to the
implementation and are not intended for general use. Their definition
may change in the future.
None
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name "None". It
is used to signify the absence of a value in many situations, e.g.,
it is returned from functions that don’t explicitly return
anything. Its truth value is false.
NotImplemented
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name
"NotImplemented". Numeric methods and rich comparison methods
should return this value if they do not implement the operation for
the operands provided. (The interpreter will then try the
reflected operation, or some other fallback, depending on the
operator.) Its truth value is true.
See Implementing the arithmetic operations for more details.
Ellipsis
This type has a single value. There is a single object with this
value. This object is accessed through the literal "..." or the
built-in name "Ellipsis". Its truth value is true.
"numbers.Number"
These are created by numeric literals and returned as results by
arithmetic operators and arithmetic built-in functions. Numeric
objects are immutable; once created their value never changes.
Python numbers are of course strongly related to mathematical
numbers, but subject to the limitations of numerical representation
in computers.
Python distinguishes between integers, floating point numbers, and
complex numbers:
"numbers.Integral"
These represent elements from the mathematical set of integers
(positive and negative).
There are two types of integers:
Integers ("int")
These represent numbers in an unlimited range, subject to
available (virtual) memory only. For the purpose of shift
and mask operations, a binary representation is assumed, and
negative numbers are represented in a variant of 2’s
complement which gives the illusion of an infinite string of
sign bits extending to the left.
Booleans ("bool")
These represent the truth values False and True. The two
objects representing the values "False" and "True" are the
only Boolean objects. The Boolean type is a subtype of the
integer type, and Boolean values behave like the values 0 and
1, respectively, in almost all contexts, the exception being
that when converted to a string, the strings ""False"" or
""True"" are returned, respectively.
The rules for integer representation are intended to give the
most meaningful interpretation of shift and mask operations
involving negative integers.
"numbers.Real" ("float")
These represent machine-level double precision floating point
numbers. You are at the mercy of the underlying machine
architecture (and C or Java implementation) for the accepted
range and handling of overflow. Python does not support single-
precision floating point numbers; the savings in processor and
memory usage that are usually the reason for using these are
dwarfed by the overhead of using objects in Python, so there is
no reason to complicate the language with two kinds of floating
point numbers.
"numbers.Complex" ("complex")
These represent complex numbers as a pair of machine-level
double precision floating point numbers. The same caveats apply
as for floating point numbers. The real and imaginary parts of a
complex number "z" can be retrieved through the read-only
attributes "z.real" and "z.imag".
Sequences
These represent finite ordered sets indexed by non-negative
numbers. The built-in function "len()" returns the number of items
of a sequence. When the length of a sequence is *n*, the index set
contains the numbers 0, 1, …, *n*-1. Item *i* of sequence *a* is
selected by "a[i]".
Sequences also support slicing: "a[i:j]" selects all items with
index *k* such that *i* "<=" *k* "<" *j*. When used as an
expression, a slice is a sequence of the same type. This implies
that the index set is renumbered so that it starts at 0.
Some sequences also support “extended slicing” with a third “step”
parameter: "a[i:j:k]" selects all items of *a* with index *x* where
"x = i + n*k", *n* ">=" "0" and *i* "<=" *x* "<" *j*.
Sequences are distinguished according to their mutability:
Immutable sequences
An object of an immutable sequence type cannot change once it is
created. (If the object contains references to other objects,
these other objects may be mutable and may be changed; however,
the collection of objects directly referenced by an immutable
object cannot change.)
The following types are immutable sequences:
Strings
A string is a sequence of values that represent Unicode code
points. All the code points in the range "U+0000 - U+10FFFF"
can be represented in a string. Python doesn’t have a "char"
type; instead, every code point in the string is represented
as a string object with length "1". The built-in function
"ord()" converts a code point from its string form to an
integer in the range "0 - 10FFFF"; "chr()" converts an
integer in the range "0 - 10FFFF" to the corresponding length
"1" string object. "str.encode()" can be used to convert a
"str" to "bytes" using the given text encoding, and
"bytes.decode()" can be used to achieve the opposite.
Tuples
The items of a tuple are arbitrary Python objects. Tuples of
two or more items are formed by comma-separated lists of
expressions. A tuple of one item (a ‘singleton’) can be
formed by affixing a comma to an expression (an expression by
itself does not create a tuple, since parentheses must be
usable for grouping of expressions). An empty tuple can be
formed by an empty pair of parentheses.
Bytes
A bytes object is an immutable array. The items are 8-bit
bytes, represented by integers in the range 0 <= x < 256.
Bytes literals (like "b'abc'") and the built-in "bytes()"
constructor can be used to create bytes objects. Also, bytes
objects can be decoded to strings via the "decode()" method.
Mutable sequences
Mutable sequences can be changed after they are created. The
subscription and slicing notations can be used as the target of
assignment and "del" (delete) statements.
There are currently two intrinsic mutable sequence types:
Lists
The items of a list are arbitrary Python objects. Lists are
formed by placing a comma-separated list of expressions in
square brackets. (Note that there are no special cases needed
to form lists of length 0 or 1.)
Byte Arrays
A bytearray object is a mutable array. They are created by
the built-in "bytearray()" constructor. Aside from being
mutable (and hence unhashable), byte arrays otherwise provide
the same interface and functionality as immutable "bytes"
objects.
The extension module "array" provides an additional example of a
mutable sequence type, as does the "collections" module.
Set types
These represent unordered, finite sets of unique, immutable
objects. As such, they cannot be indexed by any subscript. However,
they can be iterated over, and the built-in function "len()"
returns the number of items in a set. Common uses for sets are fast
membership testing, removing duplicates from a sequence, and
computing mathematical operations such as intersection, union,
difference, and symmetric difference.
For set elements, the same immutability rules apply as for
dictionary keys. Note that numeric types obey the normal rules for
numeric comparison: if two numbers compare equal (e.g., "1" and
"1.0"), only one of them can be contained in a set.
There are currently two intrinsic set types:
Sets
These represent a mutable set. They are created by the built-in
"set()" constructor and can be modified afterwards by several
methods, such as "add()".
Frozen sets
These represent an immutable set. They are created by the
built-in "frozenset()" constructor. As a frozenset is immutable
and *hashable*, it can be used again as an element of another
set, or as a dictionary key.
Mappings
These represent finite sets of objects indexed by arbitrary index
sets. The subscript notation "a[k]" selects the item indexed by "k"
from the mapping "a"; this can be used in expressions and as the
target of assignments or "del" statements. The built-in function
"len()" returns the number of items in a mapping.
There is currently a single intrinsic mapping type:
Dictionaries
These represent finite sets of objects indexed by nearly
arbitrary values. The only types of values not acceptable as
keys are values containing lists or dictionaries or other
mutable types that are compared by value rather than by object
identity, the reason being that the efficient implementation of
dictionaries requires a key’s hash value to remain constant.
Numeric types used for keys obey the normal rules for numeric
comparison: if two numbers compare equal (e.g., "1" and "1.0")
then they can be used interchangeably to index the same
dictionary entry.
Dictionaries are mutable; they can be created by the "{...}"
notation (see section Dictionary displays).
The extension modules "dbm.ndbm" and "dbm.gnu" provide
additional examples of mapping types, as does the "collections"
module.
Callable types
These are the types to which the function call operation (see
section Calls) can be applied:
User-defined functions
A user-defined function object is created by a function
definition (see section Function definitions). It should be
called with an argument list containing the same number of items
as the function’s formal parameter list.
Special attributes:
+---------------------------+---------------------------------+-------------+
| Attribute | Meaning | |
+===========================+=================================+=============+
| "__doc__" | The function’s documentation | Writable |
| | string, or "None" if | |
| | unavailable; not inherited by | |
| | subclasses | |
+---------------------------+---------------------------------+-------------+
| "__name__" | The function’s name | Writable |
+---------------------------+---------------------------------+-------------+
| "__qualname__" | The function’s *qualified name* | Writable |
| | New in version 3.3. | |
+---------------------------+---------------------------------+-------------+
| "__module__" | The name of the module the | Writable |
| | function was defined in, or | |
| | "None" if unavailable. | |
+---------------------------+---------------------------------+-------------+
| "__defaults__" | A tuple containing default | Writable |
| | argument values for those | |
| | arguments that have defaults, | |
| | or "None" if no arguments have | |
| | a default value | |
+---------------------------+---------------------------------+-------------+
| "__code__" | The code object representing | Writable |
| | the compiled function body. | |
+---------------------------+---------------------------------+-------------+
| "__globals__" | A reference to the dictionary | Read-only |
| | that holds the function’s | |
| | global variables — the global | |
| | namespace of the module in | |
| | which the function was defined. | |
+---------------------------+---------------------------------+-------------+
| "__dict__" | The namespace supporting | Writable |
| | arbitrary function attributes. | |
+---------------------------+---------------------------------+-------------+
| "__closure__" | "None" or a tuple of cells that | Read-only |
| | contain bindings for the | |
| | function’s free variables. | |
+---------------------------+---------------------------------+-------------+
| "__annotations__" | A dict containing annotations | Writable |
| | of parameters. The keys of the | |
| | dict are the parameter names, | |
| | and "'return'" for the return | |
| | annotation, if provided. | |
+---------------------------+---------------------------------+-------------+
| "__kwdefaults__" | A dict containing defaults for | Writable |
| | keyword-only parameters. | |
+---------------------------+---------------------------------+-------------+
Most of the attributes labelled “Writable” check the type of the
assigned value.
Function objects also support getting and setting arbitrary
attributes, which can be used, for example, to attach metadata
to functions. Regular attribute dot-notation is used to get and
set such attributes. *Note that the current implementation only
supports function attributes on user-defined functions. Function
attributes on built-in functions may be supported in the
future.*
Additional information about a function’s definition can be
retrieved from its code object; see the description of internal
types below.
Instance methods
An instance method object combines a class, a class instance and
any callable object (normally a user-defined function).
Special read-only attributes: "__self__" is the class instance
object, "__func__" is the function object; "__doc__" is the
method’s documentation (same as "__func__.__doc__"); "__name__"
is the method name (same as "__func__.__name__"); "__module__"
is the name of the module the method was defined in, or "None"
if unavailable.
Methods also support accessing (but not setting) the arbitrary
function attributes on the underlying function object.
User-defined method objects may be created when getting an
attribute of a class (perhaps via an instance of that class), if
that attribute is a user-defined function object or a class
method object.
When an instance method object is created by retrieving a user-
defined function object from a class via one of its instances,
its "__self__" attribute is the instance, and the method object
is said to be bound. The new method’s "__func__" attribute is
the original function object.
When a user-defined method object is created by retrieving
another method object from a class or instance, the behaviour is
the same as for a function object, except that the "__func__"
attribute of the new instance is not the original method object
but its "__func__" attribute.
When an instance method object is created by retrieving a class
method object from a class or instance, its "__self__" attribute
is the class itself, and its "__func__" attribute is the
function object underlying the class method.
When an instance method object is called, the underlying
function ("__func__") is called, inserting the class instance
("__self__") in front of the argument list. For instance, when
"C" is a class which contains a definition for a function "f()",
and "x" is an instance of "C", calling "x.f(1)" is equivalent to
calling "C.f(x, 1)".
When an instance method object is derived from a class method
object, the “class instance” stored in "__self__" will actually
be the class itself, so that calling either "x.f(1)" or "C.f(1)"
is equivalent to calling "f(C,1)" where "f" is the underlying
function.
Note that the transformation from function object to instance
method object happens each time the attribute is retrieved from
the instance. In some cases, a fruitful optimization is to
assign the attribute to a local variable and call that local
variable. Also notice that this transformation only happens for
user-defined functions; other callable objects (and all non-
callable objects) are retrieved without transformation. It is
also important to note that user-defined functions which are
attributes of a class instance are not converted to bound
methods; this *only* happens when the function is an attribute
of the class.
Generator functions
A function or method which uses the "yield" statement (see
section The yield statement) is called a *generator function*.
Such a function, when called, always returns an iterator object
which can be used to execute the body of the function: calling
the iterator’s "iterator.__next__()" method will cause the
function to execute until it provides a value using the "yield"
statement. When the function executes a "return" statement or
falls off the end, a "StopIteration" exception is raised and the
iterator will have reached the end of the set of values to be
returned.
Coroutine functions
A function or method which is defined using "async def" is
called a *coroutine function*. Such a function, when called,
returns a *coroutine* object. It may contain "await"
expressions, as well as "async with" and "async for" statements.
See also the Coroutine Objects section.
Asynchronous generator functions
A function or method which is defined using "async def" and
which uses the "yield" statement is called a *asynchronous
generator function*. Such a function, when called, returns an
asynchronous iterator object which can be used in an "async for"
statement to execute the body of the function.
Calling the asynchronous iterator’s "aiterator.__anext__()"
method will return an *awaitable* which when awaited will
execute until it provides a value using the "yield" expression.
When the function executes an empty "return" statement or falls
off the end, a "StopAsyncIteration" exception is raised and the
asynchronous iterator will have reached the end of the set of
values to be yielded.
Built-in functions
A built-in function object is a wrapper around a C function.
Examples of built-in functions are "len()" and "math.sin()"
("math" is a standard built-in module). The number and type of
the arguments are determined by the C function. Special read-
only attributes: "__doc__" is the function’s documentation
string, or "None" if unavailable; "__name__" is the function’s
name; "__self__" is set to "None" (but see the next item);
"__module__" is the name of the module the function was defined
in or "None" if unavailable.
Built-in methods
This is really a different disguise of a built-in function, this
time containing an object passed to the C function as an
implicit extra argument. An example of a built-in method is
"alist.append()", assuming *alist* is a list object. In this
case, the special read-only attribute "__self__" is set to the
object denoted by *alist*.
Classes
Classes are callable. These objects normally act as factories
for new instances of themselves, but variations are possible for
class types that override "__new__()". The arguments of the
call are passed to "__new__()" and, in the typical case, to
"__init__()" to initialize the new instance.
Class Instances
Instances of arbitrary classes can be made callable by defining
a "__call__()" method in their class.
Modules
Modules are a basic organizational unit of Python code, and are
created by the import system as invoked either by the "import"
statement (see "import"), or by calling functions such as
"importlib.import_module()" and built-in "__import__()". A module
object has a namespace implemented by a dictionary object (this is
the dictionary referenced by the "__globals__" attribute of
functions defined in the module). Attribute references are
translated to lookups in this dictionary, e.g., "m.x" is equivalent
to "m.__dict__["x"]". A module object does not contain the code
object used to initialize the module (since it isn’t needed once
the initialization is done).
Attribute assignment updates the module’s namespace dictionary,
e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".
Predefined (writable) attributes: "__name__" is the module’s name;
"__doc__" is the module’s documentation string, or "None" if
unavailable; "__annotations__" (optional) is a dictionary
containing *variable annotations* collected during module body
execution; "__file__" is the pathname of the file from which the
module was loaded, if it was loaded from a file. The "__file__"
attribute may be missing for certain types of modules, such as C
modules that are statically linked into the interpreter; for
extension modules loaded dynamically from a shared library, it is
the pathname of the shared library file.
Special read-only attribute: "__dict__" is the module’s namespace
as a dictionary object.
**CPython implementation detail:** Because of the way CPython
clears module dictionaries, the module dictionary will be cleared
when the module falls out of scope even if the dictionary still has
live references. To avoid this, copy the dictionary or keep the
module around while using its dictionary directly.
Custom classes
Custom class types are typically created by class definitions (see
section Class definitions). A class has a namespace implemented by
a dictionary object. Class attribute references are translated to
lookups in this dictionary, e.g., "C.x" is translated to
"C.__dict__["x"]" (although there are a number of hooks which allow
for other means of locating attributes). When the attribute name is
not found there, the attribute search continues in the base
classes. This search of the base classes uses the C3 method
resolution order which behaves correctly even in the presence of
‘diamond’ inheritance structures where there are multiple
inheritance paths leading back to a common ancestor. Additional
details on the C3 MRO used by Python can be found in the
documentation accompanying the 2.3 release at
https://www.python.org/download/releases/2.3/mro/.
When a class attribute reference (for class "C", say) would yield a
class method object, it is transformed into an instance method
object whose "__self__" attribute is "C". When it would yield a
static method object, it is transformed into the object wrapped by
the static method object. See section Implementing Descriptors for
another way in which attributes retrieved from a class may differ
from those actually contained in its "__dict__".
Class attribute assignments update the class’s dictionary, never
the dictionary of a base class.
A class object can be called (see above) to yield a class instance
(see below).
Special attributes: "__name__" is the class name; "__module__" is
the module name in which the class was defined; "__dict__" is the
dictionary containing the class’s namespace; "__bases__" is a tuple
containing the base classes, in the order of their occurrence in
the base class list; "__doc__" is the class’s documentation string,
or "None" if undefined; "__annotations__" (optional) is a
dictionary containing *variable annotations* collected during class
body execution.
Class instances
A class instance is created by calling a class object (see above).
A class instance has a namespace implemented as a dictionary which
is the first place in which attribute references are searched.
When an attribute is not found there, and the instance’s class has
an attribute by that name, the search continues with the class
attributes. If a class attribute is found that is a user-defined
function object, it is transformed into an instance method object
whose "__self__" attribute is the instance. Static method and
class method objects are also transformed; see above under
“Classes”. See section Implementing Descriptors for another way in
which attributes of a class retrieved via its instances may differ
from the objects actually stored in the class’s "__dict__". If no
class attribute is found, and the object’s class has a
"__getattr__()" method, that is called to satisfy the lookup.
Attribute assignments and deletions update the instance’s
dictionary, never a class’s dictionary. If the class has a
"__setattr__()" or "__delattr__()" method, this is called instead
of updating the instance dictionary directly.
Class instances can pretend to be numbers, sequences, or mappings
if they have methods with certain special names. See section
Special method names.
Special attributes: "__dict__" is the attribute dictionary;
"__class__" is the instance’s class.
I/O objects (also known as file objects)
A *file object* represents an open file. Various shortcuts are
available to create file objects: the "open()" built-in function,
and also "os.popen()", "os.fdopen()", and the "makefile()" method
of socket objects (and perhaps by other functions or methods
provided by extension modules).
The objects "sys.stdin", "sys.stdout" and "sys.stderr" are
initialized to file objects corresponding to the interpreter’s
standard input, output and error streams; they are all open in text
mode and therefore follow the interface defined by the
"io.TextIOBase" abstract class.
Internal types
A few types used internally by the interpreter are exposed to the
user. Their definitions may change with future versions of the
interpreter, but they are mentioned here for completeness.
Code objects
Code objects represent *byte-compiled* executable Python code,
or *bytecode*. The difference between a code object and a
function object is that the function object contains an explicit
reference to the function’s globals (the module in which it was
defined), while a code object contains no context; also the
default argument values are stored in the function object, not
in the code object (because they represent values calculated at
run-time). Unlike function objects, code objects are immutable
and contain no references (directly or indirectly) to mutable
objects.
Special read-only attributes: "co_name" gives the function name;
"co_argcount" is the number of positional arguments (including
arguments with default values); "co_nlocals" is the number of
local variables used by the function (including arguments);
"co_varnames" is a tuple containing the names of the local
variables (starting with the argument names); "co_cellvars" is a
tuple containing the names of local variables that are
referenced by nested functions; "co_freevars" is a tuple
containing the names of free variables; "co_code" is a string
representing the sequence of bytecode instructions; "co_consts"
is a tuple containing the literals used by the bytecode;
"co_names" is a tuple containing the names used by the bytecode;
"co_filename" is the filename from which the code was compiled;
"co_firstlineno" is the first line number of the function;
"co_lnotab" is a string encoding the mapping from bytecode
offsets to line numbers (for details see the source code of the
interpreter); "co_stacksize" is the required stack size
(including local variables); "co_flags" is an integer encoding a
number of flags for the interpreter.
The following flag bits are defined for "co_flags": bit "0x04"
is set if the function uses the "*arguments" syntax to accept an
arbitrary number of positional arguments; bit "0x08" is set if
the function uses the "**keywords" syntax to accept arbitrary
keyword arguments; bit "0x20" is set if the function is a
generator.
Future feature declarations ("from __future__ import division")
also use bits in "co_flags" to indicate whether a code object
was compiled with a particular feature enabled: bit "0x2000" is
set if the function was compiled with future division enabled;
bits "0x10" and "0x1000" were used in earlier versions of
Python.
Other bits in "co_flags" are reserved for internal use.
If a code object represents a function, the first item in
"co_consts" is the documentation string of the function, or
"None" if undefined.
Frame objects
Frame objects represent execution frames. They may occur in
traceback objects (see below).
Special read-only attributes: "f_back" is to the previous stack
frame (towards the caller), or "None" if this is the bottom
stack frame; "f_code" is the code object being executed in this
frame; "f_locals" is the dictionary used to look up local
variables; "f_globals" is used for global variables;
"f_builtins" is used for built-in (intrinsic) names; "f_lasti"
gives the precise instruction (this is an index into the
bytecode string of the code object).
Special writable attributes: "f_trace", if not "None", is a
function called at the start of each source code line (this is
used by the debugger); "f_lineno" is the current line number of
the frame — writing to this from within a trace function jumps
to the given line (only for the bottom-most frame). A debugger
can implement a Jump command (aka Set Next Statement) by writing
to f_lineno.
Frame objects support one method:
frame.clear()
This method clears all references to local variables held by
the frame. Also, if the frame belonged to a generator, the
generator is finalized. This helps break reference cycles
involving frame objects (for example when catching an
exception and storing its traceback for later use).
"RuntimeError" is raised if the frame is currently executing.
New in version 3.4.
Traceback objects
Traceback objects represent a stack trace of an exception. A
traceback object is created when an exception occurs. When the
search for an exception handler unwinds the execution stack, at
each unwound level a traceback object is inserted in front of
the current traceback. When an exception handler is entered,
the stack trace is made available to the program. (See section
The try statement.) It is accessible as the third item of the
tuple returned by "sys.exc_info()". When the program contains no
suitable handler, the stack trace is written (nicely formatted)
to the standard error stream; if the interpreter is interactive,
it is also made available to the user as "sys.last_traceback".
Special read-only attributes: "tb_next" is the next level in the
stack trace (towards the frame where the exception occurred), or
"None" if there is no next level; "tb_frame" points to the
execution frame of the current level; "tb_lineno" gives the line
number where the exception occurred; "tb_lasti" indicates the
precise instruction. The line number and last instruction in
the traceback may differ from the line number of its frame
object if the exception occurred in a "try" statement with no
matching except clause or with a finally clause.
Slice objects
Slice objects are used to represent slices for "__getitem__()"
methods. They are also created by the built-in "slice()"
function.
Special read-only attributes: "start" is the lower bound; "stop"
is the upper bound; "step" is the step value; each is "None" if
omitted. These attributes can have any type.
Slice objects support one method:
slice.indices(self, length)
This method takes a single integer argument *length* and
computes information about the slice that the slice object
would describe if applied to a sequence of *length* items.
It returns a tuple of three integers; respectively these are
the *start* and *stop* indices and the *step* or stride
length of the slice. Missing or out-of-bounds indices are
handled in a manner consistent with regular slices.
Static method objects
Static method objects provide a way of defeating the
transformation of function objects to method objects described
above. A static method object is a wrapper around any other
object, usually a user-defined method object. When a static
method object is retrieved from a class or a class instance, the
object actually returned is the wrapped object, which is not
subject to any further transformation. Static method objects are
not themselves callable, although the objects they wrap usually
are. Static method objects are created by the built-in
"staticmethod()" constructor.
Class method objects
A class method object, like a static method object, is a wrapper
around another object that alters the way in which that object
is retrieved from classes and class instances. The behaviour of
class method objects upon such retrieval is described above,
under “User-defined methods”. Class method objects are created
by the built-in "classmethod()" constructor.
a� Functions
*********
Function objects are created by function definitions. The only
operation on a function object is to call it: "func(argument-list)".
There are really two flavors of function objects: built-in functions
and user-defined functions. Both support the same operation (to call
the function), but the implementation is different, hence the
different object types.
See Function definitions for more information.
u8$ Mapping Types — "dict"
**********************
A *mapping* object maps *hashable* values to arbitrary objects.
Mappings are mutable objects. There is currently only one standard
mapping type, the *dictionary*. (For other containers see the built-
in "list", "set", and "tuple" classes, and the "collections" module.)
A dictionary’s keys are *almost* arbitrary values. Values that are
not *hashable*, that is, values containing lists, dictionaries or
other mutable types (that are compared by value rather than by object
identity) may not be used as keys. Numeric types used for keys obey
the normal rules for numeric comparison: if two numbers compare equal
(such as "1" and "1.0") then they can be used interchangeably to index
the same dictionary entry. (Note however, that since computers store
floating-point numbers as approximations it is usually unwise to use
them as dictionary keys.)
Dictionaries can be created by placing a comma-separated list of "key:
value" pairs within braces, for example: "{'jack': 4098, 'sjoerd':
4127}" or "{4098: 'jack', 4127: 'sjoerd'}", or by the "dict"
constructor.
class dict(**kwarg)
class dict(mapping, **kwarg)
class dict(iterable, **kwarg)
Return a new dictionary initialized from an optional positional
argument and a possibly empty set of keyword arguments.
If no positional argument is given, an empty dictionary is created.
If a positional argument is given and it is a mapping object, a
dictionary is created with the same key-value pairs as the mapping
object. Otherwise, the positional argument must be an *iterable*
object. Each item in the iterable must itself be an iterable with
exactly two objects. The first object of each item becomes a key
in the new dictionary, and the second object the corresponding
value. If a key occurs more than once, the last value for that key
becomes the corresponding value in the new dictionary.
If keyword arguments are given, the keyword arguments and their
values are added to the dictionary created from the positional
argument. If a key being added is already present, the value from
the keyword argument replaces the value from the positional
argument.
To illustrate, the following examples all return a dictionary equal
to "{"one": 1, "two": 2, "three": 3}":
>>> a = dict(one=1, two=2, three=3)
>>> b = {'one': 1, 'two': 2, 'three': 3}
>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
>>> e = dict({'three': 3, 'one': 1, 'two': 2})
>>> a == b == c == d == e
True
Providing keyword arguments as in the first example only works for
keys that are valid Python identifiers. Otherwise, any valid keys
can be used.
These are the operations that dictionaries support (and therefore,
custom mapping types should support too):
len(d)
Return the number of items in the dictionary *d*.
d[key]
Return the item of *d* with key *key*. Raises a "KeyError" if
*key* is not in the map.
If a subclass of dict defines a method "__missing__()" and *key*
is not present, the "d[key]" operation calls that method with
the key *key* as argument. The "d[key]" operation then returns
or raises whatever is returned or raised by the
"__missing__(key)" call. No other operations or methods invoke
"__missing__()". If "__missing__()" is not defined, "KeyError"
is raised. "__missing__()" must be a method; it cannot be an
instance variable:
>>> class Counter(dict):
... def __missing__(self, key):
... return 0
>>> c = Counter()
>>> c['red']
0
>>> c['red'] += 1
>>> c['red']
1
The example above shows part of the implementation of
"collections.Counter". A different "__missing__" method is used
by "collections.defaultdict".
d[key] = value
Set "d[key]" to *value*.
del d[key]
Remove "d[key]" from *d*. Raises a "KeyError" if *key* is not
in the map.
key in d
Return "True" if *d* has a key *key*, else "False".
key not in d
Equivalent to "not key in d".
iter(d)
Return an iterator over the keys of the dictionary. This is a
shortcut for "iter(d.keys())".
clear()
Remove all items from the dictionary.
copy()
Return a shallow copy of the dictionary.
classmethod fromkeys(seq[, value])
Create a new dictionary with keys from *seq* and values set to
*value*.
"fromkeys()" is a class method that returns a new dictionary.
*value* defaults to "None".
get(key[, default])
Return the value for *key* if *key* is in the dictionary, else
*default*. If *default* is not given, it defaults to "None", so
that this method never raises a "KeyError".
items()
Return a new view of the dictionary’s items ("(key, value)"
pairs). See the documentation of view objects.
keys()
Return a new view of the dictionary’s keys. See the
documentation of view objects.
pop(key[, default])
If *key* is in the dictionary, remove it and return its value,
else return *default*. If *default* is not given and *key* is
not in the dictionary, a "KeyError" is raised.
popitem()
Remove and return an arbitrary "(key, value)" pair from the
dictionary.
"popitem()" is useful to destructively iterate over a
dictionary, as often used in set algorithms. If the dictionary
is empty, calling "popitem()" raises a "KeyError".
setdefault(key[, default])
If *key* is in the dictionary, return its value. If not, insert
*key* with a value of *default* and return *default*. *default*
defaults to "None".
update([other])
Update the dictionary with the key/value pairs from *other*,
overwriting existing keys. Return "None".
"update()" accepts either another dictionary object or an
iterable of key/value pairs (as tuples or other iterables of
length two). If keyword arguments are specified, the dictionary
is then updated with those key/value pairs: "d.update(red=1,
blue=2)".
values()
Return a new view of the dictionary’s values. See the
documentation of view objects.
Dictionaries compare equal if and only if they have the same "(key,
value)" pairs. Order comparisons (‘<’, ‘<=’, ‘>=’, ‘>’) raise
"TypeError".
See also: "types.MappingProxyType" can be used to create a read-only
view of a "dict".
Dictionary view objects
=======================
The objects returned by "dict.keys()", "dict.values()" and
"dict.items()" are *view objects*. They provide a dynamic view on the
dictionary’s entries, which means that when the dictionary changes,
the view reflects these changes.
Dictionary views can be iterated over to yield their respective data,
and support membership tests:
len(dictview)
Return the number of entries in the dictionary.
iter(dictview)
Return an iterator over the keys, values or items (represented as
tuples of "(key, value)") in the dictionary.
Keys and values are iterated over in an arbitrary order which is
non-random, varies across Python implementations, and depends on
the dictionary’s history of insertions and deletions. If keys,
values and items views are iterated over with no intervening
modifications to the dictionary, the order of items will directly
correspond. This allows the creation of "(value, key)" pairs using
"zip()": "pairs = zip(d.values(), d.keys())". Another way to
create the same list is "pairs = [(v, k) for (k, v) in d.items()]".
Iterating views while adding or deleting entries in the dictionary
may raise a "RuntimeError" or fail to iterate over all entries.
x in dictview
Return "True" if *x* is in the underlying dictionary’s keys, values
or items (in the latter case, *x* should be a "(key, value)"
tuple).
Keys views are set-like since their entries are unique and hashable.
If all values are hashable, so that "(key, value)" pairs are unique
and hashable, then the items view is also set-like. (Values views are
not treated as set-like since the entries are generally not unique.)
For set-like views, all of the operations defined for the abstract
base class "collections.abc.Set" are available (for example, "==",
"<", or "^").
An example of dictionary view usage:
>>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500}
>>> keys = dishes.keys()
>>> values = dishes.values()
>>> # iteration
>>> n = 0
>>> for val in values:
... n += val
>>> print(n)
504
>>> # keys and values are iterated over in the same order
>>> list(keys)
['eggs', 'bacon', 'sausage', 'spam']
>>> list(values)
[2, 1, 1, 500]
>>> # view objects are dynamic and reflect dict changes
>>> del dishes['eggs']
>>> del dishes['sausage']
>>> list(keys)
['spam', 'bacon']
>>> # set operations
>>> keys & {'eggs', 'bacon', 'salad'}
{'bacon'}
>>> keys ^ {'sausage', 'juice'}
{'juice', 'sausage', 'bacon', 'spam'}
a� Methods
*******
Methods are functions that are called using the attribute notation.
There are two flavors: built-in methods (such as "append()" on lists)
and class instance methods. Built-in methods are described with the
types that support them.
If you access a method (a function defined in a class namespace)
through an instance, you get a special object: a *bound method* (also
called *instance method*) object. When called, it will add the "self"
argument to the argument list. Bound methods have two special read-
only attributes: "m.__self__" is the object on which the method
operates, and "m.__func__" is the function implementing the method.
Calling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to
calling "m.__func__(m.__self__, arg-1, arg-2, ..., arg-n)".
Like function objects, bound method objects support getting arbitrary
attributes. However, since method attributes are actually stored on
the underlying function object ("meth.__func__"), setting method
attributes on bound methods is disallowed. Attempting to set an
attribute on a method results in an "AttributeError" being raised. In
order to set a method attribute, you need to explicitly set it on the
underlying function object:
>>> class C:
... def method(self):
... pass
...
>>> c = C()
>>> c.method.whoami = 'my name is method' # can't set on the method
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'method' object has no attribute 'whoami'
>>> c.method.__func__.whoami = 'my name is method'
>>> c.method.whoami
'my name is method'
See The standard type hierarchy for more information.
u$ Modules
*******
The only special operation on a module is attribute access: "m.name",
where *m* is a module and *name* accesses a name defined in *m*’s
symbol table. Module attributes can be assigned to. (Note that the
"import" statement is not, strictly speaking, an operation on a module
object; "import foo" does not require a module object named *foo* to
exist, rather it requires an (external) *definition* for a module
named *foo* somewhere.)
A special attribute of every module is "__dict__". This is the
dictionary containing the module’s symbol table. Modifying this
dictionary will actually change the module’s symbol table, but direct
assignment to the "__dict__" attribute is not possible (you can write
"m.__dict__['a'] = 1", which defines "m.a" to be "1", but you can’t
write "m.__dict__ = {}"). Modifying "__dict__" directly is not
recommended.
Modules built into the interpreter are written like this: "<module
'sys' (built-in)>". If loaded from a file, they are written as
"<module 'os' from '/usr/local/lib/pythonX.Y/os.pyc'>".
u�Y Sequence Types — "list", "tuple", "range"
*****************************************
There are three basic sequence types: lists, tuples, and range
objects. Additional sequence types tailored for processing of binary
data and text strings are described in dedicated sections.
Common Sequence Operations
==========================
The operations in the following table are supported by most sequence
types, both mutable and immutable. The "collections.abc.Sequence" ABC
is provided to make it easier to correctly implement these operations
on custom sequence types.
This table lists the sequence operations sorted in ascending priority.
In the table, *s* and *t* are sequences of the same type, *n*, *i*,
*j* and *k* are integers and *x* is an arbitrary object that meets any
type and value restrictions imposed by *s*.
The "in" and "not in" operations have the same priorities as the
comparison operations. The "+" (concatenation) and "*" (repetition)
operations have the same priority as the corresponding numeric
operations. [3]
+----------------------------+----------------------------------+------------+
| Operation | Result | Notes |
+============================+==================================+============+
| "x in s" | "True" if an item of *s* is | (1) |
| | equal to *x*, else "False" | |
+----------------------------+----------------------------------+------------+
| "x not in s" | "False" if an item of *s* is | (1) |
| | equal to *x*, else "True" | |
+----------------------------+----------------------------------+------------+
| "s + t" | the concatenation of *s* and *t* | (6)(7) |
+----------------------------+----------------------------------+------------+
| "s * n" or "n * s" | equivalent to adding *s* to | (2)(7) |
| | itself *n* times | |
+----------------------------+----------------------------------+------------+
| "s[i]" | *i*th item of *s*, origin 0 | (3) |
+----------------------------+----------------------------------+------------+
| "s[i:j]" | slice of *s* from *i* to *j* | (3)(4) |
+----------------------------+----------------------------------+------------+
| "s[i:j:k]" | slice of *s* from *i* to *j* | (3)(5) |
| | with step *k* | |
+----------------------------+----------------------------------+------------+
| "len(s)" | length of *s* | |
+----------------------------+----------------------------------+------------+
| "min(s)" | smallest item of *s* | |
+----------------------------+----------------------------------+------------+
| "max(s)" | largest item of *s* | |
+----------------------------+----------------------------------+------------+
| "s.index(x[, i[, j]])" | index of the first occurrence of | (8) |
| | *x* in *s* (at or after index | |
| | *i* and before index *j*) | |
+----------------------------+----------------------------------+------------+
| "s.count(x)" | total number of occurrences of | |
| | *x* in *s* | |
+----------------------------+----------------------------------+------------+
Sequences of the same type also support comparisons. In particular,
tuples and lists are compared lexicographically by comparing
corresponding elements. This means that to compare equal, every
element must compare equal and the two sequences must be of the same
type and have the same length. (For full details see Comparisons in
the language reference.)
Notes:
1. While the "in" and "not in" operations are used only for simple
containment testing in the general case, some specialised sequences
(such as "str", "bytes" and "bytearray") also use them for
subsequence testing:
>>> "gg" in "eggs"
True
2. Values of *n* less than "0" are treated as "0" (which yields an
empty sequence of the same type as *s*). Note that items in the
sequence *s* are not copied; they are referenced multiple times.
This often haunts new Python programmers; consider:
>>> lists = [[]] * 3
>>> lists
[[], [], []]
>>> lists[0].append(3)
>>> lists
[[3], [3], [3]]
What has happened is that "[[]]" is a one-element list containing
an empty list, so all three elements of "[[]] * 3" are references
to this single empty list. Modifying any of the elements of
"lists" modifies this single list. You can create a list of
different lists this way:
>>> lists = [[] for i in range(3)]
>>> lists[0].append(3)
>>> lists[1].append(5)
>>> lists[2].append(7)
>>> lists
[[3], [5], [7]]
Further explanation is available in the FAQ entry How do I create a
multidimensional list?.
3. If *i* or *j* is negative, the index is relative to the end of
sequence *s*: "len(s) + i" or "len(s) + j" is substituted. But
note that "-0" is still "0".
4. The slice of *s* from *i* to *j* is defined as the sequence of
items with index *k* such that "i <= k < j". If *i* or *j* is
greater than "len(s)", use "len(s)". If *i* is omitted or "None",
use "0". If *j* is omitted or "None", use "len(s)". If *i* is
greater than or equal to *j*, the slice is empty.
5. The slice of *s* from *i* to *j* with step *k* is defined as the
sequence of items with index "x = i + n*k" such that "0 <= n <
(j-i)/k". In other words, the indices are "i", "i+k", "i+2*k",
"i+3*k" and so on, stopping when *j* is reached (but never
including *j*). When *k* is positive, *i* and *j* are reduced to
"len(s)" if they are greater. When *k* is negative, *i* and *j* are
reduced to "len(s) - 1" if they are greater. If *i* or *j* are
omitted or "None", they become “end” values (which end depends on
the sign of *k*). Note, *k* cannot be zero. If *k* is "None", it
is treated like "1".
6. Concatenating immutable sequences always results in a new
object. This means that building up a sequence by repeated
concatenation will have a quadratic runtime cost in the total
sequence length. To get a linear runtime cost, you must switch to
one of the alternatives below:
* if concatenating "str" objects, you can build a list and use
"str.join()" at the end or else write to an "io.StringIO"
instance and retrieve its value when complete
* if concatenating "bytes" objects, you can similarly use
"bytes.join()" or "io.BytesIO", or you can do in-place
concatenation with a "bytearray" object. "bytearray" objects are
mutable and have an efficient overallocation mechanism
* if concatenating "tuple" objects, extend a "list" instead
* for other types, investigate the relevant class documentation
7. Some sequence types (such as "range") only support item
sequences that follow specific patterns, and hence don’t support
sequence concatenation or repetition.
8. "index" raises "ValueError" when *x* is not found in *s*. Not
all implementations support passing the additional arguments *i*
and *j*. These arguments allow efficient searching of subsections
of the sequence. Passing the extra arguments is roughly equivalent
to using "s[i:j].index(x)", only without copying any data and with
the returned index being relative to the start of the sequence
rather than the start of the slice.
Immutable Sequence Types
========================
The only operation that immutable sequence types generally implement
that is not also implemented by mutable sequence types is support for
the "hash()" built-in.
This support allows immutable sequences, such as "tuple" instances, to
be used as "dict" keys and stored in "set" and "frozenset" instances.
Attempting to hash an immutable sequence that contains unhashable
values will result in "TypeError".
Mutable Sequence Types
======================
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
+================================+==================================+=======================+
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | appends *x* to the end of the | |
| | sequence (same as | |
| | "s[len(s):len(s)] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from *s* (same | (5) |
| | as "del s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of *s* | (5) |
| | (same as "s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | extends *s* with the contents of | |
| | *t* (for the most part the same | |
| | as "s[len(s):len(s)] = t") | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (6) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | inserts *x* into *s* at the | |
| | index given by *i* (same as | |
| | "s[i:i] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop([i])" | retrieves the item at *i* and | (2) |
| | also removes it from *s* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
| | where "s[i] == x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default
the last item is removed and returned.
3. "remove" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for
economy of space when reversing a large sequence. To remind users
that it operates by side effect, it does not return the reversed
sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don’t support slicing
operations (such as "dict" and "set")
New in version 3.3: "clear()" and "copy()" methods.
6. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the sequence.
Items in the sequence are not copied; they are referenced multiple
times, as explained for "s * n" under Common Sequence Operations.
Lists
=====
Lists are mutable sequences, typically used to store collections of
homogeneous items (where the precise degree of similarity will vary by
application).
class list([iterable])
Lists may be constructed in several ways:
* Using a pair of square brackets to denote the empty list: "[]"
* Using square brackets, separating items with commas: "[a]",
"[a, b, c]"
* Using a list comprehension: "[x for x in iterable]"
* Using the type constructor: "list()" or "list(iterable)"
The constructor builds a list whose items are the same and in the
same order as *iterable*’s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a list, a copy is made and
returned, similar to "iterable[:]". For example, "list('abc')"
returns "['a', 'b', 'c']" and "list( (1, 2, 3) )" returns "[1, 2,
3]". If no argument is given, the constructor creates a new empty
list, "[]".
Many other operations also produce lists, including the "sorted()"
built-in.
Lists implement all of the common and mutable sequence operations.
Lists also provide the following additional method:
sort(*, key=None, reverse=False)
This method sorts the list in place, using only "<" comparisons
between items. Exceptions are not suppressed - if any comparison
operations fail, the entire sort operation will fail (and the
list will likely be left in a partially modified state).
"sort()" accepts two arguments that can only be passed by
keyword (keyword-only arguments):
*key* specifies a function of one argument that is used to
extract a comparison key from each list element (for example,
"key=str.lower"). The key corresponding to each item in the list
is calculated once and then used for the entire sorting process.
The default value of "None" means that list items are sorted
directly without calculating a separate key value.
The "functools.cmp_to_key()" utility is available to convert a
2.x style *cmp* function to a *key* function.
*reverse* is a boolean value. If set to "True", then the list
elements are sorted as if each comparison were reversed.
This method modifies the sequence in place for economy of space
when sorting a large sequence. To remind users that it operates
by side effect, it does not return the sorted sequence (use
"sorted()" to explicitly request a new sorted list instance).
The "sort()" method is guaranteed to be stable. A sort is
stable if it guarantees not to change the relative order of
elements that compare equal — this is helpful for sorting in
multiple passes (for example, sort by department, then by salary
grade).
**CPython implementation detail:** While a list is being sorted,
the effect of attempting to mutate, or even inspect, the list is
undefined. The C implementation of Python makes the list appear
empty for the duration, and raises "ValueError" if it can detect
that the list has been mutated during a sort.
Tuples
======
Tuples are immutable sequences, typically used to store collections of
heterogeneous data (such as the 2-tuples produced by the "enumerate()"
built-in). Tuples are also used for cases where an immutable sequence
of homogeneous data is needed (such as allowing storage in a "set" or
"dict" instance).
class tuple([iterable])
Tuples may be constructed in a number of ways:
* Using a pair of parentheses to denote the empty tuple: "()"
* Using a trailing comma for a singleton tuple: "a," or "(a,)"
* Separating items with commas: "a, b, c" or "(a, b, c)"
* Using the "tuple()" built-in: "tuple()" or "tuple(iterable)"
The constructor builds a tuple whose items are the same and in the
same order as *iterable*’s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a tuple, it is returned
unchanged. For example, "tuple('abc')" returns "('a', 'b', 'c')"
and "tuple( [1, 2, 3] )" returns "(1, 2, 3)". If no argument is
given, the constructor creates a new empty tuple, "()".
Note that it is actually the comma which makes a tuple, not the
parentheses. The parentheses are optional, except in the empty
tuple case, or when they are needed to avoid syntactic ambiguity.
For example, "f(a, b, c)" is a function call with three arguments,
while "f((a, b, c))" is a function call with a 3-tuple as the sole
argument.
Tuples implement all of the common sequence operations.
For heterogeneous collections of data where access by name is clearer
than access by index, "collections.namedtuple()" may be a more
appropriate choice than a simple tuple object.
Ranges
======
The "range" type represents an immutable sequence of numbers and is
commonly used for looping a specific number of times in "for" loops.
class range(stop)
class range(start, stop[, step])
The arguments to the range constructor must be integers (either
built-in "int" or any object that implements the "__index__"
special method). If the *step* argument is omitted, it defaults to
"1". If the *start* argument is omitted, it defaults to "0". If
*step* is zero, "ValueError" is raised.
For a positive *step*, the contents of a range "r" are determined
by the formula "r[i] = start + step*i" where "i >= 0" and "r[i] <
stop".
For a negative *step*, the contents of the range are still
determined by the formula "r[i] = start + step*i", but the
constraints are "i >= 0" and "r[i] > stop".
A range object will be empty if "r[0]" does not meet the value
constraint. Ranges do support negative indices, but these are
interpreted as indexing from the end of the sequence determined by
the positive indices.
Ranges containing absolute values larger than "sys.maxsize" are
permitted but some features (such as "len()") may raise
"OverflowError".
Range examples:
>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(range(1, 11))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> list(range(0, 30, 5))
[0, 5, 10, 15, 20, 25]
>>> list(range(0, 10, 3))
[0, 3, 6, 9]
>>> list(range(0, -10, -1))
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
>>> list(range(0))
[]
>>> list(range(1, 0))
[]
Ranges implement all of the common sequence operations except
concatenation and repetition (due to the fact that range objects
can only represent sequences that follow a strict pattern and
repetition and concatenation will usually violate that pattern).
start
The value of the *start* parameter (or "0" if the parameter was
not supplied)
stop
The value of the *stop* parameter
step
The value of the *step* parameter (or "1" if the parameter was
not supplied)
The advantage of the "range" type over a regular "list" or "tuple" is
that a "range" object will always take the same (small) amount of
memory, no matter the size of the range it represents (as it only
stores the "start", "stop" and "step" values, calculating individual
items and subranges as needed).
Range objects implement the "collections.abc.Sequence" ABC, and
provide features such as containment tests, element index lookup,
slicing and support for negative indices (see Sequence Types — list,
tuple, range):
>>> r = range(0, 20, 2)
>>> r
range(0, 20, 2)
>>> 11 in r
False
>>> 10 in r
True
>>> r.index(10)
5
>>> r[5]
10
>>> r[:5]
range(0, 10, 2)
>>> r[-1]
18
Testing range objects for equality with "==" and "!=" compares them as
sequences. That is, two range objects are considered equal if they
represent the same sequence of values. (Note that two range objects
that compare equal might have different "start", "stop" and "step"
attributes, for example "range(0) == range(2, 1, 3)" or "range(0, 3,
2) == range(0, 4, 2)".)
Changed in version 3.2: Implement the Sequence ABC. Support slicing
and negative indices. Test "int" objects for membership in constant
time instead of iterating through all items.
Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range objects
based on the sequence of values they define (instead of comparing
based on object identity).
New in version 3.3: The "start", "stop" and "step" attributes.
See also:
* The linspace recipe shows how to implement a lazy version of
range suitable for floating point applications.
us Mutable Sequence Types
**********************
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
+================================+==================================+=======================+
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | appends *x* to the end of the | |
| | sequence (same as | |
| | "s[len(s):len(s)] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from *s* (same | (5) |
| | as "del s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of *s* | (5) |
| | (same as "s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | extends *s* with the contents of | |
| | *t* (for the most part the same | |
| | as "s[len(s):len(s)] = t") | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (6) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | inserts *x* into *s* at the | |
| | index given by *i* (same as | |
| | "s[i:i] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop([i])" | retrieves the item at *i* and | (2) |
| | also removes it from *s* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
| | where "s[i] == x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default
the last item is removed and returned.
3. "remove" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for
economy of space when reversing a large sequence. To remind users
that it operates by side effect, it does not return the reversed
sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don’t support slicing
operations (such as "dict" and "set")
New in version 3.3: "clear()" and "copy()" methods.
6. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the sequence.
Items in the sequence are not copied; they are referenced multiple
times, as explained for "s * n" under Common Sequence Operations.
a~ Unary arithmetic and bitwise operations
***************************************
All unary arithmetic and bitwise operations have the same priority:
u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr
The unary "-" (minus) operator yields the negation of its numeric
argument.
The unary "+" (plus) operator yields its numeric argument unchanged.
The unary "~" (invert) operator yields the bitwise inversion of its
integer argument. The bitwise inversion of "x" is defined as
"-(x+1)". It only applies to integral numbers.
In all three cases, if the argument does not have the proper type, a
"TypeError" exception is raised.
u� The "while" statement
*********************
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
u& The "with" statement
********************
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one “item” proceeds as
follows:
1. The context expression (the expression given in the "with_item")
is evaluated to obtain a context manager.
2. The context manager’s "__exit__()" is loaded for later use.
3. The context manager’s "__enter__()" method is invoked.
4. If a target was included in the "with" statement, the return
value from "__enter__()" is assigned to it.
Note: The "with" statement guarantees that if the "__enter__()"
method returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
5. The suite is executed.
6. The context manager’s "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
suite
is equivalent to
with A() as a:
with B() as b:
suite
Changed in version 3.1: Support for multiple context expressions.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
a, The "yield" statement
*********************
yield_stmt ::= yield_expression
A "yield" statement is semantically equivalent to a yield expression.
The yield statement can be used to omit the parentheses that would
otherwise be required in the equivalent yield expression statement.
For example, the yield statements
yield <expr>
yield from <expr>
are equivalent to the yield expression statements
(yield <expr>)
(yield from <expr>)
Yield expressions and statements are only used when defining a
*generator* function, and are only used in the body of the generator
function. Using yield in a function definition is sufficient to cause
that definition to create a generator function instead of a normal
function.
For full details of "yield" semantics, refer to the Yield expressions
section.
)M�assertZ
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ddddddddddddddd d d d d d!d"d#d$d%d&d d'd(d)d*d+d,d-d.d/d0d1d2d3d4d5d6d7d8d9d:d;d<d=d>d?d@dAdBdCdDdEdFdGdHdIdJdKdL�MZ dMS )Nau The "assert" statement
**********************
Assert statements are a convenient way to insert debugging assertions
into a program:
assert_stmt ::= "assert" expression ["," expression]
The simple form, "assert expression", is equivalent to
if __debug__:
if not expression: raise AssertionError
The extended form, "assert expression1, expression2", is equivalent to
if __debug__:
if not expression1: raise AssertionError(expression2)
These equivalences assume that "__debug__" and "AssertionError" refer
to the built-in variables with those names. In the current
implementation, the built-in variable "__debug__" is "True" under
normal circumstances, "False" when optimization is requested (command
line option "-O"). The current code generator emits no code for an
assert statement when optimization is requested at compile time. Note
that it is unnecessary to include the source code for the expression
that failed in the error message; it will be displayed as part of the
stack trace.
Assignments to "__debug__" are illegal. The value for the built-in
variable is determined when the interpreter starts.
us+ Assignment statements
*********************
Assignment statements are used to (re)bind names to values and to
modify attributes or items of mutable objects:
assignment_stmt ::= (target_list "=")+ (starred_expression | yield_expression)
target_list ::= target ("," target)* [","]
target ::= identifier
| "(" [target_list] ")"
| "[" [target_list] "]"
| attributeref
| subscription
| slicing
| "*" target
(See section Primaries for the syntax definitions for *attributeref*,
*subscription*, and *slicing*.)
An assignment statement evaluates the expression list (remember that
this can be a single expression or a comma-separated list, the latter
yielding a tuple) and assigns the single resulting object to each of
the target lists, from left to right.
Assignment is defined recursively depending on the form of the target
(list). When a target is part of a mutable object (an attribute
reference, subscription or slicing), the mutable object must
ultimately perform the assignment and decide about its validity, and
may raise an exception if the assignment is unacceptable. The rules
observed by various types and the exceptions raised are given with the
definition of the object types (see section The standard type
hierarchy).
Assignment of an object to a target list, optionally enclosed in
parentheses or square brackets, is recursively defined as follows.
* If the target list is a single target with no trailing comma,
optionally in parentheses, the object is assigned to that target.
* Else: The object must be an iterable with the same number of items
as there are targets in the target list, and the items are assigned,
from left to right, to the corresponding targets.
* If the target list contains one target prefixed with an
asterisk, called a “starred” target: The object must be an
iterable with at least as many items as there are targets in the
target list, minus one. The first items of the iterable are
assigned, from left to right, to the targets before the starred
target. The final items of the iterable are assigned to the
targets after the starred target. A list of the remaining items
in the iterable is then assigned to the starred target (the list
can be empty).
* Else: The object must be an iterable with the same number of
items as there are targets in the target list, and the items are
assigned, from left to right, to the corresponding targets.
Assignment of an object to a single target is recursively defined as
follows.
* If the target is an identifier (name):
* If the name does not occur in a "global" or "nonlocal" statement
in the current code block: the name is bound to the object in the
current local namespace.
* Otherwise: the name is bound to the object in the global
namespace or the outer namespace determined by "nonlocal",
respectively.
The name is rebound if it was already bound. This may cause the
reference count for the object previously bound to the name to reach
zero, causing the object to be deallocated and its destructor (if it
has one) to be called.
* If the target is an attribute reference: The primary expression in
the reference is evaluated. It should yield an object with
assignable attributes; if this is not the case, "TypeError" is
raised. That object is then asked to assign the assigned object to
the given attribute; if it cannot perform the assignment, it raises
an exception (usually but not necessarily "AttributeError").
Note: If the object is a class instance and the attribute reference
occurs on both sides of the assignment operator, the RHS expression,
"a.x" can access either an instance attribute or (if no instance
attribute exists) a class attribute. The LHS target "a.x" is always
set as an instance attribute, creating it if necessary. Thus, the
two occurrences of "a.x" do not necessarily refer to the same
attribute: if the RHS expression refers to a class attribute, the
LHS creates a new instance attribute as the target of the
assignment:
class Cls:
x = 3 # class variable
inst = Cls()
inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3
This description does not necessarily apply to descriptor
attributes, such as properties created with "property()".
* If the target is a subscription: The primary expression in the
reference is evaluated. It should yield either a mutable sequence
object (such as a list) or a mapping object (such as a dictionary).
Next, the subscript expression is evaluated.
If the primary is a mutable sequence object (such as a list), the
subscript must yield an integer. If it is negative, the sequence’s
length is added to it. The resulting value must be a nonnegative
integer less than the sequence’s length, and the sequence is asked
to assign the assigned object to its item with that index. If the
index is out of range, "IndexError" is raised (assignment to a
subscripted sequence cannot add new items to a list).
If the primary is a mapping object (such as a dictionary), the
subscript must have a type compatible with the mapping’s key type,
and the mapping is then asked to create a key/datum pair which maps
the subscript to the assigned object. This can either replace an
existing key/value pair with the same key value, or insert a new
key/value pair (if no key with the same value existed).
For user-defined objects, the "__setitem__()" method is called with
appropriate arguments.
* If the target is a slicing: The primary expression in the
reference is evaluated. It should yield a mutable sequence object
(such as a list). The assigned object should be a sequence object
of the same type. Next, the lower and upper bound expressions are
evaluated, insofar they are present; defaults are zero and the
sequence’s length. The bounds should evaluate to integers. If
either bound is negative, the sequence’s length is added to it. The
resulting bounds are clipped to lie between zero and the sequence’s
length, inclusive. Finally, the sequence object is asked to replace
the slice with the items of the assigned sequence. The length of
the slice may be different from the length of the assigned sequence,
thus changing the length of the target sequence, if the target
sequence allows it.
**CPython implementation detail:** In the current implementation, the
syntax for targets is taken to be the same as for expressions, and
invalid syntax is rejected during the code generation phase, causing
less detailed error messages.
Although the definition of assignment implies that overlaps between
the left-hand side and the right-hand side are ‘simultaneous’ (for
example "a, b = b, a" swaps two variables), overlaps *within* the
collection of assigned-to variables occur left-to-right, sometimes
resulting in confusion. For instance, the following program prints
"[0, 2]":
x = [0, 1]
i = 0
i, x[i] = 1, 2 # i is updated, then x[i] is updated
print(x)
See also:
**PEP 3132** - Extended Iterable Unpacking
The specification for the "*target" feature.
Augmented assignment statements
===============================
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions of the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side. For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
Annotated assignment statements
===============================
Annotation assignment is the combination, in a single statement, of a
variable or attribute annotation and an optional assignment statement:
annotated_assignment_stmt ::= augtarget ":" expression ["=" expression]
The difference from normal Assignment statements is that only single
target and only single right hand side value is allowed.
For simple names as assignment targets, if in class or module scope,
the annotations are evaluated and stored in a special class or module
attribute "__annotations__" that is a dictionary mapping from variable
names (mangled if private) to evaluated annotations. This attribute is
writable and is automatically created at the start of class or module
body execution, if annotations are found statically.
For expressions as assignment targets, the annotations are evaluated
if in class or module scope, but not stored.
If a name is annotated in a function scope, then this name is local
for that scope. Annotations are never evaluated and stored in function
scopes.
If the right hand side is present, an annotated assignment performs
the actual assignment before evaluating annotations (where
applicable). If the right hand side is not present for an expression
target, then the interpreter evaluates the target except for the last
"__setitem__()" or "__setattr__()" call.
See also:
**PEP 526** - Syntax for Variable Annotations
The proposal that added syntax for annotating the types of
variables (including class variables and instance variables),
instead of expressing them through comments.
**PEP 484** - Type hints
The proposal that added the "typing" module to provide a standard
syntax for type annotations that can be used in static analysis
tools and IDEs.
a� Identifiers (Names)
*******************
An identifier occurring as an atom is a name. See section Identifiers
and keywords for lexical definition and section Naming and binding for
documentation of naming and binding.
When the name is bound to an object, evaluation of the atom yields
that object. When a name is not bound, an attempt to evaluate it
raises a "NameError" exception.
**Private name mangling:** When an identifier that textually occurs in
a class definition begins with two or more underscore characters and
does not end in two or more underscores, it is considered a *private
name* of that class. Private names are transformed to a longer form
before code is generated for them. The transformation inserts the
class name, with leading underscores removed and a single underscore
inserted, in front of the name. For example, the identifier "__spam"
occurring in a class named "Ham" will be transformed to "_Ham__spam".
This transformation is independent of the syntactical context in which
the identifier is used. If the transformed name is extremely long
(longer than 255 characters), implementation defined truncation may
happen. If the class name consists only of underscores, no
transformation is done.
u
Literals
********
Python supports string and bytes literals and various numeric
literals:
literal ::= stringliteral | bytesliteral
| integer | floatnumber | imagnumber
Evaluation of a literal yields an object of the given type (string,
bytes, integer, floating point number, complex number) with the given
value. The value may be approximated in the case of floating point
and imaginary (complex) literals. See section Literals for details.
All literals correspond to immutable data types, and hence the
object’s identity is less important than its value. Multiple
evaluations of literals with the same value (either the same
occurrence in the program text or a different occurrence) may obtain
the same object or a different object with the same value.
u- Customizing attribute access
****************************
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
return the (computed) attribute value or raise an "AttributeError"
exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note: This method may still be bypassed when looking up special
methods as the result of implicit invocation via language syntax
or built-in functions. See Special method lookup.
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should call the base class method with the same name, for example,
"object.__setattr__(self, name, value)".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
Customizing module attribute access
===================================
For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
setattr(self, attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note: Setting module "__class__" only affects lookups made using the
attribute access syntax – directly accessing the module globals
(whether by code within the module, or via a reference to the
module’s globals dictionary) is unaffected.
Changed in version 3.5: "__class__" module attribute is now writable.
Implementing Descriptors
========================
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents). In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".
object.__get__(self, instance, owner)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). *owner* is always the owner class, while *instance* is the
instance that the attribute was accessed through, or "None" when
the attribute is accessed through the *owner*. This method should
return the (computed) attribute value or raise an "AttributeError"
exception.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
object.__set_name__(self, owner, name)
Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.
New in version 3.6.
The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).
Invoking Descriptors
====================
In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a class, "A.x" is transformed into the call:
"A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined. A descriptor can define
any combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
=========
*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of *__dict__* and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)
The space saved over using *__dict__* can be significant.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. *__slots__*
reserves space for the declared variables and prevents the
automatic creation of *__dict__* and *__weakref__* for each
instance.
Notes on using *__slots__*
--------------------------
* When inheriting from a class without *__slots__*, the *__dict__*
and *__weakref__* attribute of the instances will always be
accessible.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
* Without a *__weakref__* variable for each instance, classes
defining *__slots__* do not support weak references to its
instances. If weak reference support is needed, then add
"'__weakref__'" to the sequence of strings in the *__slots__*
declaration.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is not limited to the
class where it is defined. *__slots__* declared in parents are
available in child classes. However, child subclasses will get a
*__dict__* and *__weakref__* unless they also define *__slots__*
(which should only contain names of any *additional* slots).
* If a class defines a slot also defined in a base class, the
instance variable defined by the base class slot is inaccessible
(except by retrieving its descriptor directly from the base class).
This renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
“variable-length” built-in types such as "int", "bytes" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings
may also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".
a� Attribute references
********************
An attribute reference is a primary followed by a period and a name:
attributeref ::= primary "." identifier
The primary must evaluate to an object of a type that supports
attribute references, which most objects do. This object is then
asked to produce the attribute whose name is the identifier. This
production can be customized by overriding the "__getattr__()" method.
If this attribute is not available, the exception "AttributeError" is
raised. Otherwise, the type and value of the object produced is
determined by the object. Multiple evaluations of the same attribute
reference may yield different objects.
a� Augmented assignment statements
*******************************
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions of the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side. For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
uj Binary arithmetic operations
****************************
The binary arithmetic operations have the conventional priority
levels. Note that some of these operations also apply to certain non-
numeric types. Apart from the power operator, there are only two
levels, one for multiplicative operators and one for additive
operators:
m_expr ::= u_expr | m_expr "*" u_expr | m_expr "@" m_expr |
m_expr "//" u_expr | m_expr "/" u_expr |
m_expr "%" u_expr
a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr
The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer and the other must be a sequence. In the former case, the
numbers are converted to a common type and then multiplied together.
In the latter case, sequence repetition is performed; a negative
repetition factor yields an empty sequence.
The "@" (at) operator is intended to be used for matrix
multiplication. No builtin Python types implement this operator.
New in version 3.5.
The "/" (division) and "//" (floor division) operators yield the
quotient of their arguments. The numeric arguments are first
converted to a common type. Division of integers yields a float, while
floor division of integers results in an integer; the result is that
of mathematical division with the ‘floor’ function applied to the
result. Division by zero raises the "ZeroDivisionError" exception.
The "%" (modulo) operator yields the remainder from the division of
the first argument by the second. The numeric arguments are first
converted to a common type. A zero right argument raises the
"ZeroDivisionError" exception. The arguments may be floating point
numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 +
0.34".) The modulo operator always yields a result with the same sign
as its second operand (or zero); the absolute value of the result is
strictly smaller than the absolute value of the second operand [1].
The floor division and modulo operators are connected by the following
identity: "x == (x//y)*y + (x%y)". Floor division and modulo are also
connected with the built-in function "divmod()": "divmod(x, y) ==
(x//y, x%y)". [2].
In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string objects to perform old-style
string formatting (also known as interpolation). The syntax for
string formatting is described in the Python Library Reference,
section printf-style String Formatting.
The floor division operator, the modulo operator, and the "divmod()"
function are not defined for complex numbers. Instead, convert to a
floating point number using the "abs()" function if appropriate.
The "+" (addition) operator yields the sum of its arguments. The
arguments must either both be numbers or both be sequences of the same
type. In the former case, the numbers are converted to a common type
and then added together. In the latter case, the sequences are
concatenated.
The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.
a$ Binary bitwise operations
*************************
Each of the three bitwise operations has a different priority level:
and_expr ::= shift_expr | and_expr "&" shift_expr
xor_expr ::= and_expr | xor_expr "^" and_expr
or_expr ::= xor_expr | or_expr "|" xor_expr
The "&" operator yields the bitwise AND of its arguments, which must
be integers.
The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be integers.
The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be integers.
ux Code Objects
************
Code objects are used by the implementation to represent “pseudo-
compiled” executable Python code such as a function body. They differ
from function objects because they don’t contain a reference to their
global execution environment. Code objects are returned by the built-
in "compile()" function and can be extracted from function objects
through their "__code__" attribute. See also the "code" module.
A code object can be executed or evaluated by passing it (instead of a
source string) to the "exec()" or "eval()" built-in functions.
See The standard type hierarchy for more information.
a. The Ellipsis Object
*******************
This object is commonly used by slicing (see Slicings). It supports
no special operations. There is exactly one ellipsis object, named
"Ellipsis" (a built-in name). "type(Ellipsis)()" produces the
"Ellipsis" singleton.
It is written as "Ellipsis" or "...".
u The Null Object
***************
This object is returned by functions that don’t explicitly return a
value. It supports no special operations. There is exactly one null
object, named "None" (a built-in name). "type(None)()" produces the
same singleton.
It is written as "None".
u5 Type Objects
************
Type objects represent the various object types. An object’s type is
accessed by the built-in function "type()". There are no special
operations on types. The standard module "types" defines names for
all standard built-in types.
Types are written like this: "<class 'int'>".
a� Boolean operations
******************
or_test ::= and_test | or_test "or" and_test
and_test ::= not_test | and_test "and" not_test
not_test ::= comparison | "not" not_test
In the context of Boolean operations, and also when expressions are
used by control flow statements, the following values are interpreted
as false: "False", "None", numeric zero of all types, and empty
strings and containers (including strings, tuples, lists,
dictionaries, sets and frozensets). All other values are interpreted
as true. User-defined objects can customize their truth value by
providing a "__bool__()" method.
The operator "not" yields "True" if its argument is false, "False"
otherwise.
The expression "x and y" first evaluates *x*; if *x* is false, its
value is returned; otherwise, *y* is evaluated and the resulting value
is returned.
The expression "x or y" first evaluates *x*; if *x* is true, its value
is returned; otherwise, *y* is evaluated and the resulting value is
returned.
Note that neither "and" nor "or" restrict the value and type they
return to "False" and "True", but rather return the last evaluated
argument. This is sometimes useful, e.g., if "s" is a string that
should be replaced by a default value if it is empty, the expression
"s or 'foo'" yields the desired value. Because "not" has to create a
new value, it returns a boolean value regardless of the type of its
argument (for example, "not 'foo'" produces "False" rather than "''".)
a$ The "break" statement
*********************
break_stmt ::= "break"
"break" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop.
It terminates the nearest enclosing loop, skipping the optional "else"
clause if the loop has one.
If a "for" loop is terminated by "break", the loop control target
keeps its current value.
When "break" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
loop.
u� Emulating callable objects
**************************
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method
is defined, "x(arg1, arg2, ...)" is a shorthand for
"x.__call__(arg1, arg2, ...)".
uC Calls
*****
A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:
call ::= primary "(" [argument_list [","] | comprehension] ")"
argument_list ::= positional_arguments ["," starred_and_keywords]
["," keywords_arguments]
| starred_and_keywords ["," keywords_arguments]
| keywords_arguments
positional_arguments ::= ["*"] expression ("," ["*"] expression)*
starred_and_keywords ::= ("*" expression | keyword_item)
("," "*" expression | "," keyword_item)*
keywords_arguments ::= (keyword_item | "**" expression)
("," keyword_item | "," "**" expression)*
keyword_item ::= identifier "=" expression
An optional trailing comma may be present after the positional and
keyword arguments but does not affect the semantics.
The primary must evaluate to a callable object (user-defined
functions, built-in functions, methods of built-in objects, class
objects, methods of class instances, and all objects having a
"__call__()" method are callable). All argument expressions are
evaluated before the call is attempted. Please refer to section
Function definitions for the syntax of formal *parameter* lists.
If keyword arguments are present, they are first converted to
positional arguments, as follows. First, a list of unfilled slots is
created for the formal parameters. If there are N positional
arguments, they are placed in the first N slots. Next, for each
keyword argument, the identifier is used to determine the
corresponding slot (if the identifier is the same as the first formal
parameter name, the first slot is used, and so on). If the slot is
already filled, a "TypeError" exception is raised. Otherwise, the
value of the argument is placed in the slot, filling it (even if the
expression is "None", it fills the slot). When all arguments have
been processed, the slots that are still unfilled are filled with the
corresponding default value from the function definition. (Default
values are calculated, once, when the function is defined; thus, a
mutable object such as a list or dictionary used as default value will
be shared by all calls that don’t specify an argument value for the
corresponding slot; this should usually be avoided.) If there are any
unfilled slots for which no default value is specified, a "TypeError"
exception is raised. Otherwise, the list of filled slots is used as
the argument list for the call.
**CPython implementation detail:** An implementation may provide
built-in functions whose positional parameters do not have names, even
if they are ‘named’ for the purpose of documentation, and which
therefore cannot be supplied by keyword. In CPython, this is the case
for functions implemented in C that use "PyArg_ParseTuple()" to parse
their arguments.
If there are more positional arguments than there are formal parameter
slots, a "TypeError" exception is raised, unless a formal parameter
using the syntax "*identifier" is present; in this case, that formal
parameter receives a tuple containing the excess positional arguments
(or an empty tuple if there were no excess positional arguments).
If any keyword argument does not correspond to a formal parameter
name, a "TypeError" exception is raised, unless a formal parameter
using the syntax "**identifier" is present; in this case, that formal
parameter receives a dictionary containing the excess keyword
arguments (using the keywords as keys and the argument values as
corresponding values), or a (new) empty dictionary if there were no
excess keyword arguments.
If the syntax "*expression" appears in the function call, "expression"
must evaluate to an *iterable*. Elements from these iterables are
treated as if they were additional positional arguments. For the call
"f(x1, x2, *y, x3, x4)", if *y* evaluates to a sequence *y1*, …, *yM*,
this is equivalent to a call with M+4 positional arguments *x1*, *x2*,
*y1*, …, *yM*, *x3*, *x4*.
A consequence of this is that although the "*expression" syntax may
appear *after* explicit keyword arguments, it is processed *before*
the keyword arguments (and any "**expression" arguments – see below).
So:
>>> def f(a, b):
... print(a, b)
...
>>> f(b=1, *(2,))
2 1
>>> f(a=1, *(2,))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: f() got multiple values for keyword argument 'a'
>>> f(1, *(2,))
1 2
It is unusual for both keyword arguments and the "*expression" syntax
to be used in the same call, so in practice this confusion does not
arise.
If the syntax "**expression" appears in the function call,
"expression" must evaluate to a *mapping*, the contents of which are
treated as additional keyword arguments. If a keyword is already
present (as an explicit keyword argument, or from another unpacking),
a "TypeError" exception is raised.
Formal parameters using the syntax "*identifier" or "**identifier"
cannot be used as positional argument slots or as keyword argument
names.
Changed in version 3.5: Function calls accept any number of "*" and
"**" unpackings, positional arguments may follow iterable unpackings
("*"), and keyword arguments may follow dictionary unpackings ("**").
Originally proposed by **PEP 448**.
A call always returns some value, possibly "None", unless it raises an
exception. How this value is computed depends on the type of the
callable object.
If it is—
a user-defined function:
The code block for the function is executed, passing it the
argument list. The first thing the code block will do is bind the
formal parameters to the arguments; this is described in section
Function definitions. When the code block executes a "return"
statement, this specifies the return value of the function call.
a built-in function or method:
The result is up to the interpreter; see Built-in Functions for the
descriptions of built-in functions and methods.
a class object:
A new instance of that class is returned.
a class instance method:
The corresponding user-defined function is called, with an argument
list that is one longer than the argument list of the call: the
instance becomes the first argument.
a class instance:
The class must define a "__call__()" method; the effect is then the
same as if that method was called.
u Class definitions
*****************
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [argument_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing. Classes without an inheritance list
inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.) When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [3] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.
The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__". Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.
Class creation can be customized heavily using metaclasses.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result is then bound to the class name.
**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. Descriptors can be used to create instance
variables with different implementation details.
See also:
**PEP 3115** - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the
current syntax, and the semantics for how classes with
metaclasses are constructed.
**PEP 3129** - Class Decorators
The proposal that added class decorators. Function and method
decorators were introduced in **PEP 318**.
u4) Comparisons
***********
Unlike C, all comparison operations in Python have the same priority,
which is lower than that of any arithmetic, shifting or bitwise
operation. Also unlike C, expressions like "a < b < c" have the
interpretation that is conventional in mathematics:
comparison ::= or_expr (comp_operator or_expr)*
comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="
| "is" ["not"] | ["not"] "in"
Comparisons yield boolean values: "True" or "False".
Comparisons can be chained arbitrarily, e.g., "x < y <= z" is
equivalent to "x < y and y <= z", except that "y" is evaluated only
once (but in both cases "z" is not evaluated at all when "x < y" is
found to be false).
Formally, if *a*, *b*, *c*, …, *y*, *z* are expressions and *op1*,
*op2*, …, *opN* are comparison operators, then "a op1 b op2 c ... y
opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except
that each expression is evaluated at most once.
Note that "a op1 b op2 c" doesn’t imply any kind of comparison between
*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though
perhaps not pretty).
Value comparisons
=================
The operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects. The objects do not need to have the same type.
Chapter Objects, values and types states that objects have a value (in
addition to type and identity). The value of an object is a rather
abstract notion in Python: For example, there is no canonical access
method for an object’s value. Also, there is no requirement that the
value of an object should be constructed in a particular way, e.g.
comprised of all its data attributes. Comparison operators implement a
particular notion of what the value of an object is. One can think of
them as defining the value of an object indirectly, by means of their
comparison implementation.
Because all types are (direct or indirect) subtypes of "object", they
inherit the default comparison behavior from "object". Types can
customize their comparison behavior by implementing *rich comparison
methods* like "__lt__()", described in Basic customization.
The default behavior for equality comparison ("==" and "!=") is based
on the identity of the objects. Hence, equality comparison of
instances with the same identity results in equality, and equality
comparison of instances with different identities results in
inequality. A motivation for this default behavior is the desire that
all objects should be reflexive (i.e. "x is y" implies "x == y").
A default order comparison ("<", ">", "<=", and ">=") is not provided;
an attempt raises "TypeError". A motivation for this default behavior
is the lack of a similar invariant as for equality.
The behavior of the default equality comparison, that instances with
different identities are always unequal, may be in contrast to what
types will need that have a sensible definition of object value and
value-based equality. Such types will need to customize their
comparison behavior, and in fact, a number of built-in types have done
that.
The following list describes the comparison behavior of the most
important built-in types.
* Numbers of built-in numeric types (Numeric Types — int, float,
complex) and of the standard library types "fractions.Fraction" and
"decimal.Decimal" can be compared within and across their types,
with the restriction that complex numbers do not support order
comparison. Within the limits of the types involved, they compare
mathematically (algorithmically) correct without loss of precision.
The not-a-number values "float('NaN')" and "Decimal('NaN')" are
special. They are identical to themselves ("x is x" is true) but
are not equal to themselves ("x == x" is false). Additionally,
comparing any number to a not-a-number value will return "False".
For example, both "3 < float('NaN')" and "float('NaN') < 3" will
return "False".
* Binary sequences (instances of "bytes" or "bytearray") can be
compared within and across their types. They compare
lexicographically using the numeric values of their elements.
* Strings (instances of "str") compare lexicographically using the
numerical Unicode code points (the result of the built-in function
"ord()") of their characters. [3]
Strings and binary sequences cannot be directly compared.
* Sequences (instances of "tuple", "list", or "range") can be
compared only within each of their types, with the restriction that
ranges do not support order comparison. Equality comparison across
these types results in inequality, and ordering comparison across
these types raises "TypeError".
Sequences compare lexicographically using comparison of
corresponding elements, whereby reflexivity of the elements is
enforced.
In enforcing reflexivity of elements, the comparison of collections
assumes that for a collection element "x", "x == x" is always true.
Based on that assumption, element identity is compared first, and
element comparison is performed only for distinct elements. This
approach yields the same result as a strict element comparison
would, if the compared elements are reflexive. For non-reflexive
elements, the result is different than for strict element
comparison, and may be surprising: The non-reflexive not-a-number
values for example result in the following comparison behavior when
used in a list:
>>> nan = float('NaN')
>>> nan is nan
True
>>> nan == nan
False <-- the defined non-reflexive behavior of NaN
>>> [nan] == [nan]
True <-- list enforces reflexivity and tests identity first
Lexicographical comparison between built-in collections works as
follows:
* For two collections to compare equal, they must be of the same
type, have the same length, and each pair of corresponding
elements must compare equal (for example, "[1,2] == (1,2)" is
false because the type is not the same).
* Collections that support order comparison are ordered the same
as their first unequal elements (for example, "[1,2,x] <= [1,2,y]"
has the same value as "x <= y"). If a corresponding element does
not exist, the shorter collection is ordered first (for example,
"[1,2] < [1,2,3]" is true).
* Mappings (instances of "dict") compare equal if and only if they
have equal *(key, value)* pairs. Equality comparison of the keys and
values enforces reflexivity.
Order comparisons ("<", ">", "<=", and ">=") raise "TypeError".
* Sets (instances of "set" or "frozenset") can be compared within
and across their types.
They define order comparison operators to mean subset and superset
tests. Those relations do not define total orderings (for example,
the two sets "{1,2}" and "{2,3}" are not equal, nor subsets of one
another, nor supersets of one another). Accordingly, sets are not
appropriate arguments for functions which depend on total ordering
(for example, "min()", "max()", and "sorted()" produce undefined
results given a list of sets as inputs).
Comparison of sets enforces reflexivity of its elements.
* Most other built-in types have no comparison methods implemented,
so they inherit the default comparison behavior.
User-defined classes that customize their comparison behavior should
follow some consistency rules, if possible:
* Equality comparison should be reflexive. In other words, identical
objects should compare equal:
"x is y" implies "x == y"
* Comparison should be symmetric. In other words, the following
expressions should have the same result:
"x == y" and "y == x"
"x != y" and "y != x"
"x < y" and "y > x"
"x <= y" and "y >= x"
* Comparison should be transitive. The following (non-exhaustive)
examples illustrate that:
"x > y and y > z" implies "x > z"
"x < y and y <= z" implies "x < z"
* Inverse comparison should result in the boolean negation. In other
words, the following expressions should have the same result:
"x == y" and "not x != y"
"x < y" and "not x >= y" (for total ordering)
"x > y" and "not x <= y" (for total ordering)
The last two expressions apply to totally ordered collections (e.g.
to sequences, but not to sets or mappings). See also the
"total_ordering()" decorator.
* The "hash()" result should be consistent with equality. Objects
that are equal should either have the same hash value, or be marked
as unhashable.
Python does not enforce these consistency rules. In fact, the
not-a-number values are an example for not following these rules.
Membership test operations
==========================
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y". If an exception is raised
during the iteration, it is as if "in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse true value of
"in".
Identity comparisons
====================
The operators "is" and "is not" test for object identity: "x is y" is
true if and only if *x* and *y* are the same object. Object identity
is determined using the "id()" function. "x is not y" yields the
inverse truth value. [4]
uxe Compound statements
*******************
Compound statements contain (groups of) other statements; they affect
or control the execution of those other statements in some way. In
general, compound statements span multiple lines, although in simple
incarnations a whole compound statement may be contained in one line.
The "if", "while" and "for" statements implement traditional control
flow constructs. "try" specifies exception handlers and/or cleanup
code for a group of statements, while the "with" statement allows the
execution of initialization and finalization code around a block of
code. Function and class definitions are also syntactically compound
statements.
A compound statement consists of one or more ‘clauses.’ A clause
consists of a header and a ‘suite.’ The clause headers of a
particular compound statement are all at the same indentation level.
Each clause header begins with a uniquely identifying keyword and ends
with a colon. A suite is a group of statements controlled by a
clause. A suite can be one or more semicolon-separated simple
statements on the same line as the header, following the header’s
colon, or it can be one or more indented statements on subsequent
lines. Only the latter form of a suite can contain nested compound
statements; the following is illegal, mostly because it wouldn’t be
clear to which "if" clause a following "else" clause would belong:
if test1: if test2: print(x)
Also note that the semicolon binds tighter than the colon in this
context, so that in the following example, either all or none of the
"print()" calls are executed:
if x < y < z: print(x); print(y); print(z)
Summarizing:
compound_stmt ::= if_stmt
| while_stmt
| for_stmt
| try_stmt
| with_stmt
| funcdef
| classdef
| async_with_stmt
| async_for_stmt
| async_funcdef
suite ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT
statement ::= stmt_list NEWLINE | compound_stmt
stmt_list ::= simple_stmt (";" simple_stmt)* [";"]
Note that statements always end in a "NEWLINE" possibly followed by a
"DEDENT". Also note that optional continuation clauses always begin
with a keyword that cannot start a statement, thus there are no
ambiguities (the ‘dangling "else"’ problem is solved in Python by
requiring nested "if" statements to be indented).
The formatting of the grammar rules in the following sections places
each clause on a separate line for clarity.
The "if" statement
==================
The "if" statement is used for conditional execution:
if_stmt ::= "if" expression ":" suite
("elif" expression ":" suite)*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
The "while" statement
=====================
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
The "for" statement
===================
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator. Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.
The for-loop makes assignments to the variables(s) in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop. Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".
Note: There is a subtlety when the sequence is being modified by the
loop (this can only occur for mutable sequences, e.g. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
The "try" statement
===================
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object or a tuple containing an item compatible with the exception.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed. All except
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred. "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.
The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed. Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a ‘cleanup’ handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return" or "break" statement, the saved exception
is discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’ A "continue" statement is illegal in
the "finally" clause. (The reason is a problem with the current
implementation — this restriction may be lifted in the future).
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
The "with" statement
====================
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one “item” proceeds as
follows:
1. The context expression (the expression given in the "with_item")
is evaluated to obtain a context manager.
2. The context manager’s "__exit__()" is loaded for later use.
3. The context manager’s "__enter__()" method is invoked.
4. If a target was included in the "with" statement, the return
value from "__enter__()" is assigned to it.
Note: The "with" statement guarantees that if the "__enter__()"
method returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
5. The suite is executed.
6. The context manager’s "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
suite
is equivalent to
with A() as a:
with B() as b:
suite
Changed in version 3.1: Support for multiple context expressions.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
Function definitions
====================
A function definition defines a user-defined function object (see
section The standard type hierarchy):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
| parameter_list_starargs
parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
| "**" parameter [","]
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [2]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
except that the original function is not temporarily bound to the name
"func".
When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted. If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.
**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call. This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended. A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from position arguments, from keyword
arguments, or from default values. If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type. Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed
used keyword arguments.
Parameters may have annotations of the form “": expression"” following
the parameter name. Any parameter may have an annotation even those
of the form "*identifier" or "**identifier". Functions may have
“return” annotation of the form “"-> expression"” after the parameter
list. These annotations can be any valid Python expression and are
evaluated when the function definition is executed. Annotations may
be evaluated in a different order than they appear in the source code.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.
**Programmer’s note:** Functions are first-class objects. A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around. Free variables used
in the nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
See also:
**PEP 3107** - Function Annotations
The original specification for function annotations.
Class definitions
=================
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [argument_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing. Classes without an inheritance list
inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.) When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [3] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.
The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__". Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.
Class creation can be customized heavily using metaclasses.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result is then bound to the class name.
**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. Descriptors can be used to create instance
variables with different implementation details.
See also:
**PEP 3115** - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the
current syntax, and the semantics for how classes with
metaclasses are constructed.
**PEP 3129** - Class Decorators
The proposal that added class decorators. Function and method
decorators were introduced in **PEP 318**.
Coroutines
==========
New in version 3.5.
Coroutine function definition
-----------------------------
async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*). In the body of a coroutine, any "await" and
"async" identifiers become reserved keywords; "await" expressions,
"async for" and "async with" can only be used in coroutine bodies.
Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.
It is a "SyntaxError" to use "yield from" expressions in "async def"
coroutines.
An example of a coroutine function:
async def func(param1, param2):
do_stuff()
await some_coroutine()
The "async for" statement
-------------------------
async_for_stmt ::= "async" for_stmt
An *asynchronous iterable* is able to call asynchronous code in its
*iter* implementation, and *asynchronous iterator* can call
asynchronous code in its *next* method.
The "async for" statement allows convenient iteration over
asynchronous iterators.
The following code:
async for TARGET in ITER:
BLOCK
else:
BLOCK2
Is semantically equivalent to:
iter = (ITER)
iter = type(iter).__aiter__(iter)
running = True
while running:
try:
TARGET = await type(iter).__anext__(iter)
except StopAsyncIteration:
running = False
else:
BLOCK
else:
BLOCK2
See also "__aiter__()" and "__anext__()" for details.
It is a "SyntaxError" to use "async for" statement outside of an
"async def" function.
The "async with" statement
--------------------------
async_with_stmt ::= "async" with_stmt
An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.
The following code:
async with EXPR as VAR:
BLOCK
Is semantically equivalent to:
mgr = (EXPR)
aexit = type(mgr).__aexit__
aenter = type(mgr).__aenter__(mgr)
VAR = await aenter
try:
BLOCK
except:
if not await aexit(mgr, *sys.exc_info()):
raise
else:
await aexit(mgr, None, None, None)
See also "__aenter__()" and "__aexit__()" for details.
It is a "SyntaxError" to use "async with" statement outside of an
"async def" function.
See also:
**PEP 492** - Coroutines with async and await syntax
The proposal that made coroutines a proper standalone concept in
Python, and added supporting syntax.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless
there is a "finally" clause which happens to raise another
exception. That new exception causes the old one to be lost.
[2] A string literal appearing as the first statement in the
function body is transformed into the function’s "__doc__"
attribute and therefore the function’s *docstring*.
[3] A string literal appearing as the first statement in the class
body is transformed into the namespace’s "__doc__" item and
therefore the class’s *docstring*.
u� With Statement Context Managers
*******************************
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method’s return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller’s responsibility.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
a� The "continue" statement
************************
continue_stmt ::= "continue"
"continue" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition or "finally"
clause within that loop. It continues with the next cycle of the
nearest enclosing loop.
When "continue" passes control out of a "try" statement with a
"finally" clause, that "finally" clause is executed before really
starting the next loop cycle.
u� Arithmetic conversions
**********************
When a description of an arithmetic operator below uses the phrase
“the numeric arguments are converted to a common type,” this means
that the operator implementation for built-in types works as follows:
* If either argument is a complex number, the other is converted to
complex;
* otherwise, if either argument is a floating point number, the
other is converted to floating point;
* otherwise, both must be integers and no conversion is necessary.
Some additional rules apply for certain operators (e.g., a string as a
left argument to the ‘%’ operator). Extensions must define their own
conversion behavior.
u�3 Basic customization
*******************
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass’s "__new__()" method using
"super().__new__(cls[, ...])" with appropriate arguments and then
modifying the newly-created instance as necessary before returning
it.
If "__new__()" returns an instance of *cls*, then the new
instance’s "__init__()" method will be invoked like
"__init__(self[, ...])", where *self* is the new instance and the
remaining arguments are the same as were passed to "__new__()".
If "__new__()" does not return an instance of *cls*, then the new
instance’s "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class’s "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"super().__init__([args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.
It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.
It is not guaranteed that "__del__()" methods are called for
objects that still exist when the interpreter exits.
Note: "del x" doesn’t directly call "x.__del__()" — the former
decrements the reference count for "x" by one, and the latter is
only called when "x"’s reference count reaches zero.
**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.
See also: Documentation for the "gc" module.
Warning: Due to the precarious circumstances under which
"__del__()" methods are invoked, exceptions that occur during
their execution are ignored, and a warning is printed to
"sys.stderr" instead. In particular:
* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".
* "__del__()" can be executed during interpreter shutdown. As
a consequence, the global variables it needs to access
(including other modules) may already have been deleted or set
to "None". Python guarantees that globals whose name begins
with a single underscore are deleted from their module before
other globals are deleted; if no other references to such
globals exist, this may help in assuring that imported modules
are still available at the time when the "__del__()" method is
called.
object.__repr__(self)
Called by the "repr()" built-in function to compute the “official”
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an “informal”
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the “informal” or nicely printable string
representation of an object. The return value must be a string
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by bytes to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function, and by extension,
evaluation of formatted string literals and the "str.format()"
method, to produce a “formatted” string representation of an
object. The "format_spec" argument is a string that contains a
description of the formatting options desired. The interpretation
of the "format_spec" argument is up to the type implementing
"__format__()", however most classes will either delegate
formatting to one of the built-in types, or use a similar
formatting option syntax.
See Format Specification Mini-Language for a description of the
standard formatting syntax.
The return value must be a string object.
Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
By default, "__ne__()" delegates to "__eq__()" and inverts the
result unless it is "NotImplemented". There are no other implied
relationships among the comparison operators, for example, the
truth of "(x<y or x==y)" does not imply "x<=y". To automatically
generate ordering operations from a single root operation, see
"functools.total_ordering()".
See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
Note: "hash()" truncates the value returned from an object’s
custom "__hash__()" method to the size of a "Py_ssize_t". This
is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit
builds. If an object’s "__hash__()" must interoperate on builds
of different bit sizes, be sure to check the width on all
supported builds. An easy way to do this is with "python -c
"import sys; print(sys.hash_info.width)"".
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key’s hash value is immutable (if the object’s hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj, collections.Hashable)".
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
<ParentClass>.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.Hashable)" call.
Note: By default, the "__hash__()" values of str, bytes and
datetime objects are “salted” with an unpredictable random value.
Although they remain constant within an individual Python
process, they are not predictable between repeated invocations of
Python.This is intended to provide protection against a denial-
of-service caused by carefully-chosen inputs that exploit the
worst case performance of a dict insertion, O(n^2) complexity.
See http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of
dicts, sets and other mappings. Python has never made guarantees
about this ordering (and it typically varies between 32-bit and
64-bit builds).See also "PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
uiF "pdb" — The Python Debugger
***************************
**Source code:** Lib/pdb.py
======================================================================
The module "pdb" defines an interactive source code debugger for
Python programs. It supports setting (conditional) breakpoints and
single stepping at the source line level, inspection of stack frames,
source code listing, and evaluation of arbitrary Python code in the
context of any stack frame. It also supports post-mortem debugging
and can be called under program control.
The debugger is extensible – it is actually defined as the class
"Pdb". This is currently undocumented but easily understood by reading
the source. The extension interface uses the modules "bdb" and "cmd".
The debugger’s prompt is "(Pdb)". Typical usage to run a program under
control of the debugger is:
>>> import pdb
>>> import mymodule
>>> pdb.run('mymodule.test()')
> <string>(0)?()
(Pdb) continue
> <string>(1)?()
(Pdb) continue
NameError: 'spam'
> <string>(1)?()
(Pdb)
Changed in version 3.3: Tab-completion via the "readline" module is
available for commands and command arguments, e.g. the current global
and local names are offered as arguments of the "p" command.
"pdb.py" can also be invoked as a script to debug other scripts. For
example:
python3 -m pdb myscript.py
When invoked as a script, pdb will automatically enter post-mortem
debugging if the program being debugged exits abnormally. After post-
mortem debugging (or after normal exit of the program), pdb will
restart the program. Automatic restarting preserves pdb’s state (such
as breakpoints) and in most cases is more useful than quitting the
debugger upon program’s exit.
New in version 3.2: "pdb.py" now accepts a "-c" option that executes
commands as if given in a ".pdbrc" file, see Debugger Commands.
The typical usage to break into the debugger from a running program is
to insert
import pdb; pdb.set_trace()
at the location you want to break into the debugger. You can then
step through the code following this statement, and continue running
without the debugger using the "continue" command.
The typical usage to inspect a crashed program is:
>>> import pdb
>>> import mymodule
>>> mymodule.test()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "./mymodule.py", line 4, in test
test2()
File "./mymodule.py", line 3, in test2
print(spam)
NameError: spam
>>> pdb.pm()
> ./mymodule.py(3)test2()
-> print(spam)
(Pdb)
The module defines the following functions; each enters the debugger
in a slightly different way:
pdb.run(statement, globals=None, locals=None)
Execute the *statement* (given as a string or a code object) under
debugger control. The debugger prompt appears before any code is
executed; you can set breakpoints and type "continue", or you can
step through the statement using "step" or "next" (all these
commands are explained below). The optional *globals* and *locals*
arguments specify the environment in which the code is executed; by
default the dictionary of the module "__main__" is used. (See the
explanation of the built-in "exec()" or "eval()" functions.)
pdb.runeval(expression, globals=None, locals=None)
Evaluate the *expression* (given as a string or a code object)
under debugger control. When "runeval()" returns, it returns the
value of the expression. Otherwise this function is similar to
"run()".
pdb.runcall(function, *args, **kwds)
Call the *function* (a function or method object, not a string)
with the given arguments. When "runcall()" returns, it returns
whatever the function call returned. The debugger prompt appears
as soon as the function is entered.
pdb.set_trace()
Enter the debugger at the calling stack frame. This is useful to
hard-code a breakpoint at a given point in a program, even if the
code is not otherwise being debugged (e.g. when an assertion
fails).
pdb.post_mortem(traceback=None)
Enter post-mortem debugging of the given *traceback* object. If no
*traceback* is given, it uses the one of the exception that is
currently being handled (an exception must be being handled if the
default is to be used).
pdb.pm()
Enter post-mortem debugging of the traceback found in
"sys.last_traceback".
The "run*" functions and "set_trace()" are aliases for instantiating
the "Pdb" class and calling the method of the same name. If you want
to access further features, you have to do this yourself:
class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None, nosigint=False, readrc=True)
"Pdb" is the debugger class.
The *completekey*, *stdin* and *stdout* arguments are passed to the
underlying "cmd.Cmd" class; see the description there.
The *skip* argument, if given, must be an iterable of glob-style
module name patterns. The debugger will not step into frames that
originate in a module that matches one of these patterns. [1]
By default, Pdb sets a handler for the SIGINT signal (which is sent
when the user presses "Ctrl-C" on the console) when you give a
"continue" command. This allows you to break into the debugger
again by pressing "Ctrl-C". If you want Pdb not to touch the
SIGINT handler, set *nosigint* to true.
The *readrc* argument defaults to true and controls whether Pdb
will load .pdbrc files from the filesystem.
Example call to enable tracing with *skip*:
import pdb; pdb.Pdb(skip=['django.*']).set_trace()
New in version 3.1: The *skip* argument.
New in version 3.2: The *nosigint* argument. Previously, a SIGINT
handler was never set by Pdb.
Changed in version 3.6: The *readrc* argument.
run(statement, globals=None, locals=None)
runeval(expression, globals=None, locals=None)
runcall(function, *args, **kwds)
set_trace()
See the documentation for the functions explained above.
Debugger Commands
=================
The commands recognized by the debugger are listed below. Most
commands can be abbreviated to one or two letters as indicated; e.g.
"h(elp)" means that either "h" or "help" can be used to enter the help
command (but not "he" or "hel", nor "H" or "Help" or "HELP").
Arguments to commands must be separated by whitespace (spaces or
tabs). Optional arguments are enclosed in square brackets ("[]") in
the command syntax; the square brackets must not be typed.
Alternatives in the command syntax are separated by a vertical bar
("|").
Entering a blank line repeats the last command entered. Exception: if
the last command was a "list" command, the next 11 lines are listed.
Commands that the debugger doesn’t recognize are assumed to be Python
statements and are executed in the context of the program being
debugged. Python statements can also be prefixed with an exclamation
point ("!"). This is a powerful way to inspect the program being
debugged; it is even possible to change a variable or call a function.
When an exception occurs in such a statement, the exception name is
printed but the debugger’s state is not changed.
The debugger supports aliases. Aliases can have parameters which
allows one a certain level of adaptability to the context under
examination.
Multiple commands may be entered on a single line, separated by ";;".
(A single ";" is not used as it is the separator for multiple commands
in a line that is passed to the Python parser.) No intelligence is
applied to separating the commands; the input is split at the first
";;" pair, even if it is in the middle of a quoted string.
If a file ".pdbrc" exists in the user’s home directory or in the
current directory, it is read in and executed as if it had been typed
at the debugger prompt. This is particularly useful for aliases. If
both files exist, the one in the home directory is read first and
aliases defined there can be overridden by the local file.
Changed in version 3.2: ".pdbrc" can now contain commands that
continue debugging, such as "continue" or "next". Previously, these
commands had no effect.
h(elp) [command]
Without argument, print the list of available commands. With a
*command* as argument, print help about that command. "help pdb"
displays the full documentation (the docstring of the "pdb"
module). Since the *command* argument must be an identifier, "help
exec" must be entered to get help on the "!" command.
w(here)
Print a stack trace, with the most recent frame at the bottom. An
arrow indicates the current frame, which determines the context of
most commands.
d(own) [count]
Move the current frame *count* (default one) levels down in the
stack trace (to a newer frame).
u(p) [count]
Move the current frame *count* (default one) levels up in the stack
trace (to an older frame).
b(reak) [([filename:]lineno | function) [, condition]]
With a *lineno* argument, set a break there in the current file.
With a *function* argument, set a break at the first executable
statement within that function. The line number may be prefixed
with a filename and a colon, to specify a breakpoint in another
file (probably one that hasn’t been loaded yet). The file is
searched on "sys.path". Note that each breakpoint is assigned a
number to which all the other breakpoint commands refer.
If a second argument is present, it is an expression which must
evaluate to true before the breakpoint is honored.
Without argument, list all breaks, including for each breakpoint,
the number of times that breakpoint has been hit, the current
ignore count, and the associated condition if any.
tbreak [([filename:]lineno | function) [, condition]]
Temporary breakpoint, which is removed automatically when it is
first hit. The arguments are the same as for "break".
cl(ear) [filename:lineno | bpnumber [bpnumber ...]]
With a *filename:lineno* argument, clear all the breakpoints at
this line. With a space separated list of breakpoint numbers, clear
those breakpoints. Without argument, clear all breaks (but first
ask confirmation).
disable [bpnumber [bpnumber ...]]
Disable the breakpoints given as a space separated list of
breakpoint numbers. Disabling a breakpoint means it cannot cause
the program to stop execution, but unlike clearing a breakpoint, it
remains in the list of breakpoints and can be (re-)enabled.
enable [bpnumber [bpnumber ...]]
Enable the breakpoints specified.
ignore bpnumber [count]
Set the ignore count for the given breakpoint number. If count is
omitted, the ignore count is set to 0. A breakpoint becomes active
when the ignore count is zero. When non-zero, the count is
decremented each time the breakpoint is reached and the breakpoint
is not disabled and any associated condition evaluates to true.
condition bpnumber [condition]
Set a new *condition* for the breakpoint, an expression which must
evaluate to true before the breakpoint is honored. If *condition*
is absent, any existing condition is removed; i.e., the breakpoint
is made unconditional.
commands [bpnumber]
Specify a list of commands for breakpoint number *bpnumber*. The
commands themselves appear on the following lines. Type a line
containing just "end" to terminate the commands. An example:
(Pdb) commands 1
(com) p some_variable
(com) end
(Pdb)
To remove all commands from a breakpoint, type commands and follow
it immediately with "end"; that is, give no commands.
With no *bpnumber* argument, commands refers to the last breakpoint
set.
You can use breakpoint commands to start your program up again.
Simply use the continue command, or step, or any other command that
resumes execution.
Specifying any command resuming execution (currently continue,
step, next, return, jump, quit and their abbreviations) terminates
the command list (as if that command was immediately followed by
end). This is because any time you resume execution (even with a
simple next or step), you may encounter another breakpoint—which
could have its own command list, leading to ambiguities about which
list to execute.
If you use the ‘silent’ command in the command list, the usual
message about stopping at a breakpoint is not printed. This may be
desirable for breakpoints that are to print a specific message and
then continue. If none of the other commands print anything, you
see no sign that the breakpoint was reached.
s(tep)
Execute the current line, stop at the first possible occasion
(either in a function that is called or on the next line in the
current function).
n(ext)
Continue execution until the next line in the current function is
reached or it returns. (The difference between "next" and "step"
is that "step" stops inside a called function, while "next"
executes called functions at (nearly) full speed, only stopping at
the next line in the current function.)
unt(il) [lineno]
Without argument, continue execution until the line with a number
greater than the current one is reached.
With a line number, continue execution until a line with a number
greater or equal to that is reached. In both cases, also stop when
the current frame returns.
Changed in version 3.2: Allow giving an explicit line number.
r(eturn)
Continue execution until the current function returns.
c(ont(inue))
Continue execution, only stop when a breakpoint is encountered.
j(ump) lineno
Set the next line that will be executed. Only available in the
bottom-most frame. This lets you jump back and execute code again,
or jump forward to skip code that you don’t want to run.
It should be noted that not all jumps are allowed – for instance it
is not possible to jump into the middle of a "for" loop or out of a
"finally" clause.
l(ist) [first[, last]]
List source code for the current file. Without arguments, list 11
lines around the current line or continue the previous listing.
With "." as argument, list 11 lines around the current line. With
one argument, list 11 lines around at that line. With two
arguments, list the given range; if the second argument is less
than the first, it is interpreted as a count.
The current line in the current frame is indicated by "->". If an
exception is being debugged, the line where the exception was
originally raised or propagated is indicated by ">>", if it differs
from the current line.
New in version 3.2: The ">>" marker.
ll | longlist
List all source code for the current function or frame.
Interesting lines are marked as for "list".
New in version 3.2.
a(rgs)
Print the argument list of the current function.
p expression
Evaluate the *expression* in the current context and print its
value.
Note: "print()" can also be used, but is not a debugger command —
this executes the Python "print()" function.
pp expression
Like the "p" command, except the value of the expression is pretty-
printed using the "pprint" module.
whatis expression
Print the type of the *expression*.
source expression
Try to get source code for the given object and display it.
New in version 3.2.
display [expression]
Display the value of the expression if it changed, each time
execution stops in the current frame.
Without expression, list all display expressions for the current
frame.
New in version 3.2.
undisplay [expression]
Do not display the expression any more in the current frame.
Without expression, clear all display expressions for the current
frame.
New in version 3.2.
interact
Start an interactive interpreter (using the "code" module) whose
global namespace contains all the (global and local) names found in
the current scope.
New in version 3.2.
alias [name [command]]
Create an alias called *name* that executes *command*. The command
must *not* be enclosed in quotes. Replaceable parameters can be
indicated by "%1", "%2", and so on, while "%*" is replaced by all
the parameters. If no command is given, the current alias for
*name* is shown. If no arguments are given, all aliases are listed.
Aliases may be nested and can contain anything that can be legally
typed at the pdb prompt. Note that internal pdb commands *can* be
overridden by aliases. Such a command is then hidden until the
alias is removed. Aliasing is recursively applied to the first
word of the command line; all other words in the line are left
alone.
As an example, here are two useful aliases (especially when placed
in the ".pdbrc" file):
# Print instance variables (usage "pi classInst")
alias pi for k in %1.__dict__.keys(): print("%1.",k,"=",%1.__dict__[k])
# Print instance variables in self
alias ps pi self
unalias name
Delete the specified alias.
! statement
Execute the (one-line) *statement* in the context of the current
stack frame. The exclamation point can be omitted unless the first
word of the statement resembles a debugger command. To set a
global variable, you can prefix the assignment command with a
"global" statement on the same line, e.g.:
(Pdb) global list_options; list_options = ['-l']
(Pdb)
run [args ...]
restart [args ...]
Restart the debugged Python program. If an argument is supplied,
it is split with "shlex" and the result is used as the new
"sys.argv". History, breakpoints, actions and debugger options are
preserved. "restart" is an alias for "run".
q(uit)
Quit from the debugger. The program being executed is aborted.
-[ Footnotes ]-
[1] Whether a frame is considered to originate in a certain module
is determined by the "__name__" in the frame globals.
a� The "del" statement
*******************
del_stmt ::= "del" target_list
Deletion is recursively defined very similar to the way assignment is
defined. Rather than spelling it out in full details, here are some
hints.
Deletion of a target list recursively deletes each target, from left
to right.
Deletion of a name removes the binding of that name from the local or
global namespace, depending on whether the name occurs in a "global"
statement in the same code block. If the name is unbound, a
"NameError" exception will be raised.
Deletion of attribute references, subscriptions and slicings is passed
to the primary object involved; deletion of a slicing is in general
equivalent to assignment of an empty slice of the right type (but even
this is determined by the sliced object).
Changed in version 3.2: Previously it was illegal to delete a name
from the local namespace if it occurs as a free variable in a nested
block.
u Dictionary displays
*******************
A dictionary display is a possibly empty series of key/datum pairs
enclosed in curly braces:
dict_display ::= "{" [key_datum_list | dict_comprehension] "}"
key_datum_list ::= key_datum ("," key_datum)* [","]
key_datum ::= expression ":" expression | "**" or_expr
dict_comprehension ::= expression ":" expression comp_for
A dictionary display yields a new dictionary object.
If a comma-separated sequence of key/datum pairs is given, they are
evaluated from left to right to define the entries of the dictionary:
each key object is used as a key into the dictionary to store the
corresponding datum. This means that you can specify the same key
multiple times in the key/datum list, and the final dictionary’s value
for that key will be the last one given.
A double asterisk "**" denotes *dictionary unpacking*. Its operand
must be a *mapping*. Each mapping item is added to the new
dictionary. Later values replace values already set by earlier
key/datum pairs and earlier dictionary unpackings.
New in version 3.5: Unpacking into dictionary displays, originally
proposed by **PEP 448**.
A dict comprehension, in contrast to list and set comprehensions,
needs two expressions separated with a colon followed by the usual
“for” and “if” clauses. When the comprehension is run, the resulting
key and value elements are inserted in the new dictionary in the order
they are produced.
Restrictions on the types of the key values are listed earlier in
section The standard type hierarchy. (To summarize, the key type
should be *hashable*, which excludes all mutable objects.) Clashes
between duplicate keys are not detected; the last datum (textually
rightmost in the display) stored for a given key value prevails.
a� Interaction with dynamic features
*********************************
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
aB The "if" statement
******************
The "if" statement is used for conditional execution:
if_stmt ::= "if" expression ":" suite
("elif" expression ":" suite)*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
u� Exceptions
**********
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note: Exception messages are not part of the Python API. Their
contents may change from one version of Python to the next without
warning and should not be relied on by code which will run under
multiple versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by
these operations is not available at the time the module is
compiled.
u$ Execution model
***************
Structure of a program
======================
A Python program is constructed from code blocks. A *block* is a piece
of Python program text that is executed as a unit. The following are
blocks: a module, a function body, and a class definition. Each
command typed interactively is a block. A script file (a file given
as standard input to the interpreter or specified as a command line
argument to the interpreter) is a code block. A script command (a
command specified on the interpreter command line with the "-c"
option) is a code block. The string argument passed to the built-in
functions "eval()" and "exec()" is a code block.
A code block is executed in an *execution frame*. A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block’s execution has
completed.
Naming and binding
==================
Binding of names
----------------
*Names* refer to objects. Names are introduced by name binding
operations.
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore. This form
may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global". If a name is bound at the
module level, it is a global variable. (The variables of the module
code block are local and global.) If a variable is used in a code
block but not defined there, it is a *free variable*.
Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.
Resolution of names
-------------------
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block’s *environment*.
When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins". The global namespace is searched first. If
the name is not found there, the builtins namespace is searched. The
"global" statement must precede all uses of the name.
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope. This means that the following
will fail:
class A:
a = 42
b = list(a + i for i in range(10))
Builtins and restricted execution
---------------------------------
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.
Interaction with dynamic features
---------------------------------
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
Exceptions
==========
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note: Exception messages are not part of the Python API. Their
contents may change from one version of Python to the next without
warning and should not be relied on by code which will run under
multiple versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by
these operations is not available at the time the module is
compiled.
uo Expression lists
****************
expression_list ::= expression ("," expression)* [","]
starred_list ::= starred_item ("," starred_item)* [","]
starred_expression ::= expression | (starred_item ",")* [starred_item]
starred_item ::= expression | "*" or_expr
Except when part of a list or set display, an expression list
containing at least one comma yields a tuple. The length of the tuple
is the number of expressions in the list. The expressions are
evaluated from left to right.
An asterisk "*" denotes *iterable unpacking*. Its operand must be an
*iterable*. The iterable is expanded into a sequence of items, which
are included in the new tuple, list, or set, at the site of the
unpacking.
New in version 3.5: Iterable unpacking in expression lists, originally
proposed by **PEP 448**.
The trailing comma is required only to create a single tuple (a.k.a. a
*singleton*); it is optional in all other cases. A single expression
without a trailing comma doesn’t create a tuple, but rather yields the
value of that expression. (To create an empty tuple, use an empty pair
of parentheses: "()".)
a� Floating point literals
***********************
Floating point literals are described by the following lexical
definitions:
floatnumber ::= pointfloat | exponentfloat
pointfloat ::= [digitpart] fraction | digitpart "."
exponentfloat ::= (digitpart | pointfloat) exponent
digitpart ::= digit (["_"] digit)*
fraction ::= "." digitpart
exponent ::= ("e" | "E") ["+" | "-"] digitpart
Note that the integer and exponent parts are always interpreted using
radix 10. For example, "077e010" is legal, and denotes the same number
as "77e10". The allowed range of floating point literals is
implementation-dependent. As in integer literals, underscores are
supported for digit grouping.
Some examples of floating point literals:
3.14 10. .001 1e100 3.14e-10 0e0 3.14_15_93
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
u�
The "for" statement
*******************
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator. Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.
The for-loop makes assignments to the variables(s) in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop. Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".
Note: There is a subtlety when the sequence is being modified by the
loop (this can only occur for mutable sequences, e.g. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
u Y Format String Syntax
********************
The "str.format()" method and the "Formatter" class share the same
syntax for format strings (although in the case of "Formatter",
subclasses can define their own format string syntax). The syntax is
related to that of formatted string literals, but there are
differences.
Format strings contain “replacement fields” surrounded by curly braces
"{}". Anything that is not contained in braces is considered literal
text, which is copied unchanged to the output. If you need to include
a brace character in the literal text, it can be escaped by doubling:
"{{" and "}}".
The grammar for a replacement field is as follows:
replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}"
field_name ::= arg_name ("." attribute_name | "[" element_index "]")*
arg_name ::= [identifier | digit+]
attribute_name ::= identifier
element_index ::= digit+ | index_string
index_string ::= <any source character except "]"> +
conversion ::= "r" | "s" | "a"
format_spec ::= <described in the next section>
In less formal terms, the replacement field can start with a
*field_name* that specifies the object whose value is to be formatted
and inserted into the output instead of the replacement field. The
*field_name* is optionally followed by a *conversion* field, which is
preceded by an exclamation point "'!'", and a *format_spec*, which is
preceded by a colon "':'". These specify a non-default format for the
replacement value.
See also the Format Specification Mini-Language section.
The *field_name* itself begins with an *arg_name* that is either a
number or a keyword. If it’s a number, it refers to a positional
argument, and if it’s a keyword, it refers to a named keyword
argument. If the numerical arg_names in a format string are 0, 1, 2,
… in sequence, they can all be omitted (not just some) and the numbers
0, 1, 2, … will be automatically inserted in that order. Because
*arg_name* is not quote-delimited, it is not possible to specify
arbitrary dictionary keys (e.g., the strings "'10'" or "':-]'") within
a format string. The *arg_name* can be followed by any number of index
or attribute expressions. An expression of the form "'.name'" selects
the named attribute using "getattr()", while an expression of the form
"'[index]'" does an index lookup using "__getitem__()".
Changed in version 3.1: The positional argument specifiers can be
omitted for "str.format()", so "'{} {}'.format(a, b)" is equivalent to
"'{0} {1}'.format(a, b)".
Changed in version 3.4: The positional argument specifiers can be
omitted for "Formatter".
Some simple format string examples:
"First, thou shalt count to {0}" # References first positional argument
"Bring me a {}" # Implicitly references the first positional argument
"From {} to {}" # Same as "From {0} to {1}"
"My quest is {name}" # References keyword argument 'name'
"Weight in tons {0.weight}" # 'weight' attribute of first positional arg
"Units destroyed: {players[0]}" # First element of keyword argument 'players'.
The *conversion* field causes a type coercion before formatting.
Normally, the job of formatting a value is done by the "__format__()"
method of the value itself. However, in some cases it is desirable to
force a type to be formatted as a string, overriding its own
definition of formatting. By converting the value to a string before
calling "__format__()", the normal formatting logic is bypassed.
Three conversion flags are currently supported: "'!s'" which calls
"str()" on the value, "'!r'" which calls "repr()" and "'!a'" which
calls "ascii()".
Some examples:
"Harold's a clever {0!s}" # Calls str() on the argument first
"Bring out the holy {name!r}" # Calls repr() on the argument first
"More {!a}" # Calls ascii() on the argument first
The *format_spec* field contains a specification of how the value
should be presented, including such details as field width, alignment,
padding, decimal precision and so on. Each value type can define its
own “formatting mini-language” or interpretation of the *format_spec*.
Most built-in types support a common formatting mini-language, which
is described in the next section.
A *format_spec* field can also include nested replacement fields
within it. These nested replacement fields may contain a field name,
conversion flag and format specification, but deeper nesting is not
allowed. The replacement fields within the format_spec are
substituted before the *format_spec* string is interpreted. This
allows the formatting of a value to be dynamically specified.
See the Format examples section for some examples.
Format Specification Mini-Language
==================================
“Format specifications” are used within replacement fields contained
within a format string to define how individual values are presented
(see Format String Syntax and Formatted string literals). They can
also be passed directly to the built-in "format()" function. Each
formattable type may define how the format specification is to be
interpreted.
Most built-in types implement the following options for format
specifications, although some of the formatting options are only
supported by the numeric types.
A general convention is that an empty format string ("""") produces
the same result as if you had called "str()" on the value. A non-empty
format string typically modifies the result.
The general form of a *standard format specifier* is:
format_spec ::= [[fill]align][sign][#][0][width][grouping_option][.precision][type]
fill ::= <any character>
align ::= "<" | ">" | "=" | "^"
sign ::= "+" | "-" | " "
width ::= digit+
grouping_option ::= "_" | ","
precision ::= digit+
type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"
If a valid *align* value is specified, it can be preceded by a *fill*
character that can be any character and defaults to a space if
omitted. It is not possible to use a literal curly brace (“"{"” or
“"}"”) as the *fill* character in a formatted string literal or when
using the "str.format()" method. However, it is possible to insert a
curly brace with a nested replacement field. This limitation doesn’t
affect the "format()" function.
The meaning of the various alignment options is as follows:
+-----------+------------------------------------------------------------+
| Option | Meaning |
+===========+============================================================+
| "'<'" | Forces the field to be left-aligned within the available |
| | space (this is the default for most objects). |
+-----------+------------------------------------------------------------+
| "'>'" | Forces the field to be right-aligned within the available |
| | space (this is the default for numbers). |
+-----------+------------------------------------------------------------+
| "'='" | Forces the padding to be placed after the sign (if any) |
| | but before the digits. This is used for printing fields |
| | in the form ‘+000000120’. This alignment option is only |
| | valid for numeric types. It becomes the default when ‘0’ |
| | immediately precedes the field width. |
+-----------+------------------------------------------------------------+
| "'^'" | Forces the field to be centered within the available |
| | space. |
+-----------+------------------------------------------------------------+
Note that unless a minimum field width is defined, the field width
will always be the same size as the data to fill it, so that the
alignment option has no meaning in this case.
The *sign* option is only valid for number types, and can be one of
the following:
+-----------+------------------------------------------------------------+
| Option | Meaning |
+===========+============================================================+
| "'+'" | indicates that a sign should be used for both positive as |
| | well as negative numbers. |
+-----------+------------------------------------------------------------+
| "'-'" | indicates that a sign should be used only for negative |
| | numbers (this is the default behavior). |
+-----------+------------------------------------------------------------+
| space | indicates that a leading space should be used on positive |
| | numbers, and a minus sign on negative numbers. |
+-----------+------------------------------------------------------------+
The "'#'" option causes the “alternate form” to be used for the
conversion. The alternate form is defined differently for different
types. This option is only valid for integer, float, complex and
Decimal types. For integers, when binary, octal, or hexadecimal output
is used, this option adds the prefix respective "'0b'", "'0o'", or
"'0x'" to the output value. For floats, complex and Decimal the
alternate form causes the result of the conversion to always contain a
decimal-point character, even if no digits follow it. Normally, a
decimal-point character appears in the result of these conversions
only if a digit follows it. In addition, for "'g'" and "'G'"
conversions, trailing zeros are not removed from the result.
The "','" option signals the use of a comma for a thousands separator.
For a locale aware separator, use the "'n'" integer presentation type
instead.
Changed in version 3.1: Added the "','" option (see also **PEP 378**).
The "'_'" option signals the use of an underscore for a thousands
separator for floating point presentation types and for integer
presentation type "'d'". For integer presentation types "'b'", "'o'",
"'x'", and "'X'", underscores will be inserted every 4 digits. For
other presentation types, specifying this option is an error.
Changed in version 3.6: Added the "'_'" option (see also **PEP 515**).
*width* is a decimal integer defining the minimum field width. If not
specified, then the field width will be determined by the content.
When no explicit alignment is given, preceding the *width* field by a
zero ("'0'") character enables sign-aware zero-padding for numeric
types. This is equivalent to a *fill* character of "'0'" with an
*alignment* type of "'='".
The *precision* is a decimal number indicating how many digits should
be displayed after the decimal point for a floating point value
formatted with "'f'" and "'F'", or before and after the decimal point
for a floating point value formatted with "'g'" or "'G'". For non-
number types the field indicates the maximum field size - in other
words, how many characters will be used from the field content. The
*precision* is not allowed for integer values.
Finally, the *type* determines how the data should be presented.
The available string presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'s'" | String format. This is the default type for strings and |
| | may be omitted. |
+-----------+------------------------------------------------------------+
| None | The same as "'s'". |
+-----------+------------------------------------------------------------+
The available integer presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'b'" | Binary format. Outputs the number in base 2. |
+-----------+------------------------------------------------------------+
| "'c'" | Character. Converts the integer to the corresponding |
| | unicode character before printing. |
+-----------+------------------------------------------------------------+
| "'d'" | Decimal Integer. Outputs the number in base 10. |
+-----------+------------------------------------------------------------+
| "'o'" | Octal format. Outputs the number in base 8. |
+-----------+------------------------------------------------------------+
| "'x'" | Hex format. Outputs the number in base 16, using lower- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'X'" | Hex format. Outputs the number in base 16, using upper- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'d'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| None | The same as "'d'". |
+-----------+------------------------------------------------------------+
In addition to the above presentation types, integers can be formatted
with the floating point presentation types listed below (except "'n'"
and "None"). When doing so, "float()" is used to convert the integer
to a floating point number before formatting.
The available presentation types for floating point and decimal values
are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'e'" | Exponent notation. Prints the number in scientific |
| | notation using the letter ‘e’ to indicate the exponent. |
| | The default precision is "6". |
+-----------+------------------------------------------------------------+
| "'E'" | Exponent notation. Same as "'e'" except it uses an upper |
| | case ‘E’ as the separator character. |
+-----------+------------------------------------------------------------+
| "'f'" | Fixed-point notation. Displays the number as a fixed-point |
| | number. The default precision is "6". |
+-----------+------------------------------------------------------------+
| "'F'" | Fixed-point notation. Same as "'f'", but converts "nan" to |
| | "NAN" and "inf" to "INF". |
+-----------+------------------------------------------------------------+
| "'g'" | General format. For a given precision "p >= 1", this |
| | rounds the number to "p" significant digits and then |
| | formats the result in either fixed-point format or in |
| | scientific notation, depending on its magnitude. The |
| | precise rules are as follows: suppose that the result |
| | formatted with presentation type "'e'" and precision "p-1" |
| | would have exponent "exp". Then if "-4 <= exp < p", the |
| | number is formatted with presentation type "'f'" and |
| | precision "p-1-exp". Otherwise, the number is formatted |
| | with presentation type "'e'" and precision "p-1". In both |
| | cases insignificant trailing zeros are removed from the |
| | significand, and the decimal point is also removed if |
| | there are no remaining digits following it. Positive and |
| | negative infinity, positive and negative zero, and nans, |
| | are formatted as "inf", "-inf", "0", "-0" and "nan" |
| | respectively, regardless of the precision. A precision of |
| | "0" is treated as equivalent to a precision of "1". The |
| | default precision is "6". |
+-----------+------------------------------------------------------------+
| "'G'" | General format. Same as "'g'" except switches to "'E'" if |
| | the number gets too large. The representations of infinity |
| | and NaN are uppercased, too. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'g'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| "'%'" | Percentage. Multiplies the number by 100 and displays in |
| | fixed ("'f'") format, followed by a percent sign. |
+-----------+------------------------------------------------------------+
| None | Similar to "'g'", except that fixed-point notation, when |
| | used, has at least one digit past the decimal point. The |
| | default precision is as high as needed to represent the |
| | particular value. The overall effect is to match the |
| | output of "str()" as altered by the other format |
| | modifiers. |
+-----------+------------------------------------------------------------+
Format examples
===============
This section contains examples of the "str.format()" syntax and
comparison with the old "%"-formatting.
In most of the cases the syntax is similar to the old "%"-formatting,
with the addition of the "{}" and with ":" used instead of "%". For
example, "'%03.2f'" can be translated to "'{:03.2f}'".
The new format syntax also supports new and different options, shown
in the following examples.
Accessing arguments by position:
>>> '{0}, {1}, {2}'.format('a', 'b', 'c')
'a, b, c'
>>> '{}, {}, {}'.format('a', 'b', 'c') # 3.1+ only
'a, b, c'
>>> '{2}, {1}, {0}'.format('a', 'b', 'c')
'c, b, a'
>>> '{2}, {1}, {0}'.format(*'abc') # unpacking argument sequence
'c, b, a'
>>> '{0}{1}{0}'.format('abra', 'cad') # arguments' indices can be repeated
'abracadabra'
Accessing arguments by name:
>>> 'Coordinates: {latitude}, {longitude}'.format(latitude='37.24N', longitude='-115.81W')
'Coordinates: 37.24N, -115.81W'
>>> coord = {'latitude': '37.24N', 'longitude': '-115.81W'}
>>> 'Coordinates: {latitude}, {longitude}'.format(**coord)
'Coordinates: 37.24N, -115.81W'
Accessing arguments’ attributes:
>>> c = 3-5j
>>> ('The complex number {0} is formed from the real part {0.real} '
... 'and the imaginary part {0.imag}.').format(c)
'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.'
>>> class Point:
... def __init__(self, x, y):
... self.x, self.y = x, y
... def __str__(self):
... return 'Point({self.x}, {self.y})'.format(self=self)
...
>>> str(Point(4, 2))
'Point(4, 2)'
Accessing arguments’ items:
>>> coord = (3, 5)
>>> 'X: {0[0]}; Y: {0[1]}'.format(coord)
'X: 3; Y: 5'
Replacing "%s" and "%r":
>>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2')
"repr() shows quotes: 'test1'; str() doesn't: test2"
Aligning the text and specifying a width:
>>> '{:<30}'.format('left aligned')
'left aligned '
>>> '{:>30}'.format('right aligned')
' right aligned'
>>> '{:^30}'.format('centered')
' centered '
>>> '{:*^30}'.format('centered') # use '*' as a fill char
'***********centered***********'
Replacing "%+f", "%-f", and "% f" and specifying a sign:
>>> '{:+f}; {:+f}'.format(3.14, -3.14) # show it always
'+3.140000; -3.140000'
>>> '{: f}; {: f}'.format(3.14, -3.14) # show a space for positive numbers
' 3.140000; -3.140000'
>>> '{:-f}; {:-f}'.format(3.14, -3.14) # show only the minus -- same as '{:f}; {:f}'
'3.140000; -3.140000'
Replacing "%x" and "%o" and converting the value to different bases:
>>> # format also supports binary numbers
>>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: {0:b}".format(42)
'int: 42; hex: 2a; oct: 52; bin: 101010'
>>> # with 0x, 0o, or 0b as prefix:
>>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: {0:#b}".format(42)
'int: 42; hex: 0x2a; oct: 0o52; bin: 0b101010'
Using the comma as a thousands separator:
>>> '{:,}'.format(1234567890)
'1,234,567,890'
Expressing a percentage:
>>> points = 19
>>> total = 22
>>> 'Correct answers: {:.2%}'.format(points/total)
'Correct answers: 86.36%'
Using type-specific formatting:
>>> import datetime
>>> d = datetime.datetime(2010, 7, 4, 12, 15, 58)
>>> '{:%Y-%m-%d %H:%M:%S}'.format(d)
'2010-07-04 12:15:58'
Nesting arguments and more complex examples:
>>> for align, text in zip('<^>', ['left', 'center', 'right']):
... '{0:{fill}{align}16}'.format(text, fill=align, align=align)
...
'left<<<<<<<<<<<<'
'^^^^^center^^^^^'
'>>>>>>>>>>>right'
>>>
>>> octets = [192, 168, 0, 1]
>>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets)
'C0A80001'
>>> int(_, 16)
3232235521
>>>
>>> width = 5
>>> for num in range(5,12):
... for base in 'dXob':
... print('{0:{width}{base}}'.format(num, base=base, width=width), end=' ')
... print()
...
5 5 5 101
6 6 6 110
7 7 7 111
8 8 10 1000
9 9 11 1001
10 A 12 1010
11 B 13 1011
u[ Function definitions
********************
A function definition defines a user-defined function object (see
section The standard type hierarchy):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
| parameter_list_starargs
parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
| "**" parameter [","]
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [2]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
except that the original function is not temporarily bound to the name
"func".
When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted. If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.
**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call. This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended. A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from position arguments, from keyword
arguments, or from default values. If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type. Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed
used keyword arguments.
Parameters may have annotations of the form “": expression"” following
the parameter name. Any parameter may have an annotation even those
of the form "*identifier" or "**identifier". Functions may have
“return” annotation of the form “"-> expression"” after the parameter
list. These annotations can be any valid Python expression and are
evaluated when the function definition is executed. Annotations may
be evaluated in a different order than they appear in the source code.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.
**Programmer’s note:** Functions are first-class objects. A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around. Free variables used
in the nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
See also:
**PEP 3107** - Function Annotations
The original specification for function annotations.
u� The "global" statement
**********************
global_stmt ::= "global" identifier ("," identifier)*
The "global" statement is a declaration which holds for the entire
current code block. It means that the listed identifiers are to be
interpreted as globals. It would be impossible to assign to a global
variable without "global", although free variables may refer to
globals without being declared global.
Names listed in a "global" statement must not be used in the same code
block textually preceding that "global" statement.
Names listed in a "global" statement must not be defined as formal
parameters or in a "for" loop control target, "class" definition,
function definition, "import" statement, or variable annotation.
**CPython implementation detail:** The current implementation does not
enforce some of these restrictions, but programs should not abuse this
freedom, as future implementations may enforce them or silently change
the meaning of the program.
**Programmer’s note:** "global" is a directive to the parser. It
applies only to code parsed at the same time as the "global"
statement. In particular, a "global" statement contained in a string
or code object supplied to the built-in "exec()" function does not
affect the code block *containing* the function call, and code
contained in such a string is unaffected by "global" statements in the
code containing the function call. The same applies to the "eval()"
and "compile()" functions.
u� Reserved classes of identifiers
*******************************
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section The import statement.
Note: The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names. These names are defined by the interpreter
and its implementation (including the standard library). Current
system names are discussed in the Special method names section and
elsewhere. More will likely be defined in future versions of
Python. *Any* use of "__*__" names, in any context, that does not
follow explicitly documented use, is subject to breakage without
warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between “private” attributes of base and
derived classes. See section Identifiers (Names).
u( Identifiers and keywords
************************
Identifiers (also referred to as *names*) are described by the
following lexical definitions.
The syntax of identifiers in Python is based on the Unicode standard
annex UAX-31, with elaboration and changes as defined below; see also
**PEP 3131** for further details.
Within the ASCII range (U+0001..U+007F), the valid characters for
identifiers are the same as in Python 2.x: the uppercase and lowercase
letters "A" through "Z", the underscore "_" and, except for the first
character, the digits "0" through "9".
Python 3.0 introduces additional characters from outside the ASCII
range (see **PEP 3131**). For these characters, the classification
uses the version of the Unicode Character Database as included in the
"unicodedata" module.
Identifiers are unlimited in length. Case is significant.
identifier ::= xid_start xid_continue*
id_start ::= <all characters in general categories Lu, Ll, Lt, Lm, Lo, Nl, the underscore, and characters with the Other_ID_Start property>
id_continue ::= <all characters in id_start, plus characters in the categories Mn, Mc, Nd, Pc and others with the Other_ID_Continue property>
xid_start ::= <all characters in id_start whose NFKC normalization is in "id_start xid_continue*">
xid_continue ::= <all characters in id_continue whose NFKC normalization is in "id_continue*">
The Unicode category codes mentioned above stand for:
* *Lu* - uppercase letters
* *Ll* - lowercase letters
* *Lt* - titlecase letters
* *Lm* - modifier letters
* *Lo* - other letters
* *Nl* - letter numbers
* *Mn* - nonspacing marks
* *Mc* - spacing combining marks
* *Nd* - decimal numbers
* *Pc* - connector punctuations
* *Other_ID_Start* - explicit list of characters in PropList.txt to
support backwards compatibility
* *Other_ID_Continue* - likewise
All identifiers are converted into the normal form NFKC while parsing;
comparison of identifiers is based on NFKC.
A non-normative HTML file listing all valid identifier characters for
Unicode 4.1 can be found at https://www.dcl.hpi.uni-
potsdam.de/home/loewis/table-3131.html.
Keywords
========
The following identifiers are used as reserved words, or *keywords* of
the language, and cannot be used as ordinary identifiers. They must
be spelled exactly as written here:
False class finally is return
None continue for lambda try
True def from nonlocal while
and del global not with
as elif if or yield
assert else import pass
break except in raise
Reserved classes of identifiers
===============================
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section The import statement.
Note: The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names. These names are defined by the interpreter
and its implementation (including the standard library). Current
system names are discussed in the Special method names section and
elsewhere. More will likely be defined in future versions of
Python. *Any* use of "__*__" names, in any context, that does not
follow explicitly documented use, is subject to breakage without
warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between “private” attributes of base and
derived classes. See section Identifiers (Names).
a5 Imaginary literals
******************
Imaginary literals are described by the following lexical definitions:
imagnumber ::= (floatnumber | digitpart) ("j" | "J")
An imaginary literal yields a complex number with a real part of 0.0.
Complex numbers are represented as a pair of floating point numbers
and have the same restrictions on their range. To create a complex
number with a nonzero real part, add a floating point number to it,
e.g., "(3+4j)". Some examples of imaginary literals:
3.14j 10.j 10j .001j 1e100j 3.14e-10j 3.14_15_93j
u� The "import" statement
**********************
import_stmt ::= "import" module ["as" identifier] ("," module ["as" identifier])*
| "from" relative_module "import" identifier ["as" identifier]
("," identifier ["as" identifier])*
| "from" relative_module "import" "(" identifier ["as" identifier]
("," identifier ["as" identifier])* [","] ")"
| "from" module "import" "*"
module ::= (identifier ".")* identifier
relative_module ::= "."* module | "."+
The basic import statement (no "from" clause) is executed in two
steps:
1. find a module, loading and initializing it if necessary
2. define a name or names in the local namespace for the scope
where the "import" statement occurs.
When the statement contains multiple clauses (separated by commas) the
two steps are carried out separately for each clause, just as though
the clauses had been separated out into individual import statements.
The details of the first step, finding and loading modules are
described in greater detail in the section on the import system, which
also describes the various types of packages and modules that can be
imported, as well as all the hooks that can be used to customize the
import system. Note that failures in this step may indicate either
that the module could not be located, *or* that an error occurred
while initializing the module, which includes execution of the
module’s code.
If the requested module is retrieved successfully, it will be made
available in the local namespace in one of three ways:
* If the module name is followed by "as", then the name following
"as" is bound directly to the imported module.
* If no other name is specified, and the module being imported is a
top level module, the module’s name is bound in the local namespace
as a reference to the imported module
* If the module being imported is *not* a top level module, then the
name of the top level package that contains the module is bound in
the local namespace as a reference to the top level package. The
imported module must be accessed using its full qualified name
rather than directly
The "from" form uses a slightly more complex process:
1. find the module specified in the "from" clause, loading and
initializing it if necessary;
2. for each of the identifiers specified in the "import" clauses:
1. check if the imported module has an attribute by that name
2. if not, attempt to import a submodule with that name and then
check the imported module again for that attribute
3. if the attribute is not found, "ImportError" is raised.
4. otherwise, a reference to that value is stored in the local
namespace, using the name in the "as" clause if it is present,
otherwise using the attribute name
Examples:
import foo # foo imported and bound locally
import foo.bar.baz # foo.bar.baz imported, foo bound locally
import foo.bar.baz as fbb # foo.bar.baz imported and bound as fbb
from foo.bar import baz # foo.bar.baz imported and bound as baz
from foo import attr # foo imported and foo.attr bound as attr
If the list of identifiers is replaced by a star ("'*'"), all public
names defined in the module are bound in the local namespace for the
scope where the "import" statement occurs.
The *public names* defined by a module are determined by checking the
module’s namespace for a variable named "__all__"; if defined, it must
be a sequence of strings which are names defined or imported by that
module. The names given in "__all__" are all considered public and
are required to exist. If "__all__" is not defined, the set of public
names includes all names found in the module’s namespace which do not
begin with an underscore character ("'_'"). "__all__" should contain
the entire public API. It is intended to avoid accidentally exporting
items that are not part of the API (such as library modules which were
imported and used within the module).
The wild card form of import — "from module import *" — is only
allowed at the module level. Attempting to use it in class or
function definitions will raise a "SyntaxError".
When specifying what module to import you do not have to specify the
absolute name of the module. When a module or package is contained
within another package it is possible to make a relative import within
the same top package without having to mention the package name. By
using leading dots in the specified module or package after "from" you
can specify how high to traverse up the current package hierarchy
without specifying exact names. One leading dot means the current
package where the module making the import exists. Two dots means up
one package level. Three dots is up two levels, etc. So if you execute
"from . import mod" from a module in the "pkg" package then you will
end up importing "pkg.mod". If you execute "from ..subpkg2 import mod"
from within "pkg.subpkg1" you will import "pkg.subpkg2.mod". The
specification for relative imports is contained within **PEP 328**.
"importlib.import_module()" is provided to support applications that
determine dynamically the modules to be loaded.
Future statements
=================
A *future statement* is a directive to the compiler that a particular
module should be compiled using syntax or semantics that will be
available in a specified future release of Python where the feature
becomes standard.
The future statement is intended to ease migration to future versions
of Python that introduce incompatible changes to the language. It
allows use of the new features on a per-module basis before the
release in which the feature becomes standard.
future_stmt ::= "from" "__future__" "import" feature ["as" identifier]
("," feature ["as" identifier])*
| "from" "__future__" "import" "(" feature ["as" identifier]
("," feature ["as" identifier])* [","] ")"
feature ::= identifier
A future statement must appear near the top of the module. The only
lines that can appear before a future statement are:
* the module docstring (if any),
* comments,
* blank lines, and
* other future statements.
The features recognized by Python 3.0 are "absolute_import",
"division", "generators", "unicode_literals", "print_function",
"nested_scopes" and "with_statement". They are all redundant because
they are always enabled, and only kept for backwards compatibility.
A future statement is recognized and treated specially at compile
time: Changes to the semantics of core constructs are often
implemented by generating different code. It may even be the case
that a new feature introduces new incompatible syntax (such as a new
reserved word), in which case the compiler may need to parse the
module differently. Such decisions cannot be pushed off until
runtime.
For any given release, the compiler knows which feature names have
been defined, and raises a compile-time error if a future statement
contains a feature not known to it.
The direct runtime semantics are the same as for any import statement:
there is a standard module "__future__", described later, and it will
be imported in the usual way at the time the future statement is
executed.
The interesting runtime semantics depend on the specific feature
enabled by the future statement.
Note that there is nothing special about the statement:
import __future__ [as name]
That is not a future statement; it’s an ordinary import statement with
no special semantics or syntax restrictions.
Code compiled by calls to the built-in functions "exec()" and
"compile()" that occur in a module "M" containing a future statement
will, by default, use the new syntax or semantics associated with the
future statement. This can be controlled by optional arguments to
"compile()" — see the documentation of that function for details.
A future statement typed at an interactive interpreter prompt will
take effect for the rest of the interpreter session. If an
interpreter is started with the "-i" option, is passed a script name
to execute, and the script includes a future statement, it will be in
effect in the interactive session started after the script is
executed.
See also:
**PEP 236** - Back to the __future__
The original proposal for the __future__ mechanism.
aO Membership test operations
**************************
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y". If an exception is raised
during the iteration, it is as if "in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse true value of
"in".
aV Integer literals
****************
Integer literals are described by the following lexical definitions:
integer ::= decinteger | bininteger | octinteger | hexinteger
decinteger ::= nonzerodigit (["_"] digit)* | "0"+ (["_"] "0")*
bininteger ::= "0" ("b" | "B") (["_"] bindigit)+
octinteger ::= "0" ("o" | "O") (["_"] octdigit)+
hexinteger ::= "0" ("x" | "X") (["_"] hexdigit)+
nonzerodigit ::= "1"..."9"
digit ::= "0"..."9"
bindigit ::= "0" | "1"
octdigit ::= "0"..."7"
hexdigit ::= digit | "a"..."f" | "A"..."F"
There is no limit for the length of integer literals apart from what
can be stored in available memory.
Underscores are ignored for determining the numeric value of the
literal. They can be used to group digits for enhanced readability.
One underscore can occur between digits, and after base specifiers
like "0x".
Note that leading zeros in a non-zero decimal number are not allowed.
This is for disambiguation with C-style octal literals, which Python
used before version 3.0.
Some examples of integer literals:
7 2147483647 0o177 0b100110111
3 79228162514264337593543950336 0o377 0xdeadbeef
100_000_000_000 0b_1110_0101
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
a^ Lambdas
*******
lambda_expr ::= "lambda" [parameter_list] ":" expression
lambda_expr_nocond ::= "lambda" [parameter_list] ":" expression_nocond
Lambda expressions (sometimes called lambda forms) are used to create
anonymous functions. The expression "lambda parameters: expression"
yields a function object. The unnamed object behaves like a function
object defined with:
def <lambda>(parameters):
return expression
See section Function definitions for the syntax of parameter lists.
Note that functions created with lambda expressions cannot contain
statements or annotations.
a/ List displays
*************
A list display is a possibly empty series of expressions enclosed in
square brackets:
list_display ::= "[" [starred_list | comprehension] "]"
A list display yields a new list object, the contents being specified
by either a list of expressions or a comprehension. When a comma-
separated list of expressions is supplied, its elements are evaluated
from left to right and placed into the list object in that order.
When a comprehension is supplied, the list is constructed from the
elements resulting from the comprehension.
u� Naming and binding
******************
Binding of names
================
*Names* refer to objects. Names are introduced by name binding
operations.
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore. This form
may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global". If a name is bound at the
module level, it is a global variable. (The variables of the module
code block are local and global.) If a variable is used in a code
block but not defined there, it is a *free variable*.
Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.
Resolution of names
===================
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block’s *environment*.
When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins". The global namespace is searched first. If
the name is not found there, the builtins namespace is searched. The
"global" statement must precede all uses of the name.
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope. This means that the following
will fail:
class A:
a = 42
b = list(a + i for i in range(10))
Builtins and restricted execution
=================================
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.
Interaction with dynamic features
=================================
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
a� The "nonlocal" statement
************************
nonlocal_stmt ::= "nonlocal" identifier ("," identifier)*
The "nonlocal" statement causes the listed identifiers to refer to
previously bound variables in the nearest enclosing scope excluding
globals. This is important because the default behavior for binding is
to search the local namespace first. The statement allows
encapsulated code to rebind variables outside of the local scope
besides the global (module) scope.
Names listed in a "nonlocal" statement, unlike those listed in a
"global" statement, must refer to pre-existing bindings in an
enclosing scope (the scope in which a new binding should be created
cannot be determined unambiguously).
Names listed in a "nonlocal" statement must not collide with pre-
existing bindings in the local scope.
See also:
**PEP 3104** - Access to Names in Outer Scopes
The specification for the "nonlocal" statement.
u� Numeric literals
****************
There are three types of numeric literals: integers, floating point
numbers, and imaginary numbers. There are no complex literals
(complex numbers can be formed by adding a real number and an
imaginary number).
Note that numeric literals do not include a sign; a phrase like "-1"
is actually an expression composed of the unary operator ‘"-"‘ and the
literal "1".
u� Emulating numeric types
***********************
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the expression "x + y", where *x* is an instance of a
class that has an "__add__()" method, "x.__add__(y)" is called.
The "__divmod__()" method should be the equivalent to using
"__floordiv__()" and "__mod__()"; it should not be related to
"__truediv__()". Note that "__pow__()" should be defined to accept
an optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other)
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)
operands. These functions are only called if the left operand does
not support the corresponding operation [3] and the operands are of
different types. [4] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note: If the right operand’s type is a subclass of the left
operand’s type and that subclass provides the reflected method
for the operation, this method will be called before the left
operand’s non-reflected method. This behavior allows subclasses
to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (modifying *self*) and return the result
(which could be, but does not have to be, *self*). If a specific
method is not defined, the augmented assignment falls back to the
normal methods. For instance, if *x* is an instance of a class
with an "__iadd__()" method, "x += y" is equivalent to "x =
x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are
considered, as with the evaluation of "x + y". In certain
situations, augmented assignment can result in unexpected errors
(see Why does a_tuple[i] += [‘item’] raise an exception when the
addition works?), but this behavior is in fact part of the data
model.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()" and
"float()". Should return a value of the appropriate type.
object.__index__(self)
Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.
Note: In order to have a coherent integer type class, when
"__index__()" is defined "__int__()" should also be defined, and
both should return the same value.
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").
If "__int__()" is not defined then the built-in function "int()"
falls back to "__trunc__()".
u Objects, values and types
*************************
*Objects* are Python’s abstraction for data. All data in a Python
program is represented by objects or by relations between objects. (In
a sense, and in conformance to Von Neumann’s model of a “stored
program computer,” code is also represented by objects.)
Every object has an identity, a type and a value. An object’s
*identity* never changes once it has been created; you may think of it
as the object’s address in memory. The ‘"is"’ operator compares the
identity of two objects; the "id()" function returns an integer
representing its identity.
**CPython implementation detail:** For CPython, "id(x)" is the memory
address where "x" is stored.
An object’s type determines the operations that the object supports
(e.g., “does it have a length?”) and also defines the possible values
for objects of that type. The "type()" function returns an object’s
type (which is an object itself). Like its identity, an object’s
*type* is also unchangeable. [1]
The *value* of some objects can change. Objects whose value can
change are said to be *mutable*; objects whose value is unchangeable
once they are created are called *immutable*. (The value of an
immutable container object that contains a reference to a mutable
object can change when the latter’s value is changed; however the
container is still considered immutable, because the collection of
objects it contains cannot be changed. So, immutability is not
strictly the same as having an unchangeable value, it is more subtle.)
An object’s mutability is determined by its type; for instance,
numbers, strings and tuples are immutable, while dictionaries and
lists are mutable.
Objects are never explicitly destroyed; however, when they become
unreachable they may be garbage-collected. An implementation is
allowed to postpone garbage collection or omit it altogether — it is a
matter of implementation quality how garbage collection is
implemented, as long as no objects are collected that are still
reachable.
**CPython implementation detail:** CPython currently uses a reference-
counting scheme with (optional) delayed detection of cyclically linked
garbage, which collects most objects as soon as they become
unreachable, but is not guaranteed to collect garbage containing
circular references. See the documentation of the "gc" module for
information on controlling the collection of cyclic garbage. Other
implementations act differently and CPython may change. Do not depend
on immediate finalization of objects when they become unreachable (so
you should always close files explicitly).
Note that the use of the implementation’s tracing or debugging
facilities may keep objects alive that would normally be collectable.
Also note that catching an exception with a ‘"try"…"except"’ statement
may keep objects alive.
Some objects contain references to “external” resources such as open
files or windows. It is understood that these resources are freed
when the object is garbage-collected, but since garbage collection is
not guaranteed to happen, such objects also provide an explicit way to
release the external resource, usually a "close()" method. Programs
are strongly recommended to explicitly close such objects. The
‘"try"…"finally"’ statement and the ‘"with"’ statement provide
convenient ways to do this.
Some objects contain references to other objects; these are called
*containers*. Examples of containers are tuples, lists and
dictionaries. The references are part of a container’s value. In
most cases, when we talk about the value of a container, we imply the
values, not the identities of the contained objects; however, when we
talk about the mutability of a container, only the identities of the
immediately contained objects are implied. So, if an immutable
container (like a tuple) contains a reference to a mutable object, its
value changes if that mutable object is changed.
Types affect almost all aspects of object behavior. Even the
importance of object identity is affected in some sense: for immutable
types, operations that compute new values may actually return a
reference to any existing object with the same type and value, while
for mutable objects this is not allowed. E.g., after "a = 1; b = 1",
"a" and "b" may or may not refer to the same object with the value
one, depending on the implementation, but after "c = []; d = []", "c"
and "d" are guaranteed to refer to two different, unique, newly
created empty lists. (Note that "c = d = []" assigns the same object
to both "c" and "d".)
u� Operator precedence
*******************
The following table summarizes the operator precedence in Python, from
lowest precedence (least binding) to highest precedence (most
binding). Operators in the same box have the same precedence. Unless
the syntax is explicitly given, operators are binary. Operators in
the same box group left to right (except for exponentiation, which
groups from right to left).
Note that comparisons, membership tests, and identity tests, all have
the same precedence and have a left-to-right chaining feature as
described in the Comparisons section.
+-------------------------------------------------+---------------------------------------+
| Operator | Description |
+=================================================+=======================================+
| "lambda" | Lambda expression |
+-------------------------------------------------+---------------------------------------+
| "if" – "else" | Conditional expression |
+-------------------------------------------------+---------------------------------------+
| "or" | Boolean OR |
+-------------------------------------------------+---------------------------------------+
| "and" | Boolean AND |
+-------------------------------------------------+---------------------------------------+
| "not" "x" | Boolean NOT |
+-------------------------------------------------+---------------------------------------+
| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership |
| ">=", "!=", "==" | tests and identity tests |
+-------------------------------------------------+---------------------------------------+
| "|" | Bitwise OR |
+-------------------------------------------------+---------------------------------------+
| "^" | Bitwise XOR |
+-------------------------------------------------+---------------------------------------+
| "&" | Bitwise AND |
+-------------------------------------------------+---------------------------------------+
| "<<", ">>" | Shifts |
+-------------------------------------------------+---------------------------------------+
| "+", "-" | Addition and subtraction |
+-------------------------------------------------+---------------------------------------+
| "*", "@", "/", "//", "%" | Multiplication, matrix |
| | multiplication, division, floor |
| | division, remainder [5] |
+-------------------------------------------------+---------------------------------------+
| "+x", "-x", "~x" | Positive, negative, bitwise NOT |
+-------------------------------------------------+---------------------------------------+
| "**" | Exponentiation [6] |
+-------------------------------------------------+---------------------------------------+
| "await" "x" | Await expression |
+-------------------------------------------------+---------------------------------------+
| "x[index]", "x[index:index]", | Subscription, slicing, call, |
| "x(arguments...)", "x.attribute" | attribute reference |
+-------------------------------------------------+---------------------------------------+
| "(expressions...)", "[expressions...]", "{key: | Binding or tuple display, list |
| value...}", "{expressions...}" | display, dictionary display, set |
| | display |
+-------------------------------------------------+---------------------------------------+
-[ Footnotes ]-
[1] While "abs(x%y) < abs(y)" is true mathematically, for floats
it may not be true numerically due to roundoff. For example, and
assuming a platform on which a Python float is an IEEE 754 double-
precision number, in order that "-1e-100 % 1e100" have the same
sign as "1e100", the computed result is "-1e-100 + 1e100", which
is numerically exactly equal to "1e100". The function
"math.fmod()" returns a result whose sign matches the sign of the
first argument instead, and so returns "-1e-100" in this case.
Which approach is more appropriate depends on the application.
[2] If x is very close to an exact integer multiple of y, it’s
possible for "x//y" to be one larger than "(x-x%y)//y" due to
rounding. In such cases, Python returns the latter result, in
order to preserve that "divmod(x,y)[0] * y + x % y" be very close
to "x".
[3] The Unicode standard distinguishes between *code points* (e.g.
U+0041) and *abstract characters* (e.g. “LATIN CAPITAL LETTER A”).
While most abstract characters in Unicode are only represented
using one code point, there is a number of abstract characters
that can in addition be represented using a sequence of more than
one code point. For example, the abstract character “LATIN
CAPITAL LETTER C WITH CEDILLA” can be represented as a single
*precomposed character* at code position U+00C7, or as a sequence
of a *base character* at code position U+0043 (LATIN CAPITAL
LETTER C), followed by a *combining character* at code position
U+0327 (COMBINING CEDILLA).
The comparison operators on strings compare at the level of
Unicode code points. This may be counter-intuitive to humans. For
example, ""\u00C7" == "\u0043\u0327"" is "False", even though both
strings represent the same abstract character “LATIN CAPITAL
LETTER C WITH CEDILLA”.
To compare strings at the level of abstract characters (that is,
in a way intuitive to humans), use "unicodedata.normalize()".
[4] Due to automatic garbage-collection, free lists, and the
dynamic nature of descriptors, you may notice seemingly unusual
behaviour in certain uses of the "is" operator, like those
involving comparisons between instance methods, or constants.
Check their documentation for more info.
[5] The "%" operator is also used for string formatting; the same
precedence applies.
[6] The power operator "**" binds less tightly than an arithmetic
or bitwise unary operator on its right, that is, "2**-1" is "0.5".
uw The "pass" statement
********************
pass_stmt ::= "pass"
"pass" is a null operation — when it is executed, nothing happens. It
is useful as a placeholder when a statement is required syntactically,
but no code needs to be executed, for example:
def f(arg): pass # a function that does nothing (yet)
class C: pass # a class with no methods (yet)
a� The power operator
******************
The power operator binds more tightly than unary operators on its
left; it binds less tightly than unary operators on its right. The
syntax is:
power ::= (await_expr | primary) ["**" u_expr]
Thus, in an unparenthesized sequence of power and unary operators, the
operators are evaluated from right to left (this does not constrain
the evaluation order for the operands): "-1**2" results in "-1".
The power operator has the same semantics as the built-in "pow()"
function, when called with two arguments: it yields its left argument
raised to the power of its right argument. The numeric arguments are
first converted to a common type, and the result is of that type.
For int operands, the result has the same type as the operands unless
the second argument is negative; in that case, all arguments are
converted to float and a float result is delivered. For example,
"10**2" returns "100", but "10**-2" returns "0.01".
Raising "0.0" to a negative power results in a "ZeroDivisionError".
Raising a negative number to a fractional power results in a "complex"
number. (In earlier versions it raised a "ValueError".)
ul The "raise" statement
*********************
raise_stmt ::= "raise" [expression ["from" expression]]
If no expressions are present, "raise" re-raises the last exception
that was active in the current scope. If no exception is active in
the current scope, a "RuntimeError" exception is raised indicating
that this is an error.
Otherwise, "raise" evaluates the first expression as the exception
object. It must be either a subclass or an instance of
"BaseException". If it is a class, the exception instance will be
obtained when needed by instantiating the class with no arguments.
The *type* of the exception is the exception instance’s class, the
*value* is the instance itself.
A traceback object is normally created automatically when an exception
is raised and attached to it as the "__traceback__" attribute, which
is writable. You can create an exception and set your own traceback in
one step using the "with_traceback()" exception method (which returns
the same exception instance, with its traceback set to its argument),
like so:
raise Exception("foo occurred").with_traceback(tracebackobj)
The "from" clause is used for exception chaining: if given, the second
*expression* must be another exception class or instance, which will
then be attached to the raised exception as the "__cause__" attribute
(which is writable). If the raised exception is not handled, both
exceptions will be printed:
>>> try:
... print(1 / 0)
... except Exception as exc:
... raise RuntimeError("Something bad happened") from exc
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
A similar mechanism works implicitly if an exception is raised inside
an exception handler or a "finally" clause: the previous exception is
then attached as the new exception’s "__context__" attribute:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened")
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
Exception chaining can be explicitly suppressed by specifying "None"
in the "from" clause:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened") from None
...
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
Additional information on exceptions can be found in section
Exceptions, and information about handling exceptions is in section
The try statement.
Changed in version 3.3: "None" is now permitted as "Y" in "raise X
from Y".
New in version 3.3: The "__suppress_context__" attribute to suppress
automatic display of the exception context.
a The "return" statement
**********************
return_stmt ::= "return" [expression_list]
"return" may only occur syntactically nested in a function definition,
not within a nested class definition.
If an expression list is present, it is evaluated, else "None" is
substituted.
"return" leaves the current function call with the expression list (or
"None") as return value.
When "return" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
function.
In a generator function, the "return" statement indicates that the
generator is done and will cause "StopIteration" to be raised. The
returned value (if any) is used as an argument to construct
"StopIteration" and becomes the "StopIteration.value" attribute.
In an asynchronous generator function, an empty "return" statement
indicates that the asynchronous generator is done and will cause
"StopAsyncIteration" to be raised. A non-empty "return" statement is
a syntax error in an asynchronous generator function.
u� Emulating container types
*************************
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python’s standard dictionary objects.
The "collections" module provides a "MutableMapping" abstract base
class to help create those methods from a base set of "__getitem__()",
"__setitem__()", "__delitem__()", and "keys()". Mutable sequences
should provide methods "append()", "count()", "index()", "extend()",
"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python
standard list objects. Finally, sequence types should implement
addition (meaning concatenation) and multiplication (meaning
repetition) by defining the methods "__add__()", "__radd__()",
"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described
below; they should not define other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should search the mapping’s keys; for sequences, it
should search through the values. It is further recommended that both
mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should be the same as "keys()"; for sequences, it should iterate
through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn’t define a "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.
object.__length_hint__(self)
Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. This
method is purely an optimization and is never required for
correctness.
New in version 3.4.
Note: Slicing is done exclusively with the following three methods.
A call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note: "for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container.
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a sequence. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don’t define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
a� Shifting operations
*******************
The shifting operations have lower priority than the arithmetic
operations:
shift_expr ::= a_expr | shift_expr ("<<" | ">>") a_expr
These operators accept integers as arguments. They shift the first
argument to the left or right by the number of bits given by the
second argument.
A right shift by *n* bits is defined as floor division by "pow(2,n)".
A left shift by *n* bits is defined as multiplication with "pow(2,n)".
Note: In the current implementation, the right-hand operand is
required to be at most "sys.maxsize". If the right-hand operand is
larger than "sys.maxsize" an "OverflowError" exception is raised.
a� Slicings
********
A slicing selects a range of items in a sequence object (e.g., a
string, tuple or list). Slicings may be used as expressions or as
targets in assignment or "del" statements. The syntax for a slicing:
slicing ::= primary "[" slice_list "]"
slice_list ::= slice_item ("," slice_item)* [","]
slice_item ::= expression | proper_slice
proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" [stride] ]
lower_bound ::= expression
upper_bound ::= expression
stride ::= expression
There is ambiguity in the formal syntax here: anything that looks like
an expression list also looks like a slice list, so any subscription
can be interpreted as a slicing. Rather than further complicating the
syntax, this is disambiguated by defining that in this case the
interpretation as a subscription takes priority over the
interpretation as a slicing (this is the case if the slice list
contains no proper slice).
The semantics for a slicing are as follows. The primary is indexed
(using the same "__getitem__()" method as normal subscription) with a
key that is constructed from the slice list, as follows. If the slice
list contains at least one comma, the key is a tuple containing the
conversion of the slice items; otherwise, the conversion of the lone
slice item is the key. The conversion of a slice item that is an
expression is that expression. The conversion of a proper slice is a
slice object (see section The standard type hierarchy) whose "start",
"stop" and "step" attributes are the values of the expressions given
as lower bound, upper bound and stride, respectively, substituting
"None" for missing expressions.
u~ Special Attributes
******************
The implementation adds a few special read-only attributes to several
object types, where they are relevant. Some of these are not reported
by the "dir()" built-in function.
object.__dict__
A dictionary or other mapping object used to store an object’s
(writable) attributes.
instance.__class__
The class to which a class instance belongs.
class.__bases__
The tuple of base classes of a class object.
definition.__name__
The name of the class, function, method, descriptor, or generator
instance.
definition.__qualname__
The *qualified name* of the class, function, method, descriptor, or
generator instance.
New in version 3.3.
class.__mro__
This attribute is a tuple of classes that are considered when
looking for base classes during method resolution.
class.mro()
This method can be overridden by a metaclass to customize the
method resolution order for its instances. It is called at class
instantiation, and its result is stored in "__mro__".
class.__subclasses__()
Each class keeps a list of weak references to its immediate
subclasses. This method returns a list of all those references
still alive. Example:
>>> int.__subclasses__()
[<class 'bool'>]
-[ Footnotes ]-
[1] Additional information on these special methods may be found
in the Python Reference Manual (Basic customization).
[2] As a consequence, the list "[1, 2]" is considered equal to
"[1.0, 2.0]", and similarly for tuples.
[3] They must have since the parser can’t tell the type of the
operands.
[4] Cased characters are those with general category property
being one of “Lu” (Letter, uppercase), “Ll” (Letter, lowercase),
or “Lt” (Letter, titlecase).
[5] To format only a tuple you should therefore provide a
singleton tuple whose only element is the tuple to be formatted.
u.� Special method names
********************
A class can implement certain operations that are invoked by special
syntax (such as arithmetic operations or subscripting and slicing) by
defining methods with special names. This is Python’s approach to
*operator overloading*, allowing classes to define their own behavior
with respect to language operators. For instance, if a class defines
a method named "__getitem__()", and "x" is an instance of this class,
then "x[i]" is roughly equivalent to "type(x).__getitem__(x, i)".
Except where mentioned, attempts to execute an operation raise an
exception when no appropriate method is defined (typically
"AttributeError" or "TypeError").
Setting a special method to "None" indicates that the corresponding
operation is not available. For example, if a class sets "__iter__()"
to "None", the class is not iterable, so calling "iter()" on its
instances will raise a "TypeError" (without falling back to
"__getitem__()"). [2]
When implementing a class that emulates any built-in type, it is
important that the emulation only be implemented to the degree that it
makes sense for the object being modelled. For example, some
sequences may work well with retrieval of individual elements, but
extracting a slice may not make sense. (One example of this is the
"NodeList" interface in the W3C’s Document Object Model.)
Basic customization
===================
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass’s "__new__()" method using
"super().__new__(cls[, ...])" with appropriate arguments and then
modifying the newly-created instance as necessary before returning
it.
If "__new__()" returns an instance of *cls*, then the new
instance’s "__init__()" method will be invoked like
"__init__(self[, ...])", where *self* is the new instance and the
remaining arguments are the same as were passed to "__new__()".
If "__new__()" does not return an instance of *cls*, then the new
instance’s "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class’s "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"super().__init__([args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.
It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.
It is not guaranteed that "__del__()" methods are called for
objects that still exist when the interpreter exits.
Note: "del x" doesn’t directly call "x.__del__()" — the former
decrements the reference count for "x" by one, and the latter is
only called when "x"’s reference count reaches zero.
**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.
See also: Documentation for the "gc" module.
Warning: Due to the precarious circumstances under which
"__del__()" methods are invoked, exceptions that occur during
their execution are ignored, and a warning is printed to
"sys.stderr" instead. In particular:
* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".
* "__del__()" can be executed during interpreter shutdown. As
a consequence, the global variables it needs to access
(including other modules) may already have been deleted or set
to "None". Python guarantees that globals whose name begins
with a single underscore are deleted from their module before
other globals are deleted; if no other references to such
globals exist, this may help in assuring that imported modules
are still available at the time when the "__del__()" method is
called.
object.__repr__(self)
Called by the "repr()" built-in function to compute the “official”
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an “informal”
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the “informal” or nicely printable string
representation of an object. The return value must be a string
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by bytes to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function, and by extension,
evaluation of formatted string literals and the "str.format()"
method, to produce a “formatted” string representation of an
object. The "format_spec" argument is a string that contains a
description of the formatting options desired. The interpretation
of the "format_spec" argument is up to the type implementing
"__format__()", however most classes will either delegate
formatting to one of the built-in types, or use a similar
formatting option syntax.
See Format Specification Mini-Language for a description of the
standard formatting syntax.
The return value must be a string object.
Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
By default, "__ne__()" delegates to "__eq__()" and inverts the
result unless it is "NotImplemented". There are no other implied
relationships among the comparison operators, for example, the
truth of "(x<y or x==y)" does not imply "x<=y". To automatically
generate ordering operations from a single root operation, see
"functools.total_ordering()".
See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
Note: "hash()" truncates the value returned from an object’s
custom "__hash__()" method to the size of a "Py_ssize_t". This
is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit
builds. If an object’s "__hash__()" must interoperate on builds
of different bit sizes, be sure to check the width on all
supported builds. An easy way to do this is with "python -c
"import sys; print(sys.hash_info.width)"".
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key’s hash value is immutable (if the object’s hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj, collections.Hashable)".
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
<ParentClass>.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.Hashable)" call.
Note: By default, the "__hash__()" values of str, bytes and
datetime objects are “salted” with an unpredictable random value.
Although they remain constant within an individual Python
process, they are not predictable between repeated invocations of
Python.This is intended to provide protection against a denial-
of-service caused by carefully-chosen inputs that exploit the
worst case performance of a dict insertion, O(n^2) complexity.
See http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of
dicts, sets and other mappings. Python has never made guarantees
about this ordering (and it typically varies between 32-bit and
64-bit builds).See also "PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
Customizing attribute access
============================
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
return the (computed) attribute value or raise an "AttributeError"
exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note: This method may still be bypassed when looking up special
methods as the result of implicit invocation via language syntax
or built-in functions. See Special method lookup.
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should call the base class method with the same name, for example,
"object.__setattr__(self, name, value)".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
Customizing module attribute access
-----------------------------------
For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
setattr(self, attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note: Setting module "__class__" only affects lookups made using the
attribute access syntax – directly accessing the module globals
(whether by code within the module, or via a reference to the
module’s globals dictionary) is unaffected.
Changed in version 3.5: "__class__" module attribute is now writable.
Implementing Descriptors
------------------------
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents). In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".
object.__get__(self, instance, owner)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). *owner* is always the owner class, while *instance* is the
instance that the attribute was accessed through, or "None" when
the attribute is accessed through the *owner*. This method should
return the (computed) attribute value or raise an "AttributeError"
exception.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
object.__set_name__(self, owner, name)
Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.
New in version 3.6.
The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).
Invoking Descriptors
--------------------
In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a class, "A.x" is transformed into the call:
"A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined. A descriptor can define
any combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
---------
*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of *__dict__* and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)
The space saved over using *__dict__* can be significant.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. *__slots__*
reserves space for the declared variables and prevents the
automatic creation of *__dict__* and *__weakref__* for each
instance.
Notes on using *__slots__*
~~~~~~~~~~~~~~~~~~~~~~~~~~
* When inheriting from a class without *__slots__*, the *__dict__*
and *__weakref__* attribute of the instances will always be
accessible.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
* Without a *__weakref__* variable for each instance, classes
defining *__slots__* do not support weak references to its
instances. If weak reference support is needed, then add
"'__weakref__'" to the sequence of strings in the *__slots__*
declaration.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is not limited to the
class where it is defined. *__slots__* declared in parents are
available in child classes. However, child subclasses will get a
*__dict__* and *__weakref__* unless they also define *__slots__*
(which should only contain names of any *additional* slots).
* If a class defines a slot also defined in a base class, the
instance variable defined by the base class slot is inaccessible
(except by retrieving its descriptor directly from the base class).
This renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
“variable-length” built-in types such as "int", "bytes" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings
may also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".
Customizing class creation
==========================
Whenever a class inherits from another class, *__init_subclass__* is
called on that class. This way, it is possible to write classes which
change the behavior of subclasses. This is closely related to class
decorators, but where class decorators only affect the specific class
they’re applied to, "__init_subclass__" solely applies to future
subclasses of the class defining the method.
classmethod object.__init_subclass__(cls)
This method is called whenever the containing class is subclassed.
*cls* is then the new subclass. If defined as a normal instance
method, this method is implicitly converted to a class method.
Keyword arguments which are given to a new class are passed to the
parent’s class "__init_subclass__". For compatibility with other
classes using "__init_subclass__", one should take out the needed
keyword arguments and pass the others over to the base class, as
in:
class Philosopher:
def __init_subclass__(cls, default_name, **kwargs):
super().__init_subclass__(**kwargs)
cls.default_name = default_name
class AustralianPhilosopher(Philosopher, default_name="Bruce"):
pass
The default implementation "object.__init_subclass__" does nothing,
but raises an error if it is called with any arguments.
Note: The metaclass hint "metaclass" is consumed by the rest of
the type machinery, and is never passed to "__init_subclass__"
implementations. The actual metaclass (rather than the explicit
hint) can be accessed as "type(cls)".
New in version 3.6.
Metaclasses
-----------
By default, classes are constructed using "type()". The class body is
executed in a new namespace and the class name is bound locally to the
result of "type(name, bases, namespace)".
The class creation process can be customized by passing the
"metaclass" keyword argument in the class definition line, or by
inheriting from an existing class that included such an argument. In
the following example, both "MyClass" and "MySubclass" are instances
of "Meta":
class Meta(type):
pass
class MyClass(metaclass=Meta):
pass
class MySubclass(MyClass):
pass
Any other keyword arguments that are specified in the class definition
are passed through to all metaclass operations described below.
When a class definition is executed, the following steps occur:
* the appropriate metaclass is determined
* the class namespace is prepared
* the class body is executed
* the class object is created
Determining the appropriate metaclass
-------------------------------------
The appropriate metaclass for a class definition is determined as
follows:
* if no bases and no explicit metaclass are given, then "type()" is
used
* if an explicit metaclass is given and it is *not* an instance of
"type()", then it is used directly as the metaclass
* if an instance of "type()" is given as the explicit metaclass, or
bases are defined, then the most derived metaclass is used
The most derived metaclass is selected from the explicitly specified
metaclass (if any) and the metaclasses (i.e. "type(cls)") of all
specified base classes. The most derived metaclass is one which is a
subtype of *all* of these candidate metaclasses. If none of the
candidate metaclasses meets that criterion, then the class definition
will fail with "TypeError".
Preparing the class namespace
-----------------------------
Once the appropriate metaclass has been identified, then the class
namespace is prepared. If the metaclass has a "__prepare__" attribute,
it is called as "namespace = metaclass.__prepare__(name, bases,
**kwds)" (where the additional keyword arguments, if any, come from
the class definition).
If the metaclass has no "__prepare__" attribute, then the class
namespace is initialised as an empty ordered mapping.
See also:
**PEP 3115** - Metaclasses in Python 3000
Introduced the "__prepare__" namespace hook
Executing the class body
------------------------
The class body is executed (approximately) as "exec(body, globals(),
namespace)". The key difference from a normal call to "exec()" is that
lexical scoping allows the class body (including any methods) to
reference names from the current and outer scopes when the class
definition occurs inside a function.
However, even when the class definition occurs inside the function,
methods defined inside the class still cannot see names defined at the
class scope. Class variables must be accessed through the first
parameter of instance or class methods, or through the implicit
lexically scoped "__class__" reference described in the next section.
Creating the class object
-------------------------
Once the class namespace has been populated by executing the class
body, the class object is created by calling "metaclass(name, bases,
namespace, **kwds)" (the additional keywords passed here are the same
as those passed to "__prepare__").
This class object is the one that will be referenced by the zero-
argument form of "super()". "__class__" is an implicit closure
reference created by the compiler if any methods in a class body refer
to either "__class__" or "super". This allows the zero argument form
of "super()" to correctly identify the class being defined based on
lexical scoping, while the class or instance that was used to make the
current call is identified based on the first argument passed to the
method.
**CPython implementation detail:** In CPython 3.6 and later, the
"__class__" cell is passed to the metaclass as a "__classcell__" entry
in the class namespace. If present, this must be propagated up to the
"type.__new__" call in order for the class to be initialised
correctly. Failing to do so will result in a "DeprecationWarning" in
Python 3.6, and a "RuntimeError" in Python 3.8.
When using the default metaclass "type", or any metaclass that
ultimately calls "type.__new__", the following additional
customisation steps are invoked after creating the class object:
* first, "type.__new__" collects all of the descriptors in the class
namespace that define a "__set_name__()" method;
* second, all of these "__set_name__" methods are called with the
class being defined and the assigned name of that particular
descriptor; and
* finally, the "__init_subclass__()" hook is called on the immediate
parent of the new class in its method resolution order.
After the class object is created, it is passed to the class
decorators included in the class definition (if any) and the resulting
object is bound in the local namespace as the defined class.
When a new class is created by "type.__new__", the object provided as
the namespace parameter is copied to a new ordered mapping and the
original object is discarded. The new copy is wrapped in a read-only
proxy, which becomes the "__dict__" attribute of the class object.
See also:
**PEP 3135** - New super
Describes the implicit "__class__" closure reference
Uses for metaclasses
--------------------
The potential uses for metaclasses are boundless. Some ideas that have
been explored include enum, logging, interface checking, automatic
delegation, automatic property creation, proxies, frameworks, and
automatic resource locking/synchronization.
Customizing instance and subclass checks
========================================
The following methods are used to override the default behavior of the
"isinstance()" and "issubclass()" built-in functions.
In particular, the metaclass "abc.ABCMeta" implements these methods in
order to allow the addition of Abstract Base Classes (ABCs) as
“virtual base classes” to any class or type (including built-in
types), including other ABCs.
class.__instancecheck__(self, instance)
Return true if *instance* should be considered a (direct or
indirect) instance of *class*. If defined, called to implement
"isinstance(instance, class)".
class.__subclasscheck__(self, subclass)
Return true if *subclass* should be considered a (direct or
indirect) subclass of *class*. If defined, called to implement
"issubclass(subclass, class)".
Note that these methods are looked up on the type (metaclass) of a
class. They cannot be defined as class methods in the actual class.
This is consistent with the lookup of special methods that are called
on instances, only in this case the instance is itself a class.
See also:
**PEP 3119** - Introducing Abstract Base Classes
Includes the specification for customizing "isinstance()" and
"issubclass()" behavior through "__instancecheck__()" and
"__subclasscheck__()", with motivation for this functionality in
the context of adding Abstract Base Classes (see the "abc"
module) to the language.
Emulating callable objects
==========================
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method
is defined, "x(arg1, arg2, ...)" is a shorthand for
"x.__call__(arg1, arg2, ...)".
Emulating container types
=========================
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python’s standard dictionary objects.
The "collections" module provides a "MutableMapping" abstract base
class to help create those methods from a base set of "__getitem__()",
"__setitem__()", "__delitem__()", and "keys()". Mutable sequences
should provide methods "append()", "count()", "index()", "extend()",
"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python
standard list objects. Finally, sequence types should implement
addition (meaning concatenation) and multiplication (meaning
repetition) by defining the methods "__add__()", "__radd__()",
"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described
below; they should not define other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should search the mapping’s keys; for sequences, it
should search through the values. It is further recommended that both
mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should be the same as "keys()"; for sequences, it should iterate
through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn’t define a "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.
object.__length_hint__(self)
Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. This
method is purely an optimization and is never required for
correctness.
New in version 3.4.
Note: Slicing is done exclusively with the following three methods.
A call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note: "for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container.
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a sequence. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don’t define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
Emulating numeric types
=======================
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the expression "x + y", where *x* is an instance of a
class that has an "__add__()" method, "x.__add__(y)" is called.
The "__divmod__()" method should be the equivalent to using
"__floordiv__()" and "__mod__()"; it should not be related to
"__truediv__()". Note that "__pow__()" should be defined to accept
an optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other)
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)
operands. These functions are only called if the left operand does
not support the corresponding operation [3] and the operands are of
different types. [4] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note: If the right operand’s type is a subclass of the left
operand’s type and that subclass provides the reflected method
for the operation, this method will be called before the left
operand’s non-reflected method. This behavior allows subclasses
to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (modifying *self*) and return the result
(which could be, but does not have to be, *self*). If a specific
method is not defined, the augmented assignment falls back to the
normal methods. For instance, if *x* is an instance of a class
with an "__iadd__()" method, "x += y" is equivalent to "x =
x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are
considered, as with the evaluation of "x + y". In certain
situations, augmented assignment can result in unexpected errors
(see Why does a_tuple[i] += [‘item’] raise an exception when the
addition works?), but this behavior is in fact part of the data
model.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()" and
"float()". Should return a value of the appropriate type.
object.__index__(self)
Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.
Note: In order to have a coherent integer type class, when
"__index__()" is defined "__int__()" should also be defined, and
both should return the same value.
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").
If "__int__()" is not defined then the built-in function "int()"
falls back to "__trunc__()".
With Statement Context Managers
===============================
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method’s return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller’s responsibility.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
Special method lookup
=====================
For custom classes, implicit invocations of special methods are only
guaranteed to work correctly if defined on an object’s type, not in
the object’s instance dictionary. That behaviour is the reason why
the following code raises an exception:
>>> class C:
... pass
...
>>> c = C()
>>> c.__len__ = lambda: 5
>>> len(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: object of type 'C' has no len()
The rationale behind this behaviour lies with a number of special
methods such as "__hash__()" and "__repr__()" that are implemented by
all objects, including type objects. If the implicit lookup of these
methods used the conventional lookup process, they would fail when
invoked on the type object itself:
>>> 1 .__hash__() == hash(1)
True
>>> int.__hash__() == hash(int)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: descriptor '__hash__' of 'int' object needs an argument
Incorrectly attempting to invoke an unbound method of a class in this
way is sometimes referred to as ‘metaclass confusion’, and is avoided
by bypassing the instance when looking up special methods:
>>> type(1).__hash__(1) == hash(1)
True
>>> type(int).__hash__(int) == hash(int)
True
In addition to bypassing any instance attributes in the interest of
correctness, implicit special method lookup generally also bypasses
the "__getattribute__()" method even of the object’s metaclass:
>>> class Meta(type):
... def __getattribute__(*args):
... print("Metaclass getattribute invoked")
... return type.__getattribute__(*args)
...
>>> class C(object, metaclass=Meta):
... def __len__(self):
... return 10
... def __getattribute__(*args):
... print("Class getattribute invoked")
... return object.__getattribute__(*args)
...
>>> c = C()
>>> c.__len__() # Explicit lookup via instance
Class getattribute invoked
10
>>> type(c).__len__(c) # Explicit lookup via type
Metaclass getattribute invoked
10
>>> len(c) # Implicit lookup
10
Bypassing the "__getattribute__()" machinery in this fashion provides
significant scope for speed optimisations within the interpreter, at
the cost of some flexibility in the handling of special methods (the
special method *must* be set on the class object itself in order to be
consistently invoked by the interpreter).
u=W String Methods
**************
Strings implement all of the common sequence operations, along with
the additional methods described below.
Strings also support two styles of string formatting, one providing a
large degree of flexibility and customization (see "str.format()",
Format String Syntax and Custom String Formatting) and the other based
on C "printf" style formatting that handles a narrower range of types
and is slightly harder to use correctly, but is often faster for the
cases it can handle (printf-style String Formatting).
The Text Processing Services section of the standard library covers a
number of other modules that provide various text related utilities
(including regular expression support in the "re" module).
str.capitalize()
Return a copy of the string with its first character capitalized
and the rest lowercased.
str.casefold()
Return a casefolded copy of the string. Casefolded strings may be
used for caseless matching.
Casefolding is similar to lowercasing but more aggressive because
it is intended to remove all case distinctions in a string. For
example, the German lowercase letter "'ß'" is equivalent to ""ss"".
Since it is already lowercase, "lower()" would do nothing to "'ß'";
"casefold()" converts it to ""ss"".
The casefolding algorithm is described in section 3.13 of the
Unicode Standard.
New in version 3.3.
str.center(width[, fillchar])
Return centered in a string of length *width*. Padding is done
using the specified *fillchar* (default is an ASCII space). The
original string is returned if *width* is less than or equal to
"len(s)".
str.count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring *sub*
in the range [*start*, *end*]. Optional arguments *start* and
*end* are interpreted as in slice notation.
str.encode(encoding="utf-8", errors="strict")
Return an encoded version of the string as a bytes object. Default
encoding is "'utf-8'". *errors* may be given to set a different
error handling scheme. The default for *errors* is "'strict'",
meaning that encoding errors raise a "UnicodeError". Other possible
values are "'ignore'", "'replace'", "'xmlcharrefreplace'",
"'backslashreplace'" and any other name registered via
"codecs.register_error()", see section Error Handlers. For a list
of possible encodings, see section Standard Encodings.
Changed in version 3.1: Support for keyword arguments added.
str.endswith(suffix[, start[, end]])
Return "True" if the string ends with the specified *suffix*,
otherwise return "False". *suffix* can also be a tuple of suffixes
to look for. With optional *start*, test beginning at that
position. With optional *end*, stop comparing at that position.
str.expandtabs(tabsize=8)
Return a copy of the string where all tab characters are replaced
by one or more spaces, depending on the current column and the
given tab size. Tab positions occur every *tabsize* characters
(default is 8, giving tab positions at columns 0, 8, 16 and so on).
To expand the string, the current column is set to zero and the
string is examined character by character. If the character is a
tab ("\t"), one or more space characters are inserted in the result
until the current column is equal to the next tab position. (The
tab character itself is not copied.) If the character is a newline
("\n") or return ("\r"), it is copied and the current column is
reset to zero. Any other character is copied unchanged and the
current column is incremented by one regardless of how the
character is represented when printed.
>>> '01\t012\t0123\t01234'.expandtabs()
'01 012 0123 01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01 012 0123 01234'
str.find(sub[, start[, end]])
Return the lowest index in the string where substring *sub* is
found within the slice "s[start:end]". Optional arguments *start*
and *end* are interpreted as in slice notation. Return "-1" if
*sub* is not found.
Note: The "find()" method should be used only if you need to know
the position of *sub*. To check if *sub* is a substring or not,
use the "in" operator:
>>> 'Py' in 'Python'
True
str.format(*args, **kwargs)
Perform a string formatting operation. The string on which this
method is called can contain literal text or replacement fields
delimited by braces "{}". Each replacement field contains either
the numeric index of a positional argument, or the name of a
keyword argument. Returns a copy of the string where each
replacement field is replaced with the string value of the
corresponding argument.
>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'
See Format String Syntax for a description of the various
formatting options that can be specified in format strings.
Note: When formatting a number ("int", "float", "complex",
"decimal.Decimal" and subclasses) with the "n" type (ex:
"'{:n}'.format(1234)"), the function temporarily sets the
"LC_CTYPE" locale to the "LC_NUMERIC" locale to decode
"decimal_point" and "thousands_sep" fields of "localeconv()" if
they are non-ASCII or longer than 1 byte, and the "LC_NUMERIC"
locale is different than the "LC_CTYPE" locale. This temporary
change affects other threads.
Changed in version 3.6.5: When formatting a number with the "n"
type, the function sets temporarily the "LC_CTYPE" locale to the
"LC_NUMERIC" locale in some cases.
str.format_map(mapping)
Similar to "str.format(**mapping)", except that "mapping" is used
directly and not copied to a "dict". This is useful if for example
"mapping" is a dict subclass:
>>> class Default(dict):
... def __missing__(self, key):
... return key
...
>>> '{name} was born in {country}'.format_map(Default(name='Guido'))
'Guido was born in country'
New in version 3.2.
str.index(sub[, start[, end]])
Like "find()", but raise "ValueError" when the substring is not
found.
str.isalnum()
Return true if all characters in the string are alphanumeric and
there is at least one character, false otherwise. A character "c"
is alphanumeric if one of the following returns "True":
"c.isalpha()", "c.isdecimal()", "c.isdigit()", or "c.isnumeric()".
str.isalpha()
Return true if all characters in the string are alphabetic and
there is at least one character, false otherwise. Alphabetic
characters are those characters defined in the Unicode character
database as “Letter”, i.e., those with general category property
being one of “Lm”, “Lt”, “Lu”, “Ll”, or “Lo”. Note that this is
different from the “Alphabetic” property defined in the Unicode
Standard.
str.isdecimal()
Return true if all characters in the string are decimal characters
and there is at least one character, false otherwise. Decimal
characters are those that can be used to form numbers in base 10,
e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Formally a decimal character
is a character in the Unicode General Category “Nd”.
str.isdigit()
Return true if all characters in the string are digits and there is
at least one character, false otherwise. Digits include decimal
characters and digits that need special handling, such as the
compatibility superscript digits. This covers digits which cannot
be used to form numbers in base 10, like the Kharosthi numbers.
Formally, a digit is a character that has the property value
Numeric_Type=Digit or Numeric_Type=Decimal.
str.isidentifier()
Return true if the string is a valid identifier according to the
language definition, section Identifiers and keywords.
Use "keyword.iskeyword()" to test for reserved identifiers such as
"def" and "class".
str.islower()
Return true if all cased characters [4] in the string are lowercase
and there is at least one cased character, false otherwise.
str.isnumeric()
Return true if all characters in the string are numeric characters,
and there is at least one character, false otherwise. Numeric
characters include digit characters, and all characters that have
the Unicode numeric value property, e.g. U+2155, VULGAR FRACTION
ONE FIFTH. Formally, numeric characters are those with the
property value Numeric_Type=Digit, Numeric_Type=Decimal or
Numeric_Type=Numeric.
str.isprintable()
Return true if all characters in the string are printable or the
string is empty, false otherwise. Nonprintable characters are
those characters defined in the Unicode character database as
“Other” or “Separator”, excepting the ASCII space (0x20) which is
considered printable. (Note that printable characters in this
context are those which should not be escaped when "repr()" is
invoked on a string. It has no bearing on the handling of strings
written to "sys.stdout" or "sys.stderr".)
str.isspace()
Return true if there are only whitespace characters in the string
and there is at least one character, false otherwise. Whitespace
characters are those characters defined in the Unicode character
database as “Other” or “Separator” and those with bidirectional
property being one of “WS”, “B”, or “S”.
str.istitle()
Return true if the string is a titlecased string and there is at
least one character, for example uppercase characters may only
follow uncased characters and lowercase characters only cased ones.
Return false otherwise.
str.isupper()
Return true if all cased characters [4] in the string are uppercase
and there is at least one cased character, false otherwise.
str.join(iterable)
Return a string which is the concatenation of the strings in
*iterable*. A "TypeError" will be raised if there are any non-
string values in *iterable*, including "bytes" objects. The
separator between elements is the string providing this method.
str.ljust(width[, fillchar])
Return the string left justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is an ASCII
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.lower()
Return a copy of the string with all the cased characters [4]
converted to lowercase.
The lowercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.lstrip([chars])
Return a copy of the string with leading characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a prefix; rather,
all combinations of its values are stripped:
>>> ' spacious '.lstrip()
'spacious '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'
static str.maketrans(x[, y[, z]])
This static method returns a translation table usable for
"str.translate()".
If there is only one argument, it must be a dictionary mapping
Unicode ordinals (integers) or characters (strings of length 1) to
Unicode ordinals, strings (of arbitrary lengths) or "None".
Character keys will then be converted to ordinals.
If there are two arguments, they must be strings of equal length,
and in the resulting dictionary, each character in x will be mapped
to the character at the same position in y. If there is a third
argument, it must be a string, whose characters will be mapped to
"None" in the result.
str.partition(sep)
Split the string at the first occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing the string itself, followed by
two empty strings.
str.replace(old, new[, count])
Return a copy of the string with all occurrences of substring *old*
replaced by *new*. If the optional argument *count* is given, only
the first *count* occurrences are replaced.
str.rfind(sub[, start[, end]])
Return the highest index in the string where substring *sub* is
found, such that *sub* is contained within "s[start:end]".
Optional arguments *start* and *end* are interpreted as in slice
notation. Return "-1" on failure.
str.rindex(sub[, start[, end]])
Like "rfind()" but raises "ValueError" when the substring *sub* is
not found.
str.rjust(width[, fillchar])
Return the string right justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is an ASCII
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.rpartition(sep)
Split the string at the last occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing two empty strings, followed by
the string itself.
str.rsplit(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit* splits
are done, the *rightmost* ones. If *sep* is not specified or
"None", any whitespace string is a separator. Except for splitting
from the right, "rsplit()" behaves like "split()" which is
described in detail below.
str.rstrip([chars])
Return a copy of the string with trailing characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a suffix; rather,
all combinations of its values are stripped:
>>> ' spacious '.rstrip()
' spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'
str.split(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit*
splits are done (thus, the list will have at most "maxsplit+1"
elements). If *maxsplit* is not specified or "-1", then there is
no limit on the number of splits (all possible splits are made).
If *sep* is given, consecutive delimiters are not grouped together
and are deemed to delimit empty strings (for example,
"'1,,2'.split(',')" returns "['1', '', '2']"). The *sep* argument
may consist of multiple characters (for example,
"'1<>2<>3'.split('<>')" returns "['1', '2', '3']"). Splitting an
empty string with a specified separator returns "['']".
For example:
>>> '1,2,3'.split(',')
['1', '2', '3']
>>> '1,2,3'.split(',', maxsplit=1)
['1', '2,3']
>>> '1,2,,3,'.split(',')
['1', '2', '', '3', '']
If *sep* is not specified or is "None", a different splitting
algorithm is applied: runs of consecutive whitespace are regarded
as a single separator, and the result will contain no empty strings
at the start or end if the string has leading or trailing
whitespace. Consequently, splitting an empty string or a string
consisting of just whitespace with a "None" separator returns "[]".
For example:
>>> '1 2 3'.split()
['1', '2', '3']
>>> '1 2 3'.split(maxsplit=1)
['1', '2 3']
>>> ' 1 2 3 '.split()
['1', '2', '3']
str.splitlines([keepends])
Return a list of the lines in the string, breaking at line
boundaries. Line breaks are not included in the resulting list
unless *keepends* is given and true.
This method splits on the following line boundaries. In
particular, the boundaries are a superset of *universal newlines*.
+-------------------------+-------------------------------+
| Representation | Description |
+=========================+===============================+
| "\n" | Line Feed |
+-------------------------+-------------------------------+
| "\r" | Carriage Return |
+-------------------------+-------------------------------+
| "\r\n" | Carriage Return + Line Feed |
+-------------------------+-------------------------------+
| "\v" or "\x0b" | Line Tabulation |
+-------------------------+-------------------------------+
| "\f" or "\x0c" | Form Feed |
+-------------------------+-------------------------------+
| "\x1c" | File Separator |
+-------------------------+-------------------------------+
| "\x1d" | Group Separator |
+-------------------------+-------------------------------+
| "\x1e" | Record Separator |
+-------------------------+-------------------------------+
| "\x85" | Next Line (C1 Control Code) |
+-------------------------+-------------------------------+
| "\u2028" | Line Separator |
+-------------------------+-------------------------------+
| "\u2029" | Paragraph Separator |
+-------------------------+-------------------------------+
Changed in version 3.2: "\v" and "\f" added to list of line
boundaries.
For example:
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
['ab c', '', 'de fg', 'kl']
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True)
['ab c\n', '\n', 'de fg\r', 'kl\r\n']
Unlike "split()" when a delimiter string *sep* is given, this
method returns an empty list for the empty string, and a terminal
line break does not result in an extra line:
>>> "".splitlines()
[]
>>> "One line\n".splitlines()
['One line']
For comparison, "split('\n')" gives:
>>> ''.split('\n')
['']
>>> 'Two lines\n'.split('\n')
['Two lines', '']
str.startswith(prefix[, start[, end]])
Return "True" if string starts with the *prefix*, otherwise return
"False". *prefix* can also be a tuple of prefixes to look for.
With optional *start*, test string beginning at that position.
With optional *end*, stop comparing string at that position.
str.strip([chars])
Return a copy of the string with the leading and trailing
characters removed. The *chars* argument is a string specifying the
set of characters to be removed. If omitted or "None", the *chars*
argument defaults to removing whitespace. The *chars* argument is
not a prefix or suffix; rather, all combinations of its values are
stripped:
>>> ' spacious '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'
The outermost leading and trailing *chars* argument values are
stripped from the string. Characters are removed from the leading
end until reaching a string character that is not contained in the
set of characters in *chars*. A similar action takes place on the
trailing end. For example:
>>> comment_string = '#....... Section 3.2.1 Issue #32 .......'
>>> comment_string.strip('.#! ')
'Section 3.2.1 Issue #32'
str.swapcase()
Return a copy of the string with uppercase characters converted to
lowercase and vice versa. Note that it is not necessarily true that
"s.swapcase().swapcase() == s".
str.title()
Return a titlecased version of the string where words start with an
uppercase character and the remaining characters are lowercase.
For example:
>>> 'Hello world'.title()
'Hello World'
The algorithm uses a simple language-independent definition of a
word as groups of consecutive letters. The definition works in
many contexts but it means that apostrophes in contractions and
possessives form word boundaries, which may not be the desired
result:
>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"
A workaround for apostrophes can be constructed using regular
expressions:
>>> import re
>>> def titlecase(s):
... return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
... lambda mo: mo.group(0)[0].upper() +
... mo.group(0)[1:].lower(),
... s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."
str.translate(table)
Return a copy of the string in which each character has been mapped
through the given translation table. The table must be an object
that implements indexing via "__getitem__()", typically a *mapping*
or *sequence*. When indexed by a Unicode ordinal (an integer), the
table object can do any of the following: return a Unicode ordinal
or a string, to map the character to one or more other characters;
return "None", to delete the character from the return string; or
raise a "LookupError" exception, to map the character to itself.
You can use "str.maketrans()" to create a translation map from
character-to-character mappings in different formats.
See also the "codecs" module for a more flexible approach to custom
character mappings.
str.upper()
Return a copy of the string with all the cased characters [4]
converted to uppercase. Note that "s.upper().isupper()" might be
"False" if "s" contains uncased characters or if the Unicode
category of the resulting character(s) is not “Lu” (Letter,
uppercase), but e.g. “Lt” (Letter, titlecase).
The uppercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.zfill(width)
Return a copy of the string left filled with ASCII "'0'" digits to
make a string of length *width*. A leading sign prefix
("'+'"/"'-'") is handled by inserting the padding *after* the sign
character rather than before. The original string is returned if
*width* is less than or equal to "len(s)".
For example:
>>> "42".zfill(5)
'00042'
>>> "-42".zfill(5)
'-0042'
uw String and Bytes literals
*************************
String literals are described by the following lexical definitions:
stringliteral ::= [stringprefix](shortstring | longstring)
stringprefix ::= "r" | "u" | "R" | "U" | "f" | "F"
| "fr" | "Fr" | "fR" | "FR" | "rf" | "rF" | "Rf" | "RF"
shortstring ::= "'" shortstringitem* "'" | '"' shortstringitem* '"'
longstring ::= "'''" longstringitem* "'''" | '"""' longstringitem* '"""'
shortstringitem ::= shortstringchar | stringescapeseq
longstringitem ::= longstringchar | stringescapeseq
shortstringchar ::= <any source character except "\" or newline or the quote>
longstringchar ::= <any source character except "\">
stringescapeseq ::= "\" <any source character>
bytesliteral ::= bytesprefix(shortbytes | longbytes)
bytesprefix ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB"
shortbytes ::= "'" shortbytesitem* "'" | '"' shortbytesitem* '"'
longbytes ::= "'''" longbytesitem* "'''" | '"""' longbytesitem* '"""'
shortbytesitem ::= shortbyteschar | bytesescapeseq
longbytesitem ::= longbyteschar | bytesescapeseq
shortbyteschar ::= <any ASCII character except "\" or newline or the quote>
longbyteschar ::= <any ASCII character except "\">
bytesescapeseq ::= "\" <any ASCII character>
One syntactic restriction not indicated by these productions is that
whitespace is not allowed between the "stringprefix" or "bytesprefix"
and the rest of the literal. The source character set is defined by
the encoding declaration; it is UTF-8 if no encoding declaration is
given in the source file; see section Encoding declarations.
In plain English: Both types of literals can be enclosed in matching
single quotes ("'") or double quotes ("""). They can also be enclosed
in matching groups of three single or double quotes (these are
generally referred to as *triple-quoted strings*). The backslash
("\") character is used to escape characters that otherwise have a
special meaning, such as newline, backslash itself, or the quote
character.
Bytes literals are always prefixed with "'b'" or "'B'"; they produce
an instance of the "bytes" type instead of the "str" type. They may
only contain ASCII characters; bytes with a numeric value of 128 or
greater must be expressed with escapes.
Both string and bytes literals may optionally be prefixed with a
letter "'r'" or "'R'"; such strings are called *raw strings* and treat
backslashes as literal characters. As a result, in string literals,
"'\U'" and "'\u'" escapes in raw strings are not treated specially.
Given that Python 2.x’s raw unicode literals behave differently than
Python 3.x’s the "'ur'" syntax is not supported.
New in version 3.3: The "'rb'" prefix of raw bytes literals has been
added as a synonym of "'br'".
New in version 3.3: Support for the unicode legacy literal
("u'value'") was reintroduced to simplify the maintenance of dual
Python 2.x and 3.x codebases. See **PEP 414** for more information.
A string literal with "'f'" or "'F'" in its prefix is a *formatted
string literal*; see Formatted string literals. The "'f'" may be
combined with "'r'", but not with "'b'" or "'u'", therefore raw
formatted strings are possible, but formatted bytes literals are not.
In triple-quoted literals, unescaped newlines and quotes are allowed
(and are retained), except that three unescaped quotes in a row
terminate the literal. (A “quote” is the character used to open the
literal, i.e. either "'" or """.)
Unless an "'r'" or "'R'" prefix is present, escape sequences in string
and bytes literals are interpreted according to rules similar to those
used by Standard C. The recognized escape sequences are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
+===================+===================================+=========+
| "\newline" | Backslash and newline ignored | |
+-------------------+-----------------------------------+---------+
| "\\" | Backslash ("\") | |
+-------------------+-----------------------------------+---------+
| "\'" | Single quote ("'") | |
+-------------------+-----------------------------------+---------+
| "\"" | Double quote (""") | |
+-------------------+-----------------------------------+---------+
| "\a" | ASCII Bell (BEL) | |
+-------------------+-----------------------------------+---------+
| "\b" | ASCII Backspace (BS) | |
+-------------------+-----------------------------------+---------+
| "\f" | ASCII Formfeed (FF) | |
+-------------------+-----------------------------------+---------+
| "\n" | ASCII Linefeed (LF) | |
+-------------------+-----------------------------------+---------+
| "\r" | ASCII Carriage Return (CR) | |
+-------------------+-----------------------------------+---------+
| "\t" | ASCII Horizontal Tab (TAB) | |
+-------------------+-----------------------------------+---------+
| "\v" | ASCII Vertical Tab (VT) | |
+-------------------+-----------------------------------+---------+
| "\ooo" | Character with octal value *ooo* | (1,3) |
+-------------------+-----------------------------------+---------+
| "\xhh" | Character with hex value *hh* | (2,3) |
+-------------------+-----------------------------------+---------+
Escape sequences only recognized in string literals are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
+===================+===================================+=========+
| "\N{name}" | Character named *name* in the | (4) |
| | Unicode database | |
+-------------------+-----------------------------------+---------+
| "\uxxxx" | Character with 16-bit hex value | (5) |
| | *xxxx* | |
+-------------------+-----------------------------------+---------+
| "\Uxxxxxxxx" | Character with 32-bit hex value | (6) |
| | *xxxxxxxx* | |
+-------------------+-----------------------------------+---------+
Notes:
1. As in Standard C, up to three octal digits are accepted.
2. Unlike in Standard C, exactly two hex digits are required.
3. In a bytes literal, hexadecimal and octal escapes denote the
byte with the given value. In a string literal, these escapes
denote a Unicode character with the given value.
4. Changed in version 3.3: Support for name aliases [1] has been
added.
5. Exactly four hex digits are required.
6. Any Unicode character can be encoded this way. Exactly eight
hex digits are required.
Unlike Standard C, all unrecognized escape sequences are left in the
string unchanged, i.e., *the backslash is left in the result*. (This
behavior is useful when debugging: if an escape sequence is mistyped,
the resulting output is more easily recognized as broken.) It is also
important to note that the escape sequences only recognized in string
literals fall into the category of unrecognized escapes for bytes
literals.
Changed in version 3.6: Unrecognized escape sequences produce a
DeprecationWarning. In some future version of Python they will be
a SyntaxError.
Even in a raw literal, quotes can be escaped with a backslash, but the
backslash remains in the result; for example, "r"\""" is a valid
string literal consisting of two characters: a backslash and a double
quote; "r"\"" is not a valid string literal (even a raw string cannot
end in an odd number of backslashes). Specifically, *a raw literal
cannot end in a single backslash* (since the backslash would escape
the following quote character). Note also that a single backslash
followed by a newline is interpreted as those two characters as part
of the literal, *not* as a line continuation.
uM Subscriptions
*************
A subscription selects an item of a sequence (string, tuple or list)
or mapping (dictionary) object:
subscription ::= primary "[" expression_list "]"
The primary must evaluate to an object that supports subscription
(lists or dictionaries for example). User-defined objects can support
subscription by defining a "__getitem__()" method.
For built-in objects, there are two types of objects that support
subscription:
If the primary is a mapping, the expression list must evaluate to an
object whose value is one of the keys of the mapping, and the
subscription selects the value in the mapping that corresponds to that
key. (The expression list is a tuple except if it has exactly one
item.)
If the primary is a sequence, the expression list must evaluate to an
integer or a slice (as discussed in the following section).
The formal syntax makes no special provision for negative indices in
sequences; however, built-in sequences all provide a "__getitem__()"
method that interprets negative indices by adding the length of the
sequence to the index (so that "x[-1]" selects the last item of "x").
The resulting value must be a nonnegative integer less than the number
of items in the sequence, and the subscription selects the item whose
index is that value (counting from zero). Since the support for
negative indices and slicing occurs in the object’s "__getitem__()"
method, subclasses overriding this method will need to explicitly add
that support.
A string’s items are characters. A character is not a separate data
type but a string of exactly one character.
ax Truth Value Testing
*******************
Any object can be tested for truth value, for use in an "if" or
"while" condition or as operand of the Boolean operations below.
By default, an object is considered true unless its class defines
either a "__bool__()" method that returns "False" or a "__len__()"
method that returns zero, when called with the object. [1] Here are
most of the built-in objects considered false:
* constants defined to be false: "None" and "False".
* zero of any numeric type: "0", "0.0", "0j", "Decimal(0)",
"Fraction(0, 1)"
* empty sequences and collections: "''", "()", "[]", "{}", "set()",
"range(0)"
Operations and built-in functions that have a Boolean result always
return "0" or "False" for false and "1" or "True" for true, unless
otherwise stated. (Important exception: the Boolean operations "or"
and "and" always return one of their operands.)
u= The "try" statement
*******************
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object or a tuple containing an item compatible with the exception.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed. All except
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred. "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.
The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed. Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a ‘cleanup’ handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return" or "break" statement, the saved exception
is discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’ A "continue" statement is illegal in
the "finally" clause. (The reason is a problem with the current
implementation — this restriction may be lifted in the future).
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
u�� The standard type hierarchy
***************************
Below is a list of the types that are built into Python. Extension
modules (written in C, Java, or other languages, depending on the
implementation) can define additional types. Future versions of
Python may add types to the type hierarchy (e.g., rational numbers,
efficiently stored arrays of integers, etc.), although such additions
will often be provided via the standard library instead.
Some of the type descriptions below contain a paragraph listing
‘special attributes.’ These are attributes that provide access to the
implementation and are not intended for general use. Their definition
may change in the future.
None
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name "None". It
is used to signify the absence of a value in many situations, e.g.,
it is returned from functions that don’t explicitly return
anything. Its truth value is false.
NotImplemented
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name
"NotImplemented". Numeric methods and rich comparison methods
should return this value if they do not implement the operation for
the operands provided. (The interpreter will then try the
reflected operation, or some other fallback, depending on the
operator.) Its truth value is true.
See Implementing the arithmetic operations for more details.
Ellipsis
This type has a single value. There is a single object with this
value. This object is accessed through the literal "..." or the
built-in name "Ellipsis". Its truth value is true.
"numbers.Number"
These are created by numeric literals and returned as results by
arithmetic operators and arithmetic built-in functions. Numeric
objects are immutable; once created their value never changes.
Python numbers are of course strongly related to mathematical
numbers, but subject to the limitations of numerical representation
in computers.
Python distinguishes between integers, floating point numbers, and
complex numbers:
"numbers.Integral"
These represent elements from the mathematical set of integers
(positive and negative).
There are two types of integers:
Integers ("int")
These represent numbers in an unlimited range, subject to
available (virtual) memory only. For the purpose of shift
and mask operations, a binary representation is assumed, and
negative numbers are represented in a variant of 2’s
complement which gives the illusion of an infinite string of
sign bits extending to the left.
Booleans ("bool")
These represent the truth values False and True. The two
objects representing the values "False" and "True" are the
only Boolean objects. The Boolean type is a subtype of the
integer type, and Boolean values behave like the values 0 and
1, respectively, in almost all contexts, the exception being
that when converted to a string, the strings ""False"" or
""True"" are returned, respectively.
The rules for integer representation are intended to give the
most meaningful interpretation of shift and mask operations
involving negative integers.
"numbers.Real" ("float")
These represent machine-level double precision floating point
numbers. You are at the mercy of the underlying machine
architecture (and C or Java implementation) for the accepted
range and handling of overflow. Python does not support single-
precision floating point numbers; the savings in processor and
memory usage that are usually the reason for using these are
dwarfed by the overhead of using objects in Python, so there is
no reason to complicate the language with two kinds of floating
point numbers.
"numbers.Complex" ("complex")
These represent complex numbers as a pair of machine-level
double precision floating point numbers. The same caveats apply
as for floating point numbers. The real and imaginary parts of a
complex number "z" can be retrieved through the read-only
attributes "z.real" and "z.imag".
Sequences
These represent finite ordered sets indexed by non-negative
numbers. The built-in function "len()" returns the number of items
of a sequence. When the length of a sequence is *n*, the index set
contains the numbers 0, 1, …, *n*-1. Item *i* of sequence *a* is
selected by "a[i]".
Sequences also support slicing: "a[i:j]" selects all items with
index *k* such that *i* "<=" *k* "<" *j*. When used as an
expression, a slice is a sequence of the same type. This implies
that the index set is renumbered so that it starts at 0.
Some sequences also support “extended slicing” with a third “step”
parameter: "a[i:j:k]" selects all items of *a* with index *x* where
"x = i + n*k", *n* ">=" "0" and *i* "<=" *x* "<" *j*.
Sequences are distinguished according to their mutability:
Immutable sequences
An object of an immutable sequence type cannot change once it is
created. (If the object contains references to other objects,
these other objects may be mutable and may be changed; however,
the collection of objects directly referenced by an immutable
object cannot change.)
The following types are immutable sequences:
Strings
A string is a sequence of values that represent Unicode code
points. All the code points in the range "U+0000 - U+10FFFF"
can be represented in a string. Python doesn’t have a "char"
type; instead, every code point in the string is represented
as a string object with length "1". The built-in function
"ord()" converts a code point from its string form to an
integer in the range "0 - 10FFFF"; "chr()" converts an
integer in the range "0 - 10FFFF" to the corresponding length
"1" string object. "str.encode()" can be used to convert a
"str" to "bytes" using the given text encoding, and
"bytes.decode()" can be used to achieve the opposite.
Tuples
The items of a tuple are arbitrary Python objects. Tuples of
two or more items are formed by comma-separated lists of
expressions. A tuple of one item (a ‘singleton’) can be
formed by affixing a comma to an expression (an expression by
itself does not create a tuple, since parentheses must be
usable for grouping of expressions). An empty tuple can be
formed by an empty pair of parentheses.
Bytes
A bytes object is an immutable array. The items are 8-bit
bytes, represented by integers in the range 0 <= x < 256.
Bytes literals (like "b'abc'") and the built-in "bytes()"
constructor can be used to create bytes objects. Also, bytes
objects can be decoded to strings via the "decode()" method.
Mutable sequences
Mutable sequences can be changed after they are created. The
subscription and slicing notations can be used as the target of
assignment and "del" (delete) statements.
There are currently two intrinsic mutable sequence types:
Lists
The items of a list are arbitrary Python objects. Lists are
formed by placing a comma-separated list of expressions in
square brackets. (Note that there are no special cases needed
to form lists of length 0 or 1.)
Byte Arrays
A bytearray object is a mutable array. They are created by
the built-in "bytearray()" constructor. Aside from being
mutable (and hence unhashable), byte arrays otherwise provide
the same interface and functionality as immutable "bytes"
objects.
The extension module "array" provides an additional example of a
mutable sequence type, as does the "collections" module.
Set types
These represent unordered, finite sets of unique, immutable
objects. As such, they cannot be indexed by any subscript. However,
they can be iterated over, and the built-in function "len()"
returns the number of items in a set. Common uses for sets are fast
membership testing, removing duplicates from a sequence, and
computing mathematical operations such as intersection, union,
difference, and symmetric difference.
For set elements, the same immutability rules apply as for
dictionary keys. Note that numeric types obey the normal rules for
numeric comparison: if two numbers compare equal (e.g., "1" and
"1.0"), only one of them can be contained in a set.
There are currently two intrinsic set types:
Sets
These represent a mutable set. They are created by the built-in
"set()" constructor and can be modified afterwards by several
methods, such as "add()".
Frozen sets
These represent an immutable set. They are created by the
built-in "frozenset()" constructor. As a frozenset is immutable
and *hashable*, it can be used again as an element of another
set, or as a dictionary key.
Mappings
These represent finite sets of objects indexed by arbitrary index
sets. The subscript notation "a[k]" selects the item indexed by "k"
from the mapping "a"; this can be used in expressions and as the
target of assignments or "del" statements. The built-in function
"len()" returns the number of items in a mapping.
There is currently a single intrinsic mapping type:
Dictionaries
These represent finite sets of objects indexed by nearly
arbitrary values. The only types of values not acceptable as
keys are values containing lists or dictionaries or other
mutable types that are compared by value rather than by object
identity, the reason being that the efficient implementation of
dictionaries requires a key’s hash value to remain constant.
Numeric types used for keys obey the normal rules for numeric
comparison: if two numbers compare equal (e.g., "1" and "1.0")
then they can be used interchangeably to index the same
dictionary entry.
Dictionaries are mutable; they can be created by the "{...}"
notation (see section Dictionary displays).
The extension modules "dbm.ndbm" and "dbm.gnu" provide
additional examples of mapping types, as does the "collections"
module.
Callable types
These are the types to which the function call operation (see
section Calls) can be applied:
User-defined functions
A user-defined function object is created by a function
definition (see section Function definitions). It should be
called with an argument list containing the same number of items
as the function’s formal parameter list.
Special attributes:
+---------------------------+---------------------------------+-------------+
| Attribute | Meaning | |
+===========================+=================================+=============+
| "__doc__" | The function’s documentation | Writable |
| | string, or "None" if | |
| | unavailable; not inherited by | |
| | subclasses | |
+---------------------------+---------------------------------+-------------+
| "__name__" | The function’s name | Writable |
+---------------------------+---------------------------------+-------------+
| "__qualname__" | The function’s *qualified name* | Writable |
| | New in version 3.3. | |
+---------------------------+---------------------------------+-------------+
| "__module__" | The name of the module the | Writable |
| | function was defined in, or | |
| | "None" if unavailable. | |
+---------------------------+---------------------------------+-------------+
| "__defaults__" | A tuple containing default | Writable |
| | argument values for those | |
| | arguments that have defaults, | |
| | or "None" if no arguments have | |
| | a default value | |
+---------------------------+---------------------------------+-------------+
| "__code__" | The code object representing | Writable |
| | the compiled function body. | |
+---------------------------+---------------------------------+-------------+
| "__globals__" | A reference to the dictionary | Read-only |
| | that holds the function’s | |
| | global variables — the global | |
| | namespace of the module in | |
| | which the function was defined. | |
+---------------------------+---------------------------------+-------------+
| "__dict__" | The namespace supporting | Writable |
| | arbitrary function attributes. | |
+---------------------------+---------------------------------+-------------+
| "__closure__" | "None" or a tuple of cells that | Read-only |
| | contain bindings for the | |
| | function’s free variables. | |
+---------------------------+---------------------------------+-------------+
| "__annotations__" | A dict containing annotations | Writable |
| | of parameters. The keys of the | |
| | dict are the parameter names, | |
| | and "'return'" for the return | |
| | annotation, if provided. | |
+---------------------------+---------------------------------+-------------+
| "__kwdefaults__" | A dict containing defaults for | Writable |
| | keyword-only parameters. | |
+---------------------------+---------------------------------+-------------+
Most of the attributes labelled “Writable” check the type of the
assigned value.
Function objects also support getting and setting arbitrary
attributes, which can be used, for example, to attach metadata
to functions. Regular attribute dot-notation is used to get and
set such attributes. *Note that the current implementation only
supports function attributes on user-defined functions. Function
attributes on built-in functions may be supported in the
future.*
Additional information about a function’s definition can be
retrieved from its code object; see the description of internal
types below.
Instance methods
An instance method object combines a class, a class instance and
any callable object (normally a user-defined function).
Special read-only attributes: "__self__" is the class instance
object, "__func__" is the function object; "__doc__" is the
method’s documentation (same as "__func__.__doc__"); "__name__"
is the method name (same as "__func__.__name__"); "__module__"
is the name of the module the method was defined in, or "None"
if unavailable.
Methods also support accessing (but not setting) the arbitrary
function attributes on the underlying function object.
User-defined method objects may be created when getting an
attribute of a class (perhaps via an instance of that class), if
that attribute is a user-defined function object or a class
method object.
When an instance method object is created by retrieving a user-
defined function object from a class via one of its instances,
its "__self__" attribute is the instance, and the method object
is said to be bound. The new method’s "__func__" attribute is
the original function object.
When a user-defined method object is created by retrieving
another method object from a class or instance, the behaviour is
the same as for a function object, except that the "__func__"
attribute of the new instance is not the original method object
but its "__func__" attribute.
When an instance method object is created by retrieving a class
method object from a class or instance, its "__self__" attribute
is the class itself, and its "__func__" attribute is the
function object underlying the class method.
When an instance method object is called, the underlying
function ("__func__") is called, inserting the class instance
("__self__") in front of the argument list. For instance, when
"C" is a class which contains a definition for a function "f()",
and "x" is an instance of "C", calling "x.f(1)" is equivalent to
calling "C.f(x, 1)".
When an instance method object is derived from a class method
object, the “class instance” stored in "__self__" will actually
be the class itself, so that calling either "x.f(1)" or "C.f(1)"
is equivalent to calling "f(C,1)" where "f" is the underlying
function.
Note that the transformation from function object to instance
method object happens each time the attribute is retrieved from
the instance. In some cases, a fruitful optimization is to
assign the attribute to a local variable and call that local
variable. Also notice that this transformation only happens for
user-defined functions; other callable objects (and all non-
callable objects) are retrieved without transformation. It is
also important to note that user-defined functions which are
attributes of a class instance are not converted to bound
methods; this *only* happens when the function is an attribute
of the class.
Generator functions
A function or method which uses the "yield" statement (see
section The yield statement) is called a *generator function*.
Such a function, when called, always returns an iterator object
which can be used to execute the body of the function: calling
the iterator’s "iterator.__next__()" method will cause the
function to execute until it provides a value using the "yield"
statement. When the function executes a "return" statement or
falls off the end, a "StopIteration" exception is raised and the
iterator will have reached the end of the set of values to be
returned.
Coroutine functions
A function or method which is defined using "async def" is
called a *coroutine function*. Such a function, when called,
returns a *coroutine* object. It may contain "await"
expressions, as well as "async with" and "async for" statements.
See also the Coroutine Objects section.
Asynchronous generator functions
A function or method which is defined using "async def" and
which uses the "yield" statement is called a *asynchronous
generator function*. Such a function, when called, returns an
asynchronous iterator object which can be used in an "async for"
statement to execute the body of the function.
Calling the asynchronous iterator’s "aiterator.__anext__()"
method will return an *awaitable* which when awaited will
execute until it provides a value using the "yield" expression.
When the function executes an empty "return" statement or falls
off the end, a "StopAsyncIteration" exception is raised and the
asynchronous iterator will have reached the end of the set of
values to be yielded.
Built-in functions
A built-in function object is a wrapper around a C function.
Examples of built-in functions are "len()" and "math.sin()"
("math" is a standard built-in module). The number and type of
the arguments are determined by the C function. Special read-
only attributes: "__doc__" is the function’s documentation
string, or "None" if unavailable; "__name__" is the function’s
name; "__self__" is set to "None" (but see the next item);
"__module__" is the name of the module the function was defined
in or "None" if unavailable.
Built-in methods
This is really a different disguise of a built-in function, this
time containing an object passed to the C function as an
implicit extra argument. An example of a built-in method is
"alist.append()", assuming *alist* is a list object. In this
case, the special read-only attribute "__self__" is set to the
object denoted by *alist*.
Classes
Classes are callable. These objects normally act as factories
for new instances of themselves, but variations are possible for
class types that override "__new__()". The arguments of the
call are passed to "__new__()" and, in the typical case, to
"__init__()" to initialize the new instance.
Class Instances
Instances of arbitrary classes can be made callable by defining
a "__call__()" method in their class.
Modules
Modules are a basic organizational unit of Python code, and are
created by the import system as invoked either by the "import"
statement (see "import"), or by calling functions such as
"importlib.import_module()" and built-in "__import__()". A module
object has a namespace implemented by a dictionary object (this is
the dictionary referenced by the "__globals__" attribute of
functions defined in the module). Attribute references are
translated to lookups in this dictionary, e.g., "m.x" is equivalent
to "m.__dict__["x"]". A module object does not contain the code
object used to initialize the module (since it isn’t needed once
the initialization is done).
Attribute assignment updates the module’s namespace dictionary,
e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".
Predefined (writable) attributes: "__name__" is the module’s name;
"__doc__" is the module’s documentation string, or "None" if
unavailable; "__annotations__" (optional) is a dictionary
containing *variable annotations* collected during module body
execution; "__file__" is the pathname of the file from which the
module was loaded, if it was loaded from a file. The "__file__"
attribute may be missing for certain types of modules, such as C
modules that are statically linked into the interpreter; for
extension modules loaded dynamically from a shared library, it is
the pathname of the shared library file.
Special read-only attribute: "__dict__" is the module’s namespace
as a dictionary object.
**CPython implementation detail:** Because of the way CPython
clears module dictionaries, the module dictionary will be cleared
when the module falls out of scope even if the dictionary still has
live references. To avoid this, copy the dictionary or keep the
module around while using its dictionary directly.
Custom classes
Custom class types are typically created by class definitions (see
section Class definitions). A class has a namespace implemented by
a dictionary object. Class attribute references are translated to
lookups in this dictionary, e.g., "C.x" is translated to
"C.__dict__["x"]" (although there are a number of hooks which allow
for other means of locating attributes). When the attribute name is
not found there, the attribute search continues in the base
classes. This search of the base classes uses the C3 method
resolution order which behaves correctly even in the presence of
‘diamond’ inheritance structures where there are multiple
inheritance paths leading back to a common ancestor. Additional
details on the C3 MRO used by Python can be found in the
documentation accompanying the 2.3 release at
https://www.python.org/download/releases/2.3/mro/.
When a class attribute reference (for class "C", say) would yield a
class method object, it is transformed into an instance method
object whose "__self__" attribute is "C". When it would yield a
static method object, it is transformed into the object wrapped by
the static method object. See section Implementing Descriptors for
another way in which attributes retrieved from a class may differ
from those actually contained in its "__dict__".
Class attribute assignments update the class’s dictionary, never
the dictionary of a base class.
A class object can be called (see above) to yield a class instance
(see below).
Special attributes: "__name__" is the class name; "__module__" is
the module name in which the class was defined; "__dict__" is the
dictionary containing the class’s namespace; "__bases__" is a tuple
containing the base classes, in the order of their occurrence in
the base class list; "__doc__" is the class’s documentation string,
or "None" if undefined; "__annotations__" (optional) is a
dictionary containing *variable annotations* collected during class
body execution.
Class instances
A class instance is created by calling a class object (see above).
A class instance has a namespace implemented as a dictionary which
is the first place in which attribute references are searched.
When an attribute is not found there, and the instance’s class has
an attribute by that name, the search continues with the class
attributes. If a class attribute is found that is a user-defined
function object, it is transformed into an instance method object
whose "__self__" attribute is the instance. Static method and
class method objects are also transformed; see above under
“Classes”. See section Implementing Descriptors for another way in
which attributes of a class retrieved via its instances may differ
from the objects actually stored in the class’s "__dict__". If no
class attribute is found, and the object’s class has a
"__getattr__()" method, that is called to satisfy the lookup.
Attribute assignments and deletions update the instance’s
dictionary, never a class’s dictionary. If the class has a
"__setattr__()" or "__delattr__()" method, this is called instead
of updating the instance dictionary directly.
Class instances can pretend to be numbers, sequences, or mappings
if they have methods with certain special names. See section
Special method names.
Special attributes: "__dict__" is the attribute dictionary;
"__class__" is the instance’s class.
I/O objects (also known as file objects)
A *file object* represents an open file. Various shortcuts are
available to create file objects: the "open()" built-in function,
and also "os.popen()", "os.fdopen()", and the "makefile()" method
of socket objects (and perhaps by other functions or methods
provided by extension modules).
The objects "sys.stdin", "sys.stdout" and "sys.stderr" are
initialized to file objects corresponding to the interpreter’s
standard input, output and error streams; they are all open in text
mode and therefore follow the interface defined by the
"io.TextIOBase" abstract class.
Internal types
A few types used internally by the interpreter are exposed to the
user. Their definitions may change with future versions of the
interpreter, but they are mentioned here for completeness.
Code objects
Code objects represent *byte-compiled* executable Python code,
or *bytecode*. The difference between a code object and a
function object is that the function object contains an explicit
reference to the function’s globals (the module in which it was
defined), while a code object contains no context; also the
default argument values are stored in the function object, not
in the code object (because they represent values calculated at
run-time). Unlike function objects, code objects are immutable
and contain no references (directly or indirectly) to mutable
objects.
Special read-only attributes: "co_name" gives the function name;
"co_argcount" is the number of positional arguments (including
arguments with default values); "co_nlocals" is the number of
local variables used by the function (including arguments);
"co_varnames" is a tuple containing the names of the local
variables (starting with the argument names); "co_cellvars" is a
tuple containing the names of local variables that are
referenced by nested functions; "co_freevars" is a tuple
containing the names of free variables; "co_code" is a string
representing the sequence of bytecode instructions; "co_consts"
is a tuple containing the literals used by the bytecode;
"co_names" is a tuple containing the names used by the bytecode;
"co_filename" is the filename from which the code was compiled;
"co_firstlineno" is the first line number of the function;
"co_lnotab" is a string encoding the mapping from bytecode
offsets to line numbers (for details see the source code of the
interpreter); "co_stacksize" is the required stack size
(including local variables); "co_flags" is an integer encoding a
number of flags for the interpreter.
The following flag bits are defined for "co_flags": bit "0x04"
is set if the function uses the "*arguments" syntax to accept an
arbitrary number of positional arguments; bit "0x08" is set if
the function uses the "**keywords" syntax to accept arbitrary
keyword arguments; bit "0x20" is set if the function is a
generator.
Future feature declarations ("from __future__ import division")
also use bits in "co_flags" to indicate whether a code object
was compiled with a particular feature enabled: bit "0x2000" is
set if the function was compiled with future division enabled;
bits "0x10" and "0x1000" were used in earlier versions of
Python.
Other bits in "co_flags" are reserved for internal use.
If a code object represents a function, the first item in
"co_consts" is the documentation string of the function, or
"None" if undefined.
Frame objects
Frame objects represent execution frames. They may occur in
traceback objects (see below).
Special read-only attributes: "f_back" is to the previous stack
frame (towards the caller), or "None" if this is the bottom
stack frame; "f_code" is the code object being executed in this
frame; "f_locals" is the dictionary used to look up local
variables; "f_globals" is used for global variables;
"f_builtins" is used for built-in (intrinsic) names; "f_lasti"
gives the precise instruction (this is an index into the
bytecode string of the code object).
Special writable attributes: "f_trace", if not "None", is a
function called at the start of each source code line (this is
used by the debugger); "f_lineno" is the current line number of
the frame — writing to this from within a trace function jumps
to the given line (only for the bottom-most frame). A debugger
can implement a Jump command (aka Set Next Statement) by writing
to f_lineno.
Frame objects support one method:
frame.clear()
This method clears all references to local variables held by
the frame. Also, if the frame belonged to a generator, the
generator is finalized. This helps break reference cycles
involving frame objects (for example when catching an
exception and storing its traceback for later use).
"RuntimeError" is raised if the frame is currently executing.
New in version 3.4.
Traceback objects
Traceback objects represent a stack trace of an exception. A
traceback object is created when an exception occurs. When the
search for an exception handler unwinds the execution stack, at
each unwound level a traceback object is inserted in front of
the current traceback. When an exception handler is entered,
the stack trace is made available to the program. (See section
The try statement.) It is accessible as the third item of the
tuple returned by "sys.exc_info()". When the program contains no
suitable handler, the stack trace is written (nicely formatted)
to the standard error stream; if the interpreter is interactive,
it is also made available to the user as "sys.last_traceback".
Special read-only attributes: "tb_next" is the next level in the
stack trace (towards the frame where the exception occurred), or
"None" if there is no next level; "tb_frame" points to the
execution frame of the current level; "tb_lineno" gives the line
number where the exception occurred; "tb_lasti" indicates the
precise instruction. The line number and last instruction in
the traceback may differ from the line number of its frame
object if the exception occurred in a "try" statement with no
matching except clause or with a finally clause.
Slice objects
Slice objects are used to represent slices for "__getitem__()"
methods. They are also created by the built-in "slice()"
function.
Special read-only attributes: "start" is the lower bound; "stop"
is the upper bound; "step" is the step value; each is "None" if
omitted. These attributes can have any type.
Slice objects support one method:
slice.indices(self, length)
This method takes a single integer argument *length* and
computes information about the slice that the slice object
would describe if applied to a sequence of *length* items.
It returns a tuple of three integers; respectively these are
the *start* and *stop* indices and the *step* or stride
length of the slice. Missing or out-of-bounds indices are
handled in a manner consistent with regular slices.
Static method objects
Static method objects provide a way of defeating the
transformation of function objects to method objects described
above. A static method object is a wrapper around any other
object, usually a user-defined method object. When a static
method object is retrieved from a class or a class instance, the
object actually returned is the wrapped object, which is not
subject to any further transformation. Static method objects are
not themselves callable, although the objects they wrap usually
are. Static method objects are created by the built-in
"staticmethod()" constructor.
Class method objects
A class method object, like a static method object, is a wrapper
around another object that alters the way in which that object
is retrieved from classes and class instances. The behaviour of
class method objects upon such retrieval is described above,
under “User-defined methods”. Class method objects are created
by the built-in "classmethod()" constructor.
a� Functions
*********
Function objects are created by function definitions. The only
operation on a function object is to call it: "func(argument-list)".
There are really two flavors of function objects: built-in functions
and user-defined functions. Both support the same operation (to call
the function), but the implementation is different, hence the
different object types.
See Function definitions for more information.
u8$ Mapping Types — "dict"
**********************
A *mapping* object maps *hashable* values to arbitrary objects.
Mappings are mutable objects. There is currently only one standard
mapping type, the *dictionary*. (For other containers see the built-
in "list", "set", and "tuple" classes, and the "collections" module.)
A dictionary’s keys are *almost* arbitrary values. Values that are
not *hashable*, that is, values containing lists, dictionaries or
other mutable types (that are compared by value rather than by object
identity) may not be used as keys. Numeric types used for keys obey
the normal rules for numeric comparison: if two numbers compare equal
(such as "1" and "1.0") then they can be used interchangeably to index
the same dictionary entry. (Note however, that since computers store
floating-point numbers as approximations it is usually unwise to use
them as dictionary keys.)
Dictionaries can be created by placing a comma-separated list of "key:
value" pairs within braces, for example: "{'jack': 4098, 'sjoerd':
4127}" or "{4098: 'jack', 4127: 'sjoerd'}", or by the "dict"
constructor.
class dict(**kwarg)
class dict(mapping, **kwarg)
class dict(iterable, **kwarg)
Return a new dictionary initialized from an optional positional
argument and a possibly empty set of keyword arguments.
If no positional argument is given, an empty dictionary is created.
If a positional argument is given and it is a mapping object, a
dictionary is created with the same key-value pairs as the mapping
object. Otherwise, the positional argument must be an *iterable*
object. Each item in the iterable must itself be an iterable with
exactly two objects. The first object of each item becomes a key
in the new dictionary, and the second object the corresponding
value. If a key occurs more than once, the last value for that key
becomes the corresponding value in the new dictionary.
If keyword arguments are given, the keyword arguments and their
values are added to the dictionary created from the positional
argument. If a key being added is already present, the value from
the keyword argument replaces the value from the positional
argument.
To illustrate, the following examples all return a dictionary equal
to "{"one": 1, "two": 2, "three": 3}":
>>> a = dict(one=1, two=2, three=3)
>>> b = {'one': 1, 'two': 2, 'three': 3}
>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
>>> e = dict({'three': 3, 'one': 1, 'two': 2})
>>> a == b == c == d == e
True
Providing keyword arguments as in the first example only works for
keys that are valid Python identifiers. Otherwise, any valid keys
can be used.
These are the operations that dictionaries support (and therefore,
custom mapping types should support too):
len(d)
Return the number of items in the dictionary *d*.
d[key]
Return the item of *d* with key *key*. Raises a "KeyError" if
*key* is not in the map.
If a subclass of dict defines a method "__missing__()" and *key*
is not present, the "d[key]" operation calls that method with
the key *key* as argument. The "d[key]" operation then returns
or raises whatever is returned or raised by the
"__missing__(key)" call. No other operations or methods invoke
"__missing__()". If "__missing__()" is not defined, "KeyError"
is raised. "__missing__()" must be a method; it cannot be an
instance variable:
>>> class Counter(dict):
... def __missing__(self, key):
... return 0
>>> c = Counter()
>>> c['red']
0
>>> c['red'] += 1
>>> c['red']
1
The example above shows part of the implementation of
"collections.Counter". A different "__missing__" method is used
by "collections.defaultdict".
d[key] = value
Set "d[key]" to *value*.
del d[key]
Remove "d[key]" from *d*. Raises a "KeyError" if *key* is not
in the map.
key in d
Return "True" if *d* has a key *key*, else "False".
key not in d
Equivalent to "not key in d".
iter(d)
Return an iterator over the keys of the dictionary. This is a
shortcut for "iter(d.keys())".
clear()
Remove all items from the dictionary.
copy()
Return a shallow copy of the dictionary.
classmethod fromkeys(seq[, value])
Create a new dictionary with keys from *seq* and values set to
*value*.
"fromkeys()" is a class method that returns a new dictionary.
*value* defaults to "None".
get(key[, default])
Return the value for *key* if *key* is in the dictionary, else
*default*. If *default* is not given, it defaults to "None", so
that this method never raises a "KeyError".
items()
Return a new view of the dictionary’s items ("(key, value)"
pairs). See the documentation of view objects.
keys()
Return a new view of the dictionary’s keys. See the
documentation of view objects.
pop(key[, default])
If *key* is in the dictionary, remove it and return its value,
else return *default*. If *default* is not given and *key* is
not in the dictionary, a "KeyError" is raised.
popitem()
Remove and return an arbitrary "(key, value)" pair from the
dictionary.
"popitem()" is useful to destructively iterate over a
dictionary, as often used in set algorithms. If the dictionary
is empty, calling "popitem()" raises a "KeyError".
setdefault(key[, default])
If *key* is in the dictionary, return its value. If not, insert
*key* with a value of *default* and return *default*. *default*
defaults to "None".
update([other])
Update the dictionary with the key/value pairs from *other*,
overwriting existing keys. Return "None".
"update()" accepts either another dictionary object or an
iterable of key/value pairs (as tuples or other iterables of
length two). If keyword arguments are specified, the dictionary
is then updated with those key/value pairs: "d.update(red=1,
blue=2)".
values()
Return a new view of the dictionary’s values. See the
documentation of view objects.
Dictionaries compare equal if and only if they have the same "(key,
value)" pairs. Order comparisons (‘<’, ‘<=’, ‘>=’, ‘>’) raise
"TypeError".
See also: "types.MappingProxyType" can be used to create a read-only
view of a "dict".
Dictionary view objects
=======================
The objects returned by "dict.keys()", "dict.values()" and
"dict.items()" are *view objects*. They provide a dynamic view on the
dictionary’s entries, which means that when the dictionary changes,
the view reflects these changes.
Dictionary views can be iterated over to yield their respective data,
and support membership tests:
len(dictview)
Return the number of entries in the dictionary.
iter(dictview)
Return an iterator over the keys, values or items (represented as
tuples of "(key, value)") in the dictionary.
Keys and values are iterated over in an arbitrary order which is
non-random, varies across Python implementations, and depends on
the dictionary’s history of insertions and deletions. If keys,
values and items views are iterated over with no intervening
modifications to the dictionary, the order of items will directly
correspond. This allows the creation of "(value, key)" pairs using
"zip()": "pairs = zip(d.values(), d.keys())". Another way to
create the same list is "pairs = [(v, k) for (k, v) in d.items()]".
Iterating views while adding or deleting entries in the dictionary
may raise a "RuntimeError" or fail to iterate over all entries.
x in dictview
Return "True" if *x* is in the underlying dictionary’s keys, values
or items (in the latter case, *x* should be a "(key, value)"
tuple).
Keys views are set-like since their entries are unique and hashable.
If all values are hashable, so that "(key, value)" pairs are unique
and hashable, then the items view is also set-like. (Values views are
not treated as set-like since the entries are generally not unique.)
For set-like views, all of the operations defined for the abstract
base class "collections.abc.Set" are available (for example, "==",
"<", or "^").
An example of dictionary view usage:
>>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500}
>>> keys = dishes.keys()
>>> values = dishes.values()
>>> # iteration
>>> n = 0
>>> for val in values:
... n += val
>>> print(n)
504
>>> # keys and values are iterated over in the same order
>>> list(keys)
['eggs', 'bacon', 'sausage', 'spam']
>>> list(values)
[2, 1, 1, 500]
>>> # view objects are dynamic and reflect dict changes
>>> del dishes['eggs']
>>> del dishes['sausage']
>>> list(keys)
['spam', 'bacon']
>>> # set operations
>>> keys & {'eggs', 'bacon', 'salad'}
{'bacon'}
>>> keys ^ {'sausage', 'juice'}
{'juice', 'sausage', 'bacon', 'spam'}
a� Methods
*******
Methods are functions that are called using the attribute notation.
There are two flavors: built-in methods (such as "append()" on lists)
and class instance methods. Built-in methods are described with the
types that support them.
If you access a method (a function defined in a class namespace)
through an instance, you get a special object: a *bound method* (also
called *instance method*) object. When called, it will add the "self"
argument to the argument list. Bound methods have two special read-
only attributes: "m.__self__" is the object on which the method
operates, and "m.__func__" is the function implementing the method.
Calling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to
calling "m.__func__(m.__self__, arg-1, arg-2, ..., arg-n)".
Like function objects, bound method objects support getting arbitrary
attributes. However, since method attributes are actually stored on
the underlying function object ("meth.__func__"), setting method
attributes on bound methods is disallowed. Attempting to set an
attribute on a method results in an "AttributeError" being raised. In
order to set a method attribute, you need to explicitly set it on the
underlying function object:
>>> class C:
... def method(self):
... pass
...
>>> c = C()
>>> c.method.whoami = 'my name is method' # can't set on the method
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'method' object has no attribute 'whoami'
>>> c.method.__func__.whoami = 'my name is method'
>>> c.method.whoami
'my name is method'
See The standard type hierarchy for more information.
u$ Modules
*******
The only special operation on a module is attribute access: "m.name",
where *m* is a module and *name* accesses a name defined in *m*’s
symbol table. Module attributes can be assigned to. (Note that the
"import" statement is not, strictly speaking, an operation on a module
object; "import foo" does not require a module object named *foo* to
exist, rather it requires an (external) *definition* for a module
named *foo* somewhere.)
A special attribute of every module is "__dict__". This is the
dictionary containing the module’s symbol table. Modifying this
dictionary will actually change the module’s symbol table, but direct
assignment to the "__dict__" attribute is not possible (you can write
"m.__dict__['a'] = 1", which defines "m.a" to be "1", but you can’t
write "m.__dict__ = {}"). Modifying "__dict__" directly is not
recommended.
Modules built into the interpreter are written like this: "<module
'sys' (built-in)>". If loaded from a file, they are written as
"<module 'os' from '/usr/local/lib/pythonX.Y/os.pyc'>".
u�Y Sequence Types — "list", "tuple", "range"
*****************************************
There are three basic sequence types: lists, tuples, and range
objects. Additional sequence types tailored for processing of binary
data and text strings are described in dedicated sections.
Common Sequence Operations
==========================
The operations in the following table are supported by most sequence
types, both mutable and immutable. The "collections.abc.Sequence" ABC
is provided to make it easier to correctly implement these operations
on custom sequence types.
This table lists the sequence operations sorted in ascending priority.
In the table, *s* and *t* are sequences of the same type, *n*, *i*,
*j* and *k* are integers and *x* is an arbitrary object that meets any
type and value restrictions imposed by *s*.
The "in" and "not in" operations have the same priorities as the
comparison operations. The "+" (concatenation) and "*" (repetition)
operations have the same priority as the corresponding numeric
operations. [3]
+----------------------------+----------------------------------+------------+
| Operation | Result | Notes |
+============================+==================================+============+
| "x in s" | "True" if an item of *s* is | (1) |
| | equal to *x*, else "False" | |
+----------------------------+----------------------------------+------------+
| "x not in s" | "False" if an item of *s* is | (1) |
| | equal to *x*, else "True" | |
+----------------------------+----------------------------------+------------+
| "s + t" | the concatenation of *s* and *t* | (6)(7) |
+----------------------------+----------------------------------+------------+
| "s * n" or "n * s" | equivalent to adding *s* to | (2)(7) |
| | itself *n* times | |
+----------------------------+----------------------------------+------------+
| "s[i]" | *i*th item of *s*, origin 0 | (3) |
+----------------------------+----------------------------------+------------+
| "s[i:j]" | slice of *s* from *i* to *j* | (3)(4) |
+----------------------------+----------------------------------+------------+
| "s[i:j:k]" | slice of *s* from *i* to *j* | (3)(5) |
| | with step *k* | |
+----------------------------+----------------------------------+------------+
| "len(s)" | length of *s* | |
+----------------------------+----------------------------------+------------+
| "min(s)" | smallest item of *s* | |
+----------------------------+----------------------------------+------------+
| "max(s)" | largest item of *s* | |
+----------------------------+----------------------------------+------------+
| "s.index(x[, i[, j]])" | index of the first occurrence of | (8) |
| | *x* in *s* (at or after index | |
| | *i* and before index *j*) | |
+----------------------------+----------------------------------+------------+
| "s.count(x)" | total number of occurrences of | |
| | *x* in *s* | |
+----------------------------+----------------------------------+------------+
Sequences of the same type also support comparisons. In particular,
tuples and lists are compared lexicographically by comparing
corresponding elements. This means that to compare equal, every
element must compare equal and the two sequences must be of the same
type and have the same length. (For full details see Comparisons in
the language reference.)
Notes:
1. While the "in" and "not in" operations are used only for simple
containment testing in the general case, some specialised sequences
(such as "str", "bytes" and "bytearray") also use them for
subsequence testing:
>>> "gg" in "eggs"
True
2. Values of *n* less than "0" are treated as "0" (which yields an
empty sequence of the same type as *s*). Note that items in the
sequence *s* are not copied; they are referenced multiple times.
This often haunts new Python programmers; consider:
>>> lists = [[]] * 3
>>> lists
[[], [], []]
>>> lists[0].append(3)
>>> lists
[[3], [3], [3]]
What has happened is that "[[]]" is a one-element list containing
an empty list, so all three elements of "[[]] * 3" are references
to this single empty list. Modifying any of the elements of
"lists" modifies this single list. You can create a list of
different lists this way:
>>> lists = [[] for i in range(3)]
>>> lists[0].append(3)
>>> lists[1].append(5)
>>> lists[2].append(7)
>>> lists
[[3], [5], [7]]
Further explanation is available in the FAQ entry How do I create a
multidimensional list?.
3. If *i* or *j* is negative, the index is relative to the end of
sequence *s*: "len(s) + i" or "len(s) + j" is substituted. But
note that "-0" is still "0".
4. The slice of *s* from *i* to *j* is defined as the sequence of
items with index *k* such that "i <= k < j". If *i* or *j* is
greater than "len(s)", use "len(s)". If *i* is omitted or "None",
use "0". If *j* is omitted or "None", use "len(s)". If *i* is
greater than or equal to *j*, the slice is empty.
5. The slice of *s* from *i* to *j* with step *k* is defined as the
sequence of items with index "x = i + n*k" such that "0 <= n <
(j-i)/k". In other words, the indices are "i", "i+k", "i+2*k",
"i+3*k" and so on, stopping when *j* is reached (but never
including *j*). When *k* is positive, *i* and *j* are reduced to
"len(s)" if they are greater. When *k* is negative, *i* and *j* are
reduced to "len(s) - 1" if they are greater. If *i* or *j* are
omitted or "None", they become “end” values (which end depends on
the sign of *k*). Note, *k* cannot be zero. If *k* is "None", it
is treated like "1".
6. Concatenating immutable sequences always results in a new
object. This means that building up a sequence by repeated
concatenation will have a quadratic runtime cost in the total
sequence length. To get a linear runtime cost, you must switch to
one of the alternatives below:
* if concatenating "str" objects, you can build a list and use
"str.join()" at the end or else write to an "io.StringIO"
instance and retrieve its value when complete
* if concatenating "bytes" objects, you can similarly use
"bytes.join()" or "io.BytesIO", or you can do in-place
concatenation with a "bytearray" object. "bytearray" objects are
mutable and have an efficient overallocation mechanism
* if concatenating "tuple" objects, extend a "list" instead
* for other types, investigate the relevant class documentation
7. Some sequence types (such as "range") only support item
sequences that follow specific patterns, and hence don’t support
sequence concatenation or repetition.
8. "index" raises "ValueError" when *x* is not found in *s*. Not
all implementations support passing the additional arguments *i*
and *j*. These arguments allow efficient searching of subsections
of the sequence. Passing the extra arguments is roughly equivalent
to using "s[i:j].index(x)", only without copying any data and with
the returned index being relative to the start of the sequence
rather than the start of the slice.
Immutable Sequence Types
========================
The only operation that immutable sequence types generally implement
that is not also implemented by mutable sequence types is support for
the "hash()" built-in.
This support allows immutable sequences, such as "tuple" instances, to
be used as "dict" keys and stored in "set" and "frozenset" instances.
Attempting to hash an immutable sequence that contains unhashable
values will result in "TypeError".
Mutable Sequence Types
======================
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
+================================+==================================+=======================+
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | appends *x* to the end of the | |
| | sequence (same as | |
| | "s[len(s):len(s)] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from *s* (same | (5) |
| | as "del s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of *s* | (5) |
| | (same as "s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | extends *s* with the contents of | |
| | *t* (for the most part the same | |
| | as "s[len(s):len(s)] = t") | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (6) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | inserts *x* into *s* at the | |
| | index given by *i* (same as | |
| | "s[i:i] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop([i])" | retrieves the item at *i* and | (2) |
| | also removes it from *s* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
| | where "s[i] == x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default
the last item is removed and returned.
3. "remove" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for
economy of space when reversing a large sequence. To remind users
that it operates by side effect, it does not return the reversed
sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don’t support slicing
operations (such as "dict" and "set")
New in version 3.3: "clear()" and "copy()" methods.
6. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the sequence.
Items in the sequence are not copied; they are referenced multiple
times, as explained for "s * n" under Common Sequence Operations.
Lists
=====
Lists are mutable sequences, typically used to store collections of
homogeneous items (where the precise degree of similarity will vary by
application).
class list([iterable])
Lists may be constructed in several ways:
* Using a pair of square brackets to denote the empty list: "[]"
* Using square brackets, separating items with commas: "[a]",
"[a, b, c]"
* Using a list comprehension: "[x for x in iterable]"
* Using the type constructor: "list()" or "list(iterable)"
The constructor builds a list whose items are the same and in the
same order as *iterable*’s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a list, a copy is made and
returned, similar to "iterable[:]". For example, "list('abc')"
returns "['a', 'b', 'c']" and "list( (1, 2, 3) )" returns "[1, 2,
3]". If no argument is given, the constructor creates a new empty
list, "[]".
Many other operations also produce lists, including the "sorted()"
built-in.
Lists implement all of the common and mutable sequence operations.
Lists also provide the following additional method:
sort(*, key=None, reverse=False)
This method sorts the list in place, using only "<" comparisons
between items. Exceptions are not suppressed - if any comparison
operations fail, the entire sort operation will fail (and the
list will likely be left in a partially modified state).
"sort()" accepts two arguments that can only be passed by
keyword (keyword-only arguments):
*key* specifies a function of one argument that is used to
extract a comparison key from each list element (for example,
"key=str.lower"). The key corresponding to each item in the list
is calculated once and then used for the entire sorting process.
The default value of "None" means that list items are sorted
directly without calculating a separate key value.
The "functools.cmp_to_key()" utility is available to convert a
2.x style *cmp* function to a *key* function.
*reverse* is a boolean value. If set to "True", then the list
elements are sorted as if each comparison were reversed.
This method modifies the sequence in place for economy of space
when sorting a large sequence. To remind users that it operates
by side effect, it does not return the sorted sequence (use
"sorted()" to explicitly request a new sorted list instance).
The "sort()" method is guaranteed to be stable. A sort is
stable if it guarantees not to change the relative order of
elements that compare equal — this is helpful for sorting in
multiple passes (for example, sort by department, then by salary
grade).
**CPython implementation detail:** While a list is being sorted,
the effect of attempting to mutate, or even inspect, the list is
undefined. The C implementation of Python makes the list appear
empty for the duration, and raises "ValueError" if it can detect
that the list has been mutated during a sort.
Tuples
======
Tuples are immutable sequences, typically used to store collections of
heterogeneous data (such as the 2-tuples produced by the "enumerate()"
built-in). Tuples are also used for cases where an immutable sequence
of homogeneous data is needed (such as allowing storage in a "set" or
"dict" instance).
class tuple([iterable])
Tuples may be constructed in a number of ways:
* Using a pair of parentheses to denote the empty tuple: "()"
* Using a trailing comma for a singleton tuple: "a," or "(a,)"
* Separating items with commas: "a, b, c" or "(a, b, c)"
* Using the "tuple()" built-in: "tuple()" or "tuple(iterable)"
The constructor builds a tuple whose items are the same and in the
same order as *iterable*’s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a tuple, it is returned
unchanged. For example, "tuple('abc')" returns "('a', 'b', 'c')"
and "tuple( [1, 2, 3] )" returns "(1, 2, 3)". If no argument is
given, the constructor creates a new empty tuple, "()".
Note that it is actually the comma which makes a tuple, not the
parentheses. The parentheses are optional, except in the empty
tuple case, or when they are needed to avoid syntactic ambiguity.
For example, "f(a, b, c)" is a function call with three arguments,
while "f((a, b, c))" is a function call with a 3-tuple as the sole
argument.
Tuples implement all of the common sequence operations.
For heterogeneous collections of data where access by name is clearer
than access by index, "collections.namedtuple()" may be a more
appropriate choice than a simple tuple object.
Ranges
======
The "range" type represents an immutable sequence of numbers and is
commonly used for looping a specific number of times in "for" loops.
class range(stop)
class range(start, stop[, step])
The arguments to the range constructor must be integers (either
built-in "int" or any object that implements the "__index__"
special method). If the *step* argument is omitted, it defaults to
"1". If the *start* argument is omitted, it defaults to "0". If
*step* is zero, "ValueError" is raised.
For a positive *step*, the contents of a range "r" are determined
by the formula "r[i] = start + step*i" where "i >= 0" and "r[i] <
stop".
For a negative *step*, the contents of the range are still
determined by the formula "r[i] = start + step*i", but the
constraints are "i >= 0" and "r[i] > stop".
A range object will be empty if "r[0]" does not meet the value
constraint. Ranges do support negative indices, but these are
interpreted as indexing from the end of the sequence determined by
the positive indices.
Ranges containing absolute values larger than "sys.maxsize" are
permitted but some features (such as "len()") may raise
"OverflowError".
Range examples:
>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(range(1, 11))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> list(range(0, 30, 5))
[0, 5, 10, 15, 20, 25]
>>> list(range(0, 10, 3))
[0, 3, 6, 9]
>>> list(range(0, -10, -1))
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
>>> list(range(0))
[]
>>> list(range(1, 0))
[]
Ranges implement all of the common sequence operations except
concatenation and repetition (due to the fact that range objects
can only represent sequences that follow a strict pattern and
repetition and concatenation will usually violate that pattern).
start
The value of the *start* parameter (or "0" if the parameter was
not supplied)
stop
The value of the *stop* parameter
step
The value of the *step* parameter (or "1" if the parameter was
not supplied)
The advantage of the "range" type over a regular "list" or "tuple" is
that a "range" object will always take the same (small) amount of
memory, no matter the size of the range it represents (as it only
stores the "start", "stop" and "step" values, calculating individual
items and subranges as needed).
Range objects implement the "collections.abc.Sequence" ABC, and
provide features such as containment tests, element index lookup,
slicing and support for negative indices (see Sequence Types — list,
tuple, range):
>>> r = range(0, 20, 2)
>>> r
range(0, 20, 2)
>>> 11 in r
False
>>> 10 in r
True
>>> r.index(10)
5
>>> r[5]
10
>>> r[:5]
range(0, 10, 2)
>>> r[-1]
18
Testing range objects for equality with "==" and "!=" compares them as
sequences. That is, two range objects are considered equal if they
represent the same sequence of values. (Note that two range objects
that compare equal might have different "start", "stop" and "step"
attributes, for example "range(0) == range(2, 1, 3)" or "range(0, 3,
2) == range(0, 4, 2)".)
Changed in version 3.2: Implement the Sequence ABC. Support slicing
and negative indices. Test "int" objects for membership in constant
time instead of iterating through all items.
Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range objects
based on the sequence of values they define (instead of comparing
based on object identity).
New in version 3.3: The "start", "stop" and "step" attributes.
See also:
* The linspace recipe shows how to implement a lazy version of
range suitable for floating point applications.
us Mutable Sequence Types
**********************
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
+================================+==================================+=======================+
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | appends *x* to the end of the | |
| | sequence (same as | |
| | "s[len(s):len(s)] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from *s* (same | (5) |
| | as "del s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of *s* | (5) |
| | (same as "s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | extends *s* with the contents of | |
| | *t* (for the most part the same | |
| | as "s[len(s):len(s)] = t") | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (6) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | inserts *x* into *s* at the | |
| | index given by *i* (same as | |
| | "s[i:i] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop([i])" | retrieves the item at *i* and | (2) |
| | also removes it from *s* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
| | where "s[i] == x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default
the last item is removed and returned.
3. "remove" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for
economy of space when reversing a large sequence. To remind users
that it operates by side effect, it does not return the reversed
sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don’t support slicing
operations (such as "dict" and "set")
New in version 3.3: "clear()" and "copy()" methods.
6. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the sequence.
Items in the sequence are not copied; they are referenced multiple
times, as explained for "s * n" under Common Sequence Operations.
a~ Unary arithmetic and bitwise operations
***************************************
All unary arithmetic and bitwise operations have the same priority:
u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr
The unary "-" (minus) operator yields the negation of its numeric
argument.
The unary "+" (plus) operator yields its numeric argument unchanged.
The unary "~" (invert) operator yields the bitwise inversion of its
integer argument. The bitwise inversion of "x" is defined as
"-(x+1)". It only applies to integral numbers.
In all three cases, if the argument does not have the proper type, a
"TypeError" exception is raised.
u� The "while" statement
*********************
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
u& The "with" statement
********************
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one “item” proceeds as
follows:
1. The context expression (the expression given in the "with_item")
is evaluated to obtain a context manager.
2. The context manager’s "__exit__()" is loaded for later use.
3. The context manager’s "__enter__()" method is invoked.
4. If a target was included in the "with" statement, the return
value from "__enter__()" is assigned to it.
Note: The "with" statement guarantees that if the "__enter__()"
method returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
5. The suite is executed.
6. The context manager’s "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
suite
is equivalent to
with A() as a:
with B() as b:
suite
Changed in version 3.1: Support for multiple context expressions.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
a, The "yield" statement
*********************
yield_stmt ::= yield_expression
A "yield" statement is semantically equivalent to a yield expression.
The yield statement can be used to omit the parentheses that would
otherwise be required in the equivalent yield expression statement.
For example, the yield statements
yield <expr>
yield from <expr>
are equivalent to the yield expression statements
(yield <expr>)
(yield from <expr>)
Yield expressions and statements are only used when defining a
*generator* function, and are only used in the body of the generator
function. Using yield in a function definition is sufficient to cause
that definition to create a generator function instead of a normal
function.
For full details of "yield" semantics, refer to the Yield expressions
section.
)M�assertZ
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numeric-typesZobjectszoperator-summary�passZpower�raise�returnzsequence-typesZshiftingZslicingsZ specialattrsZ specialnameszstring-methodsZstringsZ
subscriptions�truth�try�typesZtypesfunctionsZ typesmappingZ typesmethodsZ typesmodulesZtypesseqztypesseq-mutableZunary�while�with�yieldN)Ztopics� r r �)/usr/lib64/python3.6/pydoc_data/topics.py�<module> s0 ' t( "@X 1 =` u > 8 ,L C% G W *+0 $. L A 0"i# h3U ? f9 q g9% >
JPK ���Z���� � ' __pycache__/topics.cpython-36.opt-1.pycnu �[��� 3
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d d d
ddddddddddddddd d d d d d!d"d#d$d%d&d d'd(d)d*d+d,d-d.d/d0d1d2d3d4d5d6d7d8d9d:d;d<d=d>d?d@dAdBdCdDdEdFdGdHdIdJdKdL�MZ dMS )Nau The "assert" statement
**********************
Assert statements are a convenient way to insert debugging assertions
into a program:
assert_stmt ::= "assert" expression ["," expression]
The simple form, "assert expression", is equivalent to
if __debug__:
if not expression: raise AssertionError
The extended form, "assert expression1, expression2", is equivalent to
if __debug__:
if not expression1: raise AssertionError(expression2)
These equivalences assume that "__debug__" and "AssertionError" refer
to the built-in variables with those names. In the current
implementation, the built-in variable "__debug__" is "True" under
normal circumstances, "False" when optimization is requested (command
line option "-O"). The current code generator emits no code for an
assert statement when optimization is requested at compile time. Note
that it is unnecessary to include the source code for the expression
that failed in the error message; it will be displayed as part of the
stack trace.
Assignments to "__debug__" are illegal. The value for the built-in
variable is determined when the interpreter starts.
us+ Assignment statements
*********************
Assignment statements are used to (re)bind names to values and to
modify attributes or items of mutable objects:
assignment_stmt ::= (target_list "=")+ (starred_expression | yield_expression)
target_list ::= target ("," target)* [","]
target ::= identifier
| "(" [target_list] ")"
| "[" [target_list] "]"
| attributeref
| subscription
| slicing
| "*" target
(See section Primaries for the syntax definitions for *attributeref*,
*subscription*, and *slicing*.)
An assignment statement evaluates the expression list (remember that
this can be a single expression or a comma-separated list, the latter
yielding a tuple) and assigns the single resulting object to each of
the target lists, from left to right.
Assignment is defined recursively depending on the form of the target
(list). When a target is part of a mutable object (an attribute
reference, subscription or slicing), the mutable object must
ultimately perform the assignment and decide about its validity, and
may raise an exception if the assignment is unacceptable. The rules
observed by various types and the exceptions raised are given with the
definition of the object types (see section The standard type
hierarchy).
Assignment of an object to a target list, optionally enclosed in
parentheses or square brackets, is recursively defined as follows.
* If the target list is a single target with no trailing comma,
optionally in parentheses, the object is assigned to that target.
* Else: The object must be an iterable with the same number of items
as there are targets in the target list, and the items are assigned,
from left to right, to the corresponding targets.
* If the target list contains one target prefixed with an
asterisk, called a “starred” target: The object must be an
iterable with at least as many items as there are targets in the
target list, minus one. The first items of the iterable are
assigned, from left to right, to the targets before the starred
target. The final items of the iterable are assigned to the
targets after the starred target. A list of the remaining items
in the iterable is then assigned to the starred target (the list
can be empty).
* Else: The object must be an iterable with the same number of
items as there are targets in the target list, and the items are
assigned, from left to right, to the corresponding targets.
Assignment of an object to a single target is recursively defined as
follows.
* If the target is an identifier (name):
* If the name does not occur in a "global" or "nonlocal" statement
in the current code block: the name is bound to the object in the
current local namespace.
* Otherwise: the name is bound to the object in the global
namespace or the outer namespace determined by "nonlocal",
respectively.
The name is rebound if it was already bound. This may cause the
reference count for the object previously bound to the name to reach
zero, causing the object to be deallocated and its destructor (if it
has one) to be called.
* If the target is an attribute reference: The primary expression in
the reference is evaluated. It should yield an object with
assignable attributes; if this is not the case, "TypeError" is
raised. That object is then asked to assign the assigned object to
the given attribute; if it cannot perform the assignment, it raises
an exception (usually but not necessarily "AttributeError").
Note: If the object is a class instance and the attribute reference
occurs on both sides of the assignment operator, the RHS expression,
"a.x" can access either an instance attribute or (if no instance
attribute exists) a class attribute. The LHS target "a.x" is always
set as an instance attribute, creating it if necessary. Thus, the
two occurrences of "a.x" do not necessarily refer to the same
attribute: if the RHS expression refers to a class attribute, the
LHS creates a new instance attribute as the target of the
assignment:
class Cls:
x = 3 # class variable
inst = Cls()
inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3
This description does not necessarily apply to descriptor
attributes, such as properties created with "property()".
* If the target is a subscription: The primary expression in the
reference is evaluated. It should yield either a mutable sequence
object (such as a list) or a mapping object (such as a dictionary).
Next, the subscript expression is evaluated.
If the primary is a mutable sequence object (such as a list), the
subscript must yield an integer. If it is negative, the sequence’s
length is added to it. The resulting value must be a nonnegative
integer less than the sequence’s length, and the sequence is asked
to assign the assigned object to its item with that index. If the
index is out of range, "IndexError" is raised (assignment to a
subscripted sequence cannot add new items to a list).
If the primary is a mapping object (such as a dictionary), the
subscript must have a type compatible with the mapping’s key type,
and the mapping is then asked to create a key/datum pair which maps
the subscript to the assigned object. This can either replace an
existing key/value pair with the same key value, or insert a new
key/value pair (if no key with the same value existed).
For user-defined objects, the "__setitem__()" method is called with
appropriate arguments.
* If the target is a slicing: The primary expression in the
reference is evaluated. It should yield a mutable sequence object
(such as a list). The assigned object should be a sequence object
of the same type. Next, the lower and upper bound expressions are
evaluated, insofar they are present; defaults are zero and the
sequence’s length. The bounds should evaluate to integers. If
either bound is negative, the sequence’s length is added to it. The
resulting bounds are clipped to lie between zero and the sequence’s
length, inclusive. Finally, the sequence object is asked to replace
the slice with the items of the assigned sequence. The length of
the slice may be different from the length of the assigned sequence,
thus changing the length of the target sequence, if the target
sequence allows it.
**CPython implementation detail:** In the current implementation, the
syntax for targets is taken to be the same as for expressions, and
invalid syntax is rejected during the code generation phase, causing
less detailed error messages.
Although the definition of assignment implies that overlaps between
the left-hand side and the right-hand side are ‘simultaneous’ (for
example "a, b = b, a" swaps two variables), overlaps *within* the
collection of assigned-to variables occur left-to-right, sometimes
resulting in confusion. For instance, the following program prints
"[0, 2]":
x = [0, 1]
i = 0
i, x[i] = 1, 2 # i is updated, then x[i] is updated
print(x)
See also:
**PEP 3132** - Extended Iterable Unpacking
The specification for the "*target" feature.
Augmented assignment statements
===============================
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions of the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side. For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
Annotated assignment statements
===============================
Annotation assignment is the combination, in a single statement, of a
variable or attribute annotation and an optional assignment statement:
annotated_assignment_stmt ::= augtarget ":" expression ["=" expression]
The difference from normal Assignment statements is that only single
target and only single right hand side value is allowed.
For simple names as assignment targets, if in class or module scope,
the annotations are evaluated and stored in a special class or module
attribute "__annotations__" that is a dictionary mapping from variable
names (mangled if private) to evaluated annotations. This attribute is
writable and is automatically created at the start of class or module
body execution, if annotations are found statically.
For expressions as assignment targets, the annotations are evaluated
if in class or module scope, but not stored.
If a name is annotated in a function scope, then this name is local
for that scope. Annotations are never evaluated and stored in function
scopes.
If the right hand side is present, an annotated assignment performs
the actual assignment before evaluating annotations (where
applicable). If the right hand side is not present for an expression
target, then the interpreter evaluates the target except for the last
"__setitem__()" or "__setattr__()" call.
See also:
**PEP 526** - Syntax for Variable Annotations
The proposal that added syntax for annotating the types of
variables (including class variables and instance variables),
instead of expressing them through comments.
**PEP 484** - Type hints
The proposal that added the "typing" module to provide a standard
syntax for type annotations that can be used in static analysis
tools and IDEs.
a� Identifiers (Names)
*******************
An identifier occurring as an atom is a name. See section Identifiers
and keywords for lexical definition and section Naming and binding for
documentation of naming and binding.
When the name is bound to an object, evaluation of the atom yields
that object. When a name is not bound, an attempt to evaluate it
raises a "NameError" exception.
**Private name mangling:** When an identifier that textually occurs in
a class definition begins with two or more underscore characters and
does not end in two or more underscores, it is considered a *private
name* of that class. Private names are transformed to a longer form
before code is generated for them. The transformation inserts the
class name, with leading underscores removed and a single underscore
inserted, in front of the name. For example, the identifier "__spam"
occurring in a class named "Ham" will be transformed to "_Ham__spam".
This transformation is independent of the syntactical context in which
the identifier is used. If the transformed name is extremely long
(longer than 255 characters), implementation defined truncation may
happen. If the class name consists only of underscores, no
transformation is done.
u
Literals
********
Python supports string and bytes literals and various numeric
literals:
literal ::= stringliteral | bytesliteral
| integer | floatnumber | imagnumber
Evaluation of a literal yields an object of the given type (string,
bytes, integer, floating point number, complex number) with the given
value. The value may be approximated in the case of floating point
and imaginary (complex) literals. See section Literals for details.
All literals correspond to immutable data types, and hence the
object’s identity is less important than its value. Multiple
evaluations of literals with the same value (either the same
occurrence in the program text or a different occurrence) may obtain
the same object or a different object with the same value.
u- Customizing attribute access
****************************
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
return the (computed) attribute value or raise an "AttributeError"
exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note: This method may still be bypassed when looking up special
methods as the result of implicit invocation via language syntax
or built-in functions. See Special method lookup.
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should call the base class method with the same name, for example,
"object.__setattr__(self, name, value)".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
Customizing module attribute access
===================================
For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
setattr(self, attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note: Setting module "__class__" only affects lookups made using the
attribute access syntax – directly accessing the module globals
(whether by code within the module, or via a reference to the
module’s globals dictionary) is unaffected.
Changed in version 3.5: "__class__" module attribute is now writable.
Implementing Descriptors
========================
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents). In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".
object.__get__(self, instance, owner)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). *owner* is always the owner class, while *instance* is the
instance that the attribute was accessed through, or "None" when
the attribute is accessed through the *owner*. This method should
return the (computed) attribute value or raise an "AttributeError"
exception.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
object.__set_name__(self, owner, name)
Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.
New in version 3.6.
The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).
Invoking Descriptors
====================
In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a class, "A.x" is transformed into the call:
"A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined. A descriptor can define
any combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
=========
*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of *__dict__* and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)
The space saved over using *__dict__* can be significant.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. *__slots__*
reserves space for the declared variables and prevents the
automatic creation of *__dict__* and *__weakref__* for each
instance.
Notes on using *__slots__*
--------------------------
* When inheriting from a class without *__slots__*, the *__dict__*
and *__weakref__* attribute of the instances will always be
accessible.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
* Without a *__weakref__* variable for each instance, classes
defining *__slots__* do not support weak references to its
instances. If weak reference support is needed, then add
"'__weakref__'" to the sequence of strings in the *__slots__*
declaration.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is not limited to the
class where it is defined. *__slots__* declared in parents are
available in child classes. However, child subclasses will get a
*__dict__* and *__weakref__* unless they also define *__slots__*
(which should only contain names of any *additional* slots).
* If a class defines a slot also defined in a base class, the
instance variable defined by the base class slot is inaccessible
(except by retrieving its descriptor directly from the base class).
This renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
“variable-length” built-in types such as "int", "bytes" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings
may also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".
a� Attribute references
********************
An attribute reference is a primary followed by a period and a name:
attributeref ::= primary "." identifier
The primary must evaluate to an object of a type that supports
attribute references, which most objects do. This object is then
asked to produce the attribute whose name is the identifier. This
production can be customized by overriding the "__getattr__()" method.
If this attribute is not available, the exception "AttributeError" is
raised. Otherwise, the type and value of the object produced is
determined by the object. Multiple evaluations of the same attribute
reference may yield different objects.
a� Augmented assignment statements
*******************************
Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
augtarget ::= identifier | attributeref | subscription | slicing
augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
| ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions of the last three
symbols.)
An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.
An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.
Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side. For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".
With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.
For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
uj Binary arithmetic operations
****************************
The binary arithmetic operations have the conventional priority
levels. Note that some of these operations also apply to certain non-
numeric types. Apart from the power operator, there are only two
levels, one for multiplicative operators and one for additive
operators:
m_expr ::= u_expr | m_expr "*" u_expr | m_expr "@" m_expr |
m_expr "//" u_expr | m_expr "/" u_expr |
m_expr "%" u_expr
a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr
The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer and the other must be a sequence. In the former case, the
numbers are converted to a common type and then multiplied together.
In the latter case, sequence repetition is performed; a negative
repetition factor yields an empty sequence.
The "@" (at) operator is intended to be used for matrix
multiplication. No builtin Python types implement this operator.
New in version 3.5.
The "/" (division) and "//" (floor division) operators yield the
quotient of their arguments. The numeric arguments are first
converted to a common type. Division of integers yields a float, while
floor division of integers results in an integer; the result is that
of mathematical division with the ‘floor’ function applied to the
result. Division by zero raises the "ZeroDivisionError" exception.
The "%" (modulo) operator yields the remainder from the division of
the first argument by the second. The numeric arguments are first
converted to a common type. A zero right argument raises the
"ZeroDivisionError" exception. The arguments may be floating point
numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 +
0.34".) The modulo operator always yields a result with the same sign
as its second operand (or zero); the absolute value of the result is
strictly smaller than the absolute value of the second operand [1].
The floor division and modulo operators are connected by the following
identity: "x == (x//y)*y + (x%y)". Floor division and modulo are also
connected with the built-in function "divmod()": "divmod(x, y) ==
(x//y, x%y)". [2].
In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string objects to perform old-style
string formatting (also known as interpolation). The syntax for
string formatting is described in the Python Library Reference,
section printf-style String Formatting.
The floor division operator, the modulo operator, and the "divmod()"
function are not defined for complex numbers. Instead, convert to a
floating point number using the "abs()" function if appropriate.
The "+" (addition) operator yields the sum of its arguments. The
arguments must either both be numbers or both be sequences of the same
type. In the former case, the numbers are converted to a common type
and then added together. In the latter case, the sequences are
concatenated.
The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.
a$ Binary bitwise operations
*************************
Each of the three bitwise operations has a different priority level:
and_expr ::= shift_expr | and_expr "&" shift_expr
xor_expr ::= and_expr | xor_expr "^" and_expr
or_expr ::= xor_expr | or_expr "|" xor_expr
The "&" operator yields the bitwise AND of its arguments, which must
be integers.
The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be integers.
The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be integers.
ux Code Objects
************
Code objects are used by the implementation to represent “pseudo-
compiled” executable Python code such as a function body. They differ
from function objects because they don’t contain a reference to their
global execution environment. Code objects are returned by the built-
in "compile()" function and can be extracted from function objects
through their "__code__" attribute. See also the "code" module.
A code object can be executed or evaluated by passing it (instead of a
source string) to the "exec()" or "eval()" built-in functions.
See The standard type hierarchy for more information.
a. The Ellipsis Object
*******************
This object is commonly used by slicing (see Slicings). It supports
no special operations. There is exactly one ellipsis object, named
"Ellipsis" (a built-in name). "type(Ellipsis)()" produces the
"Ellipsis" singleton.
It is written as "Ellipsis" or "...".
u The Null Object
***************
This object is returned by functions that don’t explicitly return a
value. It supports no special operations. There is exactly one null
object, named "None" (a built-in name). "type(None)()" produces the
same singleton.
It is written as "None".
u5 Type Objects
************
Type objects represent the various object types. An object’s type is
accessed by the built-in function "type()". There are no special
operations on types. The standard module "types" defines names for
all standard built-in types.
Types are written like this: "<class 'int'>".
a� Boolean operations
******************
or_test ::= and_test | or_test "or" and_test
and_test ::= not_test | and_test "and" not_test
not_test ::= comparison | "not" not_test
In the context of Boolean operations, and also when expressions are
used by control flow statements, the following values are interpreted
as false: "False", "None", numeric zero of all types, and empty
strings and containers (including strings, tuples, lists,
dictionaries, sets and frozensets). All other values are interpreted
as true. User-defined objects can customize their truth value by
providing a "__bool__()" method.
The operator "not" yields "True" if its argument is false, "False"
otherwise.
The expression "x and y" first evaluates *x*; if *x* is false, its
value is returned; otherwise, *y* is evaluated and the resulting value
is returned.
The expression "x or y" first evaluates *x*; if *x* is true, its value
is returned; otherwise, *y* is evaluated and the resulting value is
returned.
Note that neither "and" nor "or" restrict the value and type they
return to "False" and "True", but rather return the last evaluated
argument. This is sometimes useful, e.g., if "s" is a string that
should be replaced by a default value if it is empty, the expression
"s or 'foo'" yields the desired value. Because "not" has to create a
new value, it returns a boolean value regardless of the type of its
argument (for example, "not 'foo'" produces "False" rather than "''".)
a$ The "break" statement
*********************
break_stmt ::= "break"
"break" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop.
It terminates the nearest enclosing loop, skipping the optional "else"
clause if the loop has one.
If a "for" loop is terminated by "break", the loop control target
keeps its current value.
When "break" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
loop.
u� Emulating callable objects
**************************
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method
is defined, "x(arg1, arg2, ...)" is a shorthand for
"x.__call__(arg1, arg2, ...)".
uC Calls
*****
A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:
call ::= primary "(" [argument_list [","] | comprehension] ")"
argument_list ::= positional_arguments ["," starred_and_keywords]
["," keywords_arguments]
| starred_and_keywords ["," keywords_arguments]
| keywords_arguments
positional_arguments ::= ["*"] expression ("," ["*"] expression)*
starred_and_keywords ::= ("*" expression | keyword_item)
("," "*" expression | "," keyword_item)*
keywords_arguments ::= (keyword_item | "**" expression)
("," keyword_item | "," "**" expression)*
keyword_item ::= identifier "=" expression
An optional trailing comma may be present after the positional and
keyword arguments but does not affect the semantics.
The primary must evaluate to a callable object (user-defined
functions, built-in functions, methods of built-in objects, class
objects, methods of class instances, and all objects having a
"__call__()" method are callable). All argument expressions are
evaluated before the call is attempted. Please refer to section
Function definitions for the syntax of formal *parameter* lists.
If keyword arguments are present, they are first converted to
positional arguments, as follows. First, a list of unfilled slots is
created for the formal parameters. If there are N positional
arguments, they are placed in the first N slots. Next, for each
keyword argument, the identifier is used to determine the
corresponding slot (if the identifier is the same as the first formal
parameter name, the first slot is used, and so on). If the slot is
already filled, a "TypeError" exception is raised. Otherwise, the
value of the argument is placed in the slot, filling it (even if the
expression is "None", it fills the slot). When all arguments have
been processed, the slots that are still unfilled are filled with the
corresponding default value from the function definition. (Default
values are calculated, once, when the function is defined; thus, a
mutable object such as a list or dictionary used as default value will
be shared by all calls that don’t specify an argument value for the
corresponding slot; this should usually be avoided.) If there are any
unfilled slots for which no default value is specified, a "TypeError"
exception is raised. Otherwise, the list of filled slots is used as
the argument list for the call.
**CPython implementation detail:** An implementation may provide
built-in functions whose positional parameters do not have names, even
if they are ‘named’ for the purpose of documentation, and which
therefore cannot be supplied by keyword. In CPython, this is the case
for functions implemented in C that use "PyArg_ParseTuple()" to parse
their arguments.
If there are more positional arguments than there are formal parameter
slots, a "TypeError" exception is raised, unless a formal parameter
using the syntax "*identifier" is present; in this case, that formal
parameter receives a tuple containing the excess positional arguments
(or an empty tuple if there were no excess positional arguments).
If any keyword argument does not correspond to a formal parameter
name, a "TypeError" exception is raised, unless a formal parameter
using the syntax "**identifier" is present; in this case, that formal
parameter receives a dictionary containing the excess keyword
arguments (using the keywords as keys and the argument values as
corresponding values), or a (new) empty dictionary if there were no
excess keyword arguments.
If the syntax "*expression" appears in the function call, "expression"
must evaluate to an *iterable*. Elements from these iterables are
treated as if they were additional positional arguments. For the call
"f(x1, x2, *y, x3, x4)", if *y* evaluates to a sequence *y1*, …, *yM*,
this is equivalent to a call with M+4 positional arguments *x1*, *x2*,
*y1*, …, *yM*, *x3*, *x4*.
A consequence of this is that although the "*expression" syntax may
appear *after* explicit keyword arguments, it is processed *before*
the keyword arguments (and any "**expression" arguments – see below).
So:
>>> def f(a, b):
... print(a, b)
...
>>> f(b=1, *(2,))
2 1
>>> f(a=1, *(2,))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: f() got multiple values for keyword argument 'a'
>>> f(1, *(2,))
1 2
It is unusual for both keyword arguments and the "*expression" syntax
to be used in the same call, so in practice this confusion does not
arise.
If the syntax "**expression" appears in the function call,
"expression" must evaluate to a *mapping*, the contents of which are
treated as additional keyword arguments. If a keyword is already
present (as an explicit keyword argument, or from another unpacking),
a "TypeError" exception is raised.
Formal parameters using the syntax "*identifier" or "**identifier"
cannot be used as positional argument slots or as keyword argument
names.
Changed in version 3.5: Function calls accept any number of "*" and
"**" unpackings, positional arguments may follow iterable unpackings
("*"), and keyword arguments may follow dictionary unpackings ("**").
Originally proposed by **PEP 448**.
A call always returns some value, possibly "None", unless it raises an
exception. How this value is computed depends on the type of the
callable object.
If it is—
a user-defined function:
The code block for the function is executed, passing it the
argument list. The first thing the code block will do is bind the
formal parameters to the arguments; this is described in section
Function definitions. When the code block executes a "return"
statement, this specifies the return value of the function call.
a built-in function or method:
The result is up to the interpreter; see Built-in Functions for the
descriptions of built-in functions and methods.
a class object:
A new instance of that class is returned.
a class instance method:
The corresponding user-defined function is called, with an argument
list that is one longer than the argument list of the call: the
instance becomes the first argument.
a class instance:
The class must define a "__call__()" method; the effect is then the
same as if that method was called.
u Class definitions
*****************
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [argument_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing. Classes without an inheritance list
inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.) When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [3] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.
The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__". Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.
Class creation can be customized heavily using metaclasses.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result is then bound to the class name.
**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. Descriptors can be used to create instance
variables with different implementation details.
See also:
**PEP 3115** - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the
current syntax, and the semantics for how classes with
metaclasses are constructed.
**PEP 3129** - Class Decorators
The proposal that added class decorators. Function and method
decorators were introduced in **PEP 318**.
u4) Comparisons
***********
Unlike C, all comparison operations in Python have the same priority,
which is lower than that of any arithmetic, shifting or bitwise
operation. Also unlike C, expressions like "a < b < c" have the
interpretation that is conventional in mathematics:
comparison ::= or_expr (comp_operator or_expr)*
comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="
| "is" ["not"] | ["not"] "in"
Comparisons yield boolean values: "True" or "False".
Comparisons can be chained arbitrarily, e.g., "x < y <= z" is
equivalent to "x < y and y <= z", except that "y" is evaluated only
once (but in both cases "z" is not evaluated at all when "x < y" is
found to be false).
Formally, if *a*, *b*, *c*, …, *y*, *z* are expressions and *op1*,
*op2*, …, *opN* are comparison operators, then "a op1 b op2 c ... y
opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except
that each expression is evaluated at most once.
Note that "a op1 b op2 c" doesn’t imply any kind of comparison between
*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though
perhaps not pretty).
Value comparisons
=================
The operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects. The objects do not need to have the same type.
Chapter Objects, values and types states that objects have a value (in
addition to type and identity). The value of an object is a rather
abstract notion in Python: For example, there is no canonical access
method for an object’s value. Also, there is no requirement that the
value of an object should be constructed in a particular way, e.g.
comprised of all its data attributes. Comparison operators implement a
particular notion of what the value of an object is. One can think of
them as defining the value of an object indirectly, by means of their
comparison implementation.
Because all types are (direct or indirect) subtypes of "object", they
inherit the default comparison behavior from "object". Types can
customize their comparison behavior by implementing *rich comparison
methods* like "__lt__()", described in Basic customization.
The default behavior for equality comparison ("==" and "!=") is based
on the identity of the objects. Hence, equality comparison of
instances with the same identity results in equality, and equality
comparison of instances with different identities results in
inequality. A motivation for this default behavior is the desire that
all objects should be reflexive (i.e. "x is y" implies "x == y").
A default order comparison ("<", ">", "<=", and ">=") is not provided;
an attempt raises "TypeError". A motivation for this default behavior
is the lack of a similar invariant as for equality.
The behavior of the default equality comparison, that instances with
different identities are always unequal, may be in contrast to what
types will need that have a sensible definition of object value and
value-based equality. Such types will need to customize their
comparison behavior, and in fact, a number of built-in types have done
that.
The following list describes the comparison behavior of the most
important built-in types.
* Numbers of built-in numeric types (Numeric Types — int, float,
complex) and of the standard library types "fractions.Fraction" and
"decimal.Decimal" can be compared within and across their types,
with the restriction that complex numbers do not support order
comparison. Within the limits of the types involved, they compare
mathematically (algorithmically) correct without loss of precision.
The not-a-number values "float('NaN')" and "Decimal('NaN')" are
special. They are identical to themselves ("x is x" is true) but
are not equal to themselves ("x == x" is false). Additionally,
comparing any number to a not-a-number value will return "False".
For example, both "3 < float('NaN')" and "float('NaN') < 3" will
return "False".
* Binary sequences (instances of "bytes" or "bytearray") can be
compared within and across their types. They compare
lexicographically using the numeric values of their elements.
* Strings (instances of "str") compare lexicographically using the
numerical Unicode code points (the result of the built-in function
"ord()") of their characters. [3]
Strings and binary sequences cannot be directly compared.
* Sequences (instances of "tuple", "list", or "range") can be
compared only within each of their types, with the restriction that
ranges do not support order comparison. Equality comparison across
these types results in inequality, and ordering comparison across
these types raises "TypeError".
Sequences compare lexicographically using comparison of
corresponding elements, whereby reflexivity of the elements is
enforced.
In enforcing reflexivity of elements, the comparison of collections
assumes that for a collection element "x", "x == x" is always true.
Based on that assumption, element identity is compared first, and
element comparison is performed only for distinct elements. This
approach yields the same result as a strict element comparison
would, if the compared elements are reflexive. For non-reflexive
elements, the result is different than for strict element
comparison, and may be surprising: The non-reflexive not-a-number
values for example result in the following comparison behavior when
used in a list:
>>> nan = float('NaN')
>>> nan is nan
True
>>> nan == nan
False <-- the defined non-reflexive behavior of NaN
>>> [nan] == [nan]
True <-- list enforces reflexivity and tests identity first
Lexicographical comparison between built-in collections works as
follows:
* For two collections to compare equal, they must be of the same
type, have the same length, and each pair of corresponding
elements must compare equal (for example, "[1,2] == (1,2)" is
false because the type is not the same).
* Collections that support order comparison are ordered the same
as their first unequal elements (for example, "[1,2,x] <= [1,2,y]"
has the same value as "x <= y"). If a corresponding element does
not exist, the shorter collection is ordered first (for example,
"[1,2] < [1,2,3]" is true).
* Mappings (instances of "dict") compare equal if and only if they
have equal *(key, value)* pairs. Equality comparison of the keys and
values enforces reflexivity.
Order comparisons ("<", ">", "<=", and ">=") raise "TypeError".
* Sets (instances of "set" or "frozenset") can be compared within
and across their types.
They define order comparison operators to mean subset and superset
tests. Those relations do not define total orderings (for example,
the two sets "{1,2}" and "{2,3}" are not equal, nor subsets of one
another, nor supersets of one another). Accordingly, sets are not
appropriate arguments for functions which depend on total ordering
(for example, "min()", "max()", and "sorted()" produce undefined
results given a list of sets as inputs).
Comparison of sets enforces reflexivity of its elements.
* Most other built-in types have no comparison methods implemented,
so they inherit the default comparison behavior.
User-defined classes that customize their comparison behavior should
follow some consistency rules, if possible:
* Equality comparison should be reflexive. In other words, identical
objects should compare equal:
"x is y" implies "x == y"
* Comparison should be symmetric. In other words, the following
expressions should have the same result:
"x == y" and "y == x"
"x != y" and "y != x"
"x < y" and "y > x"
"x <= y" and "y >= x"
* Comparison should be transitive. The following (non-exhaustive)
examples illustrate that:
"x > y and y > z" implies "x > z"
"x < y and y <= z" implies "x < z"
* Inverse comparison should result in the boolean negation. In other
words, the following expressions should have the same result:
"x == y" and "not x != y"
"x < y" and "not x >= y" (for total ordering)
"x > y" and "not x <= y" (for total ordering)
The last two expressions apply to totally ordered collections (e.g.
to sequences, but not to sets or mappings). See also the
"total_ordering()" decorator.
* The "hash()" result should be consistent with equality. Objects
that are equal should either have the same hash value, or be marked
as unhashable.
Python does not enforce these consistency rules. In fact, the
not-a-number values are an example for not following these rules.
Membership test operations
==========================
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y". If an exception is raised
during the iteration, it is as if "in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse true value of
"in".
Identity comparisons
====================
The operators "is" and "is not" test for object identity: "x is y" is
true if and only if *x* and *y* are the same object. Object identity
is determined using the "id()" function. "x is not y" yields the
inverse truth value. [4]
uxe Compound statements
*******************
Compound statements contain (groups of) other statements; they affect
or control the execution of those other statements in some way. In
general, compound statements span multiple lines, although in simple
incarnations a whole compound statement may be contained in one line.
The "if", "while" and "for" statements implement traditional control
flow constructs. "try" specifies exception handlers and/or cleanup
code for a group of statements, while the "with" statement allows the
execution of initialization and finalization code around a block of
code. Function and class definitions are also syntactically compound
statements.
A compound statement consists of one or more ‘clauses.’ A clause
consists of a header and a ‘suite.’ The clause headers of a
particular compound statement are all at the same indentation level.
Each clause header begins with a uniquely identifying keyword and ends
with a colon. A suite is a group of statements controlled by a
clause. A suite can be one or more semicolon-separated simple
statements on the same line as the header, following the header’s
colon, or it can be one or more indented statements on subsequent
lines. Only the latter form of a suite can contain nested compound
statements; the following is illegal, mostly because it wouldn’t be
clear to which "if" clause a following "else" clause would belong:
if test1: if test2: print(x)
Also note that the semicolon binds tighter than the colon in this
context, so that in the following example, either all or none of the
"print()" calls are executed:
if x < y < z: print(x); print(y); print(z)
Summarizing:
compound_stmt ::= if_stmt
| while_stmt
| for_stmt
| try_stmt
| with_stmt
| funcdef
| classdef
| async_with_stmt
| async_for_stmt
| async_funcdef
suite ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT
statement ::= stmt_list NEWLINE | compound_stmt
stmt_list ::= simple_stmt (";" simple_stmt)* [";"]
Note that statements always end in a "NEWLINE" possibly followed by a
"DEDENT". Also note that optional continuation clauses always begin
with a keyword that cannot start a statement, thus there are no
ambiguities (the ‘dangling "else"’ problem is solved in Python by
requiring nested "if" statements to be indented).
The formatting of the grammar rules in the following sections places
each clause on a separate line for clarity.
The "if" statement
==================
The "if" statement is used for conditional execution:
if_stmt ::= "if" expression ":" suite
("elif" expression ":" suite)*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
The "while" statement
=====================
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
The "for" statement
===================
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator. Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.
The for-loop makes assignments to the variables(s) in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop. Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".
Note: There is a subtlety when the sequence is being modified by the
loop (this can only occur for mutable sequences, e.g. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
The "try" statement
===================
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object or a tuple containing an item compatible with the exception.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed. All except
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred. "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.
The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed. Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a ‘cleanup’ handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return" or "break" statement, the saved exception
is discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’ A "continue" statement is illegal in
the "finally" clause. (The reason is a problem with the current
implementation — this restriction may be lifted in the future).
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
The "with" statement
====================
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one “item” proceeds as
follows:
1. The context expression (the expression given in the "with_item")
is evaluated to obtain a context manager.
2. The context manager’s "__exit__()" is loaded for later use.
3. The context manager’s "__enter__()" method is invoked.
4. If a target was included in the "with" statement, the return
value from "__enter__()" is assigned to it.
Note: The "with" statement guarantees that if the "__enter__()"
method returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
5. The suite is executed.
6. The context manager’s "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
suite
is equivalent to
with A() as a:
with B() as b:
suite
Changed in version 3.1: Support for multiple context expressions.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
Function definitions
====================
A function definition defines a user-defined function object (see
section The standard type hierarchy):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
| parameter_list_starargs
parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
| "**" parameter [","]
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [2]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
except that the original function is not temporarily bound to the name
"func".
When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted. If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.
**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call. This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended. A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from position arguments, from keyword
arguments, or from default values. If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type. Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed
used keyword arguments.
Parameters may have annotations of the form “": expression"” following
the parameter name. Any parameter may have an annotation even those
of the form "*identifier" or "**identifier". Functions may have
“return” annotation of the form “"-> expression"” after the parameter
list. These annotations can be any valid Python expression and are
evaluated when the function definition is executed. Annotations may
be evaluated in a different order than they appear in the source code.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.
**Programmer’s note:** Functions are first-class objects. A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around. Free variables used
in the nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
See also:
**PEP 3107** - Function Annotations
The original specification for function annotations.
Class definitions
=================
A class definition defines a class object (see section The standard
type hierarchy):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [argument_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing. Classes without an inheritance list
inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.) When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [3] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.
The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__". Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.
Class creation can be customized heavily using metaclasses.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result is then bound to the class name.
**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. Descriptors can be used to create instance
variables with different implementation details.
See also:
**PEP 3115** - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the
current syntax, and the semantics for how classes with
metaclasses are constructed.
**PEP 3129** - Class Decorators
The proposal that added class decorators. Function and method
decorators were introduced in **PEP 318**.
Coroutines
==========
New in version 3.5.
Coroutine function definition
-----------------------------
async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*). In the body of a coroutine, any "await" and
"async" identifiers become reserved keywords; "await" expressions,
"async for" and "async with" can only be used in coroutine bodies.
Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.
It is a "SyntaxError" to use "yield from" expressions in "async def"
coroutines.
An example of a coroutine function:
async def func(param1, param2):
do_stuff()
await some_coroutine()
The "async for" statement
-------------------------
async_for_stmt ::= "async" for_stmt
An *asynchronous iterable* is able to call asynchronous code in its
*iter* implementation, and *asynchronous iterator* can call
asynchronous code in its *next* method.
The "async for" statement allows convenient iteration over
asynchronous iterators.
The following code:
async for TARGET in ITER:
BLOCK
else:
BLOCK2
Is semantically equivalent to:
iter = (ITER)
iter = type(iter).__aiter__(iter)
running = True
while running:
try:
TARGET = await type(iter).__anext__(iter)
except StopAsyncIteration:
running = False
else:
BLOCK
else:
BLOCK2
See also "__aiter__()" and "__anext__()" for details.
It is a "SyntaxError" to use "async for" statement outside of an
"async def" function.
The "async with" statement
--------------------------
async_with_stmt ::= "async" with_stmt
An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.
The following code:
async with EXPR as VAR:
BLOCK
Is semantically equivalent to:
mgr = (EXPR)
aexit = type(mgr).__aexit__
aenter = type(mgr).__aenter__(mgr)
VAR = await aenter
try:
BLOCK
except:
if not await aexit(mgr, *sys.exc_info()):
raise
else:
await aexit(mgr, None, None, None)
See also "__aenter__()" and "__aexit__()" for details.
It is a "SyntaxError" to use "async with" statement outside of an
"async def" function.
See also:
**PEP 492** - Coroutines with async and await syntax
The proposal that made coroutines a proper standalone concept in
Python, and added supporting syntax.
-[ Footnotes ]-
[1] The exception is propagated to the invocation stack unless
there is a "finally" clause which happens to raise another
exception. That new exception causes the old one to be lost.
[2] A string literal appearing as the first statement in the
function body is transformed into the function’s "__doc__"
attribute and therefore the function’s *docstring*.
[3] A string literal appearing as the first statement in the class
body is transformed into the namespace’s "__doc__" item and
therefore the class’s *docstring*.
u� With Statement Context Managers
*******************************
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method’s return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller’s responsibility.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
a� The "continue" statement
************************
continue_stmt ::= "continue"
"continue" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition or "finally"
clause within that loop. It continues with the next cycle of the
nearest enclosing loop.
When "continue" passes control out of a "try" statement with a
"finally" clause, that "finally" clause is executed before really
starting the next loop cycle.
u� Arithmetic conversions
**********************
When a description of an arithmetic operator below uses the phrase
“the numeric arguments are converted to a common type,” this means
that the operator implementation for built-in types works as follows:
* If either argument is a complex number, the other is converted to
complex;
* otherwise, if either argument is a floating point number, the
other is converted to floating point;
* otherwise, both must be integers and no conversion is necessary.
Some additional rules apply for certain operators (e.g., a string as a
left argument to the ‘%’ operator). Extensions must define their own
conversion behavior.
u�3 Basic customization
*******************
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass’s "__new__()" method using
"super().__new__(cls[, ...])" with appropriate arguments and then
modifying the newly-created instance as necessary before returning
it.
If "__new__()" returns an instance of *cls*, then the new
instance’s "__init__()" method will be invoked like
"__init__(self[, ...])", where *self* is the new instance and the
remaining arguments are the same as were passed to "__new__()".
If "__new__()" does not return an instance of *cls*, then the new
instance’s "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class’s "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"super().__init__([args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.
It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.
It is not guaranteed that "__del__()" methods are called for
objects that still exist when the interpreter exits.
Note: "del x" doesn’t directly call "x.__del__()" — the former
decrements the reference count for "x" by one, and the latter is
only called when "x"’s reference count reaches zero.
**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.
See also: Documentation for the "gc" module.
Warning: Due to the precarious circumstances under which
"__del__()" methods are invoked, exceptions that occur during
their execution are ignored, and a warning is printed to
"sys.stderr" instead. In particular:
* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".
* "__del__()" can be executed during interpreter shutdown. As
a consequence, the global variables it needs to access
(including other modules) may already have been deleted or set
to "None". Python guarantees that globals whose name begins
with a single underscore are deleted from their module before
other globals are deleted; if no other references to such
globals exist, this may help in assuring that imported modules
are still available at the time when the "__del__()" method is
called.
object.__repr__(self)
Called by the "repr()" built-in function to compute the “official”
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an “informal”
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the “informal” or nicely printable string
representation of an object. The return value must be a string
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by bytes to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function, and by extension,
evaluation of formatted string literals and the "str.format()"
method, to produce a “formatted” string representation of an
object. The "format_spec" argument is a string that contains a
description of the formatting options desired. The interpretation
of the "format_spec" argument is up to the type implementing
"__format__()", however most classes will either delegate
formatting to one of the built-in types, or use a similar
formatting option syntax.
See Format Specification Mini-Language for a description of the
standard formatting syntax.
The return value must be a string object.
Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
By default, "__ne__()" delegates to "__eq__()" and inverts the
result unless it is "NotImplemented". There are no other implied
relationships among the comparison operators, for example, the
truth of "(x<y or x==y)" does not imply "x<=y". To automatically
generate ordering operations from a single root operation, see
"functools.total_ordering()".
See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
Note: "hash()" truncates the value returned from an object’s
custom "__hash__()" method to the size of a "Py_ssize_t". This
is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit
builds. If an object’s "__hash__()" must interoperate on builds
of different bit sizes, be sure to check the width on all
supported builds. An easy way to do this is with "python -c
"import sys; print(sys.hash_info.width)"".
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key’s hash value is immutable (if the object’s hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj, collections.Hashable)".
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
<ParentClass>.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.Hashable)" call.
Note: By default, the "__hash__()" values of str, bytes and
datetime objects are “salted” with an unpredictable random value.
Although they remain constant within an individual Python
process, they are not predictable between repeated invocations of
Python.This is intended to provide protection against a denial-
of-service caused by carefully-chosen inputs that exploit the
worst case performance of a dict insertion, O(n^2) complexity.
See http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of
dicts, sets and other mappings. Python has never made guarantees
about this ordering (and it typically varies between 32-bit and
64-bit builds).See also "PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
uiF "pdb" — The Python Debugger
***************************
**Source code:** Lib/pdb.py
======================================================================
The module "pdb" defines an interactive source code debugger for
Python programs. It supports setting (conditional) breakpoints and
single stepping at the source line level, inspection of stack frames,
source code listing, and evaluation of arbitrary Python code in the
context of any stack frame. It also supports post-mortem debugging
and can be called under program control.
The debugger is extensible – it is actually defined as the class
"Pdb". This is currently undocumented but easily understood by reading
the source. The extension interface uses the modules "bdb" and "cmd".
The debugger’s prompt is "(Pdb)". Typical usage to run a program under
control of the debugger is:
>>> import pdb
>>> import mymodule
>>> pdb.run('mymodule.test()')
> <string>(0)?()
(Pdb) continue
> <string>(1)?()
(Pdb) continue
NameError: 'spam'
> <string>(1)?()
(Pdb)
Changed in version 3.3: Tab-completion via the "readline" module is
available for commands and command arguments, e.g. the current global
and local names are offered as arguments of the "p" command.
"pdb.py" can also be invoked as a script to debug other scripts. For
example:
python3 -m pdb myscript.py
When invoked as a script, pdb will automatically enter post-mortem
debugging if the program being debugged exits abnormally. After post-
mortem debugging (or after normal exit of the program), pdb will
restart the program. Automatic restarting preserves pdb’s state (such
as breakpoints) and in most cases is more useful than quitting the
debugger upon program’s exit.
New in version 3.2: "pdb.py" now accepts a "-c" option that executes
commands as if given in a ".pdbrc" file, see Debugger Commands.
The typical usage to break into the debugger from a running program is
to insert
import pdb; pdb.set_trace()
at the location you want to break into the debugger. You can then
step through the code following this statement, and continue running
without the debugger using the "continue" command.
The typical usage to inspect a crashed program is:
>>> import pdb
>>> import mymodule
>>> mymodule.test()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "./mymodule.py", line 4, in test
test2()
File "./mymodule.py", line 3, in test2
print(spam)
NameError: spam
>>> pdb.pm()
> ./mymodule.py(3)test2()
-> print(spam)
(Pdb)
The module defines the following functions; each enters the debugger
in a slightly different way:
pdb.run(statement, globals=None, locals=None)
Execute the *statement* (given as a string or a code object) under
debugger control. The debugger prompt appears before any code is
executed; you can set breakpoints and type "continue", or you can
step through the statement using "step" or "next" (all these
commands are explained below). The optional *globals* and *locals*
arguments specify the environment in which the code is executed; by
default the dictionary of the module "__main__" is used. (See the
explanation of the built-in "exec()" or "eval()" functions.)
pdb.runeval(expression, globals=None, locals=None)
Evaluate the *expression* (given as a string or a code object)
under debugger control. When "runeval()" returns, it returns the
value of the expression. Otherwise this function is similar to
"run()".
pdb.runcall(function, *args, **kwds)
Call the *function* (a function or method object, not a string)
with the given arguments. When "runcall()" returns, it returns
whatever the function call returned. The debugger prompt appears
as soon as the function is entered.
pdb.set_trace()
Enter the debugger at the calling stack frame. This is useful to
hard-code a breakpoint at a given point in a program, even if the
code is not otherwise being debugged (e.g. when an assertion
fails).
pdb.post_mortem(traceback=None)
Enter post-mortem debugging of the given *traceback* object. If no
*traceback* is given, it uses the one of the exception that is
currently being handled (an exception must be being handled if the
default is to be used).
pdb.pm()
Enter post-mortem debugging of the traceback found in
"sys.last_traceback".
The "run*" functions and "set_trace()" are aliases for instantiating
the "Pdb" class and calling the method of the same name. If you want
to access further features, you have to do this yourself:
class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None, nosigint=False, readrc=True)
"Pdb" is the debugger class.
The *completekey*, *stdin* and *stdout* arguments are passed to the
underlying "cmd.Cmd" class; see the description there.
The *skip* argument, if given, must be an iterable of glob-style
module name patterns. The debugger will not step into frames that
originate in a module that matches one of these patterns. [1]
By default, Pdb sets a handler for the SIGINT signal (which is sent
when the user presses "Ctrl-C" on the console) when you give a
"continue" command. This allows you to break into the debugger
again by pressing "Ctrl-C". If you want Pdb not to touch the
SIGINT handler, set *nosigint* to true.
The *readrc* argument defaults to true and controls whether Pdb
will load .pdbrc files from the filesystem.
Example call to enable tracing with *skip*:
import pdb; pdb.Pdb(skip=['django.*']).set_trace()
New in version 3.1: The *skip* argument.
New in version 3.2: The *nosigint* argument. Previously, a SIGINT
handler was never set by Pdb.
Changed in version 3.6: The *readrc* argument.
run(statement, globals=None, locals=None)
runeval(expression, globals=None, locals=None)
runcall(function, *args, **kwds)
set_trace()
See the documentation for the functions explained above.
Debugger Commands
=================
The commands recognized by the debugger are listed below. Most
commands can be abbreviated to one or two letters as indicated; e.g.
"h(elp)" means that either "h" or "help" can be used to enter the help
command (but not "he" or "hel", nor "H" or "Help" or "HELP").
Arguments to commands must be separated by whitespace (spaces or
tabs). Optional arguments are enclosed in square brackets ("[]") in
the command syntax; the square brackets must not be typed.
Alternatives in the command syntax are separated by a vertical bar
("|").
Entering a blank line repeats the last command entered. Exception: if
the last command was a "list" command, the next 11 lines are listed.
Commands that the debugger doesn’t recognize are assumed to be Python
statements and are executed in the context of the program being
debugged. Python statements can also be prefixed with an exclamation
point ("!"). This is a powerful way to inspect the program being
debugged; it is even possible to change a variable or call a function.
When an exception occurs in such a statement, the exception name is
printed but the debugger’s state is not changed.
The debugger supports aliases. Aliases can have parameters which
allows one a certain level of adaptability to the context under
examination.
Multiple commands may be entered on a single line, separated by ";;".
(A single ";" is not used as it is the separator for multiple commands
in a line that is passed to the Python parser.) No intelligence is
applied to separating the commands; the input is split at the first
";;" pair, even if it is in the middle of a quoted string.
If a file ".pdbrc" exists in the user’s home directory or in the
current directory, it is read in and executed as if it had been typed
at the debugger prompt. This is particularly useful for aliases. If
both files exist, the one in the home directory is read first and
aliases defined there can be overridden by the local file.
Changed in version 3.2: ".pdbrc" can now contain commands that
continue debugging, such as "continue" or "next". Previously, these
commands had no effect.
h(elp) [command]
Without argument, print the list of available commands. With a
*command* as argument, print help about that command. "help pdb"
displays the full documentation (the docstring of the "pdb"
module). Since the *command* argument must be an identifier, "help
exec" must be entered to get help on the "!" command.
w(here)
Print a stack trace, with the most recent frame at the bottom. An
arrow indicates the current frame, which determines the context of
most commands.
d(own) [count]
Move the current frame *count* (default one) levels down in the
stack trace (to a newer frame).
u(p) [count]
Move the current frame *count* (default one) levels up in the stack
trace (to an older frame).
b(reak) [([filename:]lineno | function) [, condition]]
With a *lineno* argument, set a break there in the current file.
With a *function* argument, set a break at the first executable
statement within that function. The line number may be prefixed
with a filename and a colon, to specify a breakpoint in another
file (probably one that hasn’t been loaded yet). The file is
searched on "sys.path". Note that each breakpoint is assigned a
number to which all the other breakpoint commands refer.
If a second argument is present, it is an expression which must
evaluate to true before the breakpoint is honored.
Without argument, list all breaks, including for each breakpoint,
the number of times that breakpoint has been hit, the current
ignore count, and the associated condition if any.
tbreak [([filename:]lineno | function) [, condition]]
Temporary breakpoint, which is removed automatically when it is
first hit. The arguments are the same as for "break".
cl(ear) [filename:lineno | bpnumber [bpnumber ...]]
With a *filename:lineno* argument, clear all the breakpoints at
this line. With a space separated list of breakpoint numbers, clear
those breakpoints. Without argument, clear all breaks (but first
ask confirmation).
disable [bpnumber [bpnumber ...]]
Disable the breakpoints given as a space separated list of
breakpoint numbers. Disabling a breakpoint means it cannot cause
the program to stop execution, but unlike clearing a breakpoint, it
remains in the list of breakpoints and can be (re-)enabled.
enable [bpnumber [bpnumber ...]]
Enable the breakpoints specified.
ignore bpnumber [count]
Set the ignore count for the given breakpoint number. If count is
omitted, the ignore count is set to 0. A breakpoint becomes active
when the ignore count is zero. When non-zero, the count is
decremented each time the breakpoint is reached and the breakpoint
is not disabled and any associated condition evaluates to true.
condition bpnumber [condition]
Set a new *condition* for the breakpoint, an expression which must
evaluate to true before the breakpoint is honored. If *condition*
is absent, any existing condition is removed; i.e., the breakpoint
is made unconditional.
commands [bpnumber]
Specify a list of commands for breakpoint number *bpnumber*. The
commands themselves appear on the following lines. Type a line
containing just "end" to terminate the commands. An example:
(Pdb) commands 1
(com) p some_variable
(com) end
(Pdb)
To remove all commands from a breakpoint, type commands and follow
it immediately with "end"; that is, give no commands.
With no *bpnumber* argument, commands refers to the last breakpoint
set.
You can use breakpoint commands to start your program up again.
Simply use the continue command, or step, or any other command that
resumes execution.
Specifying any command resuming execution (currently continue,
step, next, return, jump, quit and their abbreviations) terminates
the command list (as if that command was immediately followed by
end). This is because any time you resume execution (even with a
simple next or step), you may encounter another breakpoint—which
could have its own command list, leading to ambiguities about which
list to execute.
If you use the ‘silent’ command in the command list, the usual
message about stopping at a breakpoint is not printed. This may be
desirable for breakpoints that are to print a specific message and
then continue. If none of the other commands print anything, you
see no sign that the breakpoint was reached.
s(tep)
Execute the current line, stop at the first possible occasion
(either in a function that is called or on the next line in the
current function).
n(ext)
Continue execution until the next line in the current function is
reached or it returns. (The difference between "next" and "step"
is that "step" stops inside a called function, while "next"
executes called functions at (nearly) full speed, only stopping at
the next line in the current function.)
unt(il) [lineno]
Without argument, continue execution until the line with a number
greater than the current one is reached.
With a line number, continue execution until a line with a number
greater or equal to that is reached. In both cases, also stop when
the current frame returns.
Changed in version 3.2: Allow giving an explicit line number.
r(eturn)
Continue execution until the current function returns.
c(ont(inue))
Continue execution, only stop when a breakpoint is encountered.
j(ump) lineno
Set the next line that will be executed. Only available in the
bottom-most frame. This lets you jump back and execute code again,
or jump forward to skip code that you don’t want to run.
It should be noted that not all jumps are allowed – for instance it
is not possible to jump into the middle of a "for" loop or out of a
"finally" clause.
l(ist) [first[, last]]
List source code for the current file. Without arguments, list 11
lines around the current line or continue the previous listing.
With "." as argument, list 11 lines around the current line. With
one argument, list 11 lines around at that line. With two
arguments, list the given range; if the second argument is less
than the first, it is interpreted as a count.
The current line in the current frame is indicated by "->". If an
exception is being debugged, the line where the exception was
originally raised or propagated is indicated by ">>", if it differs
from the current line.
New in version 3.2: The ">>" marker.
ll | longlist
List all source code for the current function or frame.
Interesting lines are marked as for "list".
New in version 3.2.
a(rgs)
Print the argument list of the current function.
p expression
Evaluate the *expression* in the current context and print its
value.
Note: "print()" can also be used, but is not a debugger command —
this executes the Python "print()" function.
pp expression
Like the "p" command, except the value of the expression is pretty-
printed using the "pprint" module.
whatis expression
Print the type of the *expression*.
source expression
Try to get source code for the given object and display it.
New in version 3.2.
display [expression]
Display the value of the expression if it changed, each time
execution stops in the current frame.
Without expression, list all display expressions for the current
frame.
New in version 3.2.
undisplay [expression]
Do not display the expression any more in the current frame.
Without expression, clear all display expressions for the current
frame.
New in version 3.2.
interact
Start an interactive interpreter (using the "code" module) whose
global namespace contains all the (global and local) names found in
the current scope.
New in version 3.2.
alias [name [command]]
Create an alias called *name* that executes *command*. The command
must *not* be enclosed in quotes. Replaceable parameters can be
indicated by "%1", "%2", and so on, while "%*" is replaced by all
the parameters. If no command is given, the current alias for
*name* is shown. If no arguments are given, all aliases are listed.
Aliases may be nested and can contain anything that can be legally
typed at the pdb prompt. Note that internal pdb commands *can* be
overridden by aliases. Such a command is then hidden until the
alias is removed. Aliasing is recursively applied to the first
word of the command line; all other words in the line are left
alone.
As an example, here are two useful aliases (especially when placed
in the ".pdbrc" file):
# Print instance variables (usage "pi classInst")
alias pi for k in %1.__dict__.keys(): print("%1.",k,"=",%1.__dict__[k])
# Print instance variables in self
alias ps pi self
unalias name
Delete the specified alias.
! statement
Execute the (one-line) *statement* in the context of the current
stack frame. The exclamation point can be omitted unless the first
word of the statement resembles a debugger command. To set a
global variable, you can prefix the assignment command with a
"global" statement on the same line, e.g.:
(Pdb) global list_options; list_options = ['-l']
(Pdb)
run [args ...]
restart [args ...]
Restart the debugged Python program. If an argument is supplied,
it is split with "shlex" and the result is used as the new
"sys.argv". History, breakpoints, actions and debugger options are
preserved. "restart" is an alias for "run".
q(uit)
Quit from the debugger. The program being executed is aborted.
-[ Footnotes ]-
[1] Whether a frame is considered to originate in a certain module
is determined by the "__name__" in the frame globals.
a� The "del" statement
*******************
del_stmt ::= "del" target_list
Deletion is recursively defined very similar to the way assignment is
defined. Rather than spelling it out in full details, here are some
hints.
Deletion of a target list recursively deletes each target, from left
to right.
Deletion of a name removes the binding of that name from the local or
global namespace, depending on whether the name occurs in a "global"
statement in the same code block. If the name is unbound, a
"NameError" exception will be raised.
Deletion of attribute references, subscriptions and slicings is passed
to the primary object involved; deletion of a slicing is in general
equivalent to assignment of an empty slice of the right type (but even
this is determined by the sliced object).
Changed in version 3.2: Previously it was illegal to delete a name
from the local namespace if it occurs as a free variable in a nested
block.
u Dictionary displays
*******************
A dictionary display is a possibly empty series of key/datum pairs
enclosed in curly braces:
dict_display ::= "{" [key_datum_list | dict_comprehension] "}"
key_datum_list ::= key_datum ("," key_datum)* [","]
key_datum ::= expression ":" expression | "**" or_expr
dict_comprehension ::= expression ":" expression comp_for
A dictionary display yields a new dictionary object.
If a comma-separated sequence of key/datum pairs is given, they are
evaluated from left to right to define the entries of the dictionary:
each key object is used as a key into the dictionary to store the
corresponding datum. This means that you can specify the same key
multiple times in the key/datum list, and the final dictionary’s value
for that key will be the last one given.
A double asterisk "**" denotes *dictionary unpacking*. Its operand
must be a *mapping*. Each mapping item is added to the new
dictionary. Later values replace values already set by earlier
key/datum pairs and earlier dictionary unpackings.
New in version 3.5: Unpacking into dictionary displays, originally
proposed by **PEP 448**.
A dict comprehension, in contrast to list and set comprehensions,
needs two expressions separated with a colon followed by the usual
“for” and “if” clauses. When the comprehension is run, the resulting
key and value elements are inserted in the new dictionary in the order
they are produced.
Restrictions on the types of the key values are listed earlier in
section The standard type hierarchy. (To summarize, the key type
should be *hashable*, which excludes all mutable objects.) Clashes
between duplicate keys are not detected; the last datum (textually
rightmost in the display) stored for a given key value prevails.
a� Interaction with dynamic features
*********************************
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
aB The "if" statement
******************
The "if" statement is used for conditional execution:
if_stmt ::= "if" expression ":" suite
("elif" expression ":" suite)*
["else" ":" suite]
It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
u� Exceptions
**********
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note: Exception messages are not part of the Python API. Their
contents may change from one version of Python to the next without
warning and should not be relied on by code which will run under
multiple versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by
these operations is not available at the time the module is
compiled.
u$ Execution model
***************
Structure of a program
======================
A Python program is constructed from code blocks. A *block* is a piece
of Python program text that is executed as a unit. The following are
blocks: a module, a function body, and a class definition. Each
command typed interactively is a block. A script file (a file given
as standard input to the interpreter or specified as a command line
argument to the interpreter) is a code block. A script command (a
command specified on the interpreter command line with the "-c"
option) is a code block. The string argument passed to the built-in
functions "eval()" and "exec()" is a code block.
A code block is executed in an *execution frame*. A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block’s execution has
completed.
Naming and binding
==================
Binding of names
----------------
*Names* refer to objects. Names are introduced by name binding
operations.
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore. This form
may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global". If a name is bound at the
module level, it is a global variable. (The variables of the module
code block are local and global.) If a variable is used in a code
block but not defined there, it is a *free variable*.
Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.
Resolution of names
-------------------
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block’s *environment*.
When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins". The global namespace is searched first. If
the name is not found there, the builtins namespace is searched. The
"global" statement must precede all uses of the name.
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope. This means that the following
will fail:
class A:
a = 42
b = list(a + i for i in range(10))
Builtins and restricted execution
---------------------------------
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.
Interaction with dynamic features
---------------------------------
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
Exceptions
==========
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note: Exception messages are not part of the Python API. Their
contents may change from one version of Python to the next without
warning and should not be relied on by code which will run under
multiple versions of the interpreter.
See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by
these operations is not available at the time the module is
compiled.
uo Expression lists
****************
expression_list ::= expression ("," expression)* [","]
starred_list ::= starred_item ("," starred_item)* [","]
starred_expression ::= expression | (starred_item ",")* [starred_item]
starred_item ::= expression | "*" or_expr
Except when part of a list or set display, an expression list
containing at least one comma yields a tuple. The length of the tuple
is the number of expressions in the list. The expressions are
evaluated from left to right.
An asterisk "*" denotes *iterable unpacking*. Its operand must be an
*iterable*. The iterable is expanded into a sequence of items, which
are included in the new tuple, list, or set, at the site of the
unpacking.
New in version 3.5: Iterable unpacking in expression lists, originally
proposed by **PEP 448**.
The trailing comma is required only to create a single tuple (a.k.a. a
*singleton*); it is optional in all other cases. A single expression
without a trailing comma doesn’t create a tuple, but rather yields the
value of that expression. (To create an empty tuple, use an empty pair
of parentheses: "()".)
a� Floating point literals
***********************
Floating point literals are described by the following lexical
definitions:
floatnumber ::= pointfloat | exponentfloat
pointfloat ::= [digitpart] fraction | digitpart "."
exponentfloat ::= (digitpart | pointfloat) exponent
digitpart ::= digit (["_"] digit)*
fraction ::= "." digitpart
exponent ::= ("e" | "E") ["+" | "-"] digitpart
Note that the integer and exponent parts are always interpreted using
radix 10. For example, "077e010" is legal, and denotes the same number
as "77e10". The allowed range of floating point literals is
implementation-dependent. As in integer literals, underscores are
supported for digit grouping.
Some examples of floating point literals:
3.14 10. .001 1e100 3.14e-10 0e0 3.14_15_93
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
u�
The "for" statement
*******************
The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite
["else" ":" suite]
The expression list is evaluated once; it should yield an iterable
object. An iterator is created for the result of the
"expression_list". The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator. Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.
The for-loop makes assignments to the variables(s) in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop. Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".
Note: There is a subtlety when the sequence is being modified by the
loop (this can only occur for mutable sequences, e.g. lists). An
internal counter is used to keep track of which item is used next,
and this is incremented on each iteration. When this counter has
reached the length of the sequence the loop terminates. This means
that if the suite deletes the current (or a previous) item from the
sequence, the next item will be skipped (since it gets the index of
the current item which has already been treated). Likewise, if the
suite inserts an item in the sequence before the current item, the
current item will be treated again the next time through the loop.
This can lead to nasty bugs that can be avoided by making a
temporary copy using a slice of the whole sequence, e.g.,
for x in a[:]:
if x < 0: a.remove(x)
u Y Format String Syntax
********************
The "str.format()" method and the "Formatter" class share the same
syntax for format strings (although in the case of "Formatter",
subclasses can define their own format string syntax). The syntax is
related to that of formatted string literals, but there are
differences.
Format strings contain “replacement fields” surrounded by curly braces
"{}". Anything that is not contained in braces is considered literal
text, which is copied unchanged to the output. If you need to include
a brace character in the literal text, it can be escaped by doubling:
"{{" and "}}".
The grammar for a replacement field is as follows:
replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}"
field_name ::= arg_name ("." attribute_name | "[" element_index "]")*
arg_name ::= [identifier | digit+]
attribute_name ::= identifier
element_index ::= digit+ | index_string
index_string ::= <any source character except "]"> +
conversion ::= "r" | "s" | "a"
format_spec ::= <described in the next section>
In less formal terms, the replacement field can start with a
*field_name* that specifies the object whose value is to be formatted
and inserted into the output instead of the replacement field. The
*field_name* is optionally followed by a *conversion* field, which is
preceded by an exclamation point "'!'", and a *format_spec*, which is
preceded by a colon "':'". These specify a non-default format for the
replacement value.
See also the Format Specification Mini-Language section.
The *field_name* itself begins with an *arg_name* that is either a
number or a keyword. If it’s a number, it refers to a positional
argument, and if it’s a keyword, it refers to a named keyword
argument. If the numerical arg_names in a format string are 0, 1, 2,
… in sequence, they can all be omitted (not just some) and the numbers
0, 1, 2, … will be automatically inserted in that order. Because
*arg_name* is not quote-delimited, it is not possible to specify
arbitrary dictionary keys (e.g., the strings "'10'" or "':-]'") within
a format string. The *arg_name* can be followed by any number of index
or attribute expressions. An expression of the form "'.name'" selects
the named attribute using "getattr()", while an expression of the form
"'[index]'" does an index lookup using "__getitem__()".
Changed in version 3.1: The positional argument specifiers can be
omitted for "str.format()", so "'{} {}'.format(a, b)" is equivalent to
"'{0} {1}'.format(a, b)".
Changed in version 3.4: The positional argument specifiers can be
omitted for "Formatter".
Some simple format string examples:
"First, thou shalt count to {0}" # References first positional argument
"Bring me a {}" # Implicitly references the first positional argument
"From {} to {}" # Same as "From {0} to {1}"
"My quest is {name}" # References keyword argument 'name'
"Weight in tons {0.weight}" # 'weight' attribute of first positional arg
"Units destroyed: {players[0]}" # First element of keyword argument 'players'.
The *conversion* field causes a type coercion before formatting.
Normally, the job of formatting a value is done by the "__format__()"
method of the value itself. However, in some cases it is desirable to
force a type to be formatted as a string, overriding its own
definition of formatting. By converting the value to a string before
calling "__format__()", the normal formatting logic is bypassed.
Three conversion flags are currently supported: "'!s'" which calls
"str()" on the value, "'!r'" which calls "repr()" and "'!a'" which
calls "ascii()".
Some examples:
"Harold's a clever {0!s}" # Calls str() on the argument first
"Bring out the holy {name!r}" # Calls repr() on the argument first
"More {!a}" # Calls ascii() on the argument first
The *format_spec* field contains a specification of how the value
should be presented, including such details as field width, alignment,
padding, decimal precision and so on. Each value type can define its
own “formatting mini-language” or interpretation of the *format_spec*.
Most built-in types support a common formatting mini-language, which
is described in the next section.
A *format_spec* field can also include nested replacement fields
within it. These nested replacement fields may contain a field name,
conversion flag and format specification, but deeper nesting is not
allowed. The replacement fields within the format_spec are
substituted before the *format_spec* string is interpreted. This
allows the formatting of a value to be dynamically specified.
See the Format examples section for some examples.
Format Specification Mini-Language
==================================
“Format specifications” are used within replacement fields contained
within a format string to define how individual values are presented
(see Format String Syntax and Formatted string literals). They can
also be passed directly to the built-in "format()" function. Each
formattable type may define how the format specification is to be
interpreted.
Most built-in types implement the following options for format
specifications, although some of the formatting options are only
supported by the numeric types.
A general convention is that an empty format string ("""") produces
the same result as if you had called "str()" on the value. A non-empty
format string typically modifies the result.
The general form of a *standard format specifier* is:
format_spec ::= [[fill]align][sign][#][0][width][grouping_option][.precision][type]
fill ::= <any character>
align ::= "<" | ">" | "=" | "^"
sign ::= "+" | "-" | " "
width ::= digit+
grouping_option ::= "_" | ","
precision ::= digit+
type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"
If a valid *align* value is specified, it can be preceded by a *fill*
character that can be any character and defaults to a space if
omitted. It is not possible to use a literal curly brace (“"{"” or
“"}"”) as the *fill* character in a formatted string literal or when
using the "str.format()" method. However, it is possible to insert a
curly brace with a nested replacement field. This limitation doesn’t
affect the "format()" function.
The meaning of the various alignment options is as follows:
+-----------+------------------------------------------------------------+
| Option | Meaning |
+===========+============================================================+
| "'<'" | Forces the field to be left-aligned within the available |
| | space (this is the default for most objects). |
+-----------+------------------------------------------------------------+
| "'>'" | Forces the field to be right-aligned within the available |
| | space (this is the default for numbers). |
+-----------+------------------------------------------------------------+
| "'='" | Forces the padding to be placed after the sign (if any) |
| | but before the digits. This is used for printing fields |
| | in the form ‘+000000120’. This alignment option is only |
| | valid for numeric types. It becomes the default when ‘0’ |
| | immediately precedes the field width. |
+-----------+------------------------------------------------------------+
| "'^'" | Forces the field to be centered within the available |
| | space. |
+-----------+------------------------------------------------------------+
Note that unless a minimum field width is defined, the field width
will always be the same size as the data to fill it, so that the
alignment option has no meaning in this case.
The *sign* option is only valid for number types, and can be one of
the following:
+-----------+------------------------------------------------------------+
| Option | Meaning |
+===========+============================================================+
| "'+'" | indicates that a sign should be used for both positive as |
| | well as negative numbers. |
+-----------+------------------------------------------------------------+
| "'-'" | indicates that a sign should be used only for negative |
| | numbers (this is the default behavior). |
+-----------+------------------------------------------------------------+
| space | indicates that a leading space should be used on positive |
| | numbers, and a minus sign on negative numbers. |
+-----------+------------------------------------------------------------+
The "'#'" option causes the “alternate form” to be used for the
conversion. The alternate form is defined differently for different
types. This option is only valid for integer, float, complex and
Decimal types. For integers, when binary, octal, or hexadecimal output
is used, this option adds the prefix respective "'0b'", "'0o'", or
"'0x'" to the output value. For floats, complex and Decimal the
alternate form causes the result of the conversion to always contain a
decimal-point character, even if no digits follow it. Normally, a
decimal-point character appears in the result of these conversions
only if a digit follows it. In addition, for "'g'" and "'G'"
conversions, trailing zeros are not removed from the result.
The "','" option signals the use of a comma for a thousands separator.
For a locale aware separator, use the "'n'" integer presentation type
instead.
Changed in version 3.1: Added the "','" option (see also **PEP 378**).
The "'_'" option signals the use of an underscore for a thousands
separator for floating point presentation types and for integer
presentation type "'d'". For integer presentation types "'b'", "'o'",
"'x'", and "'X'", underscores will be inserted every 4 digits. For
other presentation types, specifying this option is an error.
Changed in version 3.6: Added the "'_'" option (see also **PEP 515**).
*width* is a decimal integer defining the minimum field width. If not
specified, then the field width will be determined by the content.
When no explicit alignment is given, preceding the *width* field by a
zero ("'0'") character enables sign-aware zero-padding for numeric
types. This is equivalent to a *fill* character of "'0'" with an
*alignment* type of "'='".
The *precision* is a decimal number indicating how many digits should
be displayed after the decimal point for a floating point value
formatted with "'f'" and "'F'", or before and after the decimal point
for a floating point value formatted with "'g'" or "'G'". For non-
number types the field indicates the maximum field size - in other
words, how many characters will be used from the field content. The
*precision* is not allowed for integer values.
Finally, the *type* determines how the data should be presented.
The available string presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'s'" | String format. This is the default type for strings and |
| | may be omitted. |
+-----------+------------------------------------------------------------+
| None | The same as "'s'". |
+-----------+------------------------------------------------------------+
The available integer presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'b'" | Binary format. Outputs the number in base 2. |
+-----------+------------------------------------------------------------+
| "'c'" | Character. Converts the integer to the corresponding |
| | unicode character before printing. |
+-----------+------------------------------------------------------------+
| "'d'" | Decimal Integer. Outputs the number in base 10. |
+-----------+------------------------------------------------------------+
| "'o'" | Octal format. Outputs the number in base 8. |
+-----------+------------------------------------------------------------+
| "'x'" | Hex format. Outputs the number in base 16, using lower- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'X'" | Hex format. Outputs the number in base 16, using upper- |
| | case letters for the digits above 9. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'d'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| None | The same as "'d'". |
+-----------+------------------------------------------------------------+
In addition to the above presentation types, integers can be formatted
with the floating point presentation types listed below (except "'n'"
and "None"). When doing so, "float()" is used to convert the integer
to a floating point number before formatting.
The available presentation types for floating point and decimal values
are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'e'" | Exponent notation. Prints the number in scientific |
| | notation using the letter ‘e’ to indicate the exponent. |
| | The default precision is "6". |
+-----------+------------------------------------------------------------+
| "'E'" | Exponent notation. Same as "'e'" except it uses an upper |
| | case ‘E’ as the separator character. |
+-----------+------------------------------------------------------------+
| "'f'" | Fixed-point notation. Displays the number as a fixed-point |
| | number. The default precision is "6". |
+-----------+------------------------------------------------------------+
| "'F'" | Fixed-point notation. Same as "'f'", but converts "nan" to |
| | "NAN" and "inf" to "INF". |
+-----------+------------------------------------------------------------+
| "'g'" | General format. For a given precision "p >= 1", this |
| | rounds the number to "p" significant digits and then |
| | formats the result in either fixed-point format or in |
| | scientific notation, depending on its magnitude. The |
| | precise rules are as follows: suppose that the result |
| | formatted with presentation type "'e'" and precision "p-1" |
| | would have exponent "exp". Then if "-4 <= exp < p", the |
| | number is formatted with presentation type "'f'" and |
| | precision "p-1-exp". Otherwise, the number is formatted |
| | with presentation type "'e'" and precision "p-1". In both |
| | cases insignificant trailing zeros are removed from the |
| | significand, and the decimal point is also removed if |
| | there are no remaining digits following it. Positive and |
| | negative infinity, positive and negative zero, and nans, |
| | are formatted as "inf", "-inf", "0", "-0" and "nan" |
| | respectively, regardless of the precision. A precision of |
| | "0" is treated as equivalent to a precision of "1". The |
| | default precision is "6". |
+-----------+------------------------------------------------------------+
| "'G'" | General format. Same as "'g'" except switches to "'E'" if |
| | the number gets too large. The representations of infinity |
| | and NaN are uppercased, too. |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'g'", except that it uses the |
| | current locale setting to insert the appropriate number |
| | separator characters. |
+-----------+------------------------------------------------------------+
| "'%'" | Percentage. Multiplies the number by 100 and displays in |
| | fixed ("'f'") format, followed by a percent sign. |
+-----------+------------------------------------------------------------+
| None | Similar to "'g'", except that fixed-point notation, when |
| | used, has at least one digit past the decimal point. The |
| | default precision is as high as needed to represent the |
| | particular value. The overall effect is to match the |
| | output of "str()" as altered by the other format |
| | modifiers. |
+-----------+------------------------------------------------------------+
Format examples
===============
This section contains examples of the "str.format()" syntax and
comparison with the old "%"-formatting.
In most of the cases the syntax is similar to the old "%"-formatting,
with the addition of the "{}" and with ":" used instead of "%". For
example, "'%03.2f'" can be translated to "'{:03.2f}'".
The new format syntax also supports new and different options, shown
in the following examples.
Accessing arguments by position:
>>> '{0}, {1}, {2}'.format('a', 'b', 'c')
'a, b, c'
>>> '{}, {}, {}'.format('a', 'b', 'c') # 3.1+ only
'a, b, c'
>>> '{2}, {1}, {0}'.format('a', 'b', 'c')
'c, b, a'
>>> '{2}, {1}, {0}'.format(*'abc') # unpacking argument sequence
'c, b, a'
>>> '{0}{1}{0}'.format('abra', 'cad') # arguments' indices can be repeated
'abracadabra'
Accessing arguments by name:
>>> 'Coordinates: {latitude}, {longitude}'.format(latitude='37.24N', longitude='-115.81W')
'Coordinates: 37.24N, -115.81W'
>>> coord = {'latitude': '37.24N', 'longitude': '-115.81W'}
>>> 'Coordinates: {latitude}, {longitude}'.format(**coord)
'Coordinates: 37.24N, -115.81W'
Accessing arguments’ attributes:
>>> c = 3-5j
>>> ('The complex number {0} is formed from the real part {0.real} '
... 'and the imaginary part {0.imag}.').format(c)
'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.'
>>> class Point:
... def __init__(self, x, y):
... self.x, self.y = x, y
... def __str__(self):
... return 'Point({self.x}, {self.y})'.format(self=self)
...
>>> str(Point(4, 2))
'Point(4, 2)'
Accessing arguments’ items:
>>> coord = (3, 5)
>>> 'X: {0[0]}; Y: {0[1]}'.format(coord)
'X: 3; Y: 5'
Replacing "%s" and "%r":
>>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2')
"repr() shows quotes: 'test1'; str() doesn't: test2"
Aligning the text and specifying a width:
>>> '{:<30}'.format('left aligned')
'left aligned '
>>> '{:>30}'.format('right aligned')
' right aligned'
>>> '{:^30}'.format('centered')
' centered '
>>> '{:*^30}'.format('centered') # use '*' as a fill char
'***********centered***********'
Replacing "%+f", "%-f", and "% f" and specifying a sign:
>>> '{:+f}; {:+f}'.format(3.14, -3.14) # show it always
'+3.140000; -3.140000'
>>> '{: f}; {: f}'.format(3.14, -3.14) # show a space for positive numbers
' 3.140000; -3.140000'
>>> '{:-f}; {:-f}'.format(3.14, -3.14) # show only the minus -- same as '{:f}; {:f}'
'3.140000; -3.140000'
Replacing "%x" and "%o" and converting the value to different bases:
>>> # format also supports binary numbers
>>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: {0:b}".format(42)
'int: 42; hex: 2a; oct: 52; bin: 101010'
>>> # with 0x, 0o, or 0b as prefix:
>>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: {0:#b}".format(42)
'int: 42; hex: 0x2a; oct: 0o52; bin: 0b101010'
Using the comma as a thousands separator:
>>> '{:,}'.format(1234567890)
'1,234,567,890'
Expressing a percentage:
>>> points = 19
>>> total = 22
>>> 'Correct answers: {:.2%}'.format(points/total)
'Correct answers: 86.36%'
Using type-specific formatting:
>>> import datetime
>>> d = datetime.datetime(2010, 7, 4, 12, 15, 58)
>>> '{:%Y-%m-%d %H:%M:%S}'.format(d)
'2010-07-04 12:15:58'
Nesting arguments and more complex examples:
>>> for align, text in zip('<^>', ['left', 'center', 'right']):
... '{0:{fill}{align}16}'.format(text, fill=align, align=align)
...
'left<<<<<<<<<<<<'
'^^^^^center^^^^^'
'>>>>>>>>>>>right'
>>>
>>> octets = [192, 168, 0, 1]
>>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets)
'C0A80001'
>>> int(_, 16)
3232235521
>>>
>>> width = 5
>>> for num in range(5,12):
... for base in 'dXob':
... print('{0:{width}{base}}'.format(num, base=base, width=width), end=' ')
... print()
...
5 5 5 101
6 6 6 110
7 7 7 111
8 8 10 1000
9 9 11 1001
10 A 12 1010
11 B 13 1011
u[ Function definitions
********************
A function definition defines a user-defined function object (see
section The standard type hierarchy):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")"
["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
| parameter_list_starargs
parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
| "**" parameter [","]
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
funcname ::= identifier
A function definition is an executable statement. Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function). This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.
The function definition does not execute the function body; this gets
executed only when the function is called. [2]
A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition. The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object. Multiple decorators are applied in
nested fashion. For example, the following code
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
except that the original function is not temporarily bound to the name
"func".
When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted. If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.
**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call. This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended. A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from position arguments, from keyword
arguments, or from default values. If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type. Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed
used keyword arguments.
Parameters may have annotations of the form “": expression"” following
the parameter name. Any parameter may have an annotation even those
of the form "*identifier" or "**identifier". Functions may have
“return” annotation of the form “"-> expression"” after the parameter
list. These annotations can be any valid Python expression and are
evaluated when the function definition is executed. Annotations may
be evaluated in a different order than they appear in the source code.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object.
It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions. This uses lambda
expressions, described in section Lambdas. Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression. The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.
**Programmer’s note:** Functions are first-class objects. A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around. Free variables used
in the nested function can access the local variables of the function
containing the def. See section Naming and binding for details.
See also:
**PEP 3107** - Function Annotations
The original specification for function annotations.
u� The "global" statement
**********************
global_stmt ::= "global" identifier ("," identifier)*
The "global" statement is a declaration which holds for the entire
current code block. It means that the listed identifiers are to be
interpreted as globals. It would be impossible to assign to a global
variable without "global", although free variables may refer to
globals without being declared global.
Names listed in a "global" statement must not be used in the same code
block textually preceding that "global" statement.
Names listed in a "global" statement must not be defined as formal
parameters or in a "for" loop control target, "class" definition,
function definition, "import" statement, or variable annotation.
**CPython implementation detail:** The current implementation does not
enforce some of these restrictions, but programs should not abuse this
freedom, as future implementations may enforce them or silently change
the meaning of the program.
**Programmer’s note:** "global" is a directive to the parser. It
applies only to code parsed at the same time as the "global"
statement. In particular, a "global" statement contained in a string
or code object supplied to the built-in "exec()" function does not
affect the code block *containing* the function call, and code
contained in such a string is unaffected by "global" statements in the
code containing the function call. The same applies to the "eval()"
and "compile()" functions.
u� Reserved classes of identifiers
*******************************
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section The import statement.
Note: The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names. These names are defined by the interpreter
and its implementation (including the standard library). Current
system names are discussed in the Special method names section and
elsewhere. More will likely be defined in future versions of
Python. *Any* use of "__*__" names, in any context, that does not
follow explicitly documented use, is subject to breakage without
warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between “private” attributes of base and
derived classes. See section Identifiers (Names).
u( Identifiers and keywords
************************
Identifiers (also referred to as *names*) are described by the
following lexical definitions.
The syntax of identifiers in Python is based on the Unicode standard
annex UAX-31, with elaboration and changes as defined below; see also
**PEP 3131** for further details.
Within the ASCII range (U+0001..U+007F), the valid characters for
identifiers are the same as in Python 2.x: the uppercase and lowercase
letters "A" through "Z", the underscore "_" and, except for the first
character, the digits "0" through "9".
Python 3.0 introduces additional characters from outside the ASCII
range (see **PEP 3131**). For these characters, the classification
uses the version of the Unicode Character Database as included in the
"unicodedata" module.
Identifiers are unlimited in length. Case is significant.
identifier ::= xid_start xid_continue*
id_start ::= <all characters in general categories Lu, Ll, Lt, Lm, Lo, Nl, the underscore, and characters with the Other_ID_Start property>
id_continue ::= <all characters in id_start, plus characters in the categories Mn, Mc, Nd, Pc and others with the Other_ID_Continue property>
xid_start ::= <all characters in id_start whose NFKC normalization is in "id_start xid_continue*">
xid_continue ::= <all characters in id_continue whose NFKC normalization is in "id_continue*">
The Unicode category codes mentioned above stand for:
* *Lu* - uppercase letters
* *Ll* - lowercase letters
* *Lt* - titlecase letters
* *Lm* - modifier letters
* *Lo* - other letters
* *Nl* - letter numbers
* *Mn* - nonspacing marks
* *Mc* - spacing combining marks
* *Nd* - decimal numbers
* *Pc* - connector punctuations
* *Other_ID_Start* - explicit list of characters in PropList.txt to
support backwards compatibility
* *Other_ID_Continue* - likewise
All identifiers are converted into the normal form NFKC while parsing;
comparison of identifiers is based on NFKC.
A non-normative HTML file listing all valid identifier characters for
Unicode 4.1 can be found at https://www.dcl.hpi.uni-
potsdam.de/home/loewis/table-3131.html.
Keywords
========
The following identifiers are used as reserved words, or *keywords* of
the language, and cannot be used as ordinary identifiers. They must
be spelled exactly as written here:
False class finally is return
None continue for lambda try
True def from nonlocal while
and del global not with
as elif if or yield
assert else import pass
break except in raise
Reserved classes of identifiers
===============================
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section The import statement.
Note: The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names. These names are defined by the interpreter
and its implementation (including the standard library). Current
system names are discussed in the Special method names section and
elsewhere. More will likely be defined in future versions of
Python. *Any* use of "__*__" names, in any context, that does not
follow explicitly documented use, is subject to breakage without
warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between “private” attributes of base and
derived classes. See section Identifiers (Names).
a5 Imaginary literals
******************
Imaginary literals are described by the following lexical definitions:
imagnumber ::= (floatnumber | digitpart) ("j" | "J")
An imaginary literal yields a complex number with a real part of 0.0.
Complex numbers are represented as a pair of floating point numbers
and have the same restrictions on their range. To create a complex
number with a nonzero real part, add a floating point number to it,
e.g., "(3+4j)". Some examples of imaginary literals:
3.14j 10.j 10j .001j 1e100j 3.14e-10j 3.14_15_93j
u� The "import" statement
**********************
import_stmt ::= "import" module ["as" identifier] ("," module ["as" identifier])*
| "from" relative_module "import" identifier ["as" identifier]
("," identifier ["as" identifier])*
| "from" relative_module "import" "(" identifier ["as" identifier]
("," identifier ["as" identifier])* [","] ")"
| "from" module "import" "*"
module ::= (identifier ".")* identifier
relative_module ::= "."* module | "."+
The basic import statement (no "from" clause) is executed in two
steps:
1. find a module, loading and initializing it if necessary
2. define a name or names in the local namespace for the scope
where the "import" statement occurs.
When the statement contains multiple clauses (separated by commas) the
two steps are carried out separately for each clause, just as though
the clauses had been separated out into individual import statements.
The details of the first step, finding and loading modules are
described in greater detail in the section on the import system, which
also describes the various types of packages and modules that can be
imported, as well as all the hooks that can be used to customize the
import system. Note that failures in this step may indicate either
that the module could not be located, *or* that an error occurred
while initializing the module, which includes execution of the
module’s code.
If the requested module is retrieved successfully, it will be made
available in the local namespace in one of three ways:
* If the module name is followed by "as", then the name following
"as" is bound directly to the imported module.
* If no other name is specified, and the module being imported is a
top level module, the module’s name is bound in the local namespace
as a reference to the imported module
* If the module being imported is *not* a top level module, then the
name of the top level package that contains the module is bound in
the local namespace as a reference to the top level package. The
imported module must be accessed using its full qualified name
rather than directly
The "from" form uses a slightly more complex process:
1. find the module specified in the "from" clause, loading and
initializing it if necessary;
2. for each of the identifiers specified in the "import" clauses:
1. check if the imported module has an attribute by that name
2. if not, attempt to import a submodule with that name and then
check the imported module again for that attribute
3. if the attribute is not found, "ImportError" is raised.
4. otherwise, a reference to that value is stored in the local
namespace, using the name in the "as" clause if it is present,
otherwise using the attribute name
Examples:
import foo # foo imported and bound locally
import foo.bar.baz # foo.bar.baz imported, foo bound locally
import foo.bar.baz as fbb # foo.bar.baz imported and bound as fbb
from foo.bar import baz # foo.bar.baz imported and bound as baz
from foo import attr # foo imported and foo.attr bound as attr
If the list of identifiers is replaced by a star ("'*'"), all public
names defined in the module are bound in the local namespace for the
scope where the "import" statement occurs.
The *public names* defined by a module are determined by checking the
module’s namespace for a variable named "__all__"; if defined, it must
be a sequence of strings which are names defined or imported by that
module. The names given in "__all__" are all considered public and
are required to exist. If "__all__" is not defined, the set of public
names includes all names found in the module’s namespace which do not
begin with an underscore character ("'_'"). "__all__" should contain
the entire public API. It is intended to avoid accidentally exporting
items that are not part of the API (such as library modules which were
imported and used within the module).
The wild card form of import — "from module import *" — is only
allowed at the module level. Attempting to use it in class or
function definitions will raise a "SyntaxError".
When specifying what module to import you do not have to specify the
absolute name of the module. When a module or package is contained
within another package it is possible to make a relative import within
the same top package without having to mention the package name. By
using leading dots in the specified module or package after "from" you
can specify how high to traverse up the current package hierarchy
without specifying exact names. One leading dot means the current
package where the module making the import exists. Two dots means up
one package level. Three dots is up two levels, etc. So if you execute
"from . import mod" from a module in the "pkg" package then you will
end up importing "pkg.mod". If you execute "from ..subpkg2 import mod"
from within "pkg.subpkg1" you will import "pkg.subpkg2.mod". The
specification for relative imports is contained within **PEP 328**.
"importlib.import_module()" is provided to support applications that
determine dynamically the modules to be loaded.
Future statements
=================
A *future statement* is a directive to the compiler that a particular
module should be compiled using syntax or semantics that will be
available in a specified future release of Python where the feature
becomes standard.
The future statement is intended to ease migration to future versions
of Python that introduce incompatible changes to the language. It
allows use of the new features on a per-module basis before the
release in which the feature becomes standard.
future_stmt ::= "from" "__future__" "import" feature ["as" identifier]
("," feature ["as" identifier])*
| "from" "__future__" "import" "(" feature ["as" identifier]
("," feature ["as" identifier])* [","] ")"
feature ::= identifier
A future statement must appear near the top of the module. The only
lines that can appear before a future statement are:
* the module docstring (if any),
* comments,
* blank lines, and
* other future statements.
The features recognized by Python 3.0 are "absolute_import",
"division", "generators", "unicode_literals", "print_function",
"nested_scopes" and "with_statement". They are all redundant because
they are always enabled, and only kept for backwards compatibility.
A future statement is recognized and treated specially at compile
time: Changes to the semantics of core constructs are often
implemented by generating different code. It may even be the case
that a new feature introduces new incompatible syntax (such as a new
reserved word), in which case the compiler may need to parse the
module differently. Such decisions cannot be pushed off until
runtime.
For any given release, the compiler knows which feature names have
been defined, and raises a compile-time error if a future statement
contains a feature not known to it.
The direct runtime semantics are the same as for any import statement:
there is a standard module "__future__", described later, and it will
be imported in the usual way at the time the future statement is
executed.
The interesting runtime semantics depend on the specific feature
enabled by the future statement.
Note that there is nothing special about the statement:
import __future__ [as name]
That is not a future statement; it’s an ordinary import statement with
no special semantics or syntax restrictions.
Code compiled by calls to the built-in functions "exec()" and
"compile()" that occur in a module "M" containing a future statement
will, by default, use the new syntax or semantics associated with the
future statement. This can be controlled by optional arguments to
"compile()" — see the documentation of that function for details.
A future statement typed at an interactive interpreter prompt will
take effect for the rest of the interpreter session. If an
interpreter is started with the "-i" option, is passed a script name
to execute, and the script includes a future statement, it will be in
effect in the interactive session started after the script is
executed.
See also:
**PEP 236** - Back to the __future__
The original proposal for the __future__ mechanism.
aO Membership test operations
**************************
The operators "in" and "not in" test for membership. "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s". All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".
For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*. An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".
For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.
For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y". If an exception is raised
during the iteration, it is as if "in" raised that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).
The operator "not in" is defined to have the inverse true value of
"in".
aV Integer literals
****************
Integer literals are described by the following lexical definitions:
integer ::= decinteger | bininteger | octinteger | hexinteger
decinteger ::= nonzerodigit (["_"] digit)* | "0"+ (["_"] "0")*
bininteger ::= "0" ("b" | "B") (["_"] bindigit)+
octinteger ::= "0" ("o" | "O") (["_"] octdigit)+
hexinteger ::= "0" ("x" | "X") (["_"] hexdigit)+
nonzerodigit ::= "1"..."9"
digit ::= "0"..."9"
bindigit ::= "0" | "1"
octdigit ::= "0"..."7"
hexdigit ::= digit | "a"..."f" | "A"..."F"
There is no limit for the length of integer literals apart from what
can be stored in available memory.
Underscores are ignored for determining the numeric value of the
literal. They can be used to group digits for enhanced readability.
One underscore can occur between digits, and after base specifiers
like "0x".
Note that leading zeros in a non-zero decimal number are not allowed.
This is for disambiguation with C-style octal literals, which Python
used before version 3.0.
Some examples of integer literals:
7 2147483647 0o177 0b100110111
3 79228162514264337593543950336 0o377 0xdeadbeef
100_000_000_000 0b_1110_0101
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
a^ Lambdas
*******
lambda_expr ::= "lambda" [parameter_list] ":" expression
lambda_expr_nocond ::= "lambda" [parameter_list] ":" expression_nocond
Lambda expressions (sometimes called lambda forms) are used to create
anonymous functions. The expression "lambda parameters: expression"
yields a function object. The unnamed object behaves like a function
object defined with:
def <lambda>(parameters):
return expression
See section Function definitions for the syntax of parameter lists.
Note that functions created with lambda expressions cannot contain
statements or annotations.
a/ List displays
*************
A list display is a possibly empty series of expressions enclosed in
square brackets:
list_display ::= "[" [starred_list | comprehension] "]"
A list display yields a new list object, the contents being specified
by either a list of expressions or a comprehension. When a comma-
separated list of expressions is supplied, its elements are evaluated
from left to right and placed into the list object in that order.
When a comprehension is supplied, the list is constructed from the
elements resulting from the comprehension.
u� Naming and binding
******************
Binding of names
================
*Names* refer to objects. Names are introduced by name binding
operations.
The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore. This form
may only be used at the module level.
A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).
Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).
If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global". If a name is bound at the
module level, it is a global variable. (The variables of the module
code block are local and global.) If a variable is used in a code
block but not defined there, it is a *free variable*.
Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.
Resolution of names
===================
A *scope* defines the visibility of a name within a block. If a local
variable is defined in a block, its scope includes that block. If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.
When a name is used in a code block, it is resolved using the nearest
enclosing scope. The set of all such scopes visible to a code block
is called the block’s *environment*.
When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".
If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block. This can lead to errors when a name is used within a
block before it is bound. This rule is subtle. Python lacks
declarations and allows name binding operations to occur anywhere
within a code block. The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.
If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins". The global namespace is searched first. If
the name is not found there, the builtins namespace is searched. The
"global" statement must precede all uses of the name.
The "global" statement has the same scope as a name binding operation
in the same block. If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.
The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.
The namespace for a module is automatically created the first time a
module is imported. The main module for a script is always called
"__main__".
Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope. This means that the following
will fail:
class A:
a = 42
b = list(a + i for i in range(10))
Builtins and restricted execution
=================================
**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail. Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.
The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used). By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.
Interaction with dynamic features
=================================
Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:
i = 10
def f():
print(i)
i = 42
f()
The "eval()" and "exec()" functions do not have access to the full
environment for resolving names. Names may be resolved in the local
and global namespaces of the caller. Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace. [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
a� The "nonlocal" statement
************************
nonlocal_stmt ::= "nonlocal" identifier ("," identifier)*
The "nonlocal" statement causes the listed identifiers to refer to
previously bound variables in the nearest enclosing scope excluding
globals. This is important because the default behavior for binding is
to search the local namespace first. The statement allows
encapsulated code to rebind variables outside of the local scope
besides the global (module) scope.
Names listed in a "nonlocal" statement, unlike those listed in a
"global" statement, must refer to pre-existing bindings in an
enclosing scope (the scope in which a new binding should be created
cannot be determined unambiguously).
Names listed in a "nonlocal" statement must not collide with pre-
existing bindings in the local scope.
See also:
**PEP 3104** - Access to Names in Outer Scopes
The specification for the "nonlocal" statement.
u� Numeric literals
****************
There are three types of numeric literals: integers, floating point
numbers, and imaginary numbers. There are no complex literals
(complex numbers can be formed by adding a real number and an
imaginary number).
Note that numeric literals do not include a sign; a phrase like "-1"
is actually an expression composed of the unary operator ‘"-"‘ and the
literal "1".
u� Emulating numeric types
***********************
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the expression "x + y", where *x* is an instance of a
class that has an "__add__()" method, "x.__add__(y)" is called.
The "__divmod__()" method should be the equivalent to using
"__floordiv__()" and "__mod__()"; it should not be related to
"__truediv__()". Note that "__pow__()" should be defined to accept
an optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other)
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)
operands. These functions are only called if the left operand does
not support the corresponding operation [3] and the operands are of
different types. [4] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note: If the right operand’s type is a subclass of the left
operand’s type and that subclass provides the reflected method
for the operation, this method will be called before the left
operand’s non-reflected method. This behavior allows subclasses
to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (modifying *self*) and return the result
(which could be, but does not have to be, *self*). If a specific
method is not defined, the augmented assignment falls back to the
normal methods. For instance, if *x* is an instance of a class
with an "__iadd__()" method, "x += y" is equivalent to "x =
x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are
considered, as with the evaluation of "x + y". In certain
situations, augmented assignment can result in unexpected errors
(see Why does a_tuple[i] += [‘item’] raise an exception when the
addition works?), but this behavior is in fact part of the data
model.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()" and
"float()". Should return a value of the appropriate type.
object.__index__(self)
Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.
Note: In order to have a coherent integer type class, when
"__index__()" is defined "__int__()" should also be defined, and
both should return the same value.
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").
If "__int__()" is not defined then the built-in function "int()"
falls back to "__trunc__()".
u Objects, values and types
*************************
*Objects* are Python’s abstraction for data. All data in a Python
program is represented by objects or by relations between objects. (In
a sense, and in conformance to Von Neumann’s model of a “stored
program computer,” code is also represented by objects.)
Every object has an identity, a type and a value. An object’s
*identity* never changes once it has been created; you may think of it
as the object’s address in memory. The ‘"is"’ operator compares the
identity of two objects; the "id()" function returns an integer
representing its identity.
**CPython implementation detail:** For CPython, "id(x)" is the memory
address where "x" is stored.
An object’s type determines the operations that the object supports
(e.g., “does it have a length?”) and also defines the possible values
for objects of that type. The "type()" function returns an object’s
type (which is an object itself). Like its identity, an object’s
*type* is also unchangeable. [1]
The *value* of some objects can change. Objects whose value can
change are said to be *mutable*; objects whose value is unchangeable
once they are created are called *immutable*. (The value of an
immutable container object that contains a reference to a mutable
object can change when the latter’s value is changed; however the
container is still considered immutable, because the collection of
objects it contains cannot be changed. So, immutability is not
strictly the same as having an unchangeable value, it is more subtle.)
An object’s mutability is determined by its type; for instance,
numbers, strings and tuples are immutable, while dictionaries and
lists are mutable.
Objects are never explicitly destroyed; however, when they become
unreachable they may be garbage-collected. An implementation is
allowed to postpone garbage collection or omit it altogether — it is a
matter of implementation quality how garbage collection is
implemented, as long as no objects are collected that are still
reachable.
**CPython implementation detail:** CPython currently uses a reference-
counting scheme with (optional) delayed detection of cyclically linked
garbage, which collects most objects as soon as they become
unreachable, but is not guaranteed to collect garbage containing
circular references. See the documentation of the "gc" module for
information on controlling the collection of cyclic garbage. Other
implementations act differently and CPython may change. Do not depend
on immediate finalization of objects when they become unreachable (so
you should always close files explicitly).
Note that the use of the implementation’s tracing or debugging
facilities may keep objects alive that would normally be collectable.
Also note that catching an exception with a ‘"try"…"except"’ statement
may keep objects alive.
Some objects contain references to “external” resources such as open
files or windows. It is understood that these resources are freed
when the object is garbage-collected, but since garbage collection is
not guaranteed to happen, such objects also provide an explicit way to
release the external resource, usually a "close()" method. Programs
are strongly recommended to explicitly close such objects. The
‘"try"…"finally"’ statement and the ‘"with"’ statement provide
convenient ways to do this.
Some objects contain references to other objects; these are called
*containers*. Examples of containers are tuples, lists and
dictionaries. The references are part of a container’s value. In
most cases, when we talk about the value of a container, we imply the
values, not the identities of the contained objects; however, when we
talk about the mutability of a container, only the identities of the
immediately contained objects are implied. So, if an immutable
container (like a tuple) contains a reference to a mutable object, its
value changes if that mutable object is changed.
Types affect almost all aspects of object behavior. Even the
importance of object identity is affected in some sense: for immutable
types, operations that compute new values may actually return a
reference to any existing object with the same type and value, while
for mutable objects this is not allowed. E.g., after "a = 1; b = 1",
"a" and "b" may or may not refer to the same object with the value
one, depending on the implementation, but after "c = []; d = []", "c"
and "d" are guaranteed to refer to two different, unique, newly
created empty lists. (Note that "c = d = []" assigns the same object
to both "c" and "d".)
u� Operator precedence
*******************
The following table summarizes the operator precedence in Python, from
lowest precedence (least binding) to highest precedence (most
binding). Operators in the same box have the same precedence. Unless
the syntax is explicitly given, operators are binary. Operators in
the same box group left to right (except for exponentiation, which
groups from right to left).
Note that comparisons, membership tests, and identity tests, all have
the same precedence and have a left-to-right chaining feature as
described in the Comparisons section.
+-------------------------------------------------+---------------------------------------+
| Operator | Description |
+=================================================+=======================================+
| "lambda" | Lambda expression |
+-------------------------------------------------+---------------------------------------+
| "if" – "else" | Conditional expression |
+-------------------------------------------------+---------------------------------------+
| "or" | Boolean OR |
+-------------------------------------------------+---------------------------------------+
| "and" | Boolean AND |
+-------------------------------------------------+---------------------------------------+
| "not" "x" | Boolean NOT |
+-------------------------------------------------+---------------------------------------+
| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership |
| ">=", "!=", "==" | tests and identity tests |
+-------------------------------------------------+---------------------------------------+
| "|" | Bitwise OR |
+-------------------------------------------------+---------------------------------------+
| "^" | Bitwise XOR |
+-------------------------------------------------+---------------------------------------+
| "&" | Bitwise AND |
+-------------------------------------------------+---------------------------------------+
| "<<", ">>" | Shifts |
+-------------------------------------------------+---------------------------------------+
| "+", "-" | Addition and subtraction |
+-------------------------------------------------+---------------------------------------+
| "*", "@", "/", "//", "%" | Multiplication, matrix |
| | multiplication, division, floor |
| | division, remainder [5] |
+-------------------------------------------------+---------------------------------------+
| "+x", "-x", "~x" | Positive, negative, bitwise NOT |
+-------------------------------------------------+---------------------------------------+
| "**" | Exponentiation [6] |
+-------------------------------------------------+---------------------------------------+
| "await" "x" | Await expression |
+-------------------------------------------------+---------------------------------------+
| "x[index]", "x[index:index]", | Subscription, slicing, call, |
| "x(arguments...)", "x.attribute" | attribute reference |
+-------------------------------------------------+---------------------------------------+
| "(expressions...)", "[expressions...]", "{key: | Binding or tuple display, list |
| value...}", "{expressions...}" | display, dictionary display, set |
| | display |
+-------------------------------------------------+---------------------------------------+
-[ Footnotes ]-
[1] While "abs(x%y) < abs(y)" is true mathematically, for floats
it may not be true numerically due to roundoff. For example, and
assuming a platform on which a Python float is an IEEE 754 double-
precision number, in order that "-1e-100 % 1e100" have the same
sign as "1e100", the computed result is "-1e-100 + 1e100", which
is numerically exactly equal to "1e100". The function
"math.fmod()" returns a result whose sign matches the sign of the
first argument instead, and so returns "-1e-100" in this case.
Which approach is more appropriate depends on the application.
[2] If x is very close to an exact integer multiple of y, it’s
possible for "x//y" to be one larger than "(x-x%y)//y" due to
rounding. In such cases, Python returns the latter result, in
order to preserve that "divmod(x,y)[0] * y + x % y" be very close
to "x".
[3] The Unicode standard distinguishes between *code points* (e.g.
U+0041) and *abstract characters* (e.g. “LATIN CAPITAL LETTER A”).
While most abstract characters in Unicode are only represented
using one code point, there is a number of abstract characters
that can in addition be represented using a sequence of more than
one code point. For example, the abstract character “LATIN
CAPITAL LETTER C WITH CEDILLA” can be represented as a single
*precomposed character* at code position U+00C7, or as a sequence
of a *base character* at code position U+0043 (LATIN CAPITAL
LETTER C), followed by a *combining character* at code position
U+0327 (COMBINING CEDILLA).
The comparison operators on strings compare at the level of
Unicode code points. This may be counter-intuitive to humans. For
example, ""\u00C7" == "\u0043\u0327"" is "False", even though both
strings represent the same abstract character “LATIN CAPITAL
LETTER C WITH CEDILLA”.
To compare strings at the level of abstract characters (that is,
in a way intuitive to humans), use "unicodedata.normalize()".
[4] Due to automatic garbage-collection, free lists, and the
dynamic nature of descriptors, you may notice seemingly unusual
behaviour in certain uses of the "is" operator, like those
involving comparisons between instance methods, or constants.
Check their documentation for more info.
[5] The "%" operator is also used for string formatting; the same
precedence applies.
[6] The power operator "**" binds less tightly than an arithmetic
or bitwise unary operator on its right, that is, "2**-1" is "0.5".
uw The "pass" statement
********************
pass_stmt ::= "pass"
"pass" is a null operation — when it is executed, nothing happens. It
is useful as a placeholder when a statement is required syntactically,
but no code needs to be executed, for example:
def f(arg): pass # a function that does nothing (yet)
class C: pass # a class with no methods (yet)
a� The power operator
******************
The power operator binds more tightly than unary operators on its
left; it binds less tightly than unary operators on its right. The
syntax is:
power ::= (await_expr | primary) ["**" u_expr]
Thus, in an unparenthesized sequence of power and unary operators, the
operators are evaluated from right to left (this does not constrain
the evaluation order for the operands): "-1**2" results in "-1".
The power operator has the same semantics as the built-in "pow()"
function, when called with two arguments: it yields its left argument
raised to the power of its right argument. The numeric arguments are
first converted to a common type, and the result is of that type.
For int operands, the result has the same type as the operands unless
the second argument is negative; in that case, all arguments are
converted to float and a float result is delivered. For example,
"10**2" returns "100", but "10**-2" returns "0.01".
Raising "0.0" to a negative power results in a "ZeroDivisionError".
Raising a negative number to a fractional power results in a "complex"
number. (In earlier versions it raised a "ValueError".)
ul The "raise" statement
*********************
raise_stmt ::= "raise" [expression ["from" expression]]
If no expressions are present, "raise" re-raises the last exception
that was active in the current scope. If no exception is active in
the current scope, a "RuntimeError" exception is raised indicating
that this is an error.
Otherwise, "raise" evaluates the first expression as the exception
object. It must be either a subclass or an instance of
"BaseException". If it is a class, the exception instance will be
obtained when needed by instantiating the class with no arguments.
The *type* of the exception is the exception instance’s class, the
*value* is the instance itself.
A traceback object is normally created automatically when an exception
is raised and attached to it as the "__traceback__" attribute, which
is writable. You can create an exception and set your own traceback in
one step using the "with_traceback()" exception method (which returns
the same exception instance, with its traceback set to its argument),
like so:
raise Exception("foo occurred").with_traceback(tracebackobj)
The "from" clause is used for exception chaining: if given, the second
*expression* must be another exception class or instance, which will
then be attached to the raised exception as the "__cause__" attribute
(which is writable). If the raised exception is not handled, both
exceptions will be printed:
>>> try:
... print(1 / 0)
... except Exception as exc:
... raise RuntimeError("Something bad happened") from exc
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
A similar mechanism works implicitly if an exception is raised inside
an exception handler or a "finally" clause: the previous exception is
then attached as the new exception’s "__context__" attribute:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened")
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
Exception chaining can be explicitly suppressed by specifying "None"
in the "from" clause:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened") from None
...
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Something bad happened
Additional information on exceptions can be found in section
Exceptions, and information about handling exceptions is in section
The try statement.
Changed in version 3.3: "None" is now permitted as "Y" in "raise X
from Y".
New in version 3.3: The "__suppress_context__" attribute to suppress
automatic display of the exception context.
a The "return" statement
**********************
return_stmt ::= "return" [expression_list]
"return" may only occur syntactically nested in a function definition,
not within a nested class definition.
If an expression list is present, it is evaluated, else "None" is
substituted.
"return" leaves the current function call with the expression list (or
"None") as return value.
When "return" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
function.
In a generator function, the "return" statement indicates that the
generator is done and will cause "StopIteration" to be raised. The
returned value (if any) is used as an argument to construct
"StopIteration" and becomes the "StopIteration.value" attribute.
In an asynchronous generator function, an empty "return" statement
indicates that the asynchronous generator is done and will cause
"StopAsyncIteration" to be raised. A non-empty "return" statement is
a syntax error in an asynchronous generator function.
u� Emulating container types
*************************
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python’s standard dictionary objects.
The "collections" module provides a "MutableMapping" abstract base
class to help create those methods from a base set of "__getitem__()",
"__setitem__()", "__delitem__()", and "keys()". Mutable sequences
should provide methods "append()", "count()", "index()", "extend()",
"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python
standard list objects. Finally, sequence types should implement
addition (meaning concatenation) and multiplication (meaning
repetition) by defining the methods "__add__()", "__radd__()",
"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described
below; they should not define other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should search the mapping’s keys; for sequences, it
should search through the values. It is further recommended that both
mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should be the same as "keys()"; for sequences, it should iterate
through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn’t define a "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.
object.__length_hint__(self)
Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. This
method is purely an optimization and is never required for
correctness.
New in version 3.4.
Note: Slicing is done exclusively with the following three methods.
A call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note: "for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container.
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a sequence. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don’t define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
a� Shifting operations
*******************
The shifting operations have lower priority than the arithmetic
operations:
shift_expr ::= a_expr | shift_expr ("<<" | ">>") a_expr
These operators accept integers as arguments. They shift the first
argument to the left or right by the number of bits given by the
second argument.
A right shift by *n* bits is defined as floor division by "pow(2,n)".
A left shift by *n* bits is defined as multiplication with "pow(2,n)".
Note: In the current implementation, the right-hand operand is
required to be at most "sys.maxsize". If the right-hand operand is
larger than "sys.maxsize" an "OverflowError" exception is raised.
a� Slicings
********
A slicing selects a range of items in a sequence object (e.g., a
string, tuple or list). Slicings may be used as expressions or as
targets in assignment or "del" statements. The syntax for a slicing:
slicing ::= primary "[" slice_list "]"
slice_list ::= slice_item ("," slice_item)* [","]
slice_item ::= expression | proper_slice
proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" [stride] ]
lower_bound ::= expression
upper_bound ::= expression
stride ::= expression
There is ambiguity in the formal syntax here: anything that looks like
an expression list also looks like a slice list, so any subscription
can be interpreted as a slicing. Rather than further complicating the
syntax, this is disambiguated by defining that in this case the
interpretation as a subscription takes priority over the
interpretation as a slicing (this is the case if the slice list
contains no proper slice).
The semantics for a slicing are as follows. The primary is indexed
(using the same "__getitem__()" method as normal subscription) with a
key that is constructed from the slice list, as follows. If the slice
list contains at least one comma, the key is a tuple containing the
conversion of the slice items; otherwise, the conversion of the lone
slice item is the key. The conversion of a slice item that is an
expression is that expression. The conversion of a proper slice is a
slice object (see section The standard type hierarchy) whose "start",
"stop" and "step" attributes are the values of the expressions given
as lower bound, upper bound and stride, respectively, substituting
"None" for missing expressions.
u~ Special Attributes
******************
The implementation adds a few special read-only attributes to several
object types, where they are relevant. Some of these are not reported
by the "dir()" built-in function.
object.__dict__
A dictionary or other mapping object used to store an object’s
(writable) attributes.
instance.__class__
The class to which a class instance belongs.
class.__bases__
The tuple of base classes of a class object.
definition.__name__
The name of the class, function, method, descriptor, or generator
instance.
definition.__qualname__
The *qualified name* of the class, function, method, descriptor, or
generator instance.
New in version 3.3.
class.__mro__
This attribute is a tuple of classes that are considered when
looking for base classes during method resolution.
class.mro()
This method can be overridden by a metaclass to customize the
method resolution order for its instances. It is called at class
instantiation, and its result is stored in "__mro__".
class.__subclasses__()
Each class keeps a list of weak references to its immediate
subclasses. This method returns a list of all those references
still alive. Example:
>>> int.__subclasses__()
[<class 'bool'>]
-[ Footnotes ]-
[1] Additional information on these special methods may be found
in the Python Reference Manual (Basic customization).
[2] As a consequence, the list "[1, 2]" is considered equal to
"[1.0, 2.0]", and similarly for tuples.
[3] They must have since the parser can’t tell the type of the
operands.
[4] Cased characters are those with general category property
being one of “Lu” (Letter, uppercase), “Ll” (Letter, lowercase),
or “Lt” (Letter, titlecase).
[5] To format only a tuple you should therefore provide a
singleton tuple whose only element is the tuple to be formatted.
u.� Special method names
********************
A class can implement certain operations that are invoked by special
syntax (such as arithmetic operations or subscripting and slicing) by
defining methods with special names. This is Python’s approach to
*operator overloading*, allowing classes to define their own behavior
with respect to language operators. For instance, if a class defines
a method named "__getitem__()", and "x" is an instance of this class,
then "x[i]" is roughly equivalent to "type(x).__getitem__(x, i)".
Except where mentioned, attempts to execute an operation raise an
exception when no appropriate method is defined (typically
"AttributeError" or "TypeError").
Setting a special method to "None" indicates that the corresponding
operation is not available. For example, if a class sets "__iter__()"
to "None", the class is not iterable, so calling "iter()" on its
instances will raise a "TypeError" (without falling back to
"__getitem__()"). [2]
When implementing a class that emulates any built-in type, it is
important that the emulation only be implemented to the degree that it
makes sense for the object being modelled. For example, some
sequences may work well with retrieval of individual elements, but
extracting a slice may not make sense. (One example of this is the
"NodeList" interface in the W3C’s Document Object Model.)
Basic customization
===================
object.__new__(cls[, ...])
Called to create a new instance of class *cls*. "__new__()" is a
static method (special-cased so you need not declare it as such)
that takes the class of which an instance was requested as its
first argument. The remaining arguments are those passed to the
object constructor expression (the call to the class). The return
value of "__new__()" should be the new object instance (usually an
instance of *cls*).
Typical implementations create a new instance of the class by
invoking the superclass’s "__new__()" method using
"super().__new__(cls[, ...])" with appropriate arguments and then
modifying the newly-created instance as necessary before returning
it.
If "__new__()" returns an instance of *cls*, then the new
instance’s "__init__()" method will be invoked like
"__init__(self[, ...])", where *self* is the new instance and the
remaining arguments are the same as were passed to "__new__()".
If "__new__()" does not return an instance of *cls*, then the new
instance’s "__init__()" method will not be invoked.
"__new__()" is intended mainly to allow subclasses of immutable
types (like int, str, or tuple) to customize instance creation. It
is also commonly overridden in custom metaclasses in order to
customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by "__new__()"), but
before it is returned to the caller. The arguments are those
passed to the class constructor expression. If a base class has an
"__init__()" method, the derived class’s "__init__()" method, if
any, must explicitly call it to ensure proper initialization of the
base class part of the instance; for example:
"super().__init__([args...])".
Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
it), no non-"None" value may be returned by "__init__()"; doing so
will cause a "TypeError" to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also
called a finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.
It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.
It is not guaranteed that "__del__()" methods are called for
objects that still exist when the interpreter exits.
Note: "del x" doesn’t directly call "x.__del__()" — the former
decrements the reference count for "x" by one, and the latter is
only called when "x"’s reference count reaches zero.
**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.
See also: Documentation for the "gc" module.
Warning: Due to the precarious circumstances under which
"__del__()" methods are invoked, exceptions that occur during
their execution are ignored, and a warning is printed to
"sys.stderr" instead. In particular:
* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".
* "__del__()" can be executed during interpreter shutdown. As
a consequence, the global variables it needs to access
(including other modules) may already have been deleted or set
to "None". Python guarantees that globals whose name begins
with a single underscore are deleted from their module before
other globals are deleted; if no other references to such
globals exist, this may help in assuring that imported modules
are still available at the time when the "__del__()" method is
called.
object.__repr__(self)
Called by the "repr()" built-in function to compute the “official”
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an “informal”
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the “informal” or nicely printable string
representation of an object. The return value must be a string
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by bytes to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function, and by extension,
evaluation of formatted string literals and the "str.format()"
method, to produce a “formatted” string representation of an
object. The "format_spec" argument is a string that contains a
description of the formatting options desired. The interpretation
of the "format_spec" argument is up to the type implementing
"__format__()", however most classes will either delegate
formatting to one of the built-in types, or use a similar
formatting option syntax.
See Format Specification Mini-Language for a description of the
standard formatting syntax.
The return value must be a string object.
Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The
correspondence between operator symbols and method names is as
follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
"x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
By default, "__ne__()" delegates to "__eq__()" and inverts the
result unless it is "NotImplemented". There are no other implied
relationships among the comparison operators, for example, the
truth of "(x<y or x==y)" does not imply "x<=y". To automatically
generate ordering operations from a single root operation, see
"functools.total_ordering()".
See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:
def __hash__(self):
return hash((self.name, self.nick, self.color))
Note: "hash()" truncates the value returned from an object’s
custom "__hash__()" method to the size of a "Py_ssize_t". This
is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit
builds. If an object’s "__hash__()" must interoperate on builds
of different bit sizes, be sure to check the width on all
supported builds. An easy way to do this is with "python -c
"import sys; print(sys.hash_info.width)"".
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key’s hash value is immutable (if the object’s hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj, collections.Hashable)".
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
<ParentClass>.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.Hashable)" call.
Note: By default, the "__hash__()" values of str, bytes and
datetime objects are “salted” with an unpredictable random value.
Although they remain constant within an individual Python
process, they are not predictable between repeated invocations of
Python.This is intended to provide protection against a denial-
of-service caused by carefully-chosen inputs that exploit the
worst case performance of a dict insertion, O(n^2) complexity.
See http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of
dicts, sets and other mappings. Python has never made guarantees
about this ordering (and it typically varies between 32-bit and
64-bit builds).See also "PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
Customizing attribute access
============================
The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
return the (computed) attribute value or raise an "AttributeError"
exception.
Note that if the attribute is found through the normal mechanism,
"__getattr__()" is not called. (This is an intentional asymmetry
between "__getattr__()" and "__setattr__()".) This is done both for
efficiency reasons and because otherwise "__getattr__()" would have
no way to access other attributes of the instance. Note that at
least for instance variables, you can fake total control by not
inserting any values in the instance attribute dictionary (but
instead inserting them in another object). See the
"__getattribute__()" method below for a way to actually get total
control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for
instances of the class. If the class also defines "__getattr__()",
the latter will not be called unless "__getattribute__()" either
calls it explicitly or raises an "AttributeError". This method
should return the (computed) attribute value or raise an
"AttributeError" exception. In order to avoid infinite recursion in
this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for
example, "object.__getattribute__(self, name)".
Note: This method may still be bypassed when looking up special
methods as the result of implicit invocation via language syntax
or built-in functions. See Special method lookup.
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called
instead of the normal mechanism (i.e. store the value in the
instance dictionary). *name* is the attribute name, *value* is the
value to be assigned to it.
If "__setattr__()" wants to assign to an instance attribute, it
should call the base class method with the same name, for example,
"object.__setattr__(self, name, value)".
object.__delattr__(self, name)
Like "__setattr__()" but for attribute deletion instead of
assignment. This should only be implemented if "del obj.name" is
meaningful for the object.
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
Customizing module attribute access
-----------------------------------
For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
setattr(self, attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note: Setting module "__class__" only affects lookups made using the
attribute access syntax – directly accessing the module globals
(whether by code within the module, or via a reference to the
module’s globals dictionary) is unaffected.
Changed in version 3.5: "__class__" module attribute is now writable.
Implementing Descriptors
------------------------
The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents). In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".
object.__get__(self, instance, owner)
Called to get the attribute of the owner class (class attribute
access) or of an instance of that class (instance attribute
access). *owner* is always the owner class, while *instance* is the
instance that the attribute was accessed through, or "None" when
the attribute is accessed through the *owner*. This method should
return the (computed) attribute value or raise an "AttributeError"
exception.
object.__set__(self, instance, value)
Called to set the attribute on an instance *instance* of the owner
class to a new value, *value*.
object.__delete__(self, instance)
Called to delete the attribute on an instance *instance* of the
owner class.
object.__set_name__(self, owner, name)
Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.
New in version 3.6.
The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).
Invoking Descriptors
--------------------
In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol: "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.
However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead. Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.
The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":
Direct Call
The simplest and least common call is when user code directly
invokes a descriptor method: "x.__get__(a)".
Instance Binding
If binding to an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a class, "A.x" is transformed into the call:
"A.__dict__['x'].__get__(None, A)".
Super Binding
If "a" is an instance of "super", then the binding "super(B,
obj).m()" searches "obj.__class__.__mro__" for the base class "A"
immediately preceding "B" and then invokes the descriptor with the
call: "A.__dict__['m'].__get__(obj, obj.__class__)".
For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined. A descriptor can define
any combination of "__get__()", "__set__()" and "__delete__()". If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary. If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor. Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method. Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary. In
contrast, non-data descriptors can be overridden by instances.
Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors. Accordingly, instances can
redefine and override methods. This allows individual instances to
acquire behaviors that differ from other instances of the same class.
The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.
__slots__
---------
*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of *__dict__* and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)
The space saved over using *__dict__* can be significant.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. *__slots__*
reserves space for the declared variables and prevents the
automatic creation of *__dict__* and *__weakref__* for each
instance.
Notes on using *__slots__*
~~~~~~~~~~~~~~~~~~~~~~~~~~
* When inheriting from a class without *__slots__*, the *__dict__*
and *__weakref__* attribute of the instances will always be
accessible.
* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name raises "AttributeError". If
dynamic assignment of new variables is desired, then add
"'__dict__'" to the sequence of strings in the *__slots__*
declaration.
* Without a *__weakref__* variable for each instance, classes
defining *__slots__* do not support weak references to its
instances. If weak reference support is needed, then add
"'__weakref__'" to the sequence of strings in the *__slots__*
declaration.
* *__slots__* are implemented at the class level by creating
descriptors (Implementing Descriptors) for each variable name. As a
result, class attributes cannot be used to set default values for
instance variables defined by *__slots__*; otherwise, the class
attribute would overwrite the descriptor assignment.
* The action of a *__slots__* declaration is not limited to the
class where it is defined. *__slots__* declared in parents are
available in child classes. However, child subclasses will get a
*__dict__* and *__weakref__* unless they also define *__slots__*
(which should only contain names of any *additional* slots).
* If a class defines a slot also defined in a base class, the
instance variable defined by the base class slot is inaccessible
(except by retrieving its descriptor directly from the base class).
This renders the meaning of the program undefined. In the future, a
check may be added to prevent this.
* Nonempty *__slots__* does not work for classes derived from
“variable-length” built-in types such as "int", "bytes" and "tuple".
* Any non-string iterable may be assigned to *__slots__*. Mappings
may also be used; however, in the future, special meaning may be
assigned to the values corresponding to each key.
* *__class__* assignment works only if both classes have the same
*__slots__*.
* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".
Customizing class creation
==========================
Whenever a class inherits from another class, *__init_subclass__* is
called on that class. This way, it is possible to write classes which
change the behavior of subclasses. This is closely related to class
decorators, but where class decorators only affect the specific class
they’re applied to, "__init_subclass__" solely applies to future
subclasses of the class defining the method.
classmethod object.__init_subclass__(cls)
This method is called whenever the containing class is subclassed.
*cls* is then the new subclass. If defined as a normal instance
method, this method is implicitly converted to a class method.
Keyword arguments which are given to a new class are passed to the
parent’s class "__init_subclass__". For compatibility with other
classes using "__init_subclass__", one should take out the needed
keyword arguments and pass the others over to the base class, as
in:
class Philosopher:
def __init_subclass__(cls, default_name, **kwargs):
super().__init_subclass__(**kwargs)
cls.default_name = default_name
class AustralianPhilosopher(Philosopher, default_name="Bruce"):
pass
The default implementation "object.__init_subclass__" does nothing,
but raises an error if it is called with any arguments.
Note: The metaclass hint "metaclass" is consumed by the rest of
the type machinery, and is never passed to "__init_subclass__"
implementations. The actual metaclass (rather than the explicit
hint) can be accessed as "type(cls)".
New in version 3.6.
Metaclasses
-----------
By default, classes are constructed using "type()". The class body is
executed in a new namespace and the class name is bound locally to the
result of "type(name, bases, namespace)".
The class creation process can be customized by passing the
"metaclass" keyword argument in the class definition line, or by
inheriting from an existing class that included such an argument. In
the following example, both "MyClass" and "MySubclass" are instances
of "Meta":
class Meta(type):
pass
class MyClass(metaclass=Meta):
pass
class MySubclass(MyClass):
pass
Any other keyword arguments that are specified in the class definition
are passed through to all metaclass operations described below.
When a class definition is executed, the following steps occur:
* the appropriate metaclass is determined
* the class namespace is prepared
* the class body is executed
* the class object is created
Determining the appropriate metaclass
-------------------------------------
The appropriate metaclass for a class definition is determined as
follows:
* if no bases and no explicit metaclass are given, then "type()" is
used
* if an explicit metaclass is given and it is *not* an instance of
"type()", then it is used directly as the metaclass
* if an instance of "type()" is given as the explicit metaclass, or
bases are defined, then the most derived metaclass is used
The most derived metaclass is selected from the explicitly specified
metaclass (if any) and the metaclasses (i.e. "type(cls)") of all
specified base classes. The most derived metaclass is one which is a
subtype of *all* of these candidate metaclasses. If none of the
candidate metaclasses meets that criterion, then the class definition
will fail with "TypeError".
Preparing the class namespace
-----------------------------
Once the appropriate metaclass has been identified, then the class
namespace is prepared. If the metaclass has a "__prepare__" attribute,
it is called as "namespace = metaclass.__prepare__(name, bases,
**kwds)" (where the additional keyword arguments, if any, come from
the class definition).
If the metaclass has no "__prepare__" attribute, then the class
namespace is initialised as an empty ordered mapping.
See also:
**PEP 3115** - Metaclasses in Python 3000
Introduced the "__prepare__" namespace hook
Executing the class body
------------------------
The class body is executed (approximately) as "exec(body, globals(),
namespace)". The key difference from a normal call to "exec()" is that
lexical scoping allows the class body (including any methods) to
reference names from the current and outer scopes when the class
definition occurs inside a function.
However, even when the class definition occurs inside the function,
methods defined inside the class still cannot see names defined at the
class scope. Class variables must be accessed through the first
parameter of instance or class methods, or through the implicit
lexically scoped "__class__" reference described in the next section.
Creating the class object
-------------------------
Once the class namespace has been populated by executing the class
body, the class object is created by calling "metaclass(name, bases,
namespace, **kwds)" (the additional keywords passed here are the same
as those passed to "__prepare__").
This class object is the one that will be referenced by the zero-
argument form of "super()". "__class__" is an implicit closure
reference created by the compiler if any methods in a class body refer
to either "__class__" or "super". This allows the zero argument form
of "super()" to correctly identify the class being defined based on
lexical scoping, while the class or instance that was used to make the
current call is identified based on the first argument passed to the
method.
**CPython implementation detail:** In CPython 3.6 and later, the
"__class__" cell is passed to the metaclass as a "__classcell__" entry
in the class namespace. If present, this must be propagated up to the
"type.__new__" call in order for the class to be initialised
correctly. Failing to do so will result in a "DeprecationWarning" in
Python 3.6, and a "RuntimeError" in Python 3.8.
When using the default metaclass "type", or any metaclass that
ultimately calls "type.__new__", the following additional
customisation steps are invoked after creating the class object:
* first, "type.__new__" collects all of the descriptors in the class
namespace that define a "__set_name__()" method;
* second, all of these "__set_name__" methods are called with the
class being defined and the assigned name of that particular
descriptor; and
* finally, the "__init_subclass__()" hook is called on the immediate
parent of the new class in its method resolution order.
After the class object is created, it is passed to the class
decorators included in the class definition (if any) and the resulting
object is bound in the local namespace as the defined class.
When a new class is created by "type.__new__", the object provided as
the namespace parameter is copied to a new ordered mapping and the
original object is discarded. The new copy is wrapped in a read-only
proxy, which becomes the "__dict__" attribute of the class object.
See also:
**PEP 3135** - New super
Describes the implicit "__class__" closure reference
Uses for metaclasses
--------------------
The potential uses for metaclasses are boundless. Some ideas that have
been explored include enum, logging, interface checking, automatic
delegation, automatic property creation, proxies, frameworks, and
automatic resource locking/synchronization.
Customizing instance and subclass checks
========================================
The following methods are used to override the default behavior of the
"isinstance()" and "issubclass()" built-in functions.
In particular, the metaclass "abc.ABCMeta" implements these methods in
order to allow the addition of Abstract Base Classes (ABCs) as
“virtual base classes” to any class or type (including built-in
types), including other ABCs.
class.__instancecheck__(self, instance)
Return true if *instance* should be considered a (direct or
indirect) instance of *class*. If defined, called to implement
"isinstance(instance, class)".
class.__subclasscheck__(self, subclass)
Return true if *subclass* should be considered a (direct or
indirect) subclass of *class*. If defined, called to implement
"issubclass(subclass, class)".
Note that these methods are looked up on the type (metaclass) of a
class. They cannot be defined as class methods in the actual class.
This is consistent with the lookup of special methods that are called
on instances, only in this case the instance is itself a class.
See also:
**PEP 3119** - Introducing Abstract Base Classes
Includes the specification for customizing "isinstance()" and
"issubclass()" behavior through "__instancecheck__()" and
"__subclasscheck__()", with motivation for this functionality in
the context of adding Abstract Base Classes (see the "abc"
module) to the language.
Emulating callable objects
==========================
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method
is defined, "x(arg1, arg2, ...)" is a shorthand for
"x.__call__(arg1, arg2, ...)".
Emulating container types
=========================
The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well. The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python’s standard dictionary objects.
The "collections" module provides a "MutableMapping" abstract base
class to help create those methods from a base set of "__getitem__()",
"__setitem__()", "__delitem__()", and "keys()". Mutable sequences
should provide methods "append()", "count()", "index()", "extend()",
"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python
standard list objects. Finally, sequence types should implement
addition (meaning concatenation) and multiplication (meaning
repetition) by defining the methods "__add__()", "__radd__()",
"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described
below; they should not define other numerical operators. It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should search the mapping’s keys; for sequences, it
should search through the values. It is further recommended that both
mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should be the same as "keys()"; for sequences, it should iterate
through the values.
object.__len__(self)
Called to implement the built-in function "len()". Should return
the length of the object, an integer ">=" 0. Also, an object that
doesn’t define a "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.
object.__length_hint__(self)
Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. This
method is purely an optimization and is never required for
correctness.
New in version 3.4.
Note: Slicing is done exclusively with the following three methods.
A call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
object.__getitem__(self, key)
Called to implement evaluation of "self[key]". For sequence types,
the accepted keys should be integers and slice objects. Note that
the special interpretation of negative indexes (if the class wishes
to emulate a sequence type) is up to the "__getitem__()" method. If
*key* is of an inappropriate type, "TypeError" may be raised; if of
a value outside the set of indexes for the sequence (after any
special interpretation of negative values), "IndexError" should be
raised. For mapping types, if *key* is missing (not in the
container), "KeyError" should be raised.
Note: "for" loops expect that an "IndexError" will be raised for
illegal indexes to allow proper detection of the end of the
sequence.
object.__setitem__(self, key, value)
Called to implement assignment to "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support changes to the values for keys, or if new keys
can be added, or for sequences if elements can be replaced. The
same exceptions should be raised for improper *key* values as for
the "__getitem__()" method.
object.__delitem__(self, key)
Called to implement deletion of "self[key]". Same note as for
"__getitem__()". This should only be implemented for mappings if
the objects support removal of keys, or for sequences if elements
can be removed from the sequence. The same exceptions should be
raised for improper *key* values as for the "__getitem__()" method.
object.__missing__(self, key)
Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an iterator is required for a container.
This method should return a new iterator object that can iterate
over all the objects in the container. For mappings, it should
iterate over the keys of the container.
Iterator objects also need to implement this method; they are
required to return themselves. For more information on iterator
objects, see Iterator Types.
object.__reversed__(self)
Called (if present) by the "reversed()" built-in to implement
reverse iteration. It should return a new iterator object that
iterates over all the objects in the container in reverse order.
If the "__reversed__()" method is not provided, the "reversed()"
built-in will fall back to using the sequence protocol ("__len__()"
and "__getitem__()"). Objects that support the sequence protocol
should only provide "__reversed__()" if they can provide an
implementation that is more efficient than the one provided by
"reversed()".
The membership test operators ("in" and "not in") are normally
implemented as an iteration through a sequence. However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.
object.__contains__(self, item)
Called to implement membership test operators. Should return true
if *item* is in *self*, false otherwise. For mapping objects, this
should consider the keys of the mapping rather than the values or
the key-item pairs.
For objects that don’t define "__contains__()", the membership test
first tries iteration via "__iter__()", then the old sequence
iteration protocol via "__getitem__()", see this section in the
language reference.
Emulating numeric types
=======================
The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the expression "x + y", where *x* is an instance of a
class that has an "__add__()" method, "x.__add__(y)" is called.
The "__divmod__()" method should be the equivalent to using
"__floordiv__()" and "__mod__()"; it should not be related to
"__truediv__()". Note that "__pow__()" should be defined to accept
an optional third argument if the ternary version of the built-in
"pow()" function is to be supported.
If one of those methods does not support the operation with the
supplied arguments, it should return "NotImplemented".
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other)
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)
operands. These functions are only called if the left operand does
not support the corresponding operation [3] and the operands are of
different types. [4] For instance, to evaluate the expression "x -
y", where *y* is an instance of a class that has an "__rsub__()"
method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
*NotImplemented*.
Note that ternary "pow()" will not try calling "__rpow__()" (the
coercion rules would become too complicated).
Note: If the right operand’s type is a subclass of the left
operand’s type and that subclass provides the reflected method
for the operation, this method will be called before the left
operand’s non-reflected method. This behavior allows subclasses
to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (modifying *self*) and return the result
(which could be, but does not have to be, *self*). If a specific
method is not defined, the augmented assignment falls back to the
normal methods. For instance, if *x* is an instance of a class
with an "__iadd__()" method, "x += y" is equivalent to "x =
x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are
considered, as with the evaluation of "x + y". In certain
situations, augmented assignment can result in unexpected errors
(see Why does a_tuple[i] += [‘item’] raise an exception when the
addition works?), but this behavior is in fact part of the data
model.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations ("-", "+",
"abs()" and "~").
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions "complex()", "int()" and
"float()". Should return a value of the appropriate type.
object.__index__(self)
Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.
Note: In order to have a coherent integer type class, when
"__index__()" is defined "__int__()" should also be defined, and
both should return the same value.
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").
If "__int__()" is not defined then the built-in function "int()"
falls back to "__trunc__()".
With Statement Context Managers
===============================
A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code. Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.
For more information on context managers, see Context Manager Types.
object.__enter__(self)
Enter the runtime context related to this object. The "with"
statement will bind this method’s return value to the target(s)
specified in the "as" clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters
describe the exception that caused the context to be exited. If the
context was exited without an exception, all three arguments will
be "None".
If an exception is supplied, and the method wishes to suppress the
exception (i.e., prevent it from being propagated), it should
return a true value. Otherwise, the exception will be processed
normally upon exit from this method.
Note that "__exit__()" methods should not reraise the passed-in
exception; this is the caller’s responsibility.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
Special method lookup
=====================
For custom classes, implicit invocations of special methods are only
guaranteed to work correctly if defined on an object’s type, not in
the object’s instance dictionary. That behaviour is the reason why
the following code raises an exception:
>>> class C:
... pass
...
>>> c = C()
>>> c.__len__ = lambda: 5
>>> len(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: object of type 'C' has no len()
The rationale behind this behaviour lies with a number of special
methods such as "__hash__()" and "__repr__()" that are implemented by
all objects, including type objects. If the implicit lookup of these
methods used the conventional lookup process, they would fail when
invoked on the type object itself:
>>> 1 .__hash__() == hash(1)
True
>>> int.__hash__() == hash(int)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: descriptor '__hash__' of 'int' object needs an argument
Incorrectly attempting to invoke an unbound method of a class in this
way is sometimes referred to as ‘metaclass confusion’, and is avoided
by bypassing the instance when looking up special methods:
>>> type(1).__hash__(1) == hash(1)
True
>>> type(int).__hash__(int) == hash(int)
True
In addition to bypassing any instance attributes in the interest of
correctness, implicit special method lookup generally also bypasses
the "__getattribute__()" method even of the object’s metaclass:
>>> class Meta(type):
... def __getattribute__(*args):
... print("Metaclass getattribute invoked")
... return type.__getattribute__(*args)
...
>>> class C(object, metaclass=Meta):
... def __len__(self):
... return 10
... def __getattribute__(*args):
... print("Class getattribute invoked")
... return object.__getattribute__(*args)
...
>>> c = C()
>>> c.__len__() # Explicit lookup via instance
Class getattribute invoked
10
>>> type(c).__len__(c) # Explicit lookup via type
Metaclass getattribute invoked
10
>>> len(c) # Implicit lookup
10
Bypassing the "__getattribute__()" machinery in this fashion provides
significant scope for speed optimisations within the interpreter, at
the cost of some flexibility in the handling of special methods (the
special method *must* be set on the class object itself in order to be
consistently invoked by the interpreter).
u=W String Methods
**************
Strings implement all of the common sequence operations, along with
the additional methods described below.
Strings also support two styles of string formatting, one providing a
large degree of flexibility and customization (see "str.format()",
Format String Syntax and Custom String Formatting) and the other based
on C "printf" style formatting that handles a narrower range of types
and is slightly harder to use correctly, but is often faster for the
cases it can handle (printf-style String Formatting).
The Text Processing Services section of the standard library covers a
number of other modules that provide various text related utilities
(including regular expression support in the "re" module).
str.capitalize()
Return a copy of the string with its first character capitalized
and the rest lowercased.
str.casefold()
Return a casefolded copy of the string. Casefolded strings may be
used for caseless matching.
Casefolding is similar to lowercasing but more aggressive because
it is intended to remove all case distinctions in a string. For
example, the German lowercase letter "'ß'" is equivalent to ""ss"".
Since it is already lowercase, "lower()" would do nothing to "'ß'";
"casefold()" converts it to ""ss"".
The casefolding algorithm is described in section 3.13 of the
Unicode Standard.
New in version 3.3.
str.center(width[, fillchar])
Return centered in a string of length *width*. Padding is done
using the specified *fillchar* (default is an ASCII space). The
original string is returned if *width* is less than or equal to
"len(s)".
str.count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring *sub*
in the range [*start*, *end*]. Optional arguments *start* and
*end* are interpreted as in slice notation.
str.encode(encoding="utf-8", errors="strict")
Return an encoded version of the string as a bytes object. Default
encoding is "'utf-8'". *errors* may be given to set a different
error handling scheme. The default for *errors* is "'strict'",
meaning that encoding errors raise a "UnicodeError". Other possible
values are "'ignore'", "'replace'", "'xmlcharrefreplace'",
"'backslashreplace'" and any other name registered via
"codecs.register_error()", see section Error Handlers. For a list
of possible encodings, see section Standard Encodings.
Changed in version 3.1: Support for keyword arguments added.
str.endswith(suffix[, start[, end]])
Return "True" if the string ends with the specified *suffix*,
otherwise return "False". *suffix* can also be a tuple of suffixes
to look for. With optional *start*, test beginning at that
position. With optional *end*, stop comparing at that position.
str.expandtabs(tabsize=8)
Return a copy of the string where all tab characters are replaced
by one or more spaces, depending on the current column and the
given tab size. Tab positions occur every *tabsize* characters
(default is 8, giving tab positions at columns 0, 8, 16 and so on).
To expand the string, the current column is set to zero and the
string is examined character by character. If the character is a
tab ("\t"), one or more space characters are inserted in the result
until the current column is equal to the next tab position. (The
tab character itself is not copied.) If the character is a newline
("\n") or return ("\r"), it is copied and the current column is
reset to zero. Any other character is copied unchanged and the
current column is incremented by one regardless of how the
character is represented when printed.
>>> '01\t012\t0123\t01234'.expandtabs()
'01 012 0123 01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01 012 0123 01234'
str.find(sub[, start[, end]])
Return the lowest index in the string where substring *sub* is
found within the slice "s[start:end]". Optional arguments *start*
and *end* are interpreted as in slice notation. Return "-1" if
*sub* is not found.
Note: The "find()" method should be used only if you need to know
the position of *sub*. To check if *sub* is a substring or not,
use the "in" operator:
>>> 'Py' in 'Python'
True
str.format(*args, **kwargs)
Perform a string formatting operation. The string on which this
method is called can contain literal text or replacement fields
delimited by braces "{}". Each replacement field contains either
the numeric index of a positional argument, or the name of a
keyword argument. Returns a copy of the string where each
replacement field is replaced with the string value of the
corresponding argument.
>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'
See Format String Syntax for a description of the various
formatting options that can be specified in format strings.
Note: When formatting a number ("int", "float", "complex",
"decimal.Decimal" and subclasses) with the "n" type (ex:
"'{:n}'.format(1234)"), the function temporarily sets the
"LC_CTYPE" locale to the "LC_NUMERIC" locale to decode
"decimal_point" and "thousands_sep" fields of "localeconv()" if
they are non-ASCII or longer than 1 byte, and the "LC_NUMERIC"
locale is different than the "LC_CTYPE" locale. This temporary
change affects other threads.
Changed in version 3.6.5: When formatting a number with the "n"
type, the function sets temporarily the "LC_CTYPE" locale to the
"LC_NUMERIC" locale in some cases.
str.format_map(mapping)
Similar to "str.format(**mapping)", except that "mapping" is used
directly and not copied to a "dict". This is useful if for example
"mapping" is a dict subclass:
>>> class Default(dict):
... def __missing__(self, key):
... return key
...
>>> '{name} was born in {country}'.format_map(Default(name='Guido'))
'Guido was born in country'
New in version 3.2.
str.index(sub[, start[, end]])
Like "find()", but raise "ValueError" when the substring is not
found.
str.isalnum()
Return true if all characters in the string are alphanumeric and
there is at least one character, false otherwise. A character "c"
is alphanumeric if one of the following returns "True":
"c.isalpha()", "c.isdecimal()", "c.isdigit()", or "c.isnumeric()".
str.isalpha()
Return true if all characters in the string are alphabetic and
there is at least one character, false otherwise. Alphabetic
characters are those characters defined in the Unicode character
database as “Letter”, i.e., those with general category property
being one of “Lm”, “Lt”, “Lu”, “Ll”, or “Lo”. Note that this is
different from the “Alphabetic” property defined in the Unicode
Standard.
str.isdecimal()
Return true if all characters in the string are decimal characters
and there is at least one character, false otherwise. Decimal
characters are those that can be used to form numbers in base 10,
e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Formally a decimal character
is a character in the Unicode General Category “Nd”.
str.isdigit()
Return true if all characters in the string are digits and there is
at least one character, false otherwise. Digits include decimal
characters and digits that need special handling, such as the
compatibility superscript digits. This covers digits which cannot
be used to form numbers in base 10, like the Kharosthi numbers.
Formally, a digit is a character that has the property value
Numeric_Type=Digit or Numeric_Type=Decimal.
str.isidentifier()
Return true if the string is a valid identifier according to the
language definition, section Identifiers and keywords.
Use "keyword.iskeyword()" to test for reserved identifiers such as
"def" and "class".
str.islower()
Return true if all cased characters [4] in the string are lowercase
and there is at least one cased character, false otherwise.
str.isnumeric()
Return true if all characters in the string are numeric characters,
and there is at least one character, false otherwise. Numeric
characters include digit characters, and all characters that have
the Unicode numeric value property, e.g. U+2155, VULGAR FRACTION
ONE FIFTH. Formally, numeric characters are those with the
property value Numeric_Type=Digit, Numeric_Type=Decimal or
Numeric_Type=Numeric.
str.isprintable()
Return true if all characters in the string are printable or the
string is empty, false otherwise. Nonprintable characters are
those characters defined in the Unicode character database as
“Other” or “Separator”, excepting the ASCII space (0x20) which is
considered printable. (Note that printable characters in this
context are those which should not be escaped when "repr()" is
invoked on a string. It has no bearing on the handling of strings
written to "sys.stdout" or "sys.stderr".)
str.isspace()
Return true if there are only whitespace characters in the string
and there is at least one character, false otherwise. Whitespace
characters are those characters defined in the Unicode character
database as “Other” or “Separator” and those with bidirectional
property being one of “WS”, “B”, or “S”.
str.istitle()
Return true if the string is a titlecased string and there is at
least one character, for example uppercase characters may only
follow uncased characters and lowercase characters only cased ones.
Return false otherwise.
str.isupper()
Return true if all cased characters [4] in the string are uppercase
and there is at least one cased character, false otherwise.
str.join(iterable)
Return a string which is the concatenation of the strings in
*iterable*. A "TypeError" will be raised if there are any non-
string values in *iterable*, including "bytes" objects. The
separator between elements is the string providing this method.
str.ljust(width[, fillchar])
Return the string left justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is an ASCII
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.lower()
Return a copy of the string with all the cased characters [4]
converted to lowercase.
The lowercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.lstrip([chars])
Return a copy of the string with leading characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a prefix; rather,
all combinations of its values are stripped:
>>> ' spacious '.lstrip()
'spacious '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'
static str.maketrans(x[, y[, z]])
This static method returns a translation table usable for
"str.translate()".
If there is only one argument, it must be a dictionary mapping
Unicode ordinals (integers) or characters (strings of length 1) to
Unicode ordinals, strings (of arbitrary lengths) or "None".
Character keys will then be converted to ordinals.
If there are two arguments, they must be strings of equal length,
and in the resulting dictionary, each character in x will be mapped
to the character at the same position in y. If there is a third
argument, it must be a string, whose characters will be mapped to
"None" in the result.
str.partition(sep)
Split the string at the first occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing the string itself, followed by
two empty strings.
str.replace(old, new[, count])
Return a copy of the string with all occurrences of substring *old*
replaced by *new*. If the optional argument *count* is given, only
the first *count* occurrences are replaced.
str.rfind(sub[, start[, end]])
Return the highest index in the string where substring *sub* is
found, such that *sub* is contained within "s[start:end]".
Optional arguments *start* and *end* are interpreted as in slice
notation. Return "-1" on failure.
str.rindex(sub[, start[, end]])
Like "rfind()" but raises "ValueError" when the substring *sub* is
not found.
str.rjust(width[, fillchar])
Return the string right justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is an ASCII
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.rpartition(sep)
Split the string at the last occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing two empty strings, followed by
the string itself.
str.rsplit(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit* splits
are done, the *rightmost* ones. If *sep* is not specified or
"None", any whitespace string is a separator. Except for splitting
from the right, "rsplit()" behaves like "split()" which is
described in detail below.
str.rstrip([chars])
Return a copy of the string with trailing characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a suffix; rather,
all combinations of its values are stripped:
>>> ' spacious '.rstrip()
' spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'
str.split(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit*
splits are done (thus, the list will have at most "maxsplit+1"
elements). If *maxsplit* is not specified or "-1", then there is
no limit on the number of splits (all possible splits are made).
If *sep* is given, consecutive delimiters are not grouped together
and are deemed to delimit empty strings (for example,
"'1,,2'.split(',')" returns "['1', '', '2']"). The *sep* argument
may consist of multiple characters (for example,
"'1<>2<>3'.split('<>')" returns "['1', '2', '3']"). Splitting an
empty string with a specified separator returns "['']".
For example:
>>> '1,2,3'.split(',')
['1', '2', '3']
>>> '1,2,3'.split(',', maxsplit=1)
['1', '2,3']
>>> '1,2,,3,'.split(',')
['1', '2', '', '3', '']
If *sep* is not specified or is "None", a different splitting
algorithm is applied: runs of consecutive whitespace are regarded
as a single separator, and the result will contain no empty strings
at the start or end if the string has leading or trailing
whitespace. Consequently, splitting an empty string or a string
consisting of just whitespace with a "None" separator returns "[]".
For example:
>>> '1 2 3'.split()
['1', '2', '3']
>>> '1 2 3'.split(maxsplit=1)
['1', '2 3']
>>> ' 1 2 3 '.split()
['1', '2', '3']
str.splitlines([keepends])
Return a list of the lines in the string, breaking at line
boundaries. Line breaks are not included in the resulting list
unless *keepends* is given and true.
This method splits on the following line boundaries. In
particular, the boundaries are a superset of *universal newlines*.
+-------------------------+-------------------------------+
| Representation | Description |
+=========================+===============================+
| "\n" | Line Feed |
+-------------------------+-------------------------------+
| "\r" | Carriage Return |
+-------------------------+-------------------------------+
| "\r\n" | Carriage Return + Line Feed |
+-------------------------+-------------------------------+
| "\v" or "\x0b" | Line Tabulation |
+-------------------------+-------------------------------+
| "\f" or "\x0c" | Form Feed |
+-------------------------+-------------------------------+
| "\x1c" | File Separator |
+-------------------------+-------------------------------+
| "\x1d" | Group Separator |
+-------------------------+-------------------------------+
| "\x1e" | Record Separator |
+-------------------------+-------------------------------+
| "\x85" | Next Line (C1 Control Code) |
+-------------------------+-------------------------------+
| "\u2028" | Line Separator |
+-------------------------+-------------------------------+
| "\u2029" | Paragraph Separator |
+-------------------------+-------------------------------+
Changed in version 3.2: "\v" and "\f" added to list of line
boundaries.
For example:
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
['ab c', '', 'de fg', 'kl']
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True)
['ab c\n', '\n', 'de fg\r', 'kl\r\n']
Unlike "split()" when a delimiter string *sep* is given, this
method returns an empty list for the empty string, and a terminal
line break does not result in an extra line:
>>> "".splitlines()
[]
>>> "One line\n".splitlines()
['One line']
For comparison, "split('\n')" gives:
>>> ''.split('\n')
['']
>>> 'Two lines\n'.split('\n')
['Two lines', '']
str.startswith(prefix[, start[, end]])
Return "True" if string starts with the *prefix*, otherwise return
"False". *prefix* can also be a tuple of prefixes to look for.
With optional *start*, test string beginning at that position.
With optional *end*, stop comparing string at that position.
str.strip([chars])
Return a copy of the string with the leading and trailing
characters removed. The *chars* argument is a string specifying the
set of characters to be removed. If omitted or "None", the *chars*
argument defaults to removing whitespace. The *chars* argument is
not a prefix or suffix; rather, all combinations of its values are
stripped:
>>> ' spacious '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'
The outermost leading and trailing *chars* argument values are
stripped from the string. Characters are removed from the leading
end until reaching a string character that is not contained in the
set of characters in *chars*. A similar action takes place on the
trailing end. For example:
>>> comment_string = '#....... Section 3.2.1 Issue #32 .......'
>>> comment_string.strip('.#! ')
'Section 3.2.1 Issue #32'
str.swapcase()
Return a copy of the string with uppercase characters converted to
lowercase and vice versa. Note that it is not necessarily true that
"s.swapcase().swapcase() == s".
str.title()
Return a titlecased version of the string where words start with an
uppercase character and the remaining characters are lowercase.
For example:
>>> 'Hello world'.title()
'Hello World'
The algorithm uses a simple language-independent definition of a
word as groups of consecutive letters. The definition works in
many contexts but it means that apostrophes in contractions and
possessives form word boundaries, which may not be the desired
result:
>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"
A workaround for apostrophes can be constructed using regular
expressions:
>>> import re
>>> def titlecase(s):
... return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
... lambda mo: mo.group(0)[0].upper() +
... mo.group(0)[1:].lower(),
... s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."
str.translate(table)
Return a copy of the string in which each character has been mapped
through the given translation table. The table must be an object
that implements indexing via "__getitem__()", typically a *mapping*
or *sequence*. When indexed by a Unicode ordinal (an integer), the
table object can do any of the following: return a Unicode ordinal
or a string, to map the character to one or more other characters;
return "None", to delete the character from the return string; or
raise a "LookupError" exception, to map the character to itself.
You can use "str.maketrans()" to create a translation map from
character-to-character mappings in different formats.
See also the "codecs" module for a more flexible approach to custom
character mappings.
str.upper()
Return a copy of the string with all the cased characters [4]
converted to uppercase. Note that "s.upper().isupper()" might be
"False" if "s" contains uncased characters or if the Unicode
category of the resulting character(s) is not “Lu” (Letter,
uppercase), but e.g. “Lt” (Letter, titlecase).
The uppercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.zfill(width)
Return a copy of the string left filled with ASCII "'0'" digits to
make a string of length *width*. A leading sign prefix
("'+'"/"'-'") is handled by inserting the padding *after* the sign
character rather than before. The original string is returned if
*width* is less than or equal to "len(s)".
For example:
>>> "42".zfill(5)
'00042'
>>> "-42".zfill(5)
'-0042'
uw String and Bytes literals
*************************
String literals are described by the following lexical definitions:
stringliteral ::= [stringprefix](shortstring | longstring)
stringprefix ::= "r" | "u" | "R" | "U" | "f" | "F"
| "fr" | "Fr" | "fR" | "FR" | "rf" | "rF" | "Rf" | "RF"
shortstring ::= "'" shortstringitem* "'" | '"' shortstringitem* '"'
longstring ::= "'''" longstringitem* "'''" | '"""' longstringitem* '"""'
shortstringitem ::= shortstringchar | stringescapeseq
longstringitem ::= longstringchar | stringescapeseq
shortstringchar ::= <any source character except "\" or newline or the quote>
longstringchar ::= <any source character except "\">
stringescapeseq ::= "\" <any source character>
bytesliteral ::= bytesprefix(shortbytes | longbytes)
bytesprefix ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB"
shortbytes ::= "'" shortbytesitem* "'" | '"' shortbytesitem* '"'
longbytes ::= "'''" longbytesitem* "'''" | '"""' longbytesitem* '"""'
shortbytesitem ::= shortbyteschar | bytesescapeseq
longbytesitem ::= longbyteschar | bytesescapeseq
shortbyteschar ::= <any ASCII character except "\" or newline or the quote>
longbyteschar ::= <any ASCII character except "\">
bytesescapeseq ::= "\" <any ASCII character>
One syntactic restriction not indicated by these productions is that
whitespace is not allowed between the "stringprefix" or "bytesprefix"
and the rest of the literal. The source character set is defined by
the encoding declaration; it is UTF-8 if no encoding declaration is
given in the source file; see section Encoding declarations.
In plain English: Both types of literals can be enclosed in matching
single quotes ("'") or double quotes ("""). They can also be enclosed
in matching groups of three single or double quotes (these are
generally referred to as *triple-quoted strings*). The backslash
("\") character is used to escape characters that otherwise have a
special meaning, such as newline, backslash itself, or the quote
character.
Bytes literals are always prefixed with "'b'" or "'B'"; they produce
an instance of the "bytes" type instead of the "str" type. They may
only contain ASCII characters; bytes with a numeric value of 128 or
greater must be expressed with escapes.
Both string and bytes literals may optionally be prefixed with a
letter "'r'" or "'R'"; such strings are called *raw strings* and treat
backslashes as literal characters. As a result, in string literals,
"'\U'" and "'\u'" escapes in raw strings are not treated specially.
Given that Python 2.x’s raw unicode literals behave differently than
Python 3.x’s the "'ur'" syntax is not supported.
New in version 3.3: The "'rb'" prefix of raw bytes literals has been
added as a synonym of "'br'".
New in version 3.3: Support for the unicode legacy literal
("u'value'") was reintroduced to simplify the maintenance of dual
Python 2.x and 3.x codebases. See **PEP 414** for more information.
A string literal with "'f'" or "'F'" in its prefix is a *formatted
string literal*; see Formatted string literals. The "'f'" may be
combined with "'r'", but not with "'b'" or "'u'", therefore raw
formatted strings are possible, but formatted bytes literals are not.
In triple-quoted literals, unescaped newlines and quotes are allowed
(and are retained), except that three unescaped quotes in a row
terminate the literal. (A “quote” is the character used to open the
literal, i.e. either "'" or """.)
Unless an "'r'" or "'R'" prefix is present, escape sequences in string
and bytes literals are interpreted according to rules similar to those
used by Standard C. The recognized escape sequences are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
+===================+===================================+=========+
| "\newline" | Backslash and newline ignored | |
+-------------------+-----------------------------------+---------+
| "\\" | Backslash ("\") | |
+-------------------+-----------------------------------+---------+
| "\'" | Single quote ("'") | |
+-------------------+-----------------------------------+---------+
| "\"" | Double quote (""") | |
+-------------------+-----------------------------------+---------+
| "\a" | ASCII Bell (BEL) | |
+-------------------+-----------------------------------+---------+
| "\b" | ASCII Backspace (BS) | |
+-------------------+-----------------------------------+---------+
| "\f" | ASCII Formfeed (FF) | |
+-------------------+-----------------------------------+---------+
| "\n" | ASCII Linefeed (LF) | |
+-------------------+-----------------------------------+---------+
| "\r" | ASCII Carriage Return (CR) | |
+-------------------+-----------------------------------+---------+
| "\t" | ASCII Horizontal Tab (TAB) | |
+-------------------+-----------------------------------+---------+
| "\v" | ASCII Vertical Tab (VT) | |
+-------------------+-----------------------------------+---------+
| "\ooo" | Character with octal value *ooo* | (1,3) |
+-------------------+-----------------------------------+---------+
| "\xhh" | Character with hex value *hh* | (2,3) |
+-------------------+-----------------------------------+---------+
Escape sequences only recognized in string literals are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
+===================+===================================+=========+
| "\N{name}" | Character named *name* in the | (4) |
| | Unicode database | |
+-------------------+-----------------------------------+---------+
| "\uxxxx" | Character with 16-bit hex value | (5) |
| | *xxxx* | |
+-------------------+-----------------------------------+---------+
| "\Uxxxxxxxx" | Character with 32-bit hex value | (6) |
| | *xxxxxxxx* | |
+-------------------+-----------------------------------+---------+
Notes:
1. As in Standard C, up to three octal digits are accepted.
2. Unlike in Standard C, exactly two hex digits are required.
3. In a bytes literal, hexadecimal and octal escapes denote the
byte with the given value. In a string literal, these escapes
denote a Unicode character with the given value.
4. Changed in version 3.3: Support for name aliases [1] has been
added.
5. Exactly four hex digits are required.
6. Any Unicode character can be encoded this way. Exactly eight
hex digits are required.
Unlike Standard C, all unrecognized escape sequences are left in the
string unchanged, i.e., *the backslash is left in the result*. (This
behavior is useful when debugging: if an escape sequence is mistyped,
the resulting output is more easily recognized as broken.) It is also
important to note that the escape sequences only recognized in string
literals fall into the category of unrecognized escapes for bytes
literals.
Changed in version 3.6: Unrecognized escape sequences produce a
DeprecationWarning. In some future version of Python they will be
a SyntaxError.
Even in a raw literal, quotes can be escaped with a backslash, but the
backslash remains in the result; for example, "r"\""" is a valid
string literal consisting of two characters: a backslash and a double
quote; "r"\"" is not a valid string literal (even a raw string cannot
end in an odd number of backslashes). Specifically, *a raw literal
cannot end in a single backslash* (since the backslash would escape
the following quote character). Note also that a single backslash
followed by a newline is interpreted as those two characters as part
of the literal, *not* as a line continuation.
uM Subscriptions
*************
A subscription selects an item of a sequence (string, tuple or list)
or mapping (dictionary) object:
subscription ::= primary "[" expression_list "]"
The primary must evaluate to an object that supports subscription
(lists or dictionaries for example). User-defined objects can support
subscription by defining a "__getitem__()" method.
For built-in objects, there are two types of objects that support
subscription:
If the primary is a mapping, the expression list must evaluate to an
object whose value is one of the keys of the mapping, and the
subscription selects the value in the mapping that corresponds to that
key. (The expression list is a tuple except if it has exactly one
item.)
If the primary is a sequence, the expression list must evaluate to an
integer or a slice (as discussed in the following section).
The formal syntax makes no special provision for negative indices in
sequences; however, built-in sequences all provide a "__getitem__()"
method that interprets negative indices by adding the length of the
sequence to the index (so that "x[-1]" selects the last item of "x").
The resulting value must be a nonnegative integer less than the number
of items in the sequence, and the subscription selects the item whose
index is that value (counting from zero). Since the support for
negative indices and slicing occurs in the object’s "__getitem__()"
method, subclasses overriding this method will need to explicitly add
that support.
A string’s items are characters. A character is not a separate data
type but a string of exactly one character.
ax Truth Value Testing
*******************
Any object can be tested for truth value, for use in an "if" or
"while" condition or as operand of the Boolean operations below.
By default, an object is considered true unless its class defines
either a "__bool__()" method that returns "False" or a "__len__()"
method that returns zero, when called with the object. [1] Here are
most of the built-in objects considered false:
* constants defined to be false: "None" and "False".
* zero of any numeric type: "0", "0.0", "0j", "Decimal(0)",
"Fraction(0, 1)"
* empty sequences and collections: "''", "()", "[]", "{}", "set()",
"range(0)"
Operations and built-in functions that have a Boolean result always
return "0" or "False" for false and "1" or "True" for true, unless
otherwise stated. (Important exception: the Boolean operations "or"
and "and" always return one of their operands.)
u= The "try" statement
*******************
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the except clauses
in turn until one is found that matches the exception. An expression-
less except clause, if present, must be last; it matches any
exception. For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception. An object is compatible
with an exception if it is the class or a base class of the exception
object or a tuple containing an item compatible with the exception.
If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]
If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).
When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed. All except
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred. "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.
The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed. Exceptions in the "else" clause are
not handled by the preceding "except" clauses.
If "finally" is present, it specifies a ‘cleanup’ handler. The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed. If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return" or "break" statement, the saved exception
is discarded:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
The exception information is not available to the program during
execution of the "finally" clause.
When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’ A "continue" statement is illegal in
the "finally" clause. (The reason is a problem with the current
implementation — this restriction may be lifted in the future).
The return value of a function is determined by the last "return"
statement executed. Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.
u�� The standard type hierarchy
***************************
Below is a list of the types that are built into Python. Extension
modules (written in C, Java, or other languages, depending on the
implementation) can define additional types. Future versions of
Python may add types to the type hierarchy (e.g., rational numbers,
efficiently stored arrays of integers, etc.), although such additions
will often be provided via the standard library instead.
Some of the type descriptions below contain a paragraph listing
‘special attributes.’ These are attributes that provide access to the
implementation and are not intended for general use. Their definition
may change in the future.
None
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name "None". It
is used to signify the absence of a value in many situations, e.g.,
it is returned from functions that don’t explicitly return
anything. Its truth value is false.
NotImplemented
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name
"NotImplemented". Numeric methods and rich comparison methods
should return this value if they do not implement the operation for
the operands provided. (The interpreter will then try the
reflected operation, or some other fallback, depending on the
operator.) Its truth value is true.
See Implementing the arithmetic operations for more details.
Ellipsis
This type has a single value. There is a single object with this
value. This object is accessed through the literal "..." or the
built-in name "Ellipsis". Its truth value is true.
"numbers.Number"
These are created by numeric literals and returned as results by
arithmetic operators and arithmetic built-in functions. Numeric
objects are immutable; once created their value never changes.
Python numbers are of course strongly related to mathematical
numbers, but subject to the limitations of numerical representation
in computers.
Python distinguishes between integers, floating point numbers, and
complex numbers:
"numbers.Integral"
These represent elements from the mathematical set of integers
(positive and negative).
There are two types of integers:
Integers ("int")
These represent numbers in an unlimited range, subject to
available (virtual) memory only. For the purpose of shift
and mask operations, a binary representation is assumed, and
negative numbers are represented in a variant of 2’s
complement which gives the illusion of an infinite string of
sign bits extending to the left.
Booleans ("bool")
These represent the truth values False and True. The two
objects representing the values "False" and "True" are the
only Boolean objects. The Boolean type is a subtype of the
integer type, and Boolean values behave like the values 0 and
1, respectively, in almost all contexts, the exception being
that when converted to a string, the strings ""False"" or
""True"" are returned, respectively.
The rules for integer representation are intended to give the
most meaningful interpretation of shift and mask operations
involving negative integers.
"numbers.Real" ("float")
These represent machine-level double precision floating point
numbers. You are at the mercy of the underlying machine
architecture (and C or Java implementation) for the accepted
range and handling of overflow. Python does not support single-
precision floating point numbers; the savings in processor and
memory usage that are usually the reason for using these are
dwarfed by the overhead of using objects in Python, so there is
no reason to complicate the language with two kinds of floating
point numbers.
"numbers.Complex" ("complex")
These represent complex numbers as a pair of machine-level
double precision floating point numbers. The same caveats apply
as for floating point numbers. The real and imaginary parts of a
complex number "z" can be retrieved through the read-only
attributes "z.real" and "z.imag".
Sequences
These represent finite ordered sets indexed by non-negative
numbers. The built-in function "len()" returns the number of items
of a sequence. When the length of a sequence is *n*, the index set
contains the numbers 0, 1, …, *n*-1. Item *i* of sequence *a* is
selected by "a[i]".
Sequences also support slicing: "a[i:j]" selects all items with
index *k* such that *i* "<=" *k* "<" *j*. When used as an
expression, a slice is a sequence of the same type. This implies
that the index set is renumbered so that it starts at 0.
Some sequences also support “extended slicing” with a third “step”
parameter: "a[i:j:k]" selects all items of *a* with index *x* where
"x = i + n*k", *n* ">=" "0" and *i* "<=" *x* "<" *j*.
Sequences are distinguished according to their mutability:
Immutable sequences
An object of an immutable sequence type cannot change once it is
created. (If the object contains references to other objects,
these other objects may be mutable and may be changed; however,
the collection of objects directly referenced by an immutable
object cannot change.)
The following types are immutable sequences:
Strings
A string is a sequence of values that represent Unicode code
points. All the code points in the range "U+0000 - U+10FFFF"
can be represented in a string. Python doesn’t have a "char"
type; instead, every code point in the string is represented
as a string object with length "1". The built-in function
"ord()" converts a code point from its string form to an
integer in the range "0 - 10FFFF"; "chr()" converts an
integer in the range "0 - 10FFFF" to the corresponding length
"1" string object. "str.encode()" can be used to convert a
"str" to "bytes" using the given text encoding, and
"bytes.decode()" can be used to achieve the opposite.
Tuples
The items of a tuple are arbitrary Python objects. Tuples of
two or more items are formed by comma-separated lists of
expressions. A tuple of one item (a ‘singleton’) can be
formed by affixing a comma to an expression (an expression by
itself does not create a tuple, since parentheses must be
usable for grouping of expressions). An empty tuple can be
formed by an empty pair of parentheses.
Bytes
A bytes object is an immutable array. The items are 8-bit
bytes, represented by integers in the range 0 <= x < 256.
Bytes literals (like "b'abc'") and the built-in "bytes()"
constructor can be used to create bytes objects. Also, bytes
objects can be decoded to strings via the "decode()" method.
Mutable sequences
Mutable sequences can be changed after they are created. The
subscription and slicing notations can be used as the target of
assignment and "del" (delete) statements.
There are currently two intrinsic mutable sequence types:
Lists
The items of a list are arbitrary Python objects. Lists are
formed by placing a comma-separated list of expressions in
square brackets. (Note that there are no special cases needed
to form lists of length 0 or 1.)
Byte Arrays
A bytearray object is a mutable array. They are created by
the built-in "bytearray()" constructor. Aside from being
mutable (and hence unhashable), byte arrays otherwise provide
the same interface and functionality as immutable "bytes"
objects.
The extension module "array" provides an additional example of a
mutable sequence type, as does the "collections" module.
Set types
These represent unordered, finite sets of unique, immutable
objects. As such, they cannot be indexed by any subscript. However,
they can be iterated over, and the built-in function "len()"
returns the number of items in a set. Common uses for sets are fast
membership testing, removing duplicates from a sequence, and
computing mathematical operations such as intersection, union,
difference, and symmetric difference.
For set elements, the same immutability rules apply as for
dictionary keys. Note that numeric types obey the normal rules for
numeric comparison: if two numbers compare equal (e.g., "1" and
"1.0"), only one of them can be contained in a set.
There are currently two intrinsic set types:
Sets
These represent a mutable set. They are created by the built-in
"set()" constructor and can be modified afterwards by several
methods, such as "add()".
Frozen sets
These represent an immutable set. They are created by the
built-in "frozenset()" constructor. As a frozenset is immutable
and *hashable*, it can be used again as an element of another
set, or as a dictionary key.
Mappings
These represent finite sets of objects indexed by arbitrary index
sets. The subscript notation "a[k]" selects the item indexed by "k"
from the mapping "a"; this can be used in expressions and as the
target of assignments or "del" statements. The built-in function
"len()" returns the number of items in a mapping.
There is currently a single intrinsic mapping type:
Dictionaries
These represent finite sets of objects indexed by nearly
arbitrary values. The only types of values not acceptable as
keys are values containing lists or dictionaries or other
mutable types that are compared by value rather than by object
identity, the reason being that the efficient implementation of
dictionaries requires a key’s hash value to remain constant.
Numeric types used for keys obey the normal rules for numeric
comparison: if two numbers compare equal (e.g., "1" and "1.0")
then they can be used interchangeably to index the same
dictionary entry.
Dictionaries are mutable; they can be created by the "{...}"
notation (see section Dictionary displays).
The extension modules "dbm.ndbm" and "dbm.gnu" provide
additional examples of mapping types, as does the "collections"
module.
Callable types
These are the types to which the function call operation (see
section Calls) can be applied:
User-defined functions
A user-defined function object is created by a function
definition (see section Function definitions). It should be
called with an argument list containing the same number of items
as the function’s formal parameter list.
Special attributes:
+---------------------------+---------------------------------+-------------+
| Attribute | Meaning | |
+===========================+=================================+=============+
| "__doc__" | The function’s documentation | Writable |
| | string, or "None" if | |
| | unavailable; not inherited by | |
| | subclasses | |
+---------------------------+---------------------------------+-------------+
| "__name__" | The function’s name | Writable |
+---------------------------+---------------------------------+-------------+
| "__qualname__" | The function’s *qualified name* | Writable |
| | New in version 3.3. | |
+---------------------------+---------------------------------+-------------+
| "__module__" | The name of the module the | Writable |
| | function was defined in, or | |
| | "None" if unavailable. | |
+---------------------------+---------------------------------+-------------+
| "__defaults__" | A tuple containing default | Writable |
| | argument values for those | |
| | arguments that have defaults, | |
| | or "None" if no arguments have | |
| | a default value | |
+---------------------------+---------------------------------+-------------+
| "__code__" | The code object representing | Writable |
| | the compiled function body. | |
+---------------------------+---------------------------------+-------------+
| "__globals__" | A reference to the dictionary | Read-only |
| | that holds the function’s | |
| | global variables — the global | |
| | namespace of the module in | |
| | which the function was defined. | |
+---------------------------+---------------------------------+-------------+
| "__dict__" | The namespace supporting | Writable |
| | arbitrary function attributes. | |
+---------------------------+---------------------------------+-------------+
| "__closure__" | "None" or a tuple of cells that | Read-only |
| | contain bindings for the | |
| | function’s free variables. | |
+---------------------------+---------------------------------+-------------+
| "__annotations__" | A dict containing annotations | Writable |
| | of parameters. The keys of the | |
| | dict are the parameter names, | |
| | and "'return'" for the return | |
| | annotation, if provided. | |
+---------------------------+---------------------------------+-------------+
| "__kwdefaults__" | A dict containing defaults for | Writable |
| | keyword-only parameters. | |
+---------------------------+---------------------------------+-------------+
Most of the attributes labelled “Writable” check the type of the
assigned value.
Function objects also support getting and setting arbitrary
attributes, which can be used, for example, to attach metadata
to functions. Regular attribute dot-notation is used to get and
set such attributes. *Note that the current implementation only
supports function attributes on user-defined functions. Function
attributes on built-in functions may be supported in the
future.*
Additional information about a function’s definition can be
retrieved from its code object; see the description of internal
types below.
Instance methods
An instance method object combines a class, a class instance and
any callable object (normally a user-defined function).
Special read-only attributes: "__self__" is the class instance
object, "__func__" is the function object; "__doc__" is the
method’s documentation (same as "__func__.__doc__"); "__name__"
is the method name (same as "__func__.__name__"); "__module__"
is the name of the module the method was defined in, or "None"
if unavailable.
Methods also support accessing (but not setting) the arbitrary
function attributes on the underlying function object.
User-defined method objects may be created when getting an
attribute of a class (perhaps via an instance of that class), if
that attribute is a user-defined function object or a class
method object.
When an instance method object is created by retrieving a user-
defined function object from a class via one of its instances,
its "__self__" attribute is the instance, and the method object
is said to be bound. The new method’s "__func__" attribute is
the original function object.
When a user-defined method object is created by retrieving
another method object from a class or instance, the behaviour is
the same as for a function object, except that the "__func__"
attribute of the new instance is not the original method object
but its "__func__" attribute.
When an instance method object is created by retrieving a class
method object from a class or instance, its "__self__" attribute
is the class itself, and its "__func__" attribute is the
function object underlying the class method.
When an instance method object is called, the underlying
function ("__func__") is called, inserting the class instance
("__self__") in front of the argument list. For instance, when
"C" is a class which contains a definition for a function "f()",
and "x" is an instance of "C", calling "x.f(1)" is equivalent to
calling "C.f(x, 1)".
When an instance method object is derived from a class method
object, the “class instance” stored in "__self__" will actually
be the class itself, so that calling either "x.f(1)" or "C.f(1)"
is equivalent to calling "f(C,1)" where "f" is the underlying
function.
Note that the transformation from function object to instance
method object happens each time the attribute is retrieved from
the instance. In some cases, a fruitful optimization is to
assign the attribute to a local variable and call that local
variable. Also notice that this transformation only happens for
user-defined functions; other callable objects (and all non-
callable objects) are retrieved without transformation. It is
also important to note that user-defined functions which are
attributes of a class instance are not converted to bound
methods; this *only* happens when the function is an attribute
of the class.
Generator functions
A function or method which uses the "yield" statement (see
section The yield statement) is called a *generator function*.
Such a function, when called, always returns an iterator object
which can be used to execute the body of the function: calling
the iterator’s "iterator.__next__()" method will cause the
function to execute until it provides a value using the "yield"
statement. When the function executes a "return" statement or
falls off the end, a "StopIteration" exception is raised and the
iterator will have reached the end of the set of values to be
returned.
Coroutine functions
A function or method which is defined using "async def" is
called a *coroutine function*. Such a function, when called,
returns a *coroutine* object. It may contain "await"
expressions, as well as "async with" and "async for" statements.
See also the Coroutine Objects section.
Asynchronous generator functions
A function or method which is defined using "async def" and
which uses the "yield" statement is called a *asynchronous
generator function*. Such a function, when called, returns an
asynchronous iterator object which can be used in an "async for"
statement to execute the body of the function.
Calling the asynchronous iterator’s "aiterator.__anext__()"
method will return an *awaitable* which when awaited will
execute until it provides a value using the "yield" expression.
When the function executes an empty "return" statement or falls
off the end, a "StopAsyncIteration" exception is raised and the
asynchronous iterator will have reached the end of the set of
values to be yielded.
Built-in functions
A built-in function object is a wrapper around a C function.
Examples of built-in functions are "len()" and "math.sin()"
("math" is a standard built-in module). The number and type of
the arguments are determined by the C function. Special read-
only attributes: "__doc__" is the function’s documentation
string, or "None" if unavailable; "__name__" is the function’s
name; "__self__" is set to "None" (but see the next item);
"__module__" is the name of the module the function was defined
in or "None" if unavailable.
Built-in methods
This is really a different disguise of a built-in function, this
time containing an object passed to the C function as an
implicit extra argument. An example of a built-in method is
"alist.append()", assuming *alist* is a list object. In this
case, the special read-only attribute "__self__" is set to the
object denoted by *alist*.
Classes
Classes are callable. These objects normally act as factories
for new instances of themselves, but variations are possible for
class types that override "__new__()". The arguments of the
call are passed to "__new__()" and, in the typical case, to
"__init__()" to initialize the new instance.
Class Instances
Instances of arbitrary classes can be made callable by defining
a "__call__()" method in their class.
Modules
Modules are a basic organizational unit of Python code, and are
created by the import system as invoked either by the "import"
statement (see "import"), or by calling functions such as
"importlib.import_module()" and built-in "__import__()". A module
object has a namespace implemented by a dictionary object (this is
the dictionary referenced by the "__globals__" attribute of
functions defined in the module). Attribute references are
translated to lookups in this dictionary, e.g., "m.x" is equivalent
to "m.__dict__["x"]". A module object does not contain the code
object used to initialize the module (since it isn’t needed once
the initialization is done).
Attribute assignment updates the module’s namespace dictionary,
e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".
Predefined (writable) attributes: "__name__" is the module’s name;
"__doc__" is the module’s documentation string, or "None" if
unavailable; "__annotations__" (optional) is a dictionary
containing *variable annotations* collected during module body
execution; "__file__" is the pathname of the file from which the
module was loaded, if it was loaded from a file. The "__file__"
attribute may be missing for certain types of modules, such as C
modules that are statically linked into the interpreter; for
extension modules loaded dynamically from a shared library, it is
the pathname of the shared library file.
Special read-only attribute: "__dict__" is the module’s namespace
as a dictionary object.
**CPython implementation detail:** Because of the way CPython
clears module dictionaries, the module dictionary will be cleared
when the module falls out of scope even if the dictionary still has
live references. To avoid this, copy the dictionary or keep the
module around while using its dictionary directly.
Custom classes
Custom class types are typically created by class definitions (see
section Class definitions). A class has a namespace implemented by
a dictionary object. Class attribute references are translated to
lookups in this dictionary, e.g., "C.x" is translated to
"C.__dict__["x"]" (although there are a number of hooks which allow
for other means of locating attributes). When the attribute name is
not found there, the attribute search continues in the base
classes. This search of the base classes uses the C3 method
resolution order which behaves correctly even in the presence of
‘diamond’ inheritance structures where there are multiple
inheritance paths leading back to a common ancestor. Additional
details on the C3 MRO used by Python can be found in the
documentation accompanying the 2.3 release at
https://www.python.org/download/releases/2.3/mro/.
When a class attribute reference (for class "C", say) would yield a
class method object, it is transformed into an instance method
object whose "__self__" attribute is "C". When it would yield a
static method object, it is transformed into the object wrapped by
the static method object. See section Implementing Descriptors for
another way in which attributes retrieved from a class may differ
from those actually contained in its "__dict__".
Class attribute assignments update the class’s dictionary, never
the dictionary of a base class.
A class object can be called (see above) to yield a class instance
(see below).
Special attributes: "__name__" is the class name; "__module__" is
the module name in which the class was defined; "__dict__" is the
dictionary containing the class’s namespace; "__bases__" is a tuple
containing the base classes, in the order of their occurrence in
the base class list; "__doc__" is the class’s documentation string,
or "None" if undefined; "__annotations__" (optional) is a
dictionary containing *variable annotations* collected during class
body execution.
Class instances
A class instance is created by calling a class object (see above).
A class instance has a namespace implemented as a dictionary which
is the first place in which attribute references are searched.
When an attribute is not found there, and the instance’s class has
an attribute by that name, the search continues with the class
attributes. If a class attribute is found that is a user-defined
function object, it is transformed into an instance method object
whose "__self__" attribute is the instance. Static method and
class method objects are also transformed; see above under
“Classes”. See section Implementing Descriptors for another way in
which attributes of a class retrieved via its instances may differ
from the objects actually stored in the class’s "__dict__". If no
class attribute is found, and the object’s class has a
"__getattr__()" method, that is called to satisfy the lookup.
Attribute assignments and deletions update the instance’s
dictionary, never a class’s dictionary. If the class has a
"__setattr__()" or "__delattr__()" method, this is called instead
of updating the instance dictionary directly.
Class instances can pretend to be numbers, sequences, or mappings
if they have methods with certain special names. See section
Special method names.
Special attributes: "__dict__" is the attribute dictionary;
"__class__" is the instance’s class.
I/O objects (also known as file objects)
A *file object* represents an open file. Various shortcuts are
available to create file objects: the "open()" built-in function,
and also "os.popen()", "os.fdopen()", and the "makefile()" method
of socket objects (and perhaps by other functions or methods
provided by extension modules).
The objects "sys.stdin", "sys.stdout" and "sys.stderr" are
initialized to file objects corresponding to the interpreter’s
standard input, output and error streams; they are all open in text
mode and therefore follow the interface defined by the
"io.TextIOBase" abstract class.
Internal types
A few types used internally by the interpreter are exposed to the
user. Their definitions may change with future versions of the
interpreter, but they are mentioned here for completeness.
Code objects
Code objects represent *byte-compiled* executable Python code,
or *bytecode*. The difference between a code object and a
function object is that the function object contains an explicit
reference to the function’s globals (the module in which it was
defined), while a code object contains no context; also the
default argument values are stored in the function object, not
in the code object (because they represent values calculated at
run-time). Unlike function objects, code objects are immutable
and contain no references (directly or indirectly) to mutable
objects.
Special read-only attributes: "co_name" gives the function name;
"co_argcount" is the number of positional arguments (including
arguments with default values); "co_nlocals" is the number of
local variables used by the function (including arguments);
"co_varnames" is a tuple containing the names of the local
variables (starting with the argument names); "co_cellvars" is a
tuple containing the names of local variables that are
referenced by nested functions; "co_freevars" is a tuple
containing the names of free variables; "co_code" is a string
representing the sequence of bytecode instructions; "co_consts"
is a tuple containing the literals used by the bytecode;
"co_names" is a tuple containing the names used by the bytecode;
"co_filename" is the filename from which the code was compiled;
"co_firstlineno" is the first line number of the function;
"co_lnotab" is a string encoding the mapping from bytecode
offsets to line numbers (for details see the source code of the
interpreter); "co_stacksize" is the required stack size
(including local variables); "co_flags" is an integer encoding a
number of flags for the interpreter.
The following flag bits are defined for "co_flags": bit "0x04"
is set if the function uses the "*arguments" syntax to accept an
arbitrary number of positional arguments; bit "0x08" is set if
the function uses the "**keywords" syntax to accept arbitrary
keyword arguments; bit "0x20" is set if the function is a
generator.
Future feature declarations ("from __future__ import division")
also use bits in "co_flags" to indicate whether a code object
was compiled with a particular feature enabled: bit "0x2000" is
set if the function was compiled with future division enabled;
bits "0x10" and "0x1000" were used in earlier versions of
Python.
Other bits in "co_flags" are reserved for internal use.
If a code object represents a function, the first item in
"co_consts" is the documentation string of the function, or
"None" if undefined.
Frame objects
Frame objects represent execution frames. They may occur in
traceback objects (see below).
Special read-only attributes: "f_back" is to the previous stack
frame (towards the caller), or "None" if this is the bottom
stack frame; "f_code" is the code object being executed in this
frame; "f_locals" is the dictionary used to look up local
variables; "f_globals" is used for global variables;
"f_builtins" is used for built-in (intrinsic) names; "f_lasti"
gives the precise instruction (this is an index into the
bytecode string of the code object).
Special writable attributes: "f_trace", if not "None", is a
function called at the start of each source code line (this is
used by the debugger); "f_lineno" is the current line number of
the frame — writing to this from within a trace function jumps
to the given line (only for the bottom-most frame). A debugger
can implement a Jump command (aka Set Next Statement) by writing
to f_lineno.
Frame objects support one method:
frame.clear()
This method clears all references to local variables held by
the frame. Also, if the frame belonged to a generator, the
generator is finalized. This helps break reference cycles
involving frame objects (for example when catching an
exception and storing its traceback for later use).
"RuntimeError" is raised if the frame is currently executing.
New in version 3.4.
Traceback objects
Traceback objects represent a stack trace of an exception. A
traceback object is created when an exception occurs. When the
search for an exception handler unwinds the execution stack, at
each unwound level a traceback object is inserted in front of
the current traceback. When an exception handler is entered,
the stack trace is made available to the program. (See section
The try statement.) It is accessible as the third item of the
tuple returned by "sys.exc_info()". When the program contains no
suitable handler, the stack trace is written (nicely formatted)
to the standard error stream; if the interpreter is interactive,
it is also made available to the user as "sys.last_traceback".
Special read-only attributes: "tb_next" is the next level in the
stack trace (towards the frame where the exception occurred), or
"None" if there is no next level; "tb_frame" points to the
execution frame of the current level; "tb_lineno" gives the line
number where the exception occurred; "tb_lasti" indicates the
precise instruction. The line number and last instruction in
the traceback may differ from the line number of its frame
object if the exception occurred in a "try" statement with no
matching except clause or with a finally clause.
Slice objects
Slice objects are used to represent slices for "__getitem__()"
methods. They are also created by the built-in "slice()"
function.
Special read-only attributes: "start" is the lower bound; "stop"
is the upper bound; "step" is the step value; each is "None" if
omitted. These attributes can have any type.
Slice objects support one method:
slice.indices(self, length)
This method takes a single integer argument *length* and
computes information about the slice that the slice object
would describe if applied to a sequence of *length* items.
It returns a tuple of three integers; respectively these are
the *start* and *stop* indices and the *step* or stride
length of the slice. Missing or out-of-bounds indices are
handled in a manner consistent with regular slices.
Static method objects
Static method objects provide a way of defeating the
transformation of function objects to method objects described
above. A static method object is a wrapper around any other
object, usually a user-defined method object. When a static
method object is retrieved from a class or a class instance, the
object actually returned is the wrapped object, which is not
subject to any further transformation. Static method objects are
not themselves callable, although the objects they wrap usually
are. Static method objects are created by the built-in
"staticmethod()" constructor.
Class method objects
A class method object, like a static method object, is a wrapper
around another object that alters the way in which that object
is retrieved from classes and class instances. The behaviour of
class method objects upon such retrieval is described above,
under “User-defined methods”. Class method objects are created
by the built-in "classmethod()" constructor.
a� Functions
*********
Function objects are created by function definitions. The only
operation on a function object is to call it: "func(argument-list)".
There are really two flavors of function objects: built-in functions
and user-defined functions. Both support the same operation (to call
the function), but the implementation is different, hence the
different object types.
See Function definitions for more information.
u8$ Mapping Types — "dict"
**********************
A *mapping* object maps *hashable* values to arbitrary objects.
Mappings are mutable objects. There is currently only one standard
mapping type, the *dictionary*. (For other containers see the built-
in "list", "set", and "tuple" classes, and the "collections" module.)
A dictionary’s keys are *almost* arbitrary values. Values that are
not *hashable*, that is, values containing lists, dictionaries or
other mutable types (that are compared by value rather than by object
identity) may not be used as keys. Numeric types used for keys obey
the normal rules for numeric comparison: if two numbers compare equal
(such as "1" and "1.0") then they can be used interchangeably to index
the same dictionary entry. (Note however, that since computers store
floating-point numbers as approximations it is usually unwise to use
them as dictionary keys.)
Dictionaries can be created by placing a comma-separated list of "key:
value" pairs within braces, for example: "{'jack': 4098, 'sjoerd':
4127}" or "{4098: 'jack', 4127: 'sjoerd'}", or by the "dict"
constructor.
class dict(**kwarg)
class dict(mapping, **kwarg)
class dict(iterable, **kwarg)
Return a new dictionary initialized from an optional positional
argument and a possibly empty set of keyword arguments.
If no positional argument is given, an empty dictionary is created.
If a positional argument is given and it is a mapping object, a
dictionary is created with the same key-value pairs as the mapping
object. Otherwise, the positional argument must be an *iterable*
object. Each item in the iterable must itself be an iterable with
exactly two objects. The first object of each item becomes a key
in the new dictionary, and the second object the corresponding
value. If a key occurs more than once, the last value for that key
becomes the corresponding value in the new dictionary.
If keyword arguments are given, the keyword arguments and their
values are added to the dictionary created from the positional
argument. If a key being added is already present, the value from
the keyword argument replaces the value from the positional
argument.
To illustrate, the following examples all return a dictionary equal
to "{"one": 1, "two": 2, "three": 3}":
>>> a = dict(one=1, two=2, three=3)
>>> b = {'one': 1, 'two': 2, 'three': 3}
>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
>>> e = dict({'three': 3, 'one': 1, 'two': 2})
>>> a == b == c == d == e
True
Providing keyword arguments as in the first example only works for
keys that are valid Python identifiers. Otherwise, any valid keys
can be used.
These are the operations that dictionaries support (and therefore,
custom mapping types should support too):
len(d)
Return the number of items in the dictionary *d*.
d[key]
Return the item of *d* with key *key*. Raises a "KeyError" if
*key* is not in the map.
If a subclass of dict defines a method "__missing__()" and *key*
is not present, the "d[key]" operation calls that method with
the key *key* as argument. The "d[key]" operation then returns
or raises whatever is returned or raised by the
"__missing__(key)" call. No other operations or methods invoke
"__missing__()". If "__missing__()" is not defined, "KeyError"
is raised. "__missing__()" must be a method; it cannot be an
instance variable:
>>> class Counter(dict):
... def __missing__(self, key):
... return 0
>>> c = Counter()
>>> c['red']
0
>>> c['red'] += 1
>>> c['red']
1
The example above shows part of the implementation of
"collections.Counter". A different "__missing__" method is used
by "collections.defaultdict".
d[key] = value
Set "d[key]" to *value*.
del d[key]
Remove "d[key]" from *d*. Raises a "KeyError" if *key* is not
in the map.
key in d
Return "True" if *d* has a key *key*, else "False".
key not in d
Equivalent to "not key in d".
iter(d)
Return an iterator over the keys of the dictionary. This is a
shortcut for "iter(d.keys())".
clear()
Remove all items from the dictionary.
copy()
Return a shallow copy of the dictionary.
classmethod fromkeys(seq[, value])
Create a new dictionary with keys from *seq* and values set to
*value*.
"fromkeys()" is a class method that returns a new dictionary.
*value* defaults to "None".
get(key[, default])
Return the value for *key* if *key* is in the dictionary, else
*default*. If *default* is not given, it defaults to "None", so
that this method never raises a "KeyError".
items()
Return a new view of the dictionary’s items ("(key, value)"
pairs). See the documentation of view objects.
keys()
Return a new view of the dictionary’s keys. See the
documentation of view objects.
pop(key[, default])
If *key* is in the dictionary, remove it and return its value,
else return *default*. If *default* is not given and *key* is
not in the dictionary, a "KeyError" is raised.
popitem()
Remove and return an arbitrary "(key, value)" pair from the
dictionary.
"popitem()" is useful to destructively iterate over a
dictionary, as often used in set algorithms. If the dictionary
is empty, calling "popitem()" raises a "KeyError".
setdefault(key[, default])
If *key* is in the dictionary, return its value. If not, insert
*key* with a value of *default* and return *default*. *default*
defaults to "None".
update([other])
Update the dictionary with the key/value pairs from *other*,
overwriting existing keys. Return "None".
"update()" accepts either another dictionary object or an
iterable of key/value pairs (as tuples or other iterables of
length two). If keyword arguments are specified, the dictionary
is then updated with those key/value pairs: "d.update(red=1,
blue=2)".
values()
Return a new view of the dictionary’s values. See the
documentation of view objects.
Dictionaries compare equal if and only if they have the same "(key,
value)" pairs. Order comparisons (‘<’, ‘<=’, ‘>=’, ‘>’) raise
"TypeError".
See also: "types.MappingProxyType" can be used to create a read-only
view of a "dict".
Dictionary view objects
=======================
The objects returned by "dict.keys()", "dict.values()" and
"dict.items()" are *view objects*. They provide a dynamic view on the
dictionary’s entries, which means that when the dictionary changes,
the view reflects these changes.
Dictionary views can be iterated over to yield their respective data,
and support membership tests:
len(dictview)
Return the number of entries in the dictionary.
iter(dictview)
Return an iterator over the keys, values or items (represented as
tuples of "(key, value)") in the dictionary.
Keys and values are iterated over in an arbitrary order which is
non-random, varies across Python implementations, and depends on
the dictionary’s history of insertions and deletions. If keys,
values and items views are iterated over with no intervening
modifications to the dictionary, the order of items will directly
correspond. This allows the creation of "(value, key)" pairs using
"zip()": "pairs = zip(d.values(), d.keys())". Another way to
create the same list is "pairs = [(v, k) for (k, v) in d.items()]".
Iterating views while adding or deleting entries in the dictionary
may raise a "RuntimeError" or fail to iterate over all entries.
x in dictview
Return "True" if *x* is in the underlying dictionary’s keys, values
or items (in the latter case, *x* should be a "(key, value)"
tuple).
Keys views are set-like since their entries are unique and hashable.
If all values are hashable, so that "(key, value)" pairs are unique
and hashable, then the items view is also set-like. (Values views are
not treated as set-like since the entries are generally not unique.)
For set-like views, all of the operations defined for the abstract
base class "collections.abc.Set" are available (for example, "==",
"<", or "^").
An example of dictionary view usage:
>>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500}
>>> keys = dishes.keys()
>>> values = dishes.values()
>>> # iteration
>>> n = 0
>>> for val in values:
... n += val
>>> print(n)
504
>>> # keys and values are iterated over in the same order
>>> list(keys)
['eggs', 'bacon', 'sausage', 'spam']
>>> list(values)
[2, 1, 1, 500]
>>> # view objects are dynamic and reflect dict changes
>>> del dishes['eggs']
>>> del dishes['sausage']
>>> list(keys)
['spam', 'bacon']
>>> # set operations
>>> keys & {'eggs', 'bacon', 'salad'}
{'bacon'}
>>> keys ^ {'sausage', 'juice'}
{'juice', 'sausage', 'bacon', 'spam'}
a� Methods
*******
Methods are functions that are called using the attribute notation.
There are two flavors: built-in methods (such as "append()" on lists)
and class instance methods. Built-in methods are described with the
types that support them.
If you access a method (a function defined in a class namespace)
through an instance, you get a special object: a *bound method* (also
called *instance method*) object. When called, it will add the "self"
argument to the argument list. Bound methods have two special read-
only attributes: "m.__self__" is the object on which the method
operates, and "m.__func__" is the function implementing the method.
Calling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to
calling "m.__func__(m.__self__, arg-1, arg-2, ..., arg-n)".
Like function objects, bound method objects support getting arbitrary
attributes. However, since method attributes are actually stored on
the underlying function object ("meth.__func__"), setting method
attributes on bound methods is disallowed. Attempting to set an
attribute on a method results in an "AttributeError" being raised. In
order to set a method attribute, you need to explicitly set it on the
underlying function object:
>>> class C:
... def method(self):
... pass
...
>>> c = C()
>>> c.method.whoami = 'my name is method' # can't set on the method
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'method' object has no attribute 'whoami'
>>> c.method.__func__.whoami = 'my name is method'
>>> c.method.whoami
'my name is method'
See The standard type hierarchy for more information.
u$ Modules
*******
The only special operation on a module is attribute access: "m.name",
where *m* is a module and *name* accesses a name defined in *m*’s
symbol table. Module attributes can be assigned to. (Note that the
"import" statement is not, strictly speaking, an operation on a module
object; "import foo" does not require a module object named *foo* to
exist, rather it requires an (external) *definition* for a module
named *foo* somewhere.)
A special attribute of every module is "__dict__". This is the
dictionary containing the module’s symbol table. Modifying this
dictionary will actually change the module’s symbol table, but direct
assignment to the "__dict__" attribute is not possible (you can write
"m.__dict__['a'] = 1", which defines "m.a" to be "1", but you can’t
write "m.__dict__ = {}"). Modifying "__dict__" directly is not
recommended.
Modules built into the interpreter are written like this: "<module
'sys' (built-in)>". If loaded from a file, they are written as
"<module 'os' from '/usr/local/lib/pythonX.Y/os.pyc'>".
u�Y Sequence Types — "list", "tuple", "range"
*****************************************
There are three basic sequence types: lists, tuples, and range
objects. Additional sequence types tailored for processing of binary
data and text strings are described in dedicated sections.
Common Sequence Operations
==========================
The operations in the following table are supported by most sequence
types, both mutable and immutable. The "collections.abc.Sequence" ABC
is provided to make it easier to correctly implement these operations
on custom sequence types.
This table lists the sequence operations sorted in ascending priority.
In the table, *s* and *t* are sequences of the same type, *n*, *i*,
*j* and *k* are integers and *x* is an arbitrary object that meets any
type and value restrictions imposed by *s*.
The "in" and "not in" operations have the same priorities as the
comparison operations. The "+" (concatenation) and "*" (repetition)
operations have the same priority as the corresponding numeric
operations. [3]
+----------------------------+----------------------------------+------------+
| Operation | Result | Notes |
+============================+==================================+============+
| "x in s" | "True" if an item of *s* is | (1) |
| | equal to *x*, else "False" | |
+----------------------------+----------------------------------+------------+
| "x not in s" | "False" if an item of *s* is | (1) |
| | equal to *x*, else "True" | |
+----------------------------+----------------------------------+------------+
| "s + t" | the concatenation of *s* and *t* | (6)(7) |
+----------------------------+----------------------------------+------------+
| "s * n" or "n * s" | equivalent to adding *s* to | (2)(7) |
| | itself *n* times | |
+----------------------------+----------------------------------+------------+
| "s[i]" | *i*th item of *s*, origin 0 | (3) |
+----------------------------+----------------------------------+------------+
| "s[i:j]" | slice of *s* from *i* to *j* | (3)(4) |
+----------------------------+----------------------------------+------------+
| "s[i:j:k]" | slice of *s* from *i* to *j* | (3)(5) |
| | with step *k* | |
+----------------------------+----------------------------------+------------+
| "len(s)" | length of *s* | |
+----------------------------+----------------------------------+------------+
| "min(s)" | smallest item of *s* | |
+----------------------------+----------------------------------+------------+
| "max(s)" | largest item of *s* | |
+----------------------------+----------------------------------+------------+
| "s.index(x[, i[, j]])" | index of the first occurrence of | (8) |
| | *x* in *s* (at or after index | |
| | *i* and before index *j*) | |
+----------------------------+----------------------------------+------------+
| "s.count(x)" | total number of occurrences of | |
| | *x* in *s* | |
+----------------------------+----------------------------------+------------+
Sequences of the same type also support comparisons. In particular,
tuples and lists are compared lexicographically by comparing
corresponding elements. This means that to compare equal, every
element must compare equal and the two sequences must be of the same
type and have the same length. (For full details see Comparisons in
the language reference.)
Notes:
1. While the "in" and "not in" operations are used only for simple
containment testing in the general case, some specialised sequences
(such as "str", "bytes" and "bytearray") also use them for
subsequence testing:
>>> "gg" in "eggs"
True
2. Values of *n* less than "0" are treated as "0" (which yields an
empty sequence of the same type as *s*). Note that items in the
sequence *s* are not copied; they are referenced multiple times.
This often haunts new Python programmers; consider:
>>> lists = [[]] * 3
>>> lists
[[], [], []]
>>> lists[0].append(3)
>>> lists
[[3], [3], [3]]
What has happened is that "[[]]" is a one-element list containing
an empty list, so all three elements of "[[]] * 3" are references
to this single empty list. Modifying any of the elements of
"lists" modifies this single list. You can create a list of
different lists this way:
>>> lists = [[] for i in range(3)]
>>> lists[0].append(3)
>>> lists[1].append(5)
>>> lists[2].append(7)
>>> lists
[[3], [5], [7]]
Further explanation is available in the FAQ entry How do I create a
multidimensional list?.
3. If *i* or *j* is negative, the index is relative to the end of
sequence *s*: "len(s) + i" or "len(s) + j" is substituted. But
note that "-0" is still "0".
4. The slice of *s* from *i* to *j* is defined as the sequence of
items with index *k* such that "i <= k < j". If *i* or *j* is
greater than "len(s)", use "len(s)". If *i* is omitted or "None",
use "0". If *j* is omitted or "None", use "len(s)". If *i* is
greater than or equal to *j*, the slice is empty.
5. The slice of *s* from *i* to *j* with step *k* is defined as the
sequence of items with index "x = i + n*k" such that "0 <= n <
(j-i)/k". In other words, the indices are "i", "i+k", "i+2*k",
"i+3*k" and so on, stopping when *j* is reached (but never
including *j*). When *k* is positive, *i* and *j* are reduced to
"len(s)" if they are greater. When *k* is negative, *i* and *j* are
reduced to "len(s) - 1" if they are greater. If *i* or *j* are
omitted or "None", they become “end” values (which end depends on
the sign of *k*). Note, *k* cannot be zero. If *k* is "None", it
is treated like "1".
6. Concatenating immutable sequences always results in a new
object. This means that building up a sequence by repeated
concatenation will have a quadratic runtime cost in the total
sequence length. To get a linear runtime cost, you must switch to
one of the alternatives below:
* if concatenating "str" objects, you can build a list and use
"str.join()" at the end or else write to an "io.StringIO"
instance and retrieve its value when complete
* if concatenating "bytes" objects, you can similarly use
"bytes.join()" or "io.BytesIO", or you can do in-place
concatenation with a "bytearray" object. "bytearray" objects are
mutable and have an efficient overallocation mechanism
* if concatenating "tuple" objects, extend a "list" instead
* for other types, investigate the relevant class documentation
7. Some sequence types (such as "range") only support item
sequences that follow specific patterns, and hence don’t support
sequence concatenation or repetition.
8. "index" raises "ValueError" when *x* is not found in *s*. Not
all implementations support passing the additional arguments *i*
and *j*. These arguments allow efficient searching of subsections
of the sequence. Passing the extra arguments is roughly equivalent
to using "s[i:j].index(x)", only without copying any data and with
the returned index being relative to the start of the sequence
rather than the start of the slice.
Immutable Sequence Types
========================
The only operation that immutable sequence types generally implement
that is not also implemented by mutable sequence types is support for
the "hash()" built-in.
This support allows immutable sequences, such as "tuple" instances, to
be used as "dict" keys and stored in "set" and "frozenset" instances.
Attempting to hash an immutable sequence that contains unhashable
values will result in "TypeError".
Mutable Sequence Types
======================
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
+================================+==================================+=======================+
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | appends *x* to the end of the | |
| | sequence (same as | |
| | "s[len(s):len(s)] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from *s* (same | (5) |
| | as "del s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of *s* | (5) |
| | (same as "s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | extends *s* with the contents of | |
| | *t* (for the most part the same | |
| | as "s[len(s):len(s)] = t") | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (6) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | inserts *x* into *s* at the | |
| | index given by *i* (same as | |
| | "s[i:i] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop([i])" | retrieves the item at *i* and | (2) |
| | also removes it from *s* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
| | where "s[i] == x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default
the last item is removed and returned.
3. "remove" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for
economy of space when reversing a large sequence. To remind users
that it operates by side effect, it does not return the reversed
sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don’t support slicing
operations (such as "dict" and "set")
New in version 3.3: "clear()" and "copy()" methods.
6. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the sequence.
Items in the sequence are not copied; they are referenced multiple
times, as explained for "s * n" under Common Sequence Operations.
Lists
=====
Lists are mutable sequences, typically used to store collections of
homogeneous items (where the precise degree of similarity will vary by
application).
class list([iterable])
Lists may be constructed in several ways:
* Using a pair of square brackets to denote the empty list: "[]"
* Using square brackets, separating items with commas: "[a]",
"[a, b, c]"
* Using a list comprehension: "[x for x in iterable]"
* Using the type constructor: "list()" or "list(iterable)"
The constructor builds a list whose items are the same and in the
same order as *iterable*’s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a list, a copy is made and
returned, similar to "iterable[:]". For example, "list('abc')"
returns "['a', 'b', 'c']" and "list( (1, 2, 3) )" returns "[1, 2,
3]". If no argument is given, the constructor creates a new empty
list, "[]".
Many other operations also produce lists, including the "sorted()"
built-in.
Lists implement all of the common and mutable sequence operations.
Lists also provide the following additional method:
sort(*, key=None, reverse=False)
This method sorts the list in place, using only "<" comparisons
between items. Exceptions are not suppressed - if any comparison
operations fail, the entire sort operation will fail (and the
list will likely be left in a partially modified state).
"sort()" accepts two arguments that can only be passed by
keyword (keyword-only arguments):
*key* specifies a function of one argument that is used to
extract a comparison key from each list element (for example,
"key=str.lower"). The key corresponding to each item in the list
is calculated once and then used for the entire sorting process.
The default value of "None" means that list items are sorted
directly without calculating a separate key value.
The "functools.cmp_to_key()" utility is available to convert a
2.x style *cmp* function to a *key* function.
*reverse* is a boolean value. If set to "True", then the list
elements are sorted as if each comparison were reversed.
This method modifies the sequence in place for economy of space
when sorting a large sequence. To remind users that it operates
by side effect, it does not return the sorted sequence (use
"sorted()" to explicitly request a new sorted list instance).
The "sort()" method is guaranteed to be stable. A sort is
stable if it guarantees not to change the relative order of
elements that compare equal — this is helpful for sorting in
multiple passes (for example, sort by department, then by salary
grade).
**CPython implementation detail:** While a list is being sorted,
the effect of attempting to mutate, or even inspect, the list is
undefined. The C implementation of Python makes the list appear
empty for the duration, and raises "ValueError" if it can detect
that the list has been mutated during a sort.
Tuples
======
Tuples are immutable sequences, typically used to store collections of
heterogeneous data (such as the 2-tuples produced by the "enumerate()"
built-in). Tuples are also used for cases where an immutable sequence
of homogeneous data is needed (such as allowing storage in a "set" or
"dict" instance).
class tuple([iterable])
Tuples may be constructed in a number of ways:
* Using a pair of parentheses to denote the empty tuple: "()"
* Using a trailing comma for a singleton tuple: "a," or "(a,)"
* Separating items with commas: "a, b, c" or "(a, b, c)"
* Using the "tuple()" built-in: "tuple()" or "tuple(iterable)"
The constructor builds a tuple whose items are the same and in the
same order as *iterable*’s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a tuple, it is returned
unchanged. For example, "tuple('abc')" returns "('a', 'b', 'c')"
and "tuple( [1, 2, 3] )" returns "(1, 2, 3)". If no argument is
given, the constructor creates a new empty tuple, "()".
Note that it is actually the comma which makes a tuple, not the
parentheses. The parentheses are optional, except in the empty
tuple case, or when they are needed to avoid syntactic ambiguity.
For example, "f(a, b, c)" is a function call with three arguments,
while "f((a, b, c))" is a function call with a 3-tuple as the sole
argument.
Tuples implement all of the common sequence operations.
For heterogeneous collections of data where access by name is clearer
than access by index, "collections.namedtuple()" may be a more
appropriate choice than a simple tuple object.
Ranges
======
The "range" type represents an immutable sequence of numbers and is
commonly used for looping a specific number of times in "for" loops.
class range(stop)
class range(start, stop[, step])
The arguments to the range constructor must be integers (either
built-in "int" or any object that implements the "__index__"
special method). If the *step* argument is omitted, it defaults to
"1". If the *start* argument is omitted, it defaults to "0". If
*step* is zero, "ValueError" is raised.
For a positive *step*, the contents of a range "r" are determined
by the formula "r[i] = start + step*i" where "i >= 0" and "r[i] <
stop".
For a negative *step*, the contents of the range are still
determined by the formula "r[i] = start + step*i", but the
constraints are "i >= 0" and "r[i] > stop".
A range object will be empty if "r[0]" does not meet the value
constraint. Ranges do support negative indices, but these are
interpreted as indexing from the end of the sequence determined by
the positive indices.
Ranges containing absolute values larger than "sys.maxsize" are
permitted but some features (such as "len()") may raise
"OverflowError".
Range examples:
>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(range(1, 11))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> list(range(0, 30, 5))
[0, 5, 10, 15, 20, 25]
>>> list(range(0, 10, 3))
[0, 3, 6, 9]
>>> list(range(0, -10, -1))
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
>>> list(range(0))
[]
>>> list(range(1, 0))
[]
Ranges implement all of the common sequence operations except
concatenation and repetition (due to the fact that range objects
can only represent sequences that follow a strict pattern and
repetition and concatenation will usually violate that pattern).
start
The value of the *start* parameter (or "0" if the parameter was
not supplied)
stop
The value of the *stop* parameter
step
The value of the *step* parameter (or "1" if the parameter was
not supplied)
The advantage of the "range" type over a regular "list" or "tuple" is
that a "range" object will always take the same (small) amount of
memory, no matter the size of the range it represents (as it only
stores the "start", "stop" and "step" values, calculating individual
items and subranges as needed).
Range objects implement the "collections.abc.Sequence" ABC, and
provide features such as containment tests, element index lookup,
slicing and support for negative indices (see Sequence Types — list,
tuple, range):
>>> r = range(0, 20, 2)
>>> r
range(0, 20, 2)
>>> 11 in r
False
>>> 10 in r
True
>>> r.index(10)
5
>>> r[5]
10
>>> r[:5]
range(0, 10, 2)
>>> r[-1]
18
Testing range objects for equality with "==" and "!=" compares them as
sequences. That is, two range objects are considered equal if they
represent the same sequence of values. (Note that two range objects
that compare equal might have different "start", "stop" and "step"
attributes, for example "range(0) == range(2, 1, 3)" or "range(0, 3,
2) == range(0, 4, 2)".)
Changed in version 3.2: Implement the Sequence ABC. Support slicing
and negative indices. Test "int" objects for membership in constant
time instead of iterating through all items.
Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range objects
based on the sequence of values they define (instead of comparing
based on object identity).
New in version 3.3: The "start", "stop" and "step" attributes.
See also:
* The linspace recipe shows how to implement a lazy version of
range suitable for floating point applications.
us Mutable Sequence Types
**********************
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
+================================+==================================+=======================+
| "s[i] = x" | item *i* of *s* is replaced by | |
| | *x* | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j] = t" | slice of *s* from *i* to *j* is | |
| | replaced by the contents of the | |
| | iterable *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j]" | same as "s[i:j] = []" | |
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
| | replaced by those of *t* | |
+--------------------------------+----------------------------------+-----------------------+
| "del s[i:j:k]" | removes the elements of | |
| | "s[i:j:k]" from the list | |
+--------------------------------+----------------------------------+-----------------------+
| "s.append(x)" | appends *x* to the end of the | |
| | sequence (same as | |
| | "s[len(s):len(s)] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from *s* (same | (5) |
| | as "del s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of *s* | (5) |
| | (same as "s[:]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.extend(t)" or "s += t" | extends *s* with the contents of | |
| | *t* (for the most part the same | |
| | as "s[len(s):len(s)] = t") | |
+--------------------------------+----------------------------------+-----------------------+
| "s *= n" | updates *s* with its contents | (6) |
| | repeated *n* times | |
+--------------------------------+----------------------------------+-----------------------+
| "s.insert(i, x)" | inserts *x* into *s* at the | |
| | index given by *i* (same as | |
| | "s[i:i] = [x]") | |
+--------------------------------+----------------------------------+-----------------------+
| "s.pop([i])" | retrieves the item at *i* and | (2) |
| | also removes it from *s* | |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
| | where "s[i] == x" | |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
| | place | |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default
the last item is removed and returned.
3. "remove" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for
economy of space when reversing a large sequence. To remind users
that it operates by side effect, it does not return the reversed
sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don’t support slicing
operations (such as "dict" and "set")
New in version 3.3: "clear()" and "copy()" methods.
6. The value *n* is an integer, or an object implementing
"__index__()". Zero and negative values of *n* clear the sequence.
Items in the sequence are not copied; they are referenced multiple
times, as explained for "s * n" under Common Sequence Operations.
a~ Unary arithmetic and bitwise operations
***************************************
All unary arithmetic and bitwise operations have the same priority:
u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr
The unary "-" (minus) operator yields the negation of its numeric
argument.
The unary "+" (plus) operator yields its numeric argument unchanged.
The unary "~" (invert) operator yields the bitwise inversion of its
integer argument. The bitwise inversion of "x" is defined as
"-(x+1)". It only applies to integral numbers.
In all three cases, if the argument does not have the proper type, a
"TypeError" exception is raised.
u� The "while" statement
*********************
The "while" statement is used for repeated execution as long as an
expression is true:
while_stmt ::= "while" expression ":" suite
["else" ":" suite]
This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.
A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite. A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.
u& The "with" statement
********************
The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.
with_stmt ::= "with" with_item ("," with_item)* ":" suite
with_item ::= expression ["as" target]
The execution of the "with" statement with one “item” proceeds as
follows:
1. The context expression (the expression given in the "with_item")
is evaluated to obtain a context manager.
2. The context manager’s "__exit__()" is loaded for later use.
3. The context manager’s "__enter__()" method is invoked.
4. If a target was included in the "with" statement, the return
value from "__enter__()" is assigned to it.
Note: The "with" statement guarantees that if the "__enter__()"
method returns without an error, then "__exit__()" will always be
called. Thus, if an error occurs during the assignment to the
target list, it will be treated the same as an error occurring
within the suite would be. See step 6 below.
5. The suite is executed.
6. The context manager’s "__exit__()" method is invoked. If an
exception caused the suite to be exited, its type, value, and
traceback are passed as arguments to "__exit__()". Otherwise, three
"None" arguments are supplied.
If the suite was exited due to an exception, and the return value
from the "__exit__()" method was false, the exception is reraised.
If the return value was true, the exception is suppressed, and
execution continues with the statement following the "with"
statement.
If the suite was exited for any reason other than an exception, the
return value from "__exit__()" is ignored, and execution proceeds
at the normal location for the kind of exit that was taken.
With more than one item, the context managers are processed as if
multiple "with" statements were nested:
with A() as a, B() as b:
suite
is equivalent to
with A() as a:
with B() as b:
suite
Changed in version 3.1: Support for multiple context expressions.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
a, The "yield" statement
*********************
yield_stmt ::= yield_expression
A "yield" statement is semantically equivalent to a yield expression.
The yield statement can be used to omit the parentheses that would
otherwise be required in the equivalent yield expression statement.
For example, the yield statements
yield <expr>
yield from <expr>
are equivalent to the yield expression statements
(yield <expr>)
(yield from <expr>)
Yield expressions and statements are only used when defining a
*generator* function, and are only used in the body of the generator
function. Using yield in a function definition is sufficient to cause
that definition to create a generator function instead of a normal
function.
For full details of "yield" semantics, refer to the Yield expressions
section.
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