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�dd�dd�dd�dd�dd�dd�dd�dd�dd�d d!�i d"d#�d$d%�d&d'�d(d)�d*d+�d,d-�d.d/�d0d1�d2d3�d4d5�d6d7�d8d9�d:d;�d<d=�d>d?�d@dA�dBdC��i dDdE�dFdG�dHdI�dJdK�dLdM�dNdO�dPdQ�dRd=�dSdT�dUdV�dWdX�dYdZ�d[d\�d]d^�d_d`�dadb�dcdd��i dedf�dgdh�didj�dkdl�dmdn�dodp�dqdr�dsdt�dudv�dwdx�dydz�d{d|�d}d~�dd��d�d��d�d��d�d���d�d�d�d�d�d�d�d�d�d�d�d���Z d�S )��assertau 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.
�
assignmentu�+ 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:
* 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/value 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 a 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 the same
expressions in the right hand side as regular assignments. Previously,
some expressions (like un-parenthesized tuple expressions) caused a
syntax error.
�asyncu� 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*). "await" expressions, "async for" and "async
with" can only be used in the body of a coroutine function.
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()
Changed in version 3.7: "await" and "async" are now keywords;
previously they were only treated as such inside the body of a
coroutine function.
The "async for" statement
=========================
async_for_stmt ::= "async" for_stmt
An *asynchronous iterable* provides an "__aiter__" method that
directly returns an *asynchronous iterator*, which can call
asynchronous code in its "__anext__" method.
The "async for" statement allows convenient iteration over
asynchronous iterables.
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)
aenter = type(manager).__aenter__
aexit = type(manager).__aexit__
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] In pattern matching, a sequence is defined as one of the
following:
* a class that inherits from "collections.abc.Sequence"
* a Python class that has been registered as
"collections.abc.Sequence"
* a builtin class that has its (CPython) "Py_TPFLAGS_SEQUENCE" bit
set
* a class that inherits from any of the above
The following standard library classes are sequences:
* "array.array"
* "collections.deque"
* "list"
* "memoryview"
* "range"
* "tuple"
Note:
Subject values of type "str", "bytes", and "bytearray" do not
match sequence patterns.
[3] In pattern matching, a mapping is defined as one of the following:
* a class that inherits from "collections.abc.Mapping"
* a Python class that has been registered as
"collections.abc.Mapping"
* a builtin class that has its (CPython) "Py_TPFLAGS_MAPPING" bit
set
* a class that inherits from any of the above
The standard library classes "dict" and "types.MappingProxyType"
are mappings.
[4] 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*.
[5] 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*.
zatom-identifiersa� 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.
z
atom-literalsu
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.
zattribute-accessu�5 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. An iterable must be
returned. "dir()" converts the returned iterable 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 an
iterable 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.
Instances of descriptors may also have the "__objclass__" attribute
present:
object.__objclass__
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
A dotted lookup such as "super(A, a).x" searches
"a.__class__.__mro__" for a base class "B" following "A" and then
returns "B.__dict__['x'].__get__(a, A)". If not a descriptor, "x"
is returned unchanged.
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 "__get__()" and "__set__()"
(and/or "__delete__()") defined always override a redefinition in an
instance dictionary. In contrast, non-data descriptors can be
overridden by instances.
Python methods (including those decorated with "@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 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.
* "TypeError" will be raised if nonempty *__slots__* are defined for a
class derived from a ""variable-length" built-in type" such as
"int", "bytes", and "tuple".
* Any non-string *iterable* may be assigned to *__slots__*.
* If a "dictionary" is used to assign *__slots__*, the dictionary keys
will be used as the slot names. The values of the dictionary can be
used to provide per-attribute docstrings that will be recognised by
"inspect.getdoc()" and displayed in the output of "help()".
* "__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.
zattribute-referencesaU 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. The type
and value produced is determined by the object. Multiple evaluations
of the same attribute reference may yield different objects.
This production can be customized by overriding the
"__getattribute__()" method or the "__getattr__()" method. The
"__getattribute__()" method is called first and either returns a value
or raises "AttributeError" if the attribute is not available.
If an "AttributeError" is raised and the object has a "__getattr__()"
method, that method is called as a fallback.
� augassigna� 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.
�awaitz�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.
�binaryu 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.
This operation can be customized using the special "__mul__()" and
"__rmul__()" methods.
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.
This operation can be customized using the special "__truediv__()" and
"__floordiv__()" methods.
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 *modulo* operation can be customized using the special "__mod__()"
method.
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.
This operation can be customized using the special "__add__()" and
"__radd__()" methods.
The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.
This operation can be customized using the special "__sub__()" method.
�bitwisea< 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 or one of them must be a custom object overriding
"__and__()" or "__rand__()" special methods.
The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be integers or one of them must be a custom
object overriding "__xor__()" or "__rxor__()" special methods.
The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be integers or one of them must be a custom object
overriding "__or__()" or "__ror__()" special methods.
zbltin-code-objectsu� 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.
zbltin-ellipsis-objecta. 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 "...".
zbltin-null-objectu 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".
zbltin-type-objectsu5 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'>".
�booleansa� 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 "''".)
�breaka$ 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.
zcallable-typesu 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, ...)".
�callsu� 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
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
often 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 parameter matching a key
has already been given a value (by an explicit keyword argument, or
from another unpacking), a "TypeError" exception is raised.
When "**expression" is used, each key in this mapping must be a
string. Each value from the mapping is assigned to the first formal
parameter eligible for keyword assignment whose name is equal to the
key. A key need not be a Python identifier (e.g. ""max-temp °F"" is
acceptable, although it will not match any formal parameter that could
be declared). If there is no match to a formal parameter the key-value
pair is collected by the "**" parameter, if there is one, or if there
is not, 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.
�classu� 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. [5] 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.
Changed in version 3.9: Classes may be decorated with any valid
"assignment_expression". Previously, the grammar was much more
restrictive; see **PEP 614** for details.
**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**.
�comparisonsu( 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". Custom *rich
comparison methods* may return non-boolean values. In this case Python
will call "bool()" on such value in boolean contexts.
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]
�compoundu�� 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
| match_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" starred_list ":" suite
["else" ":" suite]
The "starred_list" expression is evaluated once; it should yield an
*iterable* object. An *iterator* is created for that iterable. The
first item provided by the iterator is then assigned to the target
list using the standard rules for assignments (see Assignment
statements), and the suite is executed. This repeats for each item
provided by the iterator. When the iterator is exhausted, 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 type "range()" represents
immutable arithmetic sequences of integers. For instance, iterating
"range(3)" successively yields 0, 1, and then 2.
Changed in version 3.11: Starred elements are now allowed in the
expression list.
The "try" statement
===================
The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:
try_stmt ::= try1_stmt | try2_stmt | try3_stmt
try1_stmt ::= "try" ":" suite
("except" [expression ["as" identifier]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
("except" "*" expression ["as" identifier] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try3_stmt ::= "try" ":" suite
"finally" ":" suite
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.
"except" clause
---------------
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 the object is the class or a *non-
virtual base class* of the exception object, or a tuple containing an
item that is the class or a non-virtual 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, the exception is stored
in the "sys" module, where it can be accessed from within the body of
the "except" clause by calling "sys.exception()". When leaving an
exception handler, the exception stored in the "sys" module is reset
to its previous value:
>>> print(sys.exception())
None
>>> try:
... raise TypeError
... except:
... print(repr(sys.exception()))
... try:
... raise ValueError
... except:
... print(repr(sys.exception()))
... print(repr(sys.exception()))
...
TypeError()
ValueError()
TypeError()
>>> print(sys.exception())
None
"except*" clause
----------------
The "except*" clause(s) are used for handling "ExceptionGroup"s. The
exception type for matching is interpreted as in the case of "except",
but in the case of exception groups we can have partial matches when
the type matches some of the exceptions in the group. This means that
multiple "except*" clauses can execute, each handling part of the
exception group. Each clause executes at most once and handles an
exception group of all matching exceptions. Each exception in the
group is handled by at most one "except*" clause, the first that
matches it.
>>> try:
... raise ExceptionGroup("eg",
... [ValueError(1), TypeError(2), OSError(3), OSError(4)])
... except* TypeError as e:
... print(f'caught {type(e)} with nested {e.exceptions}')
... except* OSError as e:
... print(f'caught {type(e)} with nested {e.exceptions}')
...
caught <class 'ExceptionGroup'> with nested (TypeError(2),)
caught <class 'ExceptionGroup'> with nested (OSError(3), OSError(4))
+ Exception Group Traceback (most recent call last):
| File "<stdin>", line 2, in <module>
| ExceptionGroup: eg
+-+---------------- 1 ----------------
| ValueError: 1
+------------------------------------
Any remaining exceptions that were not handled by any "except*" clause
are re-raised at the end, combined into an exception group along with
all exceptions that were raised from within "except*" clauses.
From version 3.11.4, when the entire "ExceptionGroup" is handled and
only one exception is raised from an "except*" clause, this exception
is no longer wrapped to form a new "ExceptionGroup".
If the raised exception is not an exception group and its type matches
one of the "except*" clauses, it is caught and wrapped by an exception
group with an empty message string.
>>> try:
... raise BlockingIOError
... except* BlockingIOError as e:
... print(repr(e))
...
ExceptionGroup('', (BlockingIOError()))
An "except*" clause must have a matching type, and this type cannot be
a subclass of "BaseExceptionGroup". It is not possible to mix "except"
and "except*" in the same "try". "break", "continue" and "return"
cannot appear in an "except*" clause.
"else" clause
-------------
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.
"finally" clause
----------------
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'
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_stmt_contents ","? ")" | with_stmt_contents ) ":" suite
with_stmt_contents ::= with_item ("," with_item)*
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 7 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
You can also write multi-item context managers in multiple lines if
the items are surrounded by parentheses. For example:
with (
A() as a,
B() as b,
):
SUITE
Changed in version 3.1: Support for multiple context expressions.
Changed in version 3.10: Support for using grouping parentheses to
break the statement in multiple lines.
See also:
**PEP 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.
The "match" statement
=====================
New in version 3.10.
The match statement is used for pattern matching. Syntax:
match_stmt ::= 'match' subject_expr ":" NEWLINE INDENT case_block+ DEDENT
subject_expr ::= star_named_expression "," star_named_expressions?
| named_expression
case_block ::= 'case' patterns [guard] ":" block
Note:
This section uses single quotes to denote soft keywords.
Pattern matching takes a pattern as input (following "case") and a
subject value (following "match"). The pattern (which may contain
subpatterns) is matched against the subject value. The outcomes are:
* A match success or failure (also termed a pattern success or
failure).
* Possible binding of matched values to a name. The prerequisites for
this are further discussed below.
The "match" and "case" keywords are soft keywords.
See also:
* **PEP 634** – Structural Pattern Matching: Specification
* **PEP 636** – Structural Pattern Matching: Tutorial
Overview
--------
Here’s an overview of the logical flow of a match statement:
1. The subject expression "subject_expr" is evaluated and a resulting
subject value obtained. If the subject expression contains a comma,
a tuple is constructed using the standard rules.
2. Each pattern in a "case_block" is attempted to match with the
subject value. The specific rules for success or failure are
described below. The match attempt can also bind some or all of the
standalone names within the pattern. The precise pattern binding
rules vary per pattern type and are specified below. **Name
bindings made during a successful pattern match outlive the
executed block and can be used after the match statement**.
Note:
During failed pattern matches, some subpatterns may succeed. Do
not rely on bindings being made for a failed match. Conversely,
do not rely on variables remaining unchanged after a failed
match. The exact behavior is dependent on implementation and may
vary. This is an intentional decision made to allow different
implementations to add optimizations.
3. If the pattern succeeds, the corresponding guard (if present) is
evaluated. In this case all name bindings are guaranteed to have
happened.
* If the guard evaluates as true or is missing, the "block" inside
"case_block" is executed.
* Otherwise, the next "case_block" is attempted as described above.
* If there are no further case blocks, the match statement is
completed.
Note:
Users should generally never rely on a pattern being evaluated.
Depending on implementation, the interpreter may cache values or use
other optimizations which skip repeated evaluations.
A sample match statement:
>>> flag = False
>>> match (100, 200):
... case (100, 300): # Mismatch: 200 != 300
... print('Case 1')
... case (100, 200) if flag: # Successful match, but guard fails
... print('Case 2')
... case (100, y): # Matches and binds y to 200
... print(f'Case 3, y: {y}')
... case _: # Pattern not attempted
... print('Case 4, I match anything!')
...
Case 3, y: 200
In this case, "if flag" is a guard. Read more about that in the next
section.
Guards
------
guard ::= "if" named_expression
A "guard" (which is part of the "case") must succeed for code inside
the "case" block to execute. It takes the form: "if" followed by an
expression.
The logical flow of a "case" block with a "guard" follows:
1. Check that the pattern in the "case" block succeeded. If the
pattern failed, the "guard" is not evaluated and the next "case"
block is checked.
2. If the pattern succeeded, evaluate the "guard".
* If the "guard" condition evaluates as true, the case block is
selected.
* If the "guard" condition evaluates as false, the case block is
not selected.
* If the "guard" raises an exception during evaluation, the
exception bubbles up.
Guards are allowed to have side effects as they are expressions.
Guard evaluation must proceed from the first to the last case block,
one at a time, skipping case blocks whose pattern(s) don’t all
succeed. (I.e., guard evaluation must happen in order.) Guard
evaluation must stop once a case block is selected.
Irrefutable Case Blocks
-----------------------
An irrefutable case block is a match-all case block. A match
statement may have at most one irrefutable case block, and it must be
last.
A case block is considered irrefutable if it has no guard and its
pattern is irrefutable. A pattern is considered irrefutable if we can
prove from its syntax alone that it will always succeed. Only the
following patterns are irrefutable:
* AS Patterns whose left-hand side is irrefutable
* OR Patterns containing at least one irrefutable pattern
* Capture Patterns
* Wildcard Patterns
* parenthesized irrefutable patterns
Patterns
--------
Note:
This section uses grammar notations beyond standard EBNF:
* the notation "SEP.RULE+" is shorthand for "RULE (SEP RULE)*"
* the notation "!RULE" is shorthand for a negative lookahead
assertion
The top-level syntax for "patterns" is:
patterns ::= open_sequence_pattern | pattern
pattern ::= as_pattern | or_pattern
closed_pattern ::= | literal_pattern
| capture_pattern
| wildcard_pattern
| value_pattern
| group_pattern
| sequence_pattern
| mapping_pattern
| class_pattern
The descriptions below will include a description “in simple terms” of
what a pattern does for illustration purposes (credits to Raymond
Hettinger for a document that inspired most of the descriptions). Note
that these descriptions are purely for illustration purposes and **may
not** reflect the underlying implementation. Furthermore, they do not
cover all valid forms.
OR Patterns
~~~~~~~~~~~
An OR pattern is two or more patterns separated by vertical bars "|".
Syntax:
or_pattern ::= "|".closed_pattern+
Only the final subpattern may be irrefutable, and each subpattern must
bind the same set of names to avoid ambiguity.
An OR pattern matches each of its subpatterns in turn to the subject
value, until one succeeds. The OR pattern is then considered
successful. Otherwise, if none of the subpatterns succeed, the OR
pattern fails.
In simple terms, "P1 | P2 | ..." will try to match "P1", if it fails
it will try to match "P2", succeeding immediately if any succeeds,
failing otherwise.
AS Patterns
~~~~~~~~~~~
An AS pattern matches an OR pattern on the left of the "as" keyword
against a subject. Syntax:
as_pattern ::= or_pattern "as" capture_pattern
If the OR pattern fails, the AS pattern fails. Otherwise, the AS
pattern binds the subject to the name on the right of the as keyword
and succeeds. "capture_pattern" cannot be a "_".
In simple terms "P as NAME" will match with "P", and on success it
will set "NAME = <subject>".
Literal Patterns
~~~~~~~~~~~~~~~~
A literal pattern corresponds to most literals in Python. Syntax:
literal_pattern ::= signed_number
| signed_number "+" NUMBER
| signed_number "-" NUMBER
| strings
| "None"
| "True"
| "False"
| signed_number: NUMBER | "-" NUMBER
The rule "strings" and the token "NUMBER" are defined in the standard
Python grammar. Triple-quoted strings are supported. Raw strings and
byte strings are supported. f-strings are not supported.
The forms "signed_number '+' NUMBER" and "signed_number '-' NUMBER"
are for expressing complex numbers; they require a real number on the
left and an imaginary number on the right. E.g. "3 + 4j".
In simple terms, "LITERAL" will succeed only if "<subject> ==
LITERAL". For the singletons "None", "True" and "False", the "is"
operator is used.
Capture Patterns
~~~~~~~~~~~~~~~~
A capture pattern binds the subject value to a name. Syntax:
capture_pattern ::= !'_' NAME
A single underscore "_" is not a capture pattern (this is what "!'_'"
expresses). It is instead treated as a "wildcard_pattern".
In a given pattern, a given name can only be bound once. E.g. "case
x, x: ..." is invalid while "case [x] | x: ..." is allowed.
Capture patterns always succeed. The binding follows scoping rules
established by the assignment expression operator in **PEP 572**; the
name becomes a local variable in the closest containing function scope
unless there’s an applicable "global" or "nonlocal" statement.
In simple terms "NAME" will always succeed and it will set "NAME =
<subject>".
Wildcard Patterns
~~~~~~~~~~~~~~~~~
A wildcard pattern always succeeds (matches anything) and binds no
name. Syntax:
wildcard_pattern ::= '_'
"_" is a soft keyword within any pattern, but only within patterns.
It is an identifier, as usual, even within "match" subject
expressions, "guard"s, and "case" blocks.
In simple terms, "_" will always succeed.
Value Patterns
~~~~~~~~~~~~~~
A value pattern represents a named value in Python. Syntax:
value_pattern ::= attr
attr ::= name_or_attr "." NAME
name_or_attr ::= attr | NAME
The dotted name in the pattern is looked up using standard Python name
resolution rules. The pattern succeeds if the value found compares
equal to the subject value (using the "==" equality operator).
In simple terms "NAME1.NAME2" will succeed only if "<subject> ==
NAME1.NAME2"
Note:
If the same value occurs multiple times in the same match statement,
the interpreter may cache the first value found and reuse it rather
than repeat the same lookup. This cache is strictly tied to a given
execution of a given match statement.
Group Patterns
~~~~~~~~~~~~~~
A group pattern allows users to add parentheses around patterns to
emphasize the intended grouping. Otherwise, it has no additional
syntax. Syntax:
group_pattern ::= "(" pattern ")"
In simple terms "(P)" has the same effect as "P".
Sequence Patterns
~~~~~~~~~~~~~~~~~
A sequence pattern contains several subpatterns to be matched against
sequence elements. The syntax is similar to the unpacking of a list or
tuple.
sequence_pattern ::= "[" [maybe_sequence_pattern] "]"
| "(" [open_sequence_pattern] ")"
open_sequence_pattern ::= maybe_star_pattern "," [maybe_sequence_pattern]
maybe_sequence_pattern ::= ",".maybe_star_pattern+ ","?
maybe_star_pattern ::= star_pattern | pattern
star_pattern ::= "*" (capture_pattern | wildcard_pattern)
There is no difference if parentheses or square brackets are used for
sequence patterns (i.e. "(...)" vs "[...]" ).
Note:
A single pattern enclosed in parentheses without a trailing comma
(e.g. "(3 | 4)") is a group pattern. While a single pattern enclosed
in square brackets (e.g. "[3 | 4]") is still a sequence pattern.
At most one star subpattern may be in a sequence pattern. The star
subpattern may occur in any position. If no star subpattern is
present, the sequence pattern is a fixed-length sequence pattern;
otherwise it is a variable-length sequence pattern.
The following is the logical flow for matching a sequence pattern
against a subject value:
1. If the subject value is not a sequence [2], the sequence pattern
fails.
2. If the subject value is an instance of "str", "bytes" or
"bytearray" the sequence pattern fails.
3. The subsequent steps depend on whether the sequence pattern is
fixed or variable-length.
If the sequence pattern is fixed-length:
1. If the length of the subject sequence is not equal to the number
of subpatterns, the sequence pattern fails
2. Subpatterns in the sequence pattern are matched to their
corresponding items in the subject sequence from left to right.
Matching stops as soon as a subpattern fails. If all
subpatterns succeed in matching their corresponding item, the
sequence pattern succeeds.
Otherwise, if the sequence pattern is variable-length:
1. If the length of the subject sequence is less than the number of
non-star subpatterns, the sequence pattern fails.
2. The leading non-star subpatterns are matched to their
corresponding items as for fixed-length sequences.
3. If the previous step succeeds, the star subpattern matches a
list formed of the remaining subject items, excluding the
remaining items corresponding to non-star subpatterns following
the star subpattern.
4. Remaining non-star subpatterns are matched to their
corresponding subject items, as for a fixed-length sequence.
Note:
The length of the subject sequence is obtained via "len()" (i.e.
via the "__len__()" protocol). This length may be cached by the
interpreter in a similar manner as value patterns.
In simple terms "[P1, P2, P3," … ", P<N>]" matches only if all the
following happens:
* check "<subject>" is a sequence
* "len(subject) == <N>"
* "P1" matches "<subject>[0]" (note that this match can also bind
names)
* "P2" matches "<subject>[1]" (note that this match can also bind
names)
* … and so on for the corresponding pattern/element.
Mapping Patterns
~~~~~~~~~~~~~~~~
A mapping pattern contains one or more key-value patterns. The syntax
is similar to the construction of a dictionary. Syntax:
mapping_pattern ::= "{" [items_pattern] "}"
items_pattern ::= ",".key_value_pattern+ ","?
key_value_pattern ::= (literal_pattern | value_pattern) ":" pattern
| double_star_pattern
double_star_pattern ::= "**" capture_pattern
At most one double star pattern may be in a mapping pattern. The
double star pattern must be the last subpattern in the mapping
pattern.
Duplicate keys in mapping patterns are disallowed. Duplicate literal
keys will raise a "SyntaxError". Two keys that otherwise have the same
value will raise a "ValueError" at runtime.
The following is the logical flow for matching a mapping pattern
against a subject value:
1. If the subject value is not a mapping [3],the mapping pattern
fails.
2. If every key given in the mapping pattern is present in the subject
mapping, and the pattern for each key matches the corresponding
item of the subject mapping, the mapping pattern succeeds.
3. If duplicate keys are detected in the mapping pattern, the pattern
is considered invalid. A "SyntaxError" is raised for duplicate
literal values; or a "ValueError" for named keys of the same value.
Note:
Key-value pairs are matched using the two-argument form of the
mapping subject’s "get()" method. Matched key-value pairs must
already be present in the mapping, and not created on-the-fly via
"__missing__()" or "__getitem__()".
In simple terms "{KEY1: P1, KEY2: P2, ... }" matches only if all the
following happens:
* check "<subject>" is a mapping
* "KEY1 in <subject>"
* "P1" matches "<subject>[KEY1]"
* … and so on for the corresponding KEY/pattern pair.
Class Patterns
~~~~~~~~~~~~~~
A class pattern represents a class and its positional and keyword
arguments (if any). Syntax:
class_pattern ::= name_or_attr "(" [pattern_arguments ","?] ")"
pattern_arguments ::= positional_patterns ["," keyword_patterns]
| keyword_patterns
positional_patterns ::= ",".pattern+
keyword_patterns ::= ",".keyword_pattern+
keyword_pattern ::= NAME "=" pattern
The same keyword should not be repeated in class patterns.
The following is the logical flow for matching a class pattern against
a subject value:
1. If "name_or_attr" is not an instance of the builtin "type" , raise
"TypeError".
2. If the subject value is not an instance of "name_or_attr" (tested
via "isinstance()"), the class pattern fails.
3. If no pattern arguments are present, the pattern succeeds.
Otherwise, the subsequent steps depend on whether keyword or
positional argument patterns are present.
For a number of built-in types (specified below), a single
positional subpattern is accepted which will match the entire
subject; for these types keyword patterns also work as for other
types.
If only keyword patterns are present, they are processed as
follows, one by one:
I. The keyword is looked up as an attribute on the subject.
* If this raises an exception other than "AttributeError", the
exception bubbles up.
* If this raises "AttributeError", the class pattern has failed.
* Else, the subpattern associated with the keyword pattern is
matched against the subject’s attribute value. If this fails,
the class pattern fails; if this succeeds, the match proceeds
to the next keyword.
II. If all keyword patterns succeed, the class pattern succeeds.
If any positional patterns are present, they are converted to
keyword patterns using the "__match_args__" attribute on the class
"name_or_attr" before matching:
I. The equivalent of "getattr(cls, "__match_args__", ())" is
called.
* If this raises an exception, the exception bubbles up.
* If the returned value is not a tuple, the conversion fails and
"TypeError" is raised.
* If there are more positional patterns than
"len(cls.__match_args__)", "TypeError" is raised.
* Otherwise, positional pattern "i" is converted to a keyword
pattern using "__match_args__[i]" as the keyword.
"__match_args__[i]" must be a string; if not "TypeError" is
raised.
* If there are duplicate keywords, "TypeError" is raised.
See also:
Customizing positional arguments in class pattern matching
II. Once all positional patterns have been converted to keyword
patterns,
the match proceeds as if there were only keyword patterns.
For the following built-in types the handling of positional
subpatterns is different:
* "bool"
* "bytearray"
* "bytes"
* "dict"
* "float"
* "frozenset"
* "int"
* "list"
* "set"
* "str"
* "tuple"
These classes accept a single positional argument, and the pattern
there is matched against the whole object rather than an attribute.
For example "int(0|1)" matches the value "0", but not the value
"0.0".
In simple terms "CLS(P1, attr=P2)" matches only if the following
happens:
* "isinstance(<subject>, CLS)"
* convert "P1" to a keyword pattern using "CLS.__match_args__"
* For each keyword argument "attr=P2":
* "hasattr(<subject>, "attr")"
* "P2" matches "<subject>.attr"
* … and so on for the corresponding keyword argument/pattern pair.
See also:
* **PEP 634** – Structural Pattern Matching: Specification
* **PEP 636** – Structural Pattern Matching: Tutorial
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 ::= "@" assignment_expression NEWLINE
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. [4]
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".
Changed in version 3.9: Functions may be decorated with any valid
"assignment_expression". Previously, the grammar was much more
restrictive; see **PEP 614** for details.
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 value 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 parameter 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.
**PEP 318** - Decorators for Functions and Methods
Function and method decorators were introduced. Class decorators
were introduced in **PEP 3129**.
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. [5] 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.
Changed in version 3.9: Classes may be decorated with any valid
"assignment_expression". Previously, the grammar was much more
restrictive; see **PEP 614** for details.
**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*). "await" expressions, "async for" and "async
with" can only be used in the body of a coroutine function.
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()
Changed in version 3.7: "await" and "async" are now keywords;
previously they were only treated as such inside the body of a
coroutine function.
The "async for" statement
-------------------------
async_for_stmt ::= "async" for_stmt
An *asynchronous iterable* provides an "__aiter__" method that
directly returns an *asynchronous iterator*, which can call
asynchronous code in its "__anext__" method.
The "async for" statement allows convenient iteration over
asynchronous iterables.
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)
aenter = type(manager).__aenter__
aexit = type(manager).__aexit__
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] In pattern matching, a sequence is defined as one of the
following:
* a class that inherits from "collections.abc.Sequence"
* a Python class that has been registered as
"collections.abc.Sequence"
* a builtin class that has its (CPython) "Py_TPFLAGS_SEQUENCE" bit
set
* a class that inherits from any of the above
The following standard library classes are sequences:
* "array.array"
* "collections.deque"
* "list"
* "memoryview"
* "range"
* "tuple"
Note:
Subject values of type "str", "bytes", and "bytearray" do not
match sequence patterns.
[3] In pattern matching, a mapping is defined as one of the following:
* a class that inherits from "collections.abc.Mapping"
* a Python class that has been registered as
"collections.abc.Mapping"
* a builtin class that has its (CPython) "Py_TPFLAGS_MAPPING" bit
set
* a class that inherits from any of the above
The standard library classes "dict" and "types.MappingProxyType"
are mappings.
[4] 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*.
[5] 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*.
zcontext-managersu� 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.
�continuea� 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.
�conversionsu� 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.
�
customizationu6 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(x), '')".
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 the 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.
When no appropriate method returns any value other than
"NotImplemented", the "==" and "!=" operators will fall back to
"is" and "is not", respectively.
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
The "__hash__()" method 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://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.
�debuggeru�P "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".
See also:
Module "faulthandler"
Used to dump Python tracebacks explicitly, on a fault, after a
timeout, or on a user signal.
Module "traceback"
Standard interface to extract, format and print stack traces of
Python programs.
The typical usage to break into the debugger is to insert:
import pdb; pdb.set_trace()
Or:
breakpoint()
at the location you want to break into the debugger, and then run the
program. You can then step through the code following this statement,
and continue running without the debugger using the "continue"
command.
Changed in version 3.7: The built-in "breakpoint()", when called with
defaults, can be used instead of "import pdb; pdb.set_trace()".
def double(x):
breakpoint()
return x * 2
val = 3
print(f"{val} * 2 is {double(val)}")
The debugger’s prompt is "(Pdb)", which is the indicator that you are
in debug mode:
> ...(3)double()
-> return x * 2
(Pdb) p x
3
(Pdb) continue
3 * 2 is 6
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.
You can also invoke "pdb" from the command line to debug other
scripts. For example:
python -m pdb myscript.py
When invoked as a module, 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.
Changed in version 3.2: Added the "-c" option to execute commands as
if given in a ".pdbrc" file; see Debugger Commands.
Changed in version 3.7: Added the "-m" option to execute modules
similar to the way "python -m" does. As with a script, the debugger
will pause execution just before the first line of the module.
Typical usage to execute a statement under control of the debugger is:
>>> import pdb
>>> def f(x):
... print(1 / x)
>>> pdb.run("f(2)")
> <string>(1)<module>()
(Pdb) continue
0.5
>>>
The typical usage to inspect a crashed program is:
>>> import pdb
>>> def f(x):
... print(1 / x)
...
>>> f(0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 2, in f
ZeroDivisionError: division by zero
>>> pdb.pm()
> <stdin>(2)f()
(Pdb) p x
0
(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.
Changed in version 3.1: Added the *skip* parameter.
Changed in version 3.2: Added the *nosigint* parameter. 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. A
workaround for strings with double semicolons is to use implicit
string concatenation "';'';'" or "";"";"".
If a file ".pdbrc" exists in the user’s home directory or in the
current directory, it is read with "'utf-8'" encoding and executed as
if it had been typed at the debugger prompt, with the exception that
empty lines and lines starting with "#" are ignored. 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.
Changed in version 3.11: ".pdbrc" is now read with "'utf-8'" encoding.
Previously, it was read with the system locale encoding.
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 ...]
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 ...]
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 ...]
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 *lineno*, continue execution until a line with a number
greater or equal to *lineno* 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.
Changed in version 3.2: Added 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 arguments of the current function and their current
values.
p expression
Evaluate *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 *expression* is pretty-
printed using the "pprint" module.
whatis expression
Print the type of *expression*.
source expression
Try to get source code of *expression* and display it.
New in version 3.2.
display [expression]
Display the value of *expression* if it changed, each time
execution stops in the current frame.
Without *expression*, list all display expressions for the current
frame.
Note:
Display evaluates *expression* and compares to the result of the
previous evaluation of *expression*, so when the result is
mutable, display may not be able to pick up the changes.
Example:
lst = []
breakpoint()
pass
lst.append(1)
print(lst)
Display won’t realize "lst" has been changed because the result of
evaluation is modified in place by "lst.append(1)" before being
compared:
> example.py(3)<module>()
-> pass
(Pdb) display lst
display lst: []
(Pdb) n
> example.py(4)<module>()
-> lst.append(1)
(Pdb) n
> example.py(5)<module>()
-> print(lst)
(Pdb)
You can do some tricks with copy mechanism to make it work:
> example.py(3)<module>()
-> pass
(Pdb) display lst[:]
display lst[:]: []
(Pdb) n
> example.py(4)<module>()
-> lst.append(1)
(Pdb) n
> example.py(5)<module>()
-> print(lst)
display lst[:]: [1] [old: []]
(Pdb)
New in version 3.2.
undisplay [expression]
Do not display *expression* anymore 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 *command* is omitted, 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(f"%1.{k} = {%1.__dict__[k]}")
# Print instance variables in self
alias ps pi self
unalias name
Delete the specified alias *name*.
! 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 *args* 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 *code* (which is an
arbitrary expression or statement to be executed in the current
environment).
retval
Print the return value for the last return of the current function.
-[ Footnotes ]-
[1] Whether a frame is considered to originate in a certain module is
determined by the "__name__" in the frame globals.
�dela� 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.
�dictu Dictionary displays
*******************
A dictionary display is a possibly empty series of dict items
(key/value pairs) enclosed in curly braces:
dict_display ::= "{" [dict_item_list | dict_comprehension] "}"
dict_item_list ::= dict_item ("," dict_item)* [","]
dict_item ::= expression ":" expression | "**" or_expr
dict_comprehension ::= expression ":" expression comp_for
A dictionary display yields a new dictionary object.
If a comma-separated sequence of dict items 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 value. This means that you can specify the same key
multiple times in the dict item 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 dict
items 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 value (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**.
zdynamic-featuresa� 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.
�elseaX 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.
�
exceptionsu� 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 *non-virtual 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.
� execmodeluE% 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. A module run as a top level script (as module
"__main__") from the command line using a "-m" argument is also 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,
* class definitions,
* function definitions,
* assignment expressions,
* targets that are identifiers if occurring in an assignment:
* "for" loop header,
* after "as" in a "with" statement, "except" clause, "except*"
clause, or in the as-pattern in structural pattern matching,
* in a capture pattern in structural pattern matching
* "import" statements.
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. See the FAQ entry on UnboundLocalError for examples.
If the "global" statement occurs within a block, all uses of the names
specified in the statement refer to the bindings of those names 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 names are not found there, the builtins namespace is searched.
The "global" statement must precede all uses of the listed names.
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 *non-virtual 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.
� exprlistsup 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**.
A trailing comma is required only to create a one-item tuple, such as
"1,"; 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: "()".)
�floatinga� 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.
�foru" 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" starred_list ":" suite
["else" ":" suite]
The "starred_list" expression is evaluated once; it should yield an
*iterable* object. An *iterator* is created for that iterable. The
first item provided by the iterator is then assigned to the target
list using the standard rules for assignments (see Assignment
statements), and the suite is executed. This repeats for each item
provided by the iterator. When the iterator is exhausted, 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 type "range()" represents
immutable arithmetic sequences of integers. For instance, iterating
"range(3)" successively yields 0, 1, and then 2.
Changed in version 3.11: Starred elements are now allowed in the
expression list.
�
formatstringsu�b 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 ::= format-spec:format_spec
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. An *arg_name* is treated as a number if a call to
"str.isdecimal()" on the string would return true. 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 f-strings). 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]["z"]["#"]["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 for |
| | numbers 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 "'z'" option coerces negative zero floating-point values to
positive zero after rounding to the format precision. This option is
only valid for floating-point presentation types.
Changed in version 3.11: Added the "'z'" option (see also **PEP
682**).
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 respective prefix "'0b'", "'0o'", "'0x'",
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 "'='".
Changed in version 3.10: Preceding the *width* field by "'0'" no
longer affects the default alignment for strings.
The *precision* is a decimal integer indicating how many digits should
be displayed after the decimal point for presentation types "'f'" and
"'F'", or before and after the decimal point for presentation types
"'g'" or "'G'". For string presentation 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
presentation types.
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. In case "'#'" is |
| | specified, the prefix "'0x'" will be upper-cased to "'0X'" |
| | as well. |
+-----------+------------------------------------------------------------+
| "'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
�functionu� 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 ::= "@" assignment_expression NEWLINE
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. [4]
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".
Changed in version 3.9: Functions may be decorated with any valid
"assignment_expression". Previously, the grammar was much more
restrictive; see **PEP 614** for details.
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 value 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 parameter 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.
**PEP 318** - Decorators for Functions and Methods
Function and method decorators were introduced. Class decorators
were introduced in **PEP 3129**.
�globalu� 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 as targets in "with" statements or "except" clauses, or
in a "for" target list, "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.
z
id-classesu� 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 *".
"_"
In a "case" pattern within a "match" statement, "_" is a soft
keyword that denotes a wildcard.
Separately, the interactive interpreter makes the result of the
last evaluation available in the variable "_". (It is stored in the
"builtins" module, alongside built-in functions like "print".)
Elsewhere, "_" is a regular identifier. It is often used to name
“special” items, but it is not special to Python itself.
Note:
The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.It is
also commonly used for unused variables.
"__*__"
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).
�identifiersuY 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 14.0.0 can be found at
https://www.unicode.org/Public/14.0.0/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
Soft Keywords
=============
New in version 3.10.
Some identifiers are only reserved under specific contexts. These are
known as *soft keywords*. The identifiers "match", "case" and "_" can
syntactically act as keywords in contexts related to the pattern
matching statement, but this distinction is done at the parser level,
not when tokenizing.
As soft keywords, their use with pattern matching is possible while
still preserving compatibility with existing code that uses "match",
"case" and "_" as identifier names.
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 *".
"_"
In a "case" pattern within a "match" statement, "_" is a soft
keyword that denotes a wildcard.
Separately, the interactive interpreter makes the result of the
last evaluation available in the variable "_". (It is stored in the
"builtins" module, alongside built-in functions like "print".)
Elsewhere, "_" is a regular identifier. It is often used to name
“special” items, but it is not special to Python itself.
Note:
The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.It is
also commonly used for unused variables.
"__*__"
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).
�if� imaginarya5 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
�importu�"