6. Expressions¶
This chapter explains the meaning of the elements of expressions in Python.
Syntax Notes: In this and the following chapters, extended BNF notation will be used to describe syntax, not lexical analysis. When (one alternative of) a syntax rule has the form
name ::= othername
and no semantics are given, the semantics of this form of name
are the same
as for othername
.
6.1. 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.
6.2. Atoms¶
Atoms are the most basic elements of expressions. The simplest atoms are identifiers or literals. Forms enclosed in parentheses, brackets or braces are also categorized syntactically as atoms. The syntax for atoms is:
atom ::=identifier
|literal
|enclosure
enclosure ::=parenth_form
|list_display
|dict_display
|set_display
|generator_expression
|yield_atom
6.2.1. 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.
6.2.2. 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.
6.2.3. Parenthesized forms¶
A parenthesized form is an optional expression list enclosed in parentheses:
parenth_form ::= "(" [starred_expression
] ")"
A parenthesized expression list yields whatever that expression list yields: if the list contains at least one comma, it yields a tuple; otherwise, it yields the single expression that makes up the expression list.
An empty pair of parentheses yields an empty tuple object. Since tuples are immutable, the same rules as for literals apply (i.e., two occurrences of the empty tuple may or may not yield the same object).
Note that tuples are not formed by the parentheses, but rather by use of the comma. The exception is the empty tuple, for which parentheses are required — allowing unparenthesized “nothing” in expressions would cause ambiguities and allow common typos to pass uncaught.
6.2.4. Displays for lists, sets and dictionaries¶
For constructing a list, a set or a dictionary Python provides special syntax called “displays”, each of them in two flavors:
either the container contents are listed explicitly, or
they are computed via a set of looping and filtering instructions, called a comprehension.
Common syntax elements for comprehensions are:
comprehension ::=assignment_expression
comp_for
comp_for ::= ["async"] "for"target_list
"in"or_test
[comp_iter
] comp_iter ::=comp_for
|comp_if
comp_if ::= "if"or_test
[comp_iter
]
The comprehension consists of a single expression followed by at least one
for
clause and zero or more for
or if
clauses.
In this case, the elements of the new container are those that would be produced
by considering each of the for
or if
clauses a block,
nesting from left to right, and evaluating the expression to produce an element
each time the innermost block is reached.
However, aside from the iterable expression in the leftmost for
clause,
the comprehension is executed in a separate implicitly nested scope. This ensures
that names assigned to in the target list don’t “leak” into the enclosing scope.
The iterable expression in the leftmost for
clause is evaluated
directly in the enclosing scope and then passed as an argument to the implicitly
nested scope. Subsequent for
clauses and any filter condition in the
leftmost for
clause cannot be evaluated in the enclosing scope as
they may depend on the values obtained from the leftmost iterable. For example:
[x*y for x in range(10) for y in range(x, x+10)]
.
To ensure the comprehension always results in a container of the appropriate
type, yield
and yield from
expressions are prohibited in the implicitly
nested scope.
Since Python 3.6, in an async def
function, an async for
clause may be used to iterate over a asynchronous iterator.
A comprehension in an async def
function may consist of either a
for
or async for
clause following the leading
expression, may contain additional for
or async for
clauses, and may also use await
expressions.
If a comprehension contains either async for
clauses or
await
expressions or other asynchronous comprehensions it is called
an asynchronous comprehension. An asynchronous comprehension may
suspend the execution of the coroutine function in which it appears.
See also PEP 530.
New in version 3.6: Asynchronous comprehensions were introduced.
Changed in version 3.8: yield
and yield from
prohibited in the implicitly nested scope.
Changed in version 3.11: Asynchronous comprehensions are now allowed inside comprehensions in asynchronous functions. Outer comprehensions implicitly become asynchronous.
6.2.5. List displays¶
A list display is a possibly empty series of expressions enclosed in square brackets:
list_display ::= "[" [starred_list
|comprehension
] "]"
A list display yields a new list object, the contents being specified by either a list of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and placed into the list object in that order. When a comprehension is supplied, the list is constructed from the elements resulting from the comprehension.
6.2.6. Set displays¶
A set display is denoted by curly braces and distinguishable from dictionary displays by the lack of colons separating keys and values:
set_display ::= "{" (starred_list
|comprehension
) "}"
A set display yields a new mutable set object, the contents being specified by either a sequence of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and added to the set object. When a comprehension is supplied, the set is constructed from the elements resulting from the comprehension.
An empty set cannot be constructed with {}
; this literal constructs an empty
dictionary.
6.2.7. 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.
6.2.8. Generator expressions¶
A generator expression is a compact generator notation in parentheses:
generator_expression ::= "("expression
comp_for
")"
A generator expression yields a new generator object. Its syntax is the same as for comprehensions, except that it is enclosed in parentheses instead of brackets or curly braces.
Variables used in the generator expression are evaluated lazily when the
__next__()
method is called for the generator object (in the same
fashion as normal generators). However, the iterable expression in the
leftmost for
clause is immediately evaluated, so that an error
produced by it will be emitted at the point where the generator expression
is defined, rather than at the point where the first value is retrieved.
Subsequent for
clauses and any filter condition in the leftmost
for
clause cannot be evaluated in the enclosing scope as they may
depend on the values obtained from the leftmost iterable. For example:
(x*y for x in range(10) for y in range(x, x+10))
.
The parentheses can be omitted on calls with only one argument. See section Calls for details.
To avoid interfering with the expected operation of the generator expression
itself, yield
and yield from
expressions are prohibited in the
implicitly defined generator.
If a generator expression contains either async for
clauses or await
expressions it is called an
asynchronous generator expression. An asynchronous generator
expression returns a new asynchronous generator object,
which is an asynchronous iterator (see Asynchronous Iterators).
New in version 3.6: Asynchronous generator expressions were introduced.
Changed in version 3.7: Prior to Python 3.7, asynchronous generator expressions could
only appear in async def
coroutines. Starting
with 3.7, any function can use asynchronous generator expressions.
Changed in version 3.8: yield
and yield from
prohibited in the implicitly nested scope.
6.2.9. Yield expressions¶
yield_atom ::= "("yield_expression
")" yield_from ::= "yield" "from"expression
yield_expression ::= "yield"expression_list
|yield_from
The yield expression is used when defining a generator function
or an asynchronous generator function and
thus can only be used in the body of a function definition. Using a yield
expression in a function’s body causes that function to be a generator function,
and using it in an async def
function’s body causes that
coroutine function to be an asynchronous generator function. For example:
def gen(): # defines a generator function
yield 123
async def agen(): # defines an asynchronous generator function
yield 123
Due to their side effects on the containing scope, yield
expressions
are not permitted as part of the implicitly defined scopes used to
implement comprehensions and generator expressions.
Changed in version 3.8: Yield expressions prohibited in the implicitly nested scopes used to implement comprehensions and generator expressions.
Generator functions are described below, while asynchronous generator functions are described separately in section Asynchronous generator functions.
When a generator function is called, it returns an iterator known as a
generator. That generator then controls the execution of the generator
function. The execution starts when one of the generator’s methods is called.
At that time, the execution proceeds to the first yield expression, where it is
suspended again, returning the value of expression_list
to the generator’s caller,
or None
if expression_list
is omitted.
By suspended, we mean that all local state is
retained, including the current bindings of local variables, the instruction
pointer, the internal evaluation stack, and the state of any exception handling.
When the execution is resumed by calling one of the generator’s methods, the
function can proceed exactly as if the yield expression were just another
external call. The value of the yield expression after resuming depends on the
method which resumed the execution. If __next__()
is used
(typically via either a for
or the next()
builtin) then the
result is None
. Otherwise, if send()
is used, then
the result will be the value passed in to that method.
All of this makes generator functions quite similar to coroutines; they yield multiple times, they have more than one entry point and their execution can be suspended. The only difference is that a generator function cannot control where the execution should continue after it yields; the control is always transferred to the generator’s caller.
Yield expressions are allowed anywhere in a try
construct. If the
generator is not resumed before it is
finalized (by reaching a zero reference count or by being garbage collected),
the generator-iterator’s close()
method will be called,
allowing any pending finally
clauses to execute.
When yield from <expr>
is used, the supplied expression must be an
iterable. The values produced by iterating that iterable are passed directly
to the caller of the current generator’s methods. Any values passed in with
send()
and any exceptions passed in with
throw()
are passed to the underlying iterator if it has the
appropriate methods. If this is not the case, then send()
will raise AttributeError
or TypeError
, while
throw()
will just raise the passed in exception immediately.
When the underlying iterator is complete, the value
attribute of the raised StopIteration
instance becomes the value of
the yield expression. It can be either set explicitly when raising
StopIteration
, or automatically when the subiterator is a generator
(by returning a value from the subgenerator).
Changed in version 3.3: Added yield from <expr>
to delegate control flow to a subiterator.
The parentheses may be omitted when the yield expression is the sole expression on the right hand side of an assignment statement.
See also
- PEP 255 - Simple Generators
The proposal for adding generators and the
yield
statement to Python.- PEP 342 - Coroutines via Enhanced Generators
The proposal to enhance the API and syntax of generators, making them usable as simple coroutines.
- PEP 380 - Syntax for Delegating to a Subgenerator
The proposal to introduce the
yield_from
syntax, making delegation to subgenerators easy.- PEP 525 - Asynchronous Generators
The proposal that expanded on PEP 492 by adding generator capabilities to coroutine functions.
6.2.9.1. Generator-iterator methods¶
This subsection describes the methods of a generator iterator. They can be used to control the execution of a generator function.
Note that calling any of the generator methods below when the generator
is already executing raises a ValueError
exception.
- generator.__next__()¶
Starts the execution of a generator function or resumes it at the last executed yield expression. When a generator function is resumed with a
__next__()
method, the current yield expression always evaluates toNone
. The execution then continues to the next yield expression, where the generator is suspended again, and the value of theexpression_list
is returned to__next__()
’s caller. If the generator exits without yielding another value, aStopIteration
exception is raised.This method is normally called implicitly, e.g. by a
for
loop, or by the built-innext()
function.
- generator.send(value)¶
Resumes the execution and “sends” a value into the generator function. The value argument becomes the result of the current yield expression. The
send()
method returns the next value yielded by the generator, or raisesStopIteration
if the generator exits without yielding another value. Whensend()
is called to start the generator, it must be called withNone
as the argument, because there is no yield expression that could receive the value.
- generator.throw(value)¶
- generator.throw(type[, value[, traceback]])
Raises an exception at the point where the generator was paused, and returns the next value yielded by the generator function. If the generator exits without yielding another value, a
StopIteration
exception is raised. If the generator function does not catch the passed-in exception, or raises a different exception, then that exception propagates to the caller.In typical use, this is called with a single exception instance similar to the way the
raise
keyword is used.For backwards compatibility, however, the second signature is supported, following a convention from older versions of Python. The type argument should be an exception class, and value should be an exception instance. If the value is not provided, the type constructor is called to get an instance. If traceback is provided, it is set on the exception, otherwise any existing
__traceback__
attribute stored in value may be cleared.
- generator.close()¶
Raises a
GeneratorExit
at the point where the generator function was paused. If the generator function then exits gracefully, is already closed, or raisesGeneratorExit
(by not catching the exception), close returns to its caller. If the generator yields a value, aRuntimeError
is raised. If the generator raises any other exception, it is propagated to the caller.close()
does nothing if the generator has already exited due to an exception or normal exit.
6.2.9.2. Examples¶
Here is a simple example that demonstrates the behavior of generators and generator functions:
>>> def echo(value=None):
... print("Execution starts when 'next()' is called for the first time.")
... try:
... while True:
... try:
... value = (yield value)
... except Exception as e:
... value = e
... finally:
... print("Don't forget to clean up when 'close()' is called.")
...
>>> generator = echo(1)
>>> print(next(generator))
Execution starts when 'next()' is called for the first time.
1
>>> print(next(generator))
None
>>> print(generator.send(2))
2
>>> generator.throw(TypeError, "spam")
TypeError('spam',)
>>> generator.close()
Don't forget to clean up when 'close()' is called.
For examples using yield from
, see PEP 380: Syntax for Delegating to a Subgenerator in “What’s New in
Python.”
6.2.9.3. Asynchronous generator functions¶
The presence of a yield expression in a function or method defined using
async def
further defines the function as an
asynchronous generator function.
When an asynchronous generator function is called, it returns an
asynchronous iterator known as an asynchronous generator object.
That object then controls the execution of the generator function.
An asynchronous generator object is typically used in an
async for
statement in a coroutine function analogously to
how a generator object would be used in a for
statement.
Calling one of the asynchronous generator’s methods returns an awaitable
object, and the execution starts when this object is awaited on. At that time,
the execution proceeds to the first yield expression, where it is suspended
again, returning the value of expression_list
to the
awaiting coroutine. As with a generator, suspension means that all local state
is retained, including the current bindings of local variables, the instruction
pointer, the internal evaluation stack, and the state of any exception handling.
When the execution is resumed by awaiting on the next object returned by the
asynchronous generator’s methods, the function can proceed exactly as if the
yield expression were just another external call. The value of the yield
expression after resuming depends on the method which resumed the execution. If
__anext__()
is used then the result is None
. Otherwise, if
asend()
is used, then the result will be the value passed in to that
method.
If an asynchronous generator happens to exit early by break
, the caller
task being cancelled, or other exceptions, the generator’s async cleanup code
will run and possibly raise exceptions or access context variables in an
unexpected context–perhaps after the lifetime of tasks it depends, or
during the event loop shutdown when the async-generator garbage collection hook
is called.
To prevent this, the caller must explicitly close the async generator by calling
aclose()
method to finalize the generator and ultimately detach it
from the event loop.
In an asynchronous generator function, yield expressions are allowed anywhere
in a try
construct. However, if an asynchronous generator is not
resumed before it is finalized (by reaching a zero reference count or by
being garbage collected), then a yield expression within a try
construct could result in a failure to execute pending finally
clauses. In this case, it is the responsibility of the event loop or
scheduler running the asynchronous generator to call the asynchronous
generator-iterator’s aclose()
method and run the resulting
coroutine object, thus allowing any pending finally
clauses
to execute.
To take care of finalization upon event loop termination, an event loop should
define a finalizer function which takes an asynchronous generator-iterator and
presumably calls aclose()
and executes the coroutine.
This finalizer may be registered by calling sys.set_asyncgen_hooks()
.
When first iterated over, an asynchronous generator-iterator will store the
registered finalizer to be called upon finalization. For a reference example
of a finalizer method see the implementation of
asyncio.Loop.shutdown_asyncgens
in Lib/asyncio/base_events.py.
The expression yield from <expr>
is a syntax error when used in an
asynchronous generator function.
6.2.9.4. Asynchronous generator-iterator methods¶
This subsection describes the methods of an asynchronous generator iterator, which are used to control the execution of a generator function.
- coroutine agen.__anext__()¶
Returns an awaitable which when run starts to execute the asynchronous generator or resumes it at the last executed yield expression. When an asynchronous generator function is resumed with an
__anext__()
method, the current yield expression always evaluates toNone
in the returned awaitable, which when run will continue to the next yield expression. The value of theexpression_list
of the yield expression is the value of theStopIteration
exception raised by the completing coroutine. If the asynchronous generator exits without yielding another value, the awaitable instead raises aStopAsyncIteration
exception, signalling that the asynchronous iteration has completed.This method is normally called implicitly by a
async for
loop.
- coroutine agen.asend(value)¶
Returns an awaitable which when run resumes the execution of the asynchronous generator. As with the
send()
method for a generator, this “sends” a value into the asynchronous generator function, and the value argument becomes the result of the current yield expression. The awaitable returned by theasend()
method will return the next value yielded by the generator as the value of the raisedStopIteration
, or raisesStopAsyncIteration
if the asynchronous generator exits without yielding another value. Whenasend()
is called to start the asynchronous generator, it must be called withNone
as the argument, because there is no yield expression that could receive the value.
- coroutine agen.athrow(value)¶
- coroutine agen.athrow(type[, value[, traceback]])
Returns an awaitable that raises an exception of type
type
at the point where the asynchronous generator was paused, and returns the next value yielded by the generator function as the value of the raisedStopIteration
exception. If the asynchronous generator exits without yielding another value, aStopAsyncIteration
exception is raised by the awaitable. If the generator function does not catch the passed-in exception, or raises a different exception, then when the awaitable is run that exception propagates to the caller of the awaitable.
- coroutine agen.aclose()¶
Returns an awaitable that when run will throw a
GeneratorExit
into the asynchronous generator function at the point where it was paused. If the asynchronous generator function then exits gracefully, is already closed, or raisesGeneratorExit
(by not catching the exception), then the returned awaitable will raise aStopIteration
exception. Any further awaitables returned by subsequent calls to the asynchronous generator will raise aStopAsyncIteration
exception. If the asynchronous generator yields a value, aRuntimeError
is raised by the awaitable. If the asynchronous generator raises any other exception, it is propagated to the caller of the awaitable. If the asynchronous generator has already exited due to an exception or normal exit, then further calls toaclose()
will return an awaitable that does nothing.
6.3. Primaries¶
Primaries represent the most tightly bound operations of the language. Their syntax is:
primary ::=atom
|attributeref
|subscription
|slicing
|call
6.3.1. 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.
6.3.2. Subscriptions¶
The subscription of an instance of a container class will generally select an element from the container. The subscription of a generic class will generally return a GenericAlias object.
subscription ::=primary
"["expression_list
"]"
When an object is subscripted, the interpreter will evaluate the primary and the expression list.
The primary must evaluate to an object that supports subscription. An object
may support subscription through defining one or both of
__getitem__()
and __class_getitem__()
. When the
primary is subscripted, the evaluated result of the expression list will be
passed to one of these methods. For more details on when __class_getitem__
is called instead of __getitem__
, see __class_getitem__ versus __getitem__.
If the expression list contains at least one comma, it will evaluate to a
tuple
containing the items of the expression list. Otherwise, the
expression list will evaluate to the value of the list’s sole member.
For built-in objects, there are two types of objects that support subscription
via __getitem__()
:
Mappings. If the primary is a mapping, the expression list must evaluate to an object whose value is one of the keys of the mapping, and the subscription selects the value in the mapping that corresponds to that key. An example of a builtin mapping class is the
dict
class.Sequences. If the primary is a sequence, the expression list must evaluate to an
int
or aslice
(as discussed in the following section). Examples of builtin sequence classes include thestr
,list
andtuple
classes.
The formal syntax makes no special provision for negative indices in
sequences. However, built-in sequences all provide a __getitem__()
method that interprets negative indices by adding the length of the sequence
to the index so that, for example, x[-1]
selects the last item of x
. The
resulting value must be a nonnegative integer less than the number of items in
the sequence, and the subscription selects the item whose index is that value
(counting from zero). Since the support for negative indices and slicing
occurs in the object’s __getitem__()
method, subclasses overriding
this method will need to explicitly add that support.
A string
is a special kind of sequence whose items are
characters. A character is not a separate data type but a
string of exactly one character.
6.3.3. Slicings¶
A slicing selects a range of items in a sequence object (e.g., a string, tuple
or list). Slicings may be used as expressions or as targets in assignment or
del
statements. The syntax for a slicing:
slicing ::=primary
"["slice_list
"]" slice_list ::=slice_item
(","slice_item
)* [","] slice_item ::=expression
|proper_slice
proper_slice ::= [lower_bound
] ":" [upper_bound
] [ ":" [stride
] ] lower_bound ::=expression
upper_bound ::=expression
stride ::=expression
There is ambiguity in the formal syntax here: anything that looks like an expression list also looks like a slice list, so any subscription can be interpreted as a slicing. Rather than further complicating the syntax, this is disambiguated by defining that in this case the interpretation as a subscription takes priority over the interpretation as a slicing (this is the case if the slice list contains no proper slice).
The semantics for a slicing are as follows. The primary is indexed (using the
same __getitem__()
method as
normal subscription) with a key that is constructed from the slice list, as
follows. If the slice list contains at least one comma, the key is a tuple
containing the conversion of the slice items; otherwise, the conversion of the
lone slice item is the key. The conversion of a slice item that is an
expression is that expression. The conversion of a proper slice is a slice
object (see section The standard type hierarchy) whose start
,
stop
and step
attributes are the values of the
expressions given as lower bound, upper bound and stride, respectively,
substituting None
for missing expressions.
6.3.4. 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.
6.4. 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.
6.5. The power operator¶
The power operator binds more tightly than unary operators on its left; it binds less tightly than unary operators on its right. The syntax is:
power ::= (await_expr
|primary
) ["**"u_expr
]
Thus, in an unparenthesized sequence of power and unary operators, the operators
are evaluated from right to left (this does not constrain the evaluation order
for the operands): -1**2
results in -1
.
The power operator has the same semantics as the built-in pow()
function,
when called with two arguments: it yields its left argument raised to the power
of its right argument. The numeric arguments are first converted to a common
type, and the result is of that type.
For int operands, the result has the same type as the operands unless the second
argument is negative; in that case, all arguments are converted to float and a
float result is delivered. For example, 10**2
returns 100
, but
10**-2
returns 0.01
.
Raising 0.0
to a negative power results in a ZeroDivisionError
.
Raising a negative number to a fractional power results in a complex
number. (In earlier versions it raised a ValueError
.)
This operation can be customized using the special __pow__()
method.
6.6. Unary arithmetic and bitwise operations¶
All unary arithmetic and bitwise operations have the same priority:
u_expr ::=power
| "-"u_expr
| "+"u_expr
| "~"u_expr
The unary -
(minus) operator yields the negation of its numeric argument; the
operation can be overridden with the __neg__()
special method.
The unary +
(plus) operator yields its numeric argument unchanged; the
operation can be overridden with the __pos__()
special method.
The unary ~
(invert) operator yields the bitwise inversion of its integer
argument. The bitwise inversion of x
is defined as -(x+1)
. It only
applies to integral numbers or to custom objects that override the
__invert__()
special method.
In all three cases, if the argument does not have the proper type, a
TypeError
exception is raised.
6.7. 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.
6.8. Shifting operations¶
The shifting operations have lower priority than the arithmetic operations:
shift_expr ::=a_expr
|shift_expr
("<<" | ">>")a_expr
These operators accept integers as arguments. They shift the first argument to the left or right by the number of bits given by the second argument.
This operation can be customized using the special __lshift__()
and
__rshift__()
methods.
A right shift by n bits is defined as floor division by pow(2,n)
. A left
shift by n bits is defined as multiplication with pow(2,n)
.
6.9. 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.
6.10. 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).
6.10.1. 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
anddecimal.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')
anddecimal.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, ifx = float('NaN')
,3 < x
,x < 3
andx == x
are all false, whilex != x
is true. This behavior is compliant with IEEE 754.None
andNotImplemented
are singletons. PEP 8 advises that comparisons for singletons should always be done withis
oris not
, never the equality operators.Binary sequences (instances of
bytes
orbytearray
) 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 functionord()
) of their characters. [3]Strings and binary sequences cannot be directly compared.
Sequences (instances of
tuple
,list
, orrange
) 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 raisesTypeError
.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 asx <= 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>=
) raiseTypeError
.Sets (instances of
set
orfrozenset
) 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()
, andsorted()
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
impliesx == y
Comparison should be symmetric. In other words, the following expressions should have the same result:
x == y
andy == x
x != y
andy != x
x < y
andy > x
x <= y
andy >= x
Comparison should be transitive. The following (non-exhaustive) examples illustrate that:
x > y and y > z
impliesx > z
x < y and y <= z
impliesx < z
Inverse comparison should result in the boolean negation. In other words, the following expressions should have the same result:
x == y
andnot x != y
x < y
andnot x >= y
(for total ordering)x > y
andnot 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.
6.10.2. 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
.
6.10.3. 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]
6.11. 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 ''
.)
6.12. Assignment expressions¶
assignment_expression ::= [identifier
":="]expression
An assignment expression (sometimes also called a “named expression” or
“walrus”) assigns an expression
to an
identifier
, while also returning the value of the
expression
.
One common use case is when handling matched regular expressions:
if matching := pattern.search(data):
do_something(matching)
Or, when processing a file stream in chunks:
while chunk := file.read(9000):
process(chunk)
Assignment expressions must be surrounded by parentheses when
used as expression statements and when used as sub-expressions in
slicing, conditional, lambda,
keyword-argument, and comprehension-if expressions and
in assert
, with
, and assignment
statements.
In all other places where they can be used, parentheses are not required,
including in if
and while
statements.
New in version 3.8: See PEP 572 for more details about assignment expressions.
6.13. Conditional expressions¶
conditional_expression ::=or_test
["if"or_test
"else"expression
] expression ::=conditional_expression
|lambda_expr
Conditional expressions (sometimes called a “ternary operator”) have the lowest priority of all Python operations.
The expression x if C else y
first evaluates the condition, C rather than x.
If C is true, x is evaluated and its value is returned; otherwise, y is
evaluated and its value is returned.
See PEP 308 for more details about conditional expressions.
6.14. Lambdas¶
lambda_expr ::= "lambda" [parameter_list
] ":"expression
Lambda expressions (sometimes called lambda forms) are used to create anonymous
functions. The expression lambda parameters: expression
yields a function
object. The unnamed object behaves like a function object defined with:
def <lambda>(parameters):
return expression
See section Function definitions for the syntax of parameter lists. Note that functions created with lambda expressions cannot contain statements or annotations.
6.15. 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:
()
.)
6.16. Evaluation order¶
Python evaluates expressions from left to right. Notice that while evaluating an assignment, the right-hand side is evaluated before the left-hand side.
In the following lines, expressions will be evaluated in the arithmetic order of their suffixes:
expr1, expr2, expr3, expr4
(expr1, expr2, expr3, expr4)
{expr1: expr2, expr3: expr4}
expr1 + expr2 * (expr3 - expr4)
expr1(expr2, expr3, *expr4, **expr5)
expr3, expr4 = expr1, expr2
6.17. Operator precedence¶
The following table summarizes the operator precedence in Python, from highest precedence (most binding) to lowest precedence (least binding). Operators in the same box have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators in the same box group left to right (except for exponentiation and conditional expressions, which group from right to left).
Note that comparisons, membership tests, and identity tests, all have the same precedence and have a left-to-right chaining feature as described in the Comparisons section.
Operator |
Description |
---|---|
|
Binding or parenthesized expression, list display, dictionary display, set display |
|
Subscription, slicing, call, attribute reference |
Await expression |
|
|
Exponentiation [5] |
|
Positive, negative, bitwise NOT |
|
Multiplication, matrix multiplication, division, floor division, remainder [6] |
|
Addition and subtraction |
|
Shifts |
|
Bitwise AND |
|
Bitwise XOR |
|
Bitwise OR |
Comparisons, including membership tests and identity tests |
|
Boolean NOT |
|
Boolean AND |
|
Boolean OR |
|
|
Conditional expression |
Lambda expression |
|
|
Assignment expression |
Footnotes