6. Expressions

] ")"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.

6.2.5. 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:

comp_for

comp_for: ["async"] "for"

"in"

[

] comp_iter:

comp_if: "if"

[

] 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*yforxinrange(10)foryinrange(x,x+10)].

To ensure the comprehension always results in a container of the appropriate type, yield and yieldfrom expressions are prohibited in the implicitly nested scope.

Since Python 3.6, in an

The standard type hierarchy

function, an asyncfor clause may be used to iterate over a

asynchronous iterator

. A comprehension in an asyncdef function may consist of either a for or asyncfor clause following the leading expression, may contain additional for or asyncfor clauses, and may also use

await

expressions.

If a comprehension contains asyncfor clauses, or if it contains await expressions or other asynchronous comprehensions anywhere except the iterable expression in the leftmost for clause, 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

Added in version 3.6: Asynchronous comprehensions were introduced.

Changed in version 3.8: yield and yieldfrom 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.

6.2.6. List displays

A list display is a possibly empty series of expressions enclosed in square brackets:

list_display: "[" [

flexible_expression_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.

6.2.7. 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: "{" (

flexible_expression_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.

6.2.8. 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_item:

":"

| "**"

dict_comprehension:

":"

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.

Added 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

. (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

6.2.9. Generator expressions

The syntax for generator expressions is the same as for list

comprehensions

, except that they are enclosed in parentheses instead of brackets. For example:

>>> iterator=(x**2forxinrange(10))>>> iterator<generator object <genexpr> at ...>At runtime, a generator expression evaluates to a

generator iterator

which yields the same values as the corresponding list comprehension:

>>> list(iterator)[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]Thus, the example above is roughly equivalent to defining and calling the following generator function:

defmake_generator_of_squares(iterator):forxiniterator:yieldx**2make_generator_of_squares(iter(range(10)))The enclosing parentheses can be omitted in calls when the generator expression is the only positional argument and there are no keyword arguments. See the

Calls section

for details. For example:

# The parentheses after `sum` are part of the call syntax:>>>sum(x**2forxinrange(10))285# The generator needs its own parentheses if it's not the only argument:>>>sum((x**2forxinrange(10)),start=1000)1285The iterable expression in the leftmost for clause is evaluated immediately, so that an error raised by this expression will be emitted at the point where the generator expression is defined, rather than at the point where the first value is retrieved:

>>> (x**2forxinnonexistent_iterable)Traceback (most recent call last):...NameError: name 'nonexistent_iterable' is not definedAfter the expression is evaluated, an iterator is created from the result, as if

iter()

was called on it. Any error raised when creating the iterator is also emitted immediately:

>>> (x**2forxinNone)Traceback (most recent call last):...TypeError: 'NoneType' object is not iterableAll other expressions are evaluated lazily, in the same fashion as normal generators (that is, when the iterator is asked to yield a value):

>>> iterator=(nonexistent_valueforxinrange(10))>>> iterator<generator object <genexpr> at ...>>>> list(iterator)Traceback (most recent call last):...NameError: name 'nonexistent_value' is not defined>>> iterator=(x*yforxinrange(10)foryinnonexistent_iterable)>>> iterator<generator object <genexpr> at ...>>>> list(iterator)Traceback (most recent call last):...NameError: name 'nonexistent_iterable' is not definedTo avoid interfering with the expected operation of the generator expression itself, yield and yieldfrom expressions are prohibited inside the implicitly nested scope.

If a generator expression contains either asyncfor 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

The formal grammar for generator expressions is:

generator_expression: "("

comp_for

")"Added 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

coroutines. Starting with 3.7, any function can use asynchronous generator expressions.

Changed in version 3.8: yield and yieldfrom prohibited in the implicitly nested scope.

6.2.10. Yield expressions

yield_atom: "("

yield_expression

")"yield_from: "yield""from"

yield_expression: "yield"

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

Asynchronous generator functions

function’s body causes that coroutine function to be an asynchronous generator function. For example:

defgen():# defines a generator functionyield123asyncdefagen():# defines an asynchronous generator functionyield123Due 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

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

to the generator’s caller, or None if yield_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

. 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 yieldfrom<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

, while throw() will just raise the passed in exception immediately.

When the underlying iterator is complete, the

value

attribute of the raised

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 yieldfrom<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 GeneratorsThe proposal for adding generators and the

yield

statement to Python.

PEP 342

- Coroutines via Enhanced GeneratorsThe proposal to enhance the API and syntax of generators, making them usable as simple coroutines.

PEP 380

- Syntax for Delegating to a SubgeneratorThe proposal to introduce the

yield_from

syntax, making delegation to subgenerators easy.

PEP 525

- Asynchronous GeneratorsThe proposal that expanded on

PEP 492

by adding generator capabilities to coroutine functions.

6.2.10.1. 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 to

. The execution then continues to the next yield expression, where the generator is suspended again, and the value of the

is returned to __next__()’s caller. If the generator exits without yielding another value, a

exception is raised.

This method is normally called implicitly, e.g. by a

for

loop, or by the built-in

next()

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 raises

if the generator exits without yielding another value. When send() is called to start the generator, it must be called with

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

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.

Changed in version 3.12: The second signature (type[, value[, traceback]]) is deprecated and may be removed in a future version of Python.

generator.close()

Raises a

GeneratorExit

exception at the point where the generator function was paused (equivalent to calling throw(GeneratorExit)). The exception is raised by the yield expression where the generator was paused. If the generator function catches the exception and returns a value, this value is returned from close(). If the generator function is already closed, or raises GeneratorExit (by not catching the exception), close() returns

PEP 380: Syntax for Delegating to a Subgenerator

. If the generator yields a value, a

RuntimeError

is raised. If the generator raises any other exception, it is propagated to the caller. If the generator has already exited due to an exception or normal exit, close() returns None and has no other effect.

Changed in version 3.13: If a generator returns a value upon being closed, the value is returned by close().

6.2.10.2. Examples

Here is a simple example that demonstrates the behavior of generators and generator functions:

>>> defecho(value=None):... print("Execution starts when 'next()' is called for the first time.")... try:... whileTrue:... try:... value=(yieldvalue)... exceptExceptionase:... 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 yieldfrom, see

in “What’s New in Python.”

6.2.10.3. Asynchronous generator functions

The presence of a yield expression in a function or method defined using

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

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

Lib/asyncio/base_events.py

. 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

The expression yieldfrom<expr> is a syntax error when used in an asynchronous generator function.

6.2.10.4. 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.

asyncagen.__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 to

in the returned awaitable, which when run will continue to the next yield expression. The value of the

of the yield expression is the value of the

exception raised by the completing coroutine. If the asynchronous generator exits without yielding another value, the awaitable instead raises a

exception, signalling that the asynchronous iteration has completed.

This method is normally called implicitly by a

async for

loop.

asyncagen.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 the asend() method will return the next value yielded by the generator as the value of the raised

, or raises

if the asynchronous generator exits without yielding another value. When asend() is called to start the asynchronous generator, it must be called with

as the argument, because there is no yield expression that could receive the value.

asyncagen.athrow(value)

asyncagen.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 raised

exception. If the asynchronous generator exits without yielding another value, a

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.

Changed in version 3.12: The second signature (type[, value[, traceback]]) is deprecated and may be removed in a future version of Python.

asyncagen.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 raises GeneratorExit (by not catching the exception), then the returned awaitable will raise a

exception. Any further awaitables returned by subsequent calls to the asynchronous generator will raise a

exception. If the asynchronous generator yields a value, a

RuntimeError

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 to aclose() will return an awaitable that does nothing.

6.3. Primaries

Primaries represent the most tightly bound operations of the language. Their syntax is:

primary:

6.3.1. Attribute references

An attribute reference is a primary followed by a period and a name:

attributeref:

"."

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 and slicings

The subscription syntax is usually used for selecting an element from a

container

– for example, to get a value from a

dict

>>> digits_by_name={'one':1,'two':2}>>> digits_by_name['two']# Subscripting a dictionary using the key 'two'2In the subscription syntax, the object being subscribed – a

– is followed by a subscript in square brackets. In the simplest case, the subscript is a single expression.

Depending on the type of the object being subscribed, the subscript is sometimes called a

key

(for mappings),

index

(for sequences), or type argument (for

generic types

). Syntactically, these are all equivalent:

>>> colors=['red','blue','green','black']>>> colors[3]# Subscripting a list using the index 3'black'>>> list[str]# Parameterizing the list type using the type argument strlist[str]At runtime, the interpreter will evaluate the primary and the subscript, and call the primary’s

__class_getitem__ versus __getitem__

with the subscript as argument. For more details on which of these methods is called, see

To show how subscription works, we can define a custom object that implements

and prints out the value of the subscript:

>>> classSubscriptionDemo:... def__getitem__(self,key):... print(f'subscripted with: {key!r}')...>>> demo=SubscriptionDemo()>>> demo[1]subscripted with: 1>>> demo['a'*3]subscripted with: 'aaa'See

documentation for how built-in types handle subscription.

Subscriptions may also be used as targets in

assignment

deletion

statements. In these cases, the interpreter will call the subscripted object’s

__setitem__()

__delitem__()

, respectively, instead of

>>> colors=['red','blue','green','black']>>> colors[3]='white'# Setting item at index>>> colors['red', 'blue', 'green', 'white']>>> delcolors[3]# Deleting item at index 3>>> colors['red', 'blue', 'green']All advanced forms of subscript documented in the following sections are also usable for assignment and deletion.

6.3.2.1. Slicings

A more advanced form of subscription, slicing, is commonly used to extract a portion of a

sequence

. In this form, the subscript is a

slice

: up to three expressions separated by colons. Any of the expressions may be omitted, but a slice must contain at least one colon:

>>> number_names=['zero','one','two','three','four','five']>>> number_names[1:3]['one', 'two']>>> number_names[1:]['one', 'two', 'three', 'four', 'five']>>> number_names[:3]['zero', 'one', 'two']>>> number_names[:]['zero', 'one', 'two', 'three', 'four', 'five']>>> number_names[::2]['zero', 'two', 'four']>>> number_names[:-3]['zero', 'one', 'two']>>> delnumber_names[4:]>>> number_names['zero', 'one', 'two', 'three']When a slice is evaluated, the interpreter constructs a

object whose

and

attributes, respectively, are the results of the expressions between the colons. Any missing expression evaluates to

. This slice object is then passed to the

, as above.

# continuing with the SubscriptionDemo instance defined above:>>>demo[2:3]subscriptedwith:slice(2,3,None)>>>demo[::'spam']subscriptedwith:slice(None,None,'spam')6.3.2.2. Comma-separated subscripts

The subscript can also be given as two or more comma-separated expressions or slices:

# continuing with the SubscriptionDemo instance defined above:>>>demo[1,2,3]subscriptedwith:(1,2,3)>>>demo[1:2,3]subscriptedwith:(slice(1,2,None),3)This form is commonly used with numerical libraries for slicing multi-dimensional data. In this case, the interpreter constructs a

tuple

of the results of the expressions or slices, and passes this tuple to the

, as above.

The subscript may also be given as a single expression or slice followed by a comma, to specify a one-element tuple:

>>> demo['spam',]subscripted with: ('spam',)6.3.2.3. “Starred” subscriptions

Added in version 3.11: Expressions in tuple_slices may be starred. See

PEP 646

The subscript can also contain a starred expression. In this case, the interpreter unpacks the result into a tuple, and passes this tuple to

# continuing with the SubscriptionDemo instance defined above:>>>demo[*range(10)]subscriptedwith:(0,1,2,3,4,5,6,7,8,9)Starred expressions may be combined with comma-separated expressions and slices:

>>> demo['a','b',*range(3),'c']subscripted with: ('a', 'b', 0, 1, 2, 'c')6.3.2.4. Formal subscription grammar

subscription:

'['

']'subscript:

single_subscript:

proper_slice: [

] ":" [

] [ ":" [

] ] tuple_subscript: ','.(

single_subscript

)+ [','] Recall that the | operator

denotes ordered choice

. Specifically, in subscript, if both alternatives would match, the first (single_subscript) has priority.

6.3.3. Calls

A call calls a callable object (e.g., a

function

) with a possibly empty series of

call:

"(" [

[","] |

] ")"argument_list:

[","

] [","

] |

[","

] |

positional_arguments:

positional_item

(","

positional_item

)* positional_item:

| "*"

starred_and_keywords: ("*"

) (",""*"

| ","

)* keywords_arguments: (

| "**"

) (","

| ",""**"

)* keyword_item:

"="

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

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

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

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:

>>> deff(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 2It 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

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

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. If execution reaches the end of the code block without executing a return statement, the return value is None.

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"

Added 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

converted to a common type

) ["**"

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. Numeric arguments are first

, 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__()

and

__rpow__()

methods.

6.6. Unary arithmetic and bitwise operations

All unary arithmetic and bitwise operations have the same priority:

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

converted to a common real type

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:

"*"

"@"

"//"

"/"

"%"

a_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

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.

Changed in version 3.14: If only one operand is a complex number, the other operand is converted to a floating-point number.

The @ (at) operator is intended to be used for matrix multiplication. No builtin Python types implement this operator.

This operation can be customized using the special

__matmul__()

and

__rmatmul__()

methods.

Added in version 3.5.

The / (division) and // (floor division) operators yield the quotient of their arguments. The numeric arguments are first

converted to a common type

. Division of integers yields a float, while floor division of integers results in an integer; the result is that of mathematical division with the ‘floor’ function applied to the result. Division by zero raises the

ZeroDivisionError

exception.

The division operation can be customized using the special

__truediv__()

and

__rtruediv__()

methods. The floor division operation can be customized using the special

__floordiv__()

and

__rfloordiv__()

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__()

and

__rmod__()

methods.

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 real 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.

Changed in version 3.14: If only one operand is a complex number, the other operand is converted to a floating-point number.

The - (subtraction) operator yields the difference of its arguments. The numeric arguments are first

converted to a common real type

This operation can be customized using the special

__sub__()

and

__rsub__()

methods.

Changed in version 3.14: If only one operand is a complex number, the other operand is converted to a floating-point number.

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.

The left shift operation can be customized using the special

__lshift__()

and

__rlshift__()

methods. The right shift operation can be customized using the special

__rshift__()

and

__rrshift__()

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:

"&"

xor_expr:

"^"

or_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__()

__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__()

__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__()

__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<yandy<=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 aop1bop2c...yopNz is equivalent to aop1bandbop2cand...yopNz, except that each expression is evaluated at most once.

Note that aop1bop2c 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. xisy implies x==y).

A default order comparison (<, >, <=, and >=) is not provided; an attempt raises

Numeric Types — int, float, complex

. 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 (

) 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 isnot, never the equality operators.

Binary sequences (instances of

bytes

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

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

@~functools.total_ordering

Sets (instances of

set

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:

xisy 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>yandy>z implies x>z

x<yandy<=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 notx!=y

x<y and notx>=y (for total ordering)

x>y and notx<=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

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

and

not in

test for membership. xins evaluates to True if x is a member of s, and False otherwise. xnotins returns the negation of xins. 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 xiny is equivalent to any(xiseorx==eforeiny).

For the string and bytes types, xiny 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, xiny 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__()

, xiny is True if some value z, for which the expression xiszorx==z is true, is produced while iterating over y. If an exception is raised during the iteration, it is as if

raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines

, xiny is True if and only if there is a non-negative integer index i such that xisy[i]orx==y[i], and no lower integer index raises the

IndexError

exception. (If any other exception is raised, it is as if

raised that exception).

The operator

not in

is defined to have the inverse truth value of

6.10.3. Identity comparisons

The operators

and

is not

test for an object’s identity: xisy is true if and only if x and y are the same object. An Object’s identity is determined using the

id()

function. xisnoty yields the inverse truth value.

[4]

6.11. Boolean operations

or_test:

"or"

and_test:

"and"

not_test:

| "not"

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 xandy first evaluates x; if x is false, its value is returned; otherwise, y is evaluated and the resulting value is returned.

The expression xory 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

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 sor'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

":="]

An assignment expression (sometimes also called a “named expression” or “walrus”) assigns an

to an

identifier

, while also returning the value of the expression.

One common use case is when handling matched regular expressions:

ifmatching:=pattern.search(data):do_something(matching)Or, when processing a file stream in chunks:

whilechunk:=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.

Added 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:

conditional_expression

lambda_expr

A conditional expression (sometimes called a “ternary operator”) is an alternative to the if-else statement. As it is an expression, it returns a value and can appear as a sub-expression.

The expression xifCelsey 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

] ":"

Lambda expressions (sometimes called lambda forms) are used to create anonymous functions. The expression lambdaparameters: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

starred_expression: "*"

or_expr

flexible_expression:

flexible_expression_list:

flexible_expression

(","

flexible_expression

)* [","] starred_expression_list:

(","