Doc/library/typing.rst
!typing --- Support for type hints.. testsetup:: *
import typing from dataclasses import dataclass from typing import *
.. module:: typing
:synopsis: Support for type hints (see :pep:484).
.. versionadded:: 3.5
Source code: :source:Lib/typing.py
.. note::
The Python runtime does not enforce function and variable type annotations.
They can be used by third party tools such as :term:type checkers <static type checker>,
IDEs, linters, etc.
This module provides runtime support for type hints.
Consider the function below::
def surface_area_of_cube(edge_length: float) -> str: return f"The surface area of the cube is {6 * edge_length ** 2}."
The function surface_area_of_cube takes an argument expected to
be an instance of :class:float, as indicated by the :term:type hint
edge_length: float. The function is expected to return an instance
of :class:str, as indicated by the -> str hint.
While type hints can be simple classes like :class:float or :class:str,
they can also be more complex. The :mod:typing module provides a vocabulary of
more advanced type hints.
New features are frequently added to the typing module.
The :pypi:typing_extensions package
provides backports of these new features to older versions of Python.
.. seealso::
Typing cheat sheet <https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html>_
A quick overview of type hints (hosted at the mypy docs)
Type System Reference section of the mypy docs <https://mypy.readthedocs.io/en/stable/index.html>_
The Python typing system is standardised via PEPs, so this reference
should broadly apply to most Python type checkers. (Some parts may still
be specific to mypy.)
Static Typing with Python <https://typing.python.org/en/latest/>_
Type-checker-agnostic documentation written by the community detailing
type system features, useful typing related tools and typing best
practices.
.. _relevant-peps:
The canonical, up-to-date specification of the Python type system can be
found at Specification for the Python type system <https://typing.python.org/en/latest/spec/index.html>_.
.. _type-aliases:
A type alias is defined using the :keyword:type statement, which creates
an instance of :class:TypeAliasType. In this example,
Vector and list[float] will be treated equivalently by static type
checkers::
type Vector = list[float]
def scale(scalar: float, vector: Vector) -> Vector: return [scalar * num for num in vector]
new_vector = scale(2.0, [1.0, -4.2, 5.4])
Type aliases are useful for simplifying complex type signatures. For example::
from collections.abc import Sequence
type ConnectionOptions = dict[str, str] type Address = tuple[str, int] type Server = tuple[Address, ConnectionOptions]
def broadcast_message(message: str, servers: Sequence[Server]) -> None: ...
def broadcast_message( message: str, servers: Sequence[tuple[tuple[str, int], dict[str, str]]] ) -> None: ...
The :keyword:type statement is new in Python 3.12. For backwards
compatibility, type aliases can also be created through simple assignment::
Vector = list[float]
Or marked with :data:TypeAlias to make it explicit that this is a type alias,
not a normal variable assignment::
from typing import TypeAlias
Vector: TypeAlias = list[float]
.. _distinct:
Use the :class:NewType helper to create distinct types::
from typing import NewType
UserId = NewType('UserId', int) some_id = UserId(524313)
The static type checker will treat the new type as if it were a subclass of the original type. This is useful in helping catch logical errors::
def get_user_name(user_id: UserId) -> str: ...
user_a = get_user_name(UserId(42351))
user_b = get_user_name(-1)
You may still perform all int operations on a variable of type UserId,
but the result will always be of type int. This lets you pass in a
UserId wherever an int might be expected, but will prevent you from
accidentally creating a UserId in an invalid way::
output = UserId(23413) + UserId(54341)
Note that these checks are enforced only by the static type checker. At runtime,
the statement Derived = NewType('Derived', Base) will make Derived a
callable that immediately returns whatever parameter you pass it. That means
the expression Derived(some_value) does not create a new class or introduce
much overhead beyond that of a regular function call.
More precisely, the expression some_value is Derived(some_value) is always
true at runtime.
It is invalid to create a subtype of Derived::
from typing import NewType
UserId = NewType('UserId', int)
class AdminUserId(UserId): pass
However, it is possible to create a :class:NewType based on a 'derived' NewType::
from typing import NewType
UserId = NewType('UserId', int)
ProUserId = NewType('ProUserId', UserId)
and typechecking for ProUserId will work as expected.
See :pep:484 for more details.
.. note::
Recall that the use of a type alias declares two types to be equivalent to
one another. Doing type Alias = Original will make the static type checker
treat Alias as being exactly equivalent to Original in all cases.
This is useful when you want to simplify complex type signatures.
In contrast, NewType declares one type to be a subtype of another.
Doing Derived = NewType('Derived', Original) will make the static type
checker treat Derived as a subclass of Original, which means a
value of type Original cannot be used in places where a value of type
Derived is expected. This is useful when you want to prevent logic
errors with minimal runtime cost.
.. versionadded:: 3.5.2
.. versionchanged:: 3.10
NewType is now a class rather than a function. As a result, there is
some additional runtime cost when calling NewType over a regular
function.
.. versionchanged:: 3.11
The performance of calling NewType has been restored to its level in
Python 3.9.
.. _annotating-callables:
Functions -- or other :term:callable objects -- can be annotated using
:class:collections.abc.Callable or deprecated :data:typing.Callable.
Callable[[int], str] signifies a function that takes a single parameter
of type :class:int and returns a :class:str.
For example:
.. testcode::
from collections.abc import Callable, Awaitable
def feeder(get_next_item: Callable[[], str]) -> None: ... # Body
def async_query(on_success: Callable[[int], None], on_error: Callable[[int, Exception], None]) -> None: ... # Body
async def on_update(value: str) -> None: ... # Body
callback: Callable[[str], Awaitable[None]] = on_update
.. index:: single: ...; ellipsis literal
The subscription syntax must always be used with exactly two values: the
argument list and the return type. The argument list must be a list of types,
a :class:ParamSpec, :data:Concatenate, or an ellipsis (...). The return type must
be a single type.
If a literal ellipsis ... is given as the argument list, it indicates that
a callable with any arbitrary parameter list would be acceptable:
.. testcode::
def concat(x: str, y: str) -> str: return x + y
x: Callable[..., str] x = str # OK x = concat # Also OK
Callable cannot express complex signatures such as functions that take a
variadic number of arguments, :ref:overloaded functions <overload>, or
functions that have keyword-only parameters. However, these signatures can be
expressed by defining a :class:Protocol class with a
:meth:~object.__call__ method:
.. testcode::
from collections.abc import Iterable from typing import Protocol
class Combiner(Protocol): def call(self, *vals: bytes, maxlen: int | None = None) -> list[bytes]: ...
def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes: for item in data: ...
def good_cb(*vals: bytes, maxlen: int | None = None) -> list[bytes]: ... def bad_cb(*vals: bytes, maxitems: int | None) -> list[bytes]: ...
batch_proc([], good_cb) # OK batch_proc([], bad_cb) # Error! Argument 2 has incompatible type because of # different name and kind in the callback
Callables which take other callables as arguments may indicate that their
parameter types are dependent on each other using :class:ParamSpec.
Additionally, if that callable adds or removes arguments from other
callables, the :data:Concatenate operator may be used. They
take the form Callable[ParamSpecVariable, ReturnType] and
Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType]
respectively.
.. versionchanged:: 3.10
Callable now supports :class:ParamSpec and :data:Concatenate.
See :pep:612 for more details.
.. seealso::
The documentation for :class:ParamSpec and :class:Concatenate provides
examples of usage in Callable.
.. _generics:
Since type information about objects kept in containers cannot be statically inferred in a generic way, many container classes in the standard library support subscription to denote the expected types of container elements.
.. testcode::
from collections.abc import Mapping, Sequence
class Employee: ...
def notify_by_email(employees: Sequence[Employee], overrides: Mapping[str, str]) -> None: ...
Generic functions and classes can be parameterized by using
:ref:type parameter syntax <type-params>::
from collections.abc import Sequence
def first[T](l: Sequence[T]) -> T: # Function is generic over the TypeVar "T" return l[0]
Or by using the :class:TypeVar factory directly::
from collections.abc import Sequence from typing import TypeVar
U = TypeVar('U') # Declare type variable "U"
def second(l: Sequence[U]) -> U: # Function is generic over the TypeVar "U" return l[1]
.. versionchanged:: 3.12 Syntactic support for generics is new in Python 3.12.
.. _annotating-tuples:
For most containers in Python, the typing system assumes that all elements in the container will be of the same type. For example::
from collections.abc import Mapping
x are meant to be intsx: list[int] = []
list only accepts a single type argument:y: list[int, str] = [1, 'foo']
z are meant to be strings,z are meant to be either strings or intsz: Mapping[str, str | int] = {}
:class:list only accepts one type argument, so a type checker would emit an
error on the y assignment above. Similarly,
:class:~collections.abc.Mapping only accepts two type arguments: the first
indicates the type of the keys, and the second indicates the type of the
values.
Unlike most other Python containers, however, it is common in idiomatic Python
code for tuples to have elements which are not all of the same type. For this
reason, tuples are special-cased in Python's typing system. :class:tuple
accepts any number of type arguments::
x is assigned to a tuple of length 1 where the sole element is an intx: tuple[int] = (5,)
y is assigned to a tuple of length 2;y: tuple[int, str] = (5, "foo")
z has been assigned to a tuple of length 3z: tuple[int] = (1, 2, 3)
.. index:: single: ...; ellipsis literal
To denote a tuple which could be of any length, and in which all elements are
of the same type T, use the literal ellipsis ...: tuple[T, ...].
To denote an empty tuple, use
tuple[()]. Using plain tuple as an annotation is equivalent to using
tuple[Any, ...]::
x: tuple[int, ...] = (1, 2)
tuple[int, ...] indicates x can be of any lengthx = (1, 2, 3) x = ()
x must be intsx = ("foo", "bar")
y can only ever be assigned to an empty tupley: tuple[()] = ()
z: tuple = ("foo", "bar")
tuple is equivalent to tuple[Any, ...]z = (1, 2, 3) z = ()
.. _type-of-class-objects:
A variable annotated with C may accept a value of type C. In
contrast, a variable annotated with type[C] (or deprecated
:class:typing.Type[C] <Type>) may accept values that are classes
themselves -- specifically, it will accept the class object of C. For
example::
a = 3 # Has type int
b = int # Has type type[int]
c = type(a) # Also has type type[int]
Note that type[C] is covariant::
class User: ... class ProUser(User): ... class TeamUser(User): ...
def make_new_user(user_class: type[User]) -> User: # ... return user_class()
make_new_user(User) # OK
make_new_user(ProUser) # Also OK: type[ProUser] is a subtype of type[User]
make_new_user(TeamUser) # Still fine
make_new_user(User()) # Error: expected type[User] but got User
make_new_user(int) # Error: type[int] is not a subtype of type[User]
The only legal parameters for :class:type are classes, :data:Any,
:ref:type variables <generics>, and unions of any of these types.
For example::
def new_non_team_user(user_class: type[BasicUser | ProUser]): ...
new_non_team_user(BasicUser) # OK
new_non_team_user(ProUser) # OK
new_non_team_user(TeamUser) # Error: type[TeamUser] is not a subtype
# of type[BasicUser | ProUser]
new_non_team_user(User) # Also an error
type[Any] is equivalent to :class:type, which is the root of Python's
:ref:metaclass hierarchy <metaclasses>.
.. _annotating-generators-and-coroutines:
A generator can be annotated using the generic type
:class:Generator[YieldType, SendType, ReturnType] <collections.abc.Generator>.
For example::
def echo_round() -> Generator[int, float, str]: sent = yield 0 while sent >= 0: sent = yield round(sent) return 'Done'
Note that unlike many other generic classes in the standard library,
the SendType of :class:~collections.abc.Generator behaves
contravariantly, not covariantly or invariantly.
The SendType and ReturnType parameters default to :const:!None::
def infinite_stream(start: int) -> Generator[int]: while True: yield start start += 1
It is also possible to set these types explicitly::
def infinite_stream(start: int) -> Generator[int, None, None]: while True: yield start start += 1
Simple generators that only ever yield values can also be annotated
as having a return type of either
:class:Iterable[YieldType] <collections.abc.Iterable>
or :class:Iterator[YieldType] <collections.abc.Iterator>::
def infinite_stream(start: int) -> Iterator[int]: while True: yield start start += 1
Async generators are handled in a similar fashion, but don't
expect a ReturnType type argument
(:class:AsyncGenerator[YieldType, SendType] <collections.abc.AsyncGenerator>).
The SendType argument defaults to :const:!None, so the following definitions
are equivalent::
async def infinite_stream(start: int) -> AsyncGenerator[int]: while True: yield start start = await increment(start)
async def infinite_stream(start: int) -> AsyncGenerator[int, None]: while True: yield start start = await increment(start)
As in the synchronous case,
:class:AsyncIterable[YieldType] <collections.abc.AsyncIterable>
and :class:AsyncIterator[YieldType] <collections.abc.AsyncIterator> are
available as well::
async def infinite_stream(start: int) -> AsyncIterator[int]: while True: yield start start = await increment(start)
Coroutines can be annotated using
:class:Coroutine[YieldType, SendType, ReturnType] <collections.abc.Coroutine>.
Generic arguments correspond to those of :class:~collections.abc.Generator,
for example::
from collections.abc import Coroutine c: Coroutine[list[str], str, int] # Some coroutine defined elsewhere x = c.send('hi') # Inferred type of 'x' is list[str] async def bar() -> None: y = await c # Inferred type of 'y' is int
.. _user-defined-generics:
A user-defined class can be defined as a generic class.
::
from logging import Logger
class LoggedVar[T]: def init(self, value: T, name: str, logger: Logger) -> None: self.name = name self.logger = logger self.value = value
def set(self, new: T) -> None:
self.log('Set ' + repr(self.value))
self.value = new
def get(self) -> T:
self.log('Get ' + repr(self.value))
return self.value
def log(self, message: str) -> None:
self.logger.info('%s: %s', self.name, message)
This syntax indicates that the class LoggedVar is parameterised around a
single :ref:type variable <typevar> T . This also makes T valid as
a type within the class body.
Generic classes implicitly inherit from :class:Generic. For compatibility
with Python 3.11 and lower, it is also possible to inherit explicitly from
:class:Generic to indicate a generic class::
from typing import TypeVar, Generic
T = TypeVar('T')
class LoggedVar(Generic[T]): ...
Generic classes have :meth:~object.__class_getitem__ methods, meaning they
can be parameterised at runtime (e.g. LoggedVar[int] below)::
from collections.abc import Iterable
def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None: for var in vars: var.set(0)
A generic type can have any number of type variables. All varieties of
:class:TypeVar are permissible as parameters for a generic type::
from typing import TypeVar, Generic, Sequence
class WeirdTrio[T, B: Sequence[bytes], S: (int, str)]: ...
OldT = TypeVar('OldT', contravariant=True) OldB = TypeVar('OldB', bound=Sequence[bytes], covariant=True) OldS = TypeVar('OldS', int, str)
class OldWeirdTrio(Generic[OldT, OldB, OldS]): ...
Each type variable argument to :class:Generic must be distinct.
This is thus invalid::
from typing import TypeVar, Generic ...
class Pair[M, M]: # SyntaxError ...
T = TypeVar('T')
class Pair(Generic[T, T]): # INVALID ...
Generic classes can also inherit from other classes::
from collections.abc import Sized
class LinkedListT: ...
When inheriting from generic classes, some type parameters could be fixed::
from collections.abc import Mapping
class MyDict[T](Mapping[str, T]):
...
In this case MyDict has a single parameter, T.
Using a generic class without specifying type parameters assumes
:data:Any for each position. In the following example, MyIterable is
not generic but implicitly inherits from Iterable[Any]:
.. testcode::
from collections.abc import Iterable
class MyIterable(Iterable): # Same as Iterable[Any] ...
User-defined generic type aliases are also supported. Examples::
from collections.abc import Iterable
type Response[S] = Iterable[S] | int
def response(query: str) -> Response[str]: ...
type Vec[T] = Iterable[tuple[T, T]]
def inproduct[T: (int, float, complex)](v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]] return sum(x*y for x, y in v)
For backward compatibility, generic type aliases can also be created through a simple assignment::
from collections.abc import Iterable from typing import TypeVar
S = TypeVar("S") Response = Iterable[S] | int
.. versionchanged:: 3.7
:class:Generic no longer has a custom metaclass.
.. versionchanged:: 3.12
Syntactic support for generics and type aliases is new in version 3.12.
Previously, generic classes had to explicitly inherit from :class:Generic
or contain a type variable in one of their bases.
User-defined generics for parameter expressions are also supported via parameter
specification variables in the form [**P]. The behavior is consistent
with type variables' described above as parameter specification variables are
treated by the :mod:!typing module as a specialized type variable. The one exception
to this is that a list of types can be used to substitute a :class:ParamSpec::
class Z[T, **P]: ... # T is a TypeVar; P is a ParamSpec ... Z[int, [dict, float]] main.Z[int, [dict, float]]
Classes generic over a :class:ParamSpec can also be created using explicit
inheritance from :class:Generic. In this case, ** is not used::
from typing import ParamSpec, Generic
P = ParamSpec('P')
class Z(Generic[P]): ...
Another difference between :class:TypeVar and :class:ParamSpec is that a
generic with only one parameter specification variable will accept
parameter lists in the forms X[[Type1, Type2, ...]] and also
X[Type1, Type2, ...] for aesthetic reasons. Internally, the latter is converted
to the former, so the following are equivalent::
class X[**P]: ... ... X[int, str] main.X[[int, str]] X[[int, str]] main.X[[int, str]]
Note that generics with :class:ParamSpec may not have correct
__parameters__ after substitution in some cases because they
are intended primarily for static type checking.
.. versionchanged:: 3.10
:class:Generic can now be parameterized over parameter expressions.
See :class:ParamSpec and :pep:612 for more details.
A user-defined generic class can have ABCs as base classes without a metaclass
conflict. Generic metaclasses are not supported. The outcome of parameterizing
generics is cached, and most types in the :mod:!typing module are :term:hashable and
comparable for equality.
Any typeA special kind of type is :data:Any. A static type checker will treat
every type as being compatible with :data:Any and :data:Any as being
compatible with every type.
This means that it is possible to perform any operation or method call on a
value of type :data:Any and assign it to any variable::
from typing import Any
a: Any = None a = [] # OK a = 2 # OK
s: str = '' s = a # OK
def foo(item: Any) -> int: # Passes type checking; 'item' could be any type, # and that type might have a 'bar' method item.bar() ...
Notice that no type checking is performed when assigning a value of type
:data:Any to a more precise type. For example, the static type checker did
not report an error when assigning a to s even though s was
declared to be of type :class:str and receives an :class:int value at
runtime!
Furthermore, all functions without a return type or parameter types will
implicitly default to using :data:Any::
def legacy_parser(text): ... return data
def legacy_parser(text: Any) -> Any: ... return data
This behavior allows :data:Any to be used as an escape hatch when you
need to mix dynamically and statically typed code.
Contrast the behavior of :data:Any with the behavior of :class:object.
Similar to :data:Any, every type is a subtype of :class:object. However,
unlike :data:Any, the reverse is not true: :class:object is not a
subtype of every other type.
That means when the type of a value is :class:object, a type checker will
reject almost all operations on it, and assigning it to a variable (or using
it as a return value) of a more specialized type is a type error. For example::
def hash_a(item: object) -> int: # Fails type checking; an object does not have a 'magic' method. item.magic() ...
def hash_b(item: Any) -> int: # Passes type checking item.magic() ...
hash_a(42) hash_a("foo")
hash_b(42) hash_b("foo")
Use :class:object to indicate that a value could be any type in a typesafe
manner. Use :data:Any to indicate that a value is dynamically typed.
Initially :pep:484 defined the Python static type system as using
nominal subtyping. This means that a class A is allowed where
a class B is expected if and only if A is a subclass of B.
This requirement previously also applied to abstract base classes, such as
:class:~collections.abc.Iterable. The problem with this approach is that a class had
to be explicitly marked to support them, which is unpythonic and unlike
what one would normally do in idiomatic dynamically typed Python code.
For example, this conforms to :pep:484::
from collections.abc import Sized, Iterable, Iterator
class Bucket(Sized, Iterable[int]): ... def len(self) -> int: ... def iter(self) -> Iterator[int]: ...
:pep:544 solves this problem by allowing users to write
the above code without explicit base classes in the class definition,
allowing Bucket to be implicitly considered a subtype of both Sized
and Iterable[int] by static type checkers. This is known as
structural subtyping (or static duck-typing)::
from collections.abc import Iterator, Iterable
class Bucket: # Note: no base classes ... def len(self) -> int: ... def iter(self) -> Iterator[int]: ...
def collect(items: Iterable[int]) -> int: ... result = collect(Bucket()) # Passes type check
Moreover, by subclassing a special class :class:Protocol, a user
can define new custom protocols to fully enjoy structural subtyping
(see examples below).
The typing module defines the following classes, functions and decorators.
Special types """""""""""""
These can be used as types in annotations. They do not support subscription
using [].
.. data:: Any
Special type indicating an unconstrained type.
Any.Any is compatible with every type... versionchanged:: 3.11
:data:Any can now be used as a base class. This can be useful for
avoiding type checker errors with classes that can duck type anywhere or
are highly dynamic.
.. data:: AnyStr
A :ref:constrained type variable <typing-constrained-typevar>.
Definition::
AnyStr = TypeVar('AnyStr', str, bytes)
AnyStr is meant to be used for functions that may accept :class:str or
:class:bytes arguments but cannot allow the two to mix.
For example::
def concat(a: AnyStr, b: AnyStr) -> AnyStr:
return a + b
concat("foo", "bar") # OK, output has type 'str'
concat(b"foo", b"bar") # OK, output has type 'bytes'
concat("foo", b"bar") # Error, cannot mix str and bytes
Note that, despite its name, AnyStr has nothing to do with the
:class:Any type, nor does it mean "any string". In particular, AnyStr
and str | bytes are different from each other and have different use
cases::
# Invalid use of AnyStr:
# The type variable is used only once in the function signature,
# so cannot be "solved" by the type checker
def greet_bad(cond: bool) -> AnyStr:
return "hi there!" if cond else b"greetings!"
# The better way of annotating this function:
def greet_proper(cond: bool) -> str | bytes:
return "hi there!" if cond else b"greetings!"
.. deprecated-removed:: 3.13 3.18
Deprecated in favor of the new :ref:type parameter syntax <type-params>.
Use class A[T: (str, bytes)]: ... instead of importing AnyStr. See
:pep:695 for more details.
In Python 3.16, ``AnyStr`` will be removed from ``typing.__all__``, and
deprecation warnings will be emitted at runtime when it is accessed or
imported from ``typing``. ``AnyStr`` will be removed from ``typing``
in Python 3.18.
.. data:: LiteralString
Special type that includes only literal strings.
Any string
literal is compatible with LiteralString, as is another
LiteralString. However, an object typed as just str is not.
A string created by composing LiteralString-typed objects
is also acceptable as a LiteralString.
Example:
.. testcode::
def run_query(sql: LiteralString) -> None:
...
def caller(arbitrary_string: str, literal_string: LiteralString) -> None:
run_query("SELECT * FROM students") # OK
run_query(literal_string) # OK
run_query("SELECT * FROM " + literal_string) # OK
run_query(arbitrary_string) # type checker error
run_query( # type checker error
f"SELECT * FROM students WHERE name = {arbitrary_string}"
)
LiteralString is useful for sensitive APIs where arbitrary user-generated
strings could generate problems. For example, the two cases above
that generate type checker errors could be vulnerable to an SQL
injection attack.
See :pep:675 for more details.
.. versionadded:: 3.11
.. data:: Never NoReturn
:data:!Never and :data:!NoReturn represent the
bottom type <https://en.wikipedia.org/wiki/Bottom_type>_,
a type that has no members.
They can be used to indicate that a function never returns,
such as :func:sys.exit::
from typing import Never # or NoReturn
def stop() -> Never:
raise RuntimeError('no way')
Or to define a function that should never be
called, as there are no valid arguments, such as
:func:assert_never::
from typing import Never # or NoReturn
def never_call_me(arg: Never) -> None:
pass
def int_or_str(arg: int | str) -> None:
never_call_me(arg) # type checker error
match arg:
case int():
print("It's an int")
case str():
print("It's a str")
case _:
never_call_me(arg) # OK, arg is of type Never (or NoReturn)
:data:!Never and :data:!NoReturn have the same meaning in the type system
and static type checkers treat both equivalently.
.. versionadded:: 3.6.2
Added :data:`NoReturn`.
.. versionadded:: 3.11
Added :data:`Never`.
.. data:: Self
Special type to represent the current enclosed class.
For example::
from typing import Self, reveal_type
class Foo:
def return_self(self) -> Self:
...
return self
class SubclassOfFoo(Foo): pass
reveal_type(Foo().return_self()) # Revealed type is "Foo"
reveal_type(SubclassOfFoo().return_self()) # Revealed type is "SubclassOfFoo"
This annotation is semantically equivalent to the following, albeit in a more succinct fashion::
from typing import TypeVar
Self = TypeVar("Self", bound="Foo")
class Foo:
def return_self(self: Self) -> Self:
...
return self
In general, if something returns self, as in the above examples, you
should use Self as the return annotation. If Foo.return_self was
annotated as returning "Foo", then the type checker would infer the
object returned from SubclassOfFoo.return_self as being of type Foo
rather than SubclassOfFoo.
Other common use cases include:
classmethod\s that are used as alternative constructors and return instances
of the cls parameter.~object.__enter__ method which returns self.You should not use Self as the return annotation if the method is not
guaranteed to return an instance of a subclass when the class is
subclassed::
class Eggs:
# Self would be an incorrect return annotation here,
# as the object returned is always an instance of Eggs,
# even in subclasses
def returns_eggs(self) -> "Eggs":
return Eggs()
See :pep:673 for more details.
.. versionadded:: 3.11
.. data:: TypeAlias
Special annotation for explicitly declaring a :ref:type alias <type-aliases>.
For example::
from typing import TypeAlias
Factors: TypeAlias = list[int]
TypeAlias is particularly useful on older Python versions for annotating
aliases that make use of forward references, as it can be hard for type
checkers to distinguish these from normal variable assignments:
.. testcode::
from typing import Generic, TypeAlias, TypeVar
T = TypeVar("T")
# "Box" does not exist yet,
# so we have to use quotes for the forward reference on Python <3.12.
# Using ``TypeAlias`` tells the type checker that this is a type alias declaration,
# not a variable assignment to a string.
BoxOfStrings: TypeAlias = "Box[str]"
class Box(Generic[T]):
@classmethod
def make_box_of_strings(cls) -> BoxOfStrings: ...
See :pep:613 for more details.
.. versionadded:: 3.10
.. deprecated:: 3.12
:data:TypeAlias is deprecated in favor of the :keyword:type statement,
which creates instances of :class:TypeAliasType
and which natively supports forward references.
Note that while :data:TypeAlias and :class:TypeAliasType serve
similar purposes and have similar names, they are distinct and the
latter is not the type of the former.
Removal of :data:TypeAlias is not currently planned, but users
are encouraged to migrate to :keyword:type statements.
Special forms """""""""""""
These can be used as types in annotations. They all support subscription using
[], but each has a unique syntax.
.. class:: Union
Union type; Union[X, Y] is equivalent to X | Y and means either X or Y.
To define a union, use e.g. Union[int, str] or the shorthand int | str. Using that shorthand is recommended. Details:
The arguments must be types and there must be at least one.
Unions of unions are flattened, e.g.::
Union[Union[int, str], float] == Union[int, str, float]
However, this does not apply to unions referenced through a type
alias, to avoid forcing evaluation of the underlying :class:TypeAliasType::
type A = Union[int, str] Union[A, float] != Union[int, str, float]
Unions of a single argument vanish, e.g.::
Union[int] == int # The constructor actually returns int
Redundant arguments are skipped, e.g.::
Union[int, str, int] == Union[int, str] == int | str
When comparing unions, the argument order is ignored, e.g.::
Union[int, str] == Union[str, int]
You cannot subclass or instantiate a Union.
You cannot write Union[X][Y].
.. versionchanged:: 3.7 Don't remove explicit subclasses from unions at runtime.
.. versionchanged:: 3.10
Unions can now be written as X | Y. See
:ref:union type expressions<types-union>.
.. versionchanged:: 3.14
:class:types.UnionType is now an alias for :class:Union, and both
Union[int, str] and int | str create instances of the same class.
To check whether an object is a Union at runtime, use
isinstance(obj, Union). For compatibility with earlier versions of
Python, use
get_origin(obj) is typing.Union or get_origin(obj) is types.UnionType.
.. data:: Optional
Optional[X] is equivalent to X | None (or Union[X, None]).
Note that this is not the same concept as an optional argument,
which is one that has a default. An optional argument with a
default does not require the Optional qualifier on its type
annotation just because it is optional. For example::
def foo(arg: int = 0) -> None:
...
On the other hand, if an explicit value of None is allowed, the
use of Optional is appropriate, whether the argument is optional
or not. For example::
def foo(arg: Optional[int] = None) -> None:
...
.. versionchanged:: 3.10
Optional can now be written as X | None. See
:ref:union type expressions<types-union>.
.. data:: Concatenate
Special form for annotating higher-order functions.
.. index:: single: ...; ellipsis literal
Concatenate can be used in conjunction with :ref:Callable <annotating-callables> and
:class:ParamSpec to annotate a higher-order callable which adds, removes,
or transforms parameters of another
callable. Usage is in the form
Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable]. Concatenate
is currently only valid when used as the first argument to a :ref:Callable <annotating-callables>.
The last parameter to Concatenate must be a :class:ParamSpec or
ellipsis (...).
For example, to annotate a decorator with_lock which provides a
:class:threading.Lock to the decorated function, Concatenate can be
used to indicate that with_lock expects a callable which takes in a
Lock as the first argument, and returns a callable with a different type
signature. In this case, the :class:ParamSpec indicates that the returned
callable's parameter types are dependent on the parameter types of the
callable being passed in::
from collections.abc import Callable
from threading import Lock
from typing import Concatenate
# Use this lock to ensure that only one thread is executing a function
# at any time.
my_lock = Lock()
def with_lock[**P, R](f: Callable[Concatenate[Lock, P], R]) -> Callable[P, R]:
'''A type-safe decorator which provides a lock.'''
def inner(*args: P.args, **kwargs: P.kwargs) -> R:
# Provide the lock as the first argument.
return f(my_lock, *args, **kwargs)
return inner
@with_lock
def sum_threadsafe(lock: Lock, numbers: list[float]) -> float:
'''Add a list of numbers together in a thread-safe manner.'''
with lock:
return sum(numbers)
# We don't need to pass in the lock ourselves thanks to the decorator.
sum_threadsafe([1.1, 2.2, 3.3])
.. versionadded:: 3.10
.. seealso::
* :pep:`612` -- Parameter Specification Variables (the PEP which introduced
``ParamSpec`` and ``Concatenate``)
* :class:`ParamSpec`
* :ref:`annotating-callables`
.. data:: Literal
Special typing form to define "literal types".
Literal can be used to indicate to type checkers that the
annotated object has a value equivalent to one of the
provided literals.
For example::
def validate_simple(data: Any) -> Literal[True]: # always returns True
...
type Mode = Literal['r', 'rb', 'w', 'wb']
def open_helper(file: str, mode: Mode) -> str:
...
open_helper('/some/path', 'r') # Passes type check
open_helper('/other/path', 'typo') # Error in type checker
Literal[...] cannot be subclassed. At runtime, an arbitrary value
is allowed as type argument to Literal[...], but type checkers may
impose restrictions. See :pep:586 for more details about literal types.
Additional details:
The arguments must be literal values and there must be at least one.
Nested Literal types are flattened, e.g.::
assert Literal[Literal[1, 2], 3] == Literal[1, 2, 3]
However, this does not apply to Literal types referenced through a type
alias, to avoid forcing evaluation of the underlying :class:TypeAliasType::
type A = Literal[1, 2] assert Literal[A, 3] != Literal[1, 2, 3]
Redundant arguments are skipped, e.g.::
assert Literal[1, 2, 1] == Literal[1, 2]
When comparing literals, the argument order is ignored, e.g.::
assert Literal[1, 2] == Literal[2, 1]
You cannot subclass or instantiate a Literal.
You cannot write Literal[X][Y].
.. versionadded:: 3.8
.. versionchanged:: 3.9.1
Literal now de-duplicates parameters. Equality comparisons of
Literal objects are no longer order dependent. Literal objects
will now raise a :exc:TypeError exception during equality comparisons
if one of their parameters are not :term:hashable.
.. data:: ClassVar
Special type construct to mark class variables.
As introduced in :pep:526, a variable annotation wrapped in ClassVar
indicates that a given attribute is intended to be used as a class variable
and should not be set on instances of that class. Usage::
class Starship:
stats: ClassVar[dict[str, int]] = {} # class variable
damage: int = 10 # instance variable
:data:ClassVar accepts only types and cannot be further subscribed.
:data:ClassVar is not a class itself, and should not
be used with :func:isinstance or :func:issubclass.
:data:ClassVar does not change Python runtime behavior, but
it can be used by third-party type checkers. For example, a type checker
might flag the following code as an error::
enterprise_d = Starship(3000)
enterprise_d.stats = {} # Error, setting class variable on instance
Starship.stats = {} # This is OK
.. versionadded:: 3.5.3
.. versionchanged:: 3.13
:data:`ClassVar` can now be nested in :data:`Final` and vice versa.
.. data:: Final
Special typing construct to indicate final names to type checkers.
Final names cannot be reassigned in any scope. Final names declared in class scopes cannot be overridden in subclasses.
For example::
MAX_SIZE: Final = 9000
MAX_SIZE += 1 # Error reported by type checker
class Connection:
TIMEOUT: Final[int] = 10
class FastConnector(Connection):
TIMEOUT = 1 # Error reported by type checker
There is no runtime checking of these properties. See :pep:591 for
more details.
.. versionadded:: 3.8
.. versionchanged:: 3.13
:data:`Final` can now be nested in :data:`ClassVar` and vice versa.
.. data:: Required
Special typing construct to mark a :class:TypedDict key as required.
This is mainly useful for total=False TypedDicts. See :class:TypedDict
and :pep:655 for more details.
.. versionadded:: 3.11
.. data:: NotRequired
Special typing construct to mark a :class:TypedDict key as potentially
missing.
See :class:TypedDict and :pep:655 for more details.
.. versionadded:: 3.11
.. data:: ReadOnly
A special typing construct to mark an item of a :class:TypedDict as read-only.
For example::
class Movie(TypedDict):
title: ReadOnly[str]
year: int
def mutate_movie(m: Movie) -> None:
m["year"] = 1999 # allowed
m["title"] = "The Matrix" # typechecker error
There is no runtime checking for this property.
See :class:TypedDict and :pep:705 for more details.
.. versionadded:: 3.13
.. data:: Annotated
Special typing form to add context-specific metadata to an annotation.
Add metadata x to a given type T by using the annotation
Annotated[T, x]. Metadata added using Annotated can be used by
static analysis tools or at runtime. At runtime, the metadata is stored
in a :attr:!__metadata__ attribute.
If a library or tool encounters an annotation Annotated[T, x] and has
no special logic for the metadata, it should ignore the metadata and simply
treat the annotation as T. As such, Annotated can be useful for code
that wants to use annotations for purposes outside Python's static typing
system.
Using Annotated[T, x] as an annotation still allows for static
typechecking of T, as type checkers will simply ignore the metadata x.
In this way, Annotated differs from the
:deco:no_type_check decorator, which can also be used for
adding annotations outside the scope of the typing system, but
completely disables typechecking for a function or class.
The responsibility of how to interpret the metadata
lies with the tool or library encountering an
Annotated annotation. A tool or library encountering an Annotated type
can scan through the metadata elements to determine if they are of interest
(e.g., using :func:isinstance).
.. describe:: Annotated[<type>, <metadata>]
Here is an example of how you might use Annotated to add metadata to
type annotations if you were doing range analysis:
.. testcode::
@dataclass
class ValueRange:
lo: int
hi: int
T1 = Annotated[int, ValueRange(-10, 5)]
T2 = Annotated[T1, ValueRange(-20, 3)]
The first argument to Annotated must be a valid type. Multiple metadata
elements can be supplied as Annotated supports variadic arguments. The
order of the metadata elements is preserved and matters for equality checks::
@dataclass
class ctype:
kind: str
a1 = Annotated[int, ValueRange(3, 10), ctype("char")]
a2 = Annotated[int, ctype("char"), ValueRange(3, 10)]
assert a1 != a2 # Order matters
It is up to the tool consuming the annotations to decide whether the client is allowed to add multiple metadata elements to one annotation and how to merge those annotations.
Nested Annotated types are flattened. The order of the metadata elements
starts with the innermost annotation::
assert Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
int, ValueRange(3, 10), ctype("char")
]
However, this does not apply to Annotated types referenced through a type
alias, to avoid forcing evaluation of the underlying :class:TypeAliasType::
type From3To10[T] = Annotated[T, ValueRange(3, 10)]
assert Annotated[From3To10[int], ctype("char")] != Annotated[
int, ValueRange(3, 10), ctype("char")
]
Duplicated metadata elements are not removed::
assert Annotated[int, ValueRange(3, 10)] != Annotated[
int, ValueRange(3, 10), ValueRange(3, 10)
]
Annotated can be used with nested and generic aliases:
.. testcode::
@dataclass
class MaxLen:
value: int
type Vec[T] = Annotated[list[tuple[T, T]], MaxLen(10)]
# When used in a type annotation, a type checker will treat "V" the same as
# ``Annotated[list[tuple[int, int]], MaxLen(10)]``:
type V = Vec[int]
Annotated cannot be used with an unpacked :class:TypeVarTuple::
type Variadic[*Ts] = Annotated[*Ts, Ann1] = Annotated[T1, T2, T3, ..., Ann1] # NOT valid
where T1, T2, ... are :class:TypeVars <TypeVar>. This is invalid as
only one type should be passed to Annotated.
By default, :func:get_type_hints strips the metadata from annotations.
Pass include_extras=True to have the metadata preserved:
.. doctest::
>>> from typing import Annotated, get_type_hints
>>> def func(x: Annotated[int, "metadata"]) -> None: pass
...
>>> get_type_hints(func)
{'x': <class 'int'>, 'return': <class 'NoneType'>}
>>> get_type_hints(func, include_extras=True)
{'x': typing.Annotated[int, 'metadata'], 'return': <class 'NoneType'>}
At runtime, the metadata associated with an Annotated type can be
retrieved via the :attr:!__metadata__ attribute:
.. doctest::
>>> from typing import Annotated
>>> X = Annotated[int, "very", "important", "metadata"]
>>> X
typing.Annotated[int, 'very', 'important', 'metadata']
>>> X.__metadata__
('very', 'important', 'metadata')
If you want to retrieve the original type wrapped by Annotated, use the
:attr:!__origin__ attribute:
.. doctest::
>>> from typing import Annotated, get_origin
>>> Password = Annotated[str, "secret"]
>>> Password.__origin__
<class 'str'>
Note that using :func:get_origin will return Annotated itself:
.. doctest::
>>> get_origin(Password)
typing.Annotated
.. seealso::
:pep:`593` - Flexible function and variable annotations
The PEP introducing ``Annotated`` to the standard library.
.. versionadded:: 3.9
.. data:: TypeForm
A special form representing the value that results from evaluating a type expression.
This value encodes the information supplied in the type expression, and it represents the type described by that type expression.
When used in a type expression, TypeForm describes a set of type form
objects. It accepts a single type argument, which must be a valid type
expression. TypeForm[T] describes the set of all type form objects that
represent the type T or types assignable to T.
TypeForm(obj) simply returns obj unchanged. This is useful for
explicitly marking a value as a type form for static type checkers.
Example::
from typing import Any, TypeForm
def cast[T](typ: TypeForm[T], value: Any) -> T: ...
reveal_type(cast(int, "x")) # Revealed type is "int"
See :pep:747 for details.
.. versionadded:: 3.15
.. data:: TypeIs
Special typing construct for marking user-defined type predicate functions.
TypeIs can be used to annotate the return type of a user-defined
type predicate function. TypeIs only accepts a single type argument.
At runtime, functions marked this way should return a boolean and take at
least one positional argument.
TypeIs aims to benefit type narrowing -- a technique used by static
type checkers to determine a more precise type of an expression within a
program's code flow. Usually type narrowing is done by analyzing
conditional code flow and applying the narrowing to a block of code. The
conditional expression here is sometimes referred to as a "type predicate"::
def is_str(val: str | float):
# "isinstance" type predicate
if isinstance(val, str):
# Type of ``val`` is narrowed to ``str``
...
else:
# Else, type of ``val`` is narrowed to ``float``.
...
Sometimes it would be convenient to use a user-defined boolean function
as a type predicate. Such a function should use TypeIs[...] or
:data:TypeGuard as its return type to alert static type checkers to
this intention. TypeIs usually has more intuitive behavior than
TypeGuard, but it cannot be used when the input and output types
are incompatible (e.g., list[object] to list[int]) or when the
function does not return True for all instances of the narrowed type.
Using -> TypeIs[NarrowedType] tells the static type checker that for a given
function:
True, the type of its argument
is the intersection of the argument's original type and NarrowedType.False, the type of its argument
is narrowed to exclude NarrowedType.For example::
from typing import assert_type, final, TypeIs
class Parent: pass
class Child(Parent): pass
@final
class Unrelated: pass
def is_parent(val: object) -> TypeIs[Parent]:
return isinstance(val, Parent)
def run(arg: Child | Unrelated):
if is_parent(arg):
# Type of ``arg`` is narrowed to the intersection
# of ``Parent`` and ``Child``, which is equivalent to
# ``Child``.
assert_type(arg, Child)
else:
# Type of ``arg`` is narrowed to exclude ``Parent``,
# so only ``Unrelated`` is left.
assert_type(arg, Unrelated)
The type inside TypeIs must be consistent with the type of the
function's argument; if it is not, static type checkers will raise
an error. An incorrectly written TypeIs function can lead to
unsound behavior in the type system; it is the user's responsibility
to write such functions in a type-safe manner.
If a TypeIs function is a class or instance method, then the type in
TypeIs maps to the type of the second parameter (after cls or
self).
In short, the form def foo(arg: TypeA) -> TypeIs[TypeB]: ...,
means that if foo(arg) returns True, then arg is an instance
of TypeB, and if it returns False, it is not an instance of TypeB.
TypeIs also works with type variables. For more information, see
:pep:742 (Narrowing types with TypeIs).
.. versionadded:: 3.13
.. data:: TypeGuard
Special typing construct for marking user-defined type predicate functions.
Type predicate functions are user-defined functions that return whether their
argument is an instance of a particular type.
TypeGuard works similarly to :data:TypeIs, but has subtly different
effects on type checking behavior (see below).
Using -> TypeGuard tells the static type checker that for a given
function:
True, the type of its argument
is the type inside TypeGuard.TypeGuard also works with type variables. See :pep:647 for more details.
For example::
def is_str_list(val: list[object]) -> TypeGuard[list[str]]:
'''Determines whether all objects in the list are strings'''
return all(isinstance(x, str) for x in val)
def func1(val: list[object]):
if is_str_list(val):
# Type of ``val`` is narrowed to ``list[str]``.
print(" ".join(val))
else:
# Type of ``val`` remains as ``list[object]``.
print("Not a list of strings!")
TypeIs and TypeGuard differ in the following ways:
TypeIs requires the narrowed type to be a subtype of the input type, while
TypeGuard does not. The main reason is to allow for things like
narrowing list[object] to list[str] even though the latter
is not a subtype of the former, since list is invariant.TypeGuard function returns True, type checkers narrow the type of the
variable to exactly the TypeGuard type. When a TypeIs function returns True,
type checkers can infer a more precise type combining the previously known type of the
variable with the TypeIs type. (Technically, this is known as an intersection type.)TypeGuard function returns False, type checkers cannot narrow the type of
the variable at all. When a TypeIs function returns False, type checkers can narrow
the type of the variable to exclude the TypeIs type... versionadded:: 3.10
.. data:: Unpack
Typing operator to conceptually mark an object as having been unpacked.
For example, using the unpack operator * on a
:ref:type variable tuple <typevartuple> is equivalent to using Unpack
to mark the type variable tuple as having been unpacked::
Ts = TypeVarTuple('Ts')
tup: tuple[*Ts]
# Effectively does:
tup: tuple[Unpack[Ts]]
In fact, Unpack can be used interchangeably with * in the context
of :class:typing.TypeVarTuple <TypeVarTuple> and
:class:builtins.tuple <tuple> types. You might see Unpack being used
explicitly in older versions of Python, where * couldn't be used in
certain places::
# In older versions of Python, TypeVarTuple and Unpack
# are located in the `typing_extensions` backports package.
from typing_extensions import TypeVarTuple, Unpack
Ts = TypeVarTuple('Ts')
tup: tuple[*Ts] # Syntax error on Python <= 3.10!
tup: tuple[Unpack[Ts]] # Semantically equivalent, and backwards-compatible
Unpack can also be used along with :class:typing.TypedDict for typing
**kwargs in a function signature::
from typing import TypedDict, Unpack
class Movie(TypedDict):
name: str
year: int
# This function expects two keyword arguments - `name` of type `str`
# and `year` of type `int`.
def foo(**kwargs: Unpack[Movie]): ...
See :pep:692 for more details on using Unpack for **kwargs typing.
.. versionadded:: 3.11
Building generic types and type aliases """""""""""""""""""""""""""""""""""""""
The following classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating generic types and type aliases.
These objects can be created through special syntax
(:ref:type parameter lists <type-params> and the :keyword:type statement).
For compatibility with Python 3.11 and earlier, they can also be created
without the dedicated syntax, as documented below.
.. class:: Generic
Abstract base class for generic types.
A generic type is typically declared by adding a list of type parameters after the class name::
class Mapping[KT, VT]:
def __getitem__(self, key: KT) -> VT:
...
# Etc.
Such a class implicitly inherits from Generic.
The runtime semantics of this syntax are discussed in the
:ref:Language Reference <generic-classes>.
This class can then be used as follows::
def lookup_name[X, Y](mapping: Mapping[X, Y], key: X, default: Y) -> Y:
try:
return mapping[key]
except KeyError:
return default
Here the brackets after the function name indicate a
:ref:generic function <generic-functions>.
For backwards compatibility, generic classes can also be
declared by explicitly inheriting from
Generic. In this case, the type parameters must be declared
separately::
KT = TypeVar('KT')
VT = TypeVar('VT')
class Mapping(Generic[KT, VT]):
def __getitem__(self, key: KT) -> VT:
...
# Etc.
.. _typevar:
.. class:: TypeVar(name, *constraints, bound=None, covariant=False, contravariant=False, infer_variance=False, default=typing.NoDefault)
Type variable.
The preferred way to construct a type variable is via the dedicated syntax
for :ref:generic functions <generic-functions>,
:ref:generic classes <generic-classes>, and
:ref:generic type aliases <generic-type-aliases>::
class Sequence[T]: # T is a TypeVar
...
This syntax can also be used to create bounded and constrained type variables::
class StrSequence[S: str]: # S is a TypeVar with a `str` upper bound;
... # we can say that S is "bounded by `str`"
class StrOrBytesSequence[A: (str, bytes)]: # A is a TypeVar constrained to str or bytes
...
However, if desired, reusable type variables can also be constructed manually, like so::
T = TypeVar('T') # Can be anything
S = TypeVar('S', bound=str) # Can be any subtype of str
A = TypeVar('A', str, bytes) # Must be exactly str or bytes
Type variables exist primarily for the benefit of static type
checkers. They serve as the parameters for generic types as well
as for generic function and type alias definitions.
See :class:Generic for more
information on generic types. Generic functions work as follows::
def repeat[T](x: T, n: int) -> Sequence[T]:
"""Return a list containing n references to x."""
return [x]*n
def print_capitalized[S: str](x: S) -> S:
"""Print x capitalized, and return x."""
print(x.capitalize())
return x
def concatenate[A: (str, bytes)](x: A, y: A) -> A:
"""Add two strings or bytes objects together."""
return x + y
Note that type variables can be bounded, constrained, or neither, but cannot be both bounded and constrained.
The variance of type variables is inferred by type checkers when they are created
through the :ref:type parameter syntax <type-params> or when
infer_variance=True is passed.
Manually created type variables may be explicitly marked covariant or contravariant by passing
covariant=True or contravariant=True.
By default, manually created type variables are invariant.
See :pep:484 and :pep:695 for more details.
Bounded type variables and constrained type variables have different
semantics in several important ways. Using a bounded type variable means
that the TypeVar will be solved using the most specific type possible::
x = print_capitalized('a string')
reveal_type(x) # revealed type is str
class StringSubclass(str):
pass
y = print_capitalized(StringSubclass('another string'))
reveal_type(y) # revealed type is StringSubclass
z = print_capitalized(45) # error: int is not a subtype of str
The upper bound of a type variable can be a concrete type, abstract type (ABC or Protocol), or even a union of types::
# Can be anything with an __abs__ method
def print_abs[T: SupportsAbs](arg: T) -> None:
print("Absolute value:", abs(arg))
U = TypeVar('U', bound=str|bytes) # Can be any subtype of the union str|bytes
V = TypeVar('V', bound=SupportsAbs) # Can be anything with an __abs__ method
.. _typing-constrained-typevar:
Using a constrained type variable, however, means that the TypeVar
can only ever be solved as being exactly one of the constraints given::
a = concatenate('one', 'two')
reveal_type(a) # revealed type is str
b = concatenate(StringSubclass('one'), StringSubclass('two'))
reveal_type(b) # revealed type is str, despite StringSubclass being passed in
c = concatenate('one', b'two') # error: type variable 'A' can be either str or bytes in a function call, but not both
At runtime, isinstance(x, T) will raise :exc:TypeError.
.. attribute:: name
The name of the type variable.
.. attribute:: covariant
Whether the type var has been explicitly marked as covariant.
.. attribute:: contravariant
Whether the type var has been explicitly marked as contravariant.
.. attribute:: infer_variance
Whether the type variable's variance should be inferred by type checkers.
.. versionadded:: 3.12
.. attribute:: bound
The upper bound of the type variable, if any.
.. versionchanged:: 3.12
For type variables created through :ref:`type parameter syntax <type-params>`,
the bound is evaluated only when the attribute is accessed, not when
the type variable is created (see :ref:`lazy-evaluation`).
.. method:: evaluate_bound
An :term:`evaluate function` corresponding to the :attr:`~TypeVar.__bound__` attribute.
When called directly, this method supports only the :attr:`~annotationlib.Format.VALUE`
format, which is equivalent to accessing the :attr:`~TypeVar.__bound__` attribute directly,
but the method object can be passed to :func:`annotationlib.call_evaluate_function`
to evaluate the value in a different format.
.. versionadded:: 3.14
.. attribute:: constraints
A tuple containing the constraints of the type variable, if any.
.. versionchanged:: 3.12
For type variables created through :ref:`type parameter syntax <type-params>`,
the constraints are evaluated only when the attribute is accessed, not when
the type variable is created (see :ref:`lazy-evaluation`).
.. method:: evaluate_constraints
An :term:`evaluate function` corresponding to the :attr:`~TypeVar.__constraints__` attribute.
When called directly, this method supports only the :attr:`~annotationlib.Format.VALUE`
format, which is equivalent to accessing the :attr:`~TypeVar.__constraints__` attribute directly,
but the method object can be passed to :func:`annotationlib.call_evaluate_function`
to evaluate the value in a different format.
.. versionadded:: 3.14
.. attribute:: default
The default value of the type variable, or :data:`typing.NoDefault` if it
has no default.
.. versionadded:: 3.13
.. method:: evaluate_default
An :term:`evaluate function` corresponding to the :attr:`~TypeVar.__default__` attribute.
When called directly, this method supports only the :attr:`~annotationlib.Format.VALUE`
format, which is equivalent to accessing the :attr:`~TypeVar.__default__` attribute directly,
but the method object can be passed to :func:`annotationlib.call_evaluate_function`
to evaluate the value in a different format.
.. versionadded:: 3.14
.. method:: has_default()
Return whether or not the type variable has a default value. This is equivalent
to checking whether :attr:`__default__` is not the :data:`typing.NoDefault`
singleton, except that it does not force evaluation of the
:ref:`lazily evaluated <lazy-evaluation>` default value.
.. versionadded:: 3.13
.. versionchanged:: 3.12
Type variables can now be declared using the
:ref:`type parameter <type-params>` syntax introduced by :pep:`695`.
The ``infer_variance`` parameter was added.
.. versionchanged:: 3.13
Support for default values was added.
.. _typevartuple:
.. class:: TypeVarTuple(name, *, default=typing.NoDefault)
Type variable tuple. A specialized form of :ref:type variable <typevar>
that enables variadic generics.
Type variable tuples can be declared in :ref:type parameter lists <type-params>
using a single asterisk (*) before the name::
def move_first_element_to_last[T, *Ts](tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
return (*tup[1:], tup[0])
Or by explicitly invoking the TypeVarTuple constructor::
T = TypeVar("T")
Ts = TypeVarTuple("Ts")
def move_first_element_to_last(tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
return (*tup[1:], tup[0])
A normal type variable enables parameterization with a single type. A type variable tuple, in contrast, allows parameterization with an arbitrary number of types by acting like an arbitrary number of type variables wrapped in a tuple. For example::
# T is bound to int, Ts is bound to ()
# Return value is (1,), which has type tuple[int]
move_first_element_to_last(tup=(1,))
# T is bound to int, Ts is bound to (str,)
# Return value is ('spam', 1), which has type tuple[str, int]
move_first_element_to_last(tup=(1, 'spam'))
# T is bound to int, Ts is bound to (str, float)
# Return value is ('spam', 3.0, 1), which has type tuple[str, float, int]
move_first_element_to_last(tup=(1, 'spam', 3.0))
# This fails to type check (and fails at runtime)
# because tuple[()] is not compatible with tuple[T, *Ts]
# (at least one element is required)
move_first_element_to_last(tup=())
Note the use of the unpacking operator * in tuple[T, *Ts].
Conceptually, you can think of Ts as a tuple of type variables
(T1, T2, ...). tuple[T, *Ts] would then become
tuple[T, *(T1, T2, ...)], which is equivalent to
tuple[T, T1, T2, ...]. (Note that in older versions of Python, you might
see this written using :data:Unpack <Unpack> instead, as
Unpack[Ts].)
Type variable tuples must always be unpacked. This helps distinguish type variable tuples from normal type variables::
x: Ts # Not valid
x: tuple[Ts] # Not valid
x: tuple[*Ts] # The correct way to do it
Type variable tuples can be used in the same contexts as normal type variables. For example, in class definitions, arguments, and return types::
class Array[*Shape]:
def __getitem__(self, key: tuple[*Shape]) -> float: ...
def __abs__(self) -> "Array[*Shape]": ...
def get_shape(self) -> tuple[*Shape]: ...
Type variable tuples can be happily combined with normal type variables:
.. testcode::
class Array[DType, *Shape]: # This is fine
pass
class Array2[*Shape, DType]: # This would also be fine
pass
class Height: ...
class Width: ...
float_array_1d: Array[float, Height] = Array() # Totally fine
int_array_2d: Array[int, Height, Width] = Array() # Yup, fine too
However, note that at most one type variable tuple may appear in a single list of type arguments or type parameters::
x: tuple[*Ts, *Ts] # Not valid
class Array[*Shape, *Shape]: # Not valid
pass
Finally, an unpacked type variable tuple can be used as the type annotation
of *args::
def call_soon[*Ts](
callback: Callable[[*Ts], None],
*args: *Ts
) -> None:
...
callback(*args)
In contrast to non-unpacked annotations of *args - e.g. *args: int,
which would specify that all arguments are int - *args: *Ts
enables reference to the types of the individual arguments in *args.
Here, this allows us to ensure the types of the *args passed
to call_soon match the types of the (positional) arguments of
callback.
See :pep:646 for more details on type variable tuples.
.. attribute:: name
The name of the type variable tuple.
.. attribute:: default
The default value of the type variable tuple, or :data:`typing.NoDefault` if it
has no default.
.. versionadded:: 3.13
.. method:: evaluate_default
An :term:`evaluate function` corresponding to the :attr:`~TypeVarTuple.__default__` attribute.
When called directly, this method supports only the :attr:`~annotationlib.Format.VALUE`
format, which is equivalent to accessing the :attr:`~TypeVarTuple.__default__` attribute directly,
but the method object can be passed to :func:`annotationlib.call_evaluate_function`
to evaluate the value in a different format.
.. versionadded:: 3.14
.. method:: has_default()
Return whether or not the type variable tuple has a default value. This is equivalent
to checking whether :attr:`__default__` is not the :data:`typing.NoDefault`
singleton, except that it does not force evaluation of the
:ref:`lazily evaluated <lazy-evaluation>` default value.
.. versionadded:: 3.13
.. versionadded:: 3.11
.. versionchanged:: 3.12
Type variable tuples can now be declared using the
:ref:`type parameter <type-params>` syntax introduced by :pep:`695`.
.. versionchanged:: 3.13
Support for default values was added.
.. class:: ParamSpec(name, *, bound=None, covariant=False, contravariant=False, default=typing.NoDefault)
Parameter specification variable. A specialized version of
:ref:type variables <typevar>.
In :ref:type parameter lists <type-params>, parameter specifications
can be declared with two asterisks (**)::
type IntFunc[**P] = Callable[P, int]
For compatibility with Python 3.11 and earlier, ParamSpec objects
can also be created as follows::
P = ParamSpec('P')
Parameter specification variables exist primarily for the benefit of static
type checkers. They are used to forward the parameter types of one
callable to another callable -- a pattern commonly found in higher order
functions and decorators. They are only valid when used in Concatenate,
or as the first argument to Callable, or as parameters for user-defined
Generics. See :class:Generic for more information on generic types.
For example, to add basic logging to a function, one can create a decorator
add_logging to log function calls. The parameter specification variable
tells the type checker that the callable passed into the decorator and the
new callable returned by it have inter-dependent type parameters::
from collections.abc import Callable
import logging
def add_logging[T, **P](f: Callable[P, T]) -> Callable[P, T]:
'''A type-safe decorator to add logging to a function.'''
def inner(*args: P.args, **kwargs: P.kwargs) -> T:
logging.info(f'{f.__name__} was called')
return f(*args, **kwargs)
return inner
@add_logging
def add_two(x: float, y: float) -> float:
'''Add two numbers together.'''
return x + y
Without ParamSpec, the simplest way to annotate this previously was to
use a :class:TypeVar with upper bound Callable[..., Any]. However this
causes two problems:
inner function because
*args and **kwargs have to be typed :data:Any.~cast may be required in the body of the add_logging
decorator when returning the inner function, or the static type
checker must be told to ignore the return inner... attribute:: args .. attribute:: kwargs
Since ``ParamSpec`` captures both positional and keyword parameters,
``P.args`` and ``P.kwargs`` can be used to split a ``ParamSpec`` into its
components. ``P.args`` represents the tuple of positional parameters in a
given call and should only be used to annotate ``*args``. ``P.kwargs``
represents the mapping of keyword parameters to their values in a given call,
and should be only be used to annotate ``**kwargs``. Both
attributes require the annotated parameter to be in scope. At runtime,
``P.args`` and ``P.kwargs`` are instances respectively of
:class:`ParamSpecArgs` and :class:`ParamSpecKwargs`.
.. attribute:: name
The name of the parameter specification.
.. attribute:: default
The default value of the parameter specification, or :data:`typing.NoDefault` if it
has no default.
.. versionadded:: 3.13
.. method:: evaluate_default
An :term:`evaluate function` corresponding to the :attr:`~ParamSpec.__default__` attribute.
When called directly, this method supports only the :attr:`~annotationlib.Format.VALUE`
format, which is equivalent to accessing the :attr:`~ParamSpec.__default__` attribute directly,
but the method object can be passed to :func:`annotationlib.call_evaluate_function`
to evaluate the value in a different format.
.. versionadded:: 3.14
.. method:: has_default()
Return whether or not the parameter specification has a default value. This is equivalent
to checking whether :attr:`__default__` is not the :data:`typing.NoDefault`
singleton, except that it does not force evaluation of the
:ref:`lazily evaluated <lazy-evaluation>` default value.
.. versionadded:: 3.13
Parameter specification variables created with covariant=True or
contravariant=True can be used to declare covariant or contravariant
generic types. The bound argument is also accepted, similar to
:class:TypeVar. However the actual semantics of these keywords are yet to
be decided.
.. versionadded:: 3.10
.. versionchanged:: 3.12
Parameter specifications can now be declared using the
:ref:`type parameter <type-params>` syntax introduced by :pep:`695`.
.. versionchanged:: 3.13
Support for default values was added.
.. note:: Only parameter specification variables defined in global scope can be pickled.
.. seealso::
* :pep:612 -- Parameter Specification Variables (the PEP which introduced
ParamSpec and Concatenate)
* :data:Concatenate
* :ref:annotating-callables
.. data:: ParamSpecArgs ParamSpecKwargs
Arguments and keyword arguments attributes of a :class:ParamSpec. The
P.args attribute of a ParamSpec is an instance of ParamSpecArgs,
and P.kwargs is an instance of ParamSpecKwargs. They are intended
for runtime introspection and have no special meaning to static type checkers.
Calling :func:get_origin on either of these objects will return the
original ParamSpec:
.. doctest::
>>> from typing import ParamSpec, get_origin
>>> P = ParamSpec("P")
>>> get_origin(P.args) is P
True
>>> get_origin(P.kwargs) is P
True
.. versionadded:: 3.10
.. class:: TypeAliasType(name, value, *, type_params=(), qualname=None)
The type of type aliases created through the :keyword:type statement.
Example:
.. doctest::
>>> type Alias = int
>>> type(Alias)
<class 'typing.TypeAliasType'>
.. versionadded:: 3.12
.. attribute:: name
The name of the type alias:
.. doctest::
>>> type Alias = int
>>> Alias.__name__
'Alias'
.. attribute:: qualname
The :term:`qualified name` of the type alias:
.. doctest::
>>> class Class:
... type Alias = int
...
>>> Class.Alias.__qualname__
'Class.Alias'
.. versionadded:: 3.15
.. attribute:: module
The name of the module in which the type alias was defined::
>>> type Alias = int
>>> Alias.__module__
'__main__'
.. attribute:: type_params
The type parameters of the type alias, or an empty tuple if the alias is
not generic:
.. doctest::
>>> type ListOrSet[T] = list[T] | set[T]
>>> ListOrSet.__type_params__
(T,)
>>> type NotGeneric = int
>>> NotGeneric.__type_params__
()
.. attribute:: value
The type alias's value. This is :ref:`lazily evaluated <lazy-evaluation>`,
so names used in the definition of the alias are not resolved until the
``__value__`` attribute is accessed:
.. doctest::
>>> type Mutually = Recursive
>>> type Recursive = Mutually
>>> Mutually
Mutually
>>> Recursive
Recursive
>>> Mutually.__value__
Recursive
>>> Recursive.__value__
Mutually
.. method:: evaluate_value
An :term:`evaluate function` corresponding to the :attr:`__value__` attribute.
When called directly, this method supports only the :attr:`~annotationlib.Format.VALUE`
format, which is equivalent to accessing the :attr:`__value__` attribute directly,
but the method object can be passed to :func:`annotationlib.call_evaluate_function`
to evaluate the value in a different format:
.. doctest::
>>> type Alias = undefined
>>> Alias.__value__
Traceback (most recent call last):
...
NameError: name 'undefined' is not defined
>>> from annotationlib import Format, call_evaluate_function
>>> Alias.evaluate_value(Format.VALUE)
Traceback (most recent call last):
...
NameError: name 'undefined' is not defined
>>> call_evaluate_function(Alias.evaluate_value, Format.FORWARDREF)
ForwardRef('undefined')
.. versionadded:: 3.14
.. rubric:: Unpacking
Type aliases support star unpacking using the *Alias syntax.
This is equivalent to using Unpack[Alias] directly:
.. doctest::
>>> type Alias = tuple[int, str]
>>> type Unpacked = tuple[bool, *Alias]
>>> Unpacked.__value__
tuple[bool, typing.Unpack[Alias]]
.. versionadded:: 3.14
Other special directives """"""""""""""""""""""""
These functions and classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating and declaring types.
.. class:: NamedTuple
Typed version of :func:collections.namedtuple.
Usage::
class Employee(NamedTuple):
name: str
id: int
This is equivalent to::
Employee = collections.namedtuple('Employee', ['name', 'id'])
To give a field a default value, you can assign to it in the class body::
class Employee(NamedTuple):
name: str
id: int = 3
employee = Employee('Guido')
assert employee.id == 3
Fields with a default value must come after any fields without a default.
The resulting class has an extra attribute __annotations__ giving a
dict that maps the field names to the field types. (The field names are in
the _fields attribute and the default values are in the
_field_defaults attribute, both of which are part of the :func:~collections.namedtuple
API.)
NamedTuple subclasses can also have docstrings and methods::
class Employee(NamedTuple):
"""Represents an employee."""
name: str
id: int = 3
def __repr__(self) -> str:
return f'<Employee {self.name}, id={self.id}>'
NamedTuple subclasses can be generic::
class Group[T](NamedTuple):
key: T
group: list[T]
Backward-compatible usage::
# For creating a generic NamedTuple on Python 3.11
T = TypeVar("T")
class Group(NamedTuple, Generic[T]):
key: T
group: list[T]
# A functional syntax is also supported
Employee = NamedTuple('Employee', [('name', str), ('id', int)])
.. versionchanged:: 3.6
Added support for :pep:526 variable annotation syntax.
.. versionchanged:: 3.6.1 Added support for default values, methods, and docstrings.
.. versionchanged:: 3.8
The _field_types and __annotations__ attributes are
now regular dictionaries instead of instances of OrderedDict.
.. versionchanged:: 3.9
Removed the _field_types attribute in favor of the more
standard __annotations__ attribute which has the same information.
.. versionchanged:: 3.9
NamedTuple is now a function rather than a class.
It can still be used as a class base, as described above.
.. versionchanged:: 3.11 Added support for generic namedtuples.
.. versionchanged:: 3.14
Using :func:super (and the __class__ :term:closure variable) in methods of NamedTuple subclasses
is unsupported and causes a :class:TypeError.
.. class:: NewType(name, tp)
Helper class to create low-overhead :ref:distinct types <distinct>.
A NewType is considered a distinct type by a typechecker. At runtime,
however, calling a NewType returns its argument unchanged.
Usage::
UserId = NewType('UserId', int) # Declare the NewType "UserId"
first_user = UserId(1) # "UserId" returns the argument unchanged at runtime
.. attribute:: module
The name of the module in which the new type is defined.
.. attribute:: name
The name of the new type.
.. attribute:: supertype
The type that the new type is based on.
.. versionadded:: 3.5.2
.. versionchanged:: 3.10
NewType is now a class rather than a function.
.. class:: Protocol(Generic)
Base class for protocol classes.
Protocol classes are defined like this::
class Proto(Protocol):
def meth(self) -> int:
...
Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example::
class C:
def meth(self) -> int:
return 0
def func(x: Proto) -> int:
return x.meth()
func(C()) # Passes static type check
See :pep:544 for more details. Protocol classes decorated with
:func:runtime_checkable (described later) act as simple-minded runtime
protocols that check only the presence of given attributes, ignoring their
type signatures. Protocol classes without this decorator cannot be used
as the second argument to :func:isinstance or :func:issubclass.
Protocol classes can be generic, for example::
class GenProto[T](Protocol):
def meth(self) -> T:
...
In code that needs to be compatible with Python 3.11 or older, generic Protocols can be written as follows::
T = TypeVar("T")
class GenProto(Protocol[T]):
def meth(self) -> T:
...
.. versionadded:: 3.8
.. deprecated-removed:: 3.15 3.20
It is deprecated to call :func:isinstance and :func:issubclass checks on
protocol classes that were not explicitly decorated with :func:!runtime_checkable
but that inherit from a runtime-checkable protocol class. This will throw
a :exc:TypeError in Python 3.20.
.. decorator:: runtime_checkable
Mark a protocol class as a runtime protocol.
Such a protocol can be used with :func:isinstance and :func:issubclass.
This allows a simple-minded structural check, very similar to "one trick ponies"
in :mod:collections.abc such as :class:~collections.abc.Iterable. For example::
@runtime_checkable
class Closable(Protocol):
def close(self): ...
assert isinstance(open('/some/file'), Closable)
@runtime_checkable
class Named(Protocol):
name: str
import threading
assert isinstance(threading.Thread(name='Bob'), Named)
Runtime checkability of protocols is not inherited. A subclass of a runtime-checkable protocol is only runtime-checkable if it is explicitly marked as such, regardless of class hierarchy::
@runtime_checkable
class Iterable(Protocol):
def __iter__(self): ...
# Without @runtime_checkable, Reversible would no longer be runtime-checkable.
@runtime_checkable
class Reversible(Iterable, Protocol):
def __reversed__(self): ...
This decorator raises :exc:TypeError when applied to a non-protocol class.
.. note::
:func:`!runtime_checkable` will check only the presence of the required
methods or attributes, not their type signatures or types.
For example, :class:`ssl.SSLObject`
is a class, therefore it passes an :func:`issubclass`
check against :ref:`Callable <annotating-callables>`. However, the
``ssl.SSLObject.__init__`` method exists only to raise a
:exc:`TypeError` with a more informative message, therefore making
it impossible to call (instantiate) :class:`ssl.SSLObject`.
.. note::
An :func:`isinstance` check against a runtime-checkable protocol can be
surprisingly slow compared to an ``isinstance()`` check against
a non-protocol class. Consider using alternative idioms such as
:func:`hasattr` calls for structural checks in performance-sensitive
code.
.. versionadded:: 3.8
.. versionchanged:: 3.12
The internal implementation of :func:isinstance checks against
runtime-checkable protocols now uses :func:inspect.getattr_static
to look up attributes (previously, :func:hasattr was used).
As a result, some objects which used to be considered instances
of a runtime-checkable protocol may no longer be considered instances
of that protocol on Python 3.12+, and vice versa.
Most users are unlikely to be affected by this change.
.. versionchanged:: 3.12
The members of a runtime-checkable protocol are now considered "frozen"
at runtime as soon as the class has been created. Monkey-patching
attributes onto a runtime-checkable protocol will still work, but will
have no impact on :func:isinstance checks comparing objects to the
protocol. See :ref:What's new in Python 3.12 <whatsnew-typing-py312>
for more details.
.. deprecated-removed:: 3.15 3.20
It is deprecated to call :func:isinstance and :func:issubclass checks on
protocol classes that were not explicitly decorated with :func:!runtime_checkable
but that inherit from a runtime-checkable protocol class. This will throw
a :exc:TypeError in Python 3.20.
.. class:: TypedDict(dict)
Special construct to add type hints to a dictionary.
At runtime ":class:!TypedDict instances" are simply :class:dicts <dict>.
TypedDict declares a dictionary type that expects all of its
instances to have a certain set of keys, where each key is
associated with a value of a consistent type. This expectation
is not checked at runtime but is only enforced by type checkers.
Usage::
class Point2D(TypedDict):
x: int
y: int
label: str
a: Point2D = {'x': 1, 'y': 2, 'label': 'good'} # OK
b: Point2D = {'z': 3, 'label': 'bad'} # Fails type check
assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')
An alternative way to create a TypedDict is by using
function-call syntax. The second argument must be a literal :class:dict::
Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
This functional syntax allows defining keys which are not valid
:ref:identifiers <identifiers>, for example because they are
keywords or contain hyphens, or when key names must not be
:ref:mangled <private-name-mangling> like regular private names::
# raises SyntaxError
class Point2D(TypedDict):
in: int # 'in' is a keyword
x-y: int # name with hyphens
class Definition(TypedDict):
__schema: str # mangled to `_Definition__schema`
# OK, functional syntax
Point2D = TypedDict('Point2D', {'in': int, 'x-y': int})
Definition = TypedDict('Definition', {'__schema': str}) # not mangled
By default, all keys must be present in a TypedDict. It is possible to
mark individual keys as non-required using :data:NotRequired::
class Point2D(TypedDict):
x: int
y: int
label: NotRequired[str]
# Alternative syntax
Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': NotRequired[str]})
This means that a Point2D TypedDict can have the label
key omitted.
It is also possible to mark all keys as non-required by default
by specifying a totality of False::
class Point2D(TypedDict, total=False):
x: int
y: int
# Alternative syntax
Point2D = TypedDict('Point2D', {'x': int, 'y': int}, total=False)
This means that a Point2D TypedDict can have any of the keys
omitted. A type checker is only expected to support a literal False or
True as the value of the total argument. True is the default,
and makes all items defined in the class body required.
Individual keys of a total=False TypedDict can be marked as
required using :data:Required::
class Point2D(TypedDict, total=False):
x: Required[int]
y: Required[int]
label: str
# Alternative syntax
Point2D = TypedDict('Point2D', {
'x': Required[int],
'y': Required[int],
'label': str
}, total=False)
It is possible for a TypedDict type to inherit from one or more other TypedDict types
using the class-based syntax.
Usage::
class Point3D(Point2D):
z: int
Point3D has three items: x, y and z. It is equivalent to this
definition::
class Point3D(TypedDict):
x: int
y: int
z: int
A TypedDict cannot inherit from a non-\ TypedDict class,
except for :class:Generic. For example::
class X(TypedDict):
x: int
class Y(TypedDict):
y: int
class Z(object): pass # A non-TypedDict class
class XY(X, Y): pass # OK
class XZ(X, Z): pass # raises TypeError
A TypedDict can be generic::
class Group[T](TypedDict):
key: T
group: list[T]
To create a generic TypedDict that is compatible with Python 3.11
or lower, inherit from :class:Generic explicitly:
.. testcode::
T = TypeVar("T")
class Group(TypedDict, Generic[T]):
key: T
group: list[T]
A TypedDict can be introspected via annotations dicts
(see :ref:annotations-howto for more information on annotations best practices),
:attr:__total__, :attr:__required_keys__, and :attr:__optional_keys__.
.. attribute:: total
``Point2D.__total__`` gives the value of the ``total`` argument.
Example:
.. doctest::
>>> from typing import TypedDict
>>> class Point2D(TypedDict): pass
>>> Point2D.__total__
True
>>> class Point2D(TypedDict, total=False): pass
>>> Point2D.__total__
False
>>> class Point3D(Point2D): pass
>>> Point3D.__total__
True
This attribute reflects *only* the value of the ``total`` argument
to the current ``TypedDict`` class, not whether the class is semantically
total. For example, a ``TypedDict`` with ``__total__`` set to ``True`` may
have keys marked with :data:`NotRequired`, or it may inherit from another
``TypedDict`` with ``total=False``. Therefore, it is generally better to use
:attr:`__required_keys__` and :attr:`__optional_keys__` for introspection.
.. attribute:: required_keys
.. versionadded:: 3.9
.. attribute:: optional_keys
``Point2D.__required_keys__`` and ``Point2D.__optional_keys__`` return
:class:`frozenset` objects containing required and non-required keys, respectively.
Keys marked with :data:`Required` will always appear in ``__required_keys__``
and keys marked with :data:`NotRequired` will always appear in ``__optional_keys__``.
For backwards compatibility with Python 3.10 and below,
it is also possible to use inheritance to declare both required and
non-required keys in the same ``TypedDict`` . This is done by declaring a
``TypedDict`` with one value for the ``total`` argument and then
inheriting from it in another ``TypedDict`` with a different value for
``total``:
.. doctest::
>>> class Point2D(TypedDict, total=False):
... x: int
... y: int
...
>>> class Point3D(Point2D):
... z: int
...
>>> Point3D.__required_keys__ == frozenset({'z'})
True
>>> Point3D.__optional_keys__ == frozenset({'x', 'y'})
True
.. versionadded:: 3.9
.. note::
If ``from __future__ import annotations`` is used or if annotations
are given as strings, annotations are not evaluated when the
``TypedDict`` is defined. Therefore, the runtime introspection that
``__required_keys__`` and ``__optional_keys__`` rely on may not work
properly, and the values of the attributes may be incorrect.
Support for :data:ReadOnly is reflected in the following attributes:
.. attribute:: readonly_keys
A :class:`frozenset` containing the names of all read-only keys. Keys
are read-only if they carry the :data:`ReadOnly` qualifier.
.. versionadded:: 3.13
.. attribute:: mutable_keys
A :class:`frozenset` containing the names of all mutable keys. Keys
are mutable if they do not carry the :data:`ReadOnly` qualifier.
.. versionadded:: 3.13
See the TypedDict <https://typing.python.org/en/latest/spec/typeddict.html#typeddict>_ section in the typing documentation for more examples and detailed rules.
.. versionadded:: 3.8
.. versionchanged:: 3.9
TypedDict is now a function rather than a class.
It can still be used as a class base, as described above.
.. versionchanged:: 3.11
Added support for marking individual keys as :data:Required or :data:NotRequired.
See :pep:655.
.. versionchanged:: 3.11
Added support for generic TypedDict\ s.
.. versionchanged:: 3.13
Removed support for the keyword-argument method of creating TypedDict\ s.
.. versionchanged:: 3.13
Support for the :data:ReadOnly qualifier was added.
The following protocols are provided by the :mod:!typing module. All are decorated
with :deco:runtime_checkable.
.. class:: SupportsAbs
An ABC with one abstract method ``__abs__`` that is covariant
in its return type.
.. class:: SupportsBytes
An ABC with one abstract method ``__bytes__``.
.. class:: SupportsComplex
An ABC with one abstract method ``__complex__``.
.. class:: SupportsFloat
An ABC with one abstract method ``__float__``.
.. class:: SupportsIndex
An ABC with one abstract method ``__index__``.
.. versionadded:: 3.8
.. class:: SupportsInt
An ABC with one abstract method ``__int__``.
.. class:: SupportsRound
An ABC with one abstract method ``__round__``
that is covariant in its return type.
.. _typing-io:
.. class:: IO[AnyStr] TextIO BinaryIO
Generic class IO[AnyStr] and its subclasses TextIO(IO[str])
and BinaryIO(IO[bytes])
represent the types of I/O streams such as returned by
:func:open. Please note that these classes are not protocols, and
their interface is fairly broad.
The protocols :class:io.Reader and :class:io.Writer offer a simpler
alternative for argument types, when only the read() or write()
methods are accessed, respectively::
def read_and_write(reader: Reader[str], writer: Writer[bytes]): data = reader.read() writer.write(data.encode())
Also consider using :class:collections.abc.Iterable for iterating over
the lines of an input stream::
def read_config(stream: Iterable[str]): for line in stream: ...
.. function:: cast(typ, val)
Cast a value to a type.
This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don't check anything (we want this to be as fast as possible).
.. function:: assert_type(val, typ, /)
Ask a static type checker to confirm that val has an inferred type of typ.
At runtime this does nothing: it returns the first argument unchanged with no checks or side effects, no matter the actual type of the argument.
When a static type checker encounters a call to assert_type(), it
emits an error if the value is not of the specified type::
def greet(name: str) -> None:
assert_type(name, str) # OK, inferred type of `name` is `str`
assert_type(name, int) # type checker error
This function is useful for ensuring the type checker's understanding of a script is in line with the developer's intentions::
def complex_function(arg: object):
# Do some complex type-narrowing logic,
# after which we hope the inferred type will be `int`
...
# Test whether the type checker correctly understands our function
assert_type(arg, int)
.. versionadded:: 3.11
.. function:: assert_never(arg, /)
Ask a static type checker to confirm that a line of code is unreachable.
Example::
def int_or_str(arg: int | str) -> None:
match arg:
case int():
print("It's an int")
case str():
print("It's a str")
case _ as unreachable:
assert_never(unreachable)
Here, the annotations allow the type checker to infer that the
last case can never execute, because arg is either
an :class:int or a :class:str, and both options are covered by
earlier cases.
If a type checker finds that a call to assert_never() is
reachable, it will emit an error. For example, if the type annotation
for arg was instead int | str | float, the type checker would
emit an error pointing out that unreachable is of type :class:float.
For a call to assert_never to pass type checking, the inferred type of
the argument passed in must be the bottom type, :data:Never, and nothing
else.
At runtime, this throws an exception when called.
.. seealso::
Unreachable Code and Exhaustiveness Checking <https://typing.python.org/en/latest/guides/unreachable.html>__ has more
information about exhaustiveness checking with static typing.
.. versionadded:: 3.11
.. function:: reveal_type(obj, /)
Ask a static type checker to reveal the inferred type of an expression.
When a static type checker encounters a call to this function, it emits a diagnostic with the inferred type of the argument. For example::
x: int = 1
reveal_type(x) # Revealed type is "builtins.int"
This can be useful when you want to debug how your type checker handles a particular piece of code.
At runtime, this function prints the runtime type of its argument to
:data:sys.stderr and returns the argument unchanged (allowing the call to
be used within an expression)::
x = reveal_type(1) # prints "Runtime type is int"
print(x) # prints "1"
Note that the runtime type may be different from (more or less specific than) the type statically inferred by a type checker.
Most type checkers support reveal_type() anywhere, even if the
name is not imported from typing. Importing the name from
typing, however, allows your code to run without runtime errors and
communicates intent more clearly.
.. versionadded:: 3.11
.. decorator:: dataclass_transform(*, eq_default=True, order_default=False,
kw_only_default=False, frozen_default=False,
field_specifiers=(), **kwargs)
Decorator to mark an object as providing
:func:dataclass <dataclasses.dataclass>-like behavior.
dataclass_transform may be used to
decorate a class, metaclass, or a function that is itself a decorator.
The presence of @dataclass_transform() tells a static type checker that the
decorated object performs runtime "magic" that
transforms a class in a similar way to
:deco:dataclasses.dataclass.
Example usage with a decorator function:
.. testcode::
@dataclass_transform()
def create_model[T](cls: type[T]) -> type[T]:
...
return cls
@create_model
class CustomerModel:
id: int
name: str
On a base class::
@dataclass_transform()
class ModelBase: ...
class CustomerModel(ModelBase):
id: int
name: str
On a metaclass::
@dataclass_transform()
class ModelMeta(type): ...
class ModelBase(metaclass=ModelMeta): ...
class CustomerModel(ModelBase):
id: int
name: str
The CustomerModel classes defined above will
be treated by type checkers similarly to classes created with
:deco:dataclasses.dataclass.
For example, type checkers will assume these classes have
__init__ methods that accept id and name.
The decorated class, metaclass, or function may accept the following bool
arguments which type checkers will assume have the same effect as they
would have on the
:deco:dataclasses.dataclass decorator: init,
eq, order, unsafe_hash, frozen, match_args,
kw_only, and slots. It must be possible for the value of these
arguments (True or False) to be statically evaluated.
The arguments to the dataclass_transform decorator can be used to
customize the default behaviors of the decorated class, metaclass, or
function:
:param bool eq_default:
Indicates whether the eq parameter is assumed to be
True or False if it is omitted by the caller.
Defaults to True.
:param bool order_default:
Indicates whether the order parameter is
assumed to be True or False if it is omitted by the caller.
Defaults to False.
:param bool kw_only_default:
Indicates whether the kw_only parameter is
assumed to be True or False if it is omitted by the caller.
Defaults to False.
:param bool frozen_default:
Indicates whether the frozen parameter is
assumed to be True or False if it is omitted by the caller.
Defaults to False.
.. versionadded:: 3.12
:param field_specifiers:
Specifies a static list of supported classes
or functions that describe fields, similar to :func:dataclasses.field.
Defaults to ().
:type field_specifiers: tuple[Callable[..., Any], ...]
:param Any **kwargs: Arbitrary other keyword arguments are accepted in order to allow for possible future extensions.
Type checkers recognize the following optional parameters on field specifiers:
.. list-table:: Recognised parameters for field specifiers :header-rows: 1 :widths: 20 80
* - Parameter name
- Description
* - ``init``
- Indicates whether the field should be included in the
synthesized ``__init__`` method. If unspecified, ``init`` defaults to
``True``.
* - ``default``
- Provides the default value for the field.
* - ``default_factory``
- Provides a runtime callback that returns the
default value for the field. If neither ``default`` nor
``default_factory`` are specified, the field is assumed to have no
default value and must be provided a value when the class is
instantiated.
* - ``factory``
- An alias for the ``default_factory`` parameter on field specifiers.
* - ``kw_only``
- Indicates whether the field should be marked as
keyword-only. If ``True``, the field will be keyword-only. If
``False``, it will not be keyword-only. If unspecified, the value of
the ``kw_only`` parameter on the object decorated with
``dataclass_transform`` will be used, or if that is unspecified, the
value of ``kw_only_default`` on ``dataclass_transform`` will be used.
* - ``alias``
- Provides an alternative name for the field. This alternative
name is used in the synthesized ``__init__`` method.
At runtime, this decorator records its arguments in the
__dataclass_transform__ attribute on the decorated object.
It has no other runtime effect.
See :pep:681 for more details.
.. versionadded:: 3.11
.. _overload:
.. decorator:: overload
Decorator for creating overloaded functions and methods.
The @overload decorator allows describing functions and methods
that support multiple different combinations of argument types. A series
of @overload-decorated definitions must be followed by exactly one
non-@overload-decorated definition (for the same function/method).
@overload-decorated definitions are for the benefit of the
type checker only, since they will be overwritten by the
non-@overload-decorated definition. The non-@overload-decorated
definition, meanwhile, will be used at
runtime but should be ignored by a type checker. At runtime, calling
an @overload-decorated function directly will raise
:exc:NotImplementedError.
An example of overload that gives a more precise type than can be expressed using a union or a type variable:
.. testcode::
@overload
def process(response: None) -> None:
...
@overload
def process(response: int) -> tuple[int, str]:
...
@overload
def process(response: bytes) -> str:
...
def process(response):
... # actual implementation goes here
See :pep:484 for more details and comparison with other typing semantics.
.. versionchanged:: 3.11
Overloaded functions can now be introspected at runtime using
:func:get_overloads.
.. function:: get_overloads(func)
Return a sequence of :deco:overload-decorated definitions for
func.
func is the function object for the implementation of the
overloaded function. For example, given the definition of process in
the documentation for :deco:overload,
get_overloads(process) will return a sequence of three function objects
for the three defined overloads. If called on a function with no overloads,
get_overloads() returns an empty sequence.
get_overloads() can be used for introspecting an overloaded function at
runtime.
.. versionadded:: 3.11
.. function:: clear_overloads()
Clear all registered overloads in the internal registry.
This can be used to reclaim the memory used by the registry.
.. versionadded:: 3.11
.. decorator:: final
Decorator to indicate final methods and final classes.
Decorating a method with @final indicates to a type checker that the
method cannot be overridden in a subclass. Decorating a class with @final
indicates that it cannot be subclassed.
For example::
class Base:
@final
def done(self) -> None:
...
class Sub(Base):
def done(self) -> None: # Error reported by type checker
...
@final
class Leaf:
...
class Other(Leaf): # Error reported by type checker
...
There is no runtime checking of these properties. See :pep:591 for
more details.
.. versionadded:: 3.8
.. versionchanged:: 3.11
The decorator will now attempt to set a __final__ attribute to True
on the decorated object. Thus, a check like
if getattr(obj, "__final__", False) can be used at runtime
to determine whether an object obj has been marked as final.
If the decorated object does not support setting attributes,
the decorator returns the object unchanged without raising an exception.
.. decorator:: no_type_check
Decorator to indicate that annotations are not type hints.
This works as a class or function :term:decorator. With a class, it
applies recursively to all methods and classes defined in that class
(but not to methods defined in its superclasses or subclasses). Type
checkers will ignore all annotations in a function or class with this
decorator.
@no_type_check mutates the decorated object in place.
.. decorator:: override
Decorator to indicate that a method in a subclass is intended to override a method or attribute in a superclass.
Type checkers should emit an error if a method decorated with @override
does not, in fact, override anything.
This helps prevent bugs that may occur when a base class is changed without
an equivalent change to a child class.
For example:
.. testcode::
class Base:
def log_status(self) -> None:
...
class Sub(Base):
@override
def log_status(self) -> None: # Okay: overrides Base.log_status
...
@override
def done(self) -> None: # Error reported by type checker
...
There is no runtime checking of this property.
The decorator will attempt to set an __override__ attribute to True on
the decorated object. Thus, a check like
if getattr(obj, "__override__", False) can be used at runtime to determine
whether an object obj has been marked as an override. If the decorated object
does not support setting attributes, the decorator returns the object unchanged
without raising an exception.
See :pep:698 for more details.
.. versionadded:: 3.12
.. decorator:: type_check_only
Decorator to mark a class or function as unavailable at runtime.
This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class::
@type_check_only
class Response: # private or not available at runtime
code: int
def get_header(self, name: str) -> str: ...
def fetch_response() -> Response: ...
Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public.
.. function:: get_type_hints(obj, globalns=None, localns=None, include_extras=False)
Return a dictionary containing type hints for a function, method, module, class object, or other callable object.
This is often the same as obj.__annotations__, but this function makes
the following changes to the annotations dictionary:
ForwardRef
objects are handled by evaluating them in globalns, localns, and
(where applicable) obj's :ref:type parameter <type-params> namespace.
If globalns or localns is not given, appropriate namespace
dictionaries are inferred from obj.None is replaced with :class:types.NoneType.no_type_check has been applied to obj, an
empty dictionary is returned.C, the function returns a dictionary that merges
annotations from C's base classes with those on C directly. This
is done by traversing :attr:C.__mro__ <type.__mro__> and iteratively
combining
__annotations__ dictionaries. Annotations on classes appearing
earlier in the :term:method resolution order always take precedence over
annotations on classes appearing later in the method resolution order.Annotated[T, ...], Required[T], NotRequired[T], and ReadOnly[T]
with T, unless include_extras is set to True (see
:class:Annotated for more information).See also :func:annotationlib.get_annotations, a lower-level function that
returns annotations more directly.
.. caution::
This function may execute arbitrary code contained in annotations.
See :ref:`annotationlib-security` for more information.
.. note::
If any forward references in the annotations of *obj* are not resolvable
or are not valid Python code, this function will raise an exception
such as :exc:`NameError`. For example, this can happen with imported
:ref:`type aliases <type-aliases>` that include forward references,
or with names imported under :data:`if TYPE_CHECKING <TYPE_CHECKING>`.
.. note::
Calling :func:`get_type_hints` on an instance is not supported.
To retrieve annotations for an instance, call
:func:`get_type_hints` on the instance's class instead
(for example, ``get_type_hints(type(obj))``).
.. versionchanged:: 3.9
Added include_extras parameter as part of :pep:593.
See the documentation on :data:Annotated for more information.
.. versionchanged:: 3.11
Previously, Optional[t] was added for function and method annotations
if a default value equal to None was set.
Now the annotation is returned unchanged.
.. versionchanged:: 3.14
Calling :func:get_type_hints on instances is no longer supported.
Some instances were accepted in earlier versions as an undocumented
implementation detail.
.. function:: get_origin(tp)
Get the unsubscripted version of a type: for a typing object of the form
X[Y, Z, ...] return X.
If X is a typing-module alias for a builtin or
:mod:collections class, it will be normalized to the original class.
If X is an instance of :class:ParamSpecArgs or :class:ParamSpecKwargs,
return the underlying :class:ParamSpec.
Return None for unsupported objects.
Examples:
.. testcode::
assert get_origin(str) is None
assert get_origin(Dict[str, int]) is dict
assert get_origin(Union[int, str]) is Union
assert get_origin(Annotated[str, "metadata"]) is Annotated
P = ParamSpec('P')
assert get_origin(P.args) is P
assert get_origin(P.kwargs) is P
.. versionadded:: 3.8
.. function:: get_args(tp)
Get type arguments with all substitutions performed: for a typing object
of the form X[Y, Z, ...] return (Y, Z, ...).
If X is a union or :class:Literal contained in another
generic type, the order of (Y, Z, ...) may be different from the order
of the original arguments [Y, Z, ...] due to type caching.
Return () for unsupported objects.
Examples:
.. testcode::
assert get_args(int) == ()
assert get_args(Dict[int, str]) == (int, str)
assert get_args(Union[int, str]) == (int, str)
.. versionadded:: 3.8
.. function:: get_protocol_members(tp)
Return the set of members defined in a :class:Protocol.
.. doctest::
>>> from typing import Protocol, get_protocol_members
>>> class P(Protocol):
... def a(self) -> str: ...
... b: int
>>> get_protocol_members(P) == frozenset({'a', 'b'})
True
Raise :exc:TypeError for arguments that are not Protocols.
.. versionadded:: 3.13
.. function:: is_protocol(tp)
Determine if a type is a :class:Protocol.
For example::
class P(Protocol):
def a(self) -> str: ...
b: int
is_protocol(P) # => True
is_protocol(int) # => False
.. versionadded:: 3.13
.. function:: is_typeddict(tp)
Check if a type is a :class:TypedDict.
For example:
.. testcode::
class Film(TypedDict):
title: str
year: int
assert is_typeddict(Film)
assert not is_typeddict(list | str)
# TypedDict is a factory for creating typed dicts,
# not a typed dict itself
assert not is_typeddict(TypedDict)
.. versionadded:: 3.10
.. class:: ForwardRef
Class used for internal typing representation of string forward references.
For example, List["SomeClass"] is implicitly transformed into
List[ForwardRef("SomeClass")]. :class:!ForwardRef should not be instantiated by
a user, but may be used by introspection tools.
.. note::
:pep:585 generic types such as list["SomeClass"] will not be
implicitly transformed into list[ForwardRef("SomeClass")] and thus
will not automatically resolve to list[SomeClass].
.. versionadded:: 3.7.4
.. versionchanged:: 3.14
This is now an alias for :class:annotationlib.ForwardRef. Several undocumented
behaviors of this class have been changed; for example, after a ForwardRef has
been evaluated, the evaluated value is no longer cached.
.. function:: evaluate_forward_ref(forward_ref, *, owner=None, globals=None, locals=None, type_params=None, format=annotationlib.Format.VALUE)
Evaluate an :class:annotationlib.ForwardRef as a :term:type hint.
This is similar to calling :meth:annotationlib.ForwardRef.evaluate,
but unlike that method, :func:!evaluate_forward_ref also
recursively evaluates forward references nested within the type hint.
See the documentation for :meth:annotationlib.ForwardRef.evaluate for
the meaning of the owner, globals, locals, type_params, and format parameters.
.. caution::
This function may execute arbitrary code contained in annotations.
See :ref:`annotationlib-security` for more information.
.. versionadded:: 3.14
.. data:: NoDefault
A sentinel object used to indicate that a type parameter has no default value. For example:
.. doctest::
>>> T = TypeVar("T")
>>> T.__default__ is typing.NoDefault
True
>>> S = TypeVar("S", default=None)
>>> S.__default__ is None
True
.. versionadded:: 3.13
.. data:: TYPE_CHECKING
A special constant that is assumed to be True by 3rd party static
type checkers. It's False at runtime.
A module which is expensive to import, and which only contain types
used for typing annotations, can be safely imported inside an
if TYPE_CHECKING: block. This prevents the module from actually
being imported at runtime; annotations aren't eagerly evaluated
(see :pep:649) so using undefined symbols in annotations is
harmless--as long as you don't later examine them.
Your static type analysis tool will set TYPE_CHECKING to
True during static type analysis, which means the module will
be imported and the types will be checked properly during such analysis.
Usage::
if TYPE_CHECKING:
import expensive_mod
def fun(arg: expensive_mod.SomeType) -> None:
local_var: expensive_mod.AnotherType = other_fun()
If you occasionally need to examine type annotations at runtime
which may contain undefined symbols, use
:meth:annotationlib.get_annotations with a format parameter
of :attr:annotationlib.Format.STRING or
:attr:annotationlib.Format.FORWARDREF to safely retrieve the
annotations without raising :exc:NameError.
.. versionadded:: 3.5.2
.. _generic-concrete-collections: .. _deprecated-aliases:
This module defines several deprecated aliases to pre-existing
standard library classes. These were originally included in the :mod:!typing
module in order to support parameterizing these generic classes using [].
However, the aliases became redundant in Python 3.9 when the
corresponding pre-existing classes were enhanced to support [] (see
:pep:585).
The redundant types are deprecated as of Python 3.9. However, while the aliases may be removed at some point, removal of these aliases is not currently planned. As such, no deprecation warnings are currently issued by the interpreter for these aliases.
If at some point it is decided to remove these deprecated aliases, a
deprecation warning will be issued by the interpreter for at least two releases
prior to removal. The aliases are guaranteed to remain in the :mod:!typing module
without deprecation warnings until at least Python 3.14.
Type checkers are encouraged to flag uses of the deprecated types if the program they are checking targets a minimum Python version of 3.9 or newer.
.. _corresponding-to-built-in-types:
Aliases to built-in types """""""""""""""""""""""""
.. class:: Dict(dict, MutableMapping[KT, VT])
Deprecated alias to :class:dict.
Note that to annotate arguments, it is preferred
to use an abstract collection type such as :class:~collections.abc.Mapping
rather than to use :class:dict or :class:!typing.Dict.
.. deprecated:: 3.9
:class:builtins.dict <dict> now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: List(list, MutableSequence[T])
Deprecated alias to :class:list.
Note that to annotate arguments, it is preferred
to use an abstract collection type such as
:class:~collections.abc.Sequence or :class:~collections.abc.Iterable
rather than to use :class:list or :class:!typing.List.
.. deprecated:: 3.9
:class:builtins.list <list> now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: Set(set, MutableSet[T])
Deprecated alias to :class:builtins.set <set>.
Note that to annotate arguments, it is preferred
to use an abstract collection type such as :class:collections.abc.Set
rather than to use :class:set or :class:typing.Set.
.. deprecated:: 3.9
:class:builtins.set <set> now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: FrozenSet(frozenset, AbstractSet[T_co])
Deprecated alias to :class:builtins.frozenset <frozenset>.
.. deprecated:: 3.9
:class:builtins.frozenset <frozenset>
now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. data:: Tuple
Deprecated alias for :class:tuple.
:class:tuple and Tuple are special-cased in the type system; see
:ref:annotating-tuples for more details.
.. deprecated:: 3.9
:class:builtins.tuple <tuple> now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: Type(Generic[CT_co])
Deprecated alias to :class:type.
See :ref:type-of-class-objects for details on using :class:type or
typing.Type in type annotations.
.. versionadded:: 3.5.2
.. deprecated:: 3.9
:class:builtins.type <type> now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. _corresponding-to-types-in-collections:
Aliases to types in :mod:collections
""""""""""""""""""""""""""""""""""""""
.. class:: DefaultDict(collections.defaultdict, MutableMapping[KT, VT])
Deprecated alias to :class:collections.defaultdict.
.. versionadded:: 3.5.2
.. deprecated:: 3.9
:class:collections.defaultdict now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])
Deprecated alias to :class:collections.OrderedDict.
.. versionadded:: 3.7.2
.. deprecated:: 3.9
:class:collections.OrderedDict now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: ChainMap(collections.ChainMap, MutableMapping[KT, VT])
Deprecated alias to :class:collections.ChainMap.
.. versionadded:: 3.6.1
.. deprecated:: 3.9
:class:collections.ChainMap now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: Counter(collections.Counter, Dict[T, int])
Deprecated alias to :class:collections.Counter.
.. versionadded:: 3.6.1
.. deprecated:: 3.9
:class:collections.Counter now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: Deque(deque, MutableSequence[T])
Deprecated alias to :class:collections.deque.
.. versionadded:: 3.6.1
.. deprecated:: 3.9
:class:collections.deque now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. _other-concrete-types:
Aliases to other concrete types """""""""""""""""""""""""""""""
.. class:: Pattern Match
Deprecated aliases corresponding to the return types from
:func:re.compile and :func:re.match.
These types (and the corresponding functions) are generic over
:data:AnyStr. Pattern can be specialised as Pattern[str] or
Pattern[bytes]; Match can be specialised as Match[str] or
Match[bytes].
.. deprecated:: 3.9
Classes Pattern and Match from :mod:re now support [].
See :pep:585 and :ref:types-genericalias.
.. class:: Text
Deprecated alias for :class:str.
Text is provided to supply a forward
compatible path for Python 2 code: in Python 2, Text is an alias for
unicode.
Use Text to indicate that a value must contain a unicode string in
a manner that is compatible with both Python 2 and Python 3::
def add_unicode_checkmark(text: Text) -> Text:
return text + u' \u2713'
.. versionadded:: 3.5.2
.. deprecated:: 3.11
Python 2 is no longer supported, and most type checkers also no longer
support type checking Python 2 code. Removal of the alias is not
currently planned, but users are encouraged to use
:class:str instead of Text.
.. _abstract-base-classes: .. _corresponding-to-collections-in-collections-abc:
Aliases to container ABCs in :mod:collections.abc
"""""""""""""""""""""""""""""""""""""""""""""""""""
.. class:: AbstractSet(Collection[T_co])
Deprecated alias to :class:collections.abc.Set.
.. deprecated:: 3.9
:class:collections.abc.Set now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: ByteString(Sequence[int])
Deprecated alias to :class:collections.abc.ByteString.
Use isinstance(obj, collections.abc.Buffer) to test if obj
implements the :ref:buffer protocol <bufferobjects> at runtime. For use in
type annotations, either use :class:~collections.abc.Buffer or a union
that explicitly specifies the types your code supports (e.g.,
bytes | bytearray | memoryview).
:class:!ByteString was originally intended to be an abstract class that
would serve as a supertype of both :class:bytes and :class:bytearray.
However, since the ABC never had any methods, knowing that an object was an
instance of :class:!ByteString never actually told you anything useful
about the object. Other common buffer types such as :class:memoryview were
also never understood as subtypes of :class:!ByteString (either at runtime
or by static type checkers).
See :pep:PEP 688 <688#current-options> for more details.
.. deprecated-removed:: 3.9 3.17
.. class:: Collection(Sized, Iterable[T_co], Container[T_co])
Deprecated alias to :class:collections.abc.Collection.
.. versionadded:: 3.6
.. deprecated:: 3.9
:class:collections.abc.Collection now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: Container(Generic[T_co])
Deprecated alias to :class:collections.abc.Container.
.. deprecated:: 3.9
:class:collections.abc.Container now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: ItemsView(MappingView, AbstractSet[tuple[KT_co, VT_co]])
Deprecated alias to :class:collections.abc.ItemsView.
.. deprecated:: 3.9
:class:collections.abc.ItemsView now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: KeysView(MappingView, AbstractSet[KT_co])
Deprecated alias to :class:collections.abc.KeysView.
.. deprecated:: 3.9
:class:collections.abc.KeysView now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: Mapping(Collection[KT], Generic[KT, VT_co])
Deprecated alias to :class:collections.abc.Mapping.
.. deprecated:: 3.9
:class:collections.abc.Mapping now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: MappingView(Sized)
Deprecated alias to :class:collections.abc.MappingView.
.. deprecated:: 3.9
:class:collections.abc.MappingView now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: MutableMapping(Mapping[KT, VT])
Deprecated alias to :class:collections.abc.MutableMapping.
.. deprecated:: 3.9
:class:collections.abc.MutableMapping
now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: MutableSequence(Sequence[T])
Deprecated alias to :class:collections.abc.MutableSequence.
.. deprecated:: 3.9
:class:collections.abc.MutableSequence
now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: MutableSet(AbstractSet[T])
Deprecated alias to :class:collections.abc.MutableSet.
.. deprecated:: 3.9
:class:collections.abc.MutableSet now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: Sequence(Reversible[T_co], Collection[T_co])
Deprecated alias to :class:collections.abc.Sequence.
.. deprecated:: 3.9
:class:collections.abc.Sequence now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: ValuesView(MappingView, Collection[_VT_co])
Deprecated alias to :class:collections.abc.ValuesView.
.. deprecated:: 3.9
:class:collections.abc.ValuesView now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. _asynchronous-programming:
Aliases to asynchronous ABCs in :mod:collections.abc
""""""""""""""""""""""""""""""""""""""""""""""""""""""
.. class:: Coroutine(Awaitable[ReturnType], Generic[YieldType, SendType, ReturnType])
Deprecated alias to :class:collections.abc.Coroutine.
See :ref:annotating-generators-and-coroutines
for details on using :class:collections.abc.Coroutine
and typing.Coroutine in type annotations.
.. versionadded:: 3.5.3
.. deprecated:: 3.9
:class:collections.abc.Coroutine now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: AsyncGenerator(AsyncIterator[YieldType], Generic[YieldType, SendType])
Deprecated alias to :class:collections.abc.AsyncGenerator.
See :ref:annotating-generators-and-coroutines
for details on using :class:collections.abc.AsyncGenerator
and typing.AsyncGenerator in type annotations.
.. versionadded:: 3.6.1
.. deprecated:: 3.9
:class:collections.abc.AsyncGenerator
now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. versionchanged:: 3.13
The SendType parameter now has a default.
.. class:: AsyncIterable(Generic[T_co])
Deprecated alias to :class:collections.abc.AsyncIterable.
.. versionadded:: 3.5.2
.. deprecated:: 3.9
:class:collections.abc.AsyncIterable now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: AsyncIterator(AsyncIterable[T_co])
Deprecated alias to :class:collections.abc.AsyncIterator.
.. versionadded:: 3.5.2
.. deprecated:: 3.9
:class:collections.abc.AsyncIterator now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: Awaitable(Generic[T_co])
Deprecated alias to :class:collections.abc.Awaitable.
.. versionadded:: 3.5.2
.. deprecated:: 3.9
:class:collections.abc.Awaitable now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. _corresponding-to-other-types-in-collections-abc:
Aliases to other ABCs in :mod:collections.abc
"""""""""""""""""""""""""""""""""""""""""""""""
.. class:: Iterable(Generic[T_co])
Deprecated alias to :class:collections.abc.Iterable.
.. deprecated:: 3.9
:class:collections.abc.Iterable now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: Iterator(Iterable[T_co])
Deprecated alias to :class:collections.abc.Iterator.
.. deprecated:: 3.9
:class:collections.abc.Iterator now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. data:: Callable
Deprecated alias to :class:collections.abc.Callable.
See :ref:annotating-callables for details on how to use
:class:collections.abc.Callable and typing.Callable in type annotations.
.. deprecated:: 3.9
:class:collections.abc.Callable now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. versionchanged:: 3.10
Callable now supports :class:ParamSpec and :data:Concatenate.
See :pep:612 for more details.
.. class:: Generator(Iterator[YieldType], Generic[YieldType, SendType, ReturnType])
Deprecated alias to :class:collections.abc.Generator.
See :ref:annotating-generators-and-coroutines
for details on using :class:collections.abc.Generator
and typing.Generator in type annotations.
.. deprecated:: 3.9
:class:collections.abc.Generator now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. versionchanged:: 3.13 Default values for the send and return types were added.
.. class:: Hashable
Deprecated alias to :class:collections.abc.Hashable.
.. deprecated:: 3.12
Use :class:collections.abc.Hashable directly instead.
.. class:: Reversible(Iterable[T_co])
Deprecated alias to :class:collections.abc.Reversible.
.. deprecated:: 3.9
:class:collections.abc.Reversible now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. class:: Sized
Deprecated alias to :class:collections.abc.Sized.
.. deprecated:: 3.12
Use :class:collections.abc.Sized directly instead.
.. _context-manager-types:
Aliases to :mod:contextlib ABCs
"""""""""""""""""""""""""""""""""
.. class:: ContextManager(Generic[T_co, ExitT_co])
Deprecated alias to :class:contextlib.AbstractContextManager.
The first type parameter, T_co, represents the type returned by
the :meth:~object.__enter__ method. The optional second type parameter, ExitT_co,
which defaults to bool | None, represents the type returned by the
:meth:~object.__exit__ method.
.. versionadded:: 3.5.4
.. deprecated:: 3.9
:class:contextlib.AbstractContextManager
now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. versionchanged:: 3.13
Added the optional second type parameter, ExitT_co.
.. class:: AsyncContextManager(Generic[T_co, AExitT_co])
Deprecated alias to :class:contextlib.AbstractAsyncContextManager.
The first type parameter, T_co, represents the type returned by
the :meth:~object.__aenter__ method. The optional second type parameter, AExitT_co,
which defaults to bool | None, represents the type returned by the
:meth:~object.__aexit__ method.
.. versionadded:: 3.6.2
.. deprecated:: 3.9
:class:contextlib.AbstractAsyncContextManager
now supports subscripting ([]).
See :pep:585 and :ref:types-genericalias.
.. versionchanged:: 3.13
Added the optional second type parameter, AExitT_co.
Certain features in typing are deprecated and may be removed in a future
version of Python. The following table summarizes major deprecations for your
convenience. This is subject to change, and not all deprecations are listed.
.. list-table:: :header-rows: 1
typing versions of standard collectionsdeprecated-aliases for more information)585typing.ByteString91896typing.Text92332typing.Hashable and :class:typing.Sized94309typing.TypeAlias695typing.AnyStr105578