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========================================
:mod:`typing` --- Support for type hints
========================================
.. 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 type checkers, IDEs, linters,
etc.
--------------
This module provides runtime support for type hints. The most fundamental
support consists of the types :data:`Any`, :data:`Union`, :data:`Callable`,
:class:`TypeVar`, and :class:`Generic`. For a full specification, please see
:pep:`484`. For a simplified introduction to type hints, see :pep:`483`.
The function below takes and returns a string and is annotated as follows::
def greeting(name: str) -> str:
return 'Hello ' + name
In the function ``greeting``, the argument ``name`` is expected to be of type
:class:`str` and the return type :class:`str`. Subtypes are accepted as
arguments.
New features are frequently added to the ``typing`` module.
The `typing_extensions <https://pypi.org/project/typing-extensions/>`_ package
provides backports of these new features to older versions of Python.
For a summary of deprecated features and a deprecation timeline, please see
`Deprecation Timeline of Major Features`_.
.. _relevant-peps:
Relevant PEPs
=============
Since the initial introduction of type hints in :pep:`484` and :pep:`483`, a
number of PEPs have modified and enhanced Python's framework for type
annotations. These include:
* :pep:`526`: Syntax for Variable Annotations
*Introducing* syntax for annotating variables outside of function
definitions, and :data:`ClassVar`
* :pep:`544`: Protocols: Structural subtyping (static duck typing)
*Introducing* :class:`Protocol` and the
:func:`@runtime_checkable<runtime_checkable>` decorator
* :pep:`585`: Type Hinting Generics In Standard Collections
*Introducing* :class:`types.GenericAlias` and the ability to use standard
library classes as :ref:`generic types<types-genericalias>`
* :pep:`586`: Literal Types
*Introducing* :data:`Literal`
* :pep:`589`: TypedDict: Type Hints for Dictionaries with a Fixed Set of Keys
*Introducing* :class:`TypedDict`
* :pep:`591`: Adding a final qualifier to typing
*Introducing* :data:`Final` and the :func:`@final<final>` decorator
* :pep:`593`: Flexible function and variable annotations
*Introducing* :data:`Annotated`
* :pep:`604`: Allow writing union types as ``X | Y``
*Introducing* :data:`types.UnionType` and the ability to use
the binary-or operator ``|`` to signify a
:ref:`union of types<types-union>`
* :pep:`612`: Parameter Specification Variables
*Introducing* :class:`ParamSpec` and :data:`Concatenate`
* :pep:`613`: Explicit Type Aliases
*Introducing* :data:`TypeAlias`
* :pep:`646`: Variadic Generics
*Introducing* :data:`TypeVarTuple`
* :pep:`647`: User-Defined Type Guards
*Introducing* :data:`TypeGuard`
* :pep:`655`: Marking individual TypedDict items as required or potentially-missing
*Introducing* :data:`Required` and :data:`NotRequired`
* :pep:`673`: Self type
*Introducing* :data:`Self`
* :pep:`675`: Arbitrary Literal String Type
*Introducing* :data:`LiteralString`
.. _type-aliases:
Type aliases
============
A type alias is defined by assigning the type to the alias. In this example,
``Vector`` and ``list[float]`` will be treated as interchangeable synonyms::
Vector = list[float]
def scale(scalar: float, vector: Vector) -> Vector:
return [scalar * num for num in vector]
# typechecks; a list of floats qualifies as a 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
ConnectionOptions = dict[str, str]
Address = tuple[str, int]
Server = tuple[Address, ConnectionOptions]
def broadcast_message(message: str, servers: Sequence[Server]) -> None:
...
# The static type checker will treat the previous type signature as
# being exactly equivalent to this one.
def broadcast_message(
message: str,
servers: Sequence[tuple[tuple[str, int], dict[str, str]]]) -> None:
...
Note that ``None`` as a type hint is a special case and is replaced by
``type(None)``.
.. _distinct:
NewType
=======
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:
...
# typechecks
user_a = get_user_name(UserId(42351))
# does not typecheck; an int is not a UserId
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' is of type 'int', not 'UserId'
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)
# Fails at runtime and does not typecheck
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 ``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. There is some additional
runtime cost when calling ``NewType`` over a regular function. However, this
cost will be reduced in 3.11.0.
Callable
========
Frameworks expecting callback functions of specific signatures might be
type hinted using ``Callable[[Arg1Type, Arg2Type], ReturnType]``.
For example::
from collections.abc import Callable
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
It is possible to declare the return type of a callable without specifying
the call signature by substituting a literal ellipsis
for the list of arguments in the type hint: ``Callable[..., ReturnType]``.
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 information.
.. seealso::
The documentation for :class:`ParamSpec` and :class:`Concatenate` provides
examples of usage in ``Callable``.
.. _generics:
Generics
========
Since type information about objects kept in containers cannot be statically
inferred in a generic way, abstract base classes have been extended to support
subscription to denote expected types for container elements.
::
from collections.abc import Mapping, Sequence
def notify_by_email(employees: Sequence[Employee],
overrides: Mapping[str, str]) -> None: ...
Generics can be parameterized by using a factory available in typing
called :class:`TypeVar`.
::
from collections.abc import Sequence
from typing import TypeVar
T = TypeVar('T') # Declare type variable
def first(l: Sequence[T]) -> T: # Generic function
return l[0]
.. _user-defined-generics:
User-defined generic types
==========================
A user-defined class can be defined as a generic class.
::
from typing import TypeVar, Generic
from logging import Logger
T = TypeVar('T')
class LoggedVar(Generic[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)
``Generic[T]`` as a base class defines that the class ``LoggedVar`` takes a
single type parameter ``T`` . This also makes ``T`` valid as a type within the
class body.
The :class:`Generic` base class defines :meth:`~object.__class_getitem__` so
that ``LoggedVar[t]`` is valid as a type::
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
T = TypeVar('T', contravariant=True)
B = TypeVar('B', bound=Sequence[bytes], covariant=True)
S = TypeVar('S', int, str)
class WeirdTrio(Generic[T, B, S]):
...
Each type variable argument to :class:`Generic` must be distinct.
This is thus invalid::
from typing import TypeVar, Generic
...
T = TypeVar('T')
class Pair(Generic[T, T]): # INVALID
...
You can use multiple inheritance with :class:`Generic`::
from collections.abc import Sized
from typing import TypeVar, Generic
T = TypeVar('T')
class LinkedList(Sized, Generic[T]):
...
When inheriting from generic classes, some type variables could be fixed::
from collections.abc import Mapping
from typing import TypeVar
T = TypeVar('T')
class MyDict(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]``::
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
from typing import TypeVar
S = TypeVar('S')
Response = Iterable[S] | int
# Return type here is same as Iterable[str] | int
def response(query: str) -> Response[str]:
...
T = TypeVar('T', int, float, complex)
Vec = Iterable[tuple[T, T]]
def inproduct(v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]]
return sum(x*y for x, y in v)
.. versionchanged:: 3.7
:class:`Generic` no longer has a custom metaclass.
User-defined generics for parameter expressions are also supported via parameter
specification variables in the form ``Generic[P]``. The behavior is consistent
with type variables' described above as parameter specification variables are
treated by the 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`::
>>> from typing import Generic, ParamSpec, TypeVar
>>> T = TypeVar('T')
>>> P = ParamSpec('P')
>>> class Z(Generic[T, P]): ...
...
>>> Z[int, [dict, float]]
__main__.Z[int, (<class 'dict'>, <class 'float'>)]
Furthermore, 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(Generic[P]): ...
...
>>> X[int, str]
__main__.X[(<class 'int'>, <class 'str'>)]
>>> X[[int, str]]
__main__.X[(<class 'int'>, <class 'str'>)]
Do 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 typing module are hashable and
comparable for equality.
The :data:`Any` type
====================
A 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:
# Typechecks; 'item' could be any type,
# and that type might have a 'bar' method
item.bar()
...
Notice that no typechecking 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
# A static type checker will treat the above
# as having the same signature as:
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; an object does not have a 'magic' method.
item.magic()
...
def hash_b(item: Any) -> int:
# Typechecks
item.magic()
...
# Typechecks, since ints and strs are subclasses of object
hash_a(42)
hash_a("foo")
# Typechecks, since Any is compatible with all types
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.
Nominal vs structural subtyping
===============================
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` allows to solve 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).
Module contents
===============
The module defines the following classes, functions and decorators.
.. note::
This module defines several types that are subclasses of pre-existing
standard library classes which also extend :class:`Generic`
to support type variables inside ``[]``.
These types became redundant in Python 3.9 when the
corresponding pre-existing classes were enhanced to support ``[]``.
The redundant types are deprecated as of Python 3.9 but no
deprecation warnings will be issued by the interpreter.
It is expected that type checkers will flag the deprecated types
when the checked program targets Python 3.9 or newer.
The deprecated types will be removed from the :mod:`typing` module
in the first Python version released 5 years after the release of Python 3.9.0.
See details in :pep:`585`—*Type Hinting Generics In Standard Collections*.
Special typing primitives
-------------------------
Special types
"""""""""""""
These can be used as types in annotations and do not support ``[]``.
.. data:: Any
Special type indicating an unconstrained type.
* Every type is compatible with :data:`Any`.
* :data:`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:: LiteralString
Special type that includes only literal strings. A string
literal is compatible with ``LiteralString``, as is another
``LiteralString``, but an object typed as just ``str`` is not.
A string created by composing ``LiteralString``-typed objects
is also acceptable as a ``LiteralString``.
Example::
def run_query(sql: LiteralString) -> ...
...
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}"
)
This 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.
.. versionadded:: 3.11
.. data:: Never
The `bottom type <https://en.wikipedia.org/wiki/Bottom_type>`_,
a type that has no members.
This can be used to define a function that should never be
called, or a function that never returns::
from typing import Never
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
.. versionadded:: 3.11
On older Python versions, :data:`NoReturn` may be used to express the
same concept. ``Never`` was added to make the intended meaning more explicit.
.. data:: NoReturn
Special type indicating that a function never returns.
For example::
from typing import NoReturn
def stop() -> NoReturn:
raise RuntimeError('no way')
``NoReturn`` can also be used as a
`bottom type <https://en.wikipedia.org/wiki/Bottom_type>`_, a type that
has no values. Starting in Python 3.11, the :data:`Never` type should
be used for this concept instead. Type checkers should treat the two
equivalently.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.2
.. data:: Self
Special type to represent the current enclosed class.
For example::
from typing import Self
class Foo:
def returns_self(self) -> Self:
...
return self
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 returns_self(self: Self) -> Self:
...
return self
In general if something currently follows the pattern of::
class Foo:
def return_self(self) -> "Foo":
...
return self
You should use use :data:`Self` as calls to ``SubclassOfFoo.returns_self`` would have
``Foo`` as the return type and not ``SubclassOfFoo``.
Other common use cases include:
- :class:`classmethod`\s that are used as alternative constructors and return instances
of the ``cls`` parameter.
- Annotating an :meth:`~object.__enter__` method which returns self.
For more information, see :pep:`673`.
.. 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]
See :pep:`613` for more details about explicit type aliases.
.. versionadded:: 3.10
Special forms
"""""""""""""
These can be used as types in annotations using ``[]``, each having a unique syntax.
.. data:: Tuple
Tuple type; ``Tuple[X, Y]`` is the type of a tuple of two items
with the first item of type X and the second of type Y. The type of
the empty tuple can be written as ``Tuple[()]``.
Example: ``Tuple[T1, T2]`` is a tuple of two elements corresponding
to type variables T1 and T2. ``Tuple[int, float, str]`` is a tuple
of an int, a float and a string.
To specify a variable-length tuple of homogeneous type,
use literal ellipsis, e.g. ``Tuple[int, ...]``. A plain :data:`Tuple`
is equivalent to ``Tuple[Any, ...]``, and in turn to :class:`tuple`.
.. deprecated:: 3.9
:class:`builtins.tuple <tuple>` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
.. data:: 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]
* 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>`.
.. data:: Optional
Optional type.
``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:: Callable
Callable type; ``Callable[[int], str]`` is a function of (int) -> str.
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 or an ellipsis; the return type must be
a single type.
There is no syntax to indicate optional or keyword arguments;
such function types are rarely used as callback types.
``Callable[..., ReturnType]`` (literal ellipsis) can be used to
type hint a callable taking any number of arguments and returning
``ReturnType``. A plain :data:`Callable` is equivalent to
``Callable[..., Any]``, and in turn to
:class:`collections.abc.Callable`.
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.
.. deprecated:: 3.9
:class:`collections.abc.Callable` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
.. versionchanged:: 3.10
``Callable`` now supports :class:`ParamSpec` and :data:`Concatenate`.
See :pep:`612` for more information.
.. seealso::
The documentation for :class:`ParamSpec` and :class:`Concatenate` provide
examples of usage with ``Callable``.
.. data:: Concatenate
Used with :data:`Callable` and :class:`ParamSpec` to type 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 :data:`Callable`.
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, ParamSpec, TypeVar
P = ParamSpec('P')
R = TypeVar('R')
# Use this lock to ensure that only one thread is executing a function
# at any time.
my_lock = Lock()
def with_lock(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` and :class:`Callable`.
.. class:: Type(Generic[CT_co])
A variable annotated with ``C`` may accept a value of type ``C``. In
contrast, a variable annotated with ``Type[C]`` 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 BasicUser(User): ...
class ProUser(User): ...
class TeamUser(User): ...
# Accepts User, BasicUser, ProUser, TeamUser, ...
def make_new_user(user_class: Type[User]) -> User:
# ...
return user_class()
The fact that ``Type[C]`` is covariant implies that all subclasses of
``C`` should implement the same constructor signature and class method
signatures as ``C``. The type checker should flag violations of this,
but should also allow constructor calls in subclasses that match the
constructor calls in the indicated base class. How the type checker is
required to handle this particular case may change in future revisions of
:pep:`484`.
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]): ...
``Type[Any]`` is equivalent to ``Type`` which in turn is equivalent
to ``type``, which is the root of Python's metaclass hierarchy.
.. versionadded:: 3.5.2
.. deprecated:: 3.9
:class:`builtins.type <type>` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
.. data:: Literal
A type that can be used to indicate to type checkers that the
corresponding variable or function parameter has a value equivalent to
the provided literal (or one of several literals). For example::
def validate_simple(data: Any) -> Literal[True]: # always returns True
...
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.
.. 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
.. data:: Final
A special typing construct to indicate to type checkers that a name
cannot be re-assigned or overridden in a subclass. 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
.. data:: Required
.. data:: NotRequired
Special typing constructs that mark individual keys of a :class:`TypedDict`
as either required or non-required respectively.
For more information, see :class:`TypedDict` and
:pep:`655` ("Marking individual TypedDict items as required or potentially-missing").
.. versionadded:: 3.11
.. data:: Annotated
A type, introduced in :pep:`593` (``Flexible function and variable
annotations``), to decorate existing types with context-specific metadata
(possibly multiple pieces of it, as ``Annotated`` is variadic).
Specifically, a type ``T`` can be annotated with metadata ``x`` via the
typehint ``Annotated[T, x]``. This metadata can be used for either static
analysis or at runtime. If a library (or tool) encounters a typehint
``Annotated[T, x]`` and has no special logic for metadata ``x``, it
should ignore it and simply treat the type as ``T``. Unlike the
``no_type_check`` functionality that currently exists in the ``typing``
module which completely disables typechecking annotations on a function
or a class, the ``Annotated`` type allows for both static typechecking
of ``T`` (which can safely ignore ``x``)
together with runtime access to ``x`` within a specific application.
Ultimately, the responsibility of how to interpret the annotations (if
at all) is the responsibility of the tool or library encountering the
``Annotated`` type. A tool or library encountering an ``Annotated`` type
can scan through the annotations to determine if they are of interest
(e.g., using ``isinstance()``).
When a tool or a library does not support annotations or encounters an
unknown annotation it should just ignore it and treat annotated type as
the underlying type.
It's up to the tool consuming the annotations to decide whether the
client is allowed to have several annotations on one type and how to
merge those annotations.
Since the ``Annotated`` type allows you to put several annotations of
the same (or different) type(s) on any node, the tools or libraries
consuming those annotations are in charge of dealing with potential
duplicates. For example, if you are doing value range analysis you might
allow this::
T1 = Annotated[int, ValueRange(-10, 5)]
T2 = Annotated[T1, ValueRange(-20, 3)]
Passing ``include_extras=True`` to :func:`get_type_hints` lets one
access the extra annotations at runtime.
The details of the syntax:
* The first argument to ``Annotated`` must be a valid type
* Multiple type annotations are supported (``Annotated`` supports variadic
arguments)::
Annotated[int, ValueRange(3, 10), ctype("char")]
* ``Annotated`` must be called with at least two arguments (
``Annotated[int]`` is not valid)
* The order of the annotations is preserved and matters for equality
checks::
Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
int, ctype("char"), ValueRange(3, 10)
]
* Nested ``Annotated`` types are flattened, with metadata ordered
starting with the innermost annotation::
Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
int, ValueRange(3, 10), ctype("char")
]
* Duplicated annotations are not removed::
Annotated[int, ValueRange(3, 10)] != Annotated[
int, ValueRange(3, 10), ValueRange(3, 10)
]
* ``Annotated`` can be used with nested and generic aliases::
T = TypeVar('T')
Vec = Annotated[list[tuple[T, T]], MaxLen(10)]
V = Vec[int]
V == Annotated[list[tuple[int, int]], MaxLen(10)]
.. versionadded:: 3.9
.. data:: TypeGuard
Special typing form used to annotate the return type of a user-defined
type guard function. ``TypeGuard`` only accepts a single type argument.
At runtime, functions marked this way should return a boolean.
``TypeGuard`` 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 guard"::
def is_str(val: str | float):
# "isinstance" type guard
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 guard. Such a function should use ``TypeGuard[...]`` as its
return type to alert static type checkers to this intention.
Using ``-> TypeGuard`` tells the static type checker that for a given
function:
1. The return value is a boolean.
2. If the return value is ``True``, the type of its argument
is the type inside ``TypeGuard``.
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!")
If ``is_str_list`` is a class or instance method, then the type in
``TypeGuard`` maps to the type of the second parameter after ``cls`` or
``self``.
In short, the form ``def foo(arg: TypeA) -> TypeGuard[TypeB]: ...``,
means that if ``foo(arg)`` returns ``True``, then ``arg`` narrows from
``TypeA`` to ``TypeB``.
.. note::
``TypeB`` need not be a narrower form of ``TypeA`` -- it can even be a
wider form. 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.
The responsibility of writing type-safe type guards is left to the user.
``TypeGuard`` also works with type variables. For more information, see
:pep:`647` (User-Defined Type Guards).
.. versionadded:: 3.10
Building generic types
""""""""""""""""""""""
These are not used in annotations. They are building blocks for creating generic types.
.. class:: Generic
Abstract base class for generic types.
A generic type is typically declared by inheriting from an
instantiation of this class with one or more type variables.
For example, a generic mapping type might be defined as::
class Mapping(Generic[KT, VT]):
def __getitem__(self, key: KT) -> VT:
...
# Etc.
This class can then be used as follows::
X = TypeVar('X')
Y = TypeVar('Y')
def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y:
try:
return mapping[key]
except KeyError:
return default
.. class:: TypeVar
Type variable.
Usage::
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 definitions. See :class:`Generic` for more
information on generic types. Generic functions work as follows::
def repeat(x: T, n: int) -> Sequence[T]:
"""Return a list containing n references to x."""
return [x]*n
def print_capitalized(x: S) -> S:
"""Print x capitalized, and return x."""
print(x.capitalize())
return x
def concatenate(x: A, y: A) -> A:
"""Add two strings or bytes objects together."""
return x + y
Note that type variables can be *bound*, *constrained*, or neither, but
cannot be both bound *and* constrained.
Bound type variables and constrained type variables have different
semantics in several important ways. Using a *bound* 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
Type variables can be bound to concrete types, abstract types (ABCs or
protocols), and even unions of types::
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
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`. In general,
:func:`isinstance` and :func:`issubclass` should not be used with types.
Type variables may be marked covariant or contravariant by passing
``covariant=True`` or ``contravariant=True``. See :pep:`484` for more
details. By default, type variables are invariant.
.. class:: TypeVarTuple
Type variable tuple. A specialized form of :class:`type variable <TypeVar>`
that enables *variadic* generics.
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 = TypeVar('T')
Ts = TypeVarTuple('Ts')
def remove_first_element(tup: tuple[T, *Ts]) -> tuple[*Ts]:
return tup[1:]
# T is bound to int, Ts is bound to ()
# Return value is (), which has type tuple[()]
remove_first_element(tup=(1,))
# T is bound to int, Ts is bound to (str,)
# Return value is ('spam',), which has type tuple[str]
remove_first_element(tup=(1, 'spam'))
# T is bound to int, Ts is bound to (str, float)
# Return value is ('spam', 3.0), which has type tuple[str, float]
remove_first_element(tup=(1, 'spam', 3.0))
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 types from normal type variables::
x: Ts # Not valid
x: tuple[Ts] # Not valid
x: tuple[*Ts] # The correct way to 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::
Shape = TypeVarTuple('Shape')
class Array(Generic[*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::
DType = TypeVar('DType')
class Array(Generic[DType, *Shape]): # This is fine
pass
class Array2(Generic[*Shape, DType]): # This would also be fine
pass
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(Generic[*Shape, *Shape]): # Not valid
pass
Finally, an unpacked type variable tuple can be used as the type annotation
of ``*args``::
def call_soon(
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``.
For more details on type variable tuples, see :pep:`646`.
.. versionadded:: 3.11
.. data:: Unpack
A typing operator that conceptually marks an object as having been
unpacked. For example, using the unpack operator ``*`` on a
:class:`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 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
.. versionadded:: 3.11
.. class:: ParamSpec(name, *, bound=None, covariant=False, contravariant=False)
Parameter specification variable. A specialized version of
:class:`type variables <TypeVar>`.
Usage::
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
from typing import TypeVar, ParamSpec
import logging
T = TypeVar('T')
P = ParamSpec('P')
def add_logging(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 bound ``Callable[..., Any]``. However this
causes two problems:
1. The type checker can't type check the ``inner`` function because
``*args`` and ``**kwargs`` have to be typed :data:`Any`.
2. :func:`~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`.
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
.. 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``).
* :class:`Callable` and :class:`Concatenate`.
.. data:: ParamSpecArgs
.. data:: 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``::
P = ParamSpec("P")
get_origin(P.args) # returns P
get_origin(P.kwargs) # returns P
.. versionadded:: 3.10
.. data:: AnyStr
``AnyStr`` is a :class:`constrained type variable <TypeVar>` defined as
``AnyStr = TypeVar('AnyStr', str, bytes)``.
It is meant to be used for functions that may accept any kind of string
without allowing different kinds of strings to mix. For example::
def concat(a: AnyStr, b: AnyStr) -> AnyStr:
return a + b
concat(u"foo", u"bar") # Ok, output has type 'unicode'
concat(b"foo", b"bar") # Ok, output has type 'bytes'
concat(u"foo", b"bar") # Error, cannot mix unicode and bytes
.. 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 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 can be generic, for example::
class GenProto(Protocol[T]):
def meth(self) -> T:
...
.. versionadded:: 3.8
.. decorator:: runtime_checkable
Mark a protocol class as a runtime protocol.
Such a protocol can be used with :func:`isinstance` and :func:`issubclass`.
This raises :exc:`TypeError` when applied to a non-protocol class. 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)
.. note::
:func:`runtime_checkable` will check only the presence of the required
methods, not their type signatures. For example, :class:`ssl.SSLObject`
is a class, therefore it passes an :func:`issubclass`
check against :data:`Callable`. However, the
:meth:`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`.
.. versionadded:: 3.8
Other special directives
""""""""""""""""""""""""
These are not used in annotations. They are building blocks for 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(NamedTuple, Generic[T]):
key: T
group: list[T]
Backward-compatible usage::
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:: 3.11
Added support for generic namedtuples.
.. class:: NewType(name, tp)
A helper class to indicate a distinct type to a typechecker,
see :ref:`distinct`. At runtime it returns an object that returns
its argument when called.
Usage::
UserId = NewType('UserId', int)
first_user = UserId(1)
.. versionadded:: 3.5.2
.. versionchanged:: 3.10
``NewType`` is now a class rather than a function.
.. class:: TypedDict(dict)
Special construct to add type hints to a dictionary.
At runtime it is a plain :class:`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')
To allow using this feature with older versions of Python that do not
support :pep:`526`, ``TypedDict`` supports two additional equivalent
syntactic forms:
* Using a literal :class:`dict` as the second argument::
Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
* Using keyword arguments::
Point2D = TypedDict('Point2D', x=int, y=int, label=str)
.. deprecated-removed:: 3.11 3.13
The keyword-argument syntax is deprecated in 3.11 and will be removed
in 3.13. It may also be unsupported by static type checkers.
The functional syntax should also be used when any of the keys are not valid
:ref:`identifiers <identifiers>`, for example because they are keywords or contain hyphens.
Example::
# raises SyntaxError
class Point2D(TypedDict):
in: int # 'in' is a keyword
x-y: int # name with hyphens
# OK, functional syntax
Point2D = TypedDict('Point2D', {'in': int, 'x-y': int})
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
T = TypeVar('T')
class XT(X, Generic[T]): pass # raises TypeError
A ``TypedDict`` can be generic::
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::
>>> 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
.. attribute:: __required_keys__
.. 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``::
>>> 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
See :pep:`589` for more examples and detailed rules of using ``TypedDict``.
.. versionadded:: 3.8
.. 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.
Generic concrete collections
----------------------------
Corresponding to built-in types
"""""""""""""""""""""""""""""""
.. class:: Dict(dict, MutableMapping[KT, VT])
A generic version of :class:`dict`.
Useful for annotating return types. To annotate arguments it is preferred
to use an abstract collection type such as :class:`Mapping`.
This type can be used as follows::
def count_words(text: str) -> Dict[str, int]:
...
.. deprecated:: 3.9
:class:`builtins.dict <dict>` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
.. class:: List(list, MutableSequence[T])
Generic version of :class:`list`.
Useful for annotating return types. To annotate arguments it is preferred
to use an abstract collection type such as :class:`Sequence` or
:class:`Iterable`.
This type may be used as follows::
T = TypeVar('T', int, float)
def vec2(x: T, y: T) -> List[T]:
return [x, y]
def keep_positives(vector: Sequence[T]) -> List[T]:
return [item for item in vector if item > 0]
.. deprecated:: 3.9
:class:`builtins.list <list>` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
.. class:: Set(set, MutableSet[T])
A generic version of :class:`builtins.set <set>`.
Useful for annotating return types. To annotate arguments it is preferred
to use an abstract collection type such as :class:`AbstractSet`.
.. deprecated:: 3.9
:class:`builtins.set <set>` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
.. class:: FrozenSet(frozenset, AbstractSet[T_co])
A generic version of :class:`builtins.frozenset <frozenset>`.
.. deprecated:: 3.9
:class:`builtins.frozenset <frozenset>` now supports ``[]``. See
:pep:`585` and :ref:`types-genericalias`.
.. note:: :data:`Tuple` is a special form.
Corresponding to types in :mod:`collections`
""""""""""""""""""""""""""""""""""""""""""""
.. class:: DefaultDict(collections.defaultdict, MutableMapping[KT, VT])
A generic version of :class:`collections.defaultdict`.
.. versionadded:: 3.5.2
.. deprecated:: 3.9
:class:`collections.defaultdict` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
.. class:: OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])
A generic version of :class:`collections.OrderedDict`.
.. versionadded:: 3.7.2
.. deprecated:: 3.9
:class:`collections.OrderedDict` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
.. class:: ChainMap(collections.ChainMap, MutableMapping[KT, VT])
A generic version of :class:`collections.ChainMap`.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.1
.. deprecated:: 3.9
:class:`collections.ChainMap` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
.. class:: Counter(collections.Counter, Dict[T, int])
A generic version of :class:`collections.Counter`.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.1
.. deprecated:: 3.9
:class:`collections.Counter` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
.. class:: Deque(deque, MutableSequence[T])
A generic version of :class:`collections.deque`.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.1
.. deprecated:: 3.9
:class:`collections.deque` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
Other concrete types
""""""""""""""""""""
.. class:: IO
TextIO
BinaryIO
Generic type ``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`.
.. deprecated-removed:: 3.8 3.12
The ``typing.io`` namespace is deprecated and will be removed.
These types should be directly imported from ``typing`` instead.
.. class:: Pattern
Match
These type aliases
correspond to the return types from :func:`re.compile` and
:func:`re.match`. These types (and the corresponding functions)
are generic in ``AnyStr`` and can be made specific by writing
``Pattern[str]``, ``Pattern[bytes]``, ``Match[str]``, or
``Match[bytes]``.
.. deprecated-removed:: 3.8 3.12
The ``typing.re`` namespace is deprecated and will be removed.
These types should be directly imported from ``typing`` instead.
.. deprecated:: 3.9
Classes ``Pattern`` and ``Match`` from :mod:`re` now support ``[]``.
See :pep:`585` and :ref:`types-genericalias`.
.. class:: Text
``Text`` is an alias for ``str``. It 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`` wherever possible.
Abstract Base Classes
---------------------
Corresponding to collections in :mod:`collections.abc`
""""""""""""""""""""""""""""""""""""""""""""""""""""""
.. class:: AbstractSet(Sized, Collection[T_co])
A generic version of :class:`collections.abc.Set`.
.. deprecated:: 3.9
:class:`collections.abc.Set` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
.. class:: ByteString(Sequence[int])
A generic version of :class:`collections.abc.ByteString`.
This type represents the types :class:`bytes`, :class:`bytearray`,
and :class:`memoryview` of byte sequences.
As a shorthand for this type, :class:`bytes` can be used to
annotate arguments of any of the types mentioned above.
.. deprecated:: 3.9
:class:`collections.abc.ByteString` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: Collection(Sized, Iterable[T_co], Container[T_co])
A generic version of :class:`collections.abc.Collection`
.. versionadded:: 3.6.0
.. deprecated:: 3.9
:class:`collections.abc.Collection` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: Container(Generic[T_co])
A generic version of :class:`collections.abc.Container`.
.. deprecated:: 3.9
:class:`collections.abc.Container` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: ItemsView(MappingView, Generic[KT_co, VT_co])
A generic version of :class:`collections.abc.ItemsView`.
.. deprecated:: 3.9
:class:`collections.abc.ItemsView` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: KeysView(MappingView[KT_co], AbstractSet[KT_co])
A generic version of :class:`collections.abc.KeysView`.
.. deprecated:: 3.9
:class:`collections.abc.KeysView` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: Mapping(Sized, Collection[KT], Generic[VT_co])
A generic version of :class:`collections.abc.Mapping`.
This type can be used as follows::
def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
return word_list[word]
.. deprecated:: 3.9
:class:`collections.abc.Mapping` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: MappingView(Sized, Iterable[T_co])
A generic version of :class:`collections.abc.MappingView`.
.. deprecated:: 3.9
:class:`collections.abc.MappingView` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: MutableMapping(Mapping[KT, VT])
A generic version of :class:`collections.abc.MutableMapping`.
.. deprecated:: 3.9
:class:`collections.abc.MutableMapping` now supports ``[]``. See
:pep:`585` and :ref:`types-genericalias`.
.. class:: MutableSequence(Sequence[T])
A generic version of :class:`collections.abc.MutableSequence`.
.. deprecated:: 3.9
:class:`collections.abc.MutableSequence` now supports ``[]``. See
:pep:`585` and :ref:`types-genericalias`.
.. class:: MutableSet(AbstractSet[T])
A generic version of :class:`collections.abc.MutableSet`.
.. deprecated:: 3.9
:class:`collections.abc.MutableSet` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: Sequence(Reversible[T_co], Collection[T_co])
A generic version of :class:`collections.abc.Sequence`.
.. deprecated:: 3.9
:class:`collections.abc.Sequence` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: ValuesView(MappingView[VT_co])
A generic version of :class:`collections.abc.ValuesView`.
.. deprecated:: 3.9
:class:`collections.abc.ValuesView` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
Corresponding to other types in :mod:`collections.abc`
""""""""""""""""""""""""""""""""""""""""""""""""""""""
.. class:: Iterable(Generic[T_co])
A generic version of :class:`collections.abc.Iterable`.
.. deprecated:: 3.9
:class:`collections.abc.Iterable` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: Iterator(Iterable[T_co])
A generic version of :class:`collections.abc.Iterator`.
.. deprecated:: 3.9
:class:`collections.abc.Iterator` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: Generator(Iterator[T_co], Generic[T_co, T_contra, V_co])
A generator can be annotated by the generic type
``Generator[YieldType, SendType, ReturnType]``. 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 generics in the typing module, the ``SendType``
of :class:`Generator` behaves contravariantly, not covariantly or
invariantly.
If your generator will only yield values, set the ``SendType`` and
``ReturnType`` to ``None``::
def infinite_stream(start: int) -> Generator[int, None, None]:
while True:
yield start
start += 1
Alternatively, annotate your generator as having a return type of
either ``Iterable[YieldType]`` or ``Iterator[YieldType]``::
def infinite_stream(start: int) -> Iterator[int]:
while True:
yield start
start += 1
.. deprecated:: 3.9
:class:`collections.abc.Generator` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: Hashable
An alias to :class:`collections.abc.Hashable`.
.. class:: Reversible(Iterable[T_co])
A generic version of :class:`collections.abc.Reversible`.
.. deprecated:: 3.9
:class:`collections.abc.Reversible` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: Sized
An alias to :class:`collections.abc.Sized`.
Asynchronous programming
""""""""""""""""""""""""
.. class:: Coroutine(Awaitable[V_co], Generic[T_co, T_contra, V_co])
A generic version of :class:`collections.abc.Coroutine`.
The variance and order of type variables
correspond to those of :class:`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
.. versionadded:: 3.5.3
.. deprecated:: 3.9
:class:`collections.abc.Coroutine` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: AsyncGenerator(AsyncIterator[T_co], Generic[T_co, T_contra])
An async generator can be annotated by the generic type
``AsyncGenerator[YieldType, SendType]``. For example::
async def echo_round() -> AsyncGenerator[int, float]:
sent = yield 0
while sent >= 0.0:
rounded = await round(sent)
sent = yield rounded
Unlike normal generators, async generators cannot return a value, so there
is no ``ReturnType`` type parameter. As with :class:`Generator`, the
``SendType`` behaves contravariantly.
If your generator will only yield values, set the ``SendType`` to
``None``::
async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
while True:
yield start
start = await increment(start)
Alternatively, annotate your generator as having a return type of
either ``AsyncIterable[YieldType]`` or ``AsyncIterator[YieldType]``::
async def infinite_stream(start: int) -> AsyncIterator[int]:
while True:
yield start
start = await increment(start)
.. versionadded:: 3.6.1
.. deprecated:: 3.9
:class:`collections.abc.AsyncGenerator` now supports ``[]``. See
:pep:`585` and :ref:`types-genericalias`.
.. class:: AsyncIterable(Generic[T_co])
A generic version of :class:`collections.abc.AsyncIterable`.
.. versionadded:: 3.5.2
.. deprecated:: 3.9
:class:`collections.abc.AsyncIterable` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: AsyncIterator(AsyncIterable[T_co])
A generic version of :class:`collections.abc.AsyncIterator`.
.. versionadded:: 3.5.2
.. deprecated:: 3.9
:class:`collections.abc.AsyncIterator` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
.. class:: Awaitable(Generic[T_co])
A generic version of :class:`collections.abc.Awaitable`.
.. versionadded:: 3.5.2
.. deprecated:: 3.9
:class:`collections.abc.Awaitable` now supports ``[]``. See :pep:`585`
and :ref:`types-genericalias`.
Context manager types
"""""""""""""""""""""
.. class:: ContextManager(Generic[T_co])
A generic version of :class:`contextlib.AbstractContextManager`.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.0
.. deprecated:: 3.9
:class:`contextlib.AbstractContextManager` now supports ``[]``. See
:pep:`585` and :ref:`types-genericalias`.
.. class:: AsyncContextManager(Generic[T_co])
A generic version of :class:`contextlib.AbstractAsyncContextManager`.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.2
.. deprecated:: 3.9
:class:`contextlib.AbstractAsyncContextManager` now supports ``[]``. See
:pep:`585` and :ref:`types-genericalias`.
Protocols
---------
These protocols are decorated with :func:`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.
Functions and decorators
------------------------
.. 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*.
When the 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
At runtime this returns the first argument unchanged with no side effects.
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.readthedocs.io/en/latest/source/unreachable.html>`__ has more
information about exhaustiveness checking with static typing.
.. versionadded:: 3.11
.. function:: reveal_type(obj, /)
Reveal the inferred static type of an expression.
When a static type checker encounters a call to this function,
it emits a diagnostic with the 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.
The function returns its argument unchanged, which allows using
it within an expression::
x = reveal_type(1) # Revealed type is "builtins.int"
Most type checkers support ``reveal_type()`` anywhere, even if the
name is not imported from ``typing``. Importing the name from
``typing`` allows your code to run without runtime errors and
communicates intent more clearly.
At runtime, this function prints the runtime type of its argument to stderr
and returns it unchanged::
x = reveal_type(1) # prints "Runtime type is int"
print(x) # prints "1"
.. versionadded:: 3.11
.. decorator:: dataclass_transform
:data:`~typing.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, giving it :func:`dataclasses.dataclass`-like behaviors.
Example usage with a decorator function::
T = TypeVar("T")
@dataclass_transform()
def create_model(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
:func:`@dataclasses.dataclass <dataclasses.dataclass>`.
For example, type checkers will assume these classes have
``__init__`` methods that accept ``id`` and ``name``.
The arguments to this decorator can be used to customize this behavior:
* ``eq_default`` indicates whether the ``eq`` parameter is assumed to be
``True`` or ``False`` if it is omitted by the caller.
* ``order_default`` indicates whether the ``order`` parameter is
assumed to be True or False if it is omitted by the caller.
* ``kw_only_default`` indicates whether the ``kw_only`` parameter is
assumed to be True or False if it is omitted by the caller.
* ``field_specifiers`` specifies a static list of supported classes
or functions that describe fields, similar to ``dataclasses.field()``.
* Arbitrary other keyword arguments are accepted in order to allow for
possible future extensions.
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
.. decorator:: overload
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).
The ``@overload``-decorated definitions are for the benefit of the
type checker only, since they will be overwritten by the
non-``@overload``-decorated definition, while the latter is used at
runtime but should be ignored by a type checker. At runtime, calling
a ``@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::
@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>
See :pep:`484` for 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 :func:`@overload <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 :func:`@overload <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
A decorator to indicate to type checkers that the decorated method
cannot be overridden, and the decorated class 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 set the ``__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 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).
This mutates the function(s) in place.
.. decorator:: no_type_check_decorator
Decorator to give another decorator the :func:`no_type_check` effect.
This wraps the decorator with something that wraps the decorated
function in :func:`no_type_check`.
.. decorator:: type_check_only
Decorator to mark a class or function to be 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.
Introspection helpers
---------------------
.. function:: get_type_hints(obj, globalns=None, localns=None, include_extras=False)
Return a dictionary containing type hints for a function, method, module
or class object.
This is often the same as ``obj.__annotations__``. In addition,
forward references encoded as string literals are handled by evaluating
them in ``globals`` and ``locals`` namespaces. For a class ``C``, return
a dictionary constructed by merging all the ``__annotations__`` along
``C.__mro__`` in reverse order.
The function recursively replaces all ``Annotated[T, ...]`` with ``T``,
unless ``include_extras`` is set to ``True`` (see :class:`Annotated` for
more information). For example::
class Student(NamedTuple):
name: Annotated[str, 'some marker']
get_type_hints(Student) == {'name': str}
get_type_hints(Student, include_extras=False) == {'name': str}
get_type_hints(Student, include_extras=True) == {
'name': Annotated[str, 'some marker']
}
.. note::
:func:`get_type_hints` does not work with imported
:ref:`type aliases <type-aliases>` that include forward references.
Enabling postponed evaluation of annotations (:pep:`563`) may remove
the need for most forward references.
.. versionchanged:: 3.9
Added ``include_extras`` parameter as part of :pep:`593`.
.. 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.
.. function:: get_args(tp)
.. function:: get_origin(tp)
Provide basic introspection for generic types and special typing forms.
For a typing object of the form ``X[Y, Z, ...]`` these functions return
``X`` and ``(Y, Z, ...)``. If ``X`` is a generic alias for a builtin or
:mod:`collections` class, it gets normalized to the original class.
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.
For unsupported objects return ``None`` and ``()`` correspondingly.
Examples::
assert get_origin(Dict[str, int]) is dict
assert get_args(Dict[int, str]) == (int, str)
assert get_origin(Union[int, str]) is Union
assert get_args(Union[int, str]) == (int, str)
.. versionadded:: 3.8
.. function:: is_typeddict(tp)
Check if a type is a :class:`TypedDict`.
For example::
class Film(TypedDict):
title: str
year: int
is_typeddict(Film) # => True
is_typeddict(list | str) # => False
.. versionadded:: 3.10
.. class:: ForwardRef
A class used for internal typing representation of string forward references.
For example, ``List["SomeClass"]`` is implicitly transformed into
``List[ForwardRef("SomeClass")]``. This class 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
Constant
--------
.. data:: TYPE_CHECKING
A special constant that is assumed to be ``True`` by 3rd party static
type checkers. It is ``False`` at runtime. Usage::
if TYPE_CHECKING:
import expensive_mod
def fun(arg: 'expensive_mod.SomeType') -> None:
local_var: expensive_mod.AnotherType = other_fun()
The first type annotation must be enclosed in quotes, making it a
"forward reference", to hide the ``expensive_mod`` reference from the
interpreter runtime. Type annotations for local variables are not
evaluated, so the second annotation does not need to be enclosed in quotes.
.. note::
If ``from __future__ import annotations`` is used,
annotations are not evaluated at function definition time.
Instead, they are stored as strings in ``__annotations__``.
This makes it unnecessary to use quotes around the annotation
(see :pep:`563`).
.. versionadded:: 3.5.2
Deprecation Timeline of Major Features
======================================
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.
+----------------------------------+---------------+-------------------+----------------+
| Feature | Deprecated in | Projected removal | PEP/issue |
+==================================+===============+===================+================+
| ``typing.io`` and ``typing.re`` | 3.8 | 3.12 | :issue:`38291` |
| submodules | | | |
+----------------------------------+---------------+-------------------+----------------+
| ``typing`` versions of standard | 3.9 | Undecided | :pep:`585` |
| collections | | | |
+----------------------------------+---------------+-------------------+----------------+
| ``typing.Text`` | 3.11 | Undecided | :gh:`92332` |
+----------------------------------+---------------+-------------------+----------------+
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