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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`_.
.. seealso::
For a quick overview of type hints, refer to
`this cheat sheet <https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html>`_.
The "Type System Reference" section of https://mypy.readthedocs.io/ -- since
the Python typing system is standardised via PEPs, this reference should
broadly apply to most Python type checkers, although some parts may still be
specific to mypy.
The documentation at https://typing.readthedocs.io/ serves as useful reference
for type system features, useful typing related tools and typing best practices.
.. _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`
* :pep:`681`: Data Class Transforms
*Introducing* the :func:`@dataclass_transform<dataclass_transform>` decorator
* :pep:`698`: Adding an override decorator to typing
*Introducing* the :func:`@override<override>` decorator
.. _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]
# passes type checking; 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:
...
# passes type checking
user_a = get_user_name(UserId(42351))
# fails type checking; 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 pass type checking
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 details.
.. 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, [dict, 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[[int, str]]
>>> X[[int, str]]
__main__.X[[int, 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 :term:`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:
# 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
# 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 type checking; an object does not have a 'magic' method.
item.magic()
...
def hash_b(item: Any) -> int:
# Passes type checking
item.magic()
...
# Passes type checking, since ints and strs are subclasses of object
hash_a(42)
hash_a("foo")
# Passes type checking, 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.
See :pep:`675` for more details.
.. 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 return_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 return_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 :data:`Self` as calls to ``SubclassOfFoo.return_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.
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]
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 subscripting (``[]``).
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 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.
.. 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 subscripting (``[]``).
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