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========================================
:mod:`typing` --- Support for type hints
========================================
.. module:: typing
:synopsis: Support for type hints (see :pep:`484`).
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**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.
--------------
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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.
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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.
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.. _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>`
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* :pep:`586`: Literal Types
*Introducing* :class:`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* :class:`Annotated`
============
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A type alias is defined by assigning the type to the alias. In this example,
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``Vector`` and ``list[float]`` will be treated as interchangeable synonyms::
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Vector = list[float]
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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::
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from collections.abc import Sequence
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ConnectionOptions = dict[str, str]
Address = tuple[str, int]
Server = tuple[Address, ConnectionOptions]
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def broadcast_message(message: str, servers: Sequence[Server]) -> None:
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...
# The static type checker will treat the previous type signature as
# being exactly equivalent to this one.
def broadcast_message(
message: str,
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servers: Sequence[tuple[tuple[str, int], dict[str, str]]]) -> None:
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...
Note that ``None`` as a type hint is a special case and is replaced by
``type(None)``.
.. _distinct:
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NewType
=======
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Use the :func:`NewType` helper to create distinct types::
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from typing import NewType
UserId = NewType('UserId', int)
some_id = UserId(524313)
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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'
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output = UserId(23413) + UserId(54341)
Note that these checks are enforced only by the static type checker. At runtime,
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the statement ``Derived = NewType('Derived', Base)`` will make ``Derived`` a
callable that immediately returns whatever parameter you pass it. That means
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the expression ``Derived(some_value)`` does not create a new class or introduce
any overhead beyond that of a regular function call.
More precisely, the expression ``some_value is Derived(some_value)`` is always
true at runtime.
This also means that it is not possible to create a subtype of ``Derived``
since it is an identity function at runtime, not an actual type::
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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 :func:`NewType` based on a 'derived' ``NewType``::
from typing import NewType
UserId = NewType('UserId', int)
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ProUserId = NewType('ProUserId', UserId)
and typechecking for ``ProUserId`` will work as expected.
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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
========
Frameworks expecting callback functions of specific signatures might be
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type hinted using ``Callable[[Arg1Type, Arg2Type], ReturnType]``.
For example::
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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]``.
========
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.
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from collections.abc import Mapping, Sequence
def notify_by_email(employees: Sequence[Employee],
overrides: Mapping[str, str]) -> None: ...
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Generics can be parameterized by using a factory available in typing
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called :class:`TypeVar`.
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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]
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.. _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::
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from collections.abc import Iterable
def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
for var in vars:
var.set(0)
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A generic type can have any number of type variables. All varieties of
:class:`TypeVar` are permissible as parameters for a generic type::
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from typing import TypeVar, Generic, Sequence
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T = TypeVar('T', contravariant=True)
B = TypeVar('B', bound=Sequence[bytes], covariant=True)
S = TypeVar('S', int, str)
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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
...
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You can use multiple inheritance with :class:`Generic`::
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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::
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from collections.abc import Mapping
from typing import TypeVar
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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]``::
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from collections.abc import Iterable
class MyIterable(Iterable): # Same as Iterable[Any]
User defined generic type aliases are also supported. Examples::
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from collections.abc import Iterable
from typing import TypeVar, Union
S = TypeVar('S')
Response = Union[Iterable[S], int]
# Return type here is same as Union[Iterable[str], int]
def response(query: str) -> Response[str]:
...
T = TypeVar('T', int, float, complex)
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Vec = Iterable[tuple[T, T]]
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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.
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.
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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
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a: Any = None
a = [] # OK
a = 2 # OK
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s: str = ''
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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`::
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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)::
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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.
.. data:: NoReturn
Special type indicating that a function never returns.
For example::
from typing import NoReturn
def stop() -> NoReturn:
raise RuntimeError('no way')
.. versionadded:: 3.5.4
.. versionadded:: 3.6.2
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]`` means either X or Y.
To define a union, use e.g. ``Union[int, str]``. Details:
* The arguments must be types and there must be at least one.
* Unions of unions are flattened, e.g.::
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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]
* 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]``.
* You can use ``Optional[X]`` as a shorthand for ``Union[X, None]``.
.. versionchanged:: 3.7
Don't remove explicit subclasses from unions at runtime.
.. data:: Optional
Optional type.
``Optional[X]`` is equivalent to ``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:
...
.. 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`.
.. deprecated:: 3.9
:class:`collections.abc.Callable` now supports ``[]``. See :pep:`585` and
:ref:`types-genericalias`.
.. class:: Type(Generic[CT_co])
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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[Union[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
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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
...
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MODE = Literal['r', 'rb', 'w', 'wb']
def open_helper(file: str, mode: MODE) -> str:
...
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open_helper('/some/path', 'r') # Passes type check
open_helper('/other/path', 'typo') # Error in type checker
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``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
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.. versionchanged:: 3.9.1
``Literal`` now de-duplicates parameters. Equality comparisons of
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``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`.
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.. data:: ClassVar
Special type construct to mark class variables.
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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:
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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
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class Connection:
TIMEOUT: Final[int] = 10
class FastConnector(Connection):
TIMEOUT = 1 # Error reported by type checker
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There is no runtime checking of these properties. See :pep:`591` for
more details.
.. versionadded:: 3.8
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.. 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:
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* The first argument to ``Annotated`` must be a valid type
* Multiple type annotations are supported (``Annotated`` supports variadic
arguments)::
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Annotated[int, ValueRange(3, 10), ctype("char")]
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* ``Annotated`` must be called with at least two arguments (
``Annotated[int]`` is not valid)
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* 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::
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Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
int, ValueRange(3, 10), ctype("char")
]
* Duplicated annotations are not removed::
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Annotated[int, ValueRange(3, 10)] != Annotated[
int, ValueRange(3, 10), ValueRange(3, 10)
]
* ``Annotated`` can be used with nested and generic aliases::
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T = TypeVar('T')
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Vec = Annotated[list[tuple[T, T]], MaxLen(10)]
V = Vec[int]
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V == Annotated[list[tuple[int, int]], MaxLen(10)]
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.. versionadded:: 3.9
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Building generic types
""""""""""""""""""""""
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These are not used in annotations. They are building blocks for creating generic types.
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.. class:: Generic
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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.
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This class can then be used as follows::
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X = TypeVar('X')
Y = TypeVar('Y')
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def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y:
try:
return mapping[key]
except KeyError:
return default
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.. class:: TypeVar
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Type variable.
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Usage::
T = TypeVar('T') # Can be anything
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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
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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.
Constrained type variables and bound type variables have different
semantics in several important ways. Using a *constrained* type variable
means that the ``TypeVar`` can only ever be solved as being exactly one of
the constraints given::
a = concatenate('one', 'two') # Ok, variable 'a' has type 'str'
b = concatenate(StringSubclass('one'), StringSubclass('two')) # Inferred type of variable 'b' 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
Using a *bound* type variable, however, means that the ``TypeVar`` will be
solved using the most specific type possible::
print_capitalized('a string') # Ok, output has type 'str'
class StringSubclass(str):
pass
print_capitalized(StringSubclass('another string')) # Ok, output has type 'StringSubclass'
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
Bound type variables are particularly useful for annotating
:func:`classmethods <classmethod>` that serve as alternative constructors.
In the following example (©
`Raymond Hettinger <https://www.youtube.com/watch?v=HTLu2DFOdTg>`_), the
type variable ``C`` is bound to the ``Circle`` class through the use of a
forward reference. Using this type variable to annotate the
``with_circumference`` classmethod, rather than hardcoding the return type
as ``Circle``, means that a type checker can correctly infer the return
type even if the method is called on a subclass::
import math
C = TypeVar('C', bound='Circle')
class Circle:
"""An abstract circle"""
def __init__(self, radius: float) -> None:
self.radius = radius
# Use a type variable to show that the return type
# will always be an instance of whatever ``cls`` is
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@classmethod
def with_circumference(cls: type[C], circumference: float) -> C:
"""Create a circle with the specified circumference"""
radius = circumference / (math.pi * 2)
return cls(radius)
class Tire(Circle):
"""A specialised circle (made out of rubber)"""
MATERIAL = 'rubber'
c = Circle.with_circumference(3) # Ok, variable 'c' has type 'Circle'
t = Tire.with_circumference(4) # Ok, variable 't' has type 'Tire' (not 'Circle')
At runtime, ``isinstance(x, T)`` will raise :exc:`TypeError`. In general,
:func:`isinstance` and :func:`issubclass` should not be used with types.
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Type variables may be marked covariant or contravariant by passing
``covariant=True`` or ``contravariant=True``. See :pep:`484` for more
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details. By default, type variables are invariant.
.. data:: AnyStr
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``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::
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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.