:mod:`typing` --- Support for type hints
.. testsetup:: * import typing from dataclasses import dataclass from typing import *
.. module:: typing :synopsis: Support for type hints (see :pep:`484`).
.. versionadded:: 3.5
Source code: :source:`Lib/typing.py`
Note
The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.
This module provides runtime support for type hints. For the original specification of the typing system, 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 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::
`"Typing cheat sheet" <https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html>`_
A quick overview of type hints (hosted at the mypy docs)
"Type System Reference" section of `the mypy docs <https://mypy.readthedocs.io/en/stable/index.html>`_
The Python typing system is standardised via PEPs, so this reference
should broadly apply to most Python type checkers. (Some parts may still
be specific to mypy.)
`"Static Typing with Python" <https://typing.readthedocs.io/en/latest/>`_
Type-checker-agnostic documentation written by the community detailing
type system features, useful typing related tools and typing best
practices.
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:
The full list of PEPs
- 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 604: Allow writing union types as
- 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 692: Using
TypedDictfor more precise**kwargstyping - Introducing a new way of typing
**kwargswith :data:`Unpack` and :data:`TypedDict`
- PEP 692: Using
- PEP 695: Type Parameter Syntax
- Introducing builtin syntax for creating generic functions, classes, and type aliases.
- PEP 698: Adding an override decorator to typing
- Introducing the :func:`@override<override>` decorator
A type alias is defined using the :keyword:`type` statement, which creates
an instance of :class:`TypeAliasType`. In this example,
Vector and list[float] will be treated equivalently by static type
checkers:
type Vector = list[float]
def scale(scalar: float, vector: Vector) -> Vector:
return [scalar * num for num in vector]
# 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
type ConnectionOptions = dict[str, str]
type Address = tuple[str, int]
type 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:
...
The :keyword:`type` statement is new in Python 3.12. For backwards compatibility, type aliases can also be created through simple assignment:
Vector = list[float]
Or marked with :data:`TypeAlias` to make it explicit that this is a type alias, not a normal variable assignment:
from typing import TypeAlias Vector: TypeAlias = list[float]
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 type Alias = Original will make the static type checker
treat Alias as being exactly equivalent to Original in all cases.
This is useful when you want to simplify complex type signatures.
In contrast, NewType declares one type to be a subtype of another.
Doing Derived = NewType('Derived', Original) will make the static type
checker treat Derived as a subclass of Original, which means a
value of type Original cannot be used in places where a value of type
Derived is expected. This is useful when you want to prevent logic
errors with minimal runtime cost.
.. versionadded:: 3.5.2
.. versionchanged:: 3.10 ``NewType`` is now a class rather than a function. As a result, there is some additional runtime cost when calling ``NewType`` over a regular function.
.. versionchanged:: 3.11 The performance of calling ``NewType`` has been restored to its level in Python 3.9.
Functions -- or other :term:`callable` objects -- can be annotated using
:class:`collections.abc.Callable` or :data:`typing.Callable`.
Callable[[int], str] signifies a function that takes a single parameter
of type :class:`int` and returns a :class:`str`.
For example:
.. testcode::
from collections.abc import Callable, Awaitable
def feeder(get_next_item: Callable[[], str]) -> None:
... # Body
def async_query(on_success: Callable[[int], None],
on_error: Callable[[int, Exception], None]) -> None:
... # Body
async def on_update(value: str) -> None:
... # Body
callback: Callable[[str], Awaitable[None]] = on_update
The subscription syntax must always be used with exactly two values: the argument list and the return type. The argument list must be a list of types, a :class:`ParamSpec`, :data:`Concatenate`, or an ellipsis. The return type must be a single type.
If a literal ellipsis ... is given as the argument list, it indicates that
a callable with any arbitrary parameter list would be acceptable:
.. testcode::
def concat(x: str, y: str) -> str:
return x + y
x: Callable[..., str]
x = str # OK
x = concat # Also OK
Callable cannot express complex signatures such as functions that take a
variadic number of arguments, :func:`overloaded functions <overload>`, or
functions that have keyword-only parameters. However, these signatures can be
expressed by defining a :class:`Protocol` class with a
:meth:`~object.__call__` method:
.. testcode::
from collections.abc import Iterable
from typing import Protocol
class Combiner(Protocol):
def __call__(self, *vals: bytes, maxlen: int | None = None) -> list[bytes]: ...
def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes:
for item in data:
...
def good_cb(*vals: bytes, maxlen: int | None = None) -> list[bytes]:
...
def bad_cb(*vals: bytes, maxitems: int | None) -> list[bytes]:
...
batch_proc([], good_cb) # OK
batch_proc([], bad_cb) # Error! Argument 2 has incompatible type because of
# different name and kind in the callback
Callables which take other callables as arguments may indicate that their
parameter types are dependent on each other using :class:`ParamSpec`.
Additionally, if that callable adds or removes arguments from other
callables, the :data:`Concatenate` operator may be used. They
take the form Callable[ParamSpecVariable, ReturnType] and
Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType]
respectively.
.. versionchanged:: 3.10 ``Callable`` now supports :class:`ParamSpec` and :data:`Concatenate`. See :pep:`612` for more details.
.. seealso:: The documentation for :class:`ParamSpec` and :class:`Concatenate` provides examples of usage in ``Callable``.
Since type information about objects kept in containers cannot be statically inferred in a generic way, many container classes in the standard library support subscription to denote the expected types of container elements.
.. testcode::
from collections.abc import Mapping, Sequence
class Employee: ...
# Sequence[Employee] indicates that all elements in the sequence
# must be instances of "Employee".
# Mapping[str, str] indicates that all keys and all values in the mapping
# must be strings.
def notify_by_email(employees: Sequence[Employee],
overrides: Mapping[str, str]) -> None: ...
Generic functions and classes can be parameterized by using :ref:`type parameter syntax <type-params>`:
from collections.abc import Sequence
def first[T](l: Sequence[T]) -> T: # Function is generic over the TypeVar "T"
return l[0]
Or by using the :class:`TypeVar` factory directly:
from collections.abc import Sequence
from typing import TypeVar
U = TypeVar('U') # Declare type variable "U"
def second(l: Sequence[U]) -> U: # Function is generic over the TypeVar "U"
return l[1]
.. versionchanged:: 3.12 Syntactic support for generics is new in Python 3.12.
For most containers in Python, the typing system assumes that all elements in the container will be of the same type. For example:
from collections.abc import Mapping
# Type checker will infer that all elements in ``x`` are meant to be ints
x: list[int] = []
# Type checker error: ``list`` only accepts a single type argument:
y: list[int, str] = [1, 'foo']
# Type checker will infer that all keys in ``z`` are meant to be strings,
# and that all values in ``z`` are meant to be either strings or ints
z: Mapping[str, str | int] = {}
:class:`list` only accepts one type argument, so a type checker would emit an
error on the y assignment above. Similarly,
:class:`~collections.abc.Mapping` only accepts two type arguments: the first
indicates the type of the keys, and the second indicates the type of the
values.
Unlike most other Python containers, however, it is common in idiomatic Python code for tuples to have elements which are not all of the same type. For this reason, tuples are special-cased in Python's typing system. :class:`tuple` accepts any number of type arguments:
# OK: ``x`` is assigned to a tuple of length 1 where the sole element is an int x: tuple[int] = (5,) # OK: ``y`` is assigned to a tuple of length 2; # element 1 is an int, element 2 is a str y: tuple[int, str] = (5, "foo") # Error: the type annotation indicates a tuple of length 1, # but ``z`` has been assigned to a tuple of length 3 z: tuple[int] = (1, 2, 3)
To denote a tuple which could be of any length, and in which all elements are
of the same type T, use tuple[T, ...]. To denote an empty tuple, use
tuple[()]. Using plain tuple as an annotation is equivalent to using
tuple[Any, ...]:
x: tuple[int, ...] = (1, 2)
# These reassignments are OK: ``tuple[int, ...]`` indicates x can be of any length
x = (1, 2, 3)
x = ()
# This reassignment is an error: all elements in ``x`` must be ints
x = ("foo", "bar")
# ``y`` can only ever be assigned to an empty tuple
y: tuple[()] = ()
z: tuple = ("foo", "bar")
# These reassignments are OK: plain ``tuple`` is equivalent to ``tuple[Any, ...]``
z = (1, 2, 3)
z = ()
A variable annotated with C may accept a value of type C. In
contrast, a variable annotated with type[C] (or
:class:`typing.Type[C] <Type>`) may accept values that are classes
themselves -- specifically, it will accept the class object of C. For
example:
a = 3 # Has type ``int``` b = int # Has type ``type[int]`` c = type(a) # Also has type ``type[int]``
Note that type[C] is covariant:
class User: ...
class ProUser(User): ...
class TeamUser(User): ...
def make_new_user(user_class: type[User]) -> User:
# ...
return user_class()
make_new_user(User) # OK
make_new_user(ProUser) # Also OK: ``type[ProUser]`` is a subtype of ``type[User]``
make_new_user(TeamUser) # Still fine
make_new_user(User()) # Error: expected ``type[User]`` but got ``User``
make_new_user(int) # Error: ``type[int]`` is not a subtype of ``type[User]``
The only legal parameters for :class:`type` are classes, :data:`Any`, :ref:`type variables <generics>`, and unions of any of these types. For example:
def new_non_team_user(user_class: type[BasicUser | ProUser]): ...
new_non_team_user(BasicUser) # OK
new_non_team_user(ProUser) # OK
new_non_team_user(TeamUser) # Error: ``type[TeamUser]`` is not a subtype
# of ``type[BasicUser | ProUser]``
new_non_team_user(User) # Also an error
type[Any] is equivalent to :class:`type`, which is the root of Python's
:ref:`metaclass hierarchy <metaclasses>`.
A user-defined class can be defined as a generic class.
from logging import Logger
class LoggedVar[T]:
def __init__(self, value: T, name: str, logger: Logger) -> None:
self.name = name
self.logger = logger
self.value = value
def set(self, new: T) -> None:
self.log('Set ' + repr(self.value))
self.value = new
def get(self) -> T:
self.log('Get ' + repr(self.value))
return self.value
def log(self, message: str) -> None:
self.logger.info('%s: %s', self.name, message)
This syntax indicates that the class LoggedVar is parameterised around a
single :class:`type variable <TypeVar>` T . This also makes T valid as
a type within the class body.
Generic classes implicitly inherit from :class:`Generic`. For compatibility with Python 3.11 and lower, it is also possible to inherit explicitly from :class:`Generic` to indicate a generic class:
from typing import TypeVar, Generic
T = TypeVar('T')
class LoggedVar(Generic[T]):
...
Generic classes have :meth:`~object.__class_getitem__` methods, meaning they
can be parameterised at runtime (e.g. LoggedVar[int] below):
from collections.abc import Iterable
def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
for var in vars:
var.set(0)
A generic type can have any number of type variables. All varieties of :class:`TypeVar` are permissible as parameters for a generic type:
from typing import TypeVar, Generic, Sequence
class WeirdTrio[T, B: Sequence[bytes], S: (int, str)]:
...
OldT = TypeVar('OldT', contravariant=True)
OldB = TypeVar('OldB', bound=Sequence[bytes], covariant=True)
OldS = TypeVar('OldS', int, str)
class OldWeirdTrio(Generic[OldT, OldB, OldS]):
...
Each type variable argument to :class:`Generic` must be distinct. This is thus invalid:
from typing import TypeVar, Generic
...
class Pair[M, M]: # SyntaxError
...
T = TypeVar('T')
class Pair(Generic[T, T]): # INVALID
...
Generic classes can also inherit from other classes:
from collections.abc import Sized
class LinkedList[T](Sized):
...
When inheriting from generic classes, some type parameters could be fixed:
from collections.abc import Mapping
class MyDict[T](Mapping[str, T]):
...
In this case MyDict has a single parameter, T.
Using a generic class without specifying type parameters assumes
:data:`Any` for each position. In the following example, MyIterable is
not generic but implicitly inherits from Iterable[Any]:
.. testcode::
from collections.abc import Iterable
class MyIterable(Iterable): # Same as Iterable[Any]
...
User-defined generic type aliases are also supported. Examples:
from collections.abc import Iterable
type Response[S] = Iterable[S] | int
# Return type here is same as Iterable[str] | int
def response(query: str) -> Response[str]:
...
type Vec[T] = Iterable[tuple[T, T]]
def inproduct[T: (int, float, complex)](v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]]
return sum(x*y for x, y in v)
For backward compatibility, generic type aliases can also be created through a simple assignment:
from collections.abc import Iterable
from typing import TypeVar
S = TypeVar("S")
Response = Iterable[S] | int
.. versionchanged:: 3.7
:class:`Generic` no longer has a custom metaclass.
.. versionchanged:: 3.12 Syntactic support for generics and type aliases is new in version 3.12. Previously, generic classes had to explicitly inherit from :class:`Generic` or contain a type variable in one of their bases.
User-defined generics for parameter expressions are also supported via parameter
specification variables in the form [**P]. The behavior is consistent
with type variables' described above as parameter specification variables are
treated by the typing module as a specialized type variable. The one exception
to this is that a list of types can be used to substitute a :class:`ParamSpec`:
>>> class Z[T, **P]: ... # T is a TypeVar; P is a ParamSpec ... >>> Z[int, [dict, float]] __main__.Z[int, [dict, float]]
Classes generic over a :class:`ParamSpec` can also be created using explicit
inheritance from :class:`Generic`. In this case, ** is not used:
from typing import ParamSpec, Generic
P = ParamSpec('P')
class Z(Generic[P]):
...
Another difference between :class:`TypeVar` and :class:`ParamSpec` is that a
generic with only one parameter specification variable will accept
parameter lists in the forms X[[Type1, Type2, ...]] and also
X[Type1, Type2, ...] for aesthetic reasons. Internally, the latter is converted
to the former, so the following are equivalent:
>>> class X[**P]: ... ... >>> X[int, str] __main__.X[[int, str]] >>> X[[int, str]] __main__.X[[int, str]]
Note that generics with :class:`ParamSpec` may not have correct
__parameters__ after substitution in some cases because they
are intended primarily for static type checking.
.. versionchanged:: 3.10 :class:`Generic` can now be parameterized over parameter expressions. See :class:`ParamSpec` and :pep:`612` for more details.
A user-defined generic class can have ABCs as base classes without a metaclass conflict. Generic metaclasses are not supported. The outcome of parameterizing generics is cached, and most types in the 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.
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).
The typing module defines the following classes, functions and decorators.
These can be used as types in annotations. They do not support subscription
using [].
.. 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:: AnyStr
A :ref:`constrained type variable <typing-constrained-typevar>`.
Definition::
AnyStr = TypeVar('AnyStr', str, bytes)
``AnyStr`` is meant to be used for functions that may accept :class:`str` or
:class:`bytes` arguments but cannot allow the two to mix.
For example::
def concat(a: AnyStr, b: AnyStr) -> AnyStr:
return a + b
concat("foo", "bar") # OK, output has type 'str'
concat(b"foo", b"bar") # OK, output has type 'bytes'
concat("foo", b"bar") # Error, cannot mix str and bytes
Note that, despite its name, ``AnyStr`` has nothing to do with the
:class:`Any` type, nor does it mean "any string". In particular, ``AnyStr``
and ``str | bytes`` are different from each other and have different use
cases::
# Invalid use of AnyStr:
# The type variable is used only once in the function signature,
# so cannot be "solved" by the type checker
def greet_bad(cond: bool) -> AnyStr:
return "hi there!" if cond else b"greetings!"
# The better way of annotating this function:
def greet_proper(cond: bool) -> str | bytes:
return "hi there!" if cond else b"greetings!"
.. data:: LiteralString
Special type that includes only literal strings.
Any string
literal is compatible with ``LiteralString``, as is another
``LiteralString``. However, an object typed as just ``str`` is not.
A string created by composing ``LiteralString``-typed objects
is also acceptable as a ``LiteralString``.
Example:
.. testcode::
def run_query(sql: LiteralString) -> None:
...
def caller(arbitrary_string: str, literal_string: LiteralString) -> None:
run_query("SELECT * FROM students") # OK
run_query(literal_string) # OK
run_query("SELECT * FROM " + literal_string) # OK
run_query(arbitrary_string) # type checker error
run_query( # type checker error
f"SELECT * FROM students WHERE name = {arbitrary_string}"
)
``LiteralString`` is useful for sensitive APIs where arbitrary user-generated
strings could generate problems. For example, the two cases above
that generate type checker errors could be vulnerable to an SQL
injection attack.
See :pep:`675` for more details.
.. versionadded:: 3.11
.. data:: Never
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]
``TypeAlias`` is particularly useful on older Python versions for annotating
aliases that make use of forward references, as it can be hard for type
checkers to distinguish these from normal variable assignments:
.. testcode::
from typing import Generic, TypeAlias, TypeVar
T = TypeVar("T")
# "Box" does not exist yet,
# so we have to use quotes for the forward reference on Python <3.12.
# Using ``TypeAlias`` tells the type checker that this is a type alias declaration,
# not a variable assignment to a string.
BoxOfStrings: TypeAlias = "Box[str]"
class Box(Generic[T]):
@classmethod
def make_box_of_strings(cls) -> BoxOfStrings: ...
See :pep:`613` for more details.
.. versionadded:: 3.10
.. deprecated:: 3.12
:data:`TypeAlias` is deprecated in favor of the :keyword:`type` statement,
which creates instances of :class:`TypeAliasType`
and which natively supports forward references.
Note that while :data:`TypeAlias` and :class:`TypeAliasType` serve
similar purposes and have similar names, they are distinct and the
latter is not the type of the former.
Removal of :data:`TypeAlias` is not currently planned, but users
are encouraged to migrate to :keyword:`type` statements.
These can be used as types in annotations. They all support subscription using
[], but each has a unique syntax.
.. 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[X]`` is equivalent to ``X | None`` (or ``Union[X, None]``).
Note that this is not the same concept as an optional argument,
which is one that has a default. An optional argument with a
default does not require the ``Optional`` qualifier on its type
annotation just because it is optional. For example::
def foo(arg: int = 0) -> None:
...
On the other hand, if an explicit value of ``None`` is allowed, the
use of ``Optional`` is appropriate, whether the argument is optional
or not. For example::
def foo(arg: Optional[int] = None) -> None:
...
.. versionchanged:: 3.10
Optional can now be written as ``X | None``. See
:ref:`union type expressions<types-union>`.
.. data:: Concatenate
Special form for annotating higher-order functions.
``Concatenate`` can be used in conjunction with :ref:`Callable <annotating-callables>` and
:class:`ParamSpec` to annotate a higher-order callable which adds, removes,
or transforms parameters of another
callable. Usage is in the form
``Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable]``. ``Concatenate``
is currently only valid when used as the first argument to a :ref:`Callable <annotating-callables>`.
The last parameter to ``Concatenate`` must be a :class:`ParamSpec` or
ellipsis (``...``).
For example, to annotate a decorator ``with_lock`` which provides a
:class:`threading.Lock` to the decorated function, ``Concatenate`` can be
used to indicate that ``with_lock`` expects a callable which takes in a
``Lock`` as the first argument, and returns a callable with a different type
signature. In this case, the :class:`ParamSpec` indicates that the returned
callable's parameter types are dependent on the parameter types of the
callable being passed in::
from collections.abc import Callable
from threading import Lock
from typing import Concatenate, 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`
* :ref:`annotating-callables`
.. data:: Literal
Special typing form to define "literal types".
``Literal`` can be used to indicate to type checkers that the
annotated object has a value equivalent to one of the
provided literals.
For example::
def validate_simple(data: Any) -> Literal[True]: # always returns True
...
type Mode = Literal['r', 'rb', 'w', 'wb']
def open_helper(file: str, mode: Mode) -> str:
...
open_helper('/some/path', 'r') # Passes type check
open_helper('/other/path', 'typo') # Error in type checker
``Literal[...]`` cannot be subclassed. At runtime, an arbitrary value
is allowed as type argument to ``Literal[...]``, but type checkers may
impose restrictions. See :pep:`586` for more details about literal types.
.. 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
Special typing construct to indicate final names to type checkers.
Final names cannot be reassigned in any scope. Final names declared in class
scopes cannot be overridden in subclasses.
For example::
MAX_SIZE: Final = 9000
MAX_SIZE += 1 # Error reported by type checker
class Connection:
TIMEOUT: Final[int] = 10
class FastConnector(Connection):
TIMEOUT = 1 # Error reported by type checker
There is no runtime checking of these properties. See :pep:`591` for
more details.
.. versionadded:: 3.8
.. data:: Required Special typing construct to mark a :class:`TypedDict` key as required. This is mainly useful for ``total=False`` TypedDicts. See :class:`TypedDict` and :pep:`655` for more details. .. versionadded:: 3.11
.. data:: NotRequired Special typing construct to mark a :class:`TypedDict` key as potentially missing. See :class:`TypedDict` and :pep:`655` for more details. .. versionadded:: 3.11
.. data:: Annotated
Special typing form to add context-specific metadata to an annotation.
Add metadata ``x`` to a given type ``T`` by using the annotation
``Annotated[T, x]``. Metadata added using ``Annotated`` can be used by
static analysis tools or at runtime. At runtime, the metadata is stored
in a :attr:`!__metadata__` attribute.
If a library or tool encounters an annotation ``Annotated[T, x]`` and has
no special logic for the metadata, it should ignore the metadata and simply
treat the annotation as ``T``. As such, ``Annotated`` can be useful for code
that wants to use annotations for purposes outside Python's static typing
system.
Using ``Annotated[T, x]`` as an annotation still allows for static
typechecking of ``T``, as type checkers will simply ignore the metadata ``x``.
In this way, ``Annotated`` differs from the
:func:`@no_type_check <no_type_check>` decorator, which can also be used for
adding annotations outside the scope of the typing system, but
completely disables typechecking for a function or class.
The responsibility of how to interpret the metadata
lies with the the tool or library encountering an
``Annotated`` annotation. A tool or library encountering an ``Annotated`` type
can scan through the metadata elements to determine if they are of interest
(e.g., using :func:`isinstance`).
.. describe:: Annotated[<type>, <metadata>]
Here is an example of how you might use ``Annotated`` to add metadata to
type annotations if you were doing range analysis:
.. testcode::
@dataclass
class ValueRange:
lo: int
hi: int
T1 = Annotated[int, ValueRange(-10, 5)]
T2 = Annotated[T1, ValueRange(-20, 3)]
Details of the syntax:
* The first argument to ``Annotated`` must be a valid type
* Multiple metadata elements can be supplied (``Annotated`` supports variadic
arguments)::
@dataclass
class ctype:
kind: str
Annotated[int, ValueRange(3, 10), ctype("char")]
It is up to the tool consuming the annotations to decide whether the
client is allowed to add multiple metadata elements to one annotation and how to
merge those annotations.
* ``Annotated`` must be subscripted with at least two arguments (
``Annotated[int]`` is not valid)
* The order of the metadata elements is preserved and matters for equality
checks::
assert Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
int, ctype("char"), ValueRange(3, 10)
]
* Nested ``Annotated`` types are flattened. The order of the metadata elements
starts with the innermost annotation::
assert Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
int, ValueRange(3, 10), ctype("char")
]
* Duplicated metadata elements are not removed::
assert Annotated[int, ValueRange(3, 10)] != Annotated[
int, ValueRange(3, 10), ValueRange(3, 10)
]
* ``Annotated`` can be used with nested and generic aliases:
.. testcode::
@dataclass
class MaxLen:
value: int
type Vec[T] = Annotated[list[tuple[T, T]], MaxLen(10)]
# When used in a type annotation, a type checker will treat "V" the same as
# ``Annotated[list[tuple[int, int]], MaxLen(10)]``:
type V = Vec[int]
* ``Annotated`` cannot be used with an unpacked :class:`TypeVarTuple`::
type Variadic[*Ts] = Annotated[*Ts, Ann1] # NOT valid
This would be equivalent to::
Annotated[T1, T2, T3, ..., Ann1]
where ``T1``, ``T2``, etc. are :class:`TypeVars <TypeVar>`. This would be
invalid: only one type should be passed to Annotated.
* By default, :func:`get_type_hints` strips the metadata from annotations.
Pass ``include_extras=True`` to have the metadata preserved:
.. doctest::
>>> from typing import Annotated, get_type_hints
>>> def func(x: Annotated[int, "metadata"]) -> None: pass
...
>>> get_type_hints(func)
{'x': <class 'int'>, 'return': <class 'NoneType'>}
>>> get_type_hints(func, include_extras=True)
{'x': typing.Annotated[int, 'metadata'], 'return': <class 'NoneType'>}
* At runtime, the metadata associated with an ``Annotated`` type can be
retrieved via the :attr:`!__metadata__` attribute:
.. doctest::
>>> from typing import Annotated
>>> X = Annotated[int, "very", "important", "metadata"]
>>> X
typing.Annotated[int, 'very', 'important', 'metadata']
>>> X.__metadata__
('very', 'important', 'metadata')
.. seealso::
:pep:`593` - Flexible function and variable annotations
The PEP introducing ``Annotated`` to the standard library.
.. versionadded:: 3.9
.. data:: TypeGuard
Special typing construct for marking user-defined type guard functions.
``TypeGuard`` can be 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. See :pep:`647` for more details.
.. versionadded:: 3.10
.. data:: Unpack
Typing operator to conceptually mark 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 :class:`typing.TypeVarTuple <TypeVarTuple>` and
:class:`builtins.tuple <tuple>` types. You might see ``Unpack`` being used
explicitly in older versions of Python, where ``*`` couldn't be used in
certain places::
# In older versions of Python, TypeVarTuple and Unpack
# are located in the `typing_extensions` backports package.
from typing_extensions import TypeVarTuple, Unpack
Ts = TypeVarTuple('Ts')
tup: tuple[*Ts] # Syntax error on Python <= 3.10!
tup: tuple[Unpack[Ts]] # Semantically equivalent, and backwards-compatible
``Unpack`` can also be used along with :class:`typing.TypedDict` for typing
``**kwargs`` in a function signature::
from typing import TypedDict, Unpack
class Movie(TypedDict):
name: str
year: int
# This function expects two keyword arguments - `name` of type `str`
# and `year` of type `int`.
def foo(**kwargs: Unpack[Movie]): ...
See :pep:`692` for more details on using ``Unpack`` for ``**kwargs`` typing.
.. versionadded:: 3.11
The following classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating generic types and type aliases.
These objects can be created through special syntax (:ref:`type parameter lists <type-params>` and the :keyword:`type` statement). For compatibility with Python 3.11 and earlier, they can also be created without the dedicated syntax, as documented below.
Abstract base class for generic types.
A generic type is typically declared by adding a list of type parameters after the class name:
class Mapping[KT, VT]:
def __getitem__(self, key: KT) -> VT:
...
# Etc.
Such a class implicitly inherits from Generic.
The runtime semantics of this syntax are discussed in the
:ref:`Language Reference <generic-classes>`.
This class can then be used as follows:
def lookup_name[X, Y](mapping: Mapping[X, Y], key: X, default: Y) -> Y:
try:
return mapping[key]
except KeyError:
return default
Here the brackets after the function name indicate a :ref:`generic function <generic-functions>`.
For backwards compatibility, generic classes can also be
declared by explicitly inheriting from
Generic. In this case, the type parameters must be declared
separately:
KT = TypeVar('KT')
VT = TypeVar('VT')
class Mapping(Generic[KT, VT]):
def __getitem__(self, key: KT) -> VT:
...
# Etc.
Type variable.
The preferred way to construct a type variable is via the dedicated syntax for :ref:`generic functions <generic-functions>`, :ref:`generic classes <generic-classes>`, and :ref:`generic type aliases <generic-type-aliases>`:
class Sequence[T]: # T is a TypeVar
...
This syntax can also be used to create bound and constrained type variables:
class StrSequence[S: str]: # S is a TypeVar bound to str
...
class StrOrBytesSequence[A: (str, bytes)]: # A is a TypeVar constrained to str or bytes
...
However, if desired, reusable type variables can also be constructed manually, like so:
T = TypeVar('T') # Can be anything
S = TypeVar('S', bound=str) # Can be any subtype of str
A = TypeVar('A', str, bytes) # Must be exactly str or bytes
Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function and type alias definitions. See :class:`Generic` for more information on generic types. Generic functions work as follows:
def repeat[T](x: T, n: int) -> Sequence[T]:
"""Return a list containing n references to x."""
return [x]*n
def print_capitalized[S: str](x: S) -> S:
"""Print x capitalized, and return x."""
print(x.capitalize())
return x
def concatenate[A: (str, bytes)](x: A, y: A) -> A:
"""Add two strings or bytes objects together."""
return x + y
Note that type variables can be bound, constrained, or neither, but cannot be both bound and constrained.
The variance of type variables is inferred by type checkers when they are created
through the :ref:`type parameter syntax <type-params>` or when
infer_variance=True is passed.
Manually created type variables may be explicitly marked covariant or contravariant by passing
covariant=True or contravariant=True.
By default, manually created type variables are invariant.
See PEP 484 and PEP 695 for more details.
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:
# Can be anything with an __abs__ method
def print_abs[T: SupportsAbs](arg: T) -> None:
print("Absolute value:", abs(arg))
U = TypeVar('U', bound=str|bytes) # Can be any subtype of the union str|bytes
V = TypeVar('V', bound=SupportsAbs) # Can be anything with an __abs__ method
Using a constrained type variable, however, means that the TypeVar
can only ever be solved as being exactly one of the constraints given:
a = concatenate('one', 'two')
reveal_type(a) # revealed type is str
b = concatenate(StringSubclass('one'), StringSubclass('two'))
reveal_type(b) # revealed type is str, despite StringSubclass being passed in
c = concatenate('one', b'two') # error: type variable 'A' can be either str or bytes in a function call, but not both
At runtime, isinstance(x, T) will raise :exc:`TypeError`.
.. attribute:: __name__ The name of the type variable.
.. attribute:: __covariant__ Whether the type var has been explicitly marked as covariant.
.. attribute:: __contravariant__ Whether the type var has been explicitly marked as contravariant.
.. attribute:: __infer_variance__ Whether the type variable's variance should be inferred by type checkers. .. versionadded:: 3.12
.. attribute:: __bound__
The bound of the type variable, if any.
.. versionchanged:: 3.12
For type variables created through :ref:`type parameter syntax <type-params>`,
the bound is evaluated only when the attribute is accessed, not when
the type variable is created (see :ref:`lazy-evaluation`).
.. attribute:: __constraints__
A tuple containing the constraints of the type variable, if any.
.. versionchanged:: 3.12
For type variables created through :ref:`type parameter syntax <type-params>`,
the constraints are evaluated only when the attribute is accessed, not when
the type variable is created (see :ref:`lazy-evaluation`).
.. versionchanged:: 3.12 Type variables can now be declared using the :ref:`type parameter <type-params>` syntax introduced by :pep:`695`. The ``infer_variance`` parameter was added.
Type variable tuple. A specialized form of :class:`type variable <TypeVar>` that enables variadic generics.
Type variable tuples can be declared in :ref:`type parameter lists <type-params>`
using a single asterisk (*) before the name:
def move_first_element_to_last[T, *Ts](tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
return (*tup[1:], tup[0])
Or by explicitly invoking the TypeVarTuple constructor:
T = TypeVar("T")
Ts = TypeVarTuple("Ts")
def move_first_element_to_last(tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
return (*tup[1:], tup[0])
A normal type variable enables parameterization with a single type. A type variable tuple, in contrast, allows parameterization with an arbitrary number of types by acting like an arbitrary number of type variables wrapped in a tuple. For example:
# T is bound to int, Ts is bound to ()
# Return value is (1,), which has type tuple[int]
move_first_element_to_last(tup=(1,))
# T is bound to int, Ts is bound to (str,)
# Return value is ('spam', 1), which has type tuple[str, int]
move_first_element_to_last(tup=(1, 'spam'))
# T is bound to int, Ts is bound to (str, float)
# Return value is ('spam', 3.0, 1), which has type tuple[str, float, int]
move_first_element_to_last(tup=(1, 'spam', 3.0))
# This fails to type check (and fails at runtime)
# because tuple[()] is not compatible with tuple[T, *Ts]
# (at least one element is required)
move_first_element_to_last(tup=())
Note the use of the unpacking operator * in tuple[T, *Ts].
Conceptually, you can think of Ts as a tuple of type variables
(T1, T2, ...). tuple[T, *Ts] would then become
tuple[T, *(T1, T2, ...)], which is equivalent to
tuple[T, T1, T2, ...]. (Note that in older versions of Python, you might
see this written using :data:`Unpack <Unpack>` instead, as
Unpack[Ts].)
Type variable tuples must always be unpacked. This helps distinguish type variable tuples from normal type variables:
x: Ts # Not valid x: tuple[Ts] # Not valid x: tuple[*Ts] # The correct way to do it
Type variable tuples can be used in the same contexts as normal type variables. For example, in class definitions, arguments, and return types:
class Array[*Shape]:
def __getitem__(self, key: tuple[*Shape]) -> float: ...
def __abs__(self) -> "Array[*Shape]": ...
def get_shape(self) -> tuple[*Shape]: ...
Type variable tuples can be happily combined with normal type variables:
.. testcode::
class Array[DType, *Shape]: # This is fine
pass
class Array2[*Shape, DType]: # This would also be fine
pass
class Height: ...
class Width: ...
float_array_1d: Array[float, Height] = Array() # Totally fine
int_array_2d: Array[int, Height, Width] = Array() # Yup, fine too
However, note that at most one type variable tuple may appear in a single list of type arguments or type parameters:
x: tuple[*Ts, *Ts] # Not valid
class Array[*Shape, *Shape]: # Not valid
pass
Finally, an unpacked type variable tuple can be used as the type annotation
of *args:
def call_soon[*Ts](
callback: Callable[[*Ts], None],
*args: *Ts
) -> None:
...
callback(*args)
In contrast to non-unpacked annotations of *args - e.g. *args: int,
which would specify that all arguments are int - *args: *Ts
enables reference to the types of the individual arguments in *args.
Here, this allows us to ensure the types of the *args passed
to call_soon match the types of the (positional) arguments of
callback.
See PEP 646 for more details on type variable tuples.
.. attribute:: __name__ The name of the type variable tuple.
.. versionadded:: 3.11
.. versionchanged:: 3.12 Type variable tuples can now be declared using the :ref:`type parameter <type-params>` syntax introduced by :pep:`695`.
.. 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``:
.. doctest::
>>> from typing import ParamSpec
>>> P = ParamSpec("P")
>>> get_origin(P.args) is P
True
>>> get_origin(P.kwargs) is P
True
.. versionadded:: 3.10
These functions and classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating and declaring types.
Typed version of :func:`collections.namedtuple`.
Usage:
class Employee(NamedTuple):
name: str
id: int
This is equivalent to:
Employee = collections.namedtuple('Employee', ['name', 'id'])
To give a field a default value, you can assign to it in the class body:
class Employee(NamedTuple):
name: str
id: int = 3
employee = Employee('Guido')
assert employee.id == 3
Fields with a default value must come after any fields without a default.
The resulting class has an extra attribute __annotations__ giving a
dict that maps the field names to the field types. (The field names are in
the _fields attribute and the default values are in the
_field_defaults attribute, both of which are part of the :func:`~collections.namedtuple`
API.)
NamedTuple subclasses can also have docstrings and methods:
class Employee(NamedTuple):
"""Represents an employee."""
name: str
id: int = 3
def __repr__(self) -> str:
return f'<Employee {self.name}, id={self.id}>'
NamedTuple subclasses can be generic:
class Group[T](NamedTuple):
key: T
group: list[T]
Backward-compatible usage:
# For creating a generic NamedTuple on Python 3.11 or lower
class Group(NamedTuple, Generic[T]):
key: T
group: list[T]
# A functional syntax is also supported
Employee = NamedTuple('Employee', [('name', str), ('id', int)])
.. versionchanged:: 3.6 Added support for :pep:`526` variable annotation syntax.
.. versionchanged:: 3.6.1 Added support for default values, methods, and docstrings.
.. versionchanged:: 3.8 The ``_field_types`` and ``__annotations__`` attributes are now regular dictionaries instead of instances of ``OrderedDict``.
.. versionchanged:: 3.9 Removed the ``_field_types`` attribute in favor of the more standard ``__annotations__`` attribute which has the same information.
.. versionchanged:: 3.11 Added support for generic namedtuples.
.. deprecated-removed:: 3.13 3.15
The undocumented keyword argument syntax for creating NamedTuple classes
(``NT = NamedTuple("NT", x=int)``) is deprecated, and will be disallowed
in 3.15. Use the class-based syntax or the functional syntax instead.
.. deprecated-removed:: 3.13 3.15
When using the functional syntax to create a NamedTuple class, failing to
pass a value to the 'fields' parameter (``NT = NamedTuple("NT")``) is
deprecated. Passing ``None`` to the 'fields' parameter
(``NT = NamedTuple("NT", None)``) is also deprecated. Both will be
disallowed in Python 3.15. To create a NamedTuple class with 0 fields,
use ``class NT(NamedTuple): pass`` or ``NT = NamedTuple("NT", [])``.
Helper class to create low-overhead :ref:`distinct types <distinct>`.
A NewType is considered a distinct type by a typechecker. At runtime,
however, calling a NewType returns its argument unchanged.
Usage:
UserId = NewType('UserId', int) # Declare the NewType "UserId"
first_user = UserId(1) # "UserId" returns the argument unchanged at runtime
.. attribute:: __module__ The module in which the new type is defined.
.. attribute:: __name__ The name of the new type.
.. attribute:: __supertype__ The type that the new type is based on.
.. versionadded:: 3.5.2
.. versionchanged:: 3.10 ``NewType`` is now a class rather than a function.
Base class for protocol classes.
Protocol classes are defined like this:
class Proto(Protocol):
def meth(self) -> int:
...
Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:
class C:
def meth(self) -> int:
return 0
def func(x: Proto) -> int:
return x.meth()
func(C()) # Passes static type check
See PEP 544 for more details. Protocol classes decorated with :func:`runtime_checkable` (described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures.
Protocol classes can be generic, for example:
class GenProto[T](Protocol):
def meth(self) -> T:
...
In code that needs to be compatible with Python 3.11 or older, generic Protocols can be written as follows:
T = TypeVar("T")
class GenProto(Protocol[T]):
def meth(self) -> T:
...
.. versionadded:: 3.8
.. 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)
@runtime_checkable
class Named(Protocol):
name: str
import threading
assert isinstance(threading.Thread(name='Bob'), Named)
.. note::
:func:`!runtime_checkable` will check only the presence of the required
methods or attributes, not their type signatures or types.
For example, :class:`ssl.SSLObject`
is a class, therefore it passes an :func:`issubclass`
check against :ref:`Callable <annotating-callables>`. However, the
``ssl.SSLObject.__init__`` method exists only to raise a
:exc:`TypeError` with a more informative message, therefore making
it impossible to call (instantiate) :class:`ssl.SSLObject`.
.. note::
An :func:`isinstance` check against a runtime-checkable protocol can be
surprisingly slow compared to an ``isinstance()`` check against
a non-protocol class. Consider using alternative idioms such as
:func:`hasattr` calls for structural checks in performance-sensitive
code.
.. versionadded:: 3.8
.. versionchanged:: 3.12
The internal implementation of :func:`isinstance` checks against
runtime-checkable protocols now uses :func:`inspect.getattr_static`
to look up attributes (previously, :func:`hasattr` was used).
As a result, some objects which used to be considered instances
of a runtime-checkable protocol may no longer be considered instances
of that protocol on Python 3.12+, and vice versa.
Most users are unlikely to be affected by this change.
.. versionchanged:: 3.12
The members of a runtime-checkable protocol are now considered "frozen"
at runtime as soon as the class has been created. Monkey-patching
attributes onto a runtime-checkable protocol will still work, but will
have no impact on :func:`isinstance` checks comparing objects to the
protocol. See :ref:`"What's new in Python 3.12" <whatsnew-typing-py312>`
for more details.
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')
An alternative way to create a TypedDict is by using
function-call syntax. The second argument must be a literal :class:`dict`:
Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
This functional syntax allows defining keys which are not valid :ref:`identifiers <identifiers>`, for example because they are keywords or contain hyphens:
# 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
A TypedDict can be generic:
class Group[T](TypedDict):
key: T
group: list[T]
To create a generic TypedDict that is compatible with Python 3.11
or lower, inherit from :class:`Generic` explicitly:
.. testcode::
T = TypeVar("T")
class Group(TypedDict, Generic[T]):
key: T
group: list[T]
A TypedDict can be introspected via annotations dicts
(see :ref:`annotations-howto` for more information on annotations best practices),
:attr:`__total__`, :attr:`__required_keys__`, and :attr:`__optional_keys__`.
.. attribute:: __total__
``Point2D.__total__`` gives the value of the ``total`` argument.
Example:
.. doctest::
>>> from typing import TypedDict
>>> class Point2D(TypedDict): pass
>>> Point2D.__total__
True
>>> class Point2D(TypedDict, total=False): pass
>>> Point2D.__total__
False
>>> class Point3D(Point2D): pass
>>> Point3D.__total__
True
.. attribute:: __required_keys__ .. versionadded:: 3.9
.. attribute:: __optional_keys__
``Point2D.__required_keys__`` and ``Point2D.__optional_keys__`` return
:class:`frozenset` objects containing required and non-required keys, respectively.
Keys marked with :data:`Required` will always appear in ``__required_keys__``
and keys marked with :data:`NotRequired` will always appear in ``__optional_keys__``.
For backwards compatibility with Python 3.10 and below,
it is also possible to use inheritance to declare both required and
non-required keys in the same ``TypedDict`` . This is done by declaring a
``TypedDict`` with one value for the ``total`` argument and then
inheriting from it in another ``TypedDict`` with a different value for
``total``:
.. doctest::
>>> class Point2D(TypedDict, total=False):
... x: int
... y: int
...
>>> class Point3D(Point2D):
... z: int
...
>>> Point3D.__required_keys__ == frozenset({'z'})
True
>>> Point3D.__optional_keys__ == frozenset({'x', 'y'})
True
.. versionadded:: 3.9
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.
.. versionchanged:: 3.13 Removed support for the keyword-argument method of creating ``TypedDict``\ s.
.. deprecated-removed:: 3.13 3.15
When using the functional syntax to create a TypedDict class, failing to
pass a value to the 'fields' parameter (``TD = TypedDict("TD")``) is
deprecated. Passing ``None`` to the 'fields' parameter
(``TD = TypedDict("TD", None)``) is also deprecated. Both will be
disallowed in Python 3.15. To create a TypedDict class with 0 fields,
use ``class TD(TypedDict): pass`` or ``TD = TypedDict("TD", {})``.
The following protocols are provided by the typing module. All are decorated with :func:`@runtime_checkable <runtime_checkable>`.
An ABC with one abstract method __abs__ that is covariant
in its return type.
An ABC with one abstract method __bytes__.
An ABC with one abstract method __complex__.
An ABC with one abstract method __float__.
An ABC with one abstract method __index__.
.. versionadded:: 3.8
An ABC with one abstract method __int__.
An ABC with one abstract method __round__
that is covariant in its return type.
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`.
.. function:: cast(typ, val) Cast a value to a type. This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don't check anything (we want this to be as fast as possible).
.. function:: assert_type(val, typ, /)
Ask a static type checker to confirm that *val* has an inferred type of *typ*.
At runtime this does nothing: it returns the first argument unchanged with no
checks or side effects, no matter the actual type of the argument.
When a static type checker encounters a call to ``assert_type()``, it
emits an error if the value is not of the specified type::
def greet(name: str) -> None:
assert_type(name, str) # OK, inferred type of `name` is `str`
assert_type(name, int) # type checker error
This function is useful for ensuring the type checker's understanding of a
script is in line with the developer's intentions::
def complex_function(arg: object):
# Do some complex type-narrowing logic,
# after which we hope the inferred type will be `int`
...
# Test whether the type checker correctly understands our function
assert_type(arg, int)
.. versionadded:: 3.11
.. function:: assert_never(arg, /)
Ask a static type checker to confirm that a line of code is unreachable.
Example::
def int_or_str(arg: int | str) -> None:
match arg:
case int():
print("It's an int")
case str():
print("It's a str")
case _ as unreachable:
assert_never(unreachable)
Here, the annotations allow the type checker to infer that the
last case can never execute, because ``arg`` is either
an :class:`int` or a :class:`str`, and both options are covered by
earlier cases.
If a type checker finds that a call to ``assert_never()`` is
reachable, it will emit an error. For example, if the type annotation
for ``arg`` was instead ``int | str | float``, the type checker would
emit an error pointing out that ``unreachable`` is of type :class:`float`.
For a call to ``assert_never`` to pass type checking, the inferred type of
the argument passed in must be the bottom type, :data:`Never`, and nothing
else.
At runtime, this throws an exception when called.
.. seealso::
`Unreachable Code and Exhaustiveness Checking
<https://typing.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(*, eq_default=True, order_default=False, \
kw_only_default=False, frozen_default=False, \
field_specifiers=(), **kwargs)
Decorator to mark an object as providing
:func:`dataclass <dataclasses.dataclass>`-like behavior.
``dataclass_transform`` may be used to
decorate a class, metaclass, or a function that is itself a decorator.
The presence of ``@dataclass_transform()`` tells a static type checker that the
decorated object performs runtime "magic" that
transforms a class in a similar way to
:func:`@dataclasses.dataclass <dataclasses.dataclass>`.
Example usage with a decorator function:
.. testcode::
@dataclass_transform()
def create_model[T](cls: type[T]) -> type[T]:
...
return cls
@create_model
class CustomerModel:
id: int
name: str
On a base class::
@dataclass_transform()
class ModelBase: ...
class CustomerModel(ModelBase):
id: int
name: str
On a metaclass::
@dataclass_transform()
class ModelMeta(type): ...
class ModelBase(metaclass=ModelMeta): ...
class CustomerModel(ModelBase):
id: int
name: str
The ``CustomerModel`` classes defined above will
be treated by type checkers similarly to classes created with
:func:`@dataclasses.dataclass <dataclasses.dataclass>`.
For example, type checkers will assume these classes have
``__init__`` methods that accept ``id`` and ``name``.
The decorated class, metaclass, or function may accept the following bool
arguments which type checkers will assume have the same effect as they
would have on the
:func:`@dataclasses.dataclass<dataclasses.dataclass>` decorator: ``init``,
``eq``, ``order``, ``unsafe_hash``, ``frozen``, ``match_args``,
``kw_only``, and ``slots``. It must be possible for the value of these
arguments (``True`` or ``False``) to be statically evaluated.
The arguments to the ``dataclass_transform`` decorator can be used to
customize the default behaviors of the decorated class, metaclass, or
function:
:param bool eq_default:
Indicates whether the ``eq`` parameter is assumed to be
``True`` or ``False`` if it is omitted by the caller.
Defaults to ``True``.
:param bool order_default:
Indicates whether the ``order`` parameter is
assumed to be ``True`` or ``False`` if it is omitted by the caller.
Defaults to ``False``.
:param bool kw_only_default:
Indicates whether the ``kw_only`` parameter is
assumed to be ``True`` or ``False`` if it is omitted by the caller.
Defaults to ``False``.
:param bool frozen_default:
Indicates whether the ``frozen`` parameter is
assumed to be ``True`` or ``False`` if it is omitted by the caller.
Defaults to ``False``.
.. versionadded:: 3.12
:param field_specifiers:
Specifies a static list of supported classes
or functions that describe fields, similar to :func:`dataclasses.field`.
Defaults to ``()``.
:type field_specifiers: tuple[Callable[..., Any], ...]
:param Any \**kwargs:
Arbitrary other keyword arguments are accepted in order to allow for
possible future extensions.
Type checkers recognize the following optional parameters on field
specifiers:
.. list-table:: **Recognised parameters for field specifiers**
:header-rows: 1
:widths: 20 80
* - Parameter name
- Description
* - ``init``
- Indicates whether the field should be included in the
synthesized ``__init__`` method. If unspecified, ``init`` defaults to
``True``.
* - ``default``
- Provides the default value for the field.
* - ``default_factory``
- Provides a runtime callback that returns the
default value for the field. If neither ``default`` nor
``default_factory`` are specified, the field is assumed to have no
default value and must be provided a value when the class is
instantiated.
* - ``factory``
- An alias for the ``default_factory`` parameter on field specifiers.
* - ``kw_only``
- Indicates whether the field should be marked as
keyword-only. If ``True``, the field will be keyword-only. If
``False``, it will not be keyword-only. If unspecified, the value of
the ``kw_only`` parameter on the object decorated with
``dataclass_transform`` will be used, or if that is unspecified, the
value of ``kw_only_default`` on ``dataclass_transform`` will be used.
* - ``alias``
- Provides an alternative name for the field. This alternative
name is used in the synthesized ``__init__`` method.
At runtime, this decorator records its arguments in the
``__dataclass_transform__`` attribute on the decorated object.
It has no other runtime effect.
See :pep:`681` for more details.
.. versionadded:: 3.11
.. decorator:: overload
Decorator for creating overloaded functions and methods.
The ``@overload`` decorator allows describing functions and methods
that support multiple different combinations of argument types. A series
of ``@overload``-decorated definitions must be followed by exactly one
non-``@overload``-decorated definition (for the same function/method).
``@overload``-decorated definitions are for the benefit of the
type checker only, since they will be overwritten by the
non-``@overload``-decorated definition. The non-``@overload``-decorated
definition, meanwhile, will be used at
runtime but should be ignored by a type checker. At runtime, calling
an ``@overload``-decorated function directly will raise
:exc:`NotImplementedError`.
An example of overload that gives a more
precise type than can be expressed using a union or a type variable:
.. testcode::
@overload
def process(response: None) -> None:
...
@overload
def process(response: int) -> tuple[int, str]:
...
@overload
def process(response: bytes) -> str:
...
def process(response):
... # actual implementation goes here
See :pep:`484` for more details and comparison with other typing semantics.
.. versionchanged:: 3.11
Overloaded functions can now be introspected at runtime using
:func:`get_overloads`.
.. function:: get_overloads(func) Return a sequence of :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
Decorator to indicate final methods and final classes.
Decorating a method with ``@final`` indicates to a type checker that the
method cannot be overridden in a subclass. Decorating a class with ``@final``
indicates that it cannot be subclassed.
For example::
class Base:
@final
def done(self) -> None:
...
class Sub(Base):
def done(self) -> None: # Error reported by type checker
...
@final
class Leaf:
...
class Other(Leaf): # Error reported by type checker
...
There is no runtime checking of these properties. See :pep:`591` for
more details.
.. versionadded:: 3.8
.. versionchanged:: 3.11
The decorator will now attempt to set a ``__final__`` attribute to ``True``
on the decorated object. Thus, a check like
``if getattr(obj, "__final__", False)`` can be used at runtime
to determine whether an object ``obj`` has been marked as final.
If the decorated object does not support setting attributes,
the decorator returns the object unchanged without raising an exception.
.. decorator:: no_type_check Decorator to indicate that annotations are not type hints. This works as a class or function :term:`decorator`. With a class, it applies recursively to all methods and classes defined in that class (but not to methods defined in its superclasses or subclasses). Type checkers will ignore all annotations in a function or class with this decorator. ``@no_type_check`` mutates the decorated object in place.
.. decorator:: 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`.
.. deprecated-removed:: 3.13 3.15
No type checker ever added support for ``@no_type_check_decorator``. It
is therefore deprecated, and will be removed in Python 3.15.
.. decorator:: override
Decorator to indicate that a method in a subclass is intended to override a
method or attribute in a superclass.
Type checkers should emit an error if a method decorated with ``@override``
does not, in fact, override anything.
This helps prevent bugs that may occur when a base class is changed without
an equivalent change to a child class.
For example:
.. testcode::
class Base:
def log_status(self) -> None:
...
class Sub(Base):
@override
def log_status(self) -> None: # Okay: overrides Base.log_status
...
@override
def done(self) -> None: # Error reported by type checker
...
There is no runtime checking of this property.
The decorator will attempt to set an ``__override__`` attribute to ``True`` on
the decorated object. Thus, a check like
``if getattr(obj, "__override__", False)`` can be used at runtime to determine
whether an object ``obj`` has been marked as an override. If the decorated object
does not support setting attributes, the decorator returns the object unchanged
without raising an exception.
See :pep:`698` for more details.
.. versionadded:: 3.12
.. decorator:: type_check_only
Decorator to mark a class or function as unavailable at runtime.
This decorator is itself not available at runtime. It is mainly
intended to mark classes that are defined in type stub files if
an implementation returns an instance of a private class::
@type_check_only
class Response: # private or not available at runtime
code: int
def get_header(self, name: str) -> str: ...
def fetch_response() -> Response: ...
Note that returning instances of private classes is not recommended.
It is usually preferable to make such classes public.
.. function:: get_type_hints(obj, globalns=None, localns=None, include_extras=False)
Return a dictionary containing type hints for a function, method, module
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:
.. testcode::
class Student(NamedTuple):
name: Annotated[str, 'some marker']
assert get_type_hints(Student) == {'name': str}
assert get_type_hints(Student, include_extras=False) == {'name': str}
assert 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`.
See the documentation on :data:`Annotated` for more information.
.. versionchanged:: 3.11
Previously, ``Optional[t]`` was added for function and method annotations
if a default value equal to ``None`` was set.
Now the annotation is returned unchanged.
.. function:: get_origin(tp)
Get the unsubscripted version of a type: for a typing object of the form
``X[Y, Z, ...]`` return ``X``.
If ``X`` is a typing-module alias for a builtin or
:mod:`collections` class, it will be normalized to the original class.
If ``X`` is an instance of :class:`ParamSpecArgs` or :class:`ParamSpecKwargs`,
return the underlying :class:`ParamSpec`.
Return ``None`` for unsupported objects.
Examples:
.. testcode::
assert get_origin(str) is None
assert get_origin(Dict[str, int]) is dict
assert get_origin(Union[int, str]) is Union
P = ParamSpec('P')
assert get_origin(P.args) is P
assert get_origin(P.kwargs) is P
.. versionadded:: 3.8
.. function:: get_args(tp)
Get type arguments with all substitutions performed: for a typing object
of the form ``X[Y, Z, ...]`` return ``(Y, Z, ...)``.
If ``X`` is a union or :class:`Literal` contained in another
generic type, the order of ``(Y, Z, ...)`` may be different from the order
of the original arguments ``[Y, Z, ...]`` due to type caching.
Return ``()`` for unsupported objects.
Examples:
.. testcode::
assert get_args(int) == ()
assert get_args(Dict[int, str]) == (int, str)
assert get_args(Union[int, str]) == (int, str)
.. versionadded:: 3.8
.. function:: get_protocol_members(tp)
Return the set of members defined in a :class:`Protocol`.
::
>>> from typing import Protocol, get_protocol_members
>>> class P(Protocol):
... def a(self) -> str: ...
... b: int
>>> get_protocol_members(P)
frozenset({'a', 'b'})
Raise :exc:`TypeError` for arguments that are not Protocols.
.. versionadded:: 3.13
.. function:: is_protocol(tp)
Determine if a type is a :class:`Protocol`.
For example::
class P(Protocol):
def a(self) -> str: ...
b: int
is_protocol(P) # => True
is_protocol(int) # => False
.. versionadded:: 3.13
.. function:: is_typeddict(tp)
Check if a type is a :class:`TypedDict`.
For example:
.. testcode::
class Film(TypedDict):
title: str
year: int
assert is_typeddict(Film)
assert not is_typeddict(list | str)
# TypedDict is a factory for creating typed dicts,
# not a typed dict itself
assert not is_typeddict(TypedDict)
.. versionadded:: 3.10
Class used for internal typing representation of string forward references.
For example, List["SomeClass"] is implicitly transformed into
List[ForwardRef("SomeClass")]. ForwardRef should not be instantiated by
a user, but may be used by introspection tools.
Note
PEP 585 generic types such as list["SomeClass"] will not be
implicitly transformed into list[ForwardRef("SomeClass")] and thus
will not automatically resolve to list[SomeClass].
.. versionadded:: 3.7.4
.. 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
This module defines several deprecated aliases to pre-existing
standard library classes. These were originally included in the typing
module in order to support parameterizing these generic classes using [].
However, the aliases became redundant in Python 3.9 when the
corresponding pre-existing classes were enhanced to support [] (see
PEP 585).
The redundant types are deprecated as of Python 3.9. However, while the aliases may be removed at some point, removal of these aliases is not currently planned. As such, no deprecation warnings are currently issued by the interpreter for these aliases.
If at some point it is decided to remove these deprecated aliases, a deprecation warning will be issued by the interpreter for at least two releases prior to removal. The aliases are guaranteed to remain in the typing module without deprecation warnings until at least Python 3.14.
Type checkers are encouraged to flag uses of the deprecated types if the program they are checking targets a minimum Python version of 3.9 or newer.
Deprecated alias to :class:`dict`.
Note that to annotate arguments, it is preferred to use an abstract collection type such as :class:`Mapping` rather than to use :class:`dict` or :class:`!typing.Dict`.
This type can be used as follows:
def count_words(text: str) -> Dict[str, int]:
...
.. deprecated:: 3.9 :class:`builtins.dict <dict>` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`list`.
Note that to annotate arguments, it is preferred to use an abstract collection type such as :class:`Sequence` or :class:`Iterable` rather than to use :class:`list` or :class:`!typing.List`.
This type may be used as follows:
def vec2[T: (int, float)](x: T, y: T) -> List[T]:
return [x, y]
def keep_positives[T: (int, float)](vector: Sequence[T]) -> List[T]:
return [item for item in vector if item > 0]
.. deprecated:: 3.9 :class:`builtins.list <list>` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`builtins.set <set>`.
Note that to annotate arguments, it is preferred to use an abstract collection type such as :class:`AbstractSet` rather than to use :class:`set` or :class:`!typing.Set`.
.. deprecated:: 3.9 :class:`builtins.set <set>` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`builtins.frozenset <frozenset>`.
.. deprecated:: 3.9 :class:`builtins.frozenset <frozenset>` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
.. data:: Tuple
Deprecated alias for :class:`tuple`.
:class:`tuple` and ``Tuple`` are special-cased in the type system; see
:ref:`annotating-tuples` for more details.
.. deprecated:: 3.9
:class:`builtins.tuple <tuple>` now supports subscripting (``[]``).
See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`type`.
See :ref:`type-of-class-objects` for details on using :class:`type` or
typing.Type in type annotations.
.. versionadded:: 3.5.2
.. deprecated:: 3.9 :class:`builtins.type <type>` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Aliases to types in :mod:`collections`
Deprecated alias to :class:`collections.defaultdict`.
.. versionadded:: 3.5.2
.. deprecated:: 3.9 :class:`collections.defaultdict` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.OrderedDict`.
.. versionadded:: 3.7.2
.. deprecated:: 3.9 :class:`collections.OrderedDict` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.ChainMap`.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.1
.. deprecated:: 3.9 :class:`collections.ChainMap` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.Counter`.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.1
.. deprecated:: 3.9 :class:`collections.Counter` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.deque`.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.1
.. deprecated:: 3.9 :class:`collections.deque` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated aliases corresponding to the return types from :func:`re.compile` and :func:`re.match`.
These types (and the corresponding functions) are generic over
:data:`AnyStr`. Pattern can be specialised as Pattern[str] or
Pattern[bytes]; Match can be specialised as Match[str] or
Match[bytes].
.. deprecated:: 3.9 Classes ``Pattern`` and ``Match`` from :mod:`re` now support ``[]``. See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias for :class:`str`.
Text is provided to supply a forward
compatible path for Python 2 code: in Python 2, Text is an alias for
unicode.
Use Text to indicate that a value must contain a unicode string in
a manner that is compatible with both Python 2 and Python 3:
def add_unicode_checkmark(text: Text) -> Text:
return text + u' \u2713'
.. versionadded:: 3.5.2
.. deprecated:: 3.11 Python 2 is no longer supported, and most type checkers also no longer support type checking Python 2 code. Removal of the alias is not currently planned, but users are encouraged to use :class:`str` instead of ``Text``.
Aliases to container ABCs in :mod:`collections.abc`
Deprecated alias to :class:`collections.abc.Set`.
.. deprecated:: 3.9 :class:`collections.abc.Set` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
This type represents the types :class:`bytes`, :class:`bytearray`, and :class:`memoryview` of byte sequences.
.. deprecated-removed:: 3.9 3.14 Prefer :class:`collections.abc.Buffer`, or a union like ``bytes | bytearray | memoryview``.
Deprecated alias to :class:`collections.abc.Collection`.
.. versionadded:: 3.6.0
.. deprecated:: 3.9 :class:`collections.abc.Collection` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.Container`.
.. deprecated:: 3.9 :class:`collections.abc.Container` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.ItemsView`.
.. deprecated:: 3.9 :class:`collections.abc.ItemsView` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.KeysView`.
.. deprecated:: 3.9 :class:`collections.abc.KeysView` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :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 subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.MappingView`.
.. deprecated:: 3.9 :class:`collections.abc.MappingView` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.MutableMapping`.
.. deprecated:: 3.9 :class:`collections.abc.MutableMapping` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.MutableSequence`.
.. deprecated:: 3.9 :class:`collections.abc.MutableSequence` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.MutableSet`.
.. deprecated:: 3.9 :class:`collections.abc.MutableSet` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.Sequence`.
.. deprecated:: 3.9 :class:`collections.abc.Sequence` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.ValuesView`.
.. deprecated:: 3.9 :class:`collections.abc.ValuesView` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Aliases to asynchronous ABCs in :mod:`collections.abc`
Deprecated alias to :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 subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.AsyncGenerator`.
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 subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.AsyncIterable`.
.. versionadded:: 3.5.2
.. deprecated:: 3.9 :class:`collections.abc.AsyncIterable` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.AsyncIterator`.
.. versionadded:: 3.5.2
.. deprecated:: 3.9 :class:`collections.abc.AsyncIterator` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.Awaitable`.
.. versionadded:: 3.5.2
.. deprecated:: 3.9 :class:`collections.abc.Awaitable` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Aliases to other ABCs in :mod:`collections.abc`
Deprecated alias to :class:`collections.abc.Iterable`.
.. deprecated:: 3.9 :class:`collections.abc.Iterable` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.Iterator`.
.. deprecated:: 3.9 :class:`collections.abc.Iterator` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
.. data:: Callable
Deprecated alias to :class:`collections.abc.Callable`.
See :ref:`annotating-callables` for details on how to use
:class:`collections.abc.Callable` and ``typing.Callable`` in type annotations.
.. deprecated:: 3.9
:class:`collections.abc.Callable` now supports subscripting (``[]``).
See :pep:`585` and :ref:`types-genericalias`.
.. versionchanged:: 3.10
``Callable`` now supports :class:`ParamSpec` and :data:`Concatenate`.
See :pep:`612` for more details.
Deprecated alias to :class:`collections.abc.Generator`.
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 subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.Hashable`.
.. deprecated:: 3.12 Use :class:`collections.abc.Hashable` directly instead.
Deprecated alias to :class:`collections.abc.Reversible`.
.. deprecated:: 3.9 :class:`collections.abc.Reversible` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`collections.abc.Sized`.
.. deprecated:: 3.12 Use :class:`collections.abc.Sized` directly instead.
Aliases to :mod:`contextlib` ABCs
Deprecated alias to :class:`contextlib.AbstractContextManager`.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.0
.. deprecated:: 3.9 :class:`contextlib.AbstractContextManager` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
Deprecated alias to :class:`contextlib.AbstractAsyncContextManager`.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.2
.. deprecated:: 3.9 :class:`contextlib.AbstractAsyncContextManager` now supports subscripting (``[]``). See :pep:`585` and :ref:`types-genericalias`.
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 versions of standard collections |
3.9 | Undecided (see :ref:`deprecated-aliases` for more information) | PEP 585 |
| :class:`typing.ByteString` | 3.9 | 3.14 | :gh:`91896` |
| :data:`typing.Text` | 3.11 | Undecided | :gh:`92332` |
| :class:`typing.Hashable` and :class:`typing.Sized` | 3.12 | Undecided | :gh:`94309` |
| :data:`typing.TypeAlias` | 3.12 | Undecided | PEP 695 |
| :func:`@typing.no_type_check_decorator <no_type_check_decorator>` | 3.13 | 3.15 | :gh:`106309` |