typing — Soporte para type hints

Added in version 3.5.

Source code: Lib/typing.py

Nota

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.

Consider the function below:

def surface_area_of_cube(edge_length: float) -> str:
    return f"The surface area of the cube is {6 * edge_length ** 2}."

The function surface_area_of_cube takes an argument expected to be an instance of float, as indicated by the type hint edge_length: float. The function is expected to return an instance of str, as indicated by the -> str hint.

While type hints can be simple classes like float or str, they can also be more complex. The typing module provides a vocabulary of more advanced type hints.

New features are frequently added to the typing module. The typing_extensions package provides backports of these new features to older versions of Python.

Ver también

«Typing cheat sheet»

A quick overview of type hints (hosted at the mypy docs)

«Type System Reference» section of the mypy docs

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»

Type-checker-agnostic documentation written by the community detailing type system features, useful typing related tools and typing best practices.

Specification for the Python Type System

The canonical, up-to-date specification of the Python type system can be found at «Specification for the Python type system».

Alias de tipo

A type alias is defined using the type statement, which creates an instance of 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])

Los alias de tipo son útiles para simplificar firmas de tipo complejas. Por ejemplo:

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 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 TypeAlias to make it explicit that this is a type alias, not a normal variable assignment:

from typing import TypeAlias

Vector: TypeAlias = list[float]

NewType

Utilícese la clase auxiliar NewType para crear tipos distintos:

from typing import NewType

UserId = NewType('UserId', int)
some_id = UserId(524313)

El validador estático de tipos tratará el nuevo tipo como si fuera una subclase del tipo original. Esto es útil para capturar errores lógicos:

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)

Se pueden realizar todas las operaciones de int en una variable de tipo UserId, pero el resultado siempre será de tipo int. Esto permite pasar un UserId allí donde se espere un int, pero evitará la creación accidental de un UserId de manera incorrecta:

# 'output' is of type 'int', not 'UserId'
output = UserId(23413) + UserId(54341)

Tenga en cuenta que estas validaciones solo las aplica el verificador de tipo estático. En tiempo de ejecución, la declaración Derived = NewType('Derived', Base) hará que Derived sea una clase que retorna inmediatamente cualquier parámetro que le pase. Eso significa que la expresión Derived(some_value) no crea una nueva clase ni introduce mucha sobrecarga más allá de la de una llamada de función regular.

Más concretamente, la expresión some_value is Derived(some_value) será siempre verdadera en tiempo de ejecución.

No es válido crear un subtipo de Derived:

from typing import NewType

UserId = NewType('UserId', int)

# Fails at runtime and does not pass type checking
class AdminUserId(UserId): pass

Sin embargo, es posible crear un NewType basado en un NewType “derivado”:

from typing import NewType

UserId = NewType('UserId', int)

ProUserId = NewType('ProUserId', UserId)

y la comprobación de tipo para ProUserId funcionará como se espera.

Véase PEP 484 para más detalle.

Nota

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.

En cambio, NewType declara un tipo que es subtipo de otro. Haciendo Derived = NewType('Derived', Original) hará que el Validador estático de tipos trate Derived como una subclase de Original, lo que implica que un valor de tipo Original no puede ser usado allí donde se espere un valor de tipo Derived. Esto es útil para prevenir errores lógicos con un coste de ejecución mínimo.

Added in version 3.5.2.

Distinto en la versión 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.

Distinto en la versión 3.11: The performance of calling NewType has been restored to its level in Python 3.9.

Annotating callable objects

Functions – or other callable objects – can be annotated using collections.abc.Callable or deprecated typing.Callable. Callable[[int], str] signifies a function that takes a single parameter of type int and returns a str.

For example:

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 ParamSpec, 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:

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, overloaded functions, or functions that have keyword-only parameters. However, these signatures can be expressed by defining a Protocol class with a __call__() method:

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

Los invocables que toman otros invocables como argumentos pueden indicar que sus tipos de parámetros dependen unos de otros utilizando ParamSpec. Además, si ese invocable agrega o elimina argumentos de otros invocables, se puede utilizar el operador Concatenate. Toman la forma Callable[ParamSpecVariable, ReturnType] y Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType] respectivamente.

Distinto en la versión 3.10: Callable ahora es compatible con ParamSpec y Concatenate. Consulte PEP 612 para obtener más información.

Ver también

La documentación de ParamSpec y Concatenate proporciona ejemplos de uso en Callable.

Genéricos

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.

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 type parameter syntax:

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 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]

Distinto en la versión 3.12: Syntactic support for generics is new in Python 3.12.

Annotating tuples

For most containers in Python, the typing system assumes that all elements in the container will be of the same type. For example:

from collections.abc import Mapping

# 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] = {}

list only accepts one type argument, so a type checker would emit an error on the y assignment above. Similarly, 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. 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 = ()

The type of class objects

A variable annotated with C may accept a value of type C. In contrast, a variable annotated with type[C] (or deprecated typing.Type[C]) may accept values that are classes themselves – specifically, it will accept the class object of C. For example:

a = 3         # Has type ``int``
b = int       # Has type ``type[int]``
c = type(a)   # Also has type ``type[int]``

Note that type[C] is covariant:

class User: ...
class 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 type are classes, Any, type variables, 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 type, which is the root of Python’s metaclass hierarchy.

Annotating generators and coroutines

A generator can be annotated using 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 generic classes in the standard library, the SendType of Generator behaves contravariantly, not covariantly or invariantly.

The SendType and ReturnType parameters default to None:

def infinite_stream(start: int) -> Generator[int]:
    while True:
        yield start
        start += 1

It is also possible to set these types explicitly:

def infinite_stream(start: int) -> Generator[int, None, None]:
    while True:
        yield start
        start += 1

Simple generators that only ever yield values can also be annotated as having a return type of either Iterable[YieldType] or Iterator[YieldType]:

def infinite_stream(start: int) -> Iterator[int]:
    while True:
        yield start
        start += 1

Async generators are handled in a similar fashion, but don’t expect a ReturnType type argument (AsyncGenerator[YieldType, SendType]). The SendType argument defaults to None, so the following definitions are equivalent:

async def infinite_stream(start: int) -> AsyncGenerator[int]:
    while True:
        yield start
        start = await increment(start)

async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
    while True:
        yield start
        start = await increment(start)

As in the synchronous case, AsyncIterable[YieldType] and AsyncIterator[YieldType] are available as well:

async def infinite_stream(start: int) -> AsyncIterator[int]:
    while True:
        yield start
        start = await increment(start)

Coroutines can be annotated using Coroutine[YieldType, SendType, ReturnType]. Generic arguments correspond to those of 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

Tipos genéricos definidos por el usuario

Una clase definida por el usuario puede ser definida como una clase genérica.

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 type variable T . This also makes T valid as a type within the class body.

Generic classes implicitly inherit from Generic. For compatibility with Python 3.11 and lower, it is also possible to inherit explicitly from Generic to indicate a generic class:

from typing import TypeVar, Generic

T = TypeVar('T')

class LoggedVar(Generic[T]):
    ...

Generic classes have __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)

Un tipo genérico puede tener un numero cualquiera de variables de tipo. Se permiten todas las variaciones de TypeVar para ser usadas como parámetros de un tipo genérico:

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]):
    ...

Cada argumento de variable de tipo en una clase Generic debe ser distinto. Así, no será válido:

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]):
    ...

En este caso MyDict tiene un solo parámetro, T.

Using a generic class without specifying type parameters assumes Any for each position. In the following example, MyIterable is not generic but implicitly inherits from Iterable[Any]:

from collections.abc import Iterable

class MyIterable(Iterable): # Same as Iterable[Any]
    ...

User-defined generic type aliases are also supported. Examples:

from collections.abc import Iterable

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

Distinto en la versión 3.7: Generic ya no posee una metaclase personalizable.

Distinto en la versión 3.12: Syntactic support for generics and type aliases is new in version 3.12. Previously, generic classes had to explicitly inherit from 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 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 ParamSpec can also be created using explicit inheritance from Generic. In this case, ** is not used:

from typing import ParamSpec, Generic

P = ParamSpec('P')

class Z(Generic[P]):
    ...

Another difference between TypeVar and 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 ParamSpec may not have correct __parameters__ after substitution in some cases because they are intended primarily for static type checking.

Distinto en la versión 3.10: Generic ahora se puede parametrizar sobre expresiones de parámetros. Consulte ParamSpec y PEP 612 para obtener más detalles.

A user-defined generic class can have ABCs as base classes without a metaclass conflict. Generic metaclasses are not supported. The outcome of parameterizing generics is cached, and most types in the typing module are hashable and comparable for equality.

El tipo Any

Un caso especial de tipo es Any. Un Validador estático de tipos tratará cualquier tipo como compatible con Any, y Any como compatible con todos los tipos.

Esto significa que es posible realizar cualquier operación o llamada a un método en un valor de tipo Any y asignarlo a cualquier 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()
    ...

Nótese que no se realiza comprobación de tipo cuando se asigna un valor de tipo Any a un tipo más preciso. Por ejemplo, el Validador estático de tipos no reportó ningún error cuando se asignó a a s, aún cuando se declaró s como de tipo str y recibió un valor int en tiempo de ejecución!

Además, todas las funciones sin un tipo de retorno o tipos en los parámetros serán asignadas implícitamente a Any por defecto:

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

Este comportamiento permite que Any sea usado como una vía de escape cuando es necesario mezclar código tipado estática y dinámicamente.

Compárese el comportamiento de Any con el de object. De manera similar a Any, todo tipo es un subtipo de object. Sin embargo, en oposición a Any, lo contrario no es cierto: object no es un subtipo de ningún otro tipo.

Esto implica que cuando el tipo de un valor es object, un validador de tipos rechazará prácticamente todas las operaciones con él, y al asignarlo a una variable (o usarlo como valor de retorno) de un tipo más preciso será un error de tipo. Por ejemplo:

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")

Úsese object para indicar que un valor puede ser de cualquier tipo de manera segura. Úsese Any para indicar que un valor es de tipado dinámico.

Subtipado nominal vs estructural

Inicialmente, el PEP 484 definió el uso de subtipado nominal para el sistema de tipado estático de Python. Esto implica que una clase A será permitida allí donde se espere una clase B si y solo si A es una subclase de B.

Este requisito también se aplicaba anteriormente a clases base abstractas (ABC), tales como Iterable. El problema con esta estrategia es que una clase debía de ser marcada explícitamente para proporcionar tal funcionalidad, lo que resulta poco pythónico (idiomático) y poco ajustado a lo que uno normalmente haría en un código Python tipado dinámicamente. Por ejemplo, esto sí se ajusta al PEP 484:

from collections.abc import Sized, Iterable, Iterator

class Bucket(Sized, Iterable[int]):
    ...
    def __len__(self) -> int: ...
    def __iter__(self) -> Iterator[int]: ...

El PEP 544 permite resolver este problema al permitir escribir el código anterior sin una clase base explícita en la definición de la clase, permitiendo que el Validador estático de tipo considere implícitamente que Bucket es un subtipo tanto de Sized como de Iterable[int]. Esto se conoce como tipado estructural (o duck-typing estático):

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

Asimismo, creando subclases de la clase especial Protocol, el usuario puede definir nuevos protocolos personalizados y beneficiarse del tipado estructural (véanse los ejemplos de abajo).

Contenido del módulo

The typing module defines the following classes, functions and decorators.

Primitivos especiales de tipado

Tipos especiales

These can be used as types in annotations. They do not support subscription using [].

typing.Any

Tipo especial que indica un tipo sin restricciones.

• Todos los tipos son compatibles con Any.

• Any es compatible con todos los tipos.

Distinto en la versión 3.11: 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.

typing.AnyStr

A constrained type variable.

Definition:

AnyStr = TypeVar('AnyStr', str, bytes)

AnyStr is meant to be used for functions that may accept str or bytes arguments but cannot allow the two to mix.

Por ejemplo:

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 Any type, nor does it mean «any string». In particular, AnyStr and str | bytes are different from each other and have different use cases:

# Invalid use of AnyStr:
# The type variable is used only once in the function signature,
# so cannot be "solved" by the type checker
def greet_bad(cond: bool) -> AnyStr:
    return "hi there!" if cond else b"greetings!"

# The better way of annotating this function:
def greet_proper(cond: bool) -> str | bytes:
    return "hi there!" if cond else b"greetings!"

Deprecated since version 3.13, will be removed in version 3.18: Deprecated in favor of the new type parameter syntax. Use class A[T: (str, bytes)]: ... instead of importing AnyStr. See PEP 695 for more details.

In Python 3.16, AnyStr will be removed from typing.__all__, and deprecation warnings will be emitted at runtime when it is accessed or imported from typing. AnyStr will be removed from typing in Python 3.18.

typing.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:

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.

Véase PEP 675 para más detalle.

Added in version 3.11.

typing.Never

typing.NoReturn

Never and NoReturn represent the bottom type, a type that has no members.

They can be used to indicate that a function never returns, such as sys.exit():

from typing import Never  # or NoReturn

def stop() -> Never:
    raise RuntimeError('no way')

Or to define a function that should never be called, as there are no valid arguments, such as assert_never():

from typing import Never  # or NoReturn

def never_call_me(arg: Never) -> None:
    pass

def int_or_str(arg: int | str) -> None:
    never_call_me(arg)  # type checker error
    match arg:
        case int():
            print("It's an int")
        case str():
            print("It's a str")
        case _:
            never_call_me(arg)  # OK, arg is of type Never (or NoReturn)

Never and NoReturn have the same meaning in the type system and static type checkers treat both equivalently.

Added in version 3.6.2: Added NoReturn.

Added in version 3.11: Added Never.

typing.Self

Special type to represent the current enclosed class.

Por ejemplo:

from typing import Self, reveal_type

class Foo:
    def return_self(self) -> Self:
        ...
        return self

class SubclassOfFoo(Foo): pass

reveal_type(Foo().return_self())  # Revealed type is "Foo"
reveal_type(SubclassOfFoo().return_self())  # Revealed type is "SubclassOfFoo"

Esta anotación es semánticamente equivalente a lo siguiente, aunque de una manera más sucinta:

from typing import TypeVar

Self = TypeVar("Self", bound="Foo")

class Foo:
    def return_self(self: Self) -> Self:
        ...
        return self

In general, if something returns self, as in the above examples, you should use Self as the return annotation. If Foo.return_self was annotated as returning "Foo", then the type checker would infer the object returned from SubclassOfFoo.return_self as being of type Foo rather than SubclassOfFoo.

Otros casos de uso comunes incluyen:

• classmethod usados como constructores alternativos y retornan instancias del parámetro cls.

• Anotar un método __enter__() que retorna self.

You should not use Self as the return annotation if the method is not guaranteed to return an instance of a subclass when the class is subclassed:

class Eggs:
    # Self would be an incorrect return annotation here,
    # as the object returned is always an instance of Eggs,
    # even in subclasses
    def returns_eggs(self) -> "Eggs":
        return Eggs()

Véase PEP 673 para más detalle.

Added in version 3.11.

typing.TypeAlias

Special annotation for explicitly declaring a type alias.

Por ejemplo:

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:

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.

Added in version 3.10.

Obsoleto desde la versión 3.12: TypeAlias is deprecated in favor of the type statement, which creates instances of TypeAliasType and which natively supports forward references. Note that while TypeAlias and TypeAliasType serve similar purposes and have similar names, they are distinct and the latter is not the type of the former. Removal of TypeAlias is not currently planned, but users are encouraged to migrate to type statements.

Formas especiales

These can be used as types in annotations. They all support subscription using [], but each has a unique syntax.

typing.Union

Tipo de unión; Union[X, Y] es equivalente a X | Y y significa X o Y.

Para definir una unión, use p. ej. Union[int, str] o la abreviatura int | str. Se recomienda el uso de la abreviatura. Detalles:

• Los argumentos deben ser tipos y haber al menos uno.

• Las uniones de uniones se simplifican (se aplanan), p. ej.:

  Union[Union[int, str], float] == Union[int, str, float]
  

• Las uniones con un solo argumento se eliminan, p. ej.:

  Union[int] == int  # The constructor actually returns int
  

• Argumentos repetidos se omiten, p. ej.:

  Union[int, str, int] == Union[int, str] == int | str
  

• Cuando se comparan uniones, el orden de los argumentos se ignoran, p. ej.:

  Union[int, str] == Union[str, int]
  

• No es posible crear una subclase o instanciar un Union.

• No es posible escribir Union[X][Y].

Distinto en la versión 3.7: No elimina subclases explícitas de una unión en tiempo de ejecución.

Distinto en la versión 3.10: Las uniones ahora se pueden escribir como X | Y. Consulte union type expressions.

typing.Optional

Optional[X] es equivalente a X | None (o Union[X, None]).

Nótese que no es lo mismo que un argumento opcional, que es aquel que tiene un valor por defecto. Un argumento opcional con un valor por defecto no necesita el indicador Optional en su anotación de tipo simplemente por que sea opcional. Por ejemplo:

def foo(arg: int = 0) -> None:
    ...

Por otro lado, si se permite un valor None, es apropiado el uso de Optional, independientemente de que sea opcional o no. Por ejemplo:

def foo(arg: Optional[int] = None) -> None:
    ...

Distinto en la versión 3.10: Optional ahora se puede escribir como X | None. Consulte union type expressions.

typing.Concatenate

Special form for annotating higher-order functions.

Concatenate can be used in conjunction with Callable and 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 Callable. The last parameter to Concatenate must be a ParamSpec or ellipsis (...).

Por ejemplo, para anotar un decorador with_lock que proporciona un threading.Lock a la función decorada, Concatenate puede usarse para indicar que with_lock espera un invocable que toma un Lock como primer argumento y retorna un invocable con un tipo de firma diferente. En este caso, el ParamSpec indica que los tipos de parámetros de los invocables retornados dependen de los tipos de parámetros de los invocables que se pasan en

from collections.abc import Callable
from threading import Lock
from typing import Concatenate

# Use this lock to ensure that only one thread is executing a function
# at any time.
my_lock = Lock()

def with_lock[**P, R](f: Callable[Concatenate[Lock, P], R]) -> Callable[P, R]:
    '''A type-safe decorator which provides a lock.'''
    def inner(*args: P.args, **kwargs: P.kwargs) -> R:
        # Provide the lock as the first argument.
        return f(my_lock, *args, **kwargs)
    return inner

@with_lock
def sum_threadsafe(lock: Lock, numbers: list[float]) -> float:
    '''Add a list of numbers together in a thread-safe manner.'''
    with lock:
        return sum(numbers)

# We don't need to pass in the lock ourselves thanks to the decorator.
sum_threadsafe([1.1, 2.2, 3.3])

Added in version 3.10.

Ver también

• PEP 612 – Parameter Specification Variables (the PEP which introduced ParamSpec and Concatenate)

• ParamSpec

• Annotating callable objects

typing.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.

Por ejemplo:

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[...] no puede ser derivado. En tiempo de ejecución, se permite un valor arbitrario como argumento de tipo de Literal[...], pero los validadores de tipos pueden imponer sus restricciones. Véase PEP 585 para más detalles sobre tipos literales.

Added in version 3.8.

Distinto en la versión 3.9.1: Literal ahora elimina los parámetros duplicados. Las comparaciones de igualdad de los objetos Literal ya no dependen del orden. Los objetos Literal ahora lanzarán una excepción TypeError durante las comparaciones de igualdad si uno de sus parámetros no es hashable.

typing.ClassVar

Construcción especial para tipado para marcar variables de clase.

Tal y como introduce PEP 526, una anotación de variable rodeada por ClassVar indica que la intención de un atributo dado es ser usado como variable de clase y que no debería ser modificado en las instancias de esa misma clase. Uso:

class Starship:
    stats: ClassVar[dict[str, int]] = {} # class variable
    damage: int = 10                     # instance variable

ClassVar solo acepta tipos y no admite más niveles de subíndices.

ClassVar no es un clase en sí misma, y no debe ser usado con isinstance() o issubclass(). ClassVar no modifica el comportamiento de Python en tiempo de ejecución pero puede ser utilizado por validadores de terceros. Por ejemplo, un validador de tipos puede marcar el siguiente código como erróneo:

enterprise_d = Starship(3000)
enterprise_d.stats = {} # Error, setting class variable on instance
Starship.stats = {}     # This is OK

Added in version 3.5.3.

Distinto en la versión 3.13: ClassVar can now be nested in Final and vice versa.

typing.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.

Por ejemplo:

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

No hay comprobación en tiempo de ejecución para estas propiedades. Véase PEP 591 para más detalles.

Added in version 3.8.

Distinto en la versión 3.13: Final can now be nested in ClassVar and vice versa.

typing.Required

Special typing construct to mark a TypedDict key as required.

This is mainly useful for total=False TypedDicts. See TypedDict and PEP 655 for more details.

Added in version 3.11.

typing.NotRequired

Special typing construct to mark a TypedDict key as potentially missing.

Véase TypedDict y PEP 655 para más detalle.

Added in version 3.11.

typing.ReadOnly

A special typing construct to mark an item of a TypedDict as read-only.

Por ejemplo:

class Movie(TypedDict):
   title: ReadOnly[str]
   year: int

def mutate_movie(m: Movie) -> None:
   m["year"] = 1999  # allowed
   m["title"] = "The Matrix"  # typechecker error

There is no runtime checking for this property.

See TypedDict and PEP 705 for more details.

Added in version 3.13.

typing.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 __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 @no_type_check decorator, which can also be used for adding annotations outside the scope of the typing system, but completely disables typechecking for a function or class.

The responsibility of how to interpret the metadata lies with the tool or library encountering an Annotated annotation. A tool or library encountering an Annotated type can scan through the metadata elements to determine if they are of interest (e.g., using isinstance()).

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:

@dataclass
class ValueRange:
    lo: int
    hi: int

T1 = Annotated[int, ValueRange(-10, 5)]
T2 = Annotated[T1, ValueRange(-20, 3)]

Details of the syntax:

• El primer argumento en Annotated debe ser un tipo válido

• 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:

  @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 TypeVarTuple:

  type Variadic[*Ts] = Annotated[*Ts, Ann1]  # NOT valid
  

  This would be equivalent to:

  Annotated[T1, T2, T3, ..., Ann1]
  

  where T1, T2, etc. are TypeVars. This would be invalid: only one type should be passed to Annotated.

• By default, get_type_hints() strips the metadata from annotations. Pass include_extras=True to have the metadata preserved:

  >>> 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 __metadata__ attribute:

  >>> from typing import Annotated
  >>> X = Annotated[int, "very", "important", "metadata"]
  >>> X
  typing.Annotated[int, 'very', 'important', 'metadata']
  >>> X.__metadata__
  ('very', 'important', 'metadata')
  

• At runtime, if you want to retrieve the original type wrapped by Annotated, use the __origin__ attribute:

  >>> from typing import Annotated, get_origin
  >>> Password = Annotated[str, "secret"]
  >>> Password.__origin__
  <class 'str'>
  

  Note that using get_origin() will return Annotated itself:

  >>> get_origin(Password)
  typing.Annotated
  

Ver también

PEP 593 - Flexible function and variable annotations

The PEP introducing Annotated to the standard library.

Added in version 3.9.

typing.TypeIs

Special typing construct for marking user-defined type predicate functions.

TypeIs can be used to annotate the return type of a user-defined type predicate function. TypeIs only accepts a single type argument. At runtime, functions marked this way should return a boolean and take at least one positional argument.

TypeIs aims to benefit type narrowing – a technique used by static type checkers to determine a more precise type of an expression within a program’s code flow. Usually type narrowing is done by analyzing conditional code flow and applying the narrowing to a block of code. The conditional expression here is sometimes referred to as a «type predicate»:

def is_str(val: str | float):
    # "isinstance" type predicate
    if isinstance(val, str):
        # Type of ``val`` is narrowed to ``str``
        ...
    else:
        # Else, type of ``val`` is narrowed to ``float``.
        ...

Sometimes it would be convenient to use a user-defined boolean function as a type predicate. Such a function should use TypeIs[...] or TypeGuard as its return type to alert static type checkers to this intention. TypeIs usually has more intuitive behavior than TypeGuard, but it cannot be used when the input and output types are incompatible (e.g., list[object] to list[int]) or when the function does not return True for all instances of the narrowed type.

Using -> TypeIs[NarrowedType] tells the static type checker that for a given function:

1. El valor de retorno es un booleano.

2. If the return value is True, the type of its argument is the intersection of the argument’s original type and NarrowedType.

3. If the return value is False, the type of its argument is narrowed to exclude NarrowedType.

Por ejemplo:

from typing import assert_type, final, TypeIs

class Parent: pass
class Child(Parent): pass
@final
class Unrelated: pass

def is_parent(val: object) -> TypeIs[Parent]:
    return isinstance(val, Parent)

def run(arg: Child | Unrelated):
    if is_parent(arg):
        # Type of ``arg`` is narrowed to the intersection
        # of ``Parent`` and ``Child``, which is equivalent to
        # ``Child``.
        assert_type(arg, Child)
    else:
        # Type of ``arg`` is narrowed to exclude ``Parent``,
        # so only ``Unrelated`` is left.
        assert_type(arg, Unrelated)

The type inside TypeIs must be consistent with the type of the function’s argument; if it is not, static type checkers will raise an error. An incorrectly written TypeIs function can lead to unsound behavior in the type system; it is the user’s responsibility to write such functions in a type-safe manner.

If a TypeIs function is a class or instance method, then the type in TypeIs maps to the type of the second parameter (after cls or self).

In short, the form def foo(arg: TypeA) -> TypeIs[TypeB]: ..., means that if foo(arg) returns True, then arg is an instance of TypeB, and if it returns False, it is not an instance of TypeB.

TypeIs also works with type variables. For more information, see PEP 742 (Narrowing types with TypeIs).

Added in version 3.13.

typing.TypeGuard

Special typing construct for marking user-defined type predicate functions.

Type predicate functions are user-defined functions that return whether their argument is an instance of a particular type. TypeGuard works similarly to TypeIs, but has subtly different effects on type checking behavior (see below).

El uso de -> TypeGuard le dice al verificador de tipo estático que para una función determinada:

1. El valor de retorno es un booleano.

2. Si el valor de retorno es True, el tipo de su argumento es el tipo dentro de TypeGuard.

TypeGuard también funciona con variables de tipo. Véase PEP 647 para más detalles.

Por ejemplo:

def is_str_list(val: list[object]) -> TypeGuard[list[str]]:
    '''Determines whether all objects in the list are strings'''
    return all(isinstance(x, str) for x in val)

def func1(val: list[object]):
    if is_str_list(val):
        # Type of ``val`` is narrowed to ``list[str]``.
        print(" ".join(val))
    else:
        # Type of ``val`` remains as ``list[object]``.
        print("Not a list of strings!")

TypeIs and TypeGuard differ in the following ways:

• TypeIs requires the narrowed type to be a subtype of the input type, while TypeGuard does not. The main reason is to allow for things like narrowing list[object] to list[str] even though the latter is not a subtype of the former, since list is invariant.

• When a TypeGuard function returns True, type checkers narrow the type of the variable to exactly the TypeGuard type. When a TypeIs function returns True, type checkers can infer a more precise type combining the previously known type of the variable with the TypeIs type. (Technically, this is known as an intersection type.)

• When a TypeGuard function returns False, type checkers cannot narrow the type of the variable at all. When a TypeIs function returns False, type checkers can narrow the type of the variable to exclude the TypeIs type.

Added in version 3.10.

typing.Unpack

Typing operator to conceptually mark an object as having been unpacked.

For example, using the unpack operator * on a type variable tuple 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 typing.TypeVarTuple and builtins.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 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.

Added in version 3.11.

Building generic types and type aliases

The following classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating generic types and type aliases.

These objects can be created through special syntax (type parameter lists and the type statement). For compatibility with Python 3.11 and earlier, they can also be created without the dedicated syntax, as documented below.

class typing.Generic

Clase base abstracta para tipos genéricos.

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 Language Reference.

Entonces, esta clase se puede usar como sigue:

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 generic function.

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.

class typing.TypeVar(name, *constraints, bound=None, covariant=False, contravariant=False, infer_variance=False, default=typing.NoDefault)

Variable de tipo.

The preferred way to construct a type variable is via the dedicated syntax for generic functions, generic classes, and 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 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

Nótese que las variables de tipo pueden ser bound (delimitadas), constrained (restringidas), o ninguna, pero no pueden ser al mismo tiempo delimitadas y restringidas.

The variance of type variables is inferred by type checkers when they are created through the type parameter syntax 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.

Las variables de tipo delimitadas y las variables de tipo restringidas tienen semánticas distintas en varios aspectos importantes. Usar una variable de tipo bound (delimitada) significa que la TypeVar será resuelta utilizando el tipo más específico posible:

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

Las variables de tipo pueden estar delimitadas por tipos concretos, tipos abstractos (ABCs o protocols) e incluso uniones de tipos:

# 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

Sin embargo, usar una variable de tipo constrained significa que la TypeVar sólo podrá ser determinada como exactamente una de las restricciones dadas:

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 TypeError.

__name__

The name of the type variable.

__covariant__

Whether the type var has been explicitly marked as covariant.

__contravariant__

Whether the type var has been explicitly marked as contravariant.

__infer_variance__

Whether the type variable’s variance should be inferred by type checkers.

Added in version 3.12.

__bound__

The bound of the type variable, if any.

Distinto en la versión 3.12: For type variables created through type parameter syntax, the bound is evaluated only when the attribute is accessed, not when the type variable is created (see Evaluación perezosa).

evaluate_bound()

An evaluate function corresponding to the __bound__ attribute. When called directly, this method supports only the VALUE format, which is equivalent to accessing the __bound__ attribute directly, but the method object can be passed to annotationlib.call_evaluate_function() to evaluate the value in a different format.

Added in version 3.14.

__constraints__

A tuple containing the constraints of the type variable, if any.

Distinto en la versión 3.12: For type variables created through type parameter syntax, the constraints are evaluated only when the attribute is accessed, not when the type variable is created (see Evaluación perezosa).

evaluate_constraints()

An evaluate function corresponding to the __constraints__ attribute. When called directly, this method supports only the VALUE format, which is equivalent to accessing the __constraints__ attribute directly, but the method object can be passed to annotationlib.call_evaluate_function() to evaluate the value in a different format.

Added in version 3.14.

__default__

The default value of the type variable, or typing.NoDefault if it has no default.

Added in version 3.13.

evaluate_default()

An evaluate function corresponding to the __default__ attribute. When called directly, this method supports only the VALUE format, which is equivalent to accessing the __default__ attribute directly, but the method object can be passed to annotationlib.call_evaluate_function() to evaluate the value in a different format.

Added in version 3.14.

has_default()

Return whether or not the type variable has a default value. This is equivalent to checking whether __default__ is not the typing.NoDefault singleton, except that it does not force evaluation of the lazily evaluated default value.

Added in version 3.13.

Distinto en la versión 3.12: Type variables can now be declared using the type parameter syntax introduced by PEP 695. The infer_variance parameter was added.

Distinto en la versión 3.13: Support for default values was added.

class typing.TypeVarTuple(name, *, default=typing.NoDefault)

Type variable tuple. A specialized form of type variable that enables variadic generics.

Type variable tuples can be declared in type parameter lists 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])

Una variable de tipo normal permite parametrizar con un solo tipo. Una tupla de variables de tipo, en contraste, permite la parametrización con un número arbitrario de tipos, al actuar como un número arbitrario de variables de tipo envueltas en una tupla. Por ejemplo:

# 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=())

Nótese el uso del operador de desempaquetado * en tuple[T, *Ts]. Conceptualmente, puede pensarse en Ts como una tupla de variables de tipo (T1, T2, ...). tuple[T, *Ts] se convertiría en tuple[T, *(T1, T2, ...)], lo que es equivalente a tuple[T, T1, T2, ...]. (Nótese que en versiones más antiguas de Python, ésto puede verse escrito usando en cambio Unpack, en la forma 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

Las tuplas de variables de tipo pueden ser utilizadas en los mismos contextos que las variables de tipo normales. Por ejemplo en definiciones de clases, argumentos y tipos de retorno:

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:

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

Sin embargo, nótese que en una determinada lista de argumentos de tipo o de parámetros de tipo puede haber como máximo una tupla de variables de tipo:

x: tuple[*Ts, *Ts]            # Not valid
class Array[*Shape, *Shape]:  # Not valid
    pass

Finalmente, una tupla de variables de tipo desempaquetada puede ser utilizada como la anotación de tipo de *args:

def call_soon[*Ts](
    callback: Callable[[*Ts], None],
    *args: *Ts
) -> None:
    ...
    callback(*args)

En contraste con las anotaciones no-desempaquetadas de *args, por ej. *args: int, que especificaría que todos los argumentos son int - *args: *Ts permite referenciar los tipos de los argumentos individuales en *args. Aquí, ésto permite asegurarse de que los tipos de los *args que son pasados a call_soon calcen con los tipos de los argumentos (posicionales) de callback.

Véase PEP 646 para obtener más detalles sobre las tuplas de variables de tipo.

__name__

The name of the type variable tuple.

__default__

The default value of the type variable tuple, or typing.NoDefault if it has no default.

Added in version 3.13.

evaluate_default()

An evaluate function corresponding to the __default__ attribute. When called directly, this method supports only the VALUE format, which is equivalent to accessing the __default__ attribute directly, but the method object can be passed to annotationlib.call_evaluate_function() to evaluate the value in a different format.

Added in version 3.14.

has_default()

Return whether or not the type variable tuple has a default value. This is equivalent to checking whether __default__ is not the typing.NoDefault singleton, except that it does not force evaluation of the lazily evaluated default value.

Added in version 3.13.

Added in version 3.11.

Distinto en la versión 3.12: Type variable tuples can now be declared using the type parameter syntax introduced by PEP 695.

Distinto en la versión 3.13: Support for default values was added.

class typing.ParamSpec(name, *, bound=None, covariant=False, contravariant=False, default=typing.NoDefault)

Parameter specification variable. A specialized version of type variables.

In type parameter lists, parameter specifications can be declared with two asterisks (**):

type IntFunc[**P] = Callable[P, int]

For compatibility with Python 3.11 and earlier, ParamSpec objects can also be created as follows:

P = ParamSpec('P')

Las variables de especificación de parámetros existen principalmente para el beneficio de los verificadores de tipo estático. Se utilizan para reenviar los tipos de parámetros de un invocable a otro invocable, un patrón que se encuentra comúnmente en funciones y decoradores de orden superior. Solo son válidos cuando se utilizan en Concatenate, o como primer argumento de Callable, o como parámetros para genéricos definidos por el usuario. Consulte Generic para obtener más información sobre tipos genéricos.

Por ejemplo, para agregar un registro básico a una función, se puede crear un decorador add_logging para registrar llamadas a funciones. La variable de especificación de parámetros le dice al verificador de tipo que el invocable pasado al decorador y el nuevo invocable retornado por él tienen parámetros de tipo interdependientes:

from collections.abc import Callable
import logging

def add_logging[T, **P](f: Callable[P, T]) -> Callable[P, T]:
    '''A type-safe decorator to add logging to a function.'''
    def inner(*args: P.args, **kwargs: P.kwargs) -> T:
        logging.info(f'{f.__name__} was called')
        return f(*args, **kwargs)
    return inner

@add_logging
def add_two(x: float, y: float) -> float:
    '''Add two numbers together.'''
    return x + y

Sin ParamSpec, la forma más sencilla de anotar esto anteriormente era usar un TypeVar con Callable[..., Any] enlazado. Sin embargo, esto causa dos problemas:

1. El verificador de tipo no puede verificar la función inner porque *args y **kwargs deben escribirse Any.

2. Es posible que se requiera cast() en el cuerpo del decorador add_logging al retornar la función inner, o se debe indicar al verificador de tipo estático que ignore el return inner.

args

kwargs

Dado que ParamSpec captura tanto parámetros posicionales como de palabras clave, P.args y P.kwargs se pueden utilizar para dividir un ParamSpec en sus componentes. P.args representa la tupla de parámetros posicionales en una llamada determinada y solo debe usarse para anotar *args. P.kwargs representa la asignación de parámetros de palabras clave a sus valores en una llamada determinada y solo debe usarse para anotar **kwargs. Ambos atributos requieren que el parámetro anotado esté dentro del alcance. En tiempo de ejecución, P.args y P.kwargs son instancias respectivamente de ParamSpecArgs y ParamSpecKwargs.

__name__

The name of the parameter specification.

__default__

The default value of the parameter specification, or typing.NoDefault if it has no default.

Added in version 3.13.

evaluate_default()

An evaluate function corresponding to the __default__ attribute. When called directly, this method supports only the VALUE format, which is equivalent to accessing the __default__ attribute directly, but the method object can be passed to annotationlib.call_evaluate_function() to evaluate the value in a different format.

Added in version 3.14.

has_default()

Return whether or not the parameter specification has a default value. This is equivalent to checking whether __default__ is not the typing.NoDefault singleton, except that it does not force evaluation of the lazily evaluated default value.

Added in version 3.13.

Las variables de especificación de parámetros creadas con covariant=True o contravariant=True se pueden utilizar para declarar tipos genéricos covariantes o contravariantes. También se acepta el argumento bound, similar a TypeVar. Sin embargo, la semántica real de estas palabras clave aún no se ha decidido.

Added in version 3.10.

Distinto en la versión 3.12: Parameter specifications can now be declared using the type parameter syntax introduced by PEP 695.

Distinto en la versión 3.13: Support for default values was added.

Nota

Solo las variables de especificación de parámetros definidas en el ámbito global pueden ser serializadas.

Ver también

• PEP 612 – Parameter Specification Variables (the PEP which introduced ParamSpec and Concatenate)

• Concatenate

• Annotating callable objects

typing.ParamSpecArgs

typing.ParamSpecKwargs

Argumentos y atributos de argumentos de palabras clave de un ParamSpec. El atributo P.args de un ParamSpec es una instancia de ParamSpecArgs y P.kwargs es una instancia de ParamSpecKwargs. Están pensados para la introspección en tiempo de ejecución y no tienen un significado especial para los verificadores de tipo estático.

Calling get_origin() on either of these objects will return the original ParamSpec:

>>> from typing import ParamSpec, get_origin
>>> P = ParamSpec("P")
>>> get_origin(P.args) is P
True
>>> get_origin(P.kwargs) is P
True

Added in version 3.10.

class typing.TypeAliasType(name, value, *, type_params=())

The type of type aliases created through the type statement.

Example:

>>> type Alias = int
>>> type(Alias)
<class 'typing.TypeAliasType'>

Added in version 3.12.

__name__

The name of the type alias:

>>> type Alias = int
>>> Alias.__name__
'Alias'

__module__

The module in which the type alias was defined:

>>> type Alias = int
>>> Alias.__module__
'__main__'

__type_params__

The type parameters of the type alias, or an empty tuple if the alias is not generic:

>>> type ListOrSet[T] = list[T] | set[T]
>>> ListOrSet.__type_params__
(T,)
>>> type NotGeneric = int
>>> NotGeneric.__type_params__
()

__value__

The type alias’s value. This is lazily evaluated, so names used in the definition of the alias are not resolved until the __value__ attribute is accessed:

>>> type Mutually = Recursive
>>> type Recursive = Mutually
>>> Mutually
Mutually
>>> Recursive
Recursive
>>> Mutually.__value__
Recursive
>>> Recursive.__value__
Mutually

evaluate_value()

An evaluate function corresponding to the __value__ attribute. When called directly, this method supports only the VALUE format, which is equivalent to accessing the __value__ attribute directly, but the method object can be passed to annotationlib.call_evaluate_function() to evaluate the value in a different format:

>>> type Alias = undefined
>>> Alias.__value__
Traceback (most recent call last):
...
NameError: name 'undefined' is not defined
>>> from annotationlib import Format, call_evaluate_function
>>> Alias.evaluate_value(Format.VALUE)
Traceback (most recent call last):
...
NameError: name 'undefined' is not defined
>>> call_evaluate_function(Alias.evaluate_value, Format.FORWARDREF)
ForwardRef('undefined')

Added in version 3.14.

Otras directivas especiales

These functions and classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating and declaring types.

class typing.NamedTuple

Versión para anotación de tipos de collections.namedtuple().

Uso:

class Employee(NamedTuple):
    name: str
    id: int

Esto es equivalente a:

Employee = collections.namedtuple('Employee', ['name', 'id'])

Para proporcionar a un campo un valor por defecto se puede asignar en el cuerpo de la clase:

class Employee(NamedTuple):
    name: str
    id: int = 3

employee = Employee('Guido')
assert employee.id == 3

Los campos con un valor por defecto deben ir después de los campos sin valor por defecto.

La clase resultante tiene un atributo extra __annotations__ que proporciona un diccionario que mapea el nombre de los campos con sus respectivos tipos. (Los nombres de los campos están en el atributo _fields y sus valores por defecto en el atributo _field_defaults, ambos parte de la API namedtuple().)

Las subclases de NamedTuple también pueden tener docstrings y métodos:

class Employee(NamedTuple):
    """Represents an employee."""
    name: str
    id: int = 3

    def __repr__(self) -> str:
        return f'<Employee {self.name}, id={self.id}>'

Las subclases de NamedTuple pueden ser genéricas:

class Group[T](NamedTuple):
    key: T
    group: list[T]

Uso retrocompatible:

# For creating a generic NamedTuple on Python 3.11
T = TypeVar("T")

class Group(NamedTuple, Generic[T]):
    key: T
    group: list[T]

# A functional syntax is also supported
Employee = NamedTuple('Employee', [('name', str), ('id', int)])

Distinto en la versión 3.6: Soporte añadido para la sintaxis de anotación de variables propuesto en PEP 526.

Distinto en la versión 3.6.1: Soporte añadido para valores por defecto, métodos y docstrings.

Distinto en la versión 3.8: Los atributos _field_types y __annotations__ son simples diccionarios en vez de instancias de OrderedDict.

Distinto en la versión 3.9: Se remueve el atributo _field_types en favor del atributo más estándar __annotations__ que tiene la misma información.

Distinto en la versión 3.11: Se agrega soporte para namedtuples genéricas.

Deprecated since version 3.13, will be removed in version 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 since version 3.13, will be removed in version 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", []).

class typing.NewType(name, tp)

Helper class to create low-overhead distinct types.

A NewType is considered a distinct type by a typechecker. At runtime, however, calling a NewType returns its argument unchanged.

Uso:

UserId = NewType('UserId', int)  # Declare the NewType "UserId"
first_user = UserId(1)  # "UserId" returns the argument unchanged at runtime

__module__

The module in which the new type is defined.

__name__

The name of the new type.

__supertype__

The type that the new type is based on.

Added in version 3.5.2.

Distinto en la versión 3.10: NewType es ahora una clase en lugar de una función.

class typing.Protocol(Generic)

Base class for protocol classes.

Protocol classes are defined like this:

class Proto(Protocol):
    def meth(self) -> int:
        ...

Tales clases son usadas principalmente con validadores estáticos de tipos que detectan subtipado estructural (duck-typing estático), por ejemplo:

class C:
    def meth(self) -> int:
        return 0

def func(x: Proto) -> int:
    return x.meth()

func(C())  # Passes static type check

Véase PEP 544 para más detalles. Las clases protocolo decoradas con runtime_checkable() (descrito más adelante) se comportan como protocolos simplistas en tiempo de ejecución que solo comprueban la presencia de atributos dados, ignorando su firma de tipo.

Las clases protocolo pueden ser genéricas, por ejemplo:

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:
        ...

Added in version 3.8.

@typing.runtime_checkable

Marca una clase protocolo como aplicable en tiempo de ejecución (lo convierte en un runtime protocol).

Tal protocolo se puede usar con isinstance() y issubclass(). Esto lanzará una excepción TypeError cuando se aplique a una clase que no es un protocolo. Esto permite una comprobación estructural simple, muy semejante a «one trick ponies» en collections.abc con Iterable. Por ejemplo:

@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)

Nota

runtime_checkable() will check only the presence of the required methods or attributes, not their type signatures or types. For example, ssl.SSLObject is a class, therefore it passes an issubclass() check against Callable. However, the ssl.SSLObject.__init__ method exists only to raise a TypeError with a more informative message, therefore making it impossible to call (instantiate) ssl.SSLObject.

Nota

An 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 hasattr() calls for structural checks in performance-sensitive code.

Added in version 3.8.

Distinto en la versión 3.12: The internal implementation of isinstance() checks against runtime-checkable protocols now uses inspect.getattr_static() to look up attributes (previously, 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.

Distinto en la versión 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 isinstance() checks comparing objects to the protocol. See «What’s new in Python 3.12» for more details.

class typing.TypedDict(dict)

Es una construcción especial para añadir indicadores de tipo a un diccionario. En tiempo de ejecución es un dict simple.

TypedDict crea un tipo de diccionario que espera que todas sus instancias tenga un cierto conjunto de claves, donde cada clave está asociada con un valor de un tipo determinado. Esta exigencia no se comprueba en tiempo de ejecución y solo es aplicada por validadores de tipo. Uso:

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 dict:

Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})

This functional syntax allows defining keys which are not valid 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})

De forma predeterminada, todas las llaves deben estar presentes en un TypedDict. Es posible marcar llaves individuales como no requeridas utilizando NotRequired:

class Point2D(TypedDict):
    x: int
    y: int
    label: NotRequired[str]

# Alternative syntax
Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': NotRequired[str]})

Esto significa que en un TypedDict que sea una instancia de Point2D, será posible omitir la llave label.

Además, es posible marcar todas las llaves como no-requeridas por defecto, al especificar un valor de False en el argumento total:

class Point2D(TypedDict, total=False):
    x: int
    y: int

# Alternative syntax
Point2D = TypedDict('Point2D', {'x': int, 'y': int}, total=False)

Esto significa que un TypedDict Point2D puede tener cualquiera de las llaves omitidas. Solo se espera que un verificador de tipo admita un False literal o True como valor del argumento total. True es el predeterminado y hace que todos los elementos definidos en el cuerpo de la clase sean obligatorios.

Las llaves individuales de un TypedDict total=False pueden ser marcadas como requeridas utilizando 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)

Es posible que un tipo TypedDict herede de uno o más tipos TypedDict usando la sintaxis de clase. Uso:

class Point3D(Point2D):
    z: int

Point3D tiene tres elementos: x, y y z. Lo que es equivalente a la siguiente definición:

class Point3D(TypedDict):
    x: int
    y: int
    z: int

Un TypedDict no puede heredar de una clase que no sea una subclase de TypedDict, exceptuando Generic. Por ejemplo:

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

Un TypedDict puede ser genérico:

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 Generic explicitly:

T = TypeVar("T")

class Group(TypedDict, Generic[T]):
    key: T
    group: list[T]

A TypedDict can be introspected via annotations dicts (see Prácticas recomendadas para las anotaciones for more information on annotations best practices), __total__, __required_keys__, and __optional_keys__.

__total__

Point2D.__total__ gives the value of the total argument. Example:

>>> from typing import TypedDict
>>> class Point2D(TypedDict): pass
>>> Point2D.__total__
True
>>> class Point2D(TypedDict, total=False): pass
>>> Point2D.__total__
False
>>> class Point3D(Point2D): pass
>>> Point3D.__total__
True

This attribute reflects only the value of the total argument to the current TypedDict class, not whether the class is semantically total. For example, a TypedDict with __total__ set to True may have keys marked with NotRequired, or it may inherit from another TypedDict with total=False. Therefore, it is generally better to use __required_keys__ and __optional_keys__ for introspection.

__required_keys__

Added in version 3.9.

__optional_keys__

Point2D.__required_keys__ y Point2D.__optional_keys__ retornan objetos de la clase frozenset, que contienen las llaves requeridas y no requeridas, respectivamente.

Las llaves marcadas con Required siempre aparecerán en __required_keys__ y las llaves marcadas con NotRequired siempre aparecerán en __optional_keys__.

For backwards compatibility with Python 3.10 and below, it is also possible to use inheritance to declare both required and non-required keys in the same TypedDict . This is done by declaring a TypedDict with one value for the total argument and then inheriting from it in another TypedDict with a different value for total:

>>> class Point2D(TypedDict, total=False):
...     x: int
...     y: int
...
>>> class Point3D(Point2D):
...     z: int
...
>>> Point3D.__required_keys__ == frozenset({'z'})
True
>>> Point3D.__optional_keys__ == frozenset({'x', 'y'})
True

Added in version 3.9.

Nota

If from __future__ import annotations is used or if annotations are given as strings, annotations are not evaluated when the TypedDict is defined. Therefore, the runtime introspection that __required_keys__ and __optional_keys__ rely on may not work properly, and the values of the attributes may be incorrect.

Support for ReadOnly is reflected in the following attributes:

__readonly_keys__

A frozenset containing the names of all read-only keys. Keys are read-only if they carry the ReadOnly qualifier.

Added in version 3.13.

__mutable_keys__

A frozenset containing the names of all mutable keys. Keys are mutable if they do not carry the ReadOnly qualifier.

Added in version 3.13.

Véase PEP 589 para más ejemplos y reglas detalladas del uso de TypedDict.

Added in version 3.8.

Distinto en la versión 3.11: Se agrega soporte para marcar llaves individuales como Required o NotRequired. Véase PEP 655.

Distinto en la versión 3.11: Se agrega soporte para TypedDict genéricos.

Distinto en la versión 3.13: Removed support for the keyword-argument method of creating TypedDicts.

Distinto en la versión 3.13: Support for the ReadOnly qualifier was added.

Deprecated since version 3.13, will be removed in version 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", {}).

Protocolos

The following protocols are provided by the typing module. All are decorated with @runtime_checkable.

class typing.SupportsAbs

Una ABC con un método abstracto __abs__ que es covariante en su tipo retornado.

class typing.SupportsBytes

Una ABC con un método abstracto __bytes__.

class typing.SupportsComplex

Una ABC con un método abstracto __complex__.

class typing.SupportsFloat

Una ABC con un método abstracto __float__.

class typing.SupportsIndex

Una ABC con un método abstracto __index__.

Added in version 3.8.

class typing.SupportsInt

Una ABC con un método abstracto __int__.

class typing.SupportsRound

Una ABC con un método abstracto __round__ que es covariantes en su tipo retornado.

ABCs for working with IO

class typing.IO

class typing.TextIO

class typing.BinaryIO

El tipo genérico IO[AnyStr] y sus subclases TextIO(IO[str]) y BinaryIO(IO[bytes]) representan los tipos de flujos de E/S como los retornados por open().

Funciones y decoradores

typing.cast(typ, val)

Convertir un valor a un tipo.

Esto retorna el valor sin modificar. Para el validador de tipos esto indica que el valor de retorno tiene el tipo señalado pero, de manera intencionada, no se comprobará en tiempo de ejecución (para maximizar la velocidad).

typing.assert_type(val, typ, /)

Solicitar a un validador de tipos que confirme que val tiene typ por tipo inferido.

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

Esta función es útil para asegurarse de que la comprensión que el validador de tipos tiene sobre un script está alineada con las intenciones de le desarrolladores:

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)

Added in version 3.11.

typing.assert_never(arg, /)

Solicitar a un validador estático de tipos confirmar que una línea de código no es alcanzable.

Por ejemplo:

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 int or a 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 float. For a call to assert_never to pass type checking, the inferred type of the argument passed in must be the bottom type, Never, and nothing else.

En tiempo de ejecución, ésto lanza una excepción cuando es llamado.

Ver también

Unreachable Code and Exhaustiveness Checking has more information about exhaustiveness checking with static typing.

Added in version 3.11.

typing.reveal_type(obj, /)

Ask a static type checker to reveal the inferred type of an expression.

When a static type checker encounters a call to this function, it emits a diagnostic with the inferred type of the argument. For example:

x: int = 1
reveal_type(x)  # Revealed type is "builtins.int"

Ésto puede ser de utilidad cuando se desea debuguear cómo tu validador de tipos maneja una pieza particular de código.

At runtime, this function prints the runtime type of its argument to sys.stderr and returns the argument unchanged (allowing the call to be used within an expression):

x = reveal_type(1)  # prints "Runtime type is int"
print(x)  # prints "1"

Note that the runtime type may be different from (more or less specific than) the type statically inferred by a type checker.

Most type checkers support reveal_type() anywhere, even if the name is not imported from typing. Importing the name from typing, however, allows your code to run without runtime errors and communicates intent more clearly.

Added in version 3.11.

@typing.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 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 @dataclasses.dataclass.

Example usage with a decorator function:

@dataclass_transform()
def create_model[T](cls: type[T]) -> type[T]:
    ...
    return cls

@create_model
class CustomerModel:
    id: int
    name: str

En una clase base:

@dataclass_transform()
class ModelBase: ...

class CustomerModel(ModelBase):
    id: int
    name: str

En una metaclase:

@dataclass_transform()
class ModelMeta(type): ...

class ModelBase(metaclass=ModelMeta): ...

class CustomerModel(ModelBase):
    id: int
    name: str

Las clases CustomerModel definidas arribe serán tratadas por los validadores de tipo de forma similar a las clases que sean creadas con @dataclasses.dataclass. Por ejemplo, los validadores de tipo asumirán que estas clases tienen métodos __init__ que aceptan id y name.

La clase, metaclase o función decorada puede aceptar los siguientes argumentos booleanos, de los cuales los validadores de tipos asumirán que tienen el mismo efecto que tendrían en el decorador @dataclasses.dataclass: init, eq, order, unsafe_hash, frozen, match_args, kw_only, y slots. Debe ser posible evaluar estáticamente el valor de estos argumentos (True o False).

Es posible utilizar los argumentos del decorador dataclass_transform para personalizar los comportamientos por defecto de la clase, metaclase o función decorada:

Parámetros:

• eq_default (bool) – Indicates whether the eq parameter is assumed to be True or False if it is omitted by the caller. Defaults to True.

• order_default (bool) – Indicates whether the order parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.

• kw_only_default (bool) – Indicates whether the kw_only parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.

• frozen_default (bool) –

  Indicates whether the frozen parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.

  Added in version 3.12.

• field_specifiers (tuple[Callable[..., Any], ...]) – Specifies a static list of supported classes or functions that describe fields, similar to dataclasses.field(). Defaults to ().

• **kwargs (Any) – Es posible pasar arbitrariamente otros argumentos nombrados para permitir posibles extensiones futuras.

Type checkers recognize the following optional parameters on field specifiers:

Recognised parameters for field specifiers

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.

En tiempo de ejecución, este decorador registra sus argumentos en el atributo __dataclass_transform__ del objeto decorado. No tiene otro efecto en tiempo de ejecución.

Véase PEP 681 para más detalle.

Added in version 3.11.

@typing.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 NotImplementedError.

An example of overload that gives a more precise type than can be expressed using a union or a type variable:

@overload
def process(response: None) -> None:
    ...
@overload
def process(response: int) -> tuple[int, str]:
    ...
@overload
def process(response: bytes) -> str:
    ...
def process(response):
    ...  # actual implementation goes here

Véase PEP 484 para más detalle y comparación con otras semánticas de tipado.

Distinto en la versión 3.11: Ahora es posible introspectar en tiempo de ejecución las funciones sobrecargadas utilizando get_overloads().

typing.get_overloads(func)

Return a sequence of @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 @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() puede ser utilizada para introspectar en tiempo de ejecución una función sobrecargada.

Added in version 3.11.

typing.clear_overloads()

Clear all registered overloads in the internal registry.

This can be used to reclaim the memory used by the registry.

Added in version 3.11.

@typing.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.

Por ejemplo:

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
    ...

No hay comprobación en tiempo de ejecución para estas propiedades. Véase PEP 591 para más detalles.

Added in version 3.8.

Distinto en la versión 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.

@typing.no_type_check

Un decorador para indicar que las anotaciones no deben ser comprobadas como indicadores de tipo.

This works as a class or function 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.

@typing.no_type_check_decorator

Un decorador que asigna a otro decorador el efecto de no_type_check() (no comprobar tipo).

Esto hace que el decorador decorado añada el efecto de no_type_check() a la función decorada.

Deprecated since version 3.13, will be removed in version 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.

@typing.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:

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.

Added in version 3.12.

@typing.type_check_only

Decorator to mark a class or function as unavailable at runtime.

Este decorador no está disponible en tiempo de ejecución. Existe principalmente para marcar clases que se definen en archivos stub para cuando una implementación retorna una instancia de una clase privada:

@type_check_only
class Response:  # private or not available at runtime
    code: int
    def get_header(self, name: str) -> str: ...

def fetch_response() -> Response: ...

Nótese que no se recomienda retornar instancias de clases privadas. Normalmente es preferible convertirlas en clases públicas.

Ayudas de introspección

typing.get_type_hints(obj, globalns=None, localns=None, include_extras=False)

Retorna un diccionario que contiene indicaciones de tipo para una función, método, módulo o objeto clase.

This is often the same as obj.__annotations__, but this function makes the following changes to the annotations dictionary:

• Forward references encoded as string literals or ForwardRef objects are handled by evaluating them in globalns, localns, and (where applicable) obj’s type parameter namespace. If globalns or localns is not given, appropriate namespace dictionaries are inferred from obj.

• None is replaced with types.NoneType.

• If @no_type_check has been applied to obj, an empty dictionary is returned.

• If obj is a class C, the function returns a dictionary that merges annotations from C’s base classes with those on C directly. This is done by traversing C.__mro__ and iteratively combining __annotations__ dictionaries. Annotations on classes appearing earlier in the method resolution order always take precedence over annotations on classes appearing later in the method resolution order.

• The function recursively replaces all occurrences of Annotated[T, ...] with T, unless include_extras is set to True (see Annotated for more information).

See also inspect.get_annotations(), a lower-level function that returns annotations more directly.

Nota

If any forward references in the annotations of obj are not resolvable or are not valid Python code, this function will raise an exception such as NameError. For example, this can happen with imported type aliases that include forward references, or with names imported under if TYPE_CHECKING.

Distinto en la versión 3.9: Added include_extras parameter as part of PEP 593. See the documentation on Annotated for more information.

Distinto en la versión 3.11: Anteriormente, se agregaba Optional[t] en las anotaciones de funciones o métodos si se establecía un valor por defecto igual a None. Ahora la anotación es retornada sin cambios.

typing.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 collections class, it will be normalized to the original class. If X is an instance of ParamSpecArgs or ParamSpecKwargs, return the underlying ParamSpec. Return None for unsupported objects.

Examples:

assert get_origin(str) is None
assert get_origin(Dict[str, int]) is dict
assert get_origin(Union[int, str]) is Union
assert get_origin(Annotated[str, "metadata"]) is Annotated
P = ParamSpec('P')
assert get_origin(P.args) is P
assert get_origin(P.kwargs) is P

Added in version 3.8.

typing.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 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:

assert get_args(int) == ()
assert get_args(Dict[int, str]) == (int, str)
assert get_args(Union[int, str]) == (int, str)

Added in version 3.8.

typing.get_protocol_members(tp)

Return the set of members defined in a Protocol.

>>> from typing import Protocol, get_protocol_members
>>> class P(Protocol):
...     def a(self) -> str: ...
...     b: int
>>> get_protocol_members(P) == frozenset({'a', 'b'})
True

Raise TypeError for arguments that are not Protocols.

Added in version 3.13.

typing.is_protocol(tp)

Determine if a type is a Protocol.

Por ejemplo:

class P(Protocol):
    def a(self) -> str: ...
    b: int

is_protocol(P)    # => True
is_protocol(int)  # => False

Added in version 3.13.

typing.is_typeddict(tp)

Compruebe si un tipo es TypedDict.

For example:

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)

Added in version 3.10.

class typing.ForwardRef

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.

Nota

Los tipos genéricos de PEP 585, como list["SomeClass"], no se transformarán implícitamente en list[ForwardRef("SomeClass")] y, por lo tanto, no se resolverán automáticamente en list[SomeClass].

Added in version 3.7.4.

Distinto en la versión 3.14: This is now an alias for annotationlib.ForwardRef.

typing.evaluate_forward_ref(forward_ref, *, owner=None, globals=None, locals=None, type_params=None, format=annotationlib.Format.VALUE)

Evaluate an annotationlib.ForwardRef as a type hint.

This is similar to calling annotationlib.ForwardRef.evaluate(), but unlike that method, evaluate_forward_ref() also:

• Recursively evaluates forward references nested within the type hint.

• Raises TypeError when it encounters certain objects that are not valid type hints.

• Replaces type hints that evaluate to None with types.NoneType.

• Supports the FORWARDREF and STRING formats.

forward_ref must be an instance of ForwardRef. owner, if given, should be the object that holds the annotations that the forward reference derived from, such as a module, class object, or function. It is used to infer the namespaces to use for looking up names. globals and locals can also be explicitly given to provide the global and local namespaces. type_params is a tuple of type parameters that are in scope when evaluating the forward reference. This parameter must be provided (though it may be an empty tuple) if owner is not given and the forward reference does not already have an owner set. format specifies the format of the annotation and is a member of the annotationlib.Format enum.

Added in version 3.14.

typing.NoDefault

A sentinel object used to indicate that a type parameter has no default value. For example:

>>> T = TypeVar("T")
>>> T.__default__ is typing.NoDefault
True
>>> S = TypeVar("S", default=None)
>>> S.__default__ is None
True

Added in version 3.13.

Constantes

typing.TYPE_CHECKING

A special constant that is assumed to be True by 3rd party static type checkers. It is False at runtime.

Uso:

if TYPE_CHECKING:
    import expensive_mod

def fun(arg: 'expensive_mod.SomeType') -> None:
    local_var: expensive_mod.AnotherType = other_fun()

Nótese que la primera anotación de tipo debe estar rodeada por comillas, convirtiéndola en una «referencia directa», para ocultar al intérprete la referencia expensive_mod en tiempo de ejecución. Las anotaciones de tipo para variables locales no se evalúan, así que la segunda anotación no necesita comillas.

Nota

Si se utiliza from __future__ import annotations, las anotaciones no son evaluadas al momento de la definición de funciones. En cambio, serán almacenadas como cadenas de texto en __annotations__. Ésto vuelve innecesario el uso de comillas alrededor de la anotación (véase PEP 563).

Added in version 3.5.2.

Deprecated aliases

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.

Aliases to built-in types

class typing.Dict(dict, MutableMapping[KT, VT])

Deprecated alias to dict.

Note that to annotate arguments, it is preferred to use an abstract collection type such as Mapping rather than to use dict or typing.Dict.

Obsoleto desde la versión 3.9: builtins.dict ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.List(list, MutableSequence[T])

Deprecated alias to list.

Note that to annotate arguments, it is preferred to use an abstract collection type such as Sequence or Iterable rather than to use list or typing.List.

Obsoleto desde la versión 3.9: builtins.list ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.Set(set, MutableSet[T])

Deprecated alias to builtins.set.

Note that to annotate arguments, it is preferred to use an abstract collection type such as collections.abc.Set rather than to use set or typing.Set.

Obsoleto desde la versión 3.9: builtins.set ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.FrozenSet(frozenset, AbstractSet[T_co])

Deprecated alias to builtins.frozenset.

Obsoleto desde la versión 3.9: builtins.frozenset ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

typing.Tuple

Deprecated alias for tuple.

tuple and Tuple are special-cased in the type system; see Annotating tuples for more details.

Obsoleto desde la versión 3.9: builtins.tuple ahora soporta el uso de subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.Type(Generic[CT_co])

Deprecated alias to type.

See The type of class objects for details on using type or typing.Type in type annotations.

Added in version 3.5.2.

Obsoleto desde la versión 3.9: builtins.type ahora soporta el uso de subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

Aliases to types in collections

class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])

Deprecated alias to collections.defaultdict.

Added in version 3.5.2.

Obsoleto desde la versión 3.9: collections.defaultdict ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])

Deprecated alias to collections.OrderedDict.

Added in version 3.7.2.

Obsoleto desde la versión 3.9: collections.OrderedDict ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.ChainMap(collections.ChainMap, MutableMapping[KT, VT])

Deprecated alias to collections.ChainMap.

Added in version 3.6.1.

Obsoleto desde la versión 3.9: collections.ChainMap ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.Counter(collections.Counter, Dict[T, int])

Deprecated alias to collections.Counter.

Added in version 3.6.1.

Obsoleto desde la versión 3.9: collections.Counter ahora soporta subíndices ([])`. Véase PEP 585 y Tipo Alias Genérico.

class typing.Deque(deque, MutableSequence[T])

Deprecated alias to collections.deque.

Added in version 3.6.1.

Obsoleto desde la versión 3.9: collections.deque ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

Aliases to other concrete types

class typing.Pattern

class typing.Match

Deprecated aliases corresponding to the return types from re.compile() and re.match().

These types (and the corresponding functions) are generic over AnyStr. Pattern can be specialised as Pattern[str] or Pattern[bytes]; Match can be specialised as Match[str] or Match[bytes].

Obsoleto desde la versión 3.9: Las clases Pattern y Match de re ahora soportan []. Véase PEP 585 y Tipo Alias Genérico.

class typing.Text

Deprecated alias for str.

Text is provided to supply a forward compatible path for Python 2 code: in Python 2, Text is an alias for unicode.

Úsese Text para indicar que un valor debe contener una cadena de texto Unicode de manera que sea compatible con Python 2 y Python 3:

def add_unicode_checkmark(text: Text) -> Text:
    return text + u' \u2713'

Added in version 3.5.2.

Obsoleto desde la versión 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 str instead of Text.

Aliases to container ABCs in collections.abc

class typing.AbstractSet(Collection[T_co])

Deprecated alias to collections.abc.Set.

Obsoleto desde la versión 3.9: collections.abc.Set ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.Collection(Sized, Iterable[T_co], Container[T_co])

Deprecated alias to collections.abc.Collection.

Added in version 3.6.

Obsoleto desde la versión 3.9: collections.abc.Collection ahora soporta la sintaxis de subíndice ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.Container(Generic[T_co])

Deprecated alias to collections.abc.Container.

Obsoleto desde la versión 3.9: collections.abc.Container ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.ItemsView(MappingView, AbstractSet[tuple[KT_co, VT_co]])

Deprecated alias to collections.abc.ItemsView.

Obsoleto desde la versión 3.9: collections.abc.ItemsView ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.KeysView(MappingView, AbstractSet[KT_co])

Deprecated alias to collections.abc.KeysView.

Obsoleto desde la versión 3.9: collections.abc.KeysView ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.Mapping(Collection[KT], Generic[KT, VT_co])

Deprecated alias to collections.abc.Mapping.

Obsoleto desde la versión 3.9: collections.abc.Mapping ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.MappingView(Sized)

Deprecated alias to collections.abc.MappingView.

Obsoleto desde la versión 3.9: collections.abc.MappingView ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.MutableMapping(Mapping[KT, VT])

Deprecated alias to collections.abc.MutableMapping.

Obsoleto desde la versión 3.9: collections.abc.MutableMapping ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.MutableSequence(Sequence[T])

Deprecated alias to collections.abc.MutableSequence.

Obsoleto desde la versión 3.9: collections.abc.MutableSequence ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.MutableSet(AbstractSet[T])

Deprecated alias to collections.abc.MutableSet.

Obsoleto desde la versión 3.9: collections.abc.MutableSet ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.Sequence(Reversible[T_co], Collection[T_co])

Deprecated alias to collections.abc.Sequence.

Obsoleto desde la versión 3.9: collections.abc.Sequence ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.ValuesView(MappingView, Collection[_VT_co])

Deprecated alias to collections.abc.ValuesView.

Obsoleto desde la versión 3.9: collections.abc.ValuesView ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

Aliases to asynchronous ABCs in collections.abc

class typing.Coroutine(Awaitable[ReturnType], Generic[YieldType, SendType, ReturnType])

Deprecated alias to collections.abc.Coroutine.

See Annotating generators and coroutines for details on using collections.abc.Coroutine and typing.Coroutine in type annotations.

Added in version 3.5.3.

Obsoleto desde la versión 3.9: collections.abc.Coroutine ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.AsyncGenerator(AsyncIterator[YieldType], Generic[YieldType, SendType])

Deprecated alias to collections.abc.AsyncGenerator.

See Annotating generators and coroutines for details on using collections.abc.AsyncGenerator and typing.AsyncGenerator in type annotations.

Added in version 3.6.1.

Obsoleto desde la versión 3.9: collections.abc.AsycGenerator ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

Distinto en la versión 3.13: The SendType parameter now has a default.

class typing.AsyncIterable(Generic[T_co])

Deprecated alias to collections.abc.AsyncIterable.

Added in version 3.5.2.

Obsoleto desde la versión 3.9: collections.abc.AsyncIterable ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.AsyncIterator(AsyncIterable[T_co])

Deprecated alias to collections.abc.AsyncIterator.

Added in version 3.5.2.

Obsoleto desde la versión 3.9: collections.abc.AsyncIterator ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.Awaitable(Generic[T_co])

Deprecated alias to collections.abc.Awaitable.

Added in version 3.5.2.

Obsoleto desde la versión 3.9: collections.abc.Awaitable ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

Aliases to other ABCs in collections.abc

class typing.Iterable(Generic[T_co])

Deprecated alias to collections.abc.Iterable.

Obsoleto desde la versión 3.9: collections.abc.Iterable ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.Iterator(Iterable[T_co])

Deprecated alias to collections.abc.Iterator.

Obsoleto desde la versión 3.9: collections.abc.Iterator ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

typing.Callable

Deprecated alias to collections.abc.Callable.

See Annotating callable objects for details on how to use collections.abc.Callable and typing.Callable in type annotations.

Obsoleto desde la versión 3.9: collections.abc.Callable ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

Distinto en la versión 3.10: Callable ahora es compatible con ParamSpec y Concatenate. Consulte PEP 612 para obtener más información.

class typing.Generator(Iterator[YieldType], Generic[YieldType, SendType, ReturnType])

Deprecated alias to collections.abc.Generator.

See Annotating generators and coroutines for details on using collections.abc.Generator and typing.Generator in type annotations.

Obsoleto desde la versión 3.9: collections.abc.Generator ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

Distinto en la versión 3.13: Default values for the send and return types were added.

class typing.Hashable

Deprecated alias to collections.abc.Hashable.

Obsoleto desde la versión 3.12: Use collections.abc.Hashable directly instead.

class typing.Reversible(Iterable[T_co])

Deprecated alias to collections.abc.Reversible.

Obsoleto desde la versión 3.9: collections.abc.Reversible ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

class typing.Sized

Deprecated alias to collections.abc.Sized.

Obsoleto desde la versión 3.12: Use collections.abc.Sized directly instead.

Aliases to contextlib ABCs

class typing.ContextManager(Generic[T_co, ExitT_co])

Deprecated alias to contextlib.AbstractContextManager.

The first type parameter, T_co, represents the type returned by the __enter__() method. The optional second type parameter, ExitT_co, which defaults to bool | None, represents the type returned by the __exit__() method.

Added in version 3.5.4.

Obsoleto desde la versión 3.9: contextlib.AbstractContextManager ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

Distinto en la versión 3.13: Added the optional second type parameter, ExitT_co.

class typing.AsyncContextManager(Generic[T_co, AExitT_co])

Deprecated alias to contextlib.AbstractAsyncContextManager.

The first type parameter, T_co, represents the type returned by the __aenter__() method. The optional second type parameter, AExitT_co, which defaults to bool | None, represents the type returned by the __aexit__() method.

Added in version 3.6.2.

Obsoleto desde la versión 3.9: contextlib.AbstractAsyncContextManager ahora soporta subíndices ([]). Véase PEP 585 y Tipo Alias Genérico.

Distinto en la versión 3.13: Added the optional second type parameter, AExitT_co.

Línea de tiempo de obsolescencia de características principales

Algunas características de typing están obsoletas y podrán ser removidas en versiones futuras de Python. Lo que sigue es una tabla que resume las principales obsolescencias para su conveniencia. Ésto está sujeto a cambio y no todas las obsolescencias están representadas.

Característica	En desuso desde	Eliminación proyectada	PEP/issue
Versiones typing de colecciones estándares	3.9	Undecided (see Deprecated aliases for more information)	PEP 585
typing.Text	3.11	No decidido	gh-92332
typing.Hashable and typing.Sized	3.12	No decidido	gh-94309
typing.TypeAlias	3.12	No decidido	PEP 695
@typing.no_type_check_decorator	3.13	3.15	gh-106309
typing.AnyStr	3.13	3.18	gh-105578