typing — Suporte para dicas de tipo

Novo na versão 3.5.

Código-fonte: Lib/typing.py

Nota

O tempo de execução do Python não força anotações de tipos de variáveis e funções. Elas podem ser usadas por ferramentas de terceiros como verificadores de tipo, IDEs, linters, etc.

Este módulo oferece suporte para dicas de tipo ao ambiente de execução. Para a especificação original de tipagem do sistema, veja:pep:484. Para uma introdução simplificada as dicas de tipo, veja PEP 483.

A função abaixo recebe e retorna uma string e é anotada como a seguir:

def greeting(name: str) -> str:
    return 'Hello ' + name

Na função greeting, é esperado que o argumento name seja do tipo str e o retorno do tipo str. Subtipos são aceitos como argumentos.

Novos recursos são frequentemente adicionados ao módulo typing. O pacote typing_extensions provê suporte retroativo a estes novos recursos em versões anteriores do Python.

Para ter um resumo dos recursos descontinuados e um cronograma de descontinuação, por favor, veja Cronograma de Descontinuação dos Principais Recursos.

Ver também

“Guia rápido sobre Dicas de Tipo”

Uma visão geral das dicas de tipo (hospedado por mypy docs, em inglês).

“Referência sobre Sistema de Tipo” seção de the mypy docs

O sistema de tipagem do Python é padronizado pelas PEPs, portanto esta referência deve se aplicar a maioria do verificadores de tipo do Python. (Alguns trechos podem se referir especificamente ao mypy. Documento em inglês).

“Tipagem Estática com Python”

Documentação independente de verificador de tipo escrita pela comunidade, detalhando os recursos do sistema de tipo, ferramentas úteis de tipagem e melhores práticas.

PEPs Relevantes

Desde a introdução das dicas de tipo nas PEP 484 e PEP 483, várias PEPs tem modificado e aprimorado o framework do Python para anotações de tipo:

The full list of PEPs

Apelidos de tipo

A type alias is defined by assigning the type to the alias. In this example, Vector and list[float] will be treated as interchangeable synonyms:

Vector = list[float]

def scale(scalar: float, vector: Vector) -> Vector:
    return [scalar * num for num in vector]

# passes type checking; a list of floats qualifies as a Vector.
new_vector = scale(2.0, [1.0, -4.2, 5.4])

Apelidos de tipo são úteis para simplificar assinaturas de tipo complexas. Por exemplo:

from collections.abc import Sequence

ConnectionOptions = dict[str, str]
Address = tuple[str, int]
Server = tuple[Address, ConnectionOptions]

def broadcast_message(message: str, servers: Sequence[Server]) -> None:
    ...

# The static type checker will treat the previous type signature as
# being exactly equivalent to this one.
def broadcast_message(
        message: str,
        servers: Sequence[tuple[tuple[str, int], dict[str, str]]]) -> None:
    ...

Type aliases may be marked with TypeAlias to make it explicit that the statement is a type alias declaration, not a normal variable assignment:

from typing import TypeAlias

Vector: TypeAlias = list[float]

NewType

Utilize o auxiliar NewType para criar tipos únicos:

from typing import NewType

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

O verificador de tipo estático tratará o novo tipo como se fosse uma subclasse do tipo original. Isso é útil para ajudar a encontrar erros de lógica:

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)

Você ainda pode executar todas as operações int em uma variável do tipo UserId, mas o resultado sempre será do tipo int. Isso permite que você passe um UserId em qualquer ocasião que int possa ser esperado, mas previne que você acidentalmente crie um UserId de uma forma inválida:

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

Note que essas verificações são aplicadas apenas pelo verificador de tipo estático. Em tempo de execução, a instrução Derived = NewType('Derived', Base) irá tornar Derived um chamável que retornará imediatamente qualquer parâmetro que você passar. Isso significa que a expressão Derived(some_value) não cria uma nova classe ou introduz sobrecarga além de uma chamada regular de função.instrução

Mais precisamente, a expressão some_value is Derived(some_value) é sempre verdadeira em tempo de execução.

É inválido criar um subtipo de Derived:

from typing import NewType

UserId = NewType('UserId', int)

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

No entanto, é possível criar um NewType baseado em um ‘derivado’ NewType:

from typing import NewType

UserId = NewType('UserId', int)

ProUserId = NewType('ProUserId', UserId)

e a verificação de tipo para ProUserId funcionará como esperado.

Veja PEP 484 para mais detalhes.

Nota

Recall that the use of a type alias declares two types to be equivalent to one another. Doing Alias = Original will make the static type checker treat Alias as being exactly equivalent to Original in all cases. This is useful when you want to simplify complex type signatures.

Em contraste, NewType declara que um tipo será subtipo de outro. Efetuando Derived = NewType('Derived', Original) irá fazer o verificador de tipo estático tratar Derived como uma subclasse de Original, o que significa que um valor do tipo Original não pode ser utilizado onde um valor do tipo Derived é esperado. Isso é útil quando você deseja evitar erros de lógica com custo mínimo de tempo de execução.

Novo na versão 3.5.2.

Alterado na versão 3.10: NewType é agora uma classe ao invés de uma função. Como consequência, existem alguns custos em tempo de execução ao chamar NewType ao invés de uma função comum.

Alterado na versão 3.11: O desempenho de chamar NewType retornou ao mesmo nível da versão Python 3.9.

Anotações de objetos chamáveis

Funções – ou outros objetos chamáveis – podem ser anotados utilizando-se collections.abc.Callable ou typing.Callable. Callable[[int], str]. Significa uma função que recebe um único parâmetro do tipo int. e retorna um str.

Por exemplo:

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

A sintaxe da subscrição deve sempre ser usada com exatamente dois valores: uma lista de argumentos e o tipo de retorno. A lista de argumentos deve ser uma lista de tipos, um ParamSpec, Concatenate, ou reticências. O tipo de retorno deve ser um único tipo.

Se uma reticências literal ... é passada no lugar de uma lista de argumentos, indica que um chamável com umas lista de qualquer parâmetro arbitrário seria aceita.

def concat(x: str, y: str) -> str:
    return x + y

x: Callable[..., str]
x = str     # OK
x = concat  # Also OK

Callable não pode representar assinaturas complexas, como funções que aceitam um número variado de argumentos, funções sobrecarregadas, or funções que recebem apenas parâmetros somente-nomeados. No entanto, essas assinaturas podem ser expressas ao se definir uma Protocol com um método __call__():

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

Chamáveis que recebem outros chamáveis como argumentos podem indicar que seus tipos de parâmetro são dependentes uns dos outros usando ParamSpec. Além disso, se esse chamável adiciona ou retira argumentos de outros chamáveis, o operador Concatenate pode ser usado. Eles assumem a forma de Callable[ParamSpecVariable, ReturnType] e Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType], respectivamente.

Alterado na versão 3.10: Callable agora oferece suporte a ParamSpec e Concatenate. Veja PEP 612 para mais detalhes.

Ver também

A documentação para ParamSpec e Concatenate contém exemplos de uso em Callable.

Genéricos

Como a informação de tipo sobre objetos mantidos em contêineres não pode ser inferida estaticamente de uma maneira genérica, muitas classes de contêiner na biblioteca padrão suportam subscrição para denotar tipos esperados de elementos de contêiner.

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: ...

Generics can be parameterized by using a factory available in typing called TypeVar.

from collections.abc import Sequence
from typing import TypeVar

T = TypeVar('T')                  # Declare type variable "T"

def first(l: Sequence[T]) -> T:   # Function is generic over the TypeVar "T"
    return l[0]

Anotando tuplas

Para a maior parte dos tipos containers em Python, o sistema de tipagem presume que todos os elementos do contêiner são do mesmo tipo. Por exemplo:

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 aceita apenas um tipo de argumento, e assim o verificador de tipos irá emitir um erro na atribuição y acima. Da mesma forma, Mapping aceita apenas dois tipos de argumento: O primeiro indica o tipo das chaves, e o segundo indica o tipo dos valores.

Ao contrário da maioria dos outros contêineres Python, é comum no código Python idiomático que as tuplas tenham elementos que não sejam todos do mesmo tipo. Por esse motivo, as tuplas têm um caso especial no sistema de tipagem do Python. tuple aceita qualquer número do tipo argumento:

# 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)

Para indicar um tupla que pode ser de qualquer comprimento, e no qual todos os elementos são do mesmo tipo T, use tuple[T, ...]. Para denotar um tupla vazia, use tuple[()]. Usando apenas tuple como anotação, é equivalente a usar 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 = ()

O tipo de objetos de classe

Uma variável anotada com C pode aceitar um valor do tipo C. Por outro lado, uma variável anotada com type[C] (ou typing.Type[C]) pode aceitar valores que são classes – especificamente, ela aceitará o objeto classe de C. Por exemplo:

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

Observe que type[C] é covariante:

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]``

Os únicos parâmetros válidos para type são classes, Any, type variables e uniões de qualquer um desses tipos. Por exemplo:

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] é equivalente a type, que é a raiz da hierarquia de metaclasses do Python.

Tipos genéricos definidos pelo usuário

Uma classe definida pelo usuário pode ser definica como uma classe genérica.

from typing import TypeVar, Generic
from logging import Logger

T = TypeVar('T')

class LoggedVar(Generic[T]):
    def __init__(self, value: T, name: str, logger: Logger) -> None:
        self.name = name
        self.logger = logger
        self.value = value

    def set(self, new: T) -> None:
        self.log('Set ' + repr(self.value))
        self.value = new

    def get(self) -> T:
        self.log('Get ' + repr(self.value))
        return self.value

    def log(self, message: str) -> None:
        self.logger.info('%s: %s', self.name, message)

Generic[T] as a base class defines that the class LoggedVar takes a single type parameter T . This also makes T valid as a type within the class body.

The Generic base class defines __class_getitem__() so that LoggedVar[T] is valid as a type:

from collections.abc import Iterable

def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
    for var in vars:
        var.set(0)

Um tipo genérico pode ter qualquer número de tipos de variáveis. Todas as variedades de TypeVar são permitidas como parâmetros para um tipo genérico:

from typing import TypeVar, Generic, Sequence

T = TypeVar('T', contravariant=True)
B = TypeVar('B', bound=Sequence[bytes], covariant=True)
S = TypeVar('S', int, str)

class WeirdTrio(Generic[T, B, S]):
    ...

Cada tipo dos argumentos para Generic devem ser distintos. Assim, os seguintes exemplos são inválidos:

from typing import TypeVar, Generic
...

T = TypeVar('T')

class Pair(Generic[T, T]):   # INVALID
    ...

You can use multiple inheritance with Generic:

from collections.abc import Sized
from typing import TypeVar, Generic

T = TypeVar('T')

class LinkedList(Sized, Generic[T]):
    ...

Ao herdar das classes genérico, algun tipos podem ser fixos:

from collections.abc import Mapping
from typing import TypeVar

T = TypeVar('T')

class MyDict(Mapping[str, T]):
    ...

Neste caso MyDict possui um único parâmetro, T.

O uso de uma classe genérica sem especificar tipos pressupõe Any para cada posição. No exemplo a seguir, MyIterable não é genérico, mas herda implicitamente de Iterable[Any]:

from collections.abc import Iterable

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

Também há suporte para tipos genéricos definidos pelo usuário. Exemplos:

from collections.abc import Iterable
from typing import TypeVar
S = TypeVar('S')
Response = Iterable[S] | int

# Return type here is same as Iterable[str] | int
def response(query: str) -> Response[str]:
    ...

T = TypeVar('T', int, float, complex)
Vec = Iterable[tuple[T, T]]

def inproduct(v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]]
    return sum(x*y for x, y in v)

Alterado na versão 3.7: Generic não possui mais uma metaclasse personalizada.

User-defined generics for parameter expressions are also supported via parameter specification variables in the form Generic[P]. The behavior is consistent with type variables’ described above as parameter specification variables are treated by the typing module as a specialized type variable. The one exception to this is that a list of types can be used to substitute a ParamSpec:

>>> from typing import Generic, ParamSpec, TypeVar

>>> T = TypeVar('T')
>>> P = ParamSpec('P')

>>> class Z(Generic[T, P]): ...
...
>>> Z[int, [dict, float]]
__main__.Z[int, (<class 'dict'>, <class 'float'>)]

Furthermore, a generic with only one parameter specification variable will accept parameter lists in the forms X[[Type1, Type2, ...]] and also X[Type1, Type2, ...] for aesthetic reasons. Internally, the latter is converted to the former, so the following are equivalent:

>>> class X(Generic[P]): ...
...
>>> X[int, str]
__main__.X[(<class 'int'>, <class 'str'>)]
>>> X[[int, str]]
__main__.X[(<class 'int'>, <class 'str'>)]

Observe que genéricos com ParamSpec podem não ter __parameters__ corretos após a substituição em alguns casos porque eles são destinados principalmente à verificação de tipo estático.

Alterado na versão 3.10: Generic agora pode ser parametrizado através de expressões de parâmetros. Veja ParamSpec e PEP 612 para mais detalhes.

Uma classe genérica definida pelo usuário pode ter ABCs como classes base sem conflito de metaclasse. Não há suporte a metaclasses genéricas. O resultado da parametrização de genéricos é armazenado em cache, e a maioria dos tipos no módulo typing são hasheáveis e comparáveis em termos de igualdade.

O tipo Any

Um tipo especial de tipo é Any. Um verificador de tipo estático tratará cada tipo como sendo compatível com Any e Any como sendo compatível com todos os tipos.

Isso significa que é possível realizar qualquer operação ou chamada de método sobre um valor do tipo Any e atribuí-lo a qualquer variável:

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()
    ...

Observe que nenhuma verificação de tipo é realizada ao atribuir um valor do tipo Any a um tipo mais preciso. Por exemplo, o verificador de tipo estático não relatou um erro ao atribuir a a s mesmo que s tenha sido declarado como sendo do tipo str e receba um valor int em tempo de execução!

Além disso, todas as funções sem um tipo de retorno ou tipos de parâmetro terão como padrão implicitamente o uso de Any:

def legacy_parser(text):
    ...
    return data

# A static type checker will treat the above
# as having the same signature as:
def legacy_parser(text: Any) -> Any:
    ...
    return data

Este comportamento permite que Any seja usado como uma saída de emergência quando você precisar misturar código tipado dinamicamente e estaticamente.

Compare o comportamento de Any com o comportamento de object. Semelhante a Any, todo tipo é um subtipo de object. No entanto, ao contrário de Any, o inverso não é verdadeiro: object não é um subtipo de qualquer outro tipo.

Isso significa que quando o tipo de um valor é object, um verificador de tipo rejeitará quase todas as operações nele, e atribuí-lo a uma variável (ou usá-la como valor de retorno) de um tipo mais especializado é um tipo erro. Por exemplo:

def hash_a(item: object) -> int:
    # Fails type checking; an object does not have a 'magic' method.
    item.magic()
    ...

def hash_b(item: Any) -> int:
    # Passes type checking
    item.magic()
    ...

# Passes type checking, since ints and strs are subclasses of object
hash_a(42)
hash_a("foo")

# Passes type checking, since Any is compatible with all types
hash_b(42)
hash_b("foo")

Use object para indicar que um valor pode ser de qualquer tipo de maneira segura. Use Any para indicar que um valor é tipado dinamicamente.

Subtipagem nominal vs estrutural

Inicialmente a PEP 484 definiu o sistema de tipos estáticos do Python como usando subtipagem nominal. Isto significa que uma classe A é permitida onde uma classe B é esperada se e somente se A for uma subclasse de B.

Este requisito anteriormente também se aplicava a classes base abstratas, como Iterable. O problema com essa abordagem é que uma classe teve que ser marcada explicitamente para suportá-los, o que não é pythônico e diferente do que normalmente seria feito em código Python de tipo dinamicamente idiomático. Por exemplo, isso está em conformidade com PEP 484:

from collections.abc import Sized, Iterable, Iterator

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

PEP 544 permite resolver este problema permitindo que os usuários escrevam o código acima sem classes base explícitas na definição de classe, permitindo que Bucket seja implicitamente considerado um subtipo de Sized e Iterable[int] por verificador de tipo estático. Isso é conhecido como subtipagem estrutural (ou tipagem pato estática):

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

Além disso, ao criar uma subclasse de uma classe especial Protocol, um usuário pode definir novos protocolos personalizados para aproveitar ao máximo a subtipagem estrutural (veja exemplos abaixo).

Conteúdo do módulo

O módulo typing define as seguintes classes, funções e decoracores.

Tipos primitivos especiais

Tipos especiais

Eles podem ser usados como tipos em anotações. Eles não oferecem suporte a subscrição usando [].

typing.Any

Tipo especial que indica um tipo irrestrito.

  • Todos os tipos são compatíveis com Any.

  • Any é compatível com todos os tipos.

Alterado na versão 3.11: Any agora pode ser usado como classe base. Isso pode ser útil para evitar erros do verificador de tipo com classes que podem digitar em qualquer lugar ou são altamente dinâmicas.

typing.AnyStr

Uma variável de tipo restrito.

Definição:

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

AnyStr deve ser usado para funções que podem aceitar argumentos str ou bytes mas não podem permitir que os dois se misturem.

Por exemplo:

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 que, apesar do nome, AnyStr não tem nada a ver com o tipo Any, nem significa “qualquer string”. Em particular, AnyStr e str | bytes são diferentes entre si e têm casos de uso diferentes:

# 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!"
typing.LiteralString

Tipo especial que inclui apenas strings literais.

Qualquer literal de string é compatível com LiteralString, assim como outro LiteralString. Entretanto, um objeto digitado apenas str não é. Uma string criada pela composição de objetos do tipo LiteralString também é aceitável como uma LiteralString.

Exemplo:

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 é útil para APIs sensíveis onde strings arbitrárias geradas pelo usuário podem gerar problemas. Por exemplo, os dois casos acima que geram erros no verificador de tipo podem ser vulneráveis a um ataque de injeção de SQL.

Veja PEP 675 para mais detalhes.

Novo na versão 3.11.

typing.Never

The bottom type, a type that has no members.

This can be used to define a function that should never be called, or a function that never returns:

from typing import Never

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

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

Novo na versão 3.11: On older Python versions, NoReturn may be used to express the same concept. Never was added to make the intended meaning more explicit.

typing.NoReturn

Tipo especial indicando que uma função nunca retorna.

Por exemplo:

from typing import NoReturn

def stop() -> NoReturn:
    raise RuntimeError('no way')

NoReturn can also be used as a bottom type, a type that has no values. Starting in Python 3.11, the Never type should be used for this concept instead. Type checkers should treat the two equivalently.

Novo na versão 3.6.2.

typing.Self

Tipo especial para representar a classe atual inclusa.

Por exemplo:

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 anotação é semanticamente equivalente à seguinte, embora de forma mais sucinta:

from typing import TypeVar

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

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

Em geral, se algo retorna self, como nos exemplos acima, você deve usar Self como anotação de retorno. Se Foo.return_self foi anotado como retornando "Foo", então o verificador de tipo inferiria o objeto retornado de SubclassOfFoo.return_self como sendo do tipo Foo em vez de SubclassOfFoo.

Outros casos de uso comuns incluem:

  • classmethods que são usados como construtores alternativos e retornam instâncias do parâmetro cls.

  • Anotando um método __enter__() que retorna self.

Você não deveria usar Self como a anotação de retorno se não for garantido que o método retorne uma instância de uma subclasse quando a classe for subclassificada:

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()

Veja PEP 673 para mais detalhes.

Novo na versão 3.11.

typing.TypeAlias

Anotações especiais para declarar explicitamente um apelido de tipo.

Por exemplo:

from typing import TypeAlias

Factors: TypeAlias = list[int]

TypeAlias is particularly useful 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.
# 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: ...

Veja PEP 613 para mais detalhes.

Novo na versão 3.10.

Formas especiais

Eles podem ser usados ​​como tipos em anotações. Todos eles oferecem suporte a subscrição usando [], mas cada um tem uma sintaxe única.

typing.Union

Tipo de união; Union[X, Y] é equivalente a X | Y e significa X ou Y.

Para definir uma união, use, por exemplo. Union[int, str] ou a abreviatura int | str. Usar essa abreviação é recomendado. Detalhes:

  • Os argumentos devem ser tipos e deve haver pelo menos um.

  • As uniões de uniões são achatadas, por exemplo:

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

  • As uniões de um único argumento desaparecem, por exemplo:

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

  • Argumento redundantes são pulados, e.g.:

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

  • When comparing unions, the argument order is ignored, e.g.:

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

  • Você não pode estender ou instanciar uma Union

  • Você não pode escrever Union[X][Y].

Alterado na versão 3.7: Don’t remove explicit subclasses from unions at runtime.

Alterado na versão 3.10: Unions can now be written as X | Y. See union type expressions.

typing.Optional

Optional[X] is equivalent to X | None (or Union[X, None]).

Note that this is not the same concept as an optional argument, which is one that has a default. An optional argument with a default does not require the Optional qualifier on its type annotation just because it is optional. For example:

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

On the other hand, if an explicit value of None is allowed, the use of Optional is appropriate, whether the argument is optional or not. For example:

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

Alterado na versão 3.10: Optional can now be written as X | None. See union type expressions.

typing.Concatenate

Forma especial para anotar funções de ordem superior.

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 (...).

For example, to annotate a decorator with_lock which provides a threading.Lock to the decorated function, Concatenate can be used to indicate that with_lock expects a callable which takes in a Lock as the first argument, and returns a callable with a different type signature. In this case, the ParamSpec indicates that the returned callable’s parameter types are dependent on the parameter types of the callable being passed in:

from collections.abc import Callable
from threading import Lock
from typing import Concatenate, ParamSpec, TypeVar

P = ParamSpec('P')
R = TypeVar('R')

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

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

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

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

Novo na versão 3.10.

Ver também

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 exemplo:

def validate_simple(data: Any) -> Literal[True]:  # always returns True
    ...

Mode: TypeAlias = Literal['r', 'rb', 'w', 'wb']
def open_helper(file: str, mode: Mode) -> str:
    ...

open_helper('/some/path', 'r')      # Passes type check
open_helper('/other/path', 'typo')  # Error in type checker

Literal[...] cannot be subclassed. At runtime, an arbitrary value is allowed as type argument to Literal[...], but type checkers may impose restrictions. See PEP 586 for more details about literal types.

Novo na versão 3.8.

Alterado na versão 3.9.1: Literal now de-duplicates parameters. Equality comparisons of Literal objects are no longer order dependent. Literal objects will now raise a TypeError exception during equality comparisons if one of their parameters are not hashable.

typing.ClassVar

Special type construct to mark class variables.

As introduced in PEP 526, a variable annotation wrapped in ClassVar indicates that a given attribute is intended to be used as a class variable and should not be set on instances of that class. Usage:

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

ClassVar accepts only types and cannot be further subscribed.

ClassVar is not a class itself, and should not be used with isinstance() or issubclass(). ClassVar does not change Python runtime behavior, but it can be used by third-party type checkers. For example, a type checker might flag the following code as an error:

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

Novo na versão 3.5.3.

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 exemplo:

MAX_SIZE: Final = 9000
MAX_SIZE += 1  # Error reported by type checker

class Connection:
    TIMEOUT: Final[int] = 10

class FastConnector(Connection):
    TIMEOUT = 1  # Error reported by type checker

There is no runtime checking of these properties. See PEP 591 for more details.

Novo na versão 3.8.

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.

Novo na versão 3.11.

typing.NotRequired

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

See TypedDict and PEP 655 for more details.

Novo na versão 3.11.

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)]

Detalhes da sintaxe:

  • The first argument to Annotated must be a valid type

  • Multiple metadata elements can be supplied (Annotated supports variadic arguments):

    @dataclass
class ctype:
    kind: str

Annotated[int, ValueRange(3, 10), ctype("char")]

    It is up to the tool consuming the annotations to decide whether the client is allowed to add multiple metadata elements to one annotation and how to merge those annotations.

  • Annotated must be subscripted with at least two arguments ( Annotated[int] is not valid)

  • The order of the metadata elements is preserved and matters for equality checks:

    assert Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
    int, ctype("char"), ValueRange(3, 10)
]

  • Nested Annotated types are flattened. The order of the metadata elements starts with the innermost annotation:

    assert Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
    int, ValueRange(3, 10), ctype("char")
]

  • Elementos duplicados de metadata não são removidos:

    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

T = TypeVar("T")
Vec: TypeAlias = Annotated[list[tuple[T, T]], MaxLen(10)]

assert Vec[int] == Annotated[list[tuple[int, int]], MaxLen(10)]

  • Annotated cannot be used with an unpacked TypeVarTuple:

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

    Isso deve ser equivalente a

    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')

Ver também

PEP 593 - Flexible function and variable annotations

The PEP introducing Annotated to the standard library.

Novo na versão 3.9.

typing.TypeGuard

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

TypeGuard can be used to annotate the return type of a user-defined type guard function. TypeGuard only accepts a single type argument. At runtime, functions marked this way should return a boolean.

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

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

Sometimes it would be convenient to use a user-defined boolean function as a type guard. Such a function should use TypeGuard[...] as its return type to alert static type checkers to this intention.

Using -> TypeGuard tells the static type checker that for a given function:

  1. O valor de retorno é um booleano.

  2. If the return value is True, the type of its argument is the type inside TypeGuard.

Por exemplo:

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

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

If is_str_list is a class or instance method, then the type in TypeGuard maps to the type of the second parameter after cls or self.

In short, the form def foo(arg: TypeA) -> TypeGuard[TypeB]: ..., means that if foo(arg) returns True, then arg narrows from TypeA to TypeB.

Nota

TypeB need not be a narrower form of TypeA – it can even be a wider form. The main reason is to allow for things like narrowing list[object] to list[str] even though the latter is not a subtype of the former, since list is invariant. The responsibility of writing type-safe type guards is left to the user.

TypeGuard also works with type variables. See PEP 647 for more details.

Novo na versão 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

Novo na versão 3.11.

Building generic types

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

class typing.Generic

Classe base abstrata para tipos genéricos

A generic type is typically declared by inheriting from an instantiation of this class with one or more type variables. For example, a generic mapping type might be defined as:

class Mapping(Generic[KT, VT]):
    def __getitem__(self, key: KT) -> VT:
        ...
        # Etc.

Esta classe pode ser utilizada como segue:

X = TypeVar('X')
Y = TypeVar('Y')

def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y:
    try:
        return mapping[key]
    except KeyError:
        return default
class typing.TypeVar(name, *constraints, bound=None, covariant=False, contravariant=False)

Tipo variável.

Uso:

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(x: T, n: int) -> Sequence[T]:
    """Return a list containing n references to x."""
    return [x]*n


def print_capitalized(x: S) -> S:
    """Print x capitalized, and return x."""
    print(x.capitalize())
    return x


def concatenate(x: A, y: A) -> A:
    """Add two strings or bytes objects together."""
    return x + y

Note that type variables can be bound, constrained, or neither, but cannot be both bound and constrained.

Type variables may be marked covariant or contravariant by passing covariant=True or contravariant=True. See PEP 484 for more details. By default, type variables are invariant.

Bound type variables and constrained type variables have different semantics in several important ways. Using a bound type variable means that the TypeVar will be solved using the most specific type possible:

x = print_capitalized('a string')
reveal_type(x)  # revealed type is str

class StringSubclass(str):
    pass

y = print_capitalized(StringSubclass('another string'))
reveal_type(y)  # revealed type is StringSubclass

z = print_capitalized(45)  # error: int is not a subtype of str

Type variables can be bound to concrete types, abstract types (ABCs or protocols), and even unions of types:

U = TypeVar('U', bound=str|bytes)  # Can be any subtype of the union str|bytes
V = TypeVar('V', bound=SupportsAbs)  # Can be anything with an __abs__ method

Using a constrained type variable, however, means that the TypeVar can only ever be solved as being exactly one of the constraints given:

a = concatenate('one', 'two')
reveal_type(a)  # revealed type is str

b = concatenate(StringSubclass('one'), StringSubclass('two'))
reveal_type(b)  # revealed type is str, despite StringSubclass being passed in

c = concatenate('one', b'two')  # error: type variable 'A' can be either str or bytes in a function call, but not both

At runtime, isinstance(x, T) will raise TypeError.

__name__

The name of the type variable.

__covariant__

Whether the type var has been marked as covariant.

__contravariant__

Whether the type var has been marked as contravariant.

__bound__

The bound of the type variable, if any.

__constraints__

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

class typing.TypeVarTuple(name)

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

Uso:

T = TypeVar("T")
Ts = TypeVarTuple("Ts")

def move_first_element_to_last(tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
    return (*tup[1:], tup[0])

A normal type variable enables parameterization with a single type. A type variable tuple, in contrast, allows parameterization with an arbitrary number of types by acting like an arbitrary number of type variables wrapped in a tuple. For example:

# T is bound to int, Ts is bound to ()
# Return value is (1,), which has type tuple[int]
move_first_element_to_last(tup=(1,))

# T is bound to int, Ts is bound to (str,)
# Return value is ('spam', 1), which has type tuple[str, int]
move_first_element_to_last(tup=(1, 'spam'))

# T is bound to int, Ts is bound to (str, float)
# Return value is ('spam', 3.0, 1), which has type tuple[str, float, int]
move_first_element_to_last(tup=(1, 'spam', 3.0))

# This fails to type check (and fails at runtime)
# because tuple[()] is not compatible with tuple[T, *Ts]
# (at least one element is required)
move_first_element_to_last(tup=())

Note the use of the unpacking operator * in tuple[T, *Ts]. Conceptually, you can think of Ts as a tuple of type variables (T1, T2, ...). tuple[T, *Ts] would then become tuple[T, *(T1, T2, ...)], which is equivalent to tuple[T, T1, T2, ...]. (Note that in older versions of Python, you might see this written using Unpack instead, as Unpack[Ts].)

Type variable tuples must always be unpacked. This helps distinguish type variable tuples from normal type variables:

x: Ts          # Not valid
x: tuple[Ts]   # Not valid
x: tuple[*Ts]  # The correct way to do it

Type variable tuples can be used in the same contexts as normal type variables. For example, in class definitions, arguments, and return types:

Shape = TypeVarTuple("Shape")
class Array(Generic[*Shape]):
    def __getitem__(self, key: tuple[*Shape]) -> float: ...
    def __abs__(self) -> "Array[*Shape]": ...
    def get_shape(self) -> tuple[*Shape]: ...

Type variable tuples can be happily combined with normal type variables:

DType = TypeVar('DType')
Shape = TypeVarTuple('Shape')

class Array(Generic[DType, *Shape]):  # This is fine
    pass

class Array2(Generic[*Shape, DType]):  # This would also be fine
    pass

class Height: ...
class Width: ...

float_array_1d: Array[float, Height] = Array()     # Totally fine
int_array_2d: Array[int, Height, Width] = Array()  # Yup, fine too

However, note that at most one type variable tuple may appear in a single list of type arguments or type parameters:

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

Finally, an unpacked type variable tuple can be used as the type annotation of *args:

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

In contrast to non-unpacked annotations of *args - e.g. *args: int, which would specify that all arguments are int - *args: *Ts enables reference to the types of the individual arguments in *args. Here, this allows us to ensure the types of the *args passed to call_soon match the types of the (positional) arguments of callback.

See PEP 646 for more details on type variable tuples.

__name__

The name of the type variable tuple.

Novo na versão 3.11.

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

Parameter specification variable. A specialized version of type variables.

Uso:

P = ParamSpec('P')

Parameter specification variables exist primarily for the benefit of static type checkers. They are used to forward the parameter types of one callable to another callable – a pattern commonly found in higher order functions and decorators. They are only valid when used in Concatenate, or as the first argument to Callable, or as parameters for user-defined Generics. See Generic for more information on generic types.

For example, to add basic logging to a function, one can create a decorator add_logging to log function calls. The parameter specification variable tells the type checker that the callable passed into the decorator and the new callable returned by it have inter-dependent type parameters:

from collections.abc import Callable
from typing import TypeVar, ParamSpec
import logging

T = TypeVar('T')
P = ParamSpec('P')

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

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

Without ParamSpec, the simplest way to annotate this previously was to use a TypeVar with bound Callable[..., Any]. However this causes two problems:

  1. The type checker can’t type check the inner function because *args and **kwargs have to be typed Any.

  2. cast() may be required in the body of the add_logging decorator when returning the inner function, or the static type checker must be told to ignore the return inner.

args
kwargs

Since ParamSpec captures both positional and keyword parameters, P.args and P.kwargs can be used to split a ParamSpec into its components. P.args represents the tuple of positional parameters in a given call and should only be used to annotate *args. P.kwargs represents the mapping of keyword parameters to their values in a given call, and should be only be used to annotate **kwargs. Both attributes require the annotated parameter to be in scope. At runtime, P.args and P.kwargs are instances respectively of ParamSpecArgs and ParamSpecKwargs.

__name__

The name of the parameter specification.

Parameter specification variables created with covariant=True or contravariant=True can be used to declare covariant or contravariant generic types. The bound argument is also accepted, similar to TypeVar. However the actual semantics of these keywords are yet to be decided.

Novo na versão 3.10.

Nota

Only parameter specification variables defined in global scope can be pickled.

Ver também

typing.ParamSpecArgs
typing.ParamSpecKwargs

Arguments and keyword arguments attributes of a ParamSpec. The P.args attribute of a ParamSpec is an instance of ParamSpecArgs, and P.kwargs is an instance of ParamSpecKwargs. They are intended for runtime introspection and have no special meaning to static type checkers.

Calling 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

Novo na versão 3.10.

Outras diretivas especiais

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

Typed version of collections.namedtuple().

Uso:

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

Isso equivale a:

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

To give a field a default value, you can assign to it in the class body:

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

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

Fields with a default value must come after any fields without a default.

The resulting class has an extra attribute __annotations__ giving a dict that maps the field names to the field types. (The field names are in the _fields attribute and the default values are in the _field_defaults attribute, both of which are part of the namedtuple() API.)

NamedTuple subclasses can also have docstrings and methods:

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

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

NamedTuple subclasses can be generic:

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

Backward-compatible usage:

Employee = NamedTuple('Employee', [('name', str), ('id', int)])

Alterado na versão 3.6: Added support for PEP 526 variable annotation syntax.

Alterado na versão 3.6.1: Added support for default values, methods, and docstrings.

Alterado na versão 3.8: The _field_types and __annotations__ attributes are now regular dictionaries instead of instances of OrderedDict.

Alterado na versão 3.9: Removed the _field_types attribute in favor of the more standard __annotations__ attribute which has the same information.

Alterado na versão 3.11: Added support for generic namedtuples.

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__

O nome do novo tipo.

__supertype__

O tipo na qual o novo tipo é baseado.

Novo na versão 3.5.2.

Alterado na versão 3.10: NewType is now a class rather than a function.

class typing.Protocol(Generic)

Base class for protocol classes.

Protocol classes are defined like this:

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

Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:

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

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

func(C())  # Passes static type check

See PEP 544 for more details. Protocol classes decorated with runtime_checkable() (described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures.

Protocol classes can be generic, for example:

T = TypeVar("T")

class GenProto(Protocol[T]):
    def meth(self) -> T:
        ...

Novo na versão 3.8.

@typing.runtime_checkable

Mark a protocol class as a runtime protocol.

Such a protocol can be used with isinstance() and issubclass(). This raises TypeError when applied to a non-protocol class. This allows a simple-minded structural check, very similar to “one trick ponies” in collections.abc such as Iterable. For example:

@runtime_checkable
class Closable(Protocol):
    def close(self): ...

assert isinstance(open('/some/file'), Closable)

@runtime_checkable
class Named(Protocol):
    name: str

import threading
assert isinstance(threading.Thread(name='Bob'), Named)

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.

Novo na versão 3.8.

class typing.TypedDict(dict)

Special construct to add type hints to a dictionary. At runtime it is a plain dict.

TypedDict declares a dictionary type that expects all of its instances to have a certain set of keys, where each key is associated with a value of a consistent type. This expectation is not checked at runtime but is only enforced by type checkers. Usage:

class Point2D(TypedDict):
    x: int
    y: int
    label: str

a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check

assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')

To allow using this feature with older versions of Python that do not support PEP 526, TypedDict supports two additional equivalent syntactic forms:

  • Utilizando um literal dict como segundo argumento:

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

  • Using keyword arguments:

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

Descontinuado desde a versão 3.11, será removido na versão 3.13: The keyword-argument syntax is deprecated in 3.11 and will be removed in 3.13. It may also be unsupported by static type checkers.

The functional syntax should also be used when any of the keys are not valid identifiers, for example because they are keywords or contain hyphens. Example:

# raises SyntaxError
class Point2D(TypedDict):
    in: int  # 'in' is a keyword
    x-y: int  # name with hyphens

# OK, functional syntax
Point2D = TypedDict('Point2D', {'in': int, 'x-y': int})

By default, all keys must be present in a TypedDict. It is possible to mark individual keys as non-required using NotRequired:

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

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

This means that a Point2D TypedDict can have the label key omitted.

It is also possible to mark all keys as non-required by default by specifying a totality of False:

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

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

This means that a Point2D TypedDict can have any of the keys omitted. A type checker is only expected to support a literal False or True as the value of the total argument. True is the default, and makes all items defined in the class body required.

Individual keys of a total=False TypedDict can be marked as required using Required:

class Point2D(TypedDict, total=False):
    x: Required[int]
    y: Required[int]
    label: str

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

It is possible for a TypedDict type to inherit from one or more other TypedDict types using the class-based syntax. Usage:

class Point3D(Point2D):
    z: int

Point3D has three items: x, y and z. It is equivalent to this definition:

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

A TypedDict cannot inherit from a non-TypedDict class, except for Generic. For example:

class X(TypedDict):
    x: int

class Y(TypedDict):
    y: int

class Z(object): pass  # A non-TypedDict class

class XY(X, Y): pass  # OK

class XZ(X, Z): pass  # raises TypeError

A TypedDict can be generic:

T = TypeVar("T")

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

A TypedDict can be introspected via annotations dicts (see Boas práticas para anotações 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__

Novo na versão 3.9.

__optional_keys__

Point2D.__required_keys__ and Point2D.__optional_keys__ return frozenset objects containing required and non-required keys, respectively.

Keys marked with Required will always appear in __required_keys__ and keys marked with NotRequired will always appear in __optional_keys__.

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

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

Novo na versão 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.

See PEP 589 for more examples and detailed rules of using TypedDict.

Novo na versão 3.8.

Alterado na versão 3.11: Added support for marking individual keys as Required or NotRequired. See PEP 655.

Alterado na versão 3.11: Adicionado suporte para TypedDicts genéricos.

Protocolos

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

class typing.SupportsAbs

An ABC with one abstract method __abs__ that is covariant in its return type.

class typing.SupportsBytes

An ABC with one abstract method __bytes__.

class typing.SupportsComplex

An ABC with one abstract method __complex__.

class typing.SupportsFloat

An ABC with one abstract method __float__.

class typing.SupportsIndex

An ABC with one abstract method __index__.

Novo na versão 3.8.

class typing.SupportsInt

An ABC with one abstract method __int__.

class typing.SupportsRound

An ABC with one abstract method __round__ that is covariant in its return type.

ABCs para trabalhar com IO

class typing.IO
class typing.TextIO
class typing.BinaryIO

Generic type IO[AnyStr] and its subclasses TextIO(IO[str]) and BinaryIO(IO[bytes]) represent the types of I/O streams such as returned by open().

Funções e decoradores

typing.cast(typ, val)

Define um valor para um tipo.

This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don’t check anything (we want this to be as fast as possible).

typing.assert_type(val, typ, /)

Ask a static type checker to confirm that val has an inferred type of typ.

At runtime this does nothing: it returns the first argument unchanged with no checks or side effects, no matter the actual type of the argument.

When a static type checker encounters a call to assert_type(), it emits an error if the value is not of the specified type:

def greet(name: str) -> None:
    assert_type(name, str)  # OK, inferred type of `name` is `str`
    assert_type(name, int)  # type checker error

This function is useful for ensuring the type checker’s understanding of a script is in line with the developer’s intentions:

def complex_function(arg: object):
    # Do some complex type-narrowing logic,
    # after which we hope the inferred type will be `int`
    ...
    # Test whether the type checker correctly understands our function
    assert_type(arg, int)

Novo na versão 3.11.

typing.assert_never(arg, /)

Ask a static type checker to confirm that a line of code is unreachable.

Exemplo:

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.

At runtime, this throws an exception when called.

Ver também

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

Novo na versão 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"

This can be useful when you want to debug how your type checker handles a particular piece of code.

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.

Novo na versão 3.11.

@typing.dataclass_transform(*, eq_default=True, order_default=False, kw_only_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:

T = TypeVar("T")

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

@create_model
class CustomerModel:
    id: int
    name: str

On a base class:

@dataclass_transform()
class ModelBase: ...

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

On a metaclass:

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

class ModelBase(metaclass=ModelMeta): ...

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

The CustomerModel classes defined above will be treated by type checkers similarly to classes created with @dataclasses.dataclass. For example, type checkers will assume these classes have __init__ methods that accept id and name.

The decorated class, metaclass, or function may accept the following bool arguments which type checkers will assume have the same effect as they would have on the @dataclasses.dataclass decorator: init, eq, order, unsafe_hash, frozen, match_args, kw_only, and slots. It must be possible for the value of these arguments (True or False) to be statically evaluated.

The arguments to the dataclass_transform decorator can be used to customize the default behaviors of the decorated class, metaclass, or function:

Parâmetros:

  • 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.

  • 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) – Arbitrary other keyword arguments are accepted in order to allow for possible future extensions.

Type checkers recognize the following optional parameters on field specifiers:

Recognised parameters for field specifiers

Nome do parâmetro

Descrição

init

Indicates whether the field should be included in the synthesized __init__ method. If unspecified, init defaults to True.

default

Provides the default value for the field.

default_factory

Provides a runtime callback that returns the default value for the field. If neither default nor default_factory are specified, the field is assumed to have no default value and must be provided a value when the class is instantiated.

factory

An alias for the default_factory parameter on field specifiers.

kw_only

Indicates whether the field should be marked as keyword-only. If True, the field will be keyword-only. If False, it will not be keyword-only. If unspecified, the value of the kw_only parameter on the object decorated with dataclass_transform will be used, or if that is unspecified, the value of kw_only_default on dataclass_transform will be used.

alias

Provides an alternative name for the field. This alternative name is used in the synthesized __init__ method.

At runtime, this decorator records its arguments in the __dataclass_transform__ attribute on the decorated object. It has no other runtime effect.

Veja PEP 681 para mais detalhes.

Novo na versão 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

See PEP 484 for more details and comparison with other typing semantics.

Alterado na versão 3.11: Overloaded functions can now be introspected at runtime using 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() can be used for introspecting an overloaded function at runtime.

Novo na versão 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.

Novo na versão 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 exemplo:

class Base:
    @final
    def done(self) -> None:
        ...
class Sub(Base):
    def done(self) -> None:  # Error reported by type checker
        ...

@final
class Leaf:
    ...
class Other(Leaf):  # Error reported by type checker
    ...

There is no runtime checking of these properties. See PEP 591 for more details.

Novo na versão 3.8.

Alterado na versão 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

Decorator to indicate that annotations are not type hints.

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

Decorator to give another decorator the no_type_check() effect.

This wraps the decorator with something that wraps the decorated function in no_type_check().

@typing.type_check_only

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

This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class:

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

def fetch_response() -> Response: ...

Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public.

Introspection helpers

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

Return a dictionary containing type hints for a function, method, module or class object.

This is often the same as obj.__annotations__. In addition, forward references encoded as string literals are handled by evaluating them in globals and locals namespaces. For a class C, return a dictionary constructed by merging all the __annotations__ along C.__mro__ in reverse order.

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

class Student(NamedTuple):
    name: Annotated[str, 'some marker']

assert get_type_hints(Student) == {'name': str}
assert get_type_hints(Student, include_extras=False) == {'name': str}
assert get_type_hints(Student, include_extras=True) == {
    'name': Annotated[str, 'some marker']
}

Nota

get_type_hints() does not work with imported type aliases that include forward references. Enabling postponed evaluation of annotations (PEP 563) may remove the need for most forward references.

Alterado na versão 3.9: Added include_extras parameter as part of PEP 593. See the documentation on Annotated for more information.

Alterado na versão 3.11: Previously, Optional[t] was added for function and method annotations if a default value equal to None was set. Now the annotation is returned unchanged.

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.

Exemplos:

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

Novo na versão 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.

Exemplos:

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

Novo na versão 3.8.

typing.is_typeddict(tp)

Check if a type is a TypedDict.

Por exemplo:

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)

Novo na versão 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

PEP 585 generic types such as list["SomeClass"] will not be implicitly transformed into list[ForwardRef("SomeClass")] and thus will not automatically resolve to list[SomeClass].

Novo na versão 3.7.4.

Constante

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()

The first type annotation must be enclosed in quotes, making it a “forward reference”, to hide the expensive_mod reference from the interpreter runtime. Type annotations for local variables are not evaluated, so the second annotation does not need to be enclosed in quotes.

Nota

If from __future__ import annotations is used, annotations are not evaluated at function definition time. Instead, they are stored as strings in __annotations__. This makes it unnecessary to use quotes around the annotation (see PEP 563).

Novo na versão 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.

This type can be used as follows:

def count_words(text: str) -> Dict[str, int]:
    ...

Obsoleto desde a versão 3.9: builtins.dict now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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.

This type may be used as follows:

T = TypeVar('T', int, float)

def vec2(x: T, y: T) -> List[T]:
    return [x, y]

def keep_positives(vector: Sequence[T]) -> List[T]:
    return [item for item in vector if item > 0]

Obsoleto desde a versão 3.9: builtins.list now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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 AbstractSet rather than to use set or typing.Set.

Obsoleto desde a versão 3.9: builtins.set now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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

Deprecated alias to builtins.frozenset.

Obsoleto desde a versão 3.9: builtins.frozenset now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

typing.Tuple

Deprecated alias for tuple.

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

Obsoleto desde a versão 3.9: builtins.tuple now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.Type(Generic[CT_co])

Deprecated alias to type.

See O tipo de objetos de classe for details on using type or typing.Type in type annotations.

Novo na versão 3.5.2.

Obsoleto desde a versão 3.9: builtins.type now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

Aliases to types in collections

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

Deprecated alias to collections.defaultdict.

Novo na versão 3.5.2.

Obsoleto desde a versão 3.9: collections.defaultdict now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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

Deprecated alias to collections.OrderedDict.

Novo na versão 3.7.2.

Obsoleto desde a versão 3.9: collections.OrderedDict now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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

Deprecated alias to collections.ChainMap.

Novo na versão 3.6.1.

Obsoleto desde a versão 3.9: collections.ChainMap now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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

Deprecated alias to collections.Counter.

Novo na versão 3.6.1.

Obsoleto desde a versão 3.9: collections.Counter now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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

Deprecated alias to collections.deque.

Novo na versão 3.6.1.

Obsoleto desde a versão 3.9: collections.deque now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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].

Descontinuado desde a versão 3.8, será removido na versão 3.13: The typing.re namespace is deprecated and will be removed. These types should be directly imported from typing instead.

Obsoleto desde a versão 3.9: Classes Pattern and Match from re now support []. See PEP 585 and Tipo Generic Alias.

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.

Use Text to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3:

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

Novo na versão 3.5.2.

Obsoleto desde a versão 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 a versão 3.9: collections.abc.Set now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.ByteString(Sequence[int])

This type represents the types bytes, bytearray, and memoryview of byte sequences.

Descontinuado desde a versão 3.9, será removido na versão 3.14: Prefer typing_extensions.Buffer, or a union like bytes | bytearray | memoryview.

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

Deprecated alias to collections.abc.Collection.

Novo na versão 3.6.

Obsoleto desde a versão 3.9: collections.abc.Collection now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.Container(Generic[T_co])

Deprecated alias to collections.abc.Container.

Obsoleto desde a versão 3.9: collections.abc.Container now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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

Deprecated alias to collections.abc.ItemsView.

Obsoleto desde a versão 3.9: collections.abc.ItemsView now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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

Deprecated alias to collections.abc.KeysView.

Obsoleto desde a versão 3.9: collections.abc.KeysView now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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

Deprecated alias to collections.abc.Mapping.

This type can be used as follows:

def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
    return word_list[word]

Obsoleto desde a versão 3.9: collections.abc.Mapping now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.MappingView(Sized)

Deprecated alias to collections.abc.MappingView.

Obsoleto desde a versão 3.9: collections.abc.MappingView now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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

Deprecated alias to collections.abc.MutableMapping.

Obsoleto desde a versão 3.9: collections.abc.MutableMapping now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.MutableSequence(Sequence[T])

Deprecated alias to collections.abc.MutableSequence.

Obsoleto desde a versão 3.9: collections.abc.MutableSequence now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.MutableSet(AbstractSet[T])

Deprecated alias to collections.abc.MutableSet.

Obsoleto desde a versão 3.9: collections.abc.MutableSet now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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

Deprecated alias to collections.abc.Sequence.

Obsoleto desde a versão 3.9: collections.abc.Sequence now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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

Deprecated alias to collections.abc.ValuesView.

Obsoleto desde a versão 3.9: collections.abc.ValuesView now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

Aliases to asynchronous ABCs in collections.abc

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

Deprecated alias to collections.abc.Coroutine.

The variance and order of type variables 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

Novo na versão 3.5.3.

Obsoleto desde a versão 3.9: collections.abc.Coroutine now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

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

Deprecated alias to collections.abc.AsyncGenerator.

An async generator can be annotated by the generic type AsyncGenerator[YieldType, SendType]. For example:

async def echo_round() -> AsyncGenerator[int, float]:
    sent = yield 0
    while sent >= 0.0:
        rounded = await round(sent)
        sent = yield rounded

Unlike normal generators, async generators cannot return a value, so there is no ReturnType type parameter. As with Generator, the SendType behaves contravariantly.

If your generator will only yield values, set the SendType to None:

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

Alternatively, annotate your generator as having a return type of either AsyncIterable[YieldType] or AsyncIterator[YieldType]:

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

Novo na versão 3.6.1.

Obsoleto desde a versão 3.9: collections.abc.AsyncGenerator now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.AsyncIterable(Generic[T_co])

Deprecated alias to collections.abc.AsyncIterable.

Novo na versão 3.5.2.

Obsoleto desde a versão 3.9: collections.abc.AsyncIterable now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.AsyncIterator(AsyncIterable[T_co])

Deprecated alias to collections.abc.AsyncIterator.

Novo na versão 3.5.2.

Obsoleto desde a versão 3.9: collections.abc.AsyncIterator now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.Awaitable(Generic[T_co])

Deprecated alias to collections.abc.Awaitable.

Novo na versão 3.5.2.

Obsoleto desde a versão 3.9: collections.abc.Awaitable now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

Aliases to other ABCs in collections.abc

class typing.Iterable(Generic[T_co])

Deprecated alias to collections.abc.Iterable.

Obsoleto desde a versão 3.9: collections.abc.Iterable now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.Iterator(Iterable[T_co])

Deprecated alias to collections.abc.Iterator.

Obsoleto desde a versão 3.9: collections.abc.Iterator now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

typing.Callable

Deprecated alias to collections.abc.Callable.

See Anotações de objetos chamáveis for details on how to use collections.abc.Callable and typing.Callable in type annotations.

Obsoleto desde a versão 3.9: collections.abc.Callable now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

Alterado na versão 3.10: Callable agora oferece suporte a ParamSpec e Concatenate. Veja PEP 612 para mais detalhes.

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

Deprecated alias to collections.abc.Generator.

A generator can be annotated by the generic type Generator[YieldType, SendType, ReturnType]. For example:

def echo_round() -> Generator[int, float, str]:
    sent = yield 0
    while sent >= 0:
        sent = yield round(sent)
    return 'Done'

Note that unlike many other generics in the typing module, the SendType of Generator behaves contravariantly, not covariantly or invariantly.

If your generator will only yield values, set the SendType and ReturnType to None:

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

Alternatively, annotate your generator as having a return type of either Iterable[YieldType] or Iterator[YieldType]:

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

Obsoleto desde a versão 3.9: collections.abc.Generator now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.Hashable

Alias to collections.abc.Hashable.

class typing.Reversible(Iterable[T_co])

Deprecated alias to collections.abc.Reversible.

Obsoleto desde a versão 3.9: collections.abc.Reversible now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.Sized

Alias to collections.abc.Sized.

Aliases to contextlib ABCs

class typing.ContextManager(Generic[T_co])

Deprecated alias to contextlib.AbstractContextManager.

Novo na versão 3.5.4.

Obsoleto desde a versão 3.9: contextlib.AbstractContextManager now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

class typing.AsyncContextManager(Generic[T_co])

Deprecated alias to contextlib.AbstractAsyncContextManager.

Novo na versão 3.6.2.

Obsoleto desde a versão 3.9: contextlib.AbstractAsyncContextManager now supports subscripting ([]). See PEP 585 and Tipo Generic Alias.

Cronograma de Descontinuação dos Principais Recursos

Certain features in typing are deprecated and may be removed in a future version of Python. The following table summarizes major deprecations for your convenience. This is subject to change, and not all deprecations are listed.

Feature

Descontinuado em

Projected removal

PEP/issue

typing.io and typing.re submodules

3.8

3.13

bpo-38291

typing versions of standard collections

3.9

Undecided (see Deprecated aliases for more information)

PEP 585

typing.ByteString

3.9

3.14

gh-91896

typing.Text

3.11

Undecided

gh-92332