typing
— Suporte para dicas de tipo¶
Adicionado 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 fornece suporte em tempo de execução para dicas de tipo.
Considere a função abaixo:
def surface_area_of_cube(edge_length: float) -> str:
return f"The surface area of the cube is {6 * edge_length ** 2}."
A função surface_area_of_cube
recebe um argumento que se espera ser uma instância de float
, conforme indicado pela dica de tipo edge_length: float
. Espera-se que a função retorne uma instância de str
, conforme indicado pela dica -> str
.
Embora as dicas de tipo possam ser classes simples como float
ou str
, elas também podem ser mais complexas. O módulo typing
fornece um vocabulário de dicas de tipo mais avançadas.
Novos recursos são frequentemente adicionados ao módulo typing
. O pacote typing_extensions fornece backports desses novos recursos para versões mais antigas do Python.
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.
Especificação para o sistema de tipos do Python¶
A especificação canônica e atualizada do sistema de tipos Python pode ser encontrada em “Specification for the Python type system”.
Apelidos de tipo¶
Um apelido de tipo é definido utilizando a instrução type
, que por sua vez cria uma instância da classe TypeAliasType
. Neste exemplo, Vector
e list[float]
serão tratados de maneira equivalente pelos verificadores de tipo estático:
type Vector = list[float]
def scale(scalar: float, vector: Vector) -> Vector:
return [scalar * num for num in vector]
# passes type checking; a list of floats qualifies as a Vector.
new_vector = scale(2.0, [1.0, -4.2, 5.4])
Apelidos de tipo são úteis para simplificar assinaturas de tipo complexas. Por exemplo:
from collections.abc import Sequence
type ConnectionOptions = dict[str, str]
type Address = tuple[str, int]
type Server = tuple[Address, ConnectionOptions]
def broadcast_message(message: str, servers: Sequence[Server]) -> None:
...
# The static type checker will treat the previous type signature as
# being exactly equivalent to this one.
def broadcast_message(
message: str,
servers: Sequence[tuple[tuple[str, int], dict[str, str]]]
) -> None:
...
A instrução type
é nova no Python 3.12. Para compatibilidade retroativa, apelidos de tipo também podem ser criados através da simples atribuição:
Vector = list[float]
Ou marcado com TypeAlias
para tornar explícito que se trata de um apelido de tipo e não uma atribuição de variável comum:
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
Lembre-se que o uso de um apelido de tipo declara que dois tipos serão equivalentes entre si. Efetuar type Alias = Original
fará o verificador de tipo estático tratar Alias
como sendo exatamente equivalente a Original
em todos os casos. Isso é útil quando você deseja simplificar assinaturas de tipo complexas.
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.
Adicionado 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¶
Functions – or other callable objects – can be annotated using
collections.abc.Callable
or deprecated typing.Callable
.
Callable[[int], str]
signifies a function that takes a single parameter
of type int
and returns a str
.
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: ...
Funções e classes genéricas podem ser parametrizadas utilizando-se sintaxe do parâmetro de tipo:
from collections.abc import Sequence
def first[T](l: Sequence[T]) -> T: # Function is generic over the TypeVar "T"
return l[0]
Ou utilizando a fábrica TypeVar
diretamente:
from collections.abc import Sequence
from typing import TypeVar
U = TypeVar('U') # Declare type variable "U"
def second(l: Sequence[U]) -> U: # Function is generic over the TypeVar "U"
return l[1]
Alterado na versão 3.12: O suporte sintático para genéricos é novo no Python 3.12.
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¶
A variable annotated with C
may accept a value of type C
. In
contrast, a variable annotated with type[C]
(or deprecated
typing.Type[C]
) may accept values that are classes
themselves – specifically, it will accept the class object of C
. For
example:
a = 3 # Has type ``int``
b = int # Has type ``type[int]``
c = type(a) # Also has type ``type[int]``
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.
Annotating generators and coroutines¶
A generator can be annotated using the generic type
Generator[YieldType, SendType, ReturnType]
.
For example:
def echo_round() -> Generator[int, float, str]:
sent = yield 0
while sent >= 0:
sent = yield round(sent)
return 'Done'
Note that unlike many other generic classes in the standard library,
the SendType
of Generator
behaves
contravariantly, not covariantly or invariantly.
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
Async generators are handled in a similar fashion, but don’t
expect a ReturnType
type argument
(AsyncGenerator[YieldType, SendType]
):
async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
while True:
yield start
start = await increment(start)
As in the synchronous case,
AsyncIterable[YieldType]
and AsyncIterator[YieldType]
are
available as well:
async def infinite_stream(start: int) -> AsyncIterator[int]:
while True:
yield start
start = await increment(start)
Coroutines can be annotated using
Coroutine[YieldType, SendType, ReturnType]
.
Generic arguments correspond to those of Generator
,
for example:
from collections.abc import Coroutine
c: Coroutine[list[str], str, int] # Some coroutine defined elsewhere
x = c.send('hi') # Inferred type of 'x' is list[str]
async def bar() -> None:
y = await c # Inferred type of 'y' is int
Tipos genéricos definidos pelo usuário¶
Uma classe definida pelo usuário pode ser definica como uma classe genérica.
from logging import Logger
class LoggedVar[T]:
def __init__(self, value: T, name: str, logger: Logger) -> None:
self.name = name
self.logger = logger
self.value = value
def set(self, new: T) -> None:
self.log('Set ' + repr(self.value))
self.value = new
def get(self) -> T:
self.log('Get ' + repr(self.value))
return self.value
def log(self, message: str) -> None:
self.logger.info('%s: %s', self.name, message)
Esta sintaxe indica que o classe LoggedVar
é parametrizada em torno de uma única type variable T
. Isso também torna T
válido como um tipo dentro do corpo da classe.
Classes genéricas implicitamente herdar de Generic
. Para compatibilidade com Python 3.11 e versões inferiores, também é possível herdar explicitamente de Generic
para indicar uma classe genérica:
from typing import TypeVar, Generic
T = TypeVar('T')
class LoggedVar(Generic[T]):
...
Classes genéricas têm métodos __class_getitem__()
, o que significa que podem ser parametrizadas em tempo de execução (por exemplo, LoggedVar[int]
abaixo):
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
class WeirdTrio[T, B: Sequence[bytes], S: (int, str)]:
...
OldT = TypeVar('OldT', contravariant=True)
OldB = TypeVar('OldB', bound=Sequence[bytes], covariant=True)
OldS = TypeVar('OldS', int, str)
class OldWeirdTrio(Generic[OldT, OldB, OldS]):
...
Cada tipo dos argumentos para Generic
devem ser distintos. Assim, os seguintes exemplos são inválidos:
from typing import TypeVar, Generic
...
class Pair[M, M]: # SyntaxError
...
T = TypeVar('T')
class Pair(Generic[T, T]): # INVALID
...
Classes genéricas podem também herdar de outras classes:
from collections.abc import Sized
class LinkedList[T](Sized):
...
Ao herdar das classes genérico, algun tipos podem ser fixos:
from collections.abc import Mapping
class MyDict[T](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
type Response[S] = Iterable[S] | int
# Return type here is same as Iterable[str] | int
def response(query: str) -> Response[str]:
...
type Vec[T] = Iterable[tuple[T, T]]
def inproduct[T: (int, float, complex)](v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]]
return sum(x*y for x, y in v)
Para compatibilidade retroativa, os apelidos para tipos genéricos também podem ser criados por meio de um simples atribuição:
from collections.abc import Iterable
from typing import TypeVar
S = TypeVar("S")
Response = Iterable[S] | int
Alterado na versão 3.7: Generic
não possui mais uma metaclasse personalizada.
Alterado na versão 3.12: Suporte sintático para apelidos de tipo e genéricos é novo na versão 3.12. Anteriormente, as classes genéricas precisavam explicitamente herdar de Generic
ou conter um tipo de variável em uma de suas bases.
Genéricos definidos pelo usuário para expressões de parâmetros também oferecem suporte por meio de variáveis de especificação de parâmetros no formato [**P]
. O comportamento é consistente com as variáveis de tipo descritas acima, pois as variáveis de especificação de parâmetro são tratadas pelo módulo typing como uma variável de tipo especializada. A única exceção a isso é que uma lista de tipos pode ser usada para substituir um ParamSpec
:
>>> class Z[T, **P]: ... # T is a TypeVar; P is a ParamSpec
...
>>> Z[int, [dict, float]]
__main__.Z[int, [dict, float]]
Classes genéricas sobre um ParamSpec
também podem ser criadas usando herança explícita de Generic
. Neste caso, **
não é usado:
from typing import ParamSpec, Generic
P = ParamSpec('P')
class Z(Generic[P]):
...
Outra diferença entre TypeVar
e ParamSpec
é que um genérico com apenas uma variável de especificação de parâmetro aceitará listas de parâmetros nos formatos X[[Type1, Type2, ...]]
e também X[Type1, Type2, ...]
por razões estéticas. Internamente, o último é convertido no primeiro, portanto são equivalentes:
>>> class X[**P]: ...
...
>>> X[int, str]
__main__.X[[int, str]]
>>> X[[int, str]]
__main__.X[[int, 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.
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 argumentosstr
oubytes
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 tipoAny
, nem significa “qualquer string”. Em particular,AnyStr
estr | 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 outroLiteralString
. Entretanto, um objeto digitado apenasstr
não é. Uma string criada pela composição de objetos do tipoLiteralString
também é aceitável como umaLiteralString
.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.
Adicionado na versão 3.11.
- typing.Never¶
- typing.NoReturn¶
Never
eNoReturn
representam o tipo inferior, um tipo que não possui membros.Eles podem ser usados para indicar que uma função nunca retorna, como
sys.exit()
:from typing import Never # or NoReturn def stop() -> Never: raise RuntimeError('no way')
Ou para definir uma função que nunca deve ser chamada, pois não existem argumentos válidos, como
assert_never()
:from typing import Never # or NoReturn def never_call_me(arg: Never) -> None: pass def int_or_str(arg: int | str) -> None: never_call_me(arg) # type checker error match arg: case int(): print("It's an int") case str(): print("It's a str") case _: never_call_me(arg) # OK, arg is of type Never (or NoReturn)
Never
eNoReturn
têm o mesmo significado no sistema de tipos e os verificadores de tipo estático tratam ambos de forma equivalente.Adicionado na versão 3.6.2: Adicionado
NoReturn
.Adicionado na versão 3.11: Adicionado
Never
.
- 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 usarSelf
como anotação de retorno. SeFoo.return_self
foi anotado como retornando"Foo"
, então o verificador de tipo inferiria o objeto retornado deSubclassOfFoo.return_self
como sendo do tipoFoo
em vez deSubclassOfFoo
.Outros casos de uso comuns incluem:
classmethod
s que são usados como construtores alternativos e retornam instâncias do parâmetrocls
.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.
Adicionado 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
é particularmente útil em versões mais antigas do Python para anotar apelidos que fazem uso de referências futuras, pois pode ser difícil para os verificadores de tipo distingui-los das atribuições normais de variáveis:from typing import Generic, TypeAlias, TypeVar T = TypeVar("T") # "Box" does not exist yet, # so we have to use quotes for the forward reference on Python <3.12. # Using ``TypeAlias`` tells the type checker that this is a type alias declaration, # not a variable assignment to a string. BoxOfStrings: TypeAlias = "Box[str]" class Box(Generic[T]): @classmethod def make_box_of_strings(cls) -> BoxOfStrings: ...
Veja PEP 613 para mais detalhes.
Adicionado na versão 3.10.
Obsoleto desde a versão 3.12:
TypeAlias
foi descontinuado em favor da instruçãotype
, que cria instâncias deTypeAliasType
e que oferece suporte a nativamente referências futuras. Observe que emboraTypeAlias
eTypeAliasType
sirvam propósitos semelhantes e tenham nomes semelhantes, eles são distintos e o último não é o tipo do primeiro. A remoção deTypeAlias
não está planejada atualmente, mas os usuários são encorajados a migrar para instruçõestype
.
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 aX | Y
e significa X ou Y.Para definir uma união, use, por exemplo.
Union[int, str]
ou a abreviaturaint | 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 toX | None
(orUnion[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 ofOptional
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 andParamSpec
to annotate a higher-order callable which adds, removes, or transforms parameters of another callable. Usage is in the formConcatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable]
.Concatenate
is currently only valid when used as the first argument to a Callable. The last parameter toConcatenate
must be aParamSpec
or ellipsis (...
).For example, to annotate a decorator
with_lock
which provides athreading.Lock
to the decorated function,Concatenate
can be used to indicate thatwith_lock
expects a callable which takes in aLock
as the first argument, and returns a callable with a different type signature. In this case, theParamSpec
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 # Use this lock to ensure that only one thread is executing a function # at any time. my_lock = Lock() def with_lock[**P, R](f: Callable[Concatenate[Lock, P], R]) -> Callable[P, R]: '''A type-safe decorator which provides a lock.''' def inner(*args: P.args, **kwargs: P.kwargs) -> R: # Provide the lock as the first argument. return f(my_lock, *args, **kwargs) return inner @with_lock def sum_threadsafe(lock: Lock, numbers: list[float]) -> float: '''Add a list of numbers together in a thread-safe manner.''' with lock: return sum(numbers) # We don't need to pass in the lock ourselves thanks to the decorator. sum_threadsafe([1.1, 2.2, 3.3])
Adicionado na versão 3.10.
Ver também
PEP 612 – Parameter Specification Variables (the PEP which introduced
ParamSpec
andConcatenate
)
- 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 ... type Mode = Literal['r', 'rb', 'w', 'wb'] def open_helper(file: str, mode: Mode) -> str: ... open_helper('/some/path', 'r') # Passes type check open_helper('/other/path', 'typo') # Error in type checker
Literal[...]
cannot be subclassed. At runtime, an arbitrary value is allowed as type argument toLiteral[...]
, but type checkers may impose restrictions. See PEP 586 for more details about literal types.Adicionado na versão 3.8.
- 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 withisinstance()
orissubclass()
.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
Adicionado 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.
Adicionado na versão 3.8.
- typing.Required¶
Special typing construct to mark a
TypedDict
key as required.This is mainly useful for
total=False
TypedDicts. SeeTypedDict
and PEP 655 for more details.Adicionado 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.Adicionado na versão 3.11.
- typing.Annotated¶
Special typing form to add context-specific metadata to an annotation.
Add metadata
x
to a given typeT
by using the annotationAnnotated[T, x]
. Metadata added usingAnnotated
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 asT
. 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 ofT
, as type checkers will simply ignore the metadatax
. 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 anAnnotated
type can scan through the metadata elements to determine if they are of interest (e.g., usingisinstance()
).- 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 typeMultiple 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 type Vec[T] = Annotated[list[tuple[T, T]], MaxLen(10)] # When used in a type annotation, a type checker will treat "V" the same as # ``Annotated[list[tuple[int, int]], MaxLen(10)]``: type V = Vec[int]
Annotated
cannot be used with an unpackedTypeVarTuple
:type Variadic[*Ts] = Annotated[*Ts, Ann1] # NOT valid
Isso deve ser equivalente a
Annotated[T1, T2, T3, ..., Ann1]
where
T1
,T2
, etc. areTypeVars
. This would be invalid: only one type should be passed to Annotated.By default,
get_type_hints()
strips the metadata from annotations. Passinclude_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.
Adicionado 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:O valor de retorno é um booleano.
If the return value is
True
, the type of its argument is the type insideTypeGuard
.
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 inTypeGuard
maps to the type of the second parameter (aftercls
orself
).In short, the form
def foo(arg: TypeA) -> TypeGuard[TypeB]: ...
, means that iffoo(arg)
returnsTrue
, thenarg
narrows fromTypeA
toTypeB
.Nota
TypeB
need not be a narrower form ofTypeA
– it can even be a wider form. The main reason is to allow for things like narrowinglist[object]
tolist[str]
even though the latter is not a subtype of the former, sincelist
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.Adicionado 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 usingUnpack
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 oftyping.TypeVarTuple
andbuiltins.tuple
types. You might seeUnpack
being used explicitly in older versions of Python, where*
couldn’t be used in certain places:# In older versions of Python, TypeVarTuple and Unpack # are located in the `typing_extensions` backports package. from typing_extensions import TypeVarTuple, Unpack Ts = TypeVarTuple('Ts') tup: tuple[*Ts] # Syntax error on Python <= 3.10! tup: tuple[Unpack[Ts]] # Semantically equivalent, and backwards-compatible
Unpack
can also be used along withtyping.TypedDict
for typing**kwargs
in a function signature:from typing import TypedDict, Unpack class Movie(TypedDict): name: str year: int # This function expects two keyword arguments - `name` of type `str` # and `year` of type `int`. def foo(**kwargs: Unpack[Movie]): ...
See PEP 692 for more details on using
Unpack
for**kwargs
typing.Adicionado na versão 3.11.
Criando tipos genéricos e apelidos de tipo¶
The following classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating generic types and type aliases.
These objects can be created through special syntax
(type parameter lists and the type
statement).
For compatibility with Python 3.11 and earlier, they can also be created
without the dedicated syntax, as documented below.
- class typing.Generic¶
Classe base abstrata para tipos genéricos
A generic type is typically declared by adding a list of type parameters after the class name:
class Mapping[KT, VT]: def __getitem__(self, key: KT) -> VT: ... # Etc.
Such a class implicitly inherits from
Generic
. The runtime semantics of this syntax are discussed in the Language Reference.Esta classe pode ser utilizada como segue:
def lookup_name[X, Y](mapping: Mapping[X, Y], key: X, default: Y) -> Y: try: return mapping[key] except KeyError: return default
Aqui os colchetes depois no nome da função indica uma função genérica.
For backwards compatibility, generic classes can also be declared by explicitly inheriting from
Generic
. In this case, the type parameters must be declared separately:KT = TypeVar('KT') VT = TypeVar('VT') class Mapping(Generic[KT, VT]): def __getitem__(self, key: KT) -> VT: ... # Etc.
- class typing.TypeVar(name, *constraints, bound=None, covariant=False, contravariant=False, infer_variance=False)¶
Tipo variável.
The preferred way to construct a type variable is via the dedicated syntax for generic functions, generic classes, and generic type aliases:
class Sequence[T]: # T is a TypeVar ...
This syntax can also be used to create bound and constrained type variables:
class StrSequence[S: str]: # S is a TypeVar bound to str ... class StrOrBytesSequence[A: (str, bytes)]: # A is a TypeVar constrained to str or bytes ...
However, if desired, reusable type variables can also be constructed manually, like so:
T = TypeVar('T') # Can be anything S = TypeVar('S', bound=str) # Can be any subtype of str A = TypeVar('A', str, bytes) # Must be exactly str or bytes
Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function and type alias definitions. See
Generic
for more information on generic types. Generic functions work as follows:def repeat[T](x: T, n: int) -> Sequence[T]: """Return a list containing n references to x.""" return [x]*n def print_capitalized[S: str](x: S) -> S: """Print x capitalized, and return x.""" print(x.capitalize()) return x def concatenate[A: (str, bytes)](x: A, y: A) -> A: """Add two strings or bytes objects together.""" return x + y
Note that type variables can be bound, constrained, or neither, but cannot be both bound and constrained.
The variance of type variables is inferred by type checkers when they are created through the type parameter syntax or when
infer_variance=True
is passed. Manually created type variables may be explicitly marked covariant or contravariant by passingcovariant=True
orcontravariant=True
. By default, manually created type variables are invariant. See PEP 484 and PEP 695 for more details.Bound type variables and constrained type variables have different semantics in several important ways. Using a bound type variable means that the
TypeVar
will be solved using the most specific type possible:x = print_capitalized('a string') reveal_type(x) # revealed type is str class StringSubclass(str): pass y = print_capitalized(StringSubclass('another string')) reveal_type(y) # revealed type is StringSubclass z = print_capitalized(45) # error: int is not a subtype of str
Type variables can be bound to concrete types, abstract types (ABCs or protocols), and even unions of types:
# Can be anything with an __abs__ method def print_abs[T: SupportsAbs](arg: T) -> None: print("Absolute value:", abs(arg)) U = TypeVar('U', bound=str|bytes) # Can be any subtype of the union str|bytes V = TypeVar('V', bound=SupportsAbs) # Can be anything with an __abs__ method
Using a constrained type variable, however, means that the
TypeVar
can only ever be solved as being exactly one of the constraints given:a = concatenate('one', 'two') reveal_type(a) # revealed type is str b = concatenate(StringSubclass('one'), StringSubclass('two')) reveal_type(b) # revealed type is str, despite StringSubclass being passed in c = concatenate('one', b'two') # error: type variable 'A' can be either str or bytes in a function call, but not both
At runtime,
isinstance(x, T)
will raiseTypeError
.- __name__¶
The name of the type variable.
- __covariant__¶
Whether the type var has been explicitly marked as covariant.
- __contravariant__¶
Whether the type var has been explicitly marked as contravariant.
- __infer_variance__¶
Whether the type variable’s variance should be inferred by type checkers.
Adicionado na versão 3.12.
- __bound__¶
The bound of the type variable, if any.
Alterado na versão 3.12: For type variables created through type parameter syntax, the bound is evaluated only when the attribute is accessed, not when the type variable is created (see Avaliação preguiçosa).
- __constraints__¶
A tuple containing the constraints of the type variable, if any.
Alterado na versão 3.12: For type variables created through type parameter syntax, the constraints are evaluated only when the attribute is accessed, not when the type variable is created (see Avaliação preguiçosa).
Alterado na versão 3.12: Type variables can now be declared using the type parameter syntax introduced by PEP 695. The
infer_variance
parameter was added.
- class typing.TypeVarTuple(name)¶
Type variable tuple. A specialized form of type variable that enables variadic generics.
Type variable tuples can be declared in type parameter lists using a single asterisk (
*
) before the name:def move_first_element_to_last[T, *Ts](tup: tuple[T, *Ts]) -> tuple[*Ts, T]: return (*tup[1:], tup[0])
Or by explicitly invoking the
TypeVarTuple
constructor:T = TypeVar("T") Ts = TypeVarTuple("Ts") def move_first_element_to_last(tup: tuple[T, *Ts]) -> tuple[*Ts, T]: return (*tup[1:], tup[0])
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
*
intuple[T, *Ts]
. Conceptually, you can think ofTs
as a tuple of type variables(T1, T2, ...)
.tuple[T, *Ts]
would then becometuple[T, *(T1, T2, ...)]
, which is equivalent totuple[T, T1, T2, ...]
. (Note that in older versions of Python, you might see this written usingUnpack
instead, asUnpack[Ts]
.)Type variable tuples must always be unpacked. This helps distinguish type variable tuples from normal type variables:
x: Ts # Not valid x: tuple[Ts] # Not valid x: tuple[*Ts] # The correct way to do it
Type variable tuples can be used in the same contexts as normal type variables. For example, in class definitions, arguments, and return types:
class Array[*Shape]: def __getitem__(self, key: tuple[*Shape]) -> float: ... def __abs__(self) -> "Array[*Shape]": ... def get_shape(self) -> tuple[*Shape]: ...
Type variable tuples can be happily combined with normal type variables:
class Array[DType, *Shape]: # This is fine pass class Array2[*Shape, DType]: # This would also be fine pass class Height: ... class Width: ... float_array_1d: Array[float, Height] = Array() # Totally fine int_array_2d: Array[int, Height, Width] = Array() # Yup, fine too
However, note that at most one type variable tuple may appear in a single list of type arguments or type parameters:
x: tuple[*Ts, *Ts] # Not valid class Array[*Shape, *Shape]: # Not valid pass
Finally, an unpacked type variable tuple can be used as the type annotation of
*args
:def call_soon[*Ts]( callback: Callable[[*Ts], None], *args: *Ts ) -> None: ... callback(*args)
In contrast to non-unpacked annotations of
*args
- e.g.*args: int
, which would specify that all arguments areint
-*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 tocall_soon
match the types of the (positional) arguments ofcallback
.See PEP 646 for more details on type variable tuples.
- __name__¶
The name of the type variable tuple.
Adicionado na versão 3.11.
Alterado na versão 3.12: Type variable tuples can now be declared using the type parameter syntax introduced by PEP 695.
- class typing.ParamSpec(name, *, bound=None, covariant=False, contravariant=False)¶
Parameter specification variable. A specialized version of type variables.
In type parameter lists, parameter specifications can be declared with two asterisks (
**
):type IntFunc[**P] = Callable[P, int]
For compatibility with Python 3.11 and earlier,
ParamSpec
objects can also be created as follows:P = ParamSpec('P')
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 toCallable
, or as parameters for user-defined Generics. SeeGeneric
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 import logging def add_logging[T, **P](f: Callable[P, T]) -> Callable[P, T]: '''A type-safe decorator to add logging to a function.''' def inner(*args: P.args, **kwargs: P.kwargs) -> T: logging.info(f'{f.__name__} was called') return f(*args, **kwargs) return inner @add_logging def add_two(x: float, y: float) -> float: '''Add two numbers together.''' return x + y
Without
ParamSpec
, the simplest way to annotate this previously was to use aTypeVar
with boundCallable[..., Any]
. However this causes two problems:The type checker can’t type check the
inner
function because*args
and**kwargs
have to be typedAny
.cast()
may be required in the body of theadd_logging
decorator when returning theinner
function, or the static type checker must be told to ignore thereturn inner
.
- args¶
- kwargs¶
Since
ParamSpec
captures both positional and keyword parameters,P.args
andP.kwargs
can be used to split aParamSpec
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
andP.kwargs
are instances respectively ofParamSpecArgs
andParamSpecKwargs
.
- __name__¶
The name of the parameter specification.
Parameter specification variables created with
covariant=True
orcontravariant=True
can be used to declare covariant or contravariant generic types. Thebound
argument is also accepted, similar toTypeVar
. However the actual semantics of these keywords are yet to be decided.Adicionado na versão 3.10.
Alterado na versão 3.12: Parameter specifications can now be declared using the type parameter syntax introduced by PEP 695.
Nota
Only parameter specification variables defined in global scope can be pickled.
Ver também
PEP 612 – Parameter Specification Variables (the PEP which introduced
ParamSpec
andConcatenate
)
- typing.ParamSpecArgs¶
- typing.ParamSpecKwargs¶
Arguments and keyword arguments attributes of a
ParamSpec
. TheP.args
attribute of aParamSpec
is an instance ofParamSpecArgs
, andP.kwargs
is an instance ofParamSpecKwargs
. 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 originalParamSpec
:>>> from typing import ParamSpec, get_origin >>> P = ParamSpec("P") >>> get_origin(P.args) is P True >>> get_origin(P.kwargs) is P True
Adicionado na versão 3.10.
- class typing.TypeAliasType(name, value, *, type_params=())¶
The type of type aliases created through the
type
statement.Exemplo:
>>> type Alias = int >>> type(Alias) <class 'typing.TypeAliasType'>
Adicionado na versão 3.12.
- __name__¶
The name of the type alias:
>>> type Alias = int >>> Alias.__name__ 'Alias'
- __module__¶
O módulo na qual o apelido de tipo foi definido:
>>> type Alias = int >>> Alias.__module__ '__main__'
- __type_params__¶
The type parameters of the type alias, or an empty tuple if the alias is not generic:
>>> type ListOrSet[T] = list[T] | set[T] >>> ListOrSet.__type_params__ (T,) >>> type NotGeneric = int >>> NotGeneric.__type_params__ ()
- __value__¶
The type alias’s value. This is lazily evaluated, so names used in the definition of the alias are not resolved until the
__value__
attribute is accessed:>>> type Mutually = Recursive >>> type Recursive = Mutually >>> Mutually Mutually >>> Recursive Recursive >>> Mutually.__value__ Recursive >>> Recursive.__value__ Mutually
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 thenamedtuple()
API.)NamedTuple
subclasses can also have docstrings and methods:class Employee(NamedTuple): """Represents an employee.""" name: str id: int = 3 def __repr__(self) -> str: return f'<Employee {self.name}, id={self.id}>'
NamedTuple
subclasses can be generic:class Group[T](NamedTuple): key: T group: list[T]
Backward-compatible usage:
# For creating a generic NamedTuple on Python 3.11 or lower class Group(NamedTuple, Generic[T]): key: T group: list[T] # A functional syntax is also supported Employee = NamedTuple('Employee', [('name', str), ('id', int)])
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 ofOrderedDict
.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 aNewType
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.
Adicionado 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:
class GenProto[T](Protocol): def meth(self) -> T: ...
In code that needs to be compatible with Python 3.11 or older, generic Protocols can be written as follows:
T = TypeVar("T") class GenProto(Protocol[T]): def meth(self) -> T: ...
Adicionado na versão 3.8.
- @typing.runtime_checkable¶
Mark a protocol class as a runtime protocol.
Such a protocol can be used with
isinstance()
andissubclass()
. This raisesTypeError
when applied to a non-protocol class. This allows a simple-minded structural check, very similar to “one trick ponies” incollections.abc
such asIterable
. 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 anissubclass()
check against Callable. However, thessl.SSLObject.__init__
method exists only to raise aTypeError
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 anisinstance()
check against a non-protocol class. Consider using alternative idioms such ashasattr()
calls for structural checks in performance-sensitive code.Adicionado na versão 3.8.
Alterado na versão 3.12: The internal implementation of
isinstance()
checks against runtime-checkable protocols now usesinspect.getattr_static()
to look up attributes (previously,hasattr()
was used). As a result, some objects which used to be considered instances of a runtime-checkable protocol may no longer be considered instances of that protocol on Python 3.12+, and vice versa. Most users are unlikely to be affected by this change.Alterado na versão 3.12: The members of a runtime-checkable protocol are now considered “frozen” at runtime as soon as the class has been created. Monkey-patching attributes onto a runtime-checkable protocol will still work, but will have no impact on
isinstance()
checks comparing objects to the protocol. See “What’s new in Python 3.12” for more details.
- class typing.TypedDict(dict)¶
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)
Deprecated since version 3.11, will be removed in version 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 usingNotRequired
: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 thelabel
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 literalFalse
orTrue
as the value of thetotal
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 usingRequired
: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 otherTypedDict
types using the class-based syntax. Usage:class Point3D(Point2D): z: int
Point3D
has three items:x
,y
andz
. 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 forGeneric
. 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
Um
TypedDict
pode ser genérico:class Group[T](TypedDict): key: T group: list[T]
To create a generic
TypedDict
that is compatible with Python 3.11 or lower, inherit fromGeneric
explicitly: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 thetotal
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 currentTypedDict
class, not whether the class is semantically total. For example, aTypedDict
with__total__
set toTrue
may have keys marked withNotRequired
, or it may inherit from anotherTypedDict
withtotal=False
. Therefore, it is generally better to use__required_keys__
and__optional_keys__
for introspection.
- __required_keys__¶
Adicionado na versão 3.9.
- __optional_keys__¶
Point2D.__required_keys__
andPoint2D.__optional_keys__
returnfrozenset
objects containing required and non-required keys, respectively.Keys marked with
Required
will always appear in__required_keys__
and keys marked withNotRequired
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 aTypedDict
with one value for thetotal
argument and then inheriting from it in anotherTypedDict
with a different value fortotal
:>>> 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
Adicionado na versão 3.9.
Nota
If
from __future__ import annotations
is used or if annotations are given as strings, annotations are not evaluated when theTypedDict
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
.Adicionado na versão 3.8.
Alterado na versão 3.11: Added support for marking individual keys as
Required
orNotRequired
. See PEP 655.Alterado na versão 3.11: Adicionado suporte para
TypedDict
s 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__
.Adicionado 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¶
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)
Adicionado 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 anint
or astr
, 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 forarg
was insteadint | str | float
, the type checker would emit an error pointing out thatunreachable
is of typefloat
. For a call toassert_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.
Adicionado 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 fromtyping
. Importing the name fromtyping
, however, allows your code to run without runtime errors and communicates intent more clearly.Adicionado na versão 3.11.
- @typing.dataclass_transform(*, eq_default=True, order_default=False, kw_only_default=False, frozen_default=False, field_specifiers=(), **kwargs)¶
Decorator to mark an object as providing
dataclass
-like behavior.dataclass_transform
may be used to decorate a class, metaclass, or a function that is itself a decorator. The presence of@dataclass_transform()
tells a static type checker that the decorated object performs runtime “magic” that transforms a class in a similar way to@dataclasses.dataclass
.Example usage with a decorator function:
@dataclass_transform() def create_model[T](cls: type[T]) -> type[T]: ... return cls @create_model class CustomerModel: id: int name: str
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 acceptid
andname
.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
, andslots
. It must be possible for the value of these arguments (True
orFalse
) 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 beTrue
orFalse
if it is omitted by the caller. Defaults toTrue
.order_default (bool) – Indicates whether the
order
parameter is assumed to beTrue
orFalse
if it is omitted by the caller. Defaults toFalse
.kw_only_default (bool) – Indicates whether the
kw_only
parameter is assumed to beTrue
orFalse
if it is omitted by the caller. Defaults toFalse
.frozen_default (bool) –
Indicates whether the
frozen
parameter is assumed to beTrue
orFalse
if it is omitted by the caller. Defaults toFalse
.Adicionado na versão 3.12.
field_specifiers (tuple[Callable[..., Any], ...]) – Specifies a static list of supported classes or functions that describe fields, similar to
dataclasses.field()
. Defaults to()
.**kwargs (Any) – Arbitrary other keyword arguments are accepted in order to allow for possible future extensions.
Type checkers recognize the following optional parameters on field specifiers:
¶ Nome do parâmetro
Descrição
init
Indicates whether the field should be included in the synthesized
__init__
method. If unspecified,init
defaults toTrue
.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
nordefault_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. IfFalse
, it will not be keyword-only. If unspecified, the value of thekw_only
parameter on the object decorated withdataclass_transform
will be used, or if that is unspecified, the value ofkw_only_default
ondataclass_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.
Adicionado 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 raiseNotImplementedError
.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.Adicionado 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.
Adicionado 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.
Adicionado na versão 3.8.
Alterado na versão 3.11: The decorator will now attempt to set a
__final__
attribute toTrue
on the decorated object. Thus, a check likeif getattr(obj, "__final__", False)
can be used at runtime to determine whether an objectobj
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.override¶
Decorator to indicate that a method in a subclass is intended to override a method or attribute in a superclass.
Type checkers should emit an error if a method decorated with
@override
does not, in fact, override anything. This helps prevent bugs that may occur when a base class is changed without an equivalent change to a child class.Por exemplo:
class Base: def log_status(self) -> None: ... class Sub(Base): @override def log_status(self) -> None: # Okay: overrides Base.log_status ... @override def done(self) -> None: # Error reported by type checker ...
There is no runtime checking of this property.
The decorator will attempt to set an
__override__
attribute toTrue
on the decorated object. Thus, a check likeif getattr(obj, "__override__", False)
can be used at runtime to determine whether an objectobj
has been marked as an override. If the decorated object does not support setting attributes, the decorator returns the object unchanged without raising an exception.Consulte PEP 698 para obter mais detalhes.
Adicionado na versão 3.12.
- @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__
, but this function makes the following changes to the annotations dictionary:Forward references encoded as string literals or
ForwardRef
objects are handled by evaluating them in globalns, localns, and (where applicable) obj’s type parameter namespace. If globalns or localns is not given, appropriate namespace dictionaries are inferred from obj.None
is replaced withtypes.NoneType
.If
@no_type_check
has been applied to obj, an empty dictionary is returned.If obj is a class
C
, the function returns a dictionary that merges annotations fromC
’s base classes with those onC
directly. This is done by traversingC.__mro__
and iteratively combining__annotations__
dictionaries. Annotations on classes appearing earlier in the method resolution order always take precedence over annotations on classes appearing later in the method resolution order.The function recursively replaces all occurrences of
Annotated[T, ...]
withT
, unless include_extras is set toTrue
(seeAnnotated
for more information).
See also
inspect.get_annotations()
, a lower-level function that returns annotations more directly.Nota
If any forward references in the annotations of obj are not resolvable or are not valid Python code, this function will raise an exception such as
NameError
. For example, this can happen with imported type aliases that include forward references, or with names imported underif TYPE_CHECKING
.Alterado na versão 3.9: Added
include_extras
parameter as part of PEP 593. See the documentation onAnnotated
for more information.Alterado na versão 3.11: Previously,
Optional[t]
was added for function and method annotations if a default value equal toNone
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, ...]
returnX
.If
X
is a typing-module alias for a builtin orcollections
class, it will be normalized to the original class. IfX
is an instance ofParamSpecArgs
orParamSpecKwargs
, return the underlyingParamSpec
. ReturnNone
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
Adicionado 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 orLiteral
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)
Adicionado 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)
Adicionado na versão 3.10.
- class typing.ForwardRef¶
Class used for internal typing representation of string forward references.
For example,
List["SomeClass"]
is implicitly transformed intoList[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 intolist[ForwardRef("SomeClass")]
and thus will not automatically resolve tolist[SomeClass]
.Adicionado 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 isFalse
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).Adicionado 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 usedict
ortyping.Dict
.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
orIterable
rather than to uselist
ortyping.List
.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
collections.abc.Set
rather than to useset
ortyping.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
andTuple
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
ortyping.Type
in type annotations.Adicionado 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
.Adicionado 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
.Adicionado 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
.Adicionado 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
.Adicionado 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
.Adicionado 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¶
Deprecated since version 3.8, will be removed in version 3.13: The
typing.io
namespace is deprecated and will be removed. These types should be directly imported fromtyping
instead.
- class typing.Pattern¶
- class typing.Match¶
Deprecated aliases corresponding to the return types from
re.compile()
andre.match()
.These types (and the corresponding functions) are generic over
AnyStr
.Pattern
can be specialised asPattern[str]
orPattern[bytes]
;Match
can be specialised asMatch[str]
orMatch[bytes]
.Deprecated since version 3.8, will be removed in version 3.13: The
typing.re
namespace is deprecated and will be removed. These types should be directly imported fromtyping
instead.Obsoleto desde a versão 3.9: Classes
Pattern
andMatch
fromre
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 forunicode
.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'
Adicionado 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 ofText
.
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
, andmemoryview
of byte sequences.Deprecated since version 3.9, will be removed in version 3.14: Prefer
collections.abc.Buffer
, or a union likebytes | bytearray | memoryview
.
- class typing.Collection(Sized, Iterable[T_co], Container[T_co])¶
Deprecated alias to
collections.abc.Collection
.Adicionado 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
.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
.See Annotating generators and coroutines for details on using
collections.abc.Coroutine
andtyping.Coroutine
in type annotations.Adicionado 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
.See Annotating generators and coroutines for details on using
collections.abc.AsyncGenerator
andtyping.AsyncGenerator
in type annotations.Adicionado 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
.Adicionado 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
.Adicionado 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
.Adicionado 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
andtyping.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 aParamSpec
eConcatenate
. Veja PEP 612 para mais detalhes.
- class typing.Generator(Iterator[YieldType], Generic[YieldType, SendType, ReturnType])¶
Deprecated alias to
collections.abc.Generator
.See Annotating generators and coroutines for details on using
collections.abc.Generator
andtyping.Generator
in type annotations.Obsoleto desde a versão 3.9:
collections.abc.Generator
now supports subscripting ([]
). See PEP 585 and Tipo Generic Alias.
- class typing.Hashable¶
Deprecated alias to
collections.abc.Hashable
.Obsoleto desde a versão 3.12: Use
collections.abc.Hashable
directly instead.
- 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¶
Deprecated alias to
collections.abc.Sized
.Obsoleto desde a versão 3.12: Use
collections.abc.Sized
directly instead.
Aliases to contextlib
ABCs¶
- class typing.ContextManager(Generic[T_co])¶
Deprecated alias to
contextlib.AbstractContextManager
.Adicionado 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
.Adicionado 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 |
---|---|---|---|
|
3.8 |
3.13 |
|
|
3.9 |
Undecided (see Deprecated aliases for more information) |
|
3.9 |
3.14 |
||
3.11 |
Undecided |
||
3.12 |
Undecided |
||
3.12 |
Undecided |