`typing` — Suporte para dicas de tipo¶

Novo na versão 3.5.

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.

This module provides runtime support for type hints as specified by PEP 484, PEP 526, PEP 544, PEP 586, PEP 589, and PEP 591. The most fundamental support consists of the types Any, Union, Tuple, Callable, TypeVar, and Generic. For full specification please see PEP 484. For a simplified introduction to type hints see PEP 483.

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

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

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

Apelidos de tipo¶

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

from typing import List
Vector = List[float]

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

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

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

from typing import Dict, Tuple, Sequence

ConnectionOptions = Dict[str, str]
Address = Tuple[str, int]
Server = Tuple[Address, ConnectionOptions]

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

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

Note que None como uma dica de tipo é um caso especial e é substituído por type(None).

NewType¶

Use the NewType() helper function to create distinct types:

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:
    ...

# typechecks
user_a = get_user_name(UserId(42351))

# does not typecheck; 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 that these checks are enforced only by the static type checker. At runtime, the statement Derived = NewType('Derived', Base) will make Derived a function that immediately returns whatever parameter you pass it. That means the expression Derived(some_value) does not create a new class or introduce any overhead beyond that of a regular function call.

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

This also means that it is not possible to create a subtype of Derived since it is an identity function at runtime, not an actual type:

from typing import NewType

UserId = NewType('UserId', int)

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

However, it is possible to create a NewType() based on a ‘derived’ 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

Relembre que o uso de um apelido de tipo declara que dois tipos serão equivalentes entre si. Efetuar Alias = Original irá fazer 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.

Novo na versão 3.5.2.

Callable¶

Frameworks que esperam funções de retorno com assinaturas específicas podem ter seus tipos indicados usando``Callable[[Arg1Type, Arg2Type], ReturnType]``.

Por exemplo:

from typing import Callable

def feeder(get_next_item: Callable[[], str]) -> None:
    # Body

def async_query(on_success: Callable[[int], None],
                on_error: Callable[[int, Exception], None]) -> None:
    # Body

É possível declarar o tipo de retorno de um chamável sem especificar a assinatura da chamada, substituindo por reticências literais a lista de argumentos na dica de tipo: Callable[..., ReturnType].

Genéricos¶

Since type information about objects kept in containers cannot be statically inferred in a generic way, abstract base classes have been extended to support subscription to denote expected types for container elements.

from typing import Mapping, Sequence

def notify_by_email(employees: Sequence[Employee],
                    overrides: Mapping[str, str]) -> None: ...

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

from typing import Sequence, TypeVar

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

def first(l: Sequence[T]) -> T:   # Generic function
    return l[0]

Tipos genéricos definidos pelo usuário¶

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

from typing import TypeVar, Generic
from logging import Logger

T = TypeVar('T')

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

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

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

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

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

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

from typing import Iterable

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

A generic type can have any number of type variables, and type variables may be constrained:

from typing import TypeVar, Generic
...

T = TypeVar('T')
S = TypeVar('S', int, str)

class StrangePair(Generic[T, S]):
    ...

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

from typing import TypeVar, Generic
...

T = TypeVar('T')

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

You can use multiple inheritance with Generic:

from typing import TypeVar, Generic, Sized

T = TypeVar('T')

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

When inheriting from generic classes, some type variables could be fixed:

from typing import TypeVar, Mapping

T = TypeVar('T')

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

Neste caso MyDict possui um único parâmetro, T.

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

from typing import Iterable

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

User defined generic type aliases are also supported. Examples:

from typing import TypeVar, Iterable, Tuple, Union
S = TypeVar('S')
Response = Union[Iterable[S], int]

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

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

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

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

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

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 = None    # type: Any
a = []      # OK
a = 2       # OK

s = ''      # type: str
s = a       # OK

def foo(item: Any) -> int:
    # Typechecks; 'item' could be any type,
    # and that type might have a 'bar' method
    item.bar()
    ...

Notice that no typechecking is performed when assigning a value of type Any to a more precise type. For example, the static type checker did not report an error when assigning a to s even though s was declared to be of type str and receives an int value at runtime!

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; an object does not have a 'magic' method.
    item.magic()
    ...

def hash_b(item: Any) -> int:
    # Typechecks
    item.magic()
    ...

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

# Typechecks, since Any is compatible with all types
hash_b(42)
hash_b("foo")

Use 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¶

Initially PEP 484 defined Python static type system as using nominal subtyping. This means that a class A is allowed where a class B is expected if and only if A is a subclass of B.

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 typing 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 typing 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).

Classes, functions, and decorators¶

The module defines the following classes, functions and decorators:

class typing.TypeVar¶

Tipo variável.

Uso:

T = TypeVar('T')  # Can be anything
A = TypeVar('A', str, bytes)  # Must be str or bytes

Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function definitions. See class Generic for more information on generic types. Generic functions work as follows:

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

def longest(x: A, y: A) -> A:
    """Return the longest of two strings."""
    return x if len(x) >= len(y) else y

The latter example’s signature is essentially the overloading of (str, str) -> str and (bytes, bytes) -> bytes. Also note that if the arguments are instances of some subclass of str, the return type is still plain str.

At runtime, isinstance(x, T) will raise TypeError. In general, isinstance() and issubclass() should not be used with types.

Type variables may be marked covariant or contravariant by passing covariant=True or contravariant=True. See PEP 484 for more details. By default type variables are invariant. Alternatively, a type variable may specify an upper bound using bound=<type>. This means that an actual type substituted (explicitly or implicitly) for the type variable must be a subclass of the boundary type, see PEP 484.

class typing.Generic¶

Classe base abstrata para tipos genéricos

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

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

Esta classe pode ser utilizada como segue:

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

def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y:
    try:
        return mapping[key]
    except KeyError:
        return default

class typing.Protocol(Generic)¶

Base class for protocol classes. Protocol classes are defined like this:

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

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

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

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

func(C())  # Passes static type check

See PEP 544 for details. Protocol classes decorated with 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(Protocol[T]):
    def meth(self) -> T:
        ...

Novo na versão 3.8.

class typing.Type(Generic[CT_co])¶

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

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

Note that Type[C] is covariant:

class User: ...
class BasicUser(User): ...
class ProUser(User): ...
class TeamUser(User): ...

# Accepts User, BasicUser, ProUser, TeamUser, ...
def make_new_user(user_class: Type[User]) -> User:
    # ...
    return user_class()

The fact that Type[C] is covariant implies that all subclasses of C should implement the same constructor signature and class method signatures as C. The type checker should flag violations of this, but should also allow constructor calls in subclasses that match the constructor calls in the indicated base class. How the type checker is required to handle this particular case may change in future revisions of PEP 484.

The only legal parameters for Type are classes, Any, type variables, and unions of any of these types. For example:

def new_non_team_user(user_class: Type[Union[BasicUser, ProUser]]): ...

Type[Any] is equivalent to Type which in turn is equivalent to type, which is the root of Python’s metaclass hierarchy.

Novo na versão 3.5.2.

class typing.Iterable(Generic[T_co])¶: A generic version of collections.abc.Iterable.

class typing.Iterator(Iterable[T_co])¶: A generic version of collections.abc.Iterator.

class typing.Reversible(Iterable[T_co])¶: A generic version of collections.abc.Reversible.

class typing.SupportsInt¶: An ABC with one abstract method __int__.

class typing.SupportsFloat¶: An ABC with one abstract method __float__.

class typing.SupportsComplex¶: An ABC with one abstract method __complex__.

class typing.SupportsBytes¶: An ABC with one abstract method __bytes__.

class typing.SupportsIndex¶: An ABC with one abstract method __index__.

Novo na versão 3.8.

class typing.SupportsAbs¶: An ABC with one abstract method __abs__ that is covariant in its return type.

class typing.SupportsRound¶: An ABC with one abstract method __round__ that is covariant in its return type.

class typing.Container(Generic[T_co])¶: A generic version of collections.abc.Container.

class typing.Hashable¶: An alias to collections.abc.Hashable

class typing.Sized¶: An alias to collections.abc.Sized

class typing.Collection(Sized, Iterable[T_co], Container[T_co])¶: A generic version of collections.abc.Collection

Novo na versão 3.6.0.

class typing.AbstractSet(Sized, Collection[T_co])¶: A generic version of collections.abc.Set.

class typing.MutableSet(AbstractSet[T])¶: A generic version of collections.abc.MutableSet.

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

A generic version of collections.abc.Mapping. This type can be used as follows:

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

class typing.MutableMapping(Mapping[KT, VT])¶: A generic version of collections.abc.MutableMapping.

class typing.Sequence(Reversible[T_co], Collection[T_co])¶: A generic version of collections.abc.Sequence.

class typing.MutableSequence(Sequence[T])¶: A generic version of collections.abc.MutableSequence.

class typing.ByteString(Sequence[int])¶

A generic version of collections.abc.ByteString.

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

As a shorthand for this type, bytes can be used to annotate arguments of any of the types mentioned above.

class typing.Deque(deque, MutableSequence[T])¶: A generic version of collections.deque.

Novo na versão 3.5.4.

Novo na versão 3.6.1.

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

Generic version of list. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as Sequence or Iterable.

This type may be used as follows:

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

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

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

class typing.Set(set, MutableSet[T])¶: A generic version of builtins.set. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as AbstractSet.

class typing.FrozenSet(frozenset, AbstractSet[T_co])¶: A generic version of builtins.frozenset.

class typing.MappingView(Sized, Iterable[T_co])¶: A generic version of collections.abc.MappingView.

class typing.KeysView(MappingView[KT_co], AbstractSet[KT_co])¶: A generic version of collections.abc.KeysView.

class typing.ItemsView(MappingView, Generic[KT_co, VT_co])¶: A generic version of collections.abc.ItemsView.

class typing.ValuesView(MappingView[VT_co])¶: A generic version of collections.abc.ValuesView.

class typing.Awaitable(Generic[T_co])¶: A generic version of collections.abc.Awaitable.

Novo na versão 3.5.2.

class typing.Coroutine(Awaitable[V_co], Generic[T_co, T_contra, V_co])¶

A generic version of collections.abc.Coroutine. The variance and order of type variables correspond to those of Generator, for example:

from typing import List, Coroutine
c = None # type: Coroutine[List[str], str, int]
...
x = c.send('hi') # type: List[str]
async def bar() -> None:
    x = await c # type: int

Novo na versão 3.5.3.

class typing.AsyncIterable(Generic[T_co])¶: Uma versão genérica de collections.abc.AsyncIterable.

Novo na versão 3.5.2.

class typing.AsyncIterator(AsyncIterable[T_co])¶: A generic version of collections.abc.AsyncIterator.

Novo na versão 3.5.2.

class typing.ContextManager(Generic[T_co])¶: Uma versão genérica de contextlib.AbstractContextManager.

Novo na versão 3.5.4.

Novo na versão 3.6.0.

class typing.AsyncContextManager(Generic[T_co])¶: A generic version of contextlib.AbstractAsyncContextManager.

Novo na versão 3.5.4.

Novo na versão 3.6.2.

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

A generic version of dict. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as Mapping.

This type can be used as follows:

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

class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])¶: A generic version of collections.defaultdict.

Novo na versão 3.5.2.

class typing.OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])¶: A generic version of collections.OrderedDict.

Novo na versão 3.7.2.

class typing.Counter(collections.Counter, Dict[T, int])¶: A generic version of collections.Counter.

Novo na versão 3.5.4.

Novo na versão 3.6.1.

class typing.ChainMap(collections.ChainMap, MutableMapping[KT, VT])¶: A generic version of collections.ChainMap.

Novo na versão 3.5.4.

Novo na versão 3.6.1.

class typing.Generator(Iterator[T_co], Generic[T_co, T_contra, V_co])¶

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

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

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

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

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

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

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

class typing.AsyncGenerator(AsyncIterator[T_co], Generic[T_co, T_contra])¶

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

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

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

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

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

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

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

Novo na versão 3.6.1.

class typing.Text¶

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

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

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

Novo na versão 3.5.2.

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

class typing.Pattern¶
class typing.Match¶: These type aliases correspond to the return types from re.compile() and re.match(). These types (and the corresponding functions) are generic in AnyStr and can be made specific by writing Pattern[str], Pattern[bytes], Match[str], or Match[bytes].

class typing.NamedTuple¶

Typed version of collections.namedtuple().

Uso:

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

Isso equivale a:

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

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

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

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

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

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

NamedTuple subclasses can also have docstrings and methods:

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

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

Backward-compatible usage:

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

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

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

Deprecated since version 3.8, will be removed in version 3.9: Deprecated the _field_types attribute in favor of the more standard __annotations__ attribute which has the same information.

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

class typing.TypedDict(dict)¶

A simple typed namespace. At runtime it is equivalent to a plain dict.

TypedDict creates 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')

The type info for introspection can be accessed via Point2D.__annotations__ and Point2D.__total__. To allow using this feature with older versions of Python that do not support PEP 526, TypedDict supports two additional equivalent syntactic forms:

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

By default, all keys must be present in a TypedDict. It is possible to override this by specifying totality. Usage:

class point2D(TypedDict, total=False):
    x: int
    y: int

This means that a point2D TypedDict can have any of the keys omitted. A type checker is only expected to support a literal False or True as the value of the total argument. True is the default, and makes all items defined in the class body be required.

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

Novo na versão 3.8.

class typing.ForwardRef¶: A class used for internal typing representation of string forward references. For example, List["SomeClass"] is implicitly transformed into List[ForwardRef("SomeClass")]. This class should not be instantiated by a user, but may be used by introspection tools.

Novo na versão 3.7.4.

typing.NewType(name, tp)¶

A helper function to indicate a distinct type to a typechecker, see NewType. At runtime it returns a function that returns its argument. Usage:

UserId = NewType('UserId', int)
first_user = UserId(1)

Novo na versão 3.5.2.

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.get_type_hints(obj[, globals[, locals]])¶

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

This is often the same as obj.__annotations__. In addition, forward references encoded as string literals are handled by evaluating them in globals and locals namespaces. If necessary, Optional[t] is added for function and method annotations if a default value equal to None is set. For a class C, return a dictionary constructed by merging all the __annotations__ along C.__mro__ in reverse order.

typing.get_origin(tp)¶

typing.get_args(tp)¶

Provide basic introspection for generic types and special typing forms.

For a typing object of the form X[Y, Z, ...] these functions return X and (Y, Z, ...). If X is a generic alias for a builtin or collections class, it gets normalized to the original class. For unsupported objects return None and () correspondingly. Examples:

assert get_origin(Dict[str, int]) is dict
assert get_args(Dict[int, str]) == (int, str)

assert get_origin(Union[int, str]) is Union
assert get_args(Union[int, str]) == (int, str)

Novo na versão 3.8.

@typing.overload¶

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). The @overload-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-@overload-decorated definition, while the latter is used at runtime but should be ignored by a type checker. At runtime, calling a @overload-decorated function directly will raise NotImplementedError. An example of overload that gives a more precise type than can be expressed using a union or a type variable:

@overload
def process(response: None) -> None:
    ...
@overload
def process(response: int) -> Tuple[int, str]:
    ...
@overload
def process(response: bytes) -> str:
    ...
def process(response):
    <actual implementation>

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

@typing.final¶

A decorator to indicate to type checkers that the decorated method cannot be overridden, and the decorated class cannot be subclassed. For example:

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

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

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

Novo na versão 3.8.

@typing.no_type_check¶

Decorator to indicate that annotations are not type hints.

This works as class or function decorator. With a class, it applies recursively to all methods defined in that class (but not to methods defined in its superclasses or subclasses).

This mutates the function(s) in place.

@typing.no_type_check_decorator¶

Decorator to give another decorator the no_type_check() effect.

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

@typing.type_check_only¶

Decorator to mark a class or function to be 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.

@typing.runtime_checkable¶

Mark a protocol class as a runtime protocol.

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

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

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

Warning: this will check only the presence of the required methods, not their type signatures!

Novo na versão 3.8.

typing.Any¶

Tipo especial que indica um tipo irrestrito.

Todos os tipos são compatíveis com Any.
Any é compatível com todos os tipos.

typing.NoReturn¶

Tipo especial indicando que uma função nunca retorna. Por exemplo:

from typing import NoReturn

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

Novo na versão 3.5.4.

Novo na versão 3.6.2.

typing.Union¶

Union type; Union[X, Y] means either X or Y.

To define a union, use e.g. Union[int, str]. Details:

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]

When comparing unions, the argument order is ignored, e.g.:
```
Union[int, str] == Union[str, int]
```
You cannot subclass or instantiate a union.
Você não pode escrever Union[X][Y].
You can use Optional[X] as a shorthand for Union[X, None].

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

typing.Optional¶

Tipo opcional.

Optional[X] is equivalent to Union[X, None].

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

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

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

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

typing.Tuple¶

Tuple type; Tuple[X, Y] is the type of a tuple of two items with the first item of type X and the second of type Y. The type of the empty tuple can be written as Tuple[()].

Example: Tuple[T1, T2] is a tuple of two elements corresponding to type variables T1 and T2. Tuple[int, float, str] is a tuple of an int, a float and a string.

To specify a variable-length tuple of homogeneous type, use literal ellipsis, e.g. Tuple[int, ...]. A plain Tuple is equivalent to Tuple[Any, ...], and in turn to tuple.

typing.Callable¶

Callable type; Callable[[int], str] is a function of (int) -> str.

The subscription syntax must always be used with exactly two values: the argument list and the return type. The argument list must be a list of types or an ellipsis; the return type must be a single type.

There is no syntax to indicate optional or keyword arguments; such function types are rarely used as callback types. Callable[..., ReturnType] (literal ellipsis) can be used to type hint a callable taking any number of arguments and returning ReturnType. A plain Callable is equivalent to Callable[..., Any], and in turn to collections.abc.Callable.

typing.Literal¶

A type that can be used to indicate to type checkers that the corresponding variable or function parameter has a value equivalent to the provided literal (or one of several literals). For example:

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

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

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

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

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

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

Novo na versão 3.5.3.

typing.Final¶

A special typing construct to indicate to type checkers that a name cannot be re-assigned or overridden in a subclass. For example:

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

class Connection:
    TIMEOUT: Final[int] = 10

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

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

Novo na versão 3.8.

typing.AnyStr¶

AnyStr is a type variable defined as AnyStr = TypeVar('AnyStr', str, bytes).

It is meant to be used for functions that may accept any kind of string without allowing different kinds of strings to mix. For example:

def concat(a: AnyStr, b: AnyStr) -> AnyStr:
    return a + b

concat(u"foo", u"bar")  # Ok, output has type 'unicode'
concat(b"foo", b"bar")  # Ok, output has type 'bytes'
concat(u"foo", b"bar")  # Error, cannot mix unicode and bytes

typing.TYPE_CHECKING¶

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

if TYPE_CHECKING:
    import expensive_mod

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

Note that 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.

Novo na versão 3.5.2.

`typing` — Suporte para dicas de tipo¶

Apelidos de tipo¶

NewType¶

Callable¶

Genéricos¶

Tipos genéricos definidos pelo usuário¶

O tipo `Any`¶

Subtipagem nominal vs estrutural¶

Classes, functions, and decorators¶

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typing — Suporte para dicas de tipo¶

Apelidos de tipo¶

NewType¶

Callable¶

Genéricos¶

Tipos genéricos definidos pelo usuário¶

O tipo Any¶

Subtipagem nominal vs estrutural¶

Classes, functions, and decorators¶

`typing` — Suporte para dicas de tipo¶

O tipo `Any`¶