# `typing` — Support for type hints

Nuovo nella versione 3.5.

Source code: Lib/typing.py

Nota

The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.

This module provides runtime support for type hints 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.

The function below takes and returns a string and is annotated as follows:

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

In the function `greeting`, the argument `name` is expected to be of type `str` and the return type `str`. Subtypes are accepted as arguments.

## Type aliases

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])

Type aliases are useful for simplifying complex type signatures. For example:

    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 that `None` as a type hint is a special case and is replaced by `type(None)`.

## NewType

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

    from typing import NewType

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

The static type checker will treat the new type as if it were a subclass of the original type. This is useful in helping catch logical errors:

    def get_user_name(user_id: UserId) -> str:
        ...

    # typechecks
    user_a = get_user_name(UserId(42351))

    # does not typecheck; an int is not a UserId
    user_b = get_user_name(-1)

You may still perform all `int` operations on a variable of type `UserId`, but the result will always be of type `int`. This lets you pass in a `UserId` wherever an `int` might be expected, but will prevent you from accidentally creating a `UserId` in an invalid way:

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

Note that these checks are enforced only by the static type checker. At runtime, the statement `Derived = NewType('Derived', Base)` will make `Derived` a 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.

More precisely, the expression `some_value is Derived(some_value)` is always true at runtime.

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)

and typechecking for `ProUserId` will work as expected.

See PEP 484 for more details.

Nota

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

In contrast, `NewType` declares one type to be a subtype of another. Doing `Derived = NewType('Derived', Original)` will make the static type checker treat `Derived` as a subclass of `Original`, which means a value of type `Original` cannot be used in places where a value of type `Derived` is expected. This is useful when you want to prevent logic errors with minimal runtime cost.

Nuovo nella versione 3.5.2.

## Callable

Frameworks expecting callback functions of specific signatures might be type hinted using `Callable[[Arg1Type, Arg2Type], ReturnType]`.

For example:

    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

It is possible to declare the return type of a callable without specifying the call signature by substituting a literal ellipsis for the list of arguments in the type hint: `Callable[..., ReturnType]`.

## Generics

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]

## User-defined generic types

A user-defined class can be defined as a generic class.

    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]):
        ...

Each type variable argument to `Generic` must be distinct. This is thus invalid:

    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]):
        ...

In this case `MyDict` has a single parameter, `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)

Cambiato nella versione 3.7: `Generic` no longer has a custom metaclass.

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.

## The `Any` type

A special kind of type is `Any`. A static type checker will treat every type as being compatible with `Any` and `Any` as being compatible with every type.

This means that it is possible to perform any operation or method call on a value of type `Any` and assign it to any variable:

    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!

Furthermore, all functions without a return type or parameter types will implicitly default to using `Any`:

    def legacy_parser(text):
        ...
        return data

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

This behavior allows `Any` to be used as an escape hatch when you need to mix dynamically and statically typed code.

Contrast the behavior of `Any` with the behavior of `object`. Similar to `Any`, every type is a subtype of `object`. However, unlike `Any`, the reverse is not true: `object` is not a subtype of every other type.

That means when the type of a value is `object`, a type checker will reject almost all operations on it, and assigning it to a variable (or using it as a return value) of a more specialized type is a type error. For example:

    def hash_a(item: object) -> int:
        # Fails; 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` to indicate that a value could be any type in a typesafe manner. Use `Any` to indicate that a value is dynamically typed.

## Nominal vs structural subtyping

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`.

This requirement previously also applied to abstract base classes, such as `Iterable`. The problem with this approach is that a class had to be explicitly marked to support them, which is unpythonic and unlike what one would normally do in idiomatic dynamically typed Python code. For example, this conforms to PEP 484:

    from typing import Sized, Iterable, Iterator

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

PEP 544 allows to solve this problem by allowing users to write the above code without explicit base classes in the class definition, allowing `Bucket` to be implicitly considered a subtype of both `Sized` and `Iterable[int]` by static type checkers. This is known as structural subtyping (or static duck-typing):

    from 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

Moreover, by subclassing a special class `Protocol`, a user can define new custom protocols to fully enjoy structural subtyping (see examples below).

## Classes, functions, and decorators

The module defines the following classes, functions and decorators:

class `typing.``TypeVar`

  Type variable.

  Usage:

      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`

  Abstract base class for generic types.

  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.

  This class can then be used as follows:

      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:
              ...

  Nuovo nella versione 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.

  Nuovo nella versione 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__`.

  Nuovo nella versione 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`

  Nuovo nella versione 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`.

  Nuovo nella versione 3.5.4.

  Nuovo nella versione 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`.

  Nuovo nella versione 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

  Nuovo nella versione 3.5.3.

class `typing.``AsyncIterable`(Generic[T_co])

  A generic version of `collections.abc.AsyncIterable`.

  Nuovo nella versione 3.5.2.

class `typing.``AsyncIterator`(AsyncIterable[T_co])

  A generic version of `collections.abc.AsyncIterator`.

  Nuovo nella versione 3.5.2.

class `typing.``ContextManager`(Generic[T_co])

  A generic version of `contextlib.AbstractContextManager`.

  Nuovo nella versione 3.5.4.

  Nuovo nella versione 3.6.0.

class `typing.``AsyncContextManager`(Generic[T_co])

  A generic version of `contextlib.AbstractAsyncContextManager`.

  Nuovo nella versione 3.5.4.

  Nuovo nella versione 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`.

  Nuovo nella versione 3.5.2.

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

  A generic version of `collections.OrderedDict`.

  Nuovo nella versione 3.7.2.

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

  A generic version of `collections.Counter`.

  Nuovo nella versione 3.5.4.

  Nuovo nella versione 3.6.1.

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

  A generic version of `collections.ChainMap`.

  Nuovo nella versione 3.5.4.

  Nuovo nella versione 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)

  Nuovo nella versione 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'

  Nuovo nella versione 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()`.

  Usage:

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

  This is equivalent to:

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

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

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

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

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

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

  Cambiato nella versione 3.6: Added support for PEP 526 variable annotation syntax.

  Cambiato nella versione 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.

  Cambiato nella versione 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`.

  Nuovo nella versione 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.

  Nuovo nella versione 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)

  Nuovo nella versione 3.5.2.

`typing.``cast`(typ, val)

  Cast a value to a type.

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

`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)

  Nuovo nella versione 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.

  Nuovo nella versione 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!

  Nuovo nella versione 3.8.

`typing.``Any`

  Special type indicating an unconstrained type.

  - Every type is compatible with `Any`.

  - `Any` is compatible with every type.

`typing.``NoReturn`

  Special type indicating that a function never returns. For example:

      from typing import NoReturn

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

  Nuovo nella versione 3.5.4.

  Nuovo nella versione 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:

  - The arguments must be types and there must be at least one.

  - Unions of unions are flattened, e.g.:

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

  - Unions of a single argument vanish, e.g.:

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

  - Redundant arguments are skipped, e.g.:

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

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

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

  - You cannot subclass or instantiate a union.

  - You cannot write `Union[X][Y]`.

  - You can use `Optional[X]` as a shorthand for `Union[X, None]`.

  Cambiato nella versione 3.7: Don’t remove explicit subclasses from unions at runtime.

`typing.``Optional`

  Optional type.

  `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.

  Nuovo nella versione 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

  Nuovo nella versione 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.

  Nuovo nella versione 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.

  Nuovo nella versione 3.5.2.