path: root/Doc/library/typing.rst

:mod:`typing` --- Support for type hints
========================================

.. module:: typing
   :synopsis: Support for type hints (see PEP 484).

**Source code:** :source:`Lib/typing.py`

--------------

This module supports type hints as specified by :pep:`484`.  The most
fundamental support consists of the type :class:`Any`, :class:`Union`,
:class:`Tuple`, :class:`Callable`, :class:`TypeVar`, and
:class:`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 by 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::

   Vector = List[float]

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

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.

.. code-block:: python

   from typing import Mapping, Sequence

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

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

.. code-block:: python

   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.

.. code-block:: python

   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('{}: {}'.format(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 uses a metaclass that defines `__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]):
       ...

Subclassing 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]

Generic metaclasses are not supported.

The `Any` type
--------------

A special kind of type is `Any`. Every type is a subtype of `Any`.
This is also true for the builtin type object. However, to the static type
checker these are completely different.

When the type of a value is `object`, the 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. On the other hand, when a value has
type `Any`, the type checker will allow all operations on it, and a value of
type `Any` can be assigned to a variable (or used as a return value) of a more
constrained type.

Default argument values
-----------------------

Use a literal ellipsis `...` to declare an argument as having a default value::

   from typing import AnyStr

   def foo(x: AnyStr, y: AnyStr = ...) -> AnyStr: ...


Classes, functions, and decorators
----------------------------------

The module defines the following classes, functions and decorators:

.. class:: Any

   Special type indicating an unconstrained type.

   * Any object is an instance of `Any`.
   * Any class is a subclass of `Any`.
   * As a special case, `Any` and `object` are subclasses of each other.

.. class:: 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:

    .. code-block:: python

       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 `issublass` 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.

.. class:: 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]

   * If `Any` is present it is the sole survivor, e.g.::

       Union[int, Any] == Any

   * 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]`.

.. class:: Optional

   Optional type.

   `Optional[X]` is equivalent to `Union[X, type(None)]`.

.. class:: Tuple

  Tuple type; `Tuple[X, Y]` is the is the type of a tuple of two items
  with the first item of type X and the second of type Y.

  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, ...]`.

.. class:: 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; 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]` could be used to type hint a callable
   taking any number of arguments and returning `ReturnType`.
   A plain `Callable` is equivalent to `Callable[..., Any]`.

.. class:: 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:: Iterable(Generic[T_co])

.. class:: Iterator(Iterable[T_co])

.. class:: SupportsInt

.. class:: SupportsFloat

.. class:: SupportsAbs

.. class:: SupportsRound

.. class:: Reversible

.. class:: Container(Generic[T_co])

.. class:: AbstractSet(Sized, Iterable[T_co], Container[T_co])

.. class:: MutableSet(AbstractSet[T])

.. class:: Mapping(Sized, Iterable[KT_co], Container[KT_co], Generic[KT_co, VT_co])

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

.. class:: Sequence(Sized, Iterable[T_co], Container[T_co])

.. class:: MutableSequence(Sequence[T])

.. class:: ByteString(Sequence[int])

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

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

.. class:: MappingView(Sized, Iterable[T_co])

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

.. class:: ItemsView(MappingView, Generic[KT_co, VT_co])

.. class:: ValuesView(MappingView[VT_co])

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

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

.. class:: io

   Wrapper namespace for IO generic classes.

.. class:: re

   Wrapper namespace for re type classes.

.. function:: NamedTuple(typename, fields)

   Typed version of namedtuple.

   Usage::

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

   This is equivalent to::

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

   The resulting class has one extra attribute: _field_types,
   giving a dict mapping field names to types.  (The field names
   are in the _fields attribute, which is part of the namedtuple
   API.)

.. function:: 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).

.. function:: get_type_hints(obj)

   Return type hints for a function or method object.

   This is often the same as obj.__annotations__, but it handles
   forward references encoded as string literals, and if necessary
   adds Optional[t] if a default value equal to None is set.

.. decorator:: no_type_check(arg)

   Decorator to indicate that annotations are not type hints.

   The argument must be a class or function; if it is 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.

.. decorator:: no_type_check_decorator(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.