kbe/src/lib/python/Doc/library/typing.rst
typing --- Support for type hints.. module:: typing :synopsis: Support for type hints (see PEP 484).
.. versionadded:: 3.5
Source code: :source:Lib/typing.py
.. note::
The typing module has been included in the standard library on a
:term:provisional basis <provisional api>. New features might
be added and API may change even between minor releases if deemed
necessary by the core developers.
This module supports type hints as specified by :pep:484 and :pep:526.
The most fundamental support consists of the types :data:Any, :data:Union,
:data:Tuple, :data: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 be of type
:class:str and the return type :class:str. Subtypes are accepted as
arguments.
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]
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: ...
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).
.. _distinct:
Use the :func: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: ...
user_a = get_user_name(UserId(42351))
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 = 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)
class AdminUserId(UserId): pass
However, it is possible to create a :func: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.
.. note::
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.
.. versionadded:: 3.5.2
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 :class:TypeVar.
::
from typing import Sequence, TypeVar
T = TypeVar('T') # Declare type variable
def first(l: Sequence[T]) -> T: # Generic function return l[0]
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 :class:Generic base class uses a metaclass that defines
:meth:__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 :class: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 :class: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
:data: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]
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)
The metaclass used by :class:Generic is a subclass of :class:abc.ABCMeta.
A generic class can be an ABC by including abstract methods or properties,
and generic classes can also 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.
Any typeA special kind of type is :data:Any. A static type checker will treat
every type as being compatible with :data:Any and :data: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 on :data: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
:data: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 :class:str and receives an :class:int value at
runtime!
Furthermore, all functions without a return type or parameter types will
implicitly default to using :data:Any::
def legacy_parser(text): ... return data
def legacy_parser(text: Any) -> Any: ... return data
This behavior allows :data:Any to be used as an escape hatch when you
need to mix dynamically and statically typed code.
Contrast the behavior of :data:Any with the behavior of :class:object.
Similar to :data:Any, every type is a subtype of :class:object. However,
unlike :data:Any, the reverse is not true: :class:object is not a
subtype of every other type.
That means when the type of a value is :class: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() ...
hash_a(42) hash_a("foo")
hash_b(42) hash_b("foo")
Use :class:object to indicate that a value could be any type in a typesafe
manner. Use :data:Any to indicate that a value is dynamically typed.
The module defines the following classes, functions and decorators:
.. 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::
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 :class:`str`,
the return type is still plain :class:`str`.
At runtime, ``isinstance(x, T)`` will raise :exc:`TypeError`. In general,
:func:`isinstance` and :func:`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:: 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:: 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 :class:Type are classes, :data:Any,
:ref:type variables <generics>, and unions of any of these types.
For example::
def new_non_team_user(user_class: Type[Union[BaseUser, ProUser]]): ...
Type[Any] is equivalent to Type which in turn is equivalent
to type, which is the root of Python's metaclass hierarchy.
.. versionadded:: 3.5.2
.. class:: Iterable(Generic[T_co])
A generic version of :class:`collections.abc.Iterable`.
.. class:: Iterator(Iterable[T_co])
A generic version of :class:`collections.abc.Iterator`.
.. class:: Reversible(Iterable[T_co])
A generic version of :class:`collections.abc.Reversible`.
.. class:: SupportsInt
An ABC with one abstract method ``__int__``.
.. class:: SupportsFloat
An ABC with one abstract method ``__float__``.
.. class:: SupportsComplex
An ABC with one abstract method ``__complex__``.
.. class:: SupportsBytes
An ABC with one abstract method ``__bytes__``.
.. class:: SupportsAbs
An ABC with one abstract method ``__abs__`` that is covariant
in its return type.
.. class:: SupportsRound
An ABC with one abstract method ``__round__``
that is covariant in its return type.
.. class:: Container(Generic[T_co])
A generic version of :class:`collections.abc.Container`.
.. class:: Hashable
An alias to :class:collections.abc.Hashable
.. class:: Sized
An alias to :class:collections.abc.Sized
.. class:: Collection(Sized, Iterable[T_co], Container[T_co])
A generic version of :class:collections.abc.Collection
.. versionadded:: 3.6
.. class:: AbstractSet(Sized, Collection[T_co])
A generic version of :class:`collections.abc.Set`.
.. class:: MutableSet(AbstractSet[T])
A generic version of :class:`collections.abc.MutableSet`.
.. class:: Mapping(Sized, Collection[KT], Generic[VT_co])
A generic version of :class:`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:: MutableMapping(Mapping[KT, VT])
A generic version of :class:`collections.abc.MutableMapping`.
.. class:: Sequence(Reversible[T_co], Collection[T_co])
A generic version of :class:`collections.abc.Sequence`.
.. class:: MutableSequence(Sequence[T])
A generic version of :class:collections.abc.MutableSequence.
.. class:: ByteString(Sequence[int])
A generic version of :class:collections.abc.ByteString.
This type represents the types :class:bytes, :class:bytearray,
and :class:memoryview.
As a shorthand for this type, :class:bytes can be used to
annotate arguments of any of the types mentioned above.
.. class:: Deque(deque, MutableSequence[T])
A generic version of :class:collections.deque.
.. versionadded:: 3.6.1
.. class:: List(list, MutableSequence[T])
Generic version of :class:list.
Useful for annotating return types. To annotate arguments it is preferred
to use an abstract collection type such as :class:Sequence or
:class: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:: Set(set, MutableSet[T])
A generic version of :class:builtins.set <set>.
Useful for annotating return types. To annotate arguments it is preferred
to use an abstract collection type such as :class:AbstractSet.
.. class:: FrozenSet(frozenset, AbstractSet[T_co])
A generic version of :class:builtins.frozenset <frozenset>.
.. class:: MappingView(Sized, Iterable[T_co])
A generic version of :class:collections.abc.MappingView.
.. class:: KeysView(MappingView[KT_co], AbstractSet[KT_co])
A generic version of :class:collections.abc.KeysView.
.. class:: ItemsView(MappingView, Generic[KT_co, VT_co])
A generic version of :class:collections.abc.ItemsView.
.. class:: ValuesView(MappingView[VT_co])
A generic version of :class:collections.abc.ValuesView.
.. class:: Awaitable(Generic[T_co])
A generic version of :class:collections.abc.Awaitable.
.. class:: Coroutine(Awaitable[V_co], Generic[T_co T_contra, V_co])
A generic version of :class:collections.abc.Coroutine.
The variance and order of type variables
correspond to those of :class: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
.. class:: AsyncIterable(Generic[T_co])
A generic version of :class:collections.abc.AsyncIterable.
.. class:: AsyncIterator(AsyncIterable[T_co])
A generic version of :class:collections.abc.AsyncIterator.
.. class:: ContextManager(Generic[T_co])
A generic version of :class:contextlib.AbstractContextManager.
.. versionadded:: 3.6
.. class:: AsyncContextManager(Generic[T_co])
A generic version of :class:contextlib.AbstractAsyncContextManager.
.. versionadded:: 3.6
.. class:: Dict(dict, MutableMapping[KT, VT])
A generic version of :class:dict.
Useful for annotating return types. To annotate arguments it is preferred
to use an abstract collection type such as :class:Mapping.
This type can be used as follows::
def count_words(text: str) -> Dict[str, int]:
...
.. class:: DefaultDict(collections.defaultdict, MutableMapping[KT, VT])
A generic version of :class:collections.defaultdict.
.. versionadded:: 3.5.2
.. class:: OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])
A generic version of :class:collections.OrderedDict.
.. versionadded:: 3.7.2
.. class:: Counter(collections.Counter, Dict[T, int])
A generic version of :class:collections.Counter.
.. versionadded:: 3.6.1
.. class:: ChainMap(collections.ChainMap, MutableMapping[KT, VT])
A generic version of :class:collections.ChainMap.
.. versionadded:: 3.6.1
.. class:: 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 :class: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:: 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 :class: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)
.. versionadded:: 3.5.4
.. class:: 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'
.. versionadded:: 3.5.2
.. class:: IO TextIO 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
:func:open.
.. class:: Pattern Match
These type aliases
correspond to the return types from :func:re.compile and
:func: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:: NamedTuple
Typed version of 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 two extra attributes: _field_types,
giving a dict mapping field names to types, and _field_defaults, a dict
mapping field names to default values. (The field names are in the
_fields attribute, which is 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)])
.. versionchanged:: 3.6
Added support for :pep:526 variable annotation syntax.
.. versionchanged:: 3.6.1 Added support for default values, methods, and docstrings.
.. function:: NewType(typ)
A helper function to indicate a distinct types to a typechecker,
see :ref:distinct. At runtime it returns a function that returns
its argument. Usage::
UserId = NewType('UserId', int)
first_user = UserId(1)
.. versionadded:: 3.5.2
.. 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[, 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.
.. decorator:: 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
:exc: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.
.. decorator:: no_type_check
Decorator to indicate that annotations are not type hints.
This works as class or function :term: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.
.. decorator:: no_type_check_decorator
Decorator to give another decorator the :func:no_type_check effect.
This wraps the decorator with something that wraps the decorated
function in :func:no_type_check.
.. data:: Any
Special type indicating an unconstrained type.
Any.Any is compatible with every type... data:: NoReturn
Special type indicating that a function never returns. For example::
from typing import NoReturn
def stop() -> NoReturn:
raise RuntimeError('no way')
.. versionadded:: 3.5.4
.. data:: 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].
.. versionchanged:: 3.7 Don't remove explicit subclasses from unions at runtime.
.. data:: 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:
...
.. data:: 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.
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 :data:Tuple
is equivalent to Tuple[Any, ...], and in turn to :class:tuple.
.. data:: 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 :data:Callable is equivalent to
Callable[..., Any], and in turn to
:class:collections.abc.Callable.
.. data:: 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
:data:ClassVar accepts only types and cannot be further subscribed.
:data:ClassVar is not a class itself, and should not
be used with :func:isinstance or :func:issubclass.
:data: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
.. versionadded:: 3.5.3
.. data:: 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
.. data:: 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.
.. versionadded:: 3.5.2