mirrors/cpython
0
0
Fork 0
mirror of https://github.com/python/cpython.git synced 2024-12-16 21:34:44 +08:00
Code Issues Packages Projects Releases Wiki Activity
d4a09c13dd
cpython/Doc/library/typing.rst

1487 lines
46 KiB
ReStructuredText
Raw Blame History

:mod:`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 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 :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.
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)``.
.. _distinct:
NewType
-------
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:
...
# 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 :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
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:
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]
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 :class:`Generic` base class defines :meth:`__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 :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]
# 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)
.. versionchanged:: 3.7
:class:`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 :data:`Any` type
--------------------
A 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
# 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 :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()
...
# 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 :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.
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
:class:`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 the :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 :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:: 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:: 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
:func:`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:
...
.. versionadded:: 3.8
.. 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:: SupportsIndex
An ABC with one abstract method ``__index__``.
.. versionadded:: 3.8
.. 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:: 3.6.0
.. 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:: 3.5.4
.. 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`.
.. versionadded:: 3.5.2
.. 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
.. versionadded:: 3.5.3
.. class:: AsyncIterable(Generic[T_co])
A generic version of :class:`collections.abc.AsyncIterable`.
.. versionadded:: 3.5.2
.. class:: AsyncIterator(AsyncIterable[T_co])
A generic version of :class:`collections.abc.AsyncIterator`.
.. versionadded:: 3.5.2
.. class:: ContextManager(Generic[T_co])
A generic version of :class:`contextlib.AbstractContextManager`.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.0
.. class:: AsyncContextManager(Generic[T_co])
A generic version of :class:`contextlib.AbstractAsyncContextManager`.
.. versionadded:: 3.5.4
.. versionadded:: 3.6.2
.. 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:: 3.5.4
.. versionadded:: 3.6.1
.. class:: ChainMap(collections.ChainMap, MutableMapping[KT, VT])
A generic version of :class:`collections.ChainMap`.
.. versionadded:: 3.5.4
.. 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:: 3.6.1
.. 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 :func:`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)])
.. versionchanged:: 3.6
Added support for :pep:`526` variable annotation syntax.
.. versionchanged:: 3.6.1
Added support for default values, methods, and docstrings.
.. versionchanged:: 3.8
Deprecated the ``_field_types`` attribute in favor of the more
standard ``__annotations__`` attribute which has the same information.
.. versionchanged:: 3.8
The ``_field_types`` and ``__annotations__`` attributes are
now regular dictionaries instead of instances of ``OrderedDict``.
.. class:: TypedDict(dict)
A simple typed namespace. At runtime it is equivalent to
a plain :class:`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``.
.. versionadded:: 3.8
.. class:: 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.
.. 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, globalns=None, localns=None, include_extras=False)
Return a dictionary containing type hints for a function, method, module
or class object.
This is often the same as ``obj.__annotations__``. 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.
The function recursively replaces all ``Annotated[T, ...]`` with ``T``,
unless ``include_extras`` is set to ``True`` (see :class:`Annotated` for
more information). For example::
class Student(NamedTuple):
name: Annotated[str, 'some marker']
get_type_hints(Student) == {'name': str}
get_type_hints(Student, include_extras=False) == {'name': str}
get_type_hints(Student, include_extras=True) == {
'name': Annotated[str, 'some marker']
}
.. versionchanged:: 3.9
Added ``include_extras`` parameter as part of :pep:`593`.
.. function:: get_origin(tp)
.. function:: 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
:mod:`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)
.. versionadded:: 3.8
.. 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:: 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.
.. versionadded:: 3.8
.. 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`.
.. decorator:: 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.
.. decorator:: runtime_checkable
Mark a protocol class as a runtime protocol.
Such a protocol can be used with :func:`isinstance` and :func:`issubclass`.
This raises :exc:`TypeError` when applied to a non-protocol class. This
allows a simple-minded structural check, very similar to "one trick ponies"
in :mod:`collections.abc` such as :class:`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!
.. versionadded:: 3.8
.. data:: Any
Special type indicating an unconstrained type.
* Every type is compatible with :data:`Any`.
* :data:`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
.. versionadded:: 3.6.2
.. 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. 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 :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:: 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.
.. versionadded:: 3.8
.. 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:: 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.
.. versionadded:: 3.8
.. 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
.. data:: Annotated
A type, introduced in :pep:`593` (``Flexible function and variable
annotations``), to decorate existing types with context-specific metadata
(possibly multiple pieces of it, as ``Annotated`` is variadic).
Specifically, a type ``T`` can be annotated with metadata ``x`` via the
typehint ``Annotated[T, x]``. This metadata can be used for either static
analysis or at runtime. If a library (or tool) encounters a typehint
``Annotated[T, x]`` and has no special logic for metadata ``x``, it
should ignore it and simply treat the type as ``T``. Unlike the
``no_type_check`` functionality that currently exists in the ``typing``
module which completely disables typechecking annotations on a function
or a class, the ``Annotated`` type allows for both static typechecking
of ``T`` (e.g., via mypy or Pyre, which can safely ignore ``x``)
together with runtime access to ``x`` within a specific application.
Ultimately, the responsibility of how to interpret the annotations (if
at all) is the responsibility of the tool or library encountering the
``Annotated`` type. A tool or library encountering an ``Annotated`` type
can scan through the annotations to determine if they are of interest
(e.g., using ``isinstance()``).
When a tool or a library does not support annotations or encounters an
unknown annotation it should just ignore it and treat annotated type as
the underlying type.
It's up to the tool consuming the annotations to decide whether the
client is allowed to have several annotations on one type and how to
merge those annotations.
Since the ``Annotated`` type allows you to put several annotations of
the same (or different) type(s) on any node, the tools or libraries
consuming those annotations are in charge of dealing with potential
duplicates. For example, if you are doing value range analysis you might
allow this::
T1 = Annotated[int, ValueRange(-10, 5)]
T2 = Annotated[T1, ValueRange(-20, 3)]
Passing ``include_extras=True`` to :func:`get_type_hints` lets one
access the extra annotations at runtime.
The details of the syntax:
* The first argument to ``Annotated`` must be a valid type
* Multiple type annotations are supported (``Annotated`` supports variadic
arguments)::
Annotated[int, ValueRange(3, 10), ctype("char")]
* ``Annotated`` must be called with at least two arguments (
``Annotated[int]`` is not valid)
* The order of the annotations is preserved and matters for equality
checks::
Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
int, ctype("char"), ValueRange(3, 10)
]
* Nested ``Annotated`` types are flattened, with metadata ordered
starting with the innermost annotation::
Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
int, ValueRange(3, 10), ctype("char")
]
* Duplicated annotations are not removed::
Annotated[int, ValueRange(3, 10)] != Annotated[
int, ValueRange(3, 10), ValueRange(3, 10)
]
* ``Annotated`` can be used with nested and generic aliases::
T = TypeVar('T')
Vec = Annotated[List[Tuple[T, T]], MaxLen(10)]
V = Vec[int]
V == Annotated[List[Tuple[int, int]], MaxLen(10)]
.. versionadded:: 3.9
Reference in New Issue View Git Blame Copy Permalink