typing — Підтримка підказок типу

Added in version 3.5.

Вихідний код: Lib/typing.py

Примітка

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.

Consider the function below:

def surface_area_of_cube(edge_length: float) -> str:
    return f"The surface area of the cube is {6 * edge_length ** 2}."

The function surface_area_of_cube takes an argument expected to be an instance of float, as indicated by the type hint edge_length: float. The function is expected to return an instance of str, as indicated by the -> str hint.

While type hints can be simple classes like float or str, they can also be more complex. The typing module provides a vocabulary of more advanced type hints.

New features are frequently added to the typing module. The typing_extensions package provides backports of these new features to older versions of Python.

Дивись також

«Typing cheat sheet»

A quick overview of type hints (hosted at the mypy docs)

«Type System Reference» section of the mypy docs

The Python typing system is standardised via PEPs, so this reference should broadly apply to most Python type checkers. (Some parts may still be specific to mypy.)

«Static Typing with Python»

Type-checker-agnostic documentation written by the community detailing type system features, useful typing related tools and typing best practices.

Specification for the Python Type System

The canonical, up-to-date specification of the Python type system can be found at «Specification for the Python type system».

Псевдоніми типів

A type alias is defined using the type statement, which creates an instance of TypeAliasType. In this example, Vector and list[float] will be treated equivalently by static type checkers:

type Vector = list[float]

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

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

Псевдоніми типів корисні для спрощення анотацій складних типів. Наприклад:

from collections.abc import Sequence

type ConnectionOptions = dict[str, str]
type Address = tuple[str, int]
type 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:
    ...

The type statement is new in Python 3.12. For backwards compatibility, type aliases can also be created through simple assignment:

Vector = list[float]

Or marked with TypeAlias to make it explicit that this is a type alias, not a normal variable assignment:

from typing import TypeAlias

Vector: TypeAlias = list[float]

NewType

Використовуйте помічник NewType для створення різних типів:

from typing import NewType

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

Аналізатор типів розглядатиме новий тип як підклас вихідного типу. Це корисно для виявлення логічних помилок:

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

# passes type checking
user_a = get_user_name(UserId(42351))

# fails type checking; an int is not a UserId
user_b = get_user_name(-1)

Ви все ще можете виконувати всі операції int зі змінною типу UserId, але результат завжди матиме тип int. Це дозволяє передавати UserId усюди, де можна очікувати int, але запобігає випадковому створенню UserId недійсним способом:

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

Зауважте, що ці перевірки виконуються лише засобом перевірки статичного типу. Під час виконання оператор Derived = NewType('Derived', Base) зробить Derived викликом, який негайно повертає будь-який параметр, який ви йому передаєте. Це означає, що вираз Derived(some_value) не створює нового класу і не вносить багато додаткових витрат, крім звичайного виклику функції.

Точніше, вираз some_value is Derived(some_value) завжди вірний під час виконання.

Неможливо створити підтип Derived:

from typing import NewType

UserId = NewType('UserId', int)

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

Однак можна створити NewType на основі «похідного» NewType:

from typing import NewType

UserId = NewType('UserId', int)

ProUserId = NewType('ProUserId', UserId)

і аналіз типів для ProUserId працюватиме належним чином.

Дивіться PEP 484 для більш детальної інформації.

Примітка

Recall that the use of a type alias declares two types to be equivalent to one another. Doing type 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.

На відміну, NewType оголошує один тип як підтип іншого. Якщо виконати Derived = NewType('Derived', Original), аналізатор типів розглядатиме Derived як підклас Original, що означає значення типу Original не можна використовувати там, де очікується значення типу Derived. Це корисно, коли ви хочете запобігти логічним помилкам з мінімальними витратами на виконання.

Added in version 3.5.2.

Змінено в версії 3.10: NewType is now a class rather than a function. As a result, there is some additional runtime cost when calling NewType over a regular function.

Змінено в версії 3.11: The performance of calling NewType has been restored to its level in Python 3.9.

Annotating callable objects

Functions – or other callable objects – can be annotated using collections.abc.Callable or deprecated typing.Callable. Callable[[int], str] signifies a function that takes a single parameter of type int and returns a str.

Наприклад:

from collections.abc import Callable, Awaitable

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

async def on_update(value: str) -> None:
    ...  # Body

callback: Callable[[str], Awaitable[None]] = on_update

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, a ParamSpec, Concatenate, or an ellipsis. The return type must be a single type.

If a literal ellipsis ... is given as the argument list, it indicates that a callable with any arbitrary parameter list would be acceptable:

def concat(x: str, y: str) -> str:
    return x + y

x: Callable[..., str]
x = str     # OK
x = concat  # Also OK

Callable cannot express complex signatures such as functions that take a variadic number of arguments, overloaded functions, or functions that have keyword-only parameters. However, these signatures can be expressed by defining a Protocol class with a __call__() method:

from collections.abc import Iterable
from typing import Protocol

class Combiner(Protocol):
    def __call__(self, *vals: bytes, maxlen: int | None = None) -> list[bytes]: ...

def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes:
    for item in data:
        ...

def good_cb(*vals: bytes, maxlen: int | None = None) -> list[bytes]:
    ...
def bad_cb(*vals: bytes, maxitems: int | None) -> list[bytes]:
    ...

batch_proc([], good_cb)  # OK
batch_proc([], bad_cb)   # Error! Argument 2 has incompatible type because of
                         # different name and kind in the callback

Викликаються, які приймають інші викликані як аргументи, можуть вказувати, що їхні типи параметрів залежать один від одного за допомогою ParamSpec. Крім того, якщо цей виклик додає або видаляє аргументи з інших викликів, можна використовувати оператор Concatenate. Вони приймають форму Callable[ParamSpecVariable, ReturnType] і Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType] відповідно.

Змінено в версії 3.10: Callable now supports ParamSpec and Concatenate. See PEP 612 for more details.

Дивись також

Документація для ParamSpec і Concatenate містить приклади використання в Callable.

Узагальнення

Since type information about objects kept in containers cannot be statically inferred in a generic way, many container classes in the standard library support subscription to denote the expected types of container elements.

from collections.abc import Mapping, Sequence

class Employee: ...

# Sequence[Employee] indicates that all elements in the sequence
# must be instances of "Employee".
# Mapping[str, str] indicates that all keys and all values in the mapping
# must be strings.
def notify_by_email(employees: Sequence[Employee],
                    overrides: Mapping[str, str]) -> None: ...

Generic functions and classes can be parameterized by using type parameter syntax:

from collections.abc import Sequence

def first[T](l: Sequence[T]) -> T:  # Function is generic over the TypeVar "T"
    return l[0]

Or by using the TypeVar factory directly:

from collections.abc import Sequence
from typing import TypeVar

U = TypeVar('U')                  # Declare type variable "U"

def second(l: Sequence[U]) -> U:  # Function is generic over the TypeVar "U"
    return l[1]

Змінено в версії 3.12: Syntactic support for generics is new in Python 3.12.

Annotating tuples

For most containers in Python, the typing system assumes that all elements in the container will be of the same type. For example:

from collections.abc import Mapping

# Type checker will infer that all elements in ``x`` are meant to be ints
x: list[int] = []

# Type checker error: ``list`` only accepts a single type argument:
y: list[int, str] = [1, 'foo']

# Type checker will infer that all keys in ``z`` are meant to be strings,
# and that all values in ``z`` are meant to be either strings or ints
z: Mapping[str, str | int] = {}

list only accepts one type argument, so a type checker would emit an error on the y assignment above. Similarly, Mapping only accepts two type arguments: the first indicates the type of the keys, and the second indicates the type of the values.

Unlike most other Python containers, however, it is common in idiomatic Python code for tuples to have elements which are not all of the same type. For this reason, tuples are special-cased in Python’s typing system. tuple accepts any number of type arguments:

# OK: ``x`` is assigned to a tuple of length 1 where the sole element is an int
x: tuple[int] = (5,)

# OK: ``y`` is assigned to a tuple of length 2;
# element 1 is an int, element 2 is a str
y: tuple[int, str] = (5, "foo")

# Error: the type annotation indicates a tuple of length 1,
# but ``z`` has been assigned to a tuple of length 3
z: tuple[int] = (1, 2, 3)

To denote a tuple which could be of any length, and in which all elements are of the same type T, use tuple[T, ...]. To denote an empty tuple, use tuple[()]. Using plain tuple as an annotation is equivalent to using tuple[Any, ...]:

x: tuple[int, ...] = (1, 2)
# These reassignments are OK: ``tuple[int, ...]`` indicates x can be of any length
x = (1, 2, 3)
x = ()
# This reassignment is an error: all elements in ``x`` must be ints
x = ("foo", "bar")

# ``y`` can only ever be assigned to an empty tuple
y: tuple[()] = ()

z: tuple = ("foo", "bar")
# These reassignments are OK: plain ``tuple`` is equivalent to ``tuple[Any, ...]``
z = (1, 2, 3)
z = ()

The type of class objects

A variable annotated with C may accept a value of type C. In contrast, a variable annotated with type[C] (or deprecated typing.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 ProUser(User): ...
class TeamUser(User): ...

def make_new_user(user_class: type[User]) -> User:
    # ...
    return user_class()

make_new_user(User)      # OK
make_new_user(ProUser)   # Also OK: ``type[ProUser]`` is a subtype of ``type[User]``
make_new_user(TeamUser)  # Still fine
make_new_user(User())    # Error: expected ``type[User]`` but got ``User``
make_new_user(int)       # Error: ``type[int]`` is not a subtype of ``type[User]``

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[BasicUser | ProUser]): ...

new_non_team_user(BasicUser)  # OK
new_non_team_user(ProUser)    # OK
new_non_team_user(TeamUser)   # Error: ``type[TeamUser]`` is not a subtype
                              # of ``type[BasicUser | ProUser]``
new_non_team_user(User)       # Also an error

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

Annotating generators and coroutines

A generator can be annotated using 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 generic classes in the standard library, the SendType of Generator behaves contravariantly, not covariantly or invariantly.

The SendType and ReturnType parameters default to None:

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

It is also possible to set these types explicitly:

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

Simple generators that only ever yield values can also be annotated 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

Async generators are handled in a similar fashion, but don’t expect a ReturnType type argument (AsyncGenerator[YieldType, SendType]). The SendType argument defaults to None, so the following definitions are equivalent:

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

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

As in the synchronous case, AsyncIterable[YieldType] and AsyncIterator[YieldType] are available as well:

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

Coroutines can be annotated using Coroutine[YieldType, SendType, ReturnType]. Generic arguments correspond to those of Generator, for example:

from collections.abc import Coroutine
c: Coroutine[list[str], str, int]  # Some coroutine defined elsewhere
x = c.send('hi')                   # Inferred type of 'x' is list[str]
async def bar() -> None:
    y = await c                    # Inferred type of 'y' is int

Визначені користувачем загальні типи

Визначений користувачем клас можна визначити як загальний клас.

from logging import Logger

class LoggedVar[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)

This syntax indicates that the class LoggedVar is parameterised around a single type variable T . This also makes T valid as a type within the class body.

Generic classes implicitly inherit from Generic. For compatibility with Python 3.11 and lower, it is also possible to inherit explicitly from Generic to indicate a generic class:

from typing import TypeVar, Generic

T = TypeVar('T')

class LoggedVar(Generic[T]):
    ...

Generic classes have __class_getitem__() methods, meaning they can be parameterised at runtime (e.g. LoggedVar[int] below):

from collections.abc import Iterable

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

Загальний тип може мати будь-яку кількість змінних типу. Усі різновиди TypeVar допустимі як параметри для загального типу:

from typing import TypeVar, Generic, Sequence

class WeirdTrio[T, B: Sequence[bytes], S: (int, str)]:
    ...

OldT = TypeVar('OldT', contravariant=True)
OldB = TypeVar('OldB', bound=Sequence[bytes], covariant=True)
OldS = TypeVar('OldS', int, str)

class OldWeirdTrio(Generic[OldT, OldB, OldS]):
    ...

Кожен аргумент змінної типу для Generic має бути окремим. Таким чином, це недійсно:

from typing import TypeVar, Generic
...

class Pair[M, M]:  # SyntaxError
    ...

T = TypeVar('T')

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

Generic classes can also inherit from other classes:

from collections.abc import Sized

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

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

from collections.abc import Mapping

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

У цьому випадку MyDict має єдиний параметр, 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 collections.abc import Iterable

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

User-defined generic type aliases are also supported. Examples:

from collections.abc import Iterable

type Response[S] = Iterable[S] | int

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

type Vec[T] = Iterable[tuple[T, T]]

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

For backward compatibility, generic type aliases can also be created through a simple assignment:

from collections.abc import Iterable
from typing import TypeVar

S = TypeVar("S")
Response = Iterable[S] | int

Змінено в версії 3.7: Generic більше не має спеціального метакласу.

Змінено в версії 3.12: Syntactic support for generics and type aliases is new in version 3.12. Previously, generic classes had to explicitly inherit from Generic or contain a type variable in one of their bases.

User-defined generics for parameter expressions are also supported via parameter specification variables in the form [**P]. The behavior is consistent with type variables“ described above as parameter specification variables are treated by the typing module as a specialized type variable. The one exception to this is that a list of types can be used to substitute a ParamSpec:

>>> class Z[T, **P]: ...  # T is a TypeVar; P is a ParamSpec
...
>>> Z[int, [dict, float]]
__main__.Z[int, [dict, float]]

Classes generic over a ParamSpec can also be created using explicit inheritance from Generic. In this case, ** is not used:

from typing import ParamSpec, Generic

P = ParamSpec('P')

class Z(Generic[P]):
    ...

Another difference between TypeVar and ParamSpec is that a generic with only one parameter specification variable will accept parameter lists in the forms X[[Type1, Type2, ...]] and also X[Type1, Type2, ...] for aesthetic reasons. Internally, the latter is converted to the former, so the following are equivalent:

>>> class X[**P]: ...
...
>>> X[int, str]
__main__.X[[int, str]]
>>> X[[int, str]]
__main__.X[[int, str]]

Note that generics with ParamSpec may not have correct __parameters__ after substitution in some cases because they are intended primarily for static type checking.

Змінено в версії 3.10: Generic тепер можна параметризувати над виразами параметрів. Дивіться ParamSpec і PEP 612 для отримання додаткової інформації.

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.

Тип Any

Особливий вид типу — Any. Засіб перевірки статичних типів розглядатиме кожен тип як сумісний із Any і Any як сумісні з кожним типом.

Це означає, що можна виконати будь-яку операцію або викликати метод над значенням типу Any і призначити його будь-якій змінній:

from typing import Any

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

s: str = ''
s = a           # OK

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

Notice that no type checking 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!

Крім того, усі функції без типу повернення або типів параметрів неявно використовуватимуть за умовчанням 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

Така поведінка дозволяє Any використовувати як вихідний люк, коли вам потрібно змішати динамічний і статичний код.

Порівняйте поведінку Any з поведінкою object. Подібно до Any, кожен тип є підтипом object. Однак, на відміну від Any, зворотне не вірно: object не є підтипом будь-якого іншого типу.

Це означає, що коли тип значення object, засіб перевірки типів відхилить майже всі операції над ним, а присвоєння його змінній (або використання її як значення, що повертається) більш спеціалізованого типу є типом помилка. Наприклад:

def hash_a(item: object) -> int:
    # Fails type checking; an object does not have a 'magic' method.
    item.magic()
    ...

def hash_b(item: Any) -> int:
    # Passes type checking
    item.magic()
    ...

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

# Passes type checking, since Any is compatible with all types
hash_b(42)
hash_b("foo")

Використовуйте object, щоб вказати, що значення може мати будь-який тип у безпечний спосіб. Використовуйте Any, щоб вказати, що значення вводиться динамічно.

Номінальний проти структурного підтипу

Спочатку PEP 484 визначив систему статичних типів Python як таку, що використовує номінальний підтип. Це означає, що клас A дозволений там, де очікується клас B, якщо і тільки якщо A є підкласом B.

Ця вимога раніше також застосовувалася до абстрактних базових класів, таких як Iterable. Проблема з цим підходом полягає в тому, що клас повинен бути явно позначений для їх підтримки, що не є пітонічним і не схожим на те, що зазвичай робили б у ідіоматичному динамічно введеному коді Python. Наприклад, це відповідає PEP 484:

from collections.abc import Sized, Iterable, Iterator

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

PEP 544 дозволяє вирішити цю проблему, дозволяючи користувачам писати наведений вище код без явних базових класів у визначенні класу, дозволяючи Bucket неявно вважатися підтипом як Sized, так і Iterable[int] засобами перевірки статичних типів. Це відоме як структурне підтипування (або статичне качине типування):

from collections.abc 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

Крім того, створивши підклас спеціального класу Protocol, користувач може визначати нові користувальницькі протоколи, щоб повною мірою користуватися структурними підтипами (див. приклади нижче).

Зміст модуля

The typing module defines the following classes, functions and decorators.

Спеціальні примітиви типізації

Особливі види

These can be used as types in annotations. They do not support subscription using [].

typing.Any

Спеціальний тип, що вказує на необмежений тип.

• Кожен тип сумісний з Any.

• Any сумісний з усіма типами.

Змінено в версії 3.11: Any can now be used as a base class. This can be useful for avoiding type checker errors with classes that can duck type anywhere or are highly dynamic.

typing.AnyStr

A constrained type variable.

Definition:

AnyStr = TypeVar('AnyStr', str, bytes)

AnyStr is meant to be used for functions that may accept str or bytes arguments but cannot allow the two to mix.

Наприклад:

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

concat("foo", "bar")    # OK, output has type 'str'
concat(b"foo", b"bar")  # OK, output has type 'bytes'
concat("foo", b"bar")   # Error, cannot mix str and bytes

Note that, despite its name, AnyStr has nothing to do with the Any type, nor does it mean «any string». In particular, AnyStr and str | bytes are different from each other and have different use cases:

# Invalid use of AnyStr:
# The type variable is used only once in the function signature,
# so cannot be "solved" by the type checker
def greet_bad(cond: bool) -> AnyStr:
    return "hi there!" if cond else b"greetings!"

# The better way of annotating this function:
def greet_proper(cond: bool) -> str | bytes:
    return "hi there!" if cond else b"greetings!"

Deprecated since version 3.13, will be removed in version 3.18: Deprecated in favor of the new type parameter syntax. Use class A[T: (str, bytes)]: ... instead of importing AnyStr. See PEP 695 for more details.

In Python 3.16, AnyStr will be removed from typing.__all__, and deprecation warnings will be emitted at runtime when it is accessed or imported from typing. AnyStr will be removed from typing in Python 3.18.

typing.LiteralString

Special type that includes only literal strings.

Any string literal is compatible with LiteralString, as is another LiteralString. However, an object typed as just str is not. A string created by composing LiteralString-typed objects is also acceptable as a LiteralString.

приклад:

def run_query(sql: LiteralString) -> None:
    ...

def caller(arbitrary_string: str, literal_string: LiteralString) -> None:
    run_query("SELECT * FROM students")  # OK
    run_query(literal_string)  # OK
    run_query("SELECT * FROM " + literal_string)  # OK
    run_query(arbitrary_string)  # type checker error
    run_query(  # type checker error
        f"SELECT * FROM students WHERE name = {arbitrary_string}"
    )

LiteralString is useful for sensitive APIs where arbitrary user-generated strings could generate problems. For example, the two cases above that generate type checker errors could be vulnerable to an SQL injection attack.

See PEP 675 for more details.

Added in version 3.11.

typing.Never

typing.NoReturn

Never and NoReturn represent the bottom type, a type that has no members.

They can be used to indicate that a function never returns, such as sys.exit():

from typing import Never  # or NoReturn

def stop() -> Never:
    raise RuntimeError('no way')

Or to define a function that should never be called, as there are no valid arguments, such as assert_never():

from typing import Never  # or NoReturn

def never_call_me(arg: Never) -> None:
    pass

def int_or_str(arg: int | str) -> None:
    never_call_me(arg)  # type checker error
    match arg:
        case int():
            print("It's an int")
        case str():
            print("It's a str")
        case _:
            never_call_me(arg)  # OK, arg is of type Never (or NoReturn)

Never and NoReturn have the same meaning in the type system and static type checkers treat both equivalently.

Added in version 3.6.2: Added NoReturn.

Added in version 3.11: Added Never.

typing.Self

Special type to represent the current enclosed class.

Наприклад:

from typing import Self, reveal_type

class Foo:
    def return_self(self) -> Self:
        ...
        return self

class SubclassOfFoo(Foo): pass

reveal_type(Foo().return_self())  # Revealed type is "Foo"
reveal_type(SubclassOfFoo().return_self())  # Revealed type is "SubclassOfFoo"

This annotation is semantically equivalent to the following, albeit in a more succinct fashion:

from typing import TypeVar

Self = TypeVar("Self", bound="Foo")

class Foo:
    def return_self(self: Self) -> Self:
        ...
        return self

In general, if something returns self, as in the above examples, you should use Self as the return annotation. If Foo.return_self was annotated as returning "Foo", then the type checker would infer the object returned from SubclassOfFoo.return_self as being of type Foo rather than SubclassOfFoo.

Other common use cases include:

• classmethods that are used as alternative constructors and return instances of the cls parameter.

• Annotating an __enter__() method which returns self.

You should not use Self as the return annotation if the method is not guaranteed to return an instance of a subclass when the class is subclassed:

class Eggs:
    # Self would be an incorrect return annotation here,
    # as the object returned is always an instance of Eggs,
    # even in subclasses
    def returns_eggs(self) -> "Eggs":
        return Eggs()

See PEP 673 for more details.

Added in version 3.11.

typing.TypeAlias

Special annotation for explicitly declaring a type alias.

Наприклад:

from typing import TypeAlias

Factors: TypeAlias = list[int]

TypeAlias is particularly useful on older Python versions for annotating aliases that make use of forward references, as it can be hard for type checkers to distinguish these from normal variable assignments:

from typing import Generic, TypeAlias, TypeVar

T = TypeVar("T")

# "Box" does not exist yet,
# so we have to use quotes for the forward reference on Python <3.12.
# Using ``TypeAlias`` tells the type checker that this is a type alias declaration,
# not a variable assignment to a string.
BoxOfStrings: TypeAlias = "Box[str]"

class Box(Generic[T]):
    @classmethod
    def make_box_of_strings(cls) -> BoxOfStrings: ...

Дивіться PEP 613 для більш детальної інформації.

Added in version 3.10.

Застаріло починаючи з версії 3.12: TypeAlias is deprecated in favor of the type statement, which creates instances of TypeAliasType and which natively supports forward references. Note that while TypeAlias and TypeAliasType serve similar purposes and have similar names, they are distinct and the latter is not the type of the former. Removal of TypeAlias is not currently planned, but users are encouraged to migrate to type statements.

Спеціальні форми

These can be used as types in annotations. They all support subscription using [], but each has a unique syntax.

typing.Union

тип союзу; Union[X, Y] еквівалентно X | Y і означає X або Y.

Щоб визначити об’єднання, використовуйте, наприклад, Union[int, str] або скорочення int | str. Рекомендується використовувати це скорочення. Подробиці:

• Аргументи мають бути типами і має бути принаймні один.

• Союзи союзів зведені, наприклад:

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

• Союзи одного аргументу зникають, наприклад:

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

• Зайві аргументи пропускаються, наприклад:

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

• Під час порівняння об’єднань порядок аргументів ігнорується, наприклад:

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

• Ви не можете створювати підкласи або екземпляри Union.

• Ви не можете писати Union[X][Y].

Змінено в версії 3.7: Не видаляйте явні підкласи з об’єднань під час виконання.

Змінено в версії 3.10: Об’єднання тепер можна записати як X | Y. Див. вирази типу union.

typing.Optional

Optional[X] еквівалентно X | None (або Union[X, None]).

Зауважте, що це не те саме поняття, що необов’язковий аргумент, який має значення за умовчанням. Необов’язковий аргумент із значенням за замовчуванням не потребує кваліфікатора Optional в анотації свого типу лише тому, що він є необов’язковим. Наприклад:

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

З іншого боку, якщо дозволено явне значення None, використання Optional є доречним, незалежно від того, чи є аргумент необов’язковим. Наприклад:

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

Змінено в версії 3.10: Необов’язковий тепер можна записати як X | None. Див. вирази типу union.

typing.Concatenate

Special form for annotating higher-order functions.

Concatenate can be used in conjunction with Callable and ParamSpec to annotate a higher-order callable which adds, removes, or transforms parameters of another callable. Usage is in the form Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable]. Concatenate is currently only valid when used as the first argument to a Callable. The last parameter to Concatenate must be a ParamSpec or ellipsis (...).

Наприклад, щоб анотувати декоратор with_lock, який надає threading.Lock декорованій функції, Concatenate можна використовувати, щоб вказати, що with_lock очікує виклик, який приймає в Lock як перший аргумент і повертає виклик із сигнатурою іншого типу. У цьому випадку ParamSpec вказує, що типи параметрів викликаного об’єкта, що повертається, залежать від типів параметрів об’єкта виклику, який передається в:

from collections.abc import Callable
from threading import Lock
from typing import Concatenate

# Use this lock to ensure that only one thread is executing a function
# at any time.
my_lock = Lock()

def with_lock[**P, R](f: Callable[Concatenate[Lock, P], R]) -> Callable[P, R]:
    '''A type-safe decorator which provides a lock.'''
    def inner(*args: P.args, **kwargs: P.kwargs) -> R:
        # Provide the lock as the first argument.
        return f(my_lock, *args, **kwargs)
    return inner

@with_lock
def sum_threadsafe(lock: Lock, numbers: list[float]) -> float:
    '''Add a list of numbers together in a thread-safe manner.'''
    with lock:
        return sum(numbers)

# We don't need to pass in the lock ourselves thanks to the decorator.
sum_threadsafe([1.1, 2.2, 3.3])

Added in version 3.10.

Дивись також

• PEP 612 – Parameter Specification Variables (the PEP which introduced ParamSpec and Concatenate)

• ParamSpec

• Annotating callable objects

typing.Literal

Special typing form to define «literal types».

Literal can be used to indicate to type checkers that the annotated object has a value equivalent to one of the provided literals.

Наприклад:

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

type 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

Літерал[...] не може бути підкласом. Під час виконання довільне значення допускається як аргумент типу для Literal[...], але засоби перевірки типу можуть накладати обмеження. Дивіться PEP 586 для отримання додаткової інформації про літеральні типи.

Added in version 3.8.

Змінено в версії 3.9.1: Літерал тепер усуває дублікати параметрів. Порівняння рівності об’єктів Literal більше не залежить від порядку. Об’єкти Literal тепер створюватимуть виняток TypeError під час порівняння рівності, якщо один із їхніх параметрів не є hashable.

typing.ClassVar

Конструкція спеціального типу для позначення змінних класу.

Як представлено в PEP 526, анотація змінної, загорнена в ClassVar, вказує на те, що даний атрибут призначений для використання як змінна класу і не повинен встановлюватися для екземплярів цього класу. Використання:

class Starship:
    stats: ClassVar[dict[str, int]] = {} # class variable
    damage: int = 10                     # instance variable

ClassVar приймає лише типи і не може бути підписаний далі.

ClassVar сам по собі не є класом і не повинен використовуватися з isinstance() або issubclass(). ClassVar не змінює поведінку Python під час виконання, але його можуть використовувати сторонні засоби перевірки типу. Наприклад, засіб перевірки типу може позначити наступний код як помилку:

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

Added in version 3.5.3.

Змінено в версії 3.13: ClassVar can now be nested in Final and vice versa.

typing.Final

Special typing construct to indicate final names to type checkers.

Final names cannot be reassigned in any scope. Final names declared in class scopes cannot be overridden in subclasses.

Наприклад:

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

Немає жодної перевірки цих властивостей під час виконання. Дивіться PEP 591 для більш детальної інформації.

Added in version 3.8.

Змінено в версії 3.13: Final can now be nested in ClassVar and vice versa.

typing.Required

Special typing construct to mark a TypedDict key as required.

This is mainly useful for total=False TypedDicts. See TypedDict and PEP 655 for more details.

Added in version 3.11.

typing.NotRequired

Special typing construct to mark a TypedDict key as potentially missing.

See TypedDict and PEP 655 for more details.

Added in version 3.11.

typing.ReadOnly

A special typing construct to mark an item of a TypedDict as read-only.

Наприклад:

class Movie(TypedDict):
   title: ReadOnly[str]
   year: int

def mutate_movie(m: Movie) -> None:
   m["year"] = 1999  # allowed
   m["title"] = "The Matrix"  # typechecker error

There is no runtime checking for this property.

See TypedDict and PEP 705 for more details.

Added in version 3.13.

typing.Annotated

Special typing form to add context-specific metadata to an annotation.

Add metadata x to a given type T by using the annotation Annotated[T, x]. Metadata added using Annotated can be used by static analysis tools or at runtime. At runtime, the metadata is stored in a __metadata__ attribute.

If a library or tool encounters an annotation Annotated[T, x] and has no special logic for the metadata, it should ignore the metadata and simply treat the annotation as T. As such, Annotated can be useful for code that wants to use annotations for purposes outside Python’s static typing system.

Using Annotated[T, x] as an annotation still allows for static typechecking of T, as type checkers will simply ignore the metadata x. In this way, Annotated differs from the @no_type_check decorator, which can also be used for adding annotations outside the scope of the typing system, but completely disables typechecking for a function or class.

The responsibility of how to interpret the metadata lies with the tool or library encountering an Annotated annotation. A tool or library encountering an Annotated type can scan through the metadata elements to determine if they are of interest (e.g., using isinstance()).

Annotated[<type>, <metadata>]

Here is an example of how you might use Annotated to add metadata to type annotations if you were doing range analysis:

@dataclass
class ValueRange:
    lo: int
    hi: int

T1 = Annotated[int, ValueRange(-10, 5)]
T2 = Annotated[T1, ValueRange(-20, 3)]

Details of the syntax:

• Перший аргумент Анотований має бути дійсного типу

• Multiple metadata elements can be supplied (Annotated supports variadic arguments):

  @dataclass
  class ctype:
      kind: str
  
  Annotated[int, ValueRange(3, 10), ctype("char")]
  

  It is up to the tool consuming the annotations to decide whether the client is allowed to add multiple metadata elements to one annotation and how to merge those annotations.

• Annotated must be subscripted with at least two arguments ( Annotated[int] is not valid)

• The order of the metadata elements is preserved and matters for equality checks:

  assert Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
      int, ctype("char"), ValueRange(3, 10)
  ]
  

• Nested Annotated types are flattened. The order of the metadata elements starts with the innermost annotation:

  assert Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
      int, ValueRange(3, 10), ctype("char")
  ]
  

• Duplicated metadata elements are not removed:

  assert Annotated[int, ValueRange(3, 10)] != Annotated[
      int, ValueRange(3, 10), ValueRange(3, 10)
  ]
  

• Annotated can be used with nested and generic aliases:

  @dataclass
  class MaxLen:
      value: int
  
  type Vec[T] = Annotated[list[tuple[T, T]], MaxLen(10)]
  
  # When used in a type annotation, a type checker will treat "V" the same as
  # ``Annotated[list[tuple[int, int]], MaxLen(10)]``:
  type V = Vec[int]
  

• Annotated cannot be used with an unpacked TypeVarTuple:

  type Variadic[*Ts] = Annotated[*Ts, Ann1]  # NOT valid
  

  This would be equivalent to:

  Annotated[T1, T2, T3, ..., Ann1]
  

  where T1, T2, etc. are TypeVars. This would be invalid: only one type should be passed to Annotated.

• By default, get_type_hints() strips the metadata from annotations. Pass include_extras=True to have the metadata preserved:

  >>> from typing import Annotated, get_type_hints
  >>> def func(x: Annotated[int, "metadata"]) -> None: pass
  ...
  >>> get_type_hints(func)
  {'x': <class 'int'>, 'return': <class 'NoneType'>}
  >>> get_type_hints(func, include_extras=True)
  {'x': typing.Annotated[int, 'metadata'], 'return': <class 'NoneType'>}
  

• At runtime, the metadata associated with an Annotated type can be retrieved via the __metadata__ attribute:

  >>> from typing import Annotated
  >>> X = Annotated[int, "very", "important", "metadata"]
  >>> X
  typing.Annotated[int, 'very', 'important', 'metadata']
  >>> X.__metadata__
  ('very', 'important', 'metadata')
  

• At runtime, if you want to retrieve the original type wrapped by Annotated, use the __origin__ attribute:

  >>> from typing import Annotated, get_origin
  >>> Password = Annotated[str, "secret"]
  >>> Password.__origin__
  <class 'str'>
  

  Note that using get_origin() will return Annotated itself:

  >>> get_origin(Password)
  typing.Annotated
  

Дивись також

PEP 593 - Flexible function and variable annotations

The PEP introducing Annotated to the standard library.

Added in version 3.9.

typing.TypeIs

Special typing construct for marking user-defined type predicate functions.

TypeIs can be used to annotate the return type of a user-defined type predicate function. TypeIs only accepts a single type argument. At runtime, functions marked this way should return a boolean and take at least one positional argument.

TypeIs aims to benefit type narrowing – a technique used by static type checkers to determine a more precise type of an expression within a program’s code flow. Usually type narrowing is done by analyzing conditional code flow and applying the narrowing to a block of code. The conditional expression here is sometimes referred to as a «type predicate»:

def is_str(val: str | float):
    # "isinstance" type predicate
    if isinstance(val, str):
        # Type of ``val`` is narrowed to ``str``
        ...
    else:
        # Else, type of ``val`` is narrowed to ``float``.
        ...

Sometimes it would be convenient to use a user-defined boolean function as a type predicate. Such a function should use TypeIs[...] or TypeGuard as its return type to alert static type checkers to this intention. TypeIs usually has more intuitive behavior than TypeGuard, but it cannot be used when the input and output types are incompatible (e.g., list[object] to list[int]) or when the function does not return True for all instances of the narrowed type.

Using -> TypeIs[NarrowedType] tells the static type checker that for a given function:

1. Повернене значення є логічним.

2. If the return value is True, the type of its argument is the intersection of the argument’s original type and NarrowedType.

3. If the return value is False, the type of its argument is narrowed to exclude NarrowedType.

Наприклад:

from typing import assert_type, final, TypeIs

class Parent: pass
class Child(Parent): pass
@final
class Unrelated: pass

def is_parent(val: object) -> TypeIs[Parent]:
    return isinstance(val, Parent)

def run(arg: Child | Unrelated):
    if is_parent(arg):
        # Type of ``arg`` is narrowed to the intersection
        # of ``Parent`` and ``Child``, which is equivalent to
        # ``Child``.
        assert_type(arg, Child)
    else:
        # Type of ``arg`` is narrowed to exclude ``Parent``,
        # so only ``Unrelated`` is left.
        assert_type(arg, Unrelated)

The type inside TypeIs must be consistent with the type of the function’s argument; if it is not, static type checkers will raise an error. An incorrectly written TypeIs function can lead to unsound behavior in the type system; it is the user’s responsibility to write such functions in a type-safe manner.

If a TypeIs function is a class or instance method, then the type in TypeIs maps to the type of the second parameter (after cls or self).

In short, the form def foo(arg: TypeA) -> TypeIs[TypeB]: ..., means that if foo(arg) returns True, then arg is an instance of TypeB, and if it returns False, it is not an instance of TypeB.

TypeIs also works with type variables. For more information, see PEP 742 (Narrowing types with TypeIs).

Added in version 3.13.

typing.TypeGuard

Special typing construct for marking user-defined type predicate functions.

Type predicate functions are user-defined functions that return whether their argument is an instance of a particular type. TypeGuard works similarly to TypeIs, but has subtly different effects on type checking behavior (see below).

Використання -> TypeGuard повідомляє засобу перевірки статичного типу, що для даної функції:

1. Повернене значення є логічним.

2. Якщо повертається значення True, тип його аргументу є типом у TypeGuard.

TypeGuard also works with type variables. See PEP 647 for more details.

Наприклад:

def is_str_list(val: list[object]) -> TypeGuard[list[str]]:
    '''Determines whether all objects in the list are strings'''
    return all(isinstance(x, str) for x in val)

def func1(val: list[object]):
    if is_str_list(val):
        # Type of ``val`` is narrowed to ``list[str]``.
        print(" ".join(val))
    else:
        # Type of ``val`` remains as ``list[object]``.
        print("Not a list of strings!")

TypeIs and TypeGuard differ in the following ways:

• TypeIs requires the narrowed type to be a subtype of the input type, while TypeGuard does not. The main reason is to allow for things like narrowing list[object] to list[str] even though the latter is not a subtype of the former, since list is invariant.

• When a TypeGuard function returns True, type checkers narrow the type of the variable to exactly the TypeGuard type. When a TypeIs function returns True, type checkers can infer a more precise type combining the previously known type of the variable with the TypeIs type. (Technically, this is known as an intersection type.)

• When a TypeGuard function returns False, type checkers cannot narrow the type of the variable at all. When a TypeIs function returns False, type checkers can narrow the type of the variable to exclude the TypeIs type.

Added in version 3.10.

typing.Unpack

Typing operator to conceptually mark an object as having been unpacked.

For example, using the unpack operator * on a type variable tuple is equivalent to using Unpack to mark the type variable tuple as having been unpacked:

Ts = TypeVarTuple('Ts')
tup: tuple[*Ts]
# Effectively does:
tup: tuple[Unpack[Ts]]

In fact, Unpack can be used interchangeably with * in the context of typing.TypeVarTuple and builtins.tuple types. You might see Unpack being used explicitly in older versions of Python, where * couldn’t be used in certain places:

# In older versions of Python, TypeVarTuple and Unpack
# are located in the `typing_extensions` backports package.
from typing_extensions import TypeVarTuple, Unpack

Ts = TypeVarTuple('Ts')
tup: tuple[*Ts]         # Syntax error on Python <= 3.10!
tup: tuple[Unpack[Ts]]  # Semantically equivalent, and backwards-compatible

Unpack can also be used along with typing.TypedDict for typing **kwargs in a function signature:

from typing import TypedDict, Unpack

class Movie(TypedDict):
    name: str
    year: int

# This function expects two keyword arguments - `name` of type `str`
# and `year` of type `int`.
def foo(**kwargs: Unpack[Movie]): ...

See PEP 692 for more details on using Unpack for **kwargs typing.

Added in version 3.11.

Building generic types and type aliases

The following classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating generic types and type aliases.

These objects can be created through special syntax (type parameter lists and the type statement). For compatibility with Python 3.11 and earlier, they can also be created without the dedicated syntax, as documented below.

class typing.Generic

Абстрактний базовий клас для загальних типів.

A generic type is typically declared by adding a list of type parameters after the class name:

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

Such a class implicitly inherits from Generic. The runtime semantics of this syntax are discussed in the Language Reference.

Потім цей клас можна використовувати наступним чином:

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

Here the brackets after the function name indicate a generic function.

For backwards compatibility, generic classes can also be declared by explicitly inheriting from Generic. In this case, the type parameters must be declared separately:

KT = TypeVar('KT')
VT = TypeVar('VT')

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

class typing.TypeVar(name, *constraints, bound=None, covariant=False, contravariant=False, infer_variance=False, default=typing.NoDefault)

Тип змінної.

The preferred way to construct a type variable is via the dedicated syntax for generic functions, generic classes, and generic type aliases:

class Sequence[T]:  # T is a TypeVar
    ...

This syntax can also be used to create bounded and constrained type variables:

class StrSequence[S: str]:  # S is a TypeVar with a `str` upper bound;
    ...                     # we can say that S is "bounded by `str`"


class StrOrBytesSequence[A: (str, bytes)]:  # A is a TypeVar constrained to str or bytes
    ...

However, if desired, reusable type variables can also be constructed manually, like so:

T = TypeVar('T')  # Can be anything
S = TypeVar('S', bound=str)  # Can be any subtype of str
A = TypeVar('A', str, bytes)  # Must be exactly 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 and type alias definitions. See Generic for more information on generic types. Generic functions work as follows:

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


def print_capitalized[S: str](x: S) -> S:
    """Print x capitalized, and return x."""
    print(x.capitalize())
    return x


def concatenate[A: (str, bytes)](x: A, y: A) -> A:
    """Add two strings or bytes objects together."""
    return x + y

Note that type variables can be bounded, constrained, or neither, but cannot be both bounded and constrained.

The variance of type variables is inferred by type checkers when they are created through the type parameter syntax or when infer_variance=True is passed. Manually created type variables may be explicitly marked covariant or contravariant by passing covariant=True or contravariant=True. By default, manually created type variables are invariant. See PEP 484 and PEP 695 for more details.

Bounded type variables and constrained type variables have different semantics in several important ways. Using a bounded type variable means that the TypeVar will be solved using the most specific type possible:

x = print_capitalized('a string')
reveal_type(x)  # revealed type is str

class StringSubclass(str):
    pass

y = print_capitalized(StringSubclass('another string'))
reveal_type(y)  # revealed type is StringSubclass

z = print_capitalized(45)  # error: int is not a subtype of str

The upper bound of a type variable can be a concrete type, abstract type (ABC or Protocol), or even a union of types:

# Can be anything with an __abs__ method
def print_abs[T: SupportsAbs](arg: T) -> None:
    print("Absolute value:", abs(arg))

U = TypeVar('U', bound=str|bytes)  # Can be any subtype of the union str|bytes
V = TypeVar('V', bound=SupportsAbs)  # Can be anything with an __abs__ method

Using a constrained type variable, however, means that the TypeVar can only ever be solved as being exactly one of the constraints given:

a = concatenate('one', 'two')
reveal_type(a)  # revealed type is str

b = concatenate(StringSubclass('one'), StringSubclass('two'))
reveal_type(b)  # revealed type is str, despite StringSubclass being passed in

c = concatenate('one', b'two')  # error: type variable 'A' can be either str or bytes in a function call, but not both

At runtime, isinstance(x, T) will raise TypeError.

__name__

The name of the type variable.

__covariant__

Whether the type var has been explicitly marked as covariant.

__contravariant__

Whether the type var has been explicitly marked as contravariant.

__infer_variance__

Whether the type variable’s variance should be inferred by type checkers.

Added in version 3.12.

__bound__

The upper bound of the type variable, if any.

Змінено в версії 3.12: For type variables created through type parameter syntax, the bound is evaluated only when the attribute is accessed, not when the type variable is created (see Lazy evaluation).

__constraints__

A tuple containing the constraints of the type variable, if any.

Змінено в версії 3.12: For type variables created through type parameter syntax, the constraints are evaluated only when the attribute is accessed, not when the type variable is created (see Lazy evaluation).

__default__

The default value of the type variable, or typing.NoDefault if it has no default.

Added in version 3.13.

has_default()

Return whether or not the type variable has a default value. This is equivalent to checking whether __default__ is not the typing.NoDefault singleton, except that it does not force evaluation of the lazily evaluated default value.

Added in version 3.13.

Змінено в версії 3.12: Type variables can now be declared using the type parameter syntax introduced by PEP 695. The infer_variance parameter was added.

Змінено в версії 3.13: Support for default values was added.

class typing.TypeVarTuple(name, *, default=typing.NoDefault)

Type variable tuple. A specialized form of type variable that enables variadic generics.

Type variable tuples can be declared in type parameter lists using a single asterisk (*) before the name:

def move_first_element_to_last[T, *Ts](tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
    return (*tup[1:], tup[0])

Or by explicitly invoking the TypeVarTuple constructor:

T = TypeVar("T")
Ts = TypeVarTuple("Ts")

def move_first_element_to_last(tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
    return (*tup[1:], tup[0])

A normal type variable enables parameterization with a single type. A type variable tuple, in contrast, allows parameterization with an arbitrary number of types by acting like an arbitrary number of type variables wrapped in a tuple. For example:

# T is bound to int, Ts is bound to ()
# Return value is (1,), which has type tuple[int]
move_first_element_to_last(tup=(1,))

# T is bound to int, Ts is bound to (str,)
# Return value is ('spam', 1), which has type tuple[str, int]
move_first_element_to_last(tup=(1, 'spam'))

# T is bound to int, Ts is bound to (str, float)
# Return value is ('spam', 3.0, 1), which has type tuple[str, float, int]
move_first_element_to_last(tup=(1, 'spam', 3.0))

# This fails to type check (and fails at runtime)
# because tuple[()] is not compatible with tuple[T, *Ts]
# (at least one element is required)
move_first_element_to_last(tup=())

Note the use of the unpacking operator * in tuple[T, *Ts]. Conceptually, you can think of Ts as a tuple of type variables (T1, T2, ...). tuple[T, *Ts] would then become tuple[T, *(T1, T2, ...)], which is equivalent to tuple[T, T1, T2, ...]. (Note that in older versions of Python, you might see this written using Unpack instead, as Unpack[Ts].)

Type variable tuples must always be unpacked. This helps distinguish type variable tuples from normal type variables:

x: Ts          # Not valid
x: tuple[Ts]   # Not valid
x: tuple[*Ts]  # The correct way to do it

Type variable tuples can be used in the same contexts as normal type variables. For example, in class definitions, arguments, and return types:

class Array[*Shape]:
    def __getitem__(self, key: tuple[*Shape]) -> float: ...
    def __abs__(self) -> "Array[*Shape]": ...
    def get_shape(self) -> tuple[*Shape]: ...

Type variable tuples can be happily combined with normal type variables:

class Array[DType, *Shape]:  # This is fine
    pass

class Array2[*Shape, DType]:  # This would also be fine
    pass

class Height: ...
class Width: ...

float_array_1d: Array[float, Height] = Array()     # Totally fine
int_array_2d: Array[int, Height, Width] = Array()  # Yup, fine too

However, note that at most one type variable tuple may appear in a single list of type arguments or type parameters:

x: tuple[*Ts, *Ts]            # Not valid
class Array[*Shape, *Shape]:  # Not valid
    pass

Finally, an unpacked type variable tuple can be used as the type annotation of *args:

def call_soon[*Ts](
    callback: Callable[[*Ts], None],
    *args: *Ts
) -> None:
    ...
    callback(*args)

In contrast to non-unpacked annotations of *args - e.g. *args: int, which would specify that all arguments are int - *args: *Ts enables reference to the types of the individual arguments in *args. Here, this allows us to ensure the types of the *args passed to call_soon match the types of the (positional) arguments of callback.

See PEP 646 for more details on type variable tuples.

__name__

The name of the type variable tuple.

__default__

The default value of the type variable tuple, or typing.NoDefault if it has no default.

Added in version 3.13.

has_default()

Return whether or not the type variable tuple has a default value. This is equivalent to checking whether __default__ is not the typing.NoDefault singleton, except that it does not force evaluation of the lazily evaluated default value.

Added in version 3.13.

Added in version 3.11.

Змінено в версії 3.12: Type variable tuples can now be declared using the type parameter syntax introduced by PEP 695.

Змінено в версії 3.13: Support for default values was added.

class typing.ParamSpec(name, *, bound=None, covariant=False, contravariant=False, default=typing.NoDefault)

Parameter specification variable. A specialized version of type variables.

In type parameter lists, parameter specifications can be declared with two asterisks (**):

type IntFunc[**P] = Callable[P, int]

For compatibility with Python 3.11 and earlier, ParamSpec objects can also be created as follows:

P = ParamSpec('P')

Змінні специфікації параметрів існують насамперед для використання засобів перевірки статичних типів. Вони використовуються для пересилання типів параметрів одного викликаного до іншого викликаного — шаблон, який зазвичай зустрічається у функціях вищого порядку та декораторах. Вони дійсні лише тоді, коли використовуються в Concatenate, або як перший аргумент Callable, або як параметри для визначених користувачем Generics. Перегляньте Generic для отримання додаткової інформації про загальні типи.

Наприклад, щоб додати базове журналювання до функції, можна створити декоратор add_logging для журналювання викликів функцій. Змінна специфікації параметра повідомляє перевіряльнику типу, що викликаний, переданий декоратору, і новий викликаний, повернутий ним, мають взаємозалежні параметри типу:

from collections.abc import Callable
import logging

def add_logging[T, **P](f: Callable[P, T]) -> Callable[P, T]:
    '''A type-safe decorator to add logging to a function.'''
    def inner(*args: P.args, **kwargs: P.kwargs) -> T:
        logging.info(f'{f.__name__} was called')
        return f(*args, **kwargs)
    return inner

@add_logging
def add_two(x: float, y: float) -> float:
    '''Add two numbers together.'''
    return x + y

Without ParamSpec, the simplest way to annotate this previously was to use a TypeVar with upper bound Callable[..., Any]. However this causes two problems:

1. Перевірка типу не може перевірити тип функції inner, тому що *args і **kwargs мають бути введені Any.

2. cast() може знадобитися в тілі декоратора add_logging під час повернення функції inner, або засіб перевірки статичного типу повинен ігнорувати return inner.

args

kwargs

Оскільки ParamSpec фіксує як позиційні параметри, так і параметри ключових слів, P.args і P.kwargs можна використовувати для поділу ParamSpec на його компоненти. P.args представляє кортеж позиційних параметрів у заданому виклику, і його слід використовувати лише для анотації *args. P.kwargs представляє зіставлення параметрів ключового слова з їхніми значеннями в заданому виклику, і його слід використовувати лише для анотації **kwargs. Обидва атрибути вимагають, щоб анотований параметр був у межах. Під час виконання P.args і P.kwargs є екземплярами відповідно ParamSpecArgs і ParamSpecKwargs.

__name__

The name of the parameter specification.

__default__

The default value of the parameter specification, or typing.NoDefault if it has no default.

Added in version 3.13.

has_default()

Return whether or not the parameter specification has a default value. This is equivalent to checking whether __default__ is not the typing.NoDefault singleton, except that it does not force evaluation of the lazily evaluated default value.

Added in version 3.13.

Змінні специфікації параметрів, створені за допомогою covariant=True або contravariant=True, можна використовувати для оголошення коваріантних або контраваріантних загальних типів. Аргумент bound також приймається, подібно до TypeVar. Однак фактичну семантику цих ключових слів ще належить визначити.

Added in version 3.10.

Змінено в версії 3.12: Parameter specifications can now be declared using the type parameter syntax introduced by PEP 695.

Змінено в версії 3.13: Support for default values was added.

Примітка

Вибирати можна лише змінні специфікації параметрів, визначені в глобальній області видимості.

Дивись також

• PEP 612 – Parameter Specification Variables (the PEP which introduced ParamSpec and Concatenate)

• Concatenate

• Annotating callable objects

typing.ParamSpecArgs

typing.ParamSpecKwargs

Аргументи та атрибути ключових аргументів ParamSpec. Атрибут P.args ParamSpec є екземпляром ParamSpecArgs, а P.kwargs є екземпляром ParamSpecKwargs. Вони призначені для інтроспекції під час виконання і не мають особливого значення для перевірки статичних типів.

Calling get_origin() on either of these objects will return the original ParamSpec:

>>> from typing import ParamSpec, get_origin
>>> P = ParamSpec("P")
>>> get_origin(P.args) is P
True
>>> get_origin(P.kwargs) is P
True

Added in version 3.10.

class typing.TypeAliasType(name, value, *, type_params=())

The type of type aliases created through the type statement.

приклад:

>>> type Alias = int
>>> type(Alias)
<class 'typing.TypeAliasType'>

Added in version 3.12.

__name__

The name of the type alias:

>>> type Alias = int
>>> Alias.__name__
'Alias'

__module__

The module in which the type alias was defined:

>>> type Alias = int
>>> Alias.__module__
'__main__'

__type_params__

The type parameters of the type alias, or an empty tuple if the alias is not generic:

>>> type ListOrSet[T] = list[T] | set[T]
>>> ListOrSet.__type_params__
(T,)
>>> type NotGeneric = int
>>> NotGeneric.__type_params__
()

__value__

The type alias’s value. This is lazily evaluated, so names used in the definition of the alias are not resolved until the __value__ attribute is accessed:

>>> type Mutually = Recursive
>>> type Recursive = Mutually
>>> Mutually
Mutually
>>> Recursive
Recursive
>>> Mutually.__value__
Recursive
>>> Recursive.__value__
Mutually

Інші спеціальні директиви

These functions and classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating and declaring types.

class typing.NamedTuple

Введена версія collections.namedtuple().

Використання:

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

Це еквівалентно:

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

Щоб надати полю значення за замовчуванням, ви можете призначити йому в тілі класу:

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

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

Поля зі значенням за замовчуванням мають бути після будь-яких полів без значення за замовчуванням.

Отриманий клас має додатковий атрибут __annotations__, що дає диктофон, який зіставляє назви полів із типами полів. (Імена полів містяться в атрибуті _fields, а значення за замовчуванням — в атрибуті _field_defaults, обидва з яких є частиною API namedtuple().)

Підкласи NamedTuple також можуть мати рядки документації та методи:

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

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

NamedTuple subclasses can be generic:

class Group[T](NamedTuple):
    key: T
    group: list[T]

Зворотно сумісне використання:

# For creating a generic NamedTuple on Python 3.11
T = TypeVar("T")

class Group(NamedTuple, Generic[T]):
    key: T
    group: list[T]

# A functional syntax is also supported
Employee = NamedTuple('Employee', [('name', str), ('id', int)])

Змінено в версії 3.6: Додано підтримку синтаксису анотації змінної PEP 526.

Змінено в версії 3.6.1: Додано підтримку стандартних значень, методів і рядків документів.

Змінено в версії 3.8: Атрибути _field_types і __annotations__ тепер є звичайними словниками замість екземплярів OrderedDict.

Змінено в версії 3.9: Видалено атрибут _field_types на користь стандартнішого атрибута __annotations__, який містить ту саму інформацію.

Змінено в версії 3.11: Added support for generic namedtuples.

Deprecated since version 3.13, will be removed in version 3.15: The undocumented keyword argument syntax for creating NamedTuple classes (NT = NamedTuple("NT", x=int)) is deprecated, and will be disallowed in 3.15. Use the class-based syntax or the functional syntax instead.

Deprecated since version 3.13, will be removed in version 3.15: When using the functional syntax to create a NamedTuple class, failing to pass a value to the „fields“ parameter (NT = NamedTuple("NT")) is deprecated. Passing None to the „fields“ parameter (NT = NamedTuple("NT", None)) is also deprecated. Both will be disallowed in Python 3.15. To create a NamedTuple class with 0 fields, use class NT(NamedTuple): pass or NT = NamedTuple("NT", []).

class typing.NewType(name, tp)

Helper class to create low-overhead distinct types.

A NewType is considered a distinct type by a typechecker. At runtime, however, calling a NewType returns its argument unchanged.

Використання:

UserId = NewType('UserId', int)  # Declare the NewType "UserId"
first_user = UserId(1)  # "UserId" returns the argument unchanged at runtime

__module__

The module in which the new type is defined.

__name__

The name of the new type.

__supertype__

The type that the new type is based on.

Added in version 3.5.2.

Змінено в версії 3.10: NewType тепер є класом, а не функцією.

class typing.Protocol(Generic)

Base class for protocol classes.

Protocol classes are defined like this:

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

Такі класи в основному використовуються із засобами перевірки статичних типів, які розпізнають структурні підтипи (статичне типування), наприклад:

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 more 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.

Класи протоколів можуть бути загальними, наприклад:

class GenProto[T](Protocol):
    def meth(self) -> T:
        ...

In code that needs to be compatible with Python 3.11 or older, generic Protocols can be written as follows:

T = TypeVar("T")

class GenProto(Protocol[T]):
    def meth(self) -> T:
        ...

Added in version 3.8.

@typing.runtime_checkable

Позначте клас протоколу як протокол виконання.

Такий протокол можна використовувати з isinstance() і issubclass(). Це викликає TypeError, коли застосовується до непротокольного класу. Це дозволяє здійснити просту структурну перевірку, дуже схожу на «поні з одним трюком» у collections.abc, наприклад Iterable. Наприклад:

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

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

@runtime_checkable
class Named(Protocol):
    name: str

import threading
assert isinstance(threading.Thread(name='Bob'), Named)

Примітка

runtime_checkable() will check only the presence of the required methods or attributes, not their type signatures or types. For example, ssl.SSLObject is a class, therefore it passes an issubclass() check against Callable. However, the ssl.SSLObject.__init__ method exists only to raise a TypeError with a more informative message, therefore making it impossible to call (instantiate) ssl.SSLObject.

Примітка

An isinstance() check against a runtime-checkable protocol can be surprisingly slow compared to an isinstance() check against a non-protocol class. Consider using alternative idioms such as hasattr() calls for structural checks in performance-sensitive code.

Added in version 3.8.

Змінено в версії 3.12: The internal implementation of isinstance() checks against runtime-checkable protocols now uses inspect.getattr_static() to look up attributes (previously, hasattr() was used). As a result, some objects which used to be considered instances of a runtime-checkable protocol may no longer be considered instances of that protocol on Python 3.12+, and vice versa. Most users are unlikely to be affected by this change.

Змінено в версії 3.12: The members of a runtime-checkable protocol are now considered «frozen» at runtime as soon as the class has been created. Monkey-patching attributes onto a runtime-checkable protocol will still work, but will have no impact on isinstance() checks comparing objects to the protocol. See «What’s new in Python 3.12» for more details.

class typing.TypedDict(dict)

Спеціальна конструкція для додавання підказок типу до словника. Під час виконання це звичайний dict.

TypedDict оголошує тип словника, який очікує, що всі його екземпляри матимуть певний набір ключів, де кожен ключ пов’язаний зі значенням узгодженого типу. Це очікування не перевіряється під час виконання, а виконується лише засобами перевірки типів. Використання:

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')

An alternative way to create a TypedDict is by using function-call syntax. The second argument must be a literal dict:

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

This functional syntax allows defining keys which are not valid identifiers, for example because they are keywords or contain hyphens:

# raises SyntaxError
class Point2D(TypedDict):
    in: int  # 'in' is a keyword
    x-y: int  # name with hyphens

# OK, functional syntax
Point2D = TypedDict('Point2D', {'in': int, 'x-y': int})

By default, all keys must be present in a TypedDict. It is possible to mark individual keys as non-required using NotRequired:

class Point2D(TypedDict):
    x: int
    y: int
    label: NotRequired[str]

# Alternative syntax
Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': NotRequired[str]})

This means that a Point2D TypedDict can have the label key omitted.

It is also possible to mark all keys as non-required by default by specifying a totality of False:

class Point2D(TypedDict, total=False):
    x: int
    y: int

# Alternative syntax
Point2D = TypedDict('Point2D', {'x': int, 'y': int}, total=False)

Це означає, що в Point2D TypedDict може бути пропущений будь-який із ключів. Очікується, що засіб перевірки типів підтримуватиме лише літерали False або True як значення аргументу total. True є типовим і робить обов’язковими всі елементи, визначені в тілі класу.

Individual keys of a total=False TypedDict can be marked as required using Required:

class Point2D(TypedDict, total=False):
    x: Required[int]
    y: Required[int]
    label: str

# Alternative syntax
Point2D = TypedDict('Point2D', {
    'x': Required[int],
    'y': Required[int],
    'label': str
}, total=False)

Тип TypedDict може успадкувати від одного або кількох інших типів TypedDict за допомогою синтаксису на основі класу. Використання:

class Point3D(Point2D):
    z: int

Point3D має три елементи: x, y і z. Це еквівалентно цьому визначенню:

class Point3D(TypedDict):
    x: int
    y: int
    z: int

A TypedDict cannot inherit from a non-TypedDict class, except for Generic. For example:

class X(TypedDict):
    x: int

class Y(TypedDict):
    y: int

class Z(object): pass  # A non-TypedDict class

class XY(X, Y): pass  # OK

class XZ(X, Z): pass  # raises TypeError

A TypedDict can be generic:

class Group[T](TypedDict):
    key: T
    group: list[T]

To create a generic TypedDict that is compatible with Python 3.11 or lower, inherit from Generic explicitly:

T = TypeVar("T")

class Group(TypedDict, Generic[T]):
    key: T
    group: list[T]

TypedDict можна інтроспективно за допомогою dicts анотацій (перегляньте Рекомендації щодо анотацій для отримання додаткової інформації про найкращі практики анотацій), __total__, __required_keys__ та __додаткові_ключі__.

__total__

Point2D.__total__ gives the value of the total argument. Example:

>>> from typing import TypedDict
>>> class Point2D(TypedDict): pass
>>> Point2D.__total__
True
>>> class Point2D(TypedDict, total=False): pass
>>> Point2D.__total__
False
>>> class Point3D(Point2D): pass
>>> Point3D.__total__
True

This attribute reflects only the value of the total argument to the current TypedDict class, not whether the class is semantically total. For example, a TypedDict with __total__ set to True may have keys marked with NotRequired, or it may inherit from another TypedDict with total=False. Therefore, it is generally better to use __required_keys__ and __optional_keys__ for introspection.

__required_keys__

Added in version 3.9.

__optional_keys__

Point2D.__required_keys__ and Point2D.__optional_keys__ return frozenset objects containing required and non-required keys, respectively.

Keys marked with Required will always appear in __required_keys__ and keys marked with NotRequired will always appear in __optional_keys__.

For backwards compatibility with Python 3.10 and below, it is also possible to use inheritance to declare both required and non-required keys in the same TypedDict . This is done by declaring a TypedDict with one value for the total argument and then inheriting from it in another TypedDict with a different value for total:

>>> class Point2D(TypedDict, total=False):
...     x: int
...     y: int
...
>>> class Point3D(Point2D):
...     z: int
...
>>> Point3D.__required_keys__ == frozenset({'z'})
True
>>> Point3D.__optional_keys__ == frozenset({'x', 'y'})
True

Added in version 3.9.

Примітка

If from __future__ import annotations is used or if annotations are given as strings, annotations are not evaluated when the TypedDict is defined. Therefore, the runtime introspection that __required_keys__ and __optional_keys__ rely on may not work properly, and the values of the attributes may be incorrect.

Support for ReadOnly is reflected in the following attributes:

__readonly_keys__

A frozenset containing the names of all read-only keys. Keys are read-only if they carry the ReadOnly qualifier.

Added in version 3.13.

__mutable_keys__

A frozenset containing the names of all mutable keys. Keys are mutable if they do not carry the ReadOnly qualifier.

Added in version 3.13.

Перегляньте PEP 589 більше прикладів і детальних правил використання TypedDict.

Added in version 3.8.

Змінено в версії 3.11: Added support for marking individual keys as Required or NotRequired. See PEP 655.

Змінено в версії 3.11: Added support for generic TypedDicts.

Змінено в версії 3.13: Removed support for the keyword-argument method of creating TypedDicts.

Змінено в версії 3.13: Support for the ReadOnly qualifier was added.

Deprecated since version 3.13, will be removed in version 3.15: When using the functional syntax to create a TypedDict class, failing to pass a value to the „fields“ parameter (TD = TypedDict("TD")) is deprecated. Passing None to the „fields“ parameter (TD = TypedDict("TD", None)) is also deprecated. Both will be disallowed in Python 3.15. To create a TypedDict class with 0 fields, use class TD(TypedDict): pass or TD = TypedDict("TD", {}).

Протоколи

The following protocols are provided by the typing module. All are decorated with @runtime_checkable.

class typing.SupportsAbs

ABC з одним абстрактним методом __abs__, який є коваріантним у своєму типі повернення.

class typing.SupportsBytes

ABC з одним абстрактним методом __bytes__.

class typing.SupportsComplex

ABC з одним абстрактним методом __complex__.

class typing.SupportsFloat

ABC з одним абстрактним методом __float__.

class typing.SupportsIndex

ABC з одним абстрактним методом __index__.

Added in version 3.8.

class typing.SupportsInt

ABC з одним абстрактним методом __int__.

class typing.SupportsRound

ABC з одним абстрактним методом __round__, який є коваріантним у своєму типі повернення.

ABCs for working with IO

class typing.IO

class typing.TextIO

class typing.BinaryIO

Загальний тип IO[AnyStr] і його підкласи TextIO(IO[str]) і BinaryIO(IO[bytes]) представляють типи потоків вводу/виводу, такі як повертаються open().

Функції та декоратори

typing.cast(typ, val)

Приведення значення до типу.

Це повертає значення без змін. Для засобу перевірки типів це означає, що значення, що повертається, має визначений тип, але під час виконання ми навмисно нічого не перевіряємо (ми хочемо, щоб це було якомога швидше).

typing.assert_type(val, typ, /)

Ask a static type checker to confirm that val has an inferred type of typ.

At runtime this does nothing: it returns the first argument unchanged with no checks or side effects, no matter the actual type of the argument.

When a static type checker encounters a call to assert_type(), it emits an error if the value is not of the specified type:

def greet(name: str) -> None:
    assert_type(name, str)  # OK, inferred type of `name` is `str`
    assert_type(name, int)  # type checker error

This function is useful for ensuring the type checker’s understanding of a script is in line with the developer’s intentions:

def complex_function(arg: object):
    # Do some complex type-narrowing logic,
    # after which we hope the inferred type will be `int`
    ...
    # Test whether the type checker correctly understands our function
    assert_type(arg, int)

Added in version 3.11.

typing.assert_never(arg, /)

Ask a static type checker to confirm that a line of code is unreachable.

Приклад:

def int_or_str(arg: int | str) -> None:
    match arg:
        case int():
            print("It's an int")
        case str():
            print("It's a str")
        case _ as unreachable:
            assert_never(unreachable)

Here, the annotations allow the type checker to infer that the last case can never execute, because arg is either an int or a str, and both options are covered by earlier cases.

If a type checker finds that a call to assert_never() is reachable, it will emit an error. For example, if the type annotation for arg was instead int | str | float, the type checker would emit an error pointing out that unreachable is of type float. For a call to assert_never to pass type checking, the inferred type of the argument passed in must be the bottom type, Never, and nothing else.

At runtime, this throws an exception when called.

Дивись також

Unreachable Code and Exhaustiveness Checking has more information about exhaustiveness checking with static typing.

Added in version 3.11.

typing.reveal_type(obj, /)

Ask a static type checker to reveal the inferred type of an expression.

When a static type checker encounters a call to this function, it emits a diagnostic with the inferred type of the argument. For example:

x: int = 1
reveal_type(x)  # Revealed type is "builtins.int"

This can be useful when you want to debug how your type checker handles a particular piece of code.

At runtime, this function prints the runtime type of its argument to sys.stderr and returns the argument unchanged (allowing the call to be used within an expression):

x = reveal_type(1)  # prints "Runtime type is int"
print(x)  # prints "1"

Note that the runtime type may be different from (more or less specific than) the type statically inferred by a type checker.

Most type checkers support reveal_type() anywhere, even if the name is not imported from typing. Importing the name from typing, however, allows your code to run without runtime errors and communicates intent more clearly.

Added in version 3.11.

@typing.dataclass_transform(*, eq_default=True, order_default=False, kw_only_default=False, frozen_default=False, field_specifiers=(), **kwargs)

Decorator to mark an object as providing dataclass-like behavior.

dataclass_transform may be used to decorate a class, metaclass, or a function that is itself a decorator. The presence of @dataclass_transform() tells a static type checker that the decorated object performs runtime «magic» that transforms a class in a similar way to @dataclasses.dataclass.

Example usage with a decorator function:

@dataclass_transform()
def create_model[T](cls: type[T]) -> type[T]:
    ...
    return cls

@create_model
class CustomerModel:
    id: int
    name: str

On a base class:

@dataclass_transform()
class ModelBase: ...

class CustomerModel(ModelBase):
    id: int
    name: str

On a metaclass:

@dataclass_transform()
class ModelMeta(type): ...

class ModelBase(metaclass=ModelMeta): ...

class CustomerModel(ModelBase):
    id: int
    name: str

The CustomerModel classes defined above will be treated by type checkers similarly to classes created with @dataclasses.dataclass. For example, type checkers will assume these classes have __init__ methods that accept id and name.

The decorated class, metaclass, or function may accept the following bool arguments which type checkers will assume have the same effect as they would have on the @dataclasses.dataclass decorator: init, eq, order, unsafe_hash, frozen, match_args, kw_only, and slots. It must be possible for the value of these arguments (True or False) to be statically evaluated.

The arguments to the dataclass_transform decorator can be used to customize the default behaviors of the decorated class, metaclass, or function:

Параметри:

• eq_default (bool) – Indicates whether the eq parameter is assumed to be True or False if it is omitted by the caller. Defaults to True.

• order_default (bool) – Indicates whether the order parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.

• kw_only_default (bool) – Indicates whether the kw_only parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.

• frozen_default (bool) –

  Indicates whether the frozen parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.

  Added in version 3.12.

• field_specifiers (tuple[Callable[..., Any], ...]) – Specifies a static list of supported classes or functions that describe fields, similar to dataclasses.field(). Defaults to ().

• **kwargs (Any) – Arbitrary other keyword arguments are accepted in order to allow for possible future extensions.

Type checkers recognize the following optional parameters on field specifiers:

Recognised parameters for field specifiers

Parameter name	Опис
init	Indicates whether the field should be included in the synthesized __init__ method. If unspecified, init defaults to True.
за замовчуванням	Provides the default value for the field.
default_factory	Provides a runtime callback that returns the default value for the field. If neither default nor default_factory are specified, the field is assumed to have no default value and must be provided a value when the class is instantiated.
factory	An alias for the default_factory parameter on field specifiers.
kw_only	Indicates whether the field should be marked as keyword-only. If True, the field will be keyword-only. If False, it will not be keyword-only. If unspecified, the value of the kw_only parameter on the object decorated with dataclass_transform will be used, or if that is unspecified, the value of kw_only_default on dataclass_transform will be used.
alias	Provides an alternative name for the field. This alternative name is used in the synthesized __init__ method.

At runtime, this decorator records its arguments in the __dataclass_transform__ attribute on the decorated object. It has no other runtime effect.

See PEP 681 for more details.

Added in version 3.11.

@typing.overload

Decorator for creating overloaded functions and methods.

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).

@overload-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-@overload-decorated definition. The non-@overload-decorated definition, meanwhile, will be used at runtime but should be ignored by a type checker. At runtime, calling an @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 goes here

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

Змінено в версії 3.11: Overloaded functions can now be introspected at runtime using get_overloads().

typing.get_overloads(func)

Return a sequence of @overload-decorated definitions for func.

func is the function object for the implementation of the overloaded function. For example, given the definition of process in the documentation for @overload, get_overloads(process) will return a sequence of three function objects for the three defined overloads. If called on a function with no overloads, get_overloads() returns an empty sequence.

get_overloads() can be used for introspecting an overloaded function at runtime.

Added in version 3.11.

typing.clear_overloads()

Clear all registered overloads in the internal registry.

This can be used to reclaim the memory used by the registry.

Added in version 3.11.

@typing.final

Decorator to indicate final methods and final classes.

Decorating a method with @final indicates to a type checker that the method cannot be overridden in a subclass. Decorating a class with @final indicates that it cannot be subclassed.

Наприклад:

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
    ...

Немає жодної перевірки цих властивостей під час виконання. Дивіться PEP 591 для більш детальної інформації.

Added in version 3.8.

Змінено в версії 3.11: The decorator will now attempt to set a __final__ attribute to True on the decorated object. Thus, a check like if getattr(obj, "__final__", False) can be used at runtime to determine whether an object obj has been marked as final. If the decorated object does not support setting attributes, the decorator returns the object unchanged without raising an exception.

@typing.no_type_check

Декоратор, щоб вказати, що анотації не є підказками типу.

This works as a class or function decorator. With a class, it applies recursively to all methods and classes defined in that class (but not to methods defined in its superclasses or subclasses). Type checkers will ignore all annotations in a function or class with this decorator.

@no_type_check mutates the decorated object in place.

@typing.no_type_check_decorator

Декоратор, щоб надати іншому декоратору ефект no_type_check().

Це обертає декоратор чимось, що обертає декоровану функцію в no_type_check().

Deprecated since version 3.13, will be removed in version 3.15: No type checker ever added support for @no_type_check_decorator. It is therefore deprecated, and will be removed in Python 3.15.

@typing.override

Decorator to indicate that a method in a subclass is intended to override a method or attribute in a superclass.

Type checkers should emit an error if a method decorated with @override does not, in fact, override anything. This helps prevent bugs that may occur when a base class is changed without an equivalent change to a child class.

Наприклад:

class Base:
    def log_status(self) -> None:
        ...

class Sub(Base):
    @override
    def log_status(self) -> None:  # Okay: overrides Base.log_status
        ...

    @override
    def done(self) -> None:  # Error reported by type checker
        ...

There is no runtime checking of this property.

The decorator will attempt to set an __override__ attribute to True on the decorated object. Thus, a check like if getattr(obj, "__override__", False) can be used at runtime to determine whether an object obj has been marked as an override. If the decorated object does not support setting attributes, the decorator returns the object unchanged without raising an exception.

See PEP 698 for more details.

Added in version 3.12.

@typing.type_check_only

Decorator to mark a class or function as unavailable at runtime.

Сам цей декоратор недоступний під час виконання. В основному він призначений для позначення класів, визначених у файлах-заглушках типу, якщо реалізація повертає екземпляр приватного класу:

@type_check_only
class Response:  # private or not available at runtime
    code: int
    def get_header(self, name: str) -> str: ...

def fetch_response() -> Response: ...

Зауважте, що повертати екземпляри приватних класів не рекомендується. Зазвичай бажано зробити такі заняття публічними.

Помічники в самоаналізі

typing.get_type_hints(obj, globalns=None, localns=None, include_extras=False)

Повертає словник, що містить підказки типу для функції, методу, модуля або об’єкта класу.

This is often the same as obj.__annotations__, but this function makes the following changes to the annotations dictionary:

• Forward references encoded as string literals or ForwardRef objects are handled by evaluating them in globalns, localns, and (where applicable) obj’s type parameter namespace. If globalns or localns is not given, appropriate namespace dictionaries are inferred from obj.

• None is replaced with types.NoneType.

• If @no_type_check has been applied to obj, an empty dictionary is returned.

• If obj is a class C, the function returns a dictionary that merges annotations from C’s base classes with those on C directly. This is done by traversing C.__mro__ and iteratively combining __annotations__ dictionaries. Annotations on classes appearing earlier in the method resolution order always take precedence over annotations on classes appearing later in the method resolution order.

• The function recursively replaces all occurrences of Annotated[T, ...] with T, unless include_extras is set to True (see Annotated for more information).

See also inspect.get_annotations(), a lower-level function that returns annotations more directly.

Примітка

If any forward references in the annotations of obj are not resolvable or are not valid Python code, this function will raise an exception such as NameError. For example, this can happen with imported type aliases that include forward references, or with names imported under if TYPE_CHECKING.

Змінено в версії 3.9: Added include_extras parameter as part of PEP 593. See the documentation on Annotated for more information.

Змінено в версії 3.11: Previously, Optional[t] was added for function and method annotations if a default value equal to None was set. Now the annotation is returned unchanged.

typing.get_origin(tp)

Get the unsubscripted version of a type: for a typing object of the form X[Y, Z, ...] return X.

If X is a typing-module alias for a builtin or collections class, it will be normalized to the original class. If X is an instance of ParamSpecArgs or ParamSpecKwargs, return the underlying ParamSpec. Return None for unsupported objects.

приклади:

assert get_origin(str) is None
assert get_origin(Dict[str, int]) is dict
assert get_origin(Union[int, str]) is Union
assert get_origin(Annotated[str, "metadata"]) is Annotated
P = ParamSpec('P')
assert get_origin(P.args) is P
assert get_origin(P.kwargs) is P

Added in version 3.8.

typing.get_args(tp)

Get type arguments with all substitutions performed: for a typing object of the form X[Y, Z, ...] return (Y, Z, ...).

If X is a union or Literal contained in another generic type, the order of (Y, Z, ...) may be different from the order of the original arguments [Y, Z, ...] due to type caching. Return () for unsupported objects.

приклади:

assert get_args(int) == ()
assert get_args(Dict[int, str]) == (int, str)
assert get_args(Union[int, str]) == (int, str)

Added in version 3.8.

typing.get_protocol_members(tp)

Return the set of members defined in a Protocol.

>>> from typing import Protocol, get_protocol_members
>>> class P(Protocol):
...     def a(self) -> str: ...
...     b: int
>>> get_protocol_members(P) == frozenset({'a', 'b'})
True

Raise TypeError for arguments that are not Protocols.

Added in version 3.13.

typing.is_protocol(tp)

Determine if a type is a Protocol.

Наприклад:

class P(Protocol):
    def a(self) -> str: ...
    b: int

is_protocol(P)    # => True
is_protocol(int)  # => False

Added in version 3.13.

typing.is_typeddict(tp)

Перевірте, чи є тип TypedDict.

Наприклад:

class Film(TypedDict):
    title: str
    year: int

assert is_typeddict(Film)
assert not is_typeddict(list | str)

# TypedDict is a factory for creating typed dicts,
# not a typed dict itself
assert not is_typeddict(TypedDict)

Added in version 3.10.

class typing.ForwardRef

Class used for internal typing representation of string forward references.

For example, List["SomeClass"] is implicitly transformed into List[ForwardRef("SomeClass")]. ForwardRef should not be instantiated by a user, but may be used by introspection tools.

Примітка

PEP 585 загальні типи, такі як list["SomeClass"], не будуть неявно перетворені в list[ForwardRef("SomeClass")] і, таким чином, не будуть автоматично перетворені в list[ SomeClass].

Added in version 3.7.4.

typing.NoDefault

A sentinel object used to indicate that a type parameter has no default value. For example:

>>> T = TypeVar("T")
>>> T.__default__ is typing.NoDefault
True
>>> S = TypeVar("S", default=None)
>>> S.__default__ is None
True

Added in version 3.13.

Постійний

typing.TYPE_CHECKING

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

Використання:

if TYPE_CHECKING:
    import expensive_mod

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

Анотація першого типу має бути взята в лапки, що робить її «прямим посиланням», щоб приховати посилання expensive_mod від середовища виконання інтерпретатора. Анотації типу для локальних змінних не оцінюються, тому другу анотацію не потрібно брати в лапки.

Примітка

Якщо використовується from __future__ import annotations, анотації не оцінюються під час визначення функції. Натомість вони зберігаються як рядки в __annotations__. Це робить непотрібним використання лапок навколо анотації (див. PEP 563).

Added in version 3.5.2.

Застарілі псевдоніми

This module defines several deprecated aliases to pre-existing standard library classes. These were originally included in the typing module in order to support parameterizing these generic classes using []. However, the aliases became redundant in Python 3.9 when the corresponding pre-existing classes were enhanced to support [] (see PEP 585).

The redundant types are deprecated as of Python 3.9. However, while the aliases may be removed at some point, removal of these aliases is not currently planned. As such, no deprecation warnings are currently issued by the interpreter for these aliases.

If at some point it is decided to remove these deprecated aliases, a deprecation warning will be issued by the interpreter for at least two releases prior to removal. The aliases are guaranteed to remain in the typing module without deprecation warnings until at least Python 3.14.

Type checkers are encouraged to flag uses of the deprecated types if the program they are checking targets a minimum Python version of 3.9 or newer.

Aliases to built-in types

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

Deprecated alias to dict.

Note that to annotate arguments, it is preferred to use an abstract collection type such as Mapping rather than to use dict or typing.Dict.

Застаріло починаючи з версії 3.9: builtins.dict now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

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

Deprecated alias to list.

Note that to annotate arguments, it is preferred to use an abstract collection type such as Sequence or Iterable rather than to use list or typing.List.

Застаріло починаючи з версії 3.9: builtins.list now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

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

Deprecated alias to builtins.set.

Note that to annotate arguments, it is preferred to use an abstract collection type such as collections.abc.Set rather than to use set or typing.Set.

Застаріло починаючи з версії 3.9: builtins.set now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.FrozenSet(frozenset, AbstractSet[T_co])

Deprecated alias to builtins.frozenset.

Застаріло починаючи з версії 3.9: builtins.frozenset now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

typing.Tuple

Deprecated alias for tuple.

tuple and Tuple are special-cased in the type system; see Annotating tuples for more details.

Застаріло починаючи з версії 3.9: builtins.tuple now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.Type(Generic[CT_co])

Deprecated alias to type.

See The type of class objects for details on using type or typing.Type in type annotations.

Added in version 3.5.2.

Застаріло починаючи з версії 3.9: builtins.type now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

Aliases to types in collections

class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])

Deprecated alias to collections.defaultdict.

Added in version 3.5.2.

Застаріло починаючи з версії 3.9: collections.defaultdict now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

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

Deprecated alias to collections.OrderedDict.

Added in version 3.7.2.

Застаріло починаючи з версії 3.9: collections.OrderedDict now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

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

Deprecated alias to collections.ChainMap.

Added in version 3.6.1.

Застаріло починаючи з версії 3.9: collections.ChainMap now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

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

Deprecated alias to collections.Counter.

Added in version 3.6.1.

Застаріло починаючи з версії 3.9: collections.Counter now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.Deque(deque, MutableSequence[T])

Deprecated alias to collections.deque.

Added in version 3.6.1.

Застаріло починаючи з версії 3.9: collections.deque now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

Aliases to other concrete types

class typing.Pattern

class typing.Match

Deprecated aliases corresponding to the return types from re.compile() and re.match().

These types (and the corresponding functions) are generic over AnyStr. Pattern can be specialised as Pattern[str] or Pattern[bytes]; Match can be specialised as Match[str] or Match[bytes].

Застаріло починаючи з версії 3.9: Класи Pattern і Match від re тепер підтримують []. Див. PEP 585 і Загальний тип псевдоніма.

class typing.Text

Deprecated alias for str.

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

Використовуйте Текст, щоб вказати, що значення має містити рядок Юнікод у спосіб, сумісний як з Python 2, так і з Python 3:

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

Added in version 3.5.2.

Застаріло починаючи з версії 3.11: Python 2 is no longer supported, and most type checkers also no longer support type checking Python 2 code. Removal of the alias is not currently planned, but users are encouraged to use str instead of Text.

Aliases to container ABCs in collections.abc

class typing.AbstractSet(Collection[T_co])

Deprecated alias to collections.abc.Set.

Застаріло починаючи з версії 3.9: collections.abc.Set now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.ByteString(Sequence[int])

Цей тип представляє типи bytes, bytearray і memoryview послідовностей байтів.

Deprecated since version 3.9, will be removed in version 3.14: Prefer collections.abc.Buffer, or a union like bytes | bytearray | memoryview.

class typing.Collection(Sized, Iterable[T_co], Container[T_co])

Deprecated alias to collections.abc.Collection.

Added in version 3.6.

Застаріло починаючи з версії 3.9: collections.abc.Collection now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.Container(Generic[T_co])

Deprecated alias to collections.abc.Container.

Застаріло починаючи з версії 3.9: collections.abc.Container now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.ItemsView(MappingView, AbstractSet[tuple[KT_co, VT_co]])

Deprecated alias to collections.abc.ItemsView.

Застаріло починаючи з версії 3.9: collections.abc.ItemsView now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

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

Deprecated alias to collections.abc.KeysView.

Застаріло починаючи з версії 3.9: collections.abc.KeysView now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

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

Deprecated alias to collections.abc.Mapping.

Застаріло починаючи з версії 3.9: collections.abc.Mapping now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.MappingView(Sized)

Deprecated alias to collections.abc.MappingView.

Застаріло починаючи з версії 3.9: collections.abc.MappingView now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

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

Deprecated alias to collections.abc.MutableMapping.

Застаріло починаючи з версії 3.9: collections.abc.MutableMapping now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.MutableSequence(Sequence[T])

Deprecated alias to collections.abc.MutableSequence.

Застаріло починаючи з версії 3.9: collections.abc.MutableSequence now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.MutableSet(AbstractSet[T])

Deprecated alias to collections.abc.MutableSet.

Застаріло починаючи з версії 3.9: collections.abc.MutableSet now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.Sequence(Reversible[T_co], Collection[T_co])

Deprecated alias to collections.abc.Sequence.

Застаріло починаючи з версії 3.9: collections.abc.Sequence now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.ValuesView(MappingView, Collection[_VT_co])

Deprecated alias to collections.abc.ValuesView.

Застаріло починаючи з версії 3.9: collections.abc.ValuesView now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

Aliases to asynchronous ABCs in collections.abc

class typing.Coroutine(Awaitable[ReturnType], Generic[YieldType, SendType, ReturnType])

Deprecated alias to collections.abc.Coroutine.

See Annotating generators and coroutines for details on using collections.abc.Coroutine and typing.Coroutine in type annotations.

Added in version 3.5.3.

Застаріло починаючи з версії 3.9: collections.abc.Coroutine now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.AsyncGenerator(AsyncIterator[YieldType], Generic[YieldType, SendType])

Deprecated alias to collections.abc.AsyncGenerator.

See Annotating generators and coroutines for details on using collections.abc.AsyncGenerator and typing.AsyncGenerator in type annotations.

Added in version 3.6.1.

Застаріло починаючи з версії 3.9: collections.abc.AsyncGenerator now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

Змінено в версії 3.13: The SendType parameter now has a default.

class typing.AsyncIterable(Generic[T_co])

Deprecated alias to collections.abc.AsyncIterable.

Added in version 3.5.2.

Застаріло починаючи з версії 3.9: collections.abc.AsyncIterable now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.AsyncIterator(AsyncIterable[T_co])

Deprecated alias to collections.abc.AsyncIterator.

Added in version 3.5.2.

Застаріло починаючи з версії 3.9: collections.abc.AsyncIterator now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.Awaitable(Generic[T_co])

Deprecated alias to collections.abc.Awaitable.

Added in version 3.5.2.

Застаріло починаючи з версії 3.9: collections.abc.Awaitable now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

Aliases to other ABCs in collections.abc

class typing.Iterable(Generic[T_co])

Deprecated alias to collections.abc.Iterable.

Застаріло починаючи з версії 3.9: collections.abc.Iterable now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.Iterator(Iterable[T_co])

Deprecated alias to collections.abc.Iterator.

Застаріло починаючи з версії 3.9: collections.abc.Iterator now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

typing.Callable

Deprecated alias to collections.abc.Callable.

See Annotating callable objects for details on how to use collections.abc.Callable and typing.Callable in type annotations.

Застаріло починаючи з версії 3.9: collections.abc.Callable now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

Змінено в версії 3.10: Callable now supports ParamSpec and Concatenate. See PEP 612 for more details.

class typing.Generator(Iterator[YieldType], Generic[YieldType, SendType, ReturnType])

Deprecated alias to collections.abc.Generator.

See Annotating generators and coroutines for details on using collections.abc.Generator and typing.Generator in type annotations.

Застаріло починаючи з версії 3.9: collections.abc.Generator now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

Змінено в версії 3.13: Default values for the send and return types were added.

class typing.Hashable

Deprecated alias to collections.abc.Hashable.

Застаріло починаючи з версії 3.12: Use collections.abc.Hashable directly instead.

class typing.Reversible(Iterable[T_co])

Deprecated alias to collections.abc.Reversible.

Застаріло починаючи з версії 3.9: collections.abc.Reversible now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

class typing.Sized

Deprecated alias to collections.abc.Sized.

Застаріло починаючи з версії 3.12: Use collections.abc.Sized directly instead.

Aliases to contextlib ABCs

class typing.ContextManager(Generic[T_co, ExitT_co])

Deprecated alias to contextlib.AbstractContextManager.

The first type parameter, T_co, represents the type returned by the __enter__() method. The optional second type parameter, ExitT_co, which defaults to bool | None, represents the type returned by the __exit__() method.

Added in version 3.5.4.

Застаріло починаючи з версії 3.9: contextlib.AbstractContextManager now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

Змінено в версії 3.13: Added the optional second type parameter, ExitT_co.

class typing.AsyncContextManager(Generic[T_co, AExitT_co])

Deprecated alias to contextlib.AbstractAsyncContextManager.

The first type parameter, T_co, represents the type returned by the __aenter__() method. The optional second type parameter, AExitT_co, which defaults to bool | None, represents the type returned by the __aexit__() method.

Added in version 3.6.2.

Застаріло починаючи з версії 3.9: contextlib.AbstractAsyncContextManager now supports subscripting ([]). See PEP 585 and Загальний тип псевдоніма.

Змінено в версії 3.13: Added the optional second type parameter, AExitT_co.

Deprecation Timeline of Major Features

Certain features in typing are deprecated and may be removed in a future version of Python. The following table summarizes major deprecations for your convenience. This is subject to change, and not all deprecations are listed.

Feature	Deprecated in	Projected removal	PEP/issue
typing versions of standard collections	3.9	Undecided (see Застарілі псевдоніми for more information)	PEP 585
typing.ByteString	3.9	3.14	gh-91896
typing.Text	3.11	Undecided	gh-92332
typing.Hashable and typing.Sized	3.12	Undecided	gh-94309
typing.TypeAlias	3.12	Undecided	PEP 695
@typing.no_type_check_decorator	3.13	3.15	gh-106309
typing.AnyStr	3.13	3.18	gh-105578