typing --- Support for type hints

Added in version 3.5.

ソースコード: Lib/typing.py

注釈

Python ランタイムは、関数や変数の型アノテーションを強制しません。型アノテーションは、 type checkers や IDE、linterなどのサードパーティーツールで使われます。

---

このモジュールは型ヒントの実行時サポートを提供します。

以下の関数を例に考えてみます:

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

surface_area_of_cube 関数は、edge_length: float という type hint で示されているように、引数が float のインスタンスであるという前提です。 そして、-> str というヒントにある通り、この関数は str のインスタンスを返すことになっています。

型ヒントは float や str のような単純な型を指定することもできますし、もっと複雑な型も指定できます。 typing モジュールには、より高度な型ヒントを表現するためのいろいろな型が含まれています。

typing モジュールには新しい機能が頻繁に追加されます。 typing_extensions パッケージを使うことで、古い Python バージョンからもその新機能を使うことができます。

参考

Typing cheat sheet

型ヒントの簡単な概要 (mypy ドキュメンテーション)

Type System Reference section of the mypy docs

Python の型システムの規格は PEP によって定められているので、このレファレンスはほとんどの Python 型チェッカーに適用できるはずです。(mypy のみに適用される部分もあるかもしれません。)

Static Typing with Python

コミュニティーによって書かれた、型システムの機能や便利な型関連のツール、型に関するベストプラクティスを詳しく説明している、特定の型チェッカーに依らないドキュメンテーション。

Python 型システムの仕様

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

型エイリアス

型エイリアスは type 文を用いて定義され、 TypeAliasType インスタンスが生成されます。 この例では、静的型チェッカーは Vector と list[float] を等しいものとして扱います

type Vector = list[float]

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

# floatのlistはVectorとして要件を満たすため、型チェックを通過する。
new_vector = scale(2.0, [1.0, -4.2, 5.4])

型エイリアスは複雑な型シグネチャを単純化するのに有用です。例えば:

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:
    ...

# 静的型チェッカーでは、上記の型アノテーションと
# 以下のアノテーションを同等のものとして扱う。
def broadcast_message(
    message: str,
    servers: Sequence[tuple[tuple[str, int], dict[str, str]]]
) -> None:
    ...

type 文はPython3.12で新しく導入されました。後方互換性のために、単に代入によって型エイリアスを作成することもできます：

Vector = list[float]

あるいは、 TypeAlias でマークすることで、これが通常の変数代入ではなく、型エイリアスであることを明示できます

from typing import TypeAlias

Vector: TypeAlias = list[float]

NewType

異なる型を作るためには NewType ヘルパークラスを使います:

from typing import NewType

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

静的型チェッカーは新しい型を元々の型のサブクラスのように扱います。この振る舞いは論理的な誤りを見つける手助けとして役に立ちます。

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

# 型チェックを通過する
user_a = get_user_name(UserId(42351))

# 型チェックに失敗する。int型はUserId型ではないため。
user_b = get_user_name(-1)

UserId 型の変数も int の全ての演算が行えますが、その結果は常に int 型になります。 この振る舞いにより、 int が期待されるところに UserId を渡せますが、不正な方法で UserId を作ってしまうことを防ぎます。

# 'output' は'int'型となる。'UserId'型にはならない。
output = UserId(23413) + UserId(54341)

これらのチェックは静的型チェッカーのみによって強制されるということに注意してください。 実行時に Derived = NewType('Derived', Base) という文は渡された仮引数をただちに返す Derived callable を作ります。 つまり Derived(some_value) という式は新しいクラスを作ることはなく、通常の関数呼び出しより多くのオーバーヘッドがないということを意味します。

より正確に言うと、式 some_value is Derived(some_value) は実行時に常に真を返します。

Derived のサブタイプを作成することはできません

from typing import NewType

UserId = NewType('UserId', int)

# 実行時にエラーとなり、型チェックも通らない
class AdminUserId(UserId): pass

しかし、 'derived' である NewType をもとにした NewType は作ることが出来ます:

from typing import NewType

UserId = NewType('UserId', int)

ProUserId = NewType('ProUserId', UserId)

そして ProUserId に対する型検査は期待通りに動作します。

より詳しくは PEP 484 を参照してください。

注釈

型エイリアスの使用は、2つの型が互いに等価であることを宣言することを思い出してください。 type Alias = Original とすると、静的型チェッカーは Alias を Original と"正確に等価な"ものとして扱います。これは、複雑な型シグネチャを単純化したい場合に便利です。

これに対し、 NewType はある型をもう一方の型の サブタイプ として宣言します。 Derived = NewType('Derived', Original) とすると静的型検査器は Derived を Original の サブクラス として扱います。つまり Original 型の値は Derived 型の値が期待される場所で使うことが出来ないということです。これは論理的な誤りを最小の実行時のコストで防ぎたい時に有用です。

Added in version 3.5.2.

バージョン 3.10 で変更: NewType は関数ではなくクラスになりました。その結果、通常の関数よりも NewType を呼び出す際に多少の実行時コストが追加されます。

バージョン 3.11 で変更: NewType 呼び出し時のパフォーマンスが Python 3.9 のレベルに戻りました

呼び出し可能オブジェクトのアノテーション

関数、または他の callable なオブジェクトは、 collections.abc.Callable または typing.Callable を使ってアノテーションすることができます。 Callable[[int], str] は、 int 型のパラメータを1つ受け取り、 str を返す関数を意味します。

例えば:

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.

もしellipsisリテラル ... が引数リストとして与えられた場合、それは任意のパラメータリストを持つ呼び出し可能オブジェクトを受け入れることを示します。

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

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

Callable は可変引数、オーバーロード関数、キーワード専用引数などを含む複雑な型シグネチャを表現することはできません。 しかし、Protocol クラスとその __call__() メソッドを定義すれば表現することができます:

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

callable が別の callable を引数に取る場合は、ParamSpec を使えば両者のパラメータ引数の依存関係を表現することができます。 さらに、ある callable が別の callable から引数を追加したり削除したりする場合は、 Concatenate 演算子を使うことで表現できます。 その場合、callable の型は Callable[ParamSpecVariable, ReturnType] と Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType] という形になります。

バージョン 3.10 で変更: Callable は ParamSpec と Concatenate をサポートしました。詳細は PEP 612 を参照してください。

参考

ParamSpec と Concatenate のドキュメントに、Callable での使用例が記載されています。

ジェネリクス

コンテナに含まれるオブジェクトに関する型情報は、一般的な方法で静的に推論することができないため、標準ライブラリの多くのコンテナクラスは、要素に期待する型を示す添字表記をサポートしています

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]

バージョン 3.12 で変更: Syntactic support for generics is new in Python 3.12.

タプルのアノテーション

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 は引数に1つの型のみ受け入れるので、型チェッカーは上記の y への代入でエラーを出力します。同様に、Mapping は引数に2つの型のみ受け入れます。1つ目はキーの型を示し、2つ目は値の型を示します。

しかし、他の多くのPythonのコンテナとは異なり、タプルがすべて同じ型ではない要素を持つことは、慣用的なPythonコードでは一般的です。このため、タプルはPythonの型システムの中でも特殊です。tuple は任意の数の引数を受け入れます

# 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 the literal ellipsis ...: 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 = ()

クラスオブジェクトの型

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

ユーザー定義のジェネリック型

ユーザー定義のクラスを、ジェネリッククラスとして定義できます。

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)

A generic type can have any number of type variables. All varieties of TypeVar are permissible as parameters for a generic type:

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]):
    ...

Generic の引数のそれぞれの型変数は別のものでなければなりません。このため次のクラス定義は無効です:

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]):
    ...

この場合では MyDict は仮引数 T を 1 つとります。

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

バージョン 3.7 で変更: Generic にあった独自のメタクラスは無くなりました。

バージョン 3.12 で変更: 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.

バージョン 3.10 で変更: Generic can now be parameterized over parameter expressions. See ParamSpec and PEP 612 for more details.

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.

Any 型

Any は特別な種類の型です。静的型検査器はすべての型を Any と互換として扱い、 Any をすべての型と互換として扱います。

これは、 Any 型の値では、任意の演算やメソッドの呼び出しが行えることを意味します:

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()
    ...

Any 型の値をより詳細な型に代入する時に型検査が行われないことに注意してください。例えば、静的型検査器は a を s に代入する時、s が str 型として宣言されていて実行時に int の値を受け取るとしても、エラーを報告しません。

さらに、返り値や引数の型のないすべての関数は暗黙的に Any を使用します。

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

この挙動により、動的型付けと静的型付けが混在したコードを書かなければならない時に Any を 非常口 として使用することができます。

Any の挙動と object の挙動を対比しましょう。 Any と同様に、すべての型は object のサブタイプです。しかしながら、 Any と異なり、逆は成り立ちません: object はすべての他の型のサブタイプでは ありません。

これは、ある値の型が object のとき、型検査器はこれについてのほとんどすべての操作を拒否し、これをより特殊化された変数に代入する (または返り値として利用する) ことは型エラーになることを意味します。例えば:

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")

object は、ある値が型安全な方法で任意の型として使えることを示すために使用します。 Any はある値が動的に型付けられることを示すために使用します。

名前的部分型 vs 構造的部分型

初めは PEP 484 は Python の静的型システムを 名前的部分型 を使って定義していました。 名前的部分型とは、クラス B が期待されているところにクラス A が許容されるのは A が B のサブクラスの場合かつその場合に限る、ということです。

前出の必要条件は、Iterable などの抽象基底クラスにも当て嵌まります。 この型付け手法の問題は、この手法をサポートするためにクラスに明確な型付けを行う必要があることで、これは pythonic ではなく、普段行っている 慣用的な Python コードへの動的型付けとは似ていません。 例えば、次のコードは PEP 484 に従ったものです

from collections.abc import Sized, Iterable, Iterator

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

PEP 544 によって上にあるようなクラス定義で基底クラスを明示しないコードをユーザーが書け、静的型チェッカーで Bucket が Sized と Iterable[int] 両方のサブタイプだと暗黙的に見なせるようになり、この問題が解決しました。 これは structural subtyping (構造的部分型) (あるいは、静的ダックタイピング) として知られています:

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

さらに、特別なクラス Protocol のサブクラスを作ることで、新しい独自のプロトコルを作って構造的部分型というものを満喫できます。

モジュールの内容

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

特殊型付けプリミティブ

特殊型

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

typing.Any

制約のない型であることを示す特別な型です。

• 任意の型は Any と互換です。

• Any は任意の型と互換です。

バージョン 3.11 で変更: 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.

例えば:

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.

例:

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.

より詳しくは PEP 675 を参照してください。

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.

例えば:

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()

より詳しくは PEP 673 を参照してください。

Added in version 3.11.

typing.TypeAlias

Special annotation for explicitly declaring a type alias.

例えば:

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: ...

より詳しくは、 PEP 613 をご覧ください。

Added in version 3.10.

バージョン 3.12 で非推奨: 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.

特殊形式

これらはアノテーションの型として使用できます。これらは全て [] を使用した添字表記をサポートしますが、それぞれ固有の構文があります。

class typing.Union

ユニオン型; Union[X, Y] は X | Y と等価で X または Y を表します。

To define a union, use e.g. Union[int, str] or the shorthand int | str. Using that shorthand is recommended. Details:

• 引数は型でなければならず、少なくとも一つ必要です。

• ユニオン型のユニオン型は平滑化されます。例えば:

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

  However, this does not apply to unions referenced through a type alias, to avoid forcing evaluation of the underlying TypeAliasType:

  type A = Union[int, str]
  Union[A, float] != Union[int, str, float]
  

• 引数が一つのユニオン型は消えます。例えば:

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

• 冗長な実引数は飛ばされます。例えば:

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

• ユニオン型を比較するとき引数の順序は無視されます。例えば:

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

• Union のサブクラスを作成したり、インスタンスを作成することは出来ません。

• Union[X][Y] と書くことは出来ません。

バージョン 3.7 で変更: 明示的に書かれているサブクラスを、実行時に直和型から取り除かなくなりました。

バージョン 3.10 で変更: ユニオン型は X | Y のように書けるようになりました。 union型の表現 を参照ください。

バージョン 3.14 で変更: types.UnionType is now an alias for Union, and both Union[int, str] and int | str create instances of the same class. To check whether an object is a Union at runtime, use isinstance(obj, Union). For compatibility with earlier versions of Python, use get_origin(obj) is typing.Union or get_origin(obj) is types.UnionType.

typing.Optional

Optional[X] は X | None (や Union[X, None]) と同等です。

これがデフォルト値を持つオプション引数とは同じ概念ではないということに注意してください。 デフォルト値を持つオプション引数はオプション引数であるために、型アノテーションに Optional 修飾子は必要ありません。 例えば次のようになります:

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

それとは逆に、 None という値が許されていることが明示されている場合は、引数がオプションであろうとなかろうと、 Optional を使うのが好ましいです。 例えば次のようになります:

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

バージョン 3.10 で変更: Optionalは X | None のように書けるようになりました。 ref:union型の表現 <types-union> を参照ください。

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 (...).

For example, to annotate a decorator with_lock which provides a threading.Lock to the decorated function, Concatenate can be used to indicate that with_lock expects a callable which takes in a Lock as the first argument, and returns a callable with a different type signature. In this case, the ParamSpec indicates that the returned callable's parameter types are dependent on the parameter types of the callable being passed in:

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.

参考

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

• ParamSpec

• 呼び出し可能オブジェクトのアノテーション

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.

例えば:

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

Literal[...] はサブクラスにはできません。 実行時に、任意の値が Literal[...] の型引数として使えますが、型チェッカーが制約を課すことがあります。 リテラル型についてより詳しいことは PEP 586 を参照してください。

Additional details:

• The arguments must be literal values and there must be at least one.

• Nested Literal types are flattened, e.g.:

  assert Literal[Literal[1, 2], 3] == Literal[1, 2, 3]
  

  However, this does not apply to Literal types referenced through a type alias, to avoid forcing evaluation of the underlying TypeAliasType:

  type A = Literal[1, 2]
  assert Literal[A, 3] != Literal[1, 2, 3]
  

• 冗長な実引数は飛ばされます。例えば:

  assert Literal[1, 2, 1] == Literal[1, 2]
  

• When comparing literals, the argument order is ignored, e.g.:

  assert Literal[1, 2] == Literal[2, 1]
  

• You cannot subclass or instantiate a Literal.

• You cannot write Literal[X][Y].

Added in version 3.8.

バージョン 3.9.1 で変更: Literal ではパラメータの重複を解消するようになりました。Literal オブジェクトの等値比較は順序に依存しないようになりました。Literal オブジェクトは、等値比較する際に、パラメータのうち 1 つでも hashable でない場合は TypeError を送出するようになりました。

typing.ClassVar

クラス変数であることを示す特別な型構築子です。

PEP 526 で導入された通り、 ClassVar でラップされた変数アノテーションによって、ある属性はクラス変数として使うつもりであり、そのクラスのインスタンスから設定すべきではないということを示せます。使い方は次のようになります:

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

ClassVar は型のみを受け入れ、それ以外は受け付けられません。

ClassVar はクラスそのものではなく、isinstance() や issubclass() で使うべきではありません。 ClassVar は Python の実行時の挙動を変えませんが、サードパーティの型検査器で使えます。 例えば、型チェッカーは次のコードをエラーとするかもしれません:

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

Added in version 3.5.3.

バージョン 3.13 で変更: 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.

例えば:

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

この機能は実行時には検査されません。詳細については PEP 591 を参照してください。

Added in version 3.8.

バージョン 3.13 で変更: 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.

より詳しくは、 TypedDict と PEP 655 を参照してください。

Added in version 3.11.

typing.ReadOnly

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

例えば:

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)]

The first argument to Annotated must be a valid type. Multiple metadata elements can be supplied as Annotated supports variadic arguments. The order of the metadata elements is preserved and matters for equality checks:

@dataclass
class ctype:
     kind: str

a1 = Annotated[int, ValueRange(3, 10), ctype("char")]
a2 = Annotated[int, ctype("char"), ValueRange(3, 10)]

assert a1 != a2  # Order matters

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.

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")
]

However, this does not apply to Annotated types referenced through a type alias, to avoid forcing evaluation of the underlying TypeAliasType:

type From3To10[T] = Annotated[T, ValueRange(3, 10)]
assert Annotated[From3To10[int], 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] = Annotated[T1, T2, T3, ..., Ann1]  # NOT valid

where T1, T2, ... are TypeVars. This is invalid as 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')

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

参考

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. The return value is a boolean.

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.

例えば:

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).

Using -> TypeGuard tells the static type checker that for a given function:

1. The return value is a boolean.

2. If the return value is True, the type of its argument is the type inside TypeGuard.

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

例えば:

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

ジェネリック型のための抽象基底クラスです。

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.

このクラスは次のように使用することが出来ます:

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)

型変数です。

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.

バージョン 3.12 で変更: 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).

evaluate_bound()

An evaluate function corresponding to the __bound__ attribute. When called directly, this method supports only the VALUE format, which is equivalent to accessing the __bound__ attribute directly, but the method object can be passed to annotationlib.call_evaluate_function() to evaluate the value in a different format.

Added in version 3.14.

__constraints__

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

バージョン 3.12 で変更: 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).

evaluate_constraints()

An evaluate function corresponding to the __constraints__ attribute. When called directly, this method supports only the VALUE format, which is equivalent to accessing the __constraints__ attribute directly, but the method object can be passed to annotationlib.call_evaluate_function() to evaluate the value in a different format.

Added in version 3.14.

__default__

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

Added in version 3.13.

evaluate_default()

An evaluate function corresponding to the __default__ attribute. When called directly, this method supports only the VALUE format, which is equivalent to accessing the __default__ attribute directly, but the method object can be passed to annotationlib.call_evaluate_function() to evaluate the value in a different format.

Added in version 3.14.

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.

バージョン 3.12 で変更: Type variables can now be declared using the type parameter syntax introduced by PEP 695. The infer_variance parameter was added.

バージョン 3.13 で変更: 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.

evaluate_default()

An evaluate function corresponding to the __default__ attribute. When called directly, this method supports only the VALUE format, which is equivalent to accessing the __default__ attribute directly, but the method object can be passed to annotationlib.call_evaluate_function() to evaluate the value in a different format.

Added in version 3.14.

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.

バージョン 3.12 で変更: Type variable tuples can now be declared using the type parameter syntax introduced by PEP 695.

バージョン 3.13 で変更: 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')

Parameter specification variables exist primarily for the benefit of static type checkers. They are used to forward the parameter types of one callable to another callable -- a pattern commonly found in higher order functions and decorators. They are only valid when used in Concatenate, or as the first argument to Callable, or as parameters for user-defined Generics. See Generic for more information on generic types.

For example, to add basic logging to a function, one can create a decorator add_logging to log function calls. The parameter specification variable tells the type checker that the callable passed into the decorator and the new callable returned by it have inter-dependent type parameters:

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. The type checker can't type check the inner function because *args and **kwargs have to be typed Any.

2. cast() may be required in the body of the add_logging decorator when returning the inner function, or the static type checker must be told to ignore the return inner.

args

kwargs

Since ParamSpec captures both positional and keyword parameters, P.args and P.kwargs can be used to split a ParamSpec into its components. P.args represents the tuple of positional parameters in a given call and should only be used to annotate *args. P.kwargs represents the mapping of keyword parameters to their values in a given call, and should be only be used to annotate **kwargs. Both attributes require the annotated parameter to be in scope. At runtime, P.args and P.kwargs are instances respectively of ParamSpecArgs and 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.

evaluate_default()

An evaluate function corresponding to the __default__ attribute. When called directly, this method supports only the VALUE format, which is equivalent to accessing the __default__ attribute directly, but the method object can be passed to annotationlib.call_evaluate_function() to evaluate the value in a different format.

Added in version 3.14.

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.

Parameter specification variables created with covariant=True or contravariant=True can be used to declare covariant or contravariant generic types. The bound argument is also accepted, similar to TypeVar. However the actual semantics of these keywords are yet to be decided.

Added in version 3.10.

バージョン 3.12 で変更: Parameter specifications can now be declared using the type parameter syntax introduced by PEP 695.

バージョン 3.13 で変更: Support for default values was added.

注釈

Only parameter specification variables defined in global scope can be pickled.

参考

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

• Concatenate

• 呼び出し可能オブジェクトのアノテーション

typing.ParamSpecArgs

typing.ParamSpecKwargs

Arguments and keyword arguments attributes of a ParamSpec. The P.args attribute of a ParamSpec is an instance of ParamSpecArgs, and P.kwargs is an instance of ParamSpecKwargs. They are intended for runtime introspection and have no special meaning to static type checkers.

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.

例:

>>> 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 name of 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

evaluate_value()

An evaluate function corresponding to the __value__ attribute. When called directly, this method supports only the VALUE format, which is equivalent to accessing the __value__ attribute directly, but the method object can be passed to annotationlib.call_evaluate_function() to evaluate the value in a different format:

>>> type Alias = undefined
>>> Alias.__value__
Traceback (most recent call last):
...
NameError: name 'undefined' is not defined
>>> from annotationlib import Format, call_evaluate_function
>>> Alias.evaluate_value(Format.VALUE)
Traceback (most recent call last):
...
NameError: name 'undefined' is not defined
>>> call_evaluate_function(Alias.evaluate_value, Format.FORWARDREF)
ForwardRef('undefined')

Added in version 3.14.

展開

Type aliases support star unpacking using the *Alias syntax. This is equivalent to using Unpack[Alias] directly:

>>> type Alias = tuple[int, str]
>>> type Unpacked = tuple[bool, *Alias]
>>> Unpacked.__value__
tuple[bool, typing.Unpack[Alias]]

Added in version 3.14.

Other special directives

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

collections.namedtuple() の型付き版です。

使い方:

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

これは次と等価です:

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

フィールドにデフォルト値を与えるにはクラス本体で代入してください:

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

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

デフォルト値のあるフィールドはデフォルト値のないフィールドの後でなければなりません。

最終的に出来上がるクラスには、フィールド名をフィールド型へ対応付ける辞書を提供する __annotations__ 属性が追加されています。 (フィールド名は _fields 属性に、デフォルト値は _field_defaults 属性に格納されていて、両方とも namedtuple() API の一部分です。)

NamedTuple のサブクラスは docstring やメソッドも持てます:

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]

後方互換な使用法:

# 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)])

バージョン 3.6 で変更: PEP 526 変数アノテーションのシンタックスが追加されました。

バージョン 3.6.1 で変更: デフォルト値、メソッド、ドキュメンテーション文字列への対応が追加されました。

バージョン 3.8 で変更: _field_types 属性および __annotations__ 属性は OrderedDict インスタンスではなく普通の辞書になりました。

バージョン 3.9 で変更: _field_types 属性は削除されました。代わりに同じ情報を持つより標準的な __annotations__ 属性を使ってください。

バージョン 3.11 で変更: Added support for generic namedtuples.

バージョン 3.14 で変更: Using super() (and the __class__ closure variable) in methods of NamedTuple subclasses is unsupported and causes a TypeError.

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.

使い方:

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

__module__

The name of 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.

バージョン 3.10 で変更: NewType is now a class rather than a function.

class typing.Protocol(Generic)

Base class for protocol classes.

Protocol classes are defined like this:

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

このようなクラスは主に構造的部分型 (静的ダックタイピング) を認識する静的型チェッカーが使います。例えば:

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. Protocol classes without this decorator cannot be used as the second argument to isinstance() or issubclass().

プロトコルクラスはジェネリックにもできます。例えば:

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

Mark a protocol class as a runtime protocol.

Such a protocol can be used with isinstance() and issubclass(). This allows a simple-minded structural check, very similar to "one trick ponies" in collections.abc such as Iterable. For example:

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

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

@runtime_checkable
class Named(Protocol):
    name: str

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

This decorator raises TypeError when applied to a non-protocol class.

注釈

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.

注釈

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.

バージョン 3.12 で変更: 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.

バージョン 3.12 で変更: 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)

Special construct to add type hints to a dictionary. At runtime it is a plain dict.

TypedDict は、その全てのインスタンスにおいてキーの集合が固定されていて、各キーに対応する値が全てのインスタンスで同じ型を持つことが期待される辞書型を宣言します。 この期待は実行時にはチェックされず、型チェッカーでのみ強制されます。 使用方法は次の通りです:

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, or when key names must not be mangled like regular private names:

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

class Definition(TypedDict):
    __schema: str  # mangled to `_Definition__schema`

# OK, functional syntax
Point2D = TypedDict('Point2D', {'in': int, 'x-y': int})
Definition = TypedDict('Definition', {'__schema': str})  # not mangled

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)

This means that a Point2D TypedDict can have any of the keys omitted. A type checker is only expected to support a literal False or True as the value of the total argument. True is the default, and makes all items defined in the class body required.

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)

It is possible for a TypedDict type to inherit from one or more other TypedDict types using the class-based syntax. Usage:

class Point3D(Point2D):
    z: int

Point3D has three items: x, y and z. It is equivalent to this definition:

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]

A TypedDict can be introspected via annotations dicts (see Annotations Best Practices for more information on annotations best practices), __total__, __required_keys__, and __optional_keys__.

__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.

注釈

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.

See the TypedDict section in the typing documentation for more examples and detailed rules.

Added in version 3.8.

バージョン 3.11 で変更: Added support for marking individual keys as Required or NotRequired. See PEP 655.

バージョン 3.11 で変更: Added support for generic TypedDicts.

バージョン 3.13 で変更: Removed support for the keyword-argument method of creating TypedDicts.

バージョン 3.13 で変更: 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", {}).

プロトコル

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

class typing.SupportsAbs

返り値の型と共変な抽象メソッド __abs__ を備えた ABC です。

class typing.SupportsBytes

抽象メソッド __bytes__ を備えた ABC です。

class typing.SupportsComplex

抽象メソッド __complex__ を備えた ABC です。

class typing.SupportsFloat

抽象メソッド __float__ を備えた ABC です。

class typing.SupportsIndex

抽象メソッド __index__ を備えた ABC です。

Added in version 3.8.

class typing.SupportsInt

抽象メソッド __int__ を備えた ABC です。

class typing.SupportsRound

返り値の型と共変な抽象メソッド __round__ を備えた ABC です。

ABCs and Protocols for working with I/O

class typing.IO[AnyStr]

class typing.TextIO[AnyStr]

class typing.BinaryIO[AnyStr]

Generic class IO[AnyStr] and its subclasses TextIO(IO[str]) and BinaryIO(IO[bytes]) represent the types of I/O streams such as returned by open(). Please note that these classes are not protocols, and their interface is fairly broad.

The protocols io.Reader and io.Writer offer a simpler alternative for argument types, when only the read() or write() methods are accessed, respectively:

def read_and_write(reader: Reader[str], writer: Writer[bytes]):
    data = reader.read()
    writer.write(data.encode())

Also consider using collections.abc.Iterable for iterating over the lines of an input stream:

def read_config(stream: Iterable[str]):
    for line in stream:
        ...

Functions and decorators

typing.cast(typ, val)

値をある型にキャストします。

この関数は値を変更せずに返します。 型検査器に対して、返り値が指定された型を持っていることを通知しますが、実行時には意図的に何も検査しません。 (その理由は、処理をできる限り速くしたかったためです。)

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.

以下はプログラム例です:

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.

参考

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:

パラメータ:

• 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	説明
init	Indicates whether the field should be included in the synthesized __init__ method. If unspecified, init defaults to True.
default	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.

より詳しくは PEP 681 を参照してください。

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

詳細と他の型付け意味論との比較は PEP 484 を参照してください。

バージョン 3.11 で変更: 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.

例えば:

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
    ...

この機能は実行時には検査されません。詳細については PEP 591 を参照してください。

Added in version 3.8.

バージョン 3.11 で変更: 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

アノテーションが型ヒントでないことを示すデコレータです。

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

別のデコレータに no_type_check() の効果を与えるデコレータです。

これは何かの関数をラップするデコレータを no_type_check() でラップします。

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.

例えば:

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.

このデコレータ自身は実行時には使えません。 このデコレータは主に、実装がプライベートクラスのインスタンスを返す場合に、型スタブファイルに定義されているクラスに対して印を付けるためのものです:

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

def fetch_response() -> Response: ...

プライベートクラスのインスタンスを返すのは推奨されません。 そのようなクラスは公開クラスにするのが望ましいです。

Introspection helpers

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

関数、メソッド、モジュールまたはクラスのオブジェクトの型ヒントを含む辞書を返します。

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, ...], Required[T], NotRequired[T], and ReadOnly[T] with T, unless include_extras is set to True (see Annotated for more information).

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

注意

This function may execute arbitrary code contained in annotations. See Security implications of introspecting annotations for more information.

注釈

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.

バージョン 3.9 で変更: Added include_extras parameter as part of PEP 593. See the documentation on Annotated for more information.

バージョン 3.11 で変更: 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.

例:

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.

例:

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.

例えば:

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)

Check if a type is a TypedDict.

例えば:

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.

注釈

PEP 585 generic types such as list["SomeClass"] will not be implicitly transformed into list[ForwardRef("SomeClass")] and thus will not automatically resolve to list[SomeClass].

Added in version 3.7.4.

バージョン 3.14 で変更: This is now an alias for annotationlib.ForwardRef. Several undocumented behaviors of this class have been changed; for example, after a ForwardRef has been evaluated, the evaluated value is no longer cached.

typing.evaluate_forward_ref(forward_ref, *, owner=None, globals=None, locals=None, type_params=None, format=annotationlib.Format.VALUE)

Evaluate an annotationlib.ForwardRef as a type hint.

This is similar to calling annotationlib.ForwardRef.evaluate(), but unlike that method, evaluate_forward_ref() also recursively evaluates forward references nested within the type hint.

See the documentation for annotationlib.ForwardRef.evaluate() for the meaning of the owner, globals, locals, type_params, and format parameters.

注意

This function may execute arbitrary code contained in annotations. See Security implications of introspecting annotations for more information.

Added in version 3.14.

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.

定数

typing.TYPE_CHECKING

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

A module which is expensive to import, and which only contain types used for typing annotations, can be safely imported inside an if TYPE_CHECKING: block. This prevents the module from actually being imported at runtime; annotations aren't eagerly evaluated (see PEP 649) so using undefined symbols in annotations is harmless--as long as you don't later examine them. Your static type analysis tool will set TYPE_CHECKING to True during static type analysis, which means the module will be imported and the types will be checked properly during such analysis.

使い方:

if TYPE_CHECKING:
    import expensive_mod

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

If you occasionally need to examine type annotations at runtime which may contain undefined symbols, use annotationlib.get_annotations() with a format parameter of annotationlib.Format.STRING or annotationlib.Format.FORWARDREF to safely retrieve the annotations without raising NameError.

Added in version 3.5.2.

非推奨のエイリアス

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])

dict の非推奨なエイリアス。

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.

バージョン 3.9 で非推奨: builtins.dict は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

list の非推奨なエイリアス。

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.

バージョン 3.9 で非推奨: builtins.list は添字表記 ([]) をサポートするようになりました。PEP 585 と ジェネリックエイリアス型 を参照してください。

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.

バージョン 3.9 で非推奨: builtins.set は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

Deprecated alias to builtins.frozenset.

バージョン 3.9 で非推奨: builtins.frozenset は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

typing.Tuple

tuple の非推奨なエイリアス。

tuple and Tuple are special-cased in the type system; see タプルのアノテーション for more details.

バージョン 3.9 で非推奨: builtins.tuple は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Type(Generic[CT_co])

type の非推奨なエイリアス。

See クラスオブジェクトの型 for details on using type or typing.Type in type annotations.

Added in version 3.5.2.

バージョン 3.9 で非推奨: builtins.type は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

Aliases to types in collections

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

collections.defaultdict の非推奨なエイリアス。

Added in version 3.5.2.

バージョン 3.9 で非推奨: collections.defaultdict は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

collections.OrderedDict の非推奨なエイリアス。

Added in version 3.7.2.

バージョン 3.9 で非推奨: collections.OrderedDict は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

collections.ChainMap の非推奨なエイリアス。

Added in version 3.6.1.

バージョン 3.9 で非推奨: collections.ChainMap は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

collections.Counter の非推奨なエイリアス。

Added in version 3.6.1.

バージョン 3.9 で非推奨: collections.Counter は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

collections.deque の非推奨なエイリアス。

Added in version 3.6.1.

バージョン 3.9 で非推奨: collections.deque は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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].

バージョン 3.9 で非推奨: Classes Pattern and Match from re now support []. See PEP 585 and ジェネリックエイリアス型.

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.

Text は Python 2 と Python 3 の両方と互換性のある方法で値が unicode 文字列を含んでいなければならない場合に使用してください。

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

Added in version 3.5.2.

バージョン 3.11 で非推奨: 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])

collections.abc.Set の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.Set は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.ByteString(Sequence[int])

Deprecated alias to collections.abc.ByteString.

Use isinstance(obj, collections.abc.Buffer) to test if obj implements the buffer protocol at runtime. For use in type annotations, either use Buffer or a union that explicitly specifies the types your code supports (e.g., bytes | bytearray | memoryview).

ByteString was originally intended to be an abstract class that would serve as a supertype of both bytes and bytearray. However, since the ABC never had any methods, knowing that an object was an instance of ByteString never actually told you anything useful about the object. Other common buffer types such as memoryview were also never understood as subtypes of ByteString (either at runtime or by static type checkers).

See PEP 688 for more details.

Deprecated since version 3.9, will be removed in version 3.17.

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

collections.abc.Collection の非推奨なエイリアス。

Added in version 3.6.

バージョン 3.9 で非推奨: collections.abc.Collection は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Container(Generic[T_co])

collections.abc.Container の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.Container は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

collections.abc.ItemsView の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.ItemsView は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

collections.abc.KeysView の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.KeysView は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

collections.abc.Mapping の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.Mapping は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.MappingView(Sized)

collections.abc.MappingView の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.MappingView は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

collections.abc.MutableMapping の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.MutableMapping は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.MutableSequence(Sequence[T])

collections.abc.MutableSequence の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.MutableSequence は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.MutableSet(AbstractSet[T])

collections.abc.MutableSet の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.MutableSet は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

collections.abc.Sequence の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.Sequence は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

collections.abc.ValuesView の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.ValuesView は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

Aliases to asynchronous ABCs in collections.abc

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

collections.abc.Coroutine の非推奨なエイリアス。

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

Added in version 3.5.3.

バージョン 3.9 で非推奨: collections.abc.Coroutine は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

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

collections.abc.AsyncGenerator の非推奨なエイリアス。

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

Added in version 3.6.1.

バージョン 3.9 で非推奨: collections.abc.AsyncGenerator は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

バージョン 3.13 で変更: The SendType parameter now has a default.

class typing.AsyncIterable(Generic[T_co])

collections.abc.AsyncIterable の非推奨なエイリアス。

Added in version 3.5.2.

バージョン 3.9 で非推奨: collections.abc.AsyncIterable は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.AsyncIterator(AsyncIterable[T_co])

collections.abc.AsyncIterator の非推奨なエイリアス。

Added in version 3.5.2.

バージョン 3.9 で非推奨: collections.abc.AsyncIterator は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Awaitable(Generic[T_co])

collections.abc.Awaitable の非推奨なエイリアス。

Added in version 3.5.2.

バージョン 3.9 で非推奨: collections.abc.Awaitable は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

Aliases to other ABCs in collections.abc

class typing.Iterable(Generic[T_co])

collections.abc.Iterable の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.Iterable は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Iterator(Iterable[T_co])

collections.abc.Iterator の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.Iterator は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

typing.Callable

collections.abc.Callable の非推奨なエイリアス。

See 呼び出し可能オブジェクトのアノテーション for details on how to use collections.abc.Callable and typing.Callable in type annotations.

バージョン 3.9 で非推奨: collections.abc.Callable は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

バージョン 3.10 で変更: Callable は ParamSpec と Concatenate をサポートしました。詳細は PEP 612 を参照してください。

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

collections.abc.Generator の非推奨なエイリアス。

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

バージョン 3.9 で非推奨: collections.abc.Generator は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

バージョン 3.13 で変更: Default values for the send and return types were added.

class typing.Hashable

collections.abc.Hashable の非推奨なエイリアス。

バージョン 3.12 で非推奨: 代わりに collections.abc.Hashable を直接使用してください。

class typing.Reversible(Iterable[T_co])

collections.abc.Reversible の非推奨なエイリアス。

バージョン 3.9 で非推奨: collections.abc.Reversible は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Sized

collections.abc.Sized の非推奨なエイリアス。

バージョン 3.12 で非推奨: 代わりに collections.abc.Sized を直接使用してください。

Aliases to contextlib ABCs

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

contextlib.AbstractContextManager の非推奨なエイリアス。

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.

バージョン 3.9 で非推奨: contextlib.AbstractContextManager は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

バージョン 3.13 で変更: Added the optional second type parameter, ExitT_co.

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

contextlib.AbstractAsyncContextManager の非推奨なエイリアス。

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.

バージョン 3.9 で非推奨: contextlib.AbstractAsyncContextManager は添字表記 ([]) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

バージョン 3.13 で変更: Added the optional second type parameter, AExitT_co.

メジャーな機能の非推奨時系列

typing の機能の中には非推奨のものがあり、Python の将来のバージョンで削除される可能性があります。以下の表は主な非推奨機能をまとめたものです。これは変更される可能性があり、すべての非推奨機能がリストされているわけではありません。

機能	非推奨となるバージョン	削除予定のバージョン	PEP/issue
標準コレクションのエイリアス	3.9	未定（非推奨のエイリアス を参照）	PEP 585
typing.ByteString	3.9	3.17	gh-91896
typing.Text	3.11	未定	gh-92332
typing.Hashable、typing.Sized	3.12	未定	gh-94309
typing.TypeAlias	3.12	未定	PEP 695
@typing.no_type_check_decorator	3.13	3.15	gh-106309
typing.AnyStr	3.13	3.18	gh-105578