`typing` --- Support for type hints¶

バージョン 3.5 で追加.

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。

This module provides runtime support for type hints. For the original specification of the typing system, see PEP 484. For a simplified introduction to type hints, see PEP 483.

The function below takes and returns a string and is annotated as follows:

def greeting(name: str) -> str:
    return 'Hello ' + name

In the function greeting, the argument name is expected to be of type str and the return type str. Subtypes are accepted as arguments.

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

For a summary of deprecated features and a deprecation timeline, please see Deprecation Timeline of Major Features.

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)
mypy ドキュメンテーションの "Type System Reference" セクション: 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。

Relevant PEPs¶

Since the initial introduction of type hints in PEP 484 and PEP 483, a number of PEPs have modified and enhanced Python's framework for type annotations:

The full list of PEPs

PEP 526: Syntax for Variable Annotations
Introducing syntax for annotating variables outside of function definitions, and ClassVar
PEP 544: Protocols: Structural subtyping (static duck typing)
Introducing Protocol and the @runtime_checkable decorator
PEP 585: Type Hinting Generics In Standard Collections
Introducing types.GenericAlias and the ability to use standard library classes as generic types
PEP 586: Literal Types
Introducing Literal
PEP 589: TypedDict: Type Hints for Dictionaries with a Fixed Set of Keys
Introducing TypedDict
PEP 591: Adding a final qualifier to typing
Introducing Final and the @final decorator
PEP 593: Flexible function and variable annotations
Introducing Annotated
PEP 604: Allow writing union types as X | Y
Introducing types.UnionType and the ability to use the binary-or operator | to signify a union of types
PEP 612: Parameter Specification Variables
Introducing ParamSpec and Concatenate
PEP 613: Explicit Type Aliases
Introducing TypeAlias
PEP 646: Variadic Generics
Introducing TypeVarTuple
PEP 647: User-Defined Type Guards
Introducing TypeGuard
PEP 655: Marking individual TypedDict items as required or potentially missing
Introducing Required and NotRequired
PEP 673: Self type
Introducing Self
PEP 675: Arbitrary Literal String Type
Introducing LiteralString
PEP 681: Data Class Transforms
Introducing the @dataclass_transform decorator

型エイリアス¶

A type alias is defined by assigning the type to the alias. In this example, Vector and list[float] will be treated as interchangeable synonyms:

Vector = list[float]

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

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

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

ConnectionOptions = dict[str, str]
Address = tuple[str, int]
Server = tuple[Address, ConnectionOptions]

def broadcast_message(message: str, servers: Sequence[Server]) -> None:
    ...

# The static type checker will treat the previous type signature as
# being exactly equivalent to this one.
def broadcast_message(
        message: str,
        servers: Sequence[tuple[tuple[str, int], dict[str, str]]]) -> None:
    ...

Type aliases may be marked with TypeAlias to make it explicit that the statement is a type alias declaration, not a normal variable assignment:

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:
    ...

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

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

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' is of type 'int', not 'UserId'
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)

# Fails at runtime and does not pass type checking
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

Recall that the use of a type alias declares two types to be equivalent to one another. Doing Alias = Original will make the static type checker treat Alias as being exactly equivalent to Original in all cases. This is useful when you want to simplify complex type signatures.

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。

バージョン 3.5.2 で追加.

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¶

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

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

添字表記は常に2つの値とともに使われなければなりません。実引数のリストと返り値の型です。実引数のリストは型のリスト、ParamSpec、Concatenate、ellipsis のいずれかでなければなりません。返り値の型は単一の型でなければなりません。

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  # Also 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: ...

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

from collections.abc import Sequence
from typing import TypeVar

T = TypeVar('T')                  # Declare type variable "T"

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

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)

任意の 長さで、全ての要素が同じ型 T であるタプルを示すには tuple[T, ...] を使います。空のタプルを示すには tuple[()] を使います。単に tuple とアノテーションすることは、tuple[Any, ...] と等価です。

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 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.

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 typing import TypeVar, Generic
from logging import Logger

T = TypeVar('T')

class LoggedVar(Generic[T]):
    def __init__(self, value: T, name: str, logger: Logger) -> None:
        self.name = name
        self.logger = logger
        self.value = value

    def set(self, new: T) -> None:
        self.log('Set ' + repr(self.value))
        self.value = new

    def get(self) -> T:
        self.log('Get ' + repr(self.value))
        return self.value

    def log(self, message: str) -> None:
        self.logger.info('%s: %s', self.name, message)

Generic[T] as a base class defines that the class LoggedVar takes a single type parameter T . This also makes T valid as a type within the class body.

The Generic base class defines __class_getitem__() so that LoggedVar[T] is valid as a type:

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

T = TypeVar('T', contravariant=True)
B = TypeVar('B', bound=Sequence[bytes], covariant=True)
S = TypeVar('S', int, str)

class WeirdTrio(Generic[T, B, S]):
    ...

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
...

T = TypeVar('T')

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

You can use multiple inheritance with Generic:

from collections.abc import Sized
from typing import TypeVar, Generic

T = TypeVar('T')

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

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

from collections.abc import Mapping
from typing import TypeVar

T = TypeVar('T')

class MyDict(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
from typing import TypeVar
S = TypeVar('S')
Response = Iterable[S] | int

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

T = TypeVar('T', int, float, complex)
Vec = Iterable[tuple[T, T]]

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

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。

User-defined generics for parameter expressions are also supported via parameter specification variables in the form Generic[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:

>>> from typing import Generic, ParamSpec, TypeVar

>>> T = TypeVar('T')
>>> P = ParamSpec('P')

>>> class Z(Generic[T, P]): ...
...
>>> Z[int, [dict, float]]
__main__.Z[int, (<class 'dict'>, <class 'float'>)]

Furthermore, 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(Generic[P]): ...
...
>>> X[int, str]
__main__.X[(<class 'int'>, <class 'str'>)]
>>> X[[int, str]]
__main__.X[(<class 'int'>, <class '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!"

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。

バージョン 3.11 で追加.

typing.Never¶

The bottom type, a type that has no members.

This can be used to define a function that should never be called, or a function that never returns:

from typing import Never

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

バージョン 3.11 で追加: On older Python versions, NoReturn may be used to express the same concept. Never was added to make the intended meaning more explicit.

typing.NoReturn¶

Special type indicating that a function never returns.

例えば:

from typing import NoReturn

def stop() -> NoReturn:
    raise RuntimeError('no way')

NoReturn can also be used as a bottom type, a type that has no values. Starting in Python 3.11, the Never type should be used for this concept instead. Type checkers should treat the two equivalently.

バージョン 3.6.2 で追加.

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。

バージョン 3.11 で追加.

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 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.
# 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。

バージョン 3.10 で追加.

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。

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]
```

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。

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, ParamSpec, TypeVar

P = ParamSpec('P')
R = TypeVar('R')

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

def with_lock(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])

バージョン 3.10 で追加.

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
    ...

Mode: TypeAlias = 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。

バージョン 3.8 で追加.

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

バージョン 3.5.3 で追加.

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。

バージョン 3.8 で追加.

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.

バージョン 3.11 で追加.

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。

バージョン 3.11 で追加.

typing.Annotated¶

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

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

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

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

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

Annotated[<type>, <metadata>]

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

@dataclass
class ValueRange:
    lo: int
    hi: int

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

Details of the syntax:

The first argument to Annotated must be a valid type
Multiple metadata elements can be supplied (Annotated supports variadic arguments):
```
@dataclass
class ctype:
    kind: str

Annotated[int, ValueRange(3, 10), ctype("char")]
```
It is up to the tool consuming the annotations to decide whether the client is allowed to add multiple metadata elements to one annotation and how to merge those annotations.
Annotated must be subscripted with at least two arguments ( Annotated[int] is not valid)

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

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

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

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

Duplicated metadata elements are not removed:

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

Annotated can be used with nested and generic aliases:

@dataclass
class MaxLen:
    value: int

T = TypeVar("T")
Vec: TypeAlias = Annotated[list[tuple[T, T]], MaxLen(10)]

assert Vec[int] == Annotated[list[tuple[int, int]], MaxLen(10)]

Annotated cannot be used with an unpacked TypeVarTuple:
```
Variadic: TypeAlias = Annotated[*Ts, Ann1]  # NOT valid
```
This would be equivalent to:
```
Annotated[T1, T2, T3, ..., Ann1]
```
where T1, T2, etc. are TypeVars. This would be invalid: only one type should be passed to Annotated.

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

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

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

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

参考

PEP 593 - Flexible function and variable annotations: The PEP introducing Annotated to the standard library.

バージョン 3.9 で追加.

typing.TypeGuard¶

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

TypeGuard can be used to annotate the return type of a user-defined type guard function. TypeGuard only accepts a single type argument. At runtime, functions marked this way should return a boolean.

TypeGuard 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 guard":

def is_str(val: str | float):
    # "isinstance" type guard
    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 guard. Such a function should use TypeGuard[...] as its return type to alert static type checkers to this intention.

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

The return value is a boolean.
If the return value is True, the type of its argument is the type inside TypeGuard.

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!")

If is_str_list is a class or instance method, then the type in TypeGuard maps to the type of the second parameter after cls or self.

In short, the form def foo(arg: TypeA) -> TypeGuard[TypeB]: ..., means that if foo(arg) returns True, then arg narrows from TypeA to TypeB.

注釈

TypeB need not be a narrower form of TypeA -- it can even be a wider form. 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. The responsibility of writing type-safe type guards is left to the user.

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

バージョン 3.10 で追加.

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

バージョン 3.11 で追加.

Building generic types¶

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

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 inheriting from an instantiation of this class with one or more type variables. For example, a generic mapping type might be defined as:

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

このクラスは次のように使用することが出来ます:

X = TypeVar('X')
Y = TypeVar('Y')

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

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

型変数です。

使い方:

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


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


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

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

Type variables may be marked covariant or contravariant by passing covariant=True or contravariant=True. See PEP 484 for more details. By default, type variables are invariant.

Bound type variables and constrained type variables have different semantics in several important ways. Using a bound 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

Type variables can be bound to concrete types, abstract types (ABCs or protocols), and even unions of types:

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 marked as covariant.

__contravariant__¶: Whether the type var has been marked as contravariant.

__bound__¶: The bound of the type variable, if any.

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

class typing.TypeVarTuple(name)¶

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

使い方:

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:

Shape = TypeVarTuple("Shape")
class Array(Generic[*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:

DType = TypeVar('DType')
Shape = TypeVarTuple('Shape')

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

class Array2(Generic[*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(Generic[*Shape, *Shape]):  # Not valid
    pass

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

def call_soon(
         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.

バージョン 3.11 で追加.

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

Parameter specification variable. A specialized version of type variables.

使い方:

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
from typing import TypeVar, ParamSpec
import logging

T = TypeVar('T')
P = ParamSpec('P')

def add_logging(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 bound Callable[..., Any]. However this causes two problems:

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

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.

バージョン 3.10 で追加.

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

バージョン 3.10 で追加.

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

後方互換な使用法:

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.

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 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.

バージョン 3.5.2 で追加.

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.

プロトコルクラスはジェネリックにもできます。例えば:

T = TypeVar("T")

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

バージョン 3.8 で追加.

@typing.runtime_checkable¶

Mark a protocol class as a runtime protocol.

Such a protocol can be used with isinstance() and issubclass(). This raises TypeError when applied to a non-protocol class. 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)

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.

バージョン 3.8 で追加.

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')

To allow using this feature with older versions of Python that do not support PEP 526, TypedDict supports two additional equivalent syntactic forms:

Using a literal dict as the second argument:

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

Using keyword arguments:

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

バージョン 3.11 で非推奨、バージョン 3.13 で削除予定: The keyword-argument syntax is deprecated in 3.11 and will be removed in 3.13. It may also be unsupported by static type checkers.

The functional syntax should also be used when any of the keys are not valid identifiers, for example because they are keywords or contain hyphens. Example:

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

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

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

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

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

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

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

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

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

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:

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__¶: バージョン 3.9 で追加.

__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

バージョン 3.9 で追加.

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.

See PEP 589 for more examples and detailed rules of using TypedDict.

バージョン 3.8 で追加.

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.

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。

バージョン 3.8 で追加.

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 for working with IO¶

class typing.IO¶
class typing.TextIO¶
class typing.BinaryIO¶: Generic type IO[AnyStr] and its subclasses TextIO(IO[str]) and BinaryIO(IO[bytes]) represent the types of I/O streams such as returned by open().

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)

バージョン 3.11 で追加.

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.

バージョン 3.11 で追加.

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.

バージョン 3.11 で追加.

@typing.dataclass_transform(*, eq_default=True, order_default=False, kw_only_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:

T = TypeVar("T")

@dataclass_transform()
def create_model(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.
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。

バージョン 3.11 で追加.

@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.

バージョン 3.11 で追加.

typing.clear_overloads()¶

Clear all registered overloads in the internal registry.

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

バージョン 3.11 で追加.

@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。

バージョン 3.8 で追加.

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。

@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__. In addition, forward references encoded as string literals are handled by evaluating them in globals and locals namespaces. For a class C, return a dictionary constructed by merging all the __annotations__ along C.__mro__ in reverse order.

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

class Student(NamedTuple):
    name: Annotated[str, 'some marker']

assert get_type_hints(Student) == {'name': str}
assert get_type_hints(Student, include_extras=False) == {'name': str}
assert get_type_hints(Student, include_extras=True) == {
    'name': Annotated[str, 'some marker']
}

注釈

get_type_hints() does not work with imported type aliases that include forward references. Enabling postponed evaluation of annotations (PEP 563) may remove the need for most forward references.

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
P = ParamSpec('P')
assert get_origin(P.args) is P
assert get_origin(P.kwargs) is P

バージョン 3.8 で追加.

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)

バージョン 3.8 で追加.

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)

バージョン 3.10 で追加.

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].

バージョン 3.7.4 で追加.

定数¶

typing.TYPE_CHECKING¶

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

使い方:

if TYPE_CHECKING:
    import expensive_mod

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

The first type annotation must be enclosed in quotes, making it a "forward reference", to hide the expensive_mod reference from the interpreter runtime. Type annotations for local variables are not evaluated, so the second annotation does not need to be enclosed in quotes.

注釈

from __future__ import annotations が使われた場合、アノーテーションは関数定義時に評価されません。代わりにアノーテーションは __annotations__ 属性に文字列として保存されます。これによりアノーテーションをシングルクォートで囲む必要がなくなります (PEP 563 を参照してください)。

バージョン 3.5.2 で追加.

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.

This type can be used as follows:

def count_words(text: str) -> Dict[str, int]:
    ...

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.

This type may be used as follows:

T = TypeVar('T', int, float)

def vec2(x: T, y: T) -> List[T]:
    return [x, y]

def keep_positives(vector: Sequence[T]) -> List[T]:
    return [item for item in vector if item > 0]

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 AbstractSet 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.

バージョン 3.5.2 で追加.

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。

バージョン 3.5.2 で追加.

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。

バージョン 3.7.2 で追加.

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。

バージョン 3.6.1 で追加.

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。

バージョン 3.6.1 で追加.

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。

バージョン 3.6.1 で追加.

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 3.8 で非推奨、バージョン 3.13 で削除予定: The typing.re namespace is deprecated and will be removed. These types should be directly imported from typing instead.

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'

バージョン 3.5.2 で追加.

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])¶: This type represents the types bytes, bytearray, and memoryview of byte sequences.

バージョン 3.9 で非推奨、バージョン 3.14 で削除予定: Prefer typing_extensions.Buffer, or a union like bytes | bytearray | memoryview.

class typing.Collection(Sized, Iterable[T_co], Container[T_co])¶: collections.abc.Collection の非推奨なエイリアス。

バージョン 3.6 で追加.

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。

This type can be used as follows:

def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
    return word_list[word]

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。

The variance and order of type variables 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

バージョン 3.5.3 で追加.

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。

An async generator can be annotated by the generic type AsyncGenerator[YieldType, SendType]. For example:

async def echo_round() -> AsyncGenerator[int, float]:
    sent = yield 0
    while sent >= 0.0:
        rounded = await round(sent)
        sent = yield rounded

Unlike normal generators, async generators cannot return a value, so there is no ReturnType type parameter. As with Generator, the SendType behaves contravariantly.

If your generator will only yield values, set the SendType to None:

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

Alternatively, annotate your generator as having a return type of either AsyncIterable[YieldType] or AsyncIterator[YieldType]:

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

バージョン 3.6.1 で追加.

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。

class typing.AsyncIterable(Generic[T_co])¶: collections.abc.AsyncIterable の非推奨なエイリアス。

バージョン 3.5.2 で追加.

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。

バージョン 3.5.2 で追加.

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。

バージョン 3.5.2 で追加.

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。

A generator can be annotated by the generic type Generator[YieldType, SendType, ReturnType]. For example:

def echo_round() -> Generator[int, float, str]:
    sent = yield 0
    while sent >= 0:
        sent = yield round(sent)
    return 'Done'

Note that unlike many other generics in the typing module, the SendType of Generator behaves contravariantly, not covariantly or invariantly.

If your generator will only yield values, set the SendType and ReturnType to None:

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

Alternatively, annotate your generator as having a return type of either Iterable[YieldType] or Iterator[YieldType]:

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

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。

class typing.Hashable¶: Alias to collections.abc.Hashable.

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¶: Alias to collections.abc.Sized.

Aliases to `contextlib` ABCs¶

class typing.ContextManager(Generic[T_co])¶: contextlib.AbstractContextManager の非推奨なエイリアス。

バージョン 3.5.4 で追加.

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。

class typing.AsyncContextManager(Generic[T_co])¶: contextlib.AbstractAsyncContextManager の非推奨なエイリアス。

バージョン 3.6.2 で追加.

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。

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
`typing.io` and `typing.re` submodules	3.8	3.13	bpo-38291
標準コレクションのエイリアス	3.9	Undecided (see 非推奨のエイリアス for more information)	PEP 585
`typing.ByteString`	3.9	3.14	gh-91896
`typing.Text`	3.11	未定	gh-92332

`typing` --- Support for type hints¶

Relevant PEPs¶

型エイリアス¶

NewType¶

呼び出し可能オブジェクトのアノテーション¶

ジェネリクス¶

タプルのアノテーション¶

クラスオブジェクトの型¶

ユーザー定義のジェネリック型¶

`Any` 型¶

名前的部分型 vs 構造的部分型¶

モジュールの内容¶

特殊型付けプリミティブ¶

特殊型¶

特殊形式¶

Building generic types¶

Other special directives¶

プロトコル¶

ABCs for working with IO¶

Functions and decorators¶

Introspection helpers¶

定数¶

非推奨のエイリアス¶

Aliases to built-in types¶

Aliases to types in `collections`¶

Aliases to other concrete types¶

Aliases to container ABCs in `collections.abc`¶

Aliases to asynchronous ABCs in `collections.abc`¶

Aliases to other ABCs in `collections.abc`¶

Aliases to `contextlib` ABCs¶

メジャーな機能の非推奨時系列¶

目次

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typing --- Support for type hints¶

Relevant PEPs¶

型エイリアス¶

NewType¶

呼び出し可能オブジェクトのアノテーション¶

ジェネリクス¶

タプルのアノテーション¶

クラスオブジェクトの型¶

ユーザー定義のジェネリック型¶

Any 型¶

名前的部分型 vs 構造的部分型¶

モジュールの内容¶

特殊型付けプリミティブ¶

特殊型¶

特殊形式¶

Building generic types¶

Other special directives¶

プロトコル¶

ABCs for working with IO¶

Functions and decorators¶

Introspection helpers¶

定数¶

非推奨のエイリアス¶

Aliases to built-in types¶

Aliases to types in collections¶

Aliases to other concrete types¶

Aliases to container ABCs in collections.abc¶

Aliases to asynchronous ABCs in collections.abc¶

Aliases to other ABCs in collections.abc¶

Aliases to contextlib ABCs¶

メジャーな機能の非推奨時系列¶

`typing` --- Support for type hints¶

`Any` 型¶

Aliases to types in `collections`¶

Aliases to container ABCs in `collections.abc`¶

Aliases to asynchronous ABCs in `collections.abc`¶

Aliases to other ABCs in `collections.abc`¶

Aliases to `contextlib` ABCs¶