typing
--- 型ヒントのサポート¶
Added in version 3.5.
ソースコード: Lib/typing.py
注釈
The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.
This module provides runtime support for type hints.
Consider the function below:
def surface_area_of_cube(edge_length: float) -> str:
return f"The surface area of the cube is {6 * edge_length ** 2}."
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)
- 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。
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、静的型検査器は 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]
# 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
type ConnectionOptions = dict[str, str]
type Address = tuple[str, int]
type Server = tuple[Address, ConnectionOptions]
def broadcast_message(message: str, servers: Sequence[Server]) -> None:
...
# The static type checker will treat the previous type signature as
# being exactly equivalent to this one.
def broadcast_message(
message: str,
servers: Sequence[tuple[tuple[str, int], dict[str, str]]]
) -> None:
...
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)
静的型検査器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検査器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
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¶
Functions -- or other callable objects -- can be annotated using
collections.abc.Callable
or deprecated typing.Callable
.
Callable[[int], str]
signifies a function that takes a single parameter
of type int
and returns a str
.
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: ...
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)
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 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.
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
Async generators are handled in a similar fashion, but don't
expect a ReturnType
type argument
(AsyncGenerator[YieldType, SendType]
):
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.
ユーザーが定義したジェネリッククラスはメタクラスの衝突を起こすことなく基底クラスに抽象基底クラスをとれます。 ジェネリックメタクラスはサポートされません。 パラメータ化を行うジェネリクスの結果はキャッシュされていて、 typing モジュールのほとんどの型は ハッシュ可能 で等価比較できます。
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。
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¶
-
Definition:
AnyStr = TypeVar('AnyStr', str, bytes)
AnyStr
is meant to be used for functions that may acceptstr
orbytes
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 theAny
type, nor does it mean "any string". In particular,AnyStr
andstr | 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 anotherLiteralString
. However, an object typed as juststr
is not. A string created by composingLiteralString
-typed objects is also acceptable as aLiteralString
.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
andNoReturn
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
andNoReturn
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 useSelf
as the return annotation. IfFoo.return_self
was annotated as returning"Foo"
, then the type checker would infer the object returned fromSubclassOfFoo.return_self
as being of typeFoo
rather thanSubclassOfFoo
.Other common use cases include:
classmethod
s that are used as alternative constructors and return instances of thecls
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 thetype
statement, which creates instances ofTypeAliasType
and which natively supports forward references. Note that whileTypeAlias
andTypeAliasType
serve similar purposes and have similar names, they are distinct and the latter is not the type of the former. Removal ofTypeAlias
is not currently planned, but users are encouraged to migrate totype
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。
- 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 shorthandint | 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 andParamSpec
to annotate a higher-order callable which adds, removes, or transforms parameters of another callable. Usage is in the formConcatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable]
.Concatenate
is currently only valid when used as the first argument to a Callable. The last parameter toConcatenate
must be aParamSpec
or ellipsis (...
).For example, to annotate a decorator
with_lock
which provides athreading.Lock
to the decorated function,Concatenate
can be used to indicate thatwith_lock
expects a callable which takes in aLock
as the first argument, and returns a callable with a different type signature. In this case, theParamSpec
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
andConcatenate
)
- 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。Added in version 3.8.
- 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.
- 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.
- typing.Required¶
Special typing construct to mark a
TypedDict
key as required.This is mainly useful for
total=False
TypedDicts. SeeTypedDict
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.Annotated¶
Special typing form to add context-specific metadata to an annotation.
Add metadata
x
to a given typeT
by using the annotationAnnotated[T, x]
. Metadata added usingAnnotated
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 asT
. 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 ofT
, as type checkers will simply ignore the metadatax
. 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 anAnnotated
type can scan through the metadata elements to determine if they are of interest (e.g., usingisinstance()
).- 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 typeMultiple metadata elements can be supplied (
Annotated
supports variadic arguments):@dataclass class ctype: kind: str Annotated[int, ValueRange(3, 10), ctype("char")]
It is up to the tool consuming the annotations to decide whether the client is allowed to add multiple metadata elements to one annotation and how to merge those annotations.
Annotated
must be subscripted with at least two arguments (Annotated[int]
is not valid)The order of the metadata elements is preserved and matters for equality checks:
assert Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[ int, ctype("char"), ValueRange(3, 10) ]
Nested
Annotated
types are flattened. The order of the metadata elements starts with the innermost annotation:assert Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[ int, ValueRange(3, 10), ctype("char") ]
Duplicated metadata elements are not removed:
assert Annotated[int, ValueRange(3, 10)] != Annotated[ int, ValueRange(3, 10), ValueRange(3, 10) ]
Annotated
can be used with nested and generic aliases:@dataclass class MaxLen: value: int type Vec[T] = Annotated[list[tuple[T, T]], MaxLen(10)] # When used in a type annotation, a type checker will treat "V" the same as # ``Annotated[list[tuple[int, int]], MaxLen(10)]``: type V = Vec[int]
Annotated
cannot be used with an unpackedTypeVarTuple
:type Variadic[*Ts] = Annotated[*Ts, Ann1] # NOT valid
This would be equivalent to:
Annotated[T1, T2, T3, ..., Ann1]
where
T1
,T2
, etc. areTypeVars
. This would be invalid: only one type should be passed to Annotated.By default,
get_type_hints()
strips the metadata from annotations. Passinclude_extras=True
to have the metadata preserved:>>> from typing import Annotated, get_type_hints >>> def func(x: Annotated[int, "metadata"]) -> None: pass ... >>> get_type_hints(func) {'x': <class 'int'>, 'return': <class 'NoneType'>} >>> get_type_hints(func, include_extras=True) {'x': typing.Annotated[int, 'metadata'], 'return': <class 'NoneType'>}
At runtime, the metadata associated with an
Annotated
type can be retrieved via the__metadata__
attribute:>>> from typing import Annotated >>> X = Annotated[int, "very", "important", "metadata"] >>> X typing.Annotated[int, 'very', 'important', 'metadata'] >>> X.__metadata__ ('very', 'important', 'metadata')
At runtime, if you want to retrieve the original type wrapped by
Annotated
, use the__origin__
attribute:>>> from typing import Annotated, get_origin >>> Password = Annotated[str, "secret"] >>> Password.__origin__ <class 'str'>
Note that using
get_origin()
will returnAnnotated
itself:>>> get_origin(Password) <class '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.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 insideTypeGuard
.
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 inTypeGuard
maps to the type of the second parameter (aftercls
orself
).In short, the form
def foo(arg: TypeA) -> TypeGuard[TypeB]: ...
, means that iffoo(arg)
returnsTrue
, thenarg
narrows fromTypeA
toTypeB
.注釈
TypeB
need not be a narrower form ofTypeA
-- it can even be a wider form. The main reason is to allow for things like narrowinglist[object]
tolist[str]
even though the latter is not a subtype of the former, sincelist
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.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 usingUnpack
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 oftyping.TypeVarTuple
andbuiltins.tuple
types. You might seeUnpack
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 withtyping.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)¶
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 bound and constrained type variables:
class StrSequence[S: str]: # S is a TypeVar bound to 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 bound, constrained, or neither, but cannot be both bound 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 passingcovariant=True
orcontravariant=True
. By default, manually created type variables are invariant. See PEP 484 and PEP 695 for more details.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:
# 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 raiseTypeError
.- __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 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).
- __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).
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.
- class typing.TypeVarTuple(name)¶
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
*
intuple[T, *Ts]
. Conceptually, you can think ofTs
as a tuple of type variables(T1, T2, ...)
.tuple[T, *Ts]
would then becometuple[T, *(T1, T2, ...)]
, which is equivalent totuple[T, T1, T2, ...]
. (Note that in older versions of Python, you might see this written usingUnpack
instead, asUnpack[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 areint
-*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 tocall_soon
match the types of the (positional) arguments ofcallback
.See PEP 646 for more details on type variable tuples.
- __name__¶
The name of the type variable tuple.
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.
- class typing.ParamSpec(name, *, bound=None, covariant=False, contravariant=False)¶
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 toCallable
, or as parameters for user-defined Generics. SeeGeneric
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 aTypeVar
with boundCallable[..., Any]
. However this causes two problems:The type checker can't type check the
inner
function because*args
and**kwargs
have to be typedAny
.cast()
may be required in the body of theadd_logging
decorator when returning theinner
function, or the static type checker must be told to ignore thereturn inner
.
- args¶
- kwargs¶
Since
ParamSpec
captures both positional and keyword parameters,P.args
andP.kwargs
can be used to split aParamSpec
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
andP.kwargs
are instances respectively ofParamSpecArgs
andParamSpecKwargs
.
- __name__¶
The name of the parameter specification.
Parameter specification variables created with
covariant=True
orcontravariant=True
can be used to declare covariant or contravariant generic types. Thebound
argument is also accepted, similar toTypeVar
. 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.
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
andConcatenate
)
- typing.ParamSpecArgs¶
- typing.ParamSpecKwargs¶
Arguments and keyword arguments attributes of a
ParamSpec
. TheP.args
attribute of aParamSpec
is an instance ofParamSpecArgs
, andP.kwargs
is an instance ofParamSpecKwargs
. 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 originalParamSpec
:>>> 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 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
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.
- 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 aNewType
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.
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
詳細については PEP 544 を参照してください。
runtime_checkable()
(後で説明します) でデコレートされたプロトコルクラスは、与えられたメソッドがあることだけを確認し、その型シグネチャは全く見ない安直な動作をする実行時プロトコルとして振る舞います。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()
andissubclass()
. This raisesTypeError
when applied to a non-protocol class. This allows a simple-minded structural check, very similar to "one trick ponies" incollections.abc
such asIterable
. 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 anissubclass()
check against Callable. However, thessl.SSLObject.__init__
method exists only to raise aTypeError
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 anisinstance()
check against a non-protocol class. Consider using alternative idioms such ashasattr()
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 usesinspect.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')
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)
Deprecated since version 3.11, will be removed in version 3.13: 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 usingNotRequired
: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 thelabel
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 literalFalse
orTrue
as the value of thetotal
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 usingRequired
: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 otherTypedDict
types using the class-based syntax. Usage:class Point3D(Point2D): z: int
Point3D
has three items:x
,y
andz
. 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 forGeneric
. 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 fromGeneric
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 thetotal
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 currentTypedDict
class, not whether the class is semantically total. For example, aTypedDict
with__total__
set toTrue
may have keys marked withNotRequired
, or it may inherit from anotherTypedDict
withtotal=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__
andPoint2D.__optional_keys__
returnfrozenset
objects containing required and non-required keys, respectively.Keys marked with
Required
will always appear in__required_keys__
and keys marked withNotRequired
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 aTypedDict
with one value for thetotal
argument and then inheriting from it in anotherTypedDict
with a different value fortotal
:>>> 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 theTypedDict
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.
他の例や、
TypedDict
を扱う詳細な規則については PEP 589 を参照してください。Added in version 3.8.
バージョン 3.11 で変更: Added support for marking individual keys as
Required
orNotRequired
. See PEP 655.バージョン 3.11 で変更: Added support for generic
TypedDict
s.
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 for working with IO¶
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 anint
or astr
, 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 forarg
was insteadint | str | float
, the type checker would emit an error pointing out thatunreachable
is of typefloat
. For a call toassert_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 fromtyping
. Importing the name fromtyping
, 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 acceptid
andname
.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
, andslots
. It must be possible for the value of these arguments (True
orFalse
) 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 beTrue
orFalse
if it is omitted by the caller. Defaults toTrue
.order_default (bool) -- Indicates whether the
order
parameter is assumed to beTrue
orFalse
if it is omitted by the caller. Defaults toFalse
.kw_only_default (bool) -- Indicates whether the
kw_only
parameter is assumed to beTrue
orFalse
if it is omitted by the caller. Defaults toFalse
.frozen_default (bool) --
Indicates whether the
frozen
parameter is assumed to beTrue
orFalse
if it is omitted by the caller. Defaults toFalse
.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:
¶ Parameter name
説明
init
Indicates whether the field should be included in the synthesized
__init__
method. If unspecified,init
defaults toTrue
.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
nordefault_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. IfFalse
, it will not be keyword-only. If unspecified, the value of thekw_only
parameter on the object decorated withdataclass_transform
will be used, or if that is unspecified, the value ofkw_only_default
ondataclass_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 raiseNotImplementedError
.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 toTrue
on the decorated object. Thus, a check likeif getattr(obj, "__final__", False)
can be used at runtime to determine whether an objectobj
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.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 toTrue
on the decorated object. Thus, a check likeif getattr(obj, "__override__", False)
can be used at runtime to determine whether an objectobj
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 withtypes.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 fromC
's base classes with those onC
directly. This is done by traversingC.__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, ...]
withT
, unless include_extras is set toTrue
(seeAnnotated
for more information).
See also
inspect.get_annotations()
, a lower-level function that returns annotations more directly.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 underif TYPE_CHECKING
.バージョン 3.9 で変更: Added
include_extras
parameter as part of PEP 593. See the documentation onAnnotated
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 toNone
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, ...]
returnX
.If
X
is a typing-module alias for a builtin orcollections
class, it will be normalized to the original class. IfX
is an instance ofParamSpecArgs
orParamSpecKwargs
, return the underlyingParamSpec
. ReturnNone
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 orLiteral
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.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 intoList[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 intolist[ForwardRef("SomeClass")]
and thus will not automatically resolve tolist[SomeClass]
.Added in version 3.7.4.
定数¶
- typing.TYPE_CHECKING¶
A special constant that is assumed to be
True
by 3rd party static type checkers. It isFalse
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)。Added in version 3.5.2.
非推奨のエイリアス¶
このモジュールは、既存の標準ライブラリ・クラスに対するいくつかの非推奨エイリアスを定義しています。これらは元々、 []
を使ったジェネリッククラスのパラメータ化をサポートするためにtypingモジュールに含まれていました。しかしこのエイリアスは、Python 3.9 で既存の相当するクラスが []
をサポートするように拡張されたため、冗長な書き方になりました( PEP 585 を参照)。
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 usedict
ortyping.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
orIterable
rather than to uselist
ortyping.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 useset
ortyping.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
andTuple
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
ortyping.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¶
Deprecated since version 3.8, will be removed in version 3.13: The
typing.io
namespace is deprecated and will be removed. These types should be directly imported fromtyping
instead.
- class typing.Pattern¶
- class typing.Match¶
Deprecated aliases corresponding to the return types from
re.compile()
andre.match()
.These types (and the corresponding functions) are generic over
AnyStr
.Pattern
can be specialised asPattern[str]
orPattern[bytes]
;Match
can be specialised asMatch[str]
orMatch[bytes]
.Deprecated since version 3.8, will be removed in version 3.13: The
typing.re
namespace is deprecated and will be removed. These types should be directly imported fromtyping
instead.バージョン 3.9 で非推奨: Classes
Pattern
andMatch
fromre
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 forunicode
.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 ofText
.
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])¶
この型は
bytes
とbytearray
、バイト列のmemoryview
を表します。Deprecated since version 3.9, will be removed in version 3.14: Prefer
collections.abc.Buffer
, or a union likebytes | 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。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
andtyping.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
andtyping.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。
- 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
andtyping.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
andtyping.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。
- 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])¶
contextlib.AbstractContextManager
の非推奨なエイリアス。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。
- class typing.AsyncContextManager(Generic[T_co])¶
contextlib.AbstractAsyncContextManager
の非推奨なエイリアス。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。
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 |
---|---|---|---|
|
3.8 |
3.13 |
|
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) |
|
3.9 |
3.14 |
||
3.11 |
未定 |
||
3.12 |
未定 |
||
3.12 |
未定 |