`typing` —— 类型注解支持¶

3.5 新版功能.

备注

Python 运行时不强制执行函数和变量类型注解，但这些注解可用于类型检查器、IDE、静态检查器等第三方工具。

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.

下面的函数接收与返回的都是字符串，注解方式如下：

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

greeting 函数中，参数 name 的类型是 str，返回类型也是 str。子类型也可以当作参数。

新的功能频繁地被添加到 typing 模块中。typing_extensions 包提供了这些新功能对旧版本 Python 的向后移植。

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

参见

"Typing cheat sheet": A quick overview of type hints (hosted at the mypy docs)
"Type System Reference" section of the mypy docs: The Python typing system is standardised via PEPs, so this reference should broadly apply to most Python type checkers. (Some parts may still be specific to mypy.)
"Static Typing with Python": Type-checker-agnostic documentation written by the community detailing type system features, useful typing related tools and typing best practices.

类型别名¶

把类型赋给别名，就可以定义类型别名。本例中，Vector 和 list[float] 相同，可互换：

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])

类型别名适用于简化复杂的类型签名。例如：

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¶

使用 NewType 助手来创建不同的类型

from typing import NewType

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

静态类型检查器把新类型当作原始类型的子类，这种方式适用于捕捉逻辑错误：

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 类型的变量可执行所有 int 操作，但返回结果都是 int 类型。这种方式允许在预期 int 时传入 UserId，还能防止意外创建无效的 UserId：

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

注意，这些检查只由静态类型检查器强制执行。在运行时，语句 Derived = NewType('Derived', Base) 将产生一个 Derived 可调用对象，该对象立即返回你传递给它的任何参数。这意味着语句 Derived(some_value) 不会创建一个新的类，也不会引入超出常规函数调用的很多开销。

更确切地说，在运行时，some_value is Derived(some_value) 表达式总为 True。

创建 Derived 的子类型是无效的:

from typing import NewType

UserId = NewType('UserId', int)

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

然而，我们可以在 "派生的" NewType 的基础上创建一个 NewType。

from typing import NewType

UserId = NewType('UserId', int)

ProUserId = NewType('ProUserId', UserId)

同时，ProUserId 的类型检查也可以按预期执行。

详见 PEP 484。

备注

回顾上文，类型别名声明了两种彼此等价的类型。 Alias = Original 时，静态类型检查器认为 Alias 与 Original 完全等价。这种方式适用于简化复杂类型签名。

反之，NewType 声明把一种类型当作另一种类型的 子类型。Derived = NewType('Derived', Original) 时，静态类型检查器把 Derived 当作 Original 的子类，即，Original 类型的值不能用在预期 Derived 类型的位置。这种方式适用于以最小运行时成本防止逻辑错误。

3.5.2 新版功能.

在 3.10 版更改: NewType is now a class rather than a function. As a result, there is some additional runtime cost when calling NewType over a regular function.

在 3.11 版更改: The performance of calling NewType has been restored to its level in Python 3.9.

Annotating callable objects¶

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

For example:

from collections.abc import Callable, Awaitable

def feeder(get_next_item: Callable[[], str]) -> None:
    ...  # Body

def async_query(on_success: Callable[[int], None],
                on_error: Callable[[int, Exception], None]) -> None:
    ...  # Body

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

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

The subscription syntax must always be used with exactly two values: the argument list and the return type. The argument list must be a list of types, a ParamSpec, Concatenate, or an ellipsis. The return type must be a single type.

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

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

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

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

from collections.abc import Iterable
from typing import Protocol

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

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

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

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

以其他可调用对象为参数的可调用对象可以使用 ParamSpec 来表明其参数类型是相互依赖的。此外，如果该可调用对象增加或删除了其他可调用对象的参数，可以使用 Concatenate 操作符。它们分别采取``Callable[ParamSpecVariable, ReturnType]`` 和 Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType] 的形式。

在 3.10 版更改: Callable now supports ParamSpec and Concatenate. See PEP 612 for more details.

参见

ParamSpec 和 Concatenate 的文档提供了在 Callable 中使用的例子。

泛型（Generic）¶

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

from collections.abc import Mapping, Sequence

class Employee: ...

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

typing 模块中推出的 TypeVar 工厂函数实现泛型参数化。

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]

Annotating tuples¶

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

from collections.abc import Mapping

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

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

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

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

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

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

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

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

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

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

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

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

The type of class objects¶

A variable annotated with C may accept a value of type C. In contrast, a variable annotated with type[C] (or 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.

用户定义的泛型类型¶

用户定义的类可以定义为泛型类。

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] 是定义类 LoggedVar 的基类，该类使用单类型参数 T。在该类体内，T 是有效的类型。

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)

一个泛型可以有任何数量的类型变量。所有种类的 TypeVar 都可以作为泛型的参数:

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 类型变量的参数应各不相同。下列代码就是无效的：

from typing import TypeVar, Generic
...

T = TypeVar('T')

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

Generic 支持多重继承：

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]):
    ...

比如，本例中 MyDict 调用的单参数，T。

Using a generic class without specifying type parameters assumes Any for each position. In the following example, MyIterable is not generic but implicitly inherits from Iterable[Any]:

from collections.abc import Iterable

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

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

from collections.abc import Iterable
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)

在 3.7 版更改: Generic 不再支持自定义元类。

用户定义的参数表达式的泛型也通过 Generic[P] 形式的参数规范变量来支持。该行为与上面描述的类型变量一致，因为参数规范变量被类型化模块视为一个专门的类型变量。这方面的一个例外是，一个类型列表可以用来替代 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'>)]

此外，一个只有一个参数规范变量的泛型将接受表格 X[[Type1, Type2, ...]] 中的参数列表，出于美观的考虑也包括 X[Type1, Type2, ...] 。在内部，后者被转换为前者，所以下面的内容是等价的:

>>> 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.

在 3.10 版更改: Generic 现在可以通过参数表达式进行参数化。参见 ParamSpec 和 PEP 612 以了解更多细节。

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` 类型¶

Any 是一种特殊的类型。静态类型检查器认为所有类型均与 Any 兼容，同样，Any 也与所有类型兼容。

也就是说，可对 Any 类型的值执行任何操作或方法调用，并赋值给任意变量：

from typing import Any

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

s: str = ''
s = a           # OK

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

Notice that no type checking is performed when assigning a value of type Any to a more precise type. For example, the static type checker did not report an error when assigning a to s even though s was declared to be of type str and receives an int value at runtime!

此外，未指定返回值与参数类型的函数，都隐式地默认使用 Any：

def legacy_parser(text):
    ...
    return data

# A static type checker will treat the above
# as having the same signature as:
def legacy_parser(text: Any) -> Any:
    ...
    return data

需要混用动态与静态类型代码时，此操作把 Any 当作 应急出口。

Any 和 object 的区别。与 Any 相似，所有类型都是 object 的子类型。然而，与 Any 不同，object 不可逆：object 不是其它类型的子类型。

就是说，值的类型是 object 时，类型检查器几乎会拒绝所有对它的操作，并且，把它赋给更精确的类型变量（或返回值）属于类型错误。例如：

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")

使用 object，说明值能以类型安全的方式转为任何类型。使用 Any，说明值是动态类型。

名义子类型 vs 结构子类型¶

最初 PEP 484 将 Python 静态类型系统定义为使用 名义子类型。这意味着当且仅当类 A 是 B 的子类时，才满足有类 B 预期时使用类 A 。

此项要求以前也适用于抽象基类，例如，Iterable 。这种方式的问题在于，定义类时必须显式说明，既不 Pythonic，也不是动态类型式 Python 代码的惯用写法。例如，下列代码就遵从了 PEP 484 的规范：

from collections.abc import Sized, Iterable, Iterator

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

PEP 544 允许用户在类定义时不显式说明基类，从而解决了这一问题，静态类型检查器隐式认为 Bucket 既是 Sized 的子类型，又是 Iterable[int] 的子类型。这就是 结构子类型 （又称为静态鸭子类型）：

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

此外，结构子类型的优势在于，通过继承特殊类 Protocol ，用户可以定义新的自定义协议（见下文中的例子）。

模块内容¶

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

特殊类型原语¶

特殊类型¶

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

typing.Any¶

不受限的特殊类型。

所有类型都与 Any 兼容。
Any 与所有类型都兼容。

在 3.11 版更改: 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.

例如：

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

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.

Example:

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

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

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

See PEP 675 for more details.

3.11 新版功能.

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

3.11 新版功能: 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.

例如：

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.

3.5.4 新版功能.

3.6.2 新版功能.

typing.Self¶

Special type to represent the current enclosed class.

例如：

from typing import Self

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

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 currently follows the pattern of:

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

You should use Self as calls to SubclassOfFoo.return_self would have Foo as the return type and not 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.

See PEP 673 for more details.

3.11 新版功能.

typing.TypeAlias¶

Special annotation for explicitly declaring a type alias.

例如：

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: ...

See PEP 613 for more details.

3.10 新版功能.

特殊形式¶

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

typing.Union¶

联合类型； Union[X, Y] 等价于 X | Y ，意味着满足 X 或 Y 之一。

要定义一个联合类型，可以使用类似 Union[int, str] 或简写 int | str。建议使用这种简写。细节:

参数必须是某种类型，且至少有一个。

联合类型之联合类型会被展平，例如：

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

单参数之联合类型就是该参数自身，例如：

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

冗余的参数会被跳过，例如：

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

比较联合类型，不涉及参数顺序，例如：
```
Union[int, str] == Union[str, int]
```
Union 不能作为子类，也不能实例化。
不支持 Union[X][Y] 这种写法。

在 3.7 版更改: 在运行时，不要移除联合类型中的显式子类。

在 3.10 版更改: 联合类型现在可以写成 X | Y。参见联合类型表达式。

typing.Optional¶

Optional[X] 等价于 X | None （或 Union[X, None] ）。

注意，可选类型与含默认值的可选参数不同。含默认值的可选参数不需要在类型注解上添加 Optional 限定符，因为它仅是可选的。例如：

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

另一方面，显式应用 None 值时，不管该参数是否可选， Optional 都适用。例如：

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

在 3.10 版更改: 可选参数现在可以写成 X | None。参见联合类型表达式。

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 (...).

例如，为了注释一个装饰器 with_lock，它为被装饰的函数提供了 threading.Lock，Concatenate 可以用来表示 with_lock 期望一个可调用对象，该对象接收一个 Lock 作为第一个参数，并返回一个具有不同类型签名的可调用对象。在这种情况下，ParamSpec 表示返回的可调用对象的参数类型取决于被传入的可调用程序的参数类型:

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])

3.10 新版功能.

参见

PEP 612 -- Parameter Specification Variables (the PEP which introduced ParamSpec and Concatenate)
ParamSpec
Annotating callable objects

typing.Literal¶

Special typing form to define "literal types".

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

例如：

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[...] 不能创建子类。在运行时，任意值均可作为 Literal[...] 的类型参数，但类型检查器可以对此加以限制。字面量类型详见 PEP 586 。

3.8 新版功能.

在 3.9.1 版更改: Literal 现在能去除形参的重复。 Literal 对象的相等性比较不再依赖顺序。现在如果有某个参数不为 hashable，Literal 对象在相等性比较期间将引发 TypeError。

typing.ClassVar¶

标记类变量的特殊类型构造器。

如 PEP 526 所述，打包在 ClassVar 内的变量注解是指，给定属性应当用作类变量，而不应设置在类实例上。用法如下：

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

ClassVar 仅接受类型，也不能使用下标。

ClassVar 本身不是类，不应用于 isinstance() 或 issubclass()。ClassVar 不改变 Python 运行时行为，但可以用于第三方类型检查器。例如，类型检查器会认为以下代码有错：

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

3.5.3 新版功能.

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.

例如：

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

这些属性没有运行时检查。详见 PEP 591。

3.8 新版功能.

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.

3.11 新版功能.

typing.NotRequired¶

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

See TypedDict and PEP 655 for more details.

3.11 新版功能.

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 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:

Annotated 的第一个参数必须是有效类型。
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')

参见

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

3.9 新版功能.

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.

PX旨在使 类型缩小 受益--这是静态类型检查器用来确定程序代码流中表达式的更精确类型的一种技术。通常，类型缩小是通过分析条件性代码流并将缩小的结果应用于一个代码块来完成的。这里的条件表达式有时被称为 "类型保护":

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``.
        ...

有时，使用一个用户定义的布尔函数作为类型保护会很方便。这样的函数应该使用 TypeGuard[...] 作为其返回类型，以提醒静态类型检查器注意这一意图。

对于一个给定的函数，使用 -> TypeGuard 告诉静态类型检查器:

返回值是一个布尔值。
如果返回值是 True，其参数的类型是 TypeGuard 里面的类型。

例如：

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!")

如果 is_str_list 是一个类或实例方法，那么 TypeGuard 中的类型映射到 cls 或 self 之后的第二个参数的类型。

简而言之，def foo(arg: TypeA) -> TypeGuard[TypeB]: ... 形式的意思是：如果 foo(arg) 返回 True，那么 arg 将把 TypeA 缩小为 TypeB。

备注

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.

3.10 新版功能.

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

3.11 新版功能.

构建泛型类型¶

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¶

用于泛型类型的抽象基类。

泛型类型一般通过继承含一个或多个类型变量的类实例进行声明。例如，泛型映射类型定义如下：

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

该类的用法如下：

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)¶

类型变量。

用法：

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

请注意，类型变量可以是 被绑定的 ， 被约束的 ，或者两者都不是，但不能既是被绑定的又是被约束的。

类型变量可以通过传递 covariant=True 或 contravariant=True 来标记协变或反变。更多细节请参见 PEP 484 。默认情况下，类型变量是不变的。

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

类型变量可以被绑定到具体类型、抽象类型（ ABC 或 protocol ），甚至是类型的联合:

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.

用法：

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.

3.11 新版功能.

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

参数规范变量。 类型变量 的一个专门版本。

用法：

P = ParamSpec('P')

参数规范变量的存在主要是为了使静态类型检查器受益。它们被用来将一个可调用对象的参数类型转发给另一个可调用对象的参数类型——这种模式通常出现在高阶函数和装饰器中。它们只有在 Concatenate 中使用时才有效，或者作为 Callable 的第一个参数，或者作为用户定义的泛型的参数。参见 Generic 以了解更多关于泛型的信息。

例如，为了给一个函数添加基本的日志记录，我们可以创建一个装饰器 add_logging 来记录函数调用。参数规范变量告诉类型检查器，传入装饰器的可调用对象和由其返回的新可调用对象有相互依赖的类型参数:

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

如果没有 ParamSpec，以前注释这个的最简单的方法是使用一个 TypeVar 与绑定 Callable[..., Any]。

类型检查器不能对 inner 函数进行类型检查，因为 *args 和 **kwargs 的类型必须是 Any。
cast() 在返回 inner 函数时，可能需要在 add_logging 装饰器的主体中进行，或者必须告诉静态类型检查器忽略 return inner。

args¶

kwargs¶: 由于 ParamSpec 同时捕获了位置参数和关键字参数，P.args 和 P.kwargs 可以用来将 ParamSpec 分割成其组成部分。 P.args 代表给定调用中的位置参数的元组，只能用于注释 *args。 P.kwargs 代表给定调用中的关键字参数到其值的映射，只能用于注释 **kwargs。在运行时，P.args 和 P.kwargs 分别是 ParamSpecArgs 和 ParamSpecKwargs 的实例。

__name__¶: The name of the parameter specification.

用 covariant=True 或 contravariant=True 创建的参数规范变量可以用来声明协变或禁变的通用类型。参数 bound 也被接受，类似于 TypeVar。然而这些关键字的实际语义还有待决定。

3.10 新版功能.

备注

只有在全局范围内定义的参数规范变量可以被 pickle。

参见

PEP 612 -- Parameter Specification Variables (the PEP which introduced ParamSpec and Concatenate)
Concatenate
Annotating callable objects

typing.ParamSpecArgs¶

typing.ParamSpecKwargs¶

ParamSpec`的参数和关键字参数属性。``ParamSpec` 的 P.args 属性是 ParamSpecArgs 的一个实例，P.kwargs 是 ParamSpecKwargs 的一个实例。它们的目的是用于运行时内部检查的，对静态类型检查器没有特殊意义。

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

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

3.10 新版功能.

其他特殊指令¶

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() 的类型版本。

用法：

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

这相当于：

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

为字段提供默认值，要在类体内赋值：

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

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

带默认值的字段必须在不带默认值的字段后面。

由此产生的类有一个额外的属性 __annotations__ ，给出一个 dict ，将字段名映射到字段类型。（字段名在 _fields 属性中，默认值在 _field_defaults 属性中，这两者都是 namedtuple() API 的一部分。）

NamedTuple 子类也支持文档字符串与方法：

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]

反向兼容用法：

Employee = NamedTuple('Employee', [('name', str), ('id', int)])

在 3.6 版更改: 添加了对 PEP 526 中变量注解句法的支持。

在 3.6.1 版更改: 添加了对默认值、方法、文档字符串的支持。

在 3.8 版更改: _field_types 和 __annotations__ 属性现已使用常规字典，不再使用 OrderedDict 实例。

在 3.9 版更改: 移除了 _field_types 属性，改用具有相同信息，但更标准的 __annotations__ 属性。

在 3.11 版更改: 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.

用法：

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.

3.5.2 新版功能.

在 3.10 版更改: NewType 现在是一个类而不是函数。

class typing.Protocol(Generic)¶

Base class for protocol classes.

Protocol classes are defined like this:

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

这些类主要与静态类型检查器搭配使用，用来识别结构子类型（静态鸭子类型），例如：

class C:
    def meth(self) -> int:
        return 0

def func(x: Proto) -> int:
    return x.meth()

func(C())  # Passes static type check

See PEP 544 for more details. Protocol classes decorated with runtime_checkable() (described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures.

Protocol 类可以是泛型，例如：

T = TypeVar("T")

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

3.8 新版功能.

@typing.runtime_checkable¶

用于把 Protocol 类标记为运行时协议。

该协议可以与 isinstance() 和 issubclass() 一起使用。应用于非协议的类时，会触发 TypeError。该指令支持简易结构检查，与 collections.abc 的 Iterable 非常类似，只擅长做一件事。例如：

@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)

备注

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.

备注

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.

3.8 新版功能.

class typing.TypedDict(dict)¶

把类型提示添加至字典的特殊构造器。在运行时，它是纯 dict。

TypedDict 声明一个字典类型，该类型预期所有实例都具有一组键集，其中，每个键都与对应类型的值关联。运行时不检查此预期，而是由类型检查器强制执行。用法如下：

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)

从版本 3.11 开始标记为过时，将在版本 3.13 中移除。: 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.

当任何一个键不是有效的标识符时，例如因为它们是关键字或包含连字符，也应该使用函数式语法。例子:

# 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)

这意味着一个 Point2D TypedDict 可以省略任何一个键。类型检查器只需要支持一个字面的 False 或 True 作为 total 参数的值。 True 是默认的，它使类主体中定义的所有项目都是必需的。

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)

一个 TypedDict 类型有可能使用基于类的语法从一个或多个其他 TypedDict 类型继承。用法:

class Point3D(Point2D):
    z: int

Point3D 有三个项目 : x , y 和 z 。其等价于定义:

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

T = TypeVar('T')
class XT(X, Generic[T]): pass  # raises TypeError

A TypedDict can be generic:

T = TypeVar("T")

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

TypedDict 可以通过注解字典（参见对象注解属性的最佳实践了解更多关于注解的最佳实践）、 __total__ 、 __required_keys__ 和 __optional_keys__ 进行内省。

__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

__required_keys__¶: 3.9 新版功能.

__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

3.9 新版功能.

更多示例与 TypedDict 的详细规则，详见 PEP 589。

3.8 新版功能.

在 3.11 版更改: Added support for marking individual keys as Required or NotRequired. See PEP 655.

在 3.11 版更改: Added support for generic TypedDicts.

协议¶

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

class typing.SupportsAbs¶: 含抽象方法 __abs__ 的抽象基类，是其返回类型里的协变量。

class typing.SupportsBytes¶: 含抽象方法 __bytes__ 的抽象基类。

class typing.SupportsComplex¶: 含抽象方法 __complex__ 的抽象基类。

class typing.SupportsFloat¶: 含抽象方法 __float__ 的抽象基类。

class typing.SupportsIndex¶: 含抽象方法 __index__ 的抽象基类。

3.8 新版功能.

class typing.SupportsInt¶: 含抽象方法 __int__ 的抽象基类。

class typing.SupportsRound¶: 含抽象方法 __round__ 的抽象基类，是其返回类型的协变量。

ABCs for working with IO¶

class typing.IO¶
class typing.TextIO¶
class typing.BinaryIO¶: 泛型类型 IO[AnyStr] 及其子类 TextIO(IO[str]) 与 BinaryIO(IO[bytes]) 表示 I/O 流的类型，例如 open() 所返回的对象。

函数与装饰器¶

typing.cast(typ, val)¶

把值强制转换为类型。

不变更返回值。对类型检查器而言，代表了返回值具有指定的类型，但运行时故意不做任何检查（以便让检查速度尽量快）。

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)

3.11 新版功能.

typing.assert_never(arg, /)¶

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

Example:

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.

参见

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

3.11 新版功能.

typing.reveal_type(obj, /)¶

Reveal the inferred static type of an expression.

When a static type checker encounters a call to this function, it emits a diagnostic with the 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.

The function returns its argument unchanged, which allows using it within an expression:

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

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

At runtime, this function prints the runtime type of its argument to stderr and returns it unchanged:

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

3.11 新版功能.

@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:

参数

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	Description
`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.

See PEP 681 for more details.

3.11 新版功能.

@typing.overload¶

Decorator for creating overloaded functions and methods.

The @overload decorator allows describing functions and methods that support multiple different combinations of argument types. A series of @overload-decorated definitions must be followed by exactly one non-@overload-decorated definition (for the same function/method).

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

An example of overload that gives a more precise type than can be expressed using a union or a type variable:

@overload
def process(response: None) -> None:
    ...
@overload
def process(response: int) -> tuple[int, str]:
    ...
@overload
def process(response: bytes) -> str:
    ...
def process(response):
    ...  # actual implementation goes here

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

在 3.11 版更改: 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.

3.11 新版功能.

typing.clear_overloads()¶

Clear all registered overloads in the internal registry.

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

3.11 新版功能.

@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.

例如：

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
    ...

这些属性没有运行时检查。详见 PEP 591。

3.8 新版功能.

在 3.11 版更改: 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¶

标明注解不是类型提示的装饰器。

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¶

让其他装饰器具有 no_type_check() 效果的装饰器。

本装饰器用 no_type_check() 里的装饰函数打包其他装饰器。

@typing.type_check_only¶

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

在运行时，该装饰器本身不可用。实现返回的是私有类实例时，它主要是用于标记在类型存根文件中定义的类。

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

def fetch_response() -> Response: ...

注意，建议不要返回私有类实例，最好将之设为公共类。

内省辅助器¶

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

返回函数、方法、模块、类对象的类型提示的字典。

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']
}

备注

get_type_hints() 在导入的类型别名中不工作，包括前向引用。启用注解的延迟评估（ PEP 563 ）可能会消除对大多数前向引用的需要。

在 3.9 版更改: Added include_extras parameter as part of PEP 593. See the documentation on Annotated for more information.

在 3.11 版更改: 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.

Examples:

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

3.8 新版功能.

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.

Examples:

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

3.8 新版功能.

typing.is_typeddict(tp)¶

检查一个类型是否为 TypedDict。

For example:

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)

3.10 新版功能.

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.

备注

PEP 585 泛型类型例如 list["SomeClass"] 将不会被隐式地转换为 list[ForwardRef("SomeClass")] 因而将不会自动解析为 list[SomeClass]。

3.7.4 新版功能.

常量¶

typing.TYPE_CHECKING¶

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

用法：

if TYPE_CHECKING:
    import expensive_mod

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

第一个类型注解必须用引号标注，才能把它当作“前向引用”，从而在解释器运行时中隐藏 expensive_mod 引用。局部变量的类型注释不会被评估，因此，第二个注解不需要用引号引起来。

备注

使用 from __future__ import 时，函数定义时不处理注解，而是把注解当作字符串存在 __annotations__ 里，这样就不必为注解使用引号。（详见 PEP 563）。

3.5.2 新版功能.

Deprecated aliases¶

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

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

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

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

Aliases to built-in types¶

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

Deprecated alias to dict.

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

该类型用法如下：

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

3.9 版后已移除: builtins.dict now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

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

Deprecated alias to list.

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

该类型用法如下：

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]

3.9 版后已移除: builtins.list now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

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.

3.9 版后已移除: builtins.set now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.FrozenSet(frozenset, AbstractSet[T_co])¶: Deprecated alias to builtins.frozenset.

3.9 版后已移除: builtins.frozenset now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

typing.Tuple¶

Deprecated alias for tuple.

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

3.9 版后已移除: builtins.tuple now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.Type(Generic[CT_co])¶

Deprecated alias to type.

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

3.5.2 新版功能.

3.9 版后已移除: builtins.type now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

Aliases to types in `collections`¶

class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])¶: Deprecated alias to collections.defaultdict.

3.5.2 新版功能.

3.9 版后已移除: collections.defaultdict now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])¶: Deprecated alias to collections.OrderedDict.

3.7.2 新版功能.

3.9 版后已移除: collections.OrderedDict now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.ChainMap(collections.ChainMap, MutableMapping[KT, VT])¶: Deprecated alias to collections.ChainMap.

3.5.4 新版功能.

3.6.1 新版功能.

3.9 版后已移除: collections.ChainMap now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.Counter(collections.Counter, Dict[T, int])¶: Deprecated alias to collections.Counter.

3.5.4 新版功能.

3.6.1 新版功能.

3.9 版后已移除: collections.Counter now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.Deque(deque, MutableSequence[T])¶: Deprecated alias to collections.deque.

3.5.4 新版功能.

3.6.1 新版功能.

3.9 版后已移除: collections.deque now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

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].

从版本 3.8 开始标记为过时，将在版本 3.13 中移除。: typing.re 命名空间已被废弃并将被删除。这些类型应该被直接从 typing 导入。

3.9 版后已移除: re 模块中的 Pattern 与 Match 类现已支持 []。详见 PEP 585 与 GenericAlias 类型。

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.

使用 Text 时，值中必须包含 unicode 字符串，以兼容 Python 2 和 Python 3：

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

3.5.2 新版功能.

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

Aliases to container ABCs in `collections.abc`¶

class typing.AbstractSet(Collection[T_co])¶: Deprecated alias to collections.abc.Set.

3.9 版后已移除: collections.abc.Set now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.ByteString(Sequence[int])¶: 该类型代表了 bytes、bytearray、memoryview 等字节序列类型。

从版本 3.9 开始标记为过时，将在版本 3.14 中移除。: Prefer typing_extensions.Buffer, or a union like bytes | bytearray | memoryview.

class typing.Collection(Sized, Iterable[T_co], Container[T_co])¶: Deprecated alias to collections.abc.Collection.

3.6.0 新版功能.

3.9 版后已移除: collections.abc.Collection now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.Container(Generic[T_co])¶: Deprecated alias to collections.abc.Container.

3.9 版后已移除: collections.abc.Container now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.ItemsView(MappingView, AbstractSet[tuple[KT_co, VT_co]])¶: Deprecated alias to collections.abc.ItemsView.

3.9 版后已移除: collections.abc.ItemsView now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.KeysView(MappingView, AbstractSet[KT_co])¶: Deprecated alias to collections.abc.KeysView.

3.9 版后已移除: collections.abc.KeysView now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

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

Deprecated alias to collections.abc.Mapping.

该类型用法如下：

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

3.9 版后已移除: collections.abc.Mapping now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.MappingView(Sized)¶: Deprecated alias to collections.abc.MappingView.

3.9 版后已移除: collections.abc.MappingView now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.MutableMapping(Mapping[KT, VT])¶: Deprecated alias to collections.abc.MutableMapping.

3.9 版后已移除: collections.abc.MutableMapping now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.MutableSequence(Sequence[T])¶: Deprecated alias to collections.abc.MutableSequence.

3.9 版后已移除: collections.abc.MutableSequence now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.MutableSet(AbstractSet[T])¶: Deprecated alias to collections.abc.MutableSet.

3.9 版后已移除: collections.abc.MutableSet now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.Sequence(Reversible[T_co], Collection[T_co])¶: Deprecated alias to collections.abc.Sequence.

3.9 版后已移除: collections.abc.Sequence now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.ValuesView(MappingView, Collection[_VT_co])¶: Deprecated alias to collections.abc.ValuesView.

3.9 版后已移除: collections.abc.ValuesView now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

Aliases to asynchronous ABCs in `collections.abc`¶

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

Deprecated alias to collections.abc.Coroutine.

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

3.5.3 新版功能.

3.9 版后已移除: collections.abc.Coroutine now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

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

Deprecated alias to collections.abc.AsyncGenerator.

异步生成器可由泛型类型 AsyncGenerator[YieldType, SendType] 注解。例如：

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

与常规生成器不同，异步生成器不能返回值，因此没有 ReturnType 类型参数。与 Generator 类似，SendType 也属于逆变行为。

如果生成器只产生值，可将 SendType 设置为 None：

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

此外，可用 AsyncIterable[YieldType] 或 AsyncIterator[YieldType] 注解生成器的返回类型：

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

3.6.1 新版功能.

3.9 版后已移除: collections.abc.AsyncGenerator now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.AsyncIterable(Generic[T_co])¶: Deprecated alias to collections.abc.AsyncIterable.

3.5.2 新版功能.

3.9 版后已移除: collections.abc.AsyncIterable now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.AsyncIterator(AsyncIterable[T_co])¶: Deprecated alias to collections.abc.AsyncIterator.

3.5.2 新版功能.

3.9 版后已移除: collections.abc.AsyncIterator now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.Awaitable(Generic[T_co])¶: Deprecated alias to collections.abc.Awaitable.

3.5.2 新版功能.

3.9 版后已移除: collections.abc.Awaitable now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

Aliases to other ABCs in `collections.abc`¶

class typing.Iterable(Generic[T_co])¶: Deprecated alias to collections.abc.Iterable.

3.9 版后已移除: collections.abc.Iterable now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.Iterator(Iterable[T_co])¶: Deprecated alias to collections.abc.Iterator.

3.9 版后已移除: collections.abc.Iterator now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

typing.Callable¶

Deprecated alias to collections.abc.Callable.

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

3.9 版后已移除: collections.abc.Callable now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

在 3.10 版更改: Callable now supports ParamSpec and Concatenate. See PEP 612 for more details.

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

Deprecated alias to collections.abc.Generator.

生成器可以由泛型类型 Generator[YieldType, SendType, ReturnType] 注解。例如：

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

注意，与 typing 模块里的其他泛型不同， Generator 的 SendType 属于逆变行为，不是协变行为，也是不变行为。

如果生成器只产生值，可将 SendType 与 ReturnType 设为 None：

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

此外，还可以把生成器的返回类型注解为 Iterable[YieldType] 或 Iterator[YieldType]：

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

3.9 版后已移除: collections.abc.Generator now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

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

class typing.Reversible(Iterable[T_co])¶: Deprecated alias to collections.abc.Reversible.

3.9 版后已移除: collections.abc.Reversible now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

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

Aliases to `contextlib` ABCs¶

class typing.ContextManager(Generic[T_co])¶: Deprecated alias to contextlib.AbstractContextManager.

3.5.4 新版功能.

3.6.0 新版功能.

3.9 版后已移除: contextlib.AbstractContextManager now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

class typing.AsyncContextManager(Generic[T_co])¶: Deprecated alias to contextlib.AbstractAsyncContextManager.

3.5.4 新版功能.

3.6.2 新版功能.

3.9 版后已移除: contextlib.AbstractAsyncContextManager now supports subscripting ([]). See PEP 585 and GenericAlias 类型.

Deprecation Timeline of Major Features¶

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

Feature	Deprecated in	Projected removal	PEP/issue
`typing.io` and `typing.re` submodules	3.8	3.13	bpo-38291
`typing` versions of standard collections	3.9	Undecided (see Deprecated aliases for more information)	PEP 585
`typing.ByteString`	3.9	3.14	gh-91896
`typing.Text`	3.11	Undecided	gh-92332

`typing` —— 类型注解支持¶

相关的 PEP¶

类型别名¶

NewType¶

Annotating callable objects¶

泛型（Generic）¶

Annotating tuples¶

The type of class objects¶

用户定义的泛型类型¶

`Any` 类型¶

名义子类型 vs 结构子类型¶

模块内容¶

特殊类型原语¶

特殊类型¶

特殊形式¶

构建泛型类型¶

其他特殊指令¶

协议¶

ABCs for working with IO¶

函数与装饰器¶

内省辅助器¶

常量¶

Deprecated aliases¶

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¶

Deprecation Timeline of Major Features¶

目录

上一个主题

下一个主题

当前页面

typing —— 类型注解支持¶

相关的 PEP¶

类型别名¶

NewType¶

Annotating callable objects¶

泛型（Generic）¶

Annotating tuples¶

The type of class objects¶

用户定义的泛型类型¶

Any 类型¶

名义子类型 vs 结构子类型¶

模块内容¶

特殊类型原语¶

特殊类型¶

特殊形式¶

构建泛型类型¶

其他特殊指令¶

协议¶

ABCs for working with IO¶

函数与装饰器¶

内省辅助器¶

常量¶

Deprecated aliases¶

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¶

Deprecation Timeline of Major Features¶

`typing` —— 类型注解支持¶

`Any` 类型¶

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¶