typing — 支持类型提示 ¶

特殊类型 ¶

These can be used as types in annotations and do not support [] .

typing. Any ¶

Special type indicating an unconstrained type.

• Every type is compatible with Any .

• Any is compatible with every type.

typing. NoReturn ¶

Special type indicating that a function never returns. For example:

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

3.5.4 版新增。

3.6.2 版新增。

特殊形式 ¶

These can be used as types in annotations using [] , each having a unique syntax.

typing. Tuple ¶

元组类型； Tuple[X, Y] is the type of a tuple of two items with the first item of type X and the second of type Y. The type of the empty tuple can be written as Tuple[()] .

范例： Tuple[T1, T2] is a tuple of two elements corresponding to type variables T1 and T2. Tuple[int, float, str] is a tuple of an int, a float and a string.

To specify a variable-length tuple of homogeneous type, use literal ellipsis, e.g. Tuple[int, ...] . A plain Tuple 相当于 Tuple[Any, ...] , and in turn to tuple .

从 3.9 版起弃用： builtins.tuple 现在支持 [] 。见 PEP 585 and 一般别名类型 .

typing. Union ¶

并集类型； Union[X, Y] means either X or Y.

To define a union, use e.g. Union[int, str] . Details:

• The arguments must be types and there must be at least one.

• Unions of unions are flattened, e.g.:

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

• Unions of a single argument vanish, e.g.:

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

• Redundant arguments are skipped, e.g.:

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

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

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

• You cannot subclass or instantiate a union.

• You cannot write Union[X][Y] .

• 可以使用 Optional[X] as a shorthand for Union[X, None] .

3.7 版改变： Don’t remove explicit subclasses from unions at runtime.

typing. Optional ¶

可选类型。

Optional[X] 相当于 Union[X, None] .

Note that this is not the same concept as an optional argument, which is one that has a default. An optional argument with a default does not require the Optional qualifier on its type annotation just because it is optional. For example:

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

On the other hand, if an explicit value of None is allowed, the use of Optional is appropriate, whether the argument is optional or not. For example:

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

typing. Callable ¶

Callable type; Callable[[int], str] is a function of (int) -> str.

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 or an ellipsis; the return type must be a single type.

There is no syntax to indicate optional or keyword arguments; such function types are rarely used as callback types. Callable[..., ReturnType] (literal ellipsis) can be used to type hint a callable taking any number of arguments and returning ReturnType . A plain Callable 相当于 Callable[..., Any] , and in turn to collections.abc.Callable .

从 3.9 版起弃用： collections.abc.Callable 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. Type ( Generic[CT_co] ) ¶

A variable annotated with C may accept a value of type C . In contrast, a variable annotated with Type[C] may accept values that are classes themselves – specifically, it will accept the class object of C 。例如：

a = 3         # Has type 'int'
b = int       # Has type 'Type[int]'
c = type(a)   # Also has type 'Type[int]'
												

注意， Type[C] 是协变：

class User: ...
class BasicUser(User): ...
class ProUser(User): ...
class TeamUser(User): ...
# Accepts User, BasicUser, ProUser, TeamUser, ...
def make_new_user(user_class: Type[User]) -> User:
    # ...
    return user_class()
												

The fact that Type[C] is covariant implies that all subclasses of C should implement the same constructor signature and class method signatures as C . The type checker should flag violations of this, but should also allow constructor calls in subclasses that match the constructor calls in the indicated base class. How the type checker is required to handle this particular case may change in future revisions of PEP 484 .

The only legal parameters for Type are classes, Any , 类型变量 , and unions of any of these types. For example:

def new_non_team_user(user_class: Type[Union[BasicUser, ProUser]]): ...
												

Type[Any] 相当于 Type which in turn is equivalent to type , which is the root of Python’s metaclass hierarchy.

3.5.2 版新增。

从 3.9 版起弃用： builtins.type 现在支持 [] 。见 PEP 585 and 一般别名类型 .

typing. Literal ¶

A type that can be used to indicate to type checkers that the corresponding variable or function parameter has a value equivalent to the provided literal (or one of several literals). For example:

def validate_simple(data: Any) -> Literal[True]:  # always returns True
    ...
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[...] cannot be subclassed. At runtime, an arbitrary value is allowed as type argument to Literal[...] , but type checkers may impose restrictions. See PEP 586 for more details about literal types.

3.8 版新增。

3.9.1 版改变： Literal now de-duplicates parameters. Equality comparisons of Literal objects are no longer order dependent. Literal objects will now raise a TypeError exception during equality comparisons if one of their parameters are not hashable .

typing. ClassVar ¶

Special type construct to mark class variables.

As introduced in PEP 526 , a variable annotation wrapped in ClassVar indicates that a given attribute is intended to be used as a class variable and should not be set on instances of that class. Usage:

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

ClassVar accepts only types and cannot be further subscribed.

ClassVar is not a class itself, and should not be used with isinstance() or issubclass() . ClassVar does not change Python runtime behavior, but it can be used by third-party type checkers. For example, a type checker might flag the following code as an error:

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

3.5.3 版新增。

typing. Final ¶

A special typing construct to indicate to type checkers that a name cannot be re-assigned or overridden in a subclass. For example:

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
												

There is no runtime checking of these properties. See PEP 591 了解更多细节。

3.8 版新增。

typing. Annotated ¶

A type, introduced in PEP 593 ( Flexible function and variable annotations ), to decorate existing types with context-specific metadata (possibly multiple pieces of it, as Annotated is variadic). Specifically, a type T can be annotated with metadata x via the typehint Annotated[T, x] . This metadata can be used for either static analysis or at runtime. If a library (or tool) encounters a typehint Annotated[T, x] and has no special logic for metadata x , it should ignore it and simply treat the type as T . Unlike the no_type_check functionality that currently exists in the typing module which completely disables typechecking annotations on a function or a class, the Annotated type allows for both static typechecking of T (e.g., via mypy or Pyre, which can safely ignore x ) together with runtime access to x within a specific application.

Ultimately, the responsibility of how to interpret the annotations (if at all) is the responsibility of the tool or library encountering the Annotated type. A tool or library encountering an Annotated type can scan through the annotations to determine if they are of interest (e.g., using isinstance() ).

When a tool or a library does not support annotations or encounters an unknown annotation it should just ignore it and treat annotated type as the underlying type.

It’s up to the tool consuming the annotations to decide whether the client is allowed to have several annotations on one type and how to merge those annotations.

由于 Annotated type allows you to put several annotations of the same (or different) type(s) on any node, the tools or libraries consuming those annotations are in charge of dealing with potential duplicates. For example, if you are doing value range analysis you might allow this:

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

传递 include_extras=True to get_type_hints() lets one access the extra annotations at runtime.

句法细节：

• The first argument to Annotated must be a valid type

• Multiple type annotations are supported ( Annotated supports variadic arguments):

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

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

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

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

• Nested Annotated types are flattened, with metadata ordered starting with the innermost annotation:

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

• Duplicated annotations are not removed:

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

• Annotated can be used with nested and generic aliases:

  T = TypeVar('T')
  Vec = Annotated[list[tuple[T, T]], MaxLen(10)]
  V = Vec[int]
  V == Annotated[list[tuple[int, int]], MaxLen(10)]
  														

3.9 版新增。

构建一般类型 ¶

These are not used in annotations. They are building blocks for creating generic types.

class typing. Generic ¶

用于一般类型的 ABC (抽象基类)。

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

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

This class can then be used as follows:

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 ¶

类型变量。

用法：

T = TypeVar('T')  # Can be anything
A = TypeVar('A', str, bytes)  # Must be 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 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 longest(x: A, y: A) -> A:
    """Return the longest of two strings."""
    return x if len(x) >= len(y) else y
												

The latter example’s signature is essentially the overloading of (str, str) -> str and (bytes, bytes) -> bytes . Also note that if the arguments are instances of some subclass of str , the return type is still plain str .

At runtime, isinstance(x, T) 会引发 TypeError . In general, isinstance() and issubclass() should not be used with types.

Type variables may be marked covariant or contravariant by passing covariant=True or contravariant=True 。见 PEP 484 for more details. By default type variables are invariant. Alternatively, a type variable may specify an upper bound using bound=<type> . This means that an actual type substituted (explicitly or implicitly) for the type variable must be a subclass of the boundary type, see PEP 484 .

typing. AnyStr ¶

AnyStr is a type variable defined as AnyStr = TypeVar('AnyStr', str, bytes) .

It is meant to be used for functions that may accept any kind of string without allowing different kinds of strings to mix. For example:

def concat(a: AnyStr, b: AnyStr) -> AnyStr:
    return a + b
concat(u"foo", u"bar")  # Ok, output has type 'unicode'
concat(b"foo", b"bar")  # Ok, output has type 'bytes'
concat(u"foo", b"bar")  # Error, cannot mix unicode and bytes
												

class typing. Protocol ( Generic ) ¶

Base class for protocol classes. Protocol classes are defined like this:

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

Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:

class C:
    def meth(self) -> int:
        return 0
def func(x: Proto) -> int:
    return x.meth()
func(C())  # Passes static type check
												

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

Protocol classes can be generic, for example:

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

3.8 版新增。

@ typing. runtime_checkable ¶

Mark a protocol class as a runtime protocol.

Such a protocol can be used with isinstance() and issubclass() . This raises TypeError when applied to a non-protocol class. This allows a simple-minded structural check, very similar to “one trick ponies” in collections.abc 譬如 Iterable 。例如：

@runtime_checkable
class Closable(Protocol):
    def close(self): ...
assert isinstance(open('/some/file'), Closable)
												

注意

runtime_checkable() will check only the presence of the required methods, not their type signatures! For example, builtins.complex 实现 __float__() , therefore it passes an issubclass() check against SupportsFloat . However, the complex.__float__ method exists only to raise a TypeError with a more informative message.

3.8 版新增。

其它特殊指令 ¶

These are not used in annotations. They are building blocks for declaring types.

class typing. NamedTuple ¶

Typed version of collections.namedtuple() .

用法：

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

这相当于：

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

To give a field a default value, you can assign to it in the class body:

class Employee(NamedTuple):
    name: str
    id: int = 3
employee = Employee('Guido')
assert employee.id == 3
												

Fields with a default value must come after any fields without a default.

The resulting class has an extra attribute __annotations__ giving a dict that maps the field names to the field types. (The field names are in the _fields attribute and the default values are in the _field_defaults attribute both of which are part of the namedtuple API.)

NamedTuple subclasses can also have docstrings and methods:

class Employee(NamedTuple):
    """Represents an employee."""
    name: str
    id: int = 3
    def __repr__(self) -> str:
        return f'<Employee {self.name}, id={self.id}>'
												

Backward-compatible usage:

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

3.6 版改变： 添加支持 PEP 526 变量注解句法。

3.6.1 版改变： Added support for default values, methods, and docstrings.

3.8 版改变： _field_types and __annotations__ attributes are now regular dictionaries instead of instances of OrderedDict .

3.9 版改变： 移除 _field_types attribute in favor of the more standard __annotations__ attribute which has the same information.

typing. NewType ( name , tp ) ¶

A helper function to indicate a distinct type to a typechecker, see NewType . At runtime it returns a function that returns its argument. Usage:

UserId = NewType('UserId', int)
first_user = UserId(1)
												

3.5.2 版新增。

class typing. TypedDict ( dict ) ¶

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

TypedDict declares a dictionary type that expects all of its instances to have a certain set of keys, where each key is associated with a value of a consistent type. This expectation is not checked at runtime but is only enforced by type checkers. Usage:

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')
												

The type info for introspection can be accessed via Point2D.__annotations__ and Point2D.__total__ . To allow using this feature with older versions of Python that do not support PEP 526 , TypedDict supports two additional equivalent syntactic forms:

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

By default, all keys must be present in a TypedDict. It is possible to override this by specifying totality. Usage:

class point2D(TypedDict, total=False):
    x: int
    y: int
												

This means that a point2D TypedDict can have any of the keys omitted. A type checker is only expected to support a literal False or True as the value of the total argument. True is the default, and makes all items defined in the class body be required.

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

3.8 版新增。

对应内置类型 ¶

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

一般版本的 dict . Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as Mapping .

This type can be used as follows:

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

从 3.9 版起弃用： builtins.dict 现在支持 [] 。见 PEP 585 and 一般别名类型 .

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

Generic version of list . Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as Sequence or Iterable .

此类型可以按如下方式使用：

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]
												

从 3.9 版起弃用： builtins.list 现在支持 [] 。见 PEP 585 and 一般别名类型 .

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

一般版本的 builtins.set . Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as AbstractSet .

从 3.9 版起弃用： builtins.set 现在支持 [] 。见 PEP 585 and 一般别名类型 .

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

一般版本的 builtins.frozenset .

从 3.9 版起弃用： builtins.frozenset 现在支持 [] 。见 PEP 585 and 一般别名类型 .

Corresponding to types in collections ¶

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

一般版本的 collections.defaultdict .

3.5.2 版新增。

从 3.9 版起弃用： collections.defaultdict 现在支持 [] 。见 PEP 585 and 一般别名类型 .

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

一般版本的 collections.OrderedDict .

3.7.2 版新增。

从 3.9 版起弃用： collections.OrderedDict 现在支持 [] 。见 PEP 585 and 一般别名类型 .

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

一般版本的 collections.ChainMap .

3.5.4 版新增。

3.6.1 版新增。

从 3.9 版起弃用： collections.ChainMap 现在支持 [] 。见 PEP 585 and 一般别名类型 .

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

一般版本的 collections.Counter .

3.5.4 版新增。

3.6.1 版新增。

从 3.9 版起弃用： collections.Counter 现在支持 [] 。见 PEP 585 and 一般别名类型 .

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

一般版本的 collections.deque .

3.5.4 版新增。

3.6.1 版新增。

从 3.9 版起弃用： collections.deque 现在支持 [] 。见 PEP 585 and 一般别名类型 .

其它具体类型 ¶

class typing. IO ¶

class typing. TextIO ¶

class typing. BinaryIO ¶

一般类型 IO[AnyStr] 及其子类 TextIO(IO[str]) and BinaryIO(IO[bytes]) represent the types of I/O streams such as returned by open() .

Deprecated since version 3.8, will be removed in version 3.12: These types are also in the typing.io namespace, which was never supported by type checkers and will be removed.

class typing. Pattern ¶

class typing. Match ¶

These type aliases correspond to the return types from re.compile() and re.match() . These types (and the corresponding functions) are generic in AnyStr and can be made specific by writing Pattern[str] , Pattern[bytes] , Match[str] ，或 Match[bytes] .

Deprecated since version 3.8, will be removed in version 3.12: These types are also in the typing.re namespace, which was never supported by type checkers and will be removed.

从 3.9 版起弃用： 类 Pattern and Match from re 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. Text ¶

Text 是别名化的 str . It is provided to supply a forward compatible path for Python 2 code: in Python 2, Text 是别名化的 unicode .

使用 Text to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3:

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

3.5.2 版新增。

Corresponding to collections in collections.abc ¶

class typing. AbstractSet ( Sized, Collection[T_co] ) ¶

一般版本的 collections.abc.Set .

从 3.9 版起弃用： collections.abc.Set 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. ByteString ( Sequence[int] ) ¶

一般版本的 collections.abc.ByteString .

此类型表示类型 bytes , bytearray ，和 memoryview of byte sequences.

As a shorthand for this type, bytes can be used to annotate arguments of any of the types mentioned above.

从 3.9 版起弃用： collections.abc.ByteString 现在支持 [] 。见 PEP 585 and 一般别名类型 .

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

一般版本的 collections.abc.Collection

3.6.0 版新增。

从 3.9 版起弃用： collections.abc.Collection 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. Container ( Generic[T_co] ) ¶

一般版本的 collections.abc.Container .

从 3.9 版起弃用： collections.abc.Container 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. ItemsView ( MappingView, Generic[KT_co, VT_co] ) ¶

一般版本的 collections.abc.ItemsView .

从 3.9 版起弃用： collections.abc.ItemsView 现在支持 [] 。见 PEP 585 and 一般别名类型 .

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

一般版本的 collections.abc.KeysView .

从 3.9 版起弃用： collections.abc.KeysView 现在支持 [] 。见 PEP 585 and 一般别名类型 .

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

一般版本的 collections.abc.Mapping . This type can be used as follows:

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

从 3.9 版起弃用： collections.abc.Mapping 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. MappingView ( Sized, Iterable[T_co] ) ¶

一般版本的 collections.abc.MappingView .

从 3.9 版起弃用： collections.abc.MappingView 现在支持 [] 。见 PEP 585 and 一般别名类型 .

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

一般版本的 collections.abc.MutableMapping .

从 3.9 版起弃用： collections.abc.MutableMapping 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. MutableSequence ( Sequence[T] ) ¶

一般版本的 collections.abc.MutableSequence .

从 3.9 版起弃用： collections.abc.MutableSequence 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. MutableSet ( AbstractSet[T] ) ¶

一般版本的 collections.abc.MutableSet .

从 3.9 版起弃用： collections.abc.MutableSet 现在支持 [] 。见 PEP 585 and 一般别名类型 .

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

一般版本的 collections.abc.Sequence .

从 3.9 版起弃用： collections.abc.Sequence 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. ValuesView ( MappingView[VT_co] ) ¶

一般版本的 collections.abc.ValuesView .

从 3.9 版起弃用： collections.abc.ValuesView 现在支持 [] 。见 PEP 585 and 一般别名类型 .

Corresponding to other types in collections.abc ¶

class typing. Iterable ( Generic[T_co] ) ¶

一般版本的 collections.abc.Iterable .

从 3.9 版起弃用： collections.abc.Iterable 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. Iterator ( Iterable[T_co] ) ¶

一般版本的 collections.abc.Iterator .

从 3.9 版起弃用： collections.abc.Iterator 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. Generator ( Iterator[T_co], Generic[T_co, T_contra, V_co] ) ¶

A generator can be annotated by the generic type Generator[YieldType, SendType, ReturnType] 。例如：

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

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

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

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

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

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

从 3.9 版起弃用： collections.abc.Generator 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. Hashable ¶

An alias to collections.abc.Hashable

class typing. Reversible ( Iterable[T_co] ) ¶

一般版本的 collections.abc.Reversible .

从 3.9 版起弃用： collections.abc.Reversible 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. Sized ¶

An alias to collections.abc.Sized

异步编程 ¶

class typing. Coroutine ( Awaitable[V_co], Generic[T_co, T_contra, V_co] ) ¶

一般版本的 collections.abc.Coroutine . The variance and order of type variables correspond to those of Generator ，例如：

from collections.abc import Coroutine
c = None # type: Coroutine[list[str], str, int]
...
x = c.send('hi') # type: list[str]
async def bar() -> None:
    x = await c # type: int
												

3.5.3 版新增。

从 3.9 版起弃用： collections.abc.Coroutine 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. AsyncGenerator ( AsyncIterator[T_co], Generic[T_co, T_contra] ) ¶

An async generator can be annotated by the generic type AsyncGenerator[YieldType, SendType] 。例如：

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

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

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

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

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

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

3.6.1 版新增。

从 3.9 版起弃用： collections.abc.AsyncGenerator 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. AsyncIterable ( Generic[T_co] ) ¶

一般版本的 collections.abc.AsyncIterable .

3.5.2 版新增。

从 3.9 版起弃用： collections.abc.AsyncIterable 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. AsyncIterator ( AsyncIterable[T_co] ) ¶

一般版本的 collections.abc.AsyncIterator .

3.5.2 版新增。

从 3.9 版起弃用： collections.abc.AsyncIterator 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. Awaitable ( Generic[T_co] ) ¶

一般版本的 collections.abc.Awaitable .

3.5.2 版新增。

从 3.9 版起弃用： collections.abc.Awaitable 现在支持 [] 。见 PEP 585 and 一般别名类型 .

上下文管理器类型 ¶

class typing. ContextManager ( Generic[T_co] ) ¶

一般版本的 contextlib.AbstractContextManager .

3.5.4 版新增。

3.6.0 版新增。

从 3.9 版起弃用： contextlib.AbstractContextManager 现在支持 [] 。见 PEP 585 and 一般别名类型 .

class typing. AsyncContextManager ( Generic[T_co] ) ¶

一般版本的 contextlib.AbstractAsyncContextManager .

3.5.4 版新增。

3.6.2 版新增。

从 3.9 版起弃用： contextlib.AbstractAsyncContextManager 现在支持 [] 。见 PEP 585 and 一般别名类型 .