26.1. `typing` — 支持类型提示 ¶

3.5 版新增。

This module supports type hints as specified by PEP 484 。最基础支持包含类型 Any , Union , Tuple , Callable , TypeVar ，和 Generic 。对于完整规范，请参阅 PEP 484 。对于类型提示的简化介绍，见 PEP 483 .

以下函数接受并返回字符串，注解如下：

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

在函数 greeting ，自变量 name 期望为类型 str 和返回类型 str 。子类型被接受作为自变量。

26.1.1. 类型别名 ¶

类型别名是通过对类型赋值别名来定义的。在此范例中， Vector and List[float] 将被视为可互换的同义词：

from typing import List
Vector = List[float]
def scale(scalar: float, vector: Vector) -> Vector:
    return [scalar * num for num in vector]
# typechecks; a list of floats qualifies as a Vector.
new_vector = scale(2.0, [1.0, -4.2, 5.4])

类型别名很有用为简化复杂类型签名。例如：

from typing import Dict, Tuple, List
ConnectionOptions = Dict[str, str]
Address = Tuple[str, int]
Server = Tuple[Address, ConnectionOptions]
def broadcast_message(message: str, servers: List[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: List[Tuple[Tuple[str, int], Dict[str, str]]]) -> None:
    ...

注意， None 作为类型提示是特殊情况且替换通过 type(None) .

26.1.2. NewType ¶

使用 NewType() 帮手函数以创建截然不同的类型：

from typing import NewType
UserId = NewType('UserId', int)
some_id = UserId(524313)

The static type checker will treat the new type as if it were a subclass of the original type. This is useful in helping catch logical errors:

def get_user_name(user_id: UserId) -> str:
    ...
# typechecks
user_a = get_user_name(UserId(42351))
# does not typecheck; an int is not a UserId
user_b = get_user_name(-1)

可以仍然履行所有 int operations on a variable of type UserId , but the result will always be of type int . This lets you pass in a UserId wherever an int might be expected, but will prevent you from accidentally creating a UserId in an invalid way:

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

Note that these checks are enforced only by the static type checker. At runtime the statement Derived = NewType('Derived', Base) will make Derived a function that immediately returns whatever parameter you pass it. That means the expression Derived(some_value) does not create a new class or introduce any overhead beyond that of a regular function call.

More precisely, the expression some_value is Derived(some_value) is always true at runtime.

This also means that it is not possible to create a subtype of Derived since it is an identity function at runtime, not an actual type. Similarly, it is not possible to create another NewType() based on a Derived 类型：

from typing import NewType
UserId = NewType('UserId', int)
# Fails at runtime and does not typecheck
class AdminUserId(UserId): pass
# Also does not typecheck
ProUserId = NewType('ProUserId', UserId)

见 PEP 484 了解更多细节。

注意

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

In contrast, NewType declares one type to be a subtype of another. Doing Derived = NewType('Derived', Original) will make the static type checker treat Derived 作为 subclass of Original , which means a value of type Original cannot be used in places where a value of type Derived is expected. This is useful when you want to prevent logic errors with minimal runtime cost.

26.1.3. 可调用 ¶

Frameworks expecting callback functions of specific signatures might be type hinted using Callable[[Arg1Type, Arg2Type], ReturnType] .

例如：

from typing import Callable
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

It is possible to declare the return type of a callable without specifying the call signature by substituting a literal ellipsis for the list of arguments in the type hint: Callable[..., ReturnType] .

26.1.4. 一般 ¶

Since type information about objects kept in containers cannot be statically inferred in a generic way, abstract base classes have been extended to support subscription to denote expected types for container elements.

from typing import Mapping, Sequence
def notify_by_email(employees: Sequence[Employee],
                    overrides: Mapping[str, str]) -> None: ...

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

from typing import Sequence, TypeVar
T = TypeVar('T')      # Declare type variable
def first(l: Sequence[T]) -> T:   # Generic function
    return l[0]

26.1.5. 用户定义一般类型 ¶

A user-defined class can be defined as a generic class.

from typing import TypeVar, Generic
from logging import Logger
T = TypeVar('T')
class LoggedVar(Generic[T]):
    def __init__(self, value: T, name: str, logger: Logger) -> None:
        self.name = name
        self.logger = logger
        self.value = value
    def set(self, new: T) -> None:
        self.log('Set ' + repr(self.value))
        self.value = new
    def get(self) -> T:
        self.log('Get ' + repr(self.value))
        return self.value
    def log(self, message: str) -> None:
        self.logger.info('%s: %s', self.name, message)

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

The Generic base class uses a metaclass that defines __getitem__() so that LoggedVar[t] is valid as a type:

from typing import Iterable
def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
    for var in vars:
        var.set(0)

A generic type can have any number of type variables, and type variables may be constrained:

from typing import TypeVar, Generic
...
T = TypeVar('T')
S = TypeVar('S', int, str)
class StrangePair(Generic[T, S]):
    ...

Each type variable argument to Generic must be distinct. This is thus invalid:

from typing import TypeVar, Generic
...
T = TypeVar('T')
class Pair(Generic[T, T]):   # INVALID
    ...

You can use multiple inheritance with Generic :

from typing import TypeVar, Generic, Sized
T = TypeVar('T')
class LinkedList(Sized, Generic[T]):
    ...

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

from typing import TypeVar, Mapping
T = TypeVar('T')
class MyDict(Mapping[str, T]):
    ...

在此情况下 MyDict 拥有单参数， 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 typing import Iterable
class MyIterable(Iterable): # Same as Iterable[Any]

User defined generic type aliases are also supported. Examples:

from typing import TypeVar, Iterable, Tuple, Union
S = TypeVar('S')
Response = Union[Iterable[S], int]
# Return type here is same as Union[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)

The metaclass used by Generic 是子类化的 abc.ABCMeta . A generic class can be an ABC by including abstract methods or properties, and generic classes can also 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.

26.1.6. The `Any` type ¶

A special kind of type is Any . A static type checker will treat every type as being compatible with Any and Any as being compatible with every type.

This means that it is possible to perform any operation or method call on a value of type on Any and assign it to any variable:

from typing import Any
a = None    # type: Any
a = []      # OK
a = 2       # OK
s = ''      # type: str
s = a       # OK
def foo(item: Any) -> int:
    # Typechecks; 'item' could be any type,
    # and that type might have a 'bar' method
    item.bar()
    ...

Notice that no typechecking 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!

Furthermore, all functions without a return type or parameter types will implicitly default to using 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

This behavior allows Any to be used as an escape hatch when you need to mix dynamically and statically typed code.

Contrast the behavior of Any with the behavior of object . Similar to Any , every type is a subtype of object . However, unlike Any , the reverse is not true: object is not a subtype of every other type.

That means when the type of a value is object , a type checker will reject almost all operations on it, and assigning it to a variable (or using it as a return value) of a more specialized type is a type error. For example:

def hash_a(item: object) -> int:
    # Fails; an object does not have a 'magic' method.
    item.magic()
    ...
def hash_b(item: Any) -> int:
    # Typechecks
    item.magic()
    ...
# Typechecks, since ints and strs are subclasses of object
hash_a(42)
hash_a("foo")
# Typechecks, since Any is compatible with all types
hash_b(42)
hash_b("foo")

使用 object to indicate that a value could be any type in a typesafe manner. Use Any to indicate that a value is dynamically typed.

26.1.7. Classes, functions, and decorators ¶

The module defines the following classes, functions and decorators:

class typing. TypeVar ¶

class typing. 一般 ¶

class typing. 类型 ( Generic[CT_co] ) ¶

class typing. Iterable ( Generic[T_co] ) ¶

class typing. 迭代器 ( Iterable[T_co] ) ¶

class typing. Reversible ( Iterable[T_co] ) ¶

class typing. SupportsInt ¶

class typing. SupportsFloat ¶

class typing. SupportsAbs ¶

class typing. SupportsRound ¶

class typing. Container ( Generic[T_co] ) ¶

class typing. Hashable ¶

class typing. Sized ¶

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

class typing. MutableSet ( AbstractSet[T] ) ¶

class typing. 映射 ( Sized, Iterable[KT], Container[KT], Generic[VT_co] ) ¶

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

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

class typing. MutableSequence ( Sequence[T] ) ¶

class typing. ByteString ( Sequence[int] ) ¶

一般版本的 collections.abc.ByteString .

此类型表示类型 bytes , bytearray ，和 memoryview .

作为此类型的简写， bytes can be used to annotate arguments of any of the types mentioned above.

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

一般版本的 collections.deque .

3.5.4 版新增。

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

Generic version of list . Useful for annotating return types. To annotate arguments it is preferred to use abstract collection types such as Mapping , Sequence ，或 AbstractSet .

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]

class typing. Set ( set, MutableSet[T] ) ¶: 一般版本的 builtins.set .

class typing. FrozenSet ( frozenset, AbstractSet[T_co] ) ¶: 一般版本的 builtins.frozenset .

class typing. MappingView ( Sized, Iterable[T_co] ) ¶: 一般版本的 collections.abc.MappingView .

class typing. KeysView ( MappingView[KT_co], AbstractSet[KT_co] ) ¶: 一般版本的 collections.abc.KeysView .

class typing. ItemsView ( MappingView, Generic[KT_co, VT_co] ) ¶: 一般版本的 collections.abc.ItemsView .

class typing. ValuesView ( MappingView[VT_co] ) ¶: 一般版本的 collections.abc.ValuesView .

class typing. Awaitable ( Generic[T_co] ) ¶: 一般版本的 collections.abc.Awaitable .

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 typing import List, 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

class typing. AsyncIterable ( Generic[T_co] ) ¶: 一般版本的 collections.abc.AsyncIterable .

class typing. AsyncIterator ( AsyncIterable[T_co] ) ¶: 一般版本的 collections.abc.AsyncIterator .

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

一般版本的 dict . The usage of this type is as follows:

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

class typing. DefaultDict ( collections.defaultdict, MutableMapping[KT, VT] ) ¶: 一般版本的 collections.defaultdict

class typing. 生成器 ( 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

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)

3.5.4 版新增。

class typing. 文本 ¶

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'

class typing. io ¶

Wrapper namespace for I/O stream types.

This defines the generic type IO[AnyStr] and aliases TextIO and BinaryIO for respectively IO[str] and IO[bytes] . These representing the types of I/O streams such as returned by open() .

class typing. re ¶

Wrapper namespace for regular expression matching types.

This defines the type aliases Pattern and Match which 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] .

typing. NamedTuple ( typename , 字段 ) ¶

typing. NewType ( typ ) ¶

typing. cast ( typ , val ) ¶

typing. get_type_hints ( obj [ , globals [ , locals ] ] ) ¶

@ typing. overload ¶

@ typing. no_type_check ( arg ) ¶

@ typing. no_type_check_decorator ( 装饰器 ) ¶

Decorator to give another decorator the no_type_check() 效果。

This wraps the decorator with something that wraps the decorated function in no_type_check() .

typing. 任何 ¶

指示无约束类型的特殊类型。

Every type is compatible with Any .
Any is compatible with every type.

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]
									
```
When a class and its subclass are present, the latter is skipped, e.g.:
```
Union[int, object] == object
									
```
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] .

typing. 可选 ¶

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 needn’t use the Optional qualifier on its type annotation (although it is inferred if the default is None ). A mandatory argument may still have an Optional type if an explicit value of None is allowed.

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.

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 .

typing. 可调用 ¶

可调用类型； 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 .

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 = {}  # type: ClassVar[Dict[str, int]] # class variable
    damage = 10 # type: int                      # 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() 。注意， ClassVar does not change Python runtime behavior; it can be used by 3rd party type checkers, so that the following code might flagged as an error by those:

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. 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

typing. TYPE_CHECKING ¶

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

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

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

26.1. typing — 支持类型提示 ¶