python新特性详解及版本

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python新特性详解及版本
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python新特性详解及版本

typing

— Support for type hints¶

New in version 3.5.

Source code: Lib/typing.py

Note

The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.

This module provides runtime support for type hints as specified by PEP 484, PEP 526, PEP 544, PEP 586, PEP 589, and PEP 591. The most fundamental support consists of the types Any, Union, Tuple, Callable, TypeVar, and Generic. For full specification please see PEP 484. For a simplified introduction to type hints see PEP 483.

The function below takes and returns a string and is annotated as follows:
```
def
						greeting(name: str) ->
												str:
```
```
					return
							'Hello '
									+ name
```
In the function greeting, the argument name is expected to be of type str and the return type str. Subtypes are accepted as arguments.

Type aliases

A type alias is defined by assigning the type to the alias. In this example, Vector and List[float] will be treated as interchangeable synonyms:
```
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])
```
Type aliases are useful for simplifying complex type signatures. For example:
```
from
						typing
								import Dict, Tuple, 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:
```
```
					...
```
Note that None as a type hint is a special case and is replaced by type(None).

NewType

Use the NewType() helper function to create distinct types:
```
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)
```
You may still perform all 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:
```
from
						typing
								import NewType
```
```
UserId = NewType('UserId', int)
```
```
# Fails at runtime and does not typecheck
```
```
class
						AdminUserId(UserId): pass
```
However, it is possible to create a NewType() based on a 'derived' NewType:
```
from
						typing
								import NewType
```
```
UserId = NewType('UserId', int)
```
```
ProUserId = NewType('ProUserId', UserId)
```
and typechecking for ProUserId will work as expected.

See PEP 484 for more details.

Note

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 as a 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.

New in version 3.5.2.

Callable

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

For example:
```
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].

Generics

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 parameterized 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]
```
User-defined generic types

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 Tvalid as a type within the class body.

The Generic base class defines __class_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]):
```
```
					...
```
In this case MyDict has a single parameter, 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)
```
Changed in version 3.7: Generic no longer has a custom metaclass.

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.

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

Nominal vs structural subtyping

Initially PEP 484 defined Python static type system as using nominal subtyping. This means that a class A is allowed where a class B is expected if and only if A is a subclass of B.

This requirement previously also applied to abstract base classes, such as Iterable. The problem with this approach is that a class had to be explicitly marked to support them, which is unpythonic and unlike what one would normally do in idiomatic dynamically typed Python code. For example, this conforms to the PEP 484:
```
from
						typing
								import Sized, Iterable, Iterator
```
```
class
						Bucket(Sized, Iterable[int]):
```
```
					...
```
```
					def
							__len__(self) ->
													int: ...
```
```
					def
							__iter__(self) -> Iterator[int]: ...
```
PEP 544 allows to solve this problem by allowing users to write the above code without explicit base classes in the class definition, allowing Bucket to be implicitly considered a subtype of both Sized and Iterable[int] by static type checkers. This is known as structural subtyping (or static duck-typing):
```
from
						typing
								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
```
Moreover, by subclassing a special class Protocol, a user can define new custom protocols to fully enjoy structural subtyping (see examples below).

Classes, functions, and decorators

The module defines the following classes, functions and decorators:

class typing.TypeVar

Type variable.

Usage:
```
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 class 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) will raise 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. See 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.

class typing.Generic

Abstract base class for generic types.

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.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
```
See 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:
```
```
					...
```
New in version 3.8.

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. 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
						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, type variables, and unions of any of these types. For example:
```
def
						new_non_team_user(user_class: Type[Union[BaseUser, ProUser]]): ...
```
Type[Any] is equivalent to Type which in turn is equivalent to type, which is the root of Python's metaclass hierarchy.

New in version 3.5.2.

class typing.Iterable(Generic[T_co])

A generic version of collections.abc.Iterable.

class typing.Iterator(Iterable[T_co])

A generic version of collections.abc.Iterator.

class typing.Reversible(Iterable[T_co])

A generic version of collections.abc.Reversible.

class typing.SupportsInt

An ABC with one abstract method __int__.

class typing.SupportsFloat

An ABC with one abstract method __float__.

class typing.SupportsComplex

An ABC with one abstract method __complex__.

class typing.SupportsBytes

An ABC with one abstract method __bytes__.

class typing.SupportsIndex

An ABC with one abstract method __index__.

New in version 3.8.

class typing.SupportsAbs

An ABC with one abstract method __abs__ that is covariant in its return type.

class typing.SupportsRound

An ABC with one abstract method __round__ that is covariant in its return type.

class typing.Container(Generic[T_co])

A generic version of collections.abc.Container.

class typing.Hashable

An alias to collections.abc.Hashable

class typing.Sized

An alias to collections.abc.Sized

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

A generic version of collections.abc.Collection

New in version 3.6.0.

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

A generic version of collections.abc.Set.

class typing.MutableSet(AbstractSet[T])

A generic version of collections.abc.MutableSet.

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

A generic version of 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]
```
class typing.MutableMapping(Mapping[KT, VT])

A generic version of collections.abc.MutableMapping.

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

A generic version of collections.abc.Sequence.

class typing.MutableSequence(Sequence[T])

A generic version of collections.abc.MutableSequence.

class typing.ByteString(Sequence[int])

A generic version of collections.abc.ByteString.

This type represents the types bytes, bytearray, and memoryview of byte sequences.

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

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

A generic version of collections.deque.

New in version 3.5.4.

New in version 3.6.1.

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.

This type may be used as follows:
```
T = TypeVar('T', int, float)
```
```
def
						vec2(x: T, y: T) -> List[T]:
```
```
					return [x, y]
```
```
def
						keep_positives(vector: Sequence[T]) -> List[T]:
```
```
					return [item for item in vector if item >
															0]
```
class typing.Set(set, MutableSet[T])

A generic version of builtins.set. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as AbstractSet.

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

A generic version of builtins.frozenset.

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

A generic version of collections.abc.MappingView.

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

A generic version of collections.abc.KeysView.

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

A generic version of collections.abc.ItemsView.

class typing.ValuesView(MappingView[VT_co])

A generic version of collections.abc.ValuesView.

class typing.Awaitable(Generic[T_co])

A generic version of collections.abc.Awaitable.

New in version 3.5.2.

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

A generic version of collections.abc.Coroutine. The variance and order of type variables correspond to those of Generator, for example:
```
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
```
New in version 3.5.3.

class typing.AsyncIterable(Generic[T_co])

A generic version of collections.abc.AsyncIterable.

New in version 3.5.2.

class typing.AsyncIterator(AsyncIterable[T_co])

A generic version of collections.abc.AsyncIterator.

New in version 3.5.2.

class typing.ContextManager(Generic[T_co])

A generic version of contextlib.AbstractContextManager.

New in version 3.5.4.

New in version 3.6.0.

class typing.AsyncContextManager(Generic[T_co])

A generic version of contextlib.AbstractAsyncContextManager.

New in version 3.5.4.

New in version 3.6.2.

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

A generic version of 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]:
```
```
					...
```
class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])

A generic version of collections.defaultdict.

New in version 3.5.2.

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

A generic version of collections.OrderedDict.

New in version 3.7.2.

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

A generic version of collections.Counter.

New in version 3.5.4.

New in version 3.6.1.

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

A generic version of collections.ChainMap.

New in version 3.5.4.

New in version 3.6.1.

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]. For example:
```
def
						echo_round() -> Generator[int, float, str]:
```
```
    sent =
							yield
									0
```
```
					while sent >=
									0:
```
```
        sent =
							yield
									round(sent)
```
```
					return
							'Done'
```
Note that unlike many other generics in the typing module, the SendType of Generator behaves contravariantly, not covariantly or invariantly.

If your generator will only yield values, set the SendType and ReturnType to None:
```
def
						infinite_stream(start: int) -> Generator[int, None, None]:
```
```
					while
							True:
```
```
					yield start
```
```
        start +=
							1
```
Alternatively, annotate your generator as having a return type of either Iterable[YieldType] or Iterator[YieldType]:
```
def
						infinite_stream(start: int) -> Iterator[int]:
```
```
					while
							True:
```
```
					yield start
```
```
        start +=
							1
```
class typing.AsyncGenerator(AsyncIterator[T_co], Generic[T_co, T_contra])

An async generator can be annotated by the generic type AsyncGenerator[YieldType, SendType]. For example:
```
async
						def
								echo_round() -> AsyncGenerator[int, float]:
```
```
    sent =
							yield
									0
```
```
					while sent >=
									0.0:
```
```
        rounded =
							await
									round(sent)
```
```
        sent =
							yield rounded
```
Unlike normal generators, async generators cannot return a value, so there is no ReturnType type parameter. As with Generator, the SendType behaves contravariantly.

If your generator will only yield values, set the SendType to None:
```
async
						def
								infinite_stream(start: int) -> AsyncGenerator[int, None]:
```
```
					while
							True:
```
```
					yield start
```
```
        start =
							await increment(start)
```
Alternatively, annotate your generator as having a return type of either AsyncIterable[YieldType] or AsyncIterator[YieldType]:
```
async
						def
								infinite_stream(start: int) -> AsyncIterator[int]:
```
```
					while
							True:
```
```
					yield start
```
```
        start =
							await increment(start)
```
New in version 3.6.1.

class typing.Text

Text is an alias for str. It is provided to supply a forward compatible path for Python 2 code: in Python 2, Text is an alias for unicode.

Use 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'
```
New in version 3.5.2.

class typing.IO

class typing.TextIO

class typing.BinaryIO

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

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], or Match[bytes].

class typing.NamedTuple

Typed version of collections.namedtuple().

Usage:
```
class
						Employee(NamedTuple):
```
```
    name: str
```
```
					id: int
```
This is equivalent to:
```
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_defaultsattribute 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)])
```
Changed in version 3.6: Added support for PEP 526 variable annotation syntax.

Changed in version 3.6.1: Added support for default values, methods, and docstrings.

Deprecated since version 3.8, will be removed in version 3.9: Deprecated the _field_types attribute in favor of the more standard __annotations__ attribute which has the same information.

Changed in version 3.8: The _field_types and __annotations__ attributes are now regular dictionaries instead of instances of OrderedDict.

class typing.TypedDict(dict)

A simple typed namespace. At runtime it is equivalent to a plain dict.

TypedDict creates 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.

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

New in version 3.8.

class typing.ForwardRef

A class used for internal typing representation of string forward references. For example, List["SomeClass"]is implicitly transformed into List[ForwardRef("SomeClass")]. This class should not be instantiated by a user, but may be used by introspection tools.

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)
```
New in version 3.5.2.

typing.cast(typ, val)

Cast a value to a type.

This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don't check anything (we want this to be as fast as possible).

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

Return a dictionary containing type hints for a function, method, module or class object.

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. If necessary, Optional[t] is added for function and method annotations if a default value equal to None is set. For a class C, return a dictionary constructed by merging all the __annotations__ along C.__mro__ in reverse order.

typing.get_origin(tp)

typing.get_args(tp)

Provide basic introspection for generic types and special typing forms.

For a typing object of the form X[Y, Z, ...] these functions return X and (Y, Z, ...). If X is a generic alias for a builtin or collections class, it gets normalized to the original class. For unsupported objects return Noneand () correspondingly. Examples:
```
assert get_origin(Dict[str, int]) is
												dict
```
```
assert get_args(Dict[int, str]) == (int, str)
```
```
assert get_origin(Union[int, str]) is Union
```
```
assert get_args(Union[int, str]) == (int, str)
```
New in version 3.8.

@typing.overload

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). The @overload-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-@overload-decorated definition, while the latter is used at runtime but should be ignored by a type checker. At runtime, calling a @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>
```
See PEP 484 for details and comparison with other typing semantics.

@typing.final

A decorator to indicate to type checkers that the decorated method cannot be overridden, and the decorated class cannot be subclassed. For example:
```
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
```
```
					...
```
There is no runtime checking of these properties. See PEP 591 for more details.

New in version 3.8.

@typing.no_type_check

Decorator to indicate that annotations are not type hints.

This works as class or function decorator. With a class, it applies recursively to all methods defined in that class (but not to methods defined in its superclasses or subclasses).

This mutates the function(s) in place.

@typing.no_type_check_decorator

Decorator to give another decorator the no_type_check() effect.

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

@typing.type_check_only

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

This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class:
```
@type_check_only
```
```
class
						Response:  # private or not available at runtime
```
```
    code: int
```
```
					def
							get_header(self, name: str) ->
															str: ...
```
```
def
						fetch_response() -> Response: ...
```
Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public.

@typing.runtime_checkable

Mark a protocol class as a runtime protocol.

Such a protocol can be used with isinstance() and issubclass(). This raises TypeError when applied to a non-protocol class. This allows a simple-minded structural check, very similar to "one trick ponies" in collections.abc such as Iterable. For example:
```
@runtime_checkable
```
```
class
						Closable(Protocol):
```
```
					def
							close(self): ...
```
```
assert
						isinstance(open('/some/file'), Closable)
```
Warning: this will check only the presence of the required methods, not their type signatures!

New in version 3.8.

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')
  
  New in version 3.5.4.
  
  New in version 3.6.2.
  
  typing.Union
  
  Union type; 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].
- You can use Optional[X] as a shorthand for Union[X, None].
  
  Changed in version 3.7: Don't remove explicit subclasses from unions at runtime.
  
  typing.Optional
  
  Optional type.
  
  Optional[X] is equivalent to 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.Tuple
  
  Tuple type; 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[()].
  
  Example: 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 is equivalent to Tuple[Any, ...], and in turn to tuple.
  
  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 is equivalent to Callable[..., Any], and in turn to collections.abc.Callable.
  
  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.
  
  New in version 3.8.
  
  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
  
  New in version 3.5.3.
  
  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 for more details.
  
  New in version 3.8.
  
  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.
  
  New in version 3.5.2.
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原文地址：https://www.cnblogs.com/xiexiaokui/p/13614940.html

python新特性详解及版本

python新特性详解及版本

typing

— Support for type hints¶

Type aliases

NewType

Callable

Generics

User-defined generic types

The Any type

Nominal vs structural subtyping

Classes, functions, and decorators