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# [`typing`](#module-typing "typing: Support for type hints (see PEP 484).") --- 類型標注支持
3\.5 新版功能.
**源碼：** [Lib/typing.py](https://github.com/python/cpython/tree/3.7/Lib/typing.py) \[https://github.com/python/cpython/tree/3.7/Lib/typing.py\]
注解
typing 模塊以 [暫定狀態](../glossary.xhtml#term-provisional-api) 包含在標準庫中。如果核心開發人員認為有必要，可能會添加新功能，甚至可能會在次要版本之間改變 API。
- - - - - -
此模塊支持 [**PEP 484**](https://www.python.org/dev/peps/pep-0484) \[https://www.python.org/dev/peps/pep-0484\] 和 [**PEP 526**](https://www.python.org/dev/peps/pep-0526) \[https://www.python.org/dev/peps/pep-0526\] 指定的類型提示。最基本的支持由 [`Any`](#typing.Any "typing.Any")，[`Union`](#typing.Union "typing.Union")，[`Tuple`](#typing.Tuple "typing.Tuple")，[`Callable`](#typing.Callable "typing.Callable")，[`TypeVar`](#typing.TypeVar "typing.TypeVar") 和 [`Generic`](#typing.Generic "typing.Generic") 類型組成。有關完整的規范，請參閱 [**PEP 484**](https://www.python.org/dev/peps/pep-0484) \[https://www.python.org/dev/peps/pep-0484\]。有關類型提示的簡單介紹，請參閱 [**PEP 483**](https://www.python.org/dev/peps/pep-0483) \[https://www.python.org/dev/peps/pep-0483\]。
函數接受并返回一個字符串，注釋像下面這樣:
```
def greeting(name: str) -> str:
return 'Hello ' + name
```
在函數 `greeting` 中，參數 `name` 預期是 [`str`](stdtypes.xhtml#str "str") 類型，并且返回 [`str`](stdtypes.xhtml#str "str") 類型。子類型允許作為參數。
## 類型別名
類型別名通過將類型分配給別名來定義。在這個例子中， `Vector` 和 `List[float]` 將被視為可互換的同義詞:
```
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])
```
類型別名可用于簡化復雜類型簽名。例如:
```
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:
...
```
請注意，`None` 作為類型提示是一種特殊情況，并且由 `type(None)` 取代。
## NewType
使用 [`NewType()`](#typing.NewType "typing.NewType") 輔助函數創建不同的類型:
```
from typing import NewType
UserId = NewType('UserId', int)
some_id = UserId(524313)
```
靜態類型檢查器會將新類型視為它是原始類型的子類。這對于幫助捕捉邏輯錯誤非常有用:
```
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)
```
您仍然可以對 `UserId` 類型的變量執行所有的 `int` 支持的操作，但結果將始終為 `int` 類型。這可以讓你在需要 `int` 的地方傳入 `UserId`，但會阻止你以無效的方式無意中創建 `UserId`:
```
# 'output' is of type 'int', not 'UserId'
output = UserId(23413) + UserId(54341)
```
請注意，這些檢查僅通過靜態類型檢查程序強制執行。在運行時，`Derived = NewType('Derived'，Base)` 將 `Derived` 一個函數，該函數立即返回您傳遞它的任何參數。這意味著表達式 `Derived(some_value)` 不會創建一個新的類或引入任何超出常規函數調用的開銷。
更確切地說，表達式 `some_value is Derived(some_value)` 在運行時總是為真。
這也意味著無法創建 `Derived` 的子類型，因為它是運行時的標識函數，而不是實際的類型:
```
from typing import NewType
UserId = NewType('UserId', int)
# Fails at runtime and does not typecheck
class AdminUserId(UserId): pass
```
但是，可以基于'derived' `NewType` 創建 [`NewType()`](#typing.NewType "typing.NewType")
```
from typing import NewType
UserId = NewType('UserId', int)
ProUserId = NewType('ProUserId', UserId)
```
并且 `ProUserId` 的類型檢查將按預期工作。
有關更多詳細信息，請參閱 [**PEP 484**](https://www.python.org/dev/peps/pep-0484) \[https://www.python.org/dev/peps/pep-0484\]。
注解
回想一下，使用類型別名聲明兩種類型彼此 *等效* 。`Alias = Original` 將使靜態類型檢查對待所有情況下 `Alias` *完全等同于*`Original`。當您想簡化復雜類型簽名時，這很有用。
相反，`NewType` 聲明一種類型是另一種類型的子類型。`Derived = NewType('Derived', Original)` 將使靜態類型檢查器將 `Derived` 當作 `Original` 的 *子類* ，這意味著 `Original` 類型的值不能用于 `Derived` 類型的值需要的地方。當您想以最小的運行時間成本防止邏輯錯誤時，這非常有用。
3\.5.2 新版功能.
## Callable
期望特定簽名的回調函數的框架可以將類型標注為 `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
```
通過用文字省略號替換類型提示中的參數列表： `Callable[...，ReturnType]`，可以聲明可調用的返回類型，而無需指定調用簽名。
## 泛型(Generic)
由于無法以通用方式靜態推斷有關保存在容器中的對象的類型信息，因此抽象基類已擴展為支持訂閱以表示容器元素的預期類型。
```
from typing import Mapping, Sequence
def notify_by_email(employees: Sequence[Employee],
overrides: Mapping[str, str]) -> None: ...
```
泛型可以通過使用typing模塊中名為 [`TypeVar`](#typing.TypeVar "typing.TypeVar") 的新工廠進行參數化。
```
from typing import Sequence, TypeVar
T = TypeVar('T') # Declare type variable
def first(l: Sequence[T]) -> T: # Generic function
return l[0]
```
## 用戶定義的泛型類型
用戶定義的類可以定義為泛型類。
```
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]` 作為基類定義了類 `LoggedVar` 采用單個類型參數 `T`。這也使得 `T` 作為類體內的一個類型有效。
[`Generic`](#typing.Generic "typing.Generic") 基類使用定義了 [`__getitem__()`](../reference/datamodel.xhtml#object.__getitem__ "object.__getitem__") 的元類，以便 `LoggedVar[t]` 作為類型有效:
```
from typing import Iterable
def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
for var in vars:
var.set(0)
```
泛型類型可以有任意數量的類型變量，并且類型變量可能會受到限制:
```
from typing import TypeVar, Generic
...
T = TypeVar('T')
S = TypeVar('S', int, str)
class StrangePair(Generic[T, S]):
...
```
[`Generic`](#typing.Generic "typing.Generic") 每個參數的類型變量必須是不同的。這是無效的:
```
from typing import TypeVar, Generic
...
T = TypeVar('T')
class Pair(Generic[T, T]): # INVALID
...
```
您可以對 [`Generic`](#typing.Generic "typing.Generic") 使用多重繼承:
```
from typing import TypeVar, Generic, Sized
T = TypeVar('T')
class LinkedList(Sized, Generic[T]):
...
```
從泛型類繼承時，某些類型變量可能是固定的:
```
from typing import TypeVar, Mapping
T = TypeVar('T')
class MyDict(Mapping[str, T]):
...
```
在這種情況下，`MyDict` 只有一個參數，`T`。
在不指定類型參數的情況下使用泛型類別會為每個位置假設 [`Any`](#typing.Any "typing.Any")。在下面的例子中，`MyIterable` 不是泛型，但是隱式繼承自 `Iterable[Any]`:
```
from typing import Iterable
class MyIterable(Iterable): # Same as Iterable[Any]
```
用戶定義的通用類型別名也受支持。例子:
```
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)
```
[`Generic`](#typing.Generic "typing.Generic") 使用的元類是 [`abc.ABCMeta`](abc.xhtml#abc.ABCMeta "abc.ABCMeta") 的子類。泛型類可以通過包含抽象方法或屬性成為ABC，并且泛型類也可以使用ABCs作為基類而不存在元類沖突。不支持泛型元類。參數化泛型的結果被緩存，并且typing模塊中的大部分類型都是可散列的，并且可以比較是否相等。
## [`Any`](#typing.Any "typing.Any") 類型
[`Any`](#typing.Any "typing.Any") 是一種特殊的類型。靜態類型檢查器將所有類型視為與 [`Any`](#typing.Any "typing.Any") 兼容，反之亦然， [`Any`](#typing.Any "typing.Any") 也與所有類型相兼容。
這意味著可對類型為 [`Any`](#typing.Any "typing.Any") 的值執行任何操作或方法調用，并將其賦值給任何變量:
```
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()
...
```
需要注意的是，將 [`Any`](#typing.Any "typing.Any") 類型的值賦值給另一個更具體的類型時，Python不會執行類型檢查。例如，當把 `a` 賦值給 `s` 時，即使 `s` 被聲明為 [`str`](stdtypes.xhtml#str "str") 類型，在運行時接收到的是 [`int`](functions.xhtml#int "int") 值，靜態類型檢查器也不會報錯。
此外，所有返回值無類型或形參無類型的函數將隱式地默認使用 [`Any`](#typing.Any "typing.Any") 類型:
```
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
```
當需要混用動態類型和靜態類型的代碼時，上述行為可以讓 [`Any`](#typing.Any "typing.Any") 被用作 *應急出口* 。
[`Any`](#typing.Any "typing.Any") 和 [`object`](functions.xhtml#object "object") 的行為對比。與 [`Any`](#typing.Any "typing.Any") 相似，所有的類型都是 [`object`](functions.xhtml#object "object") 的子類型。然而不同于 [`Any`](#typing.Any "typing.Any")，反之并不成立： [`object`](functions.xhtml#object "object") *不是* 其他所有類型的子類型。
這意味著當一個值的類型是 [`object`](functions.xhtml#object "object") 的時候，類型檢查器會拒絕對它的幾乎所有的操作。把它賦值給一個指定了類型的變量（或者當作返回值）是一個類型錯誤。比如說：
```
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`](functions.xhtml#object "object") 示意一個值可以類型安全地兼容任何類型。使用 [`Any`](#typing.Any "typing.Any") 示意一個值地類型是動態定義的。
## 類,函數和修飾器.
這個模塊定義了如下的類,模塊和修飾器.
*class* `typing.``TypeVar`類型變量
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 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`](stdtypes.xhtml#str "str"), the return type is still plain [`str`](stdtypes.xhtml#str "str").
`isinstance(x, T)` 會在運行時拋出 [`TypeError`](exceptions.xhtml#TypeError "TypeError") 異常。一般地說， [`isinstance()`](functions.xhtml#isinstance "isinstance") 和 [`issubclass()`](functions.xhtml#issubclass "issubclass") 不應該和類型一起使用。
Type variables may be marked covariant or contravariant by passing `covariant=True` or `contravariant=True`. See [**PEP 484**](https://www.python.org/dev/peps/pep-0484) \[https://www.python.org/dev/peps/pep-0484\] 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**](https://www.python.org/dev/peps/pep-0484) \[https://www.python.org/dev/peps/pep-0484\].
*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.
```
這個類之后可以被這樣用:
```
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.``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**](https://www.python.org/dev/peps/pep-0484) \[https://www.python.org/dev/peps/pep-0484\].
The only legal parameters for [`Type`](#typing.Type "typing.Type") are classes, [`Any`](#typing.Any "typing.Any"), [type variables](#generics), 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.
3\.5.2 新版功能.
*class* `typing.``Iterable`(*Generic\[T\_co\]*)[`collections.abc.Iterable`](collections.abc.xhtml#collections.abc.Iterable "collections.abc.Iterable") 的泛型版本。
*class* `typing.``Iterator`(*Iterable\[T\_co\]*)[`collections.abc.Iterator`](collections.abc.xhtml#collections.abc.Iterator "collections.abc.Iterator") 的泛型版本。
*class* `typing.``Reversible`(*Iterable\[T\_co\]*)[`collections.abc.Reversible`](collections.abc.xhtml#collections.abc.Reversible "collections.abc.Reversible") 的泛型版本。
*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.``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\[T\_co\]*)[`collections.abc.Container`](collections.abc.xhtml#collections.abc.Container "collections.abc.Container") 的泛型版本。
*class* `typing.``Hashable`[`collections.abc.Hashable`](collections.abc.xhtml#collections.abc.Hashable "collections.abc.Hashable") 的別名。
*class* `typing.``Sized`[`collections.abc.Sized`](collections.abc.xhtml#collections.abc.Sized "collections.abc.Sized") 的別名。
*class* `typing.``Collection`(*Sized, Iterable\[T\_co\], Container\[T\_co\]*)[`collections.abc.Collection`](collections.abc.xhtml#collections.abc.Collection "collections.abc.Collection") 的泛型版本。
3\.6 新版功能.
*class* `typing.``AbstractSet`(*Sized, Collection\[T\_co\]*)[`collections.abc.Set`](collections.abc.xhtml#collections.abc.Set "collections.abc.Set") 的泛型版本。
*class* `typing.``MutableSet`(*AbstractSet\[T\]*)[`collections.abc.MutableSet`](collections.abc.xhtml#collections.abc.MutableSet "collections.abc.MutableSet") 的泛型版本。
*class* `typing.``Mapping`(*Sized, Collection\[KT\], Generic\[VT\_co\]*)[`collections.abc.Mapping`](collections.abc.xhtml#collections.abc.Mapping "collections.abc.Mapping") 的泛型版本。這個類型可以如下使用:
```
def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
return word_list[word]
```
*class* `typing.``MutableMapping`(*Mapping\[KT, VT\]*)[`collections.abc.MutableMapping`](collections.abc.xhtml#collections.abc.MutableMapping "collections.abc.MutableMapping") 的泛型版本。
*class* `typing.``Sequence`(*Reversible\[T\_co\], Collection\[T\_co\]*)[`collections.abc.Sequence`](collections.abc.xhtml#collections.abc.Sequence "collections.abc.Sequence") 的泛型版本。
*class* `typing.``MutableSequence`(*Sequence\[T\]*)[`collections.abc.MutableSequence`](collections.abc.xhtml#collections.abc.MutableSequence "collections.abc.MutableSequence") 的泛型版本。
*class* `typing.``ByteString`(*Sequence\[int\]*)[`collections.abc.ByteString`](collections.abc.xhtml#collections.abc.ByteString "collections.abc.ByteString") 的泛型版本。
This type represents the types [`bytes`](stdtypes.xhtml#bytes "bytes"), [`bytearray`](stdtypes.xhtml#bytearray "bytearray"), and [`memoryview`](stdtypes.xhtml#memoryview "memoryview").
As a shorthand for this type, [`bytes`](stdtypes.xhtml#bytes "bytes") can be used to annotate arguments of any of the types mentioned above.
*class* `typing.``Deque`(*deque, MutableSequence\[T\]*)[`collections.deque`](collections.xhtml#collections.deque "collections.deque") 的泛型版本。
3\.6.1 新版功能.
*class* `typing.``List`(*list, MutableSequence\[T\]*)Generic version of [`list`](stdtypes.xhtml#list "list"). Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as [`Sequence`](#typing.Sequence "typing.Sequence") or [`Iterable`](#typing.Iterable "typing.Iterable").
這個類型可以這樣用:
```
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\[T\]*)A generic version of [`builtins.set`](stdtypes.xhtml#set "set"). Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as [`AbstractSet`](#typing.AbstractSet "typing.AbstractSet").
*class* `typing.``FrozenSet`(*frozenset, AbstractSet\[T\_co\]*)A generic version of [`builtins.frozenset`](stdtypes.xhtml#frozenset "frozenset").
*class* `typing.``MappingView`(*Sized, Iterable\[T\_co\]*)[`collections.abc.MappingView`](collections.abc.xhtml#collections.abc.MappingView "collections.abc.MappingView") 的泛型版本。
*class* `typing.``KeysView`(*MappingView\[KT\_co\], AbstractSet\[KT\_co\]*)[`collections.abc.KeysView`](collections.abc.xhtml#collections.abc.KeysView "collections.abc.KeysView") 的泛型版本。
*class* `typing.``ItemsView`(*MappingView, Generic\[KT\_co, VT\_co\]*)[`collections.abc.ItemsView`](collections.abc.xhtml#collections.abc.ItemsView "collections.abc.ItemsView") 的泛型版本。
*class* `typing.``ValuesView`(*MappingView\[VT\_co\]*)[`collections.abc.ValuesView`](collections.abc.xhtml#collections.abc.ValuesView "collections.abc.ValuesView") 的泛型版本。
*class* `typing.``Awaitable`(*Generic\[T\_co\]*)[`collections.abc.Awaitable`](collections.abc.xhtml#collections.abc.Awaitable "collections.abc.Awaitable") 的泛型版本。
*class* `typing.``Coroutine`(*Awaitable\[V\_co\], Generic\[T\_co T\_contra, V\_co\]*)A generic version of [`collections.abc.Coroutine`](collections.abc.xhtml#collections.abc.Coroutine "collections.abc.Coroutine"). The variance and order of type variables correspond to those of [`Generator`](#typing.Generator "typing.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
```
*class* `typing.``AsyncIterable`(*Generic\[T\_co\]*)[`collections.abc.AsyncIterable`](collections.abc.xhtml#collections.abc.AsyncIterable "collections.abc.AsyncIterable") 的泛型版本。
*class* `typing.``AsyncIterator`(*AsyncIterable\[T\_co\]*)[`collections.abc.AsyncIterator`](collections.abc.xhtml#collections.abc.AsyncIterator "collections.abc.AsyncIterator") 的泛型版本。
*class* `typing.``ContextManager`(*Generic\[T\_co\]*)[`contextlib.AbstractContextManager`](contextlib.xhtml#contextlib.AbstractContextManager "contextlib.AbstractContextManager") 的泛型版本。
3\.6 新版功能.
*class* `typing.``AsyncContextManager`(*Generic\[T\_co\]*)[`contextlib.AbstractAsyncContextManager`](contextlib.xhtml#contextlib.AbstractAsyncContextManager "contextlib.AbstractAsyncContextManager") 的泛型版本。
3\.6 新版功能.
*class* `typing.``Dict`(*dict, MutableMapping\[KT, VT\]*)[`dict`](stdtypes.xhtml#dict "dict") 的泛型版本。對標注返回類型比較有用。如果要標注參數的話，使用如 [`Mapping`](#typing.Mapping "typing.Mapping") 的抽象容器類型是更好的選擇。
這個類型可以這樣使用:
```
def count_words(text: str) -> Dict[str, int]:
...
```
*class* `typing.``DefaultDict`(*collections.defaultdict, MutableMapping\[KT, VT\]*)[`collections.defaultdict`](collections.xhtml#collections.defaultdict "collections.defaultdict") 的泛型版本。
3\.5.2 新版功能.
*class* `typing.``OrderedDict`(*collections.OrderedDict, MutableMapping\[KT, VT\]*)[`collections.OrderedDict`](collections.xhtml#collections.OrderedDict "collections.OrderedDict") 的泛型版本。
3\.7.2 新版功能.
*class* `typing.``Counter`(*collections.Counter, Dict\[T, int\]*)[`collections.Counter`](collections.xhtml#collections.Counter "collections.Counter") 的泛型版本。
3\.6.1 新版功能.
*class* `typing.``ChainMap`(*collections.ChainMap, MutableMapping\[KT, VT\]*)[`collections.ChainMap`](collections.xhtml#collections.ChainMap "collections.ChainMap") 的泛型版本。
3\.6.1 新版功能.
*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`](#typing.Generator "typing.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`](#typing.Generator "typing.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)
```
3\.5.4 新版功能.
*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'
```
3\.5.2 新版功能.
*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()`](functions.xhtml#open "open").
*class* `typing.``Pattern`*class* `typing.``Match`These type aliases correspond to the return types from [`re.compile()`](re.xhtml#re.compile "re.compile") and [`re.match()`](re.xhtml#re.match "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()`](collections.xhtml#collections.namedtuple "collections.namedtuple").
用法:
```
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 two extra attributes: `_field_types`, giving a dict mapping field names to types, and `_field_defaults`, a dict mapping field names to default values. (The field names are in the `_fields` attribute, which is 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)])
```
在 3.6 版更改: Added support for [**PEP 526**](https://www.python.org/dev/peps/pep-0526) \[https://www.python.org/dev/peps/pep-0526\] variable annotation syntax.
在 3.6.1 版更改: Added support for default values, methods, and docstrings.
`typing.``NewType`(*typ*)A helper function to indicate a distinct types to a typechecker, see [NewType](#distinct). At runtime it returns a function that returns its argument. Usage:
```
UserId = NewType('UserId', int)
first_user = UserId(1)
```
3\.5.2 新版功能.
`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*\[, *locals*\]\])返回一個字典，字典內含有函數、方法、模塊或類對象的類型提示。
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.``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`](exceptions.xhtml#NotImplementedError "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**](https://www.python.org/dev/peps/pep-0484) \[https://www.python.org/dev/peps/pep-0484\] for details and comparison with other typing semantics.
`@``typing.``no_type_check`用于指明標注不是類型提示的裝飾器。
此 [decorator](../glossary.xhtml#term-decorator) 裝飾器生效于類或函數上。如果作用于類上的話，它會遞歸地作用于這個類的所定義的所有方法上（但是對于超類或子類所定義的方法不會生效）。
此方法會就地地修改函數。
`@``typing.``no_type_check_decorator`使其它裝飾器起到 [`no_type_check()`](#typing.no_type_check "typing.no_type_check") 效果的裝飾器。
This wraps the decorator with something that wraps the decorated function in [`no_type_check()`](#typing.no_type_check "typing.no_type_check").
`@``typing.``type_check_only`標記一個類或函數在運行時內不可用的裝飾器。
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.``Any`特殊類型，表明類型沒有任何限制。
- 每一個類型都對 [`Any`](#typing.Any "typing.Any") 兼容。
- [`Any`](#typing.Any "typing.Any") 對每一個類型都兼容。
`typing.``NoReturn`標記一個函數沒有返回值的特殊類型。比如說:
```
from typing import NoReturn
def stop() -> NoReturn:
raise RuntimeError('no way')
```
3\.5.4 新版功能.
`typing.``Union`聯合類型； `Union[X, Y]` 意味著：要不是 X，要不是 Y。
使用形如 `Union[int, str]` 的形式來定義一個聯合類型。細節如下:
- 參數必須是類型，而且必須至少有一個參數。
- 聯合類型的聯合類型會被展開打平，比如:
```
Union[Union[int, str], float] == Union[int, str, float]
```
- 僅有一個參數的聯合類型會坍縮成參數自身，比如:
```
Union[int] == int # The constructor actually returns int
```
- 多余的參數會被跳過，比如:
```
Union[int, str, int] == Union[int, str]
```
- 在比較聯合類型的時候，參數順序會被忽略，比如:
```
Union[int, str] == Union[str, int]
```
- 你不能繼承或者實例化一個聯合類型。
- 你不能寫成 `Union[X][Y]` 。
- 你可以使用 `Optional[X]` 作為 `Union[X, None]` 的縮寫。
在 3.7 版更改: 不要在運行時內從聯合類型中移除顯式說明的子類。
`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.
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`](#typing.Tuple "typing.Tuple")is equivalent to `Tuple[Any, ...]`, and in turn to [`tuple`](stdtypes.xhtml#tuple "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`](#typing.Callable "typing.Callable") is equivalent to `Callable[..., Any]`, and in turn to [`collections.abc.Callable`](collections.abc.xhtml#collections.abc.Callable "collections.abc.Callable").
`typing.``ClassVar`Special type construct to mark class variables.
As introduced in [**PEP 526**](https://www.python.org/dev/peps/pep-0526) \[https://www.python.org/dev/peps/pep-0526\], 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`](#typing.ClassVar "typing.ClassVar") accepts only types and cannot be further subscribed.
[`ClassVar`](#typing.ClassVar "typing.ClassVar") is not a class itself, and should not be used with [`isinstance()`](functions.xhtml#isinstance "isinstance") or [`issubclass()`](functions.xhtml#issubclass "issubclass"). [`ClassVar`](#typing.ClassVar "typing.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 新版功能.
`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.
3\.5.2 新版功能.
### 導航
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- [模塊](../py-modindex.xhtml "Python 模塊索引") |
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- Python文檔內容
- Python 有什么新變化？
- Python 3.7 有什么新變化
- 摘要 - 發布重點
- 新的特性
- 其他語言特性修改
- 新增模塊
- 改進的模塊
- C API 的改變
- 構建的改變
- 性能優化
- 其他 CPython 實現的改變
- 已棄用的 Python 行為
- 已棄用的 Python 模塊、函數和方法
- 已棄用的 C API 函數和類型
- 平臺支持的移除
- API 與特性的移除
- 移除的模塊
- Windows 專屬的改變
- 移植到 Python 3.7
- Python 3.7.1 中的重要變化
- Python 3.7.2 中的重要變化
- Python 3.6 有什么新變化A
- 摘要 - 發布重點
- 新的特性
- 其他語言特性修改
- 新增模塊
- 改進的模塊
- 性能優化
- Build and C API Changes
- 其他改進
- 棄用
- 移除
- 移植到Python 3.6
- Python 3.6.2 中的重要變化
- Python 3.6.4 中的重要變化
- Python 3.6.5 中的重要變化
- Python 3.6.7 中的重要變化
- Python 3.5 有什么新變化
- 摘要 - 發布重點
- 新的特性
- 其他語言特性修改
- 新增模塊
- 改進的模塊
- Other module-level changes
- 性能優化
- Build and C API Changes
- 棄用
- 移除
- Porting to Python 3.5
- Notable changes in Python 3.5.4
- What's New In Python 3.4
- 摘要 - 發布重點
- 新的特性
- 新增模塊
- 改進的模塊
- CPython Implementation Changes
- 棄用
- 移除
- Porting to Python 3.4
- Changed in 3.4.3
- What's New In Python 3.3
- 摘要 - 發布重點
- PEP 405: Virtual Environments
- PEP 420: Implicit Namespace Packages
- PEP 3118: New memoryview implementation and buffer protocol documentation
- PEP 393: Flexible String Representation
- PEP 397: Python Launcher for Windows
- PEP 3151: Reworking the OS and IO exception hierarchy
- PEP 380: Syntax for Delegating to a Subgenerator
- PEP 409: Suppressing exception context
- PEP 414: Explicit Unicode literals
- PEP 3155: Qualified name for classes and functions
- PEP 412: Key-Sharing Dictionary
- PEP 362: Function Signature Object
- PEP 421: Adding sys.implementation
- Using importlib as the Implementation of Import
- 其他語言特性修改
- A Finer-Grained Import Lock
- Builtin functions and types
- 新增模塊
- 改進的模塊
- 性能優化
- Build and C API Changes
- 棄用
- Porting to Python 3.3
- What's New In Python 3.2
- PEP 384: Defining a Stable ABI
- PEP 389: Argparse Command Line Parsing Module
- PEP 391: Dictionary Based Configuration for Logging
- PEP 3148: The concurrent.futures module
- PEP 3147: PYC Repository Directories
- PEP 3149: ABI Version Tagged .so Files
- PEP 3333: Python Web Server Gateway Interface v1.0.1
- 其他語言特性修改
- New, Improved, and Deprecated Modules
- 多線程
- 性能優化
- Unicode
- Codecs
- 文檔
- IDLE
- Code Repository
- Build and C API Changes
- Porting to Python 3.2
- What's New In Python 3.1
- PEP 372: Ordered Dictionaries
- PEP 378: Format Specifier for Thousands Separator
- 其他語言特性修改
- New, Improved, and Deprecated Modules
- 性能優化
- IDLE
- Build and C API Changes
- Porting to Python 3.1
- What's New In Python 3.0
- Common Stumbling Blocks
- Overview Of Syntax Changes
- Changes Already Present In Python 2.6
- Library Changes
- PEP 3101: A New Approach To String Formatting
- Changes To Exceptions
- Miscellaneous Other Changes
- Build and C API Changes
- 性能
- Porting To Python 3.0
- What's New in Python 2.7
- The Future for Python 2.x
- Changes to the Handling of Deprecation Warnings
- Python 3.1 Features
- PEP 372: Adding an Ordered Dictionary to collections
- PEP 378: Format Specifier for Thousands Separator
- PEP 389: The argparse Module for Parsing Command Lines
- PEP 391: Dictionary-Based Configuration For Logging
- PEP 3106: Dictionary Views
- PEP 3137: The memoryview Object
- 其他語言特性修改
- New and Improved Modules
- Build and C API Changes
- Other Changes and Fixes
- Porting to Python 2.7
- New Features Added to Python 2.7 Maintenance Releases
- Acknowledgements
- Python 2.6 有什么新變化
- Python 3.0
- Changes to the Development Process
- PEP 343: The 'with' statement
- PEP 366: Explicit Relative Imports From a Main Module
- PEP 370: Per-user site-packages Directory
- PEP 371: The multiprocessing Package
- PEP 3101: Advanced String Formatting
- PEP 3105: print As a Function
- PEP 3110: Exception-Handling Changes
- PEP 3112: Byte Literals
- PEP 3116: New I/O Library
- PEP 3118: Revised Buffer Protocol
- PEP 3119: Abstract Base Classes
- PEP 3127: Integer Literal Support and Syntax
- PEP 3129: Class Decorators
- PEP 3141: A Type Hierarchy for Numbers
- 其他語言特性修改
- New and Improved Modules
- Deprecations and Removals
- Build and C API Changes
- Porting to Python 2.6
- Acknowledgements
- What's New in Python 2.5
- PEP 308: Conditional Expressions
- PEP 309: Partial Function Application
- PEP 314: Metadata for Python Software Packages v1.1
- PEP 328: Absolute and Relative Imports
- PEP 338: Executing Modules as Scripts
- PEP 341: Unified try/except/finally
- PEP 342: New Generator Features
- PEP 343: The 'with' statement
- PEP 352: Exceptions as New-Style Classes
- PEP 353: Using ssize_t as the index type
- PEP 357: The 'index' method
- 其他語言特性修改
- New, Improved, and Removed Modules
- Build and C API Changes
- Porting to Python 2.5
- Acknowledgements
- What's New in Python 2.4
- PEP 218: Built-In Set Objects
- PEP 237: Unifying Long Integers and Integers
- PEP 289: Generator Expressions
- PEP 292: Simpler String Substitutions
- PEP 318: Decorators for Functions and Methods
- PEP 322: Reverse Iteration
- PEP 324: New subprocess Module
- PEP 327: Decimal Data Type
- PEP 328: Multi-line Imports
- PEP 331: Locale-Independent Float/String Conversions
- 其他語言特性修改
- New, Improved, and Deprecated Modules
- Build and C API Changes
- Porting to Python 2.4
- Acknowledgements
- What's New in Python 2.3
- PEP 218: A Standard Set Datatype
- PEP 255: Simple Generators
- PEP 263: Source Code Encodings
- PEP 273: Importing Modules from ZIP Archives
- PEP 277: Unicode file name support for Windows NT
- PEP 278: Universal Newline Support
- PEP 279: enumerate()
- PEP 282: The logging Package
- PEP 285: A Boolean Type
- PEP 293: Codec Error Handling Callbacks
- PEP 301: Package Index and Metadata for Distutils
- PEP 302: New Import Hooks
- PEP 305: Comma-separated Files
- PEP 307: Pickle Enhancements
- Extended Slices
- 其他語言特性修改
- New, Improved, and Deprecated Modules
- Pymalloc: A Specialized Object Allocator
- Build and C API Changes
- Other Changes and Fixes
- Porting to Python 2.3
- Acknowledgements
- What's New in Python 2.2
- 概述
- PEPs 252 and 253: Type and Class Changes
- PEP 234: Iterators
- PEP 255: Simple Generators
- PEP 237: Unifying Long Integers and Integers
- PEP 238: Changing the Division Operator
- Unicode Changes
- PEP 227: Nested Scopes
- New and Improved Modules
- Interpreter Changes and Fixes
- Other Changes and Fixes
- Acknowledgements
- What's New in Python 2.1
- 概述
- PEP 227: Nested Scopes
- PEP 236: future Directives
- PEP 207: Rich Comparisons
- PEP 230: Warning Framework
- PEP 229: New Build System
- PEP 205: Weak References
- PEP 232: Function Attributes
- PEP 235: Importing Modules on Case-Insensitive Platforms
- PEP 217: Interactive Display Hook
- PEP 208: New Coercion Model
- PEP 241: Metadata in Python Packages
- New and Improved Modules
- Other Changes and Fixes
- Acknowledgements
- What's New in Python 2.0
- 概述
- What About Python 1.6?
- New Development Process
- Unicode
- 列表推導式
- Augmented Assignment
- 字符串的方法
- Garbage Collection of Cycles
- Other Core Changes
- Porting to 2.0
- Extending/Embedding Changes
- Distutils: Making Modules Easy to Install
- XML Modules
- Module changes
- New modules
- IDLE Improvements
- Deleted and Deprecated Modules
- Acknowledgements
- 更新日志
- Python 下一版
- Python 3.7.3 最終版
- Python 3.7.3 發布候選版 1
- Python 3.7.2 最終版
- Python 3.7.2 發布候選版 1
- Python 3.7.1 最終版
- Python 3.7.1 RC 2版本
- Python 3.7.1 發布候選版 1
- Python 3.7.0 正式版
- Python 3.7.0 release candidate 1
- Python 3.7.0 beta 5
- Python 3.7.0 beta 4
- Python 3.7.0 beta 3
- Python 3.7.0 beta 2
- Python 3.7.0 beta 1
- Python 3.7.0 alpha 4
- Python 3.7.0 alpha 3
- Python 3.7.0 alpha 2
- Python 3.7.0 alpha 1
- Python 3.6.6 final
- Python 3.6.6 RC 1
- Python 3.6.5 final
- Python 3.6.5 release candidate 1
- Python 3.6.4 final
- Python 3.6.4 release candidate 1
- Python 3.6.3 final
- Python 3.6.3 release candidate 1
- Python 3.6.2 final
- Python 3.6.2 release candidate 2
- Python 3.6.2 release candidate 1
- Python 3.6.1 final
- Python 3.6.1 release candidate 1
- Python 3.6.0 final
- Python 3.6.0 release candidate 2
- Python 3.6.0 release candidate 1
- Python 3.6.0 beta 4
- Python 3.6.0 beta 3
- Python 3.6.0 beta 2
- Python 3.6.0 beta 1
- Python 3.6.0 alpha 4
- Python 3.6.0 alpha 3
- Python 3.6.0 alpha 2
- Python 3.6.0 alpha 1
- Python 3.5.5 final
- Python 3.5.5 release candidate 1
- Python 3.5.4 final
- Python 3.5.4 release candidate 1
- Python 3.5.3 final
- Python 3.5.3 release candidate 1
- Python 3.5.2 final
- Python 3.5.2 release candidate 1
- Python 3.5.1 final
- Python 3.5.1 release candidate 1
- Python 3.5.0 final
- Python 3.5.0 release candidate 4
- Python 3.5.0 release candidate 3
- Python 3.5.0 release candidate 2
- Python 3.5.0 release candidate 1
- Python 3.5.0 beta 4
- Python 3.5.0 beta 3
- Python 3.5.0 beta 2
- Python 3.5.0 beta 1
- Python 3.5.0 alpha 4
- Python 3.5.0 alpha 3
- Python 3.5.0 alpha 2
- Python 3.5.0 alpha 1
- Python 教程
- 課前甜點
- 使用 Python 解釋器
- 調用解釋器
- 解釋器的運行環境
- Python 的非正式介紹
- Python 作為計算器使用
- 走向編程的第一步
- 其他流程控制工具
- if 語句
- for 語句
- range() 函數
- break 和 continue 語句，以及循環中的 else 子句
- pass 語句
- 定義函數
- 函數定義的更多形式
- 小插曲：編碼風格
- 數據結構
- 列表的更多特性
- del 語句
- 元組和序列
- 集合
- 字典
- 循環的技巧
- 深入條件控制
- 序列和其它類型的比較
- 模塊
- 有關模塊的更多信息
- 標準模塊
- dir() 函數
- 包
- 輸入輸出
- 更漂亮的輸出格式
- 讀寫文件
- 錯誤和異常
- 語法錯誤
- 異常
- 處理異常
- 拋出異常
- 用戶自定義異常
- 定義清理操作
- 預定義的清理操作
- 類
- 名稱和對象
- Python 作用域和命名空間
- 初探類
- 補充說明
- 繼承
- 私有變量
- 雜項說明
- 迭代器
- 生成器
- 生成器表達式
- 標準庫簡介
- 操作系統接口
- 文件通配符
- 命令行參數
- 錯誤輸出重定向和程序終止
- 字符串模式匹配
- 數學
- 互聯網訪問
- 日期和時間
- 數據壓縮
- 性能測量
- 質量控制
- 自帶電池
- 標準庫簡介 —— 第二部分
- 格式化輸出
- 模板
- 使用二進制數據記錄格式
- 多線程
- 日志
- 弱引用
- 用于操作列表的工具
- 十進制浮點運算
- 虛擬環境和包
- 概述
- 創建虛擬環境
- 使用pip管理包
- 接下來？
- 交互式編輯和編輯歷史
- Tab 補全和編輯歷史
- 默認交互式解釋器的替代品
- 浮點算術：爭議和限制
- 表示性錯誤
- 附錄
- 交互模式
- 安裝和使用 Python
- 命令行與環境
- 命令行
- 環境變量
- 在Unix平臺中使用Python
- 獲取最新版本的Python
- 構建Python
- 與Python相關的路徑和文件
- 雜項
- 編輯器和集成開發環境
- 在Windows上使用 Python
- 完整安裝程序
- Microsoft Store包
- nuget.org 安裝包
- 可嵌入的包
- 替代捆綁包
- 配置Python
- 適用于Windows的Python啟動器
- 查找模塊
- 附加模塊
- 在Windows上編譯Python
- 其他平臺
- 在蘋果系統上使用 Python
- 獲取和安裝 MacPython
- IDE
- 安裝額外的 Python 包
- Mac 上的圖形界面編程
- 在 Mac 上分發 Python 應用程序
- 其他資源
- Python 語言參考
- 概述
- 其他實現
- 標注
- 詞法分析
- 行結構
- 其他形符
- 標識符和關鍵字
- 字面值
- 運算符
- 分隔符
- 數據模型
- 對象、值與類型
- 標準類型層級結構
- 特殊方法名稱
- 協程
- 執行模型
- 程序的結構
- 命名與綁定
- 異常
- 導入系統
- importlib
- 包
- 搜索
- 加載
- 基于路徑的查找器
- 替換標準導入系統
- Package Relative Imports
- 有關 main 的特殊事項
- 開放問題項
- 參考文獻
- 表達式
- 算術轉換
- 原子
- 原型
- await 表達式
- 冪運算符
- 一元算術和位運算
- 二元算術運算符
- 移位運算
- 二元位運算
- 比較運算
- 布爾運算
- 條件表達式
- lambda 表達式
- 表達式列表
- 求值順序
- 運算符優先級
- 簡單語句
- 表達式語句
- 賦值語句
- assert 語句
- pass 語句
- del 語句
- return 語句
- yield 語句
- raise 語句
- break 語句
- continue 語句
- import 語句
- global 語句
- nonlocal 語句
- 復合語句
- if 語句
- while 語句
- for 語句
- try 語句
- with 語句
- 函數定義
- 類定義
- 協程
- 最高層級組件
- 完整的 Python 程序
- 文件輸入
- 交互式輸入
- 表達式輸入
- 完整的語法規范
- Python 標準庫
- 概述
- 可用性注釋
- 內置函數
- 內置常量
- 由 site 模塊添加的常量
- 內置類型
- 邏輯值檢測
- 布爾運算 — and, or, not
- 比較
- 數字類型 — int, float, complex
- 迭代器類型
- 序列類型 — list, tuple, range
- 文本序列類型 — str
- 二進制序列類型 — bytes, bytearray, memoryview
- 集合類型 — set, frozenset
- 映射類型 — dict
- 上下文管理器類型
- 其他內置類型
- 特殊屬性
- 內置異常
- 基類
- 具體異常
- 警告
- 異常層次結構
- 文本處理服務
- string — 常見的字符串操作
- re — 正則表達式操作
- 模塊 difflib 是一個計算差異的助手
- textwrap — Text wrapping and filling
- unicodedata — Unicode 數據庫
- stringprep — Internet String Preparation
- readline — GNU readline interface
- rlcompleter — GNU readline的完成函數
- 二進制數據服務
- struct — Interpret bytes as packed binary data
- codecs — Codec registry and base classes
- 數據類型
- datetime — 基礎日期/時間數據類型
- calendar — General calendar-related functions
- collections — 容器數據類型
- collections.abc — 容器的抽象基類
- heapq — 堆隊列算法
- bisect — Array bisection algorithm
- array — Efficient arrays of numeric values
- weakref — 弱引用
- types — Dynamic type creation and names for built-in types
- copy — 淺層 (shallow) 和深層 (deep) 復制操作
- pprint — 數據美化輸出
- reprlib — Alternate repr() implementation
- enum — Support for enumerations
- 數字和數學模塊
- numbers — 數字的抽象基類
- math — 數學函數
- cmath — Mathematical functions for complex numbers
- decimal — 十進制定點和浮點運算
- fractions — 分數
- random — 生成偽隨機數
- statistics — Mathematical statistics functions
- 函數式編程模塊
- itertools — 為高效循環而創建迭代器的函數
- functools — 高階函數和可調用對象上的操作
- operator — 標準運算符替代函數
- 文件和目錄訪問
- pathlib — 面向對象的文件系統路徑
- os.path — 常見路徑操作
- fileinput — Iterate over lines from multiple input streams
- stat — Interpreting stat() results
- filecmp — File and Directory Comparisons
- tempfile — Generate temporary files and directories
- glob — Unix style pathname pattern expansion
- fnmatch — Unix filename pattern matching
- linecache — Random access to text lines
- shutil — High-level file operations
- macpath — Mac OS 9 路徑操作函數
- 數據持久化
- pickle —— Python 對象序列化
- copyreg — Register pickle support functions
- shelve — Python object persistence
- marshal — Internal Python object serialization
- dbm — Interfaces to Unix “databases”
- sqlite3 — SQLite 數據庫 DB-API 2.0 接口模塊
- 數據壓縮和存檔
- zlib — 與 gzip 兼容的壓縮
- gzip — 對 gzip 格式的支持
- bz2 — 對 bzip2 壓縮算法的支持
- lzma — 用 LZMA 算法壓縮
- zipfile — 在 ZIP 歸檔中工作
- tarfile — Read and write tar archive files
- 文件格式
- csv — CSV 文件讀寫
- configparser — Configuration file parser
- netrc — netrc file processing
- xdrlib — Encode and decode XDR data
- plistlib — Generate and parse Mac OS X .plist files
- 加密服務
- hashlib — 安全哈希與消息摘要
- hmac — 基于密鑰的消息驗證
- secrets — Generate secure random numbers for managing secrets
- 通用操作系統服務
- os — 操作系統接口模塊
- io — 處理流的核心工具
- time — 時間的訪問和轉換
- argparse — 命令行選項、參數和子命令解析器
- getopt — C-style parser for command line options
- 模塊 logging — Python 的日志記錄工具
- logging.config — 日志記錄配置
- logging.handlers — Logging handlers
- getpass — 便攜式密碼輸入工具
- curses — 終端字符單元顯示的處理
- curses.textpad — Text input widget for curses programs
- curses.ascii — Utilities for ASCII characters
- curses.panel — A panel stack extension for curses
- platform — Access to underlying platform's identifying data
- errno — Standard errno system symbols
- ctypes — Python 的外部函數庫
- 并發執行
- threading — 基于線程的并行
- multiprocessing — 基于進程的并行
- concurrent 包
- concurrent.futures — 啟動并行任務
- subprocess — 子進程管理
- sched — 事件調度器
- queue — 一個同步的隊列類
- _thread — 底層多線程 API
- _dummy_thread — _thread 的替代模塊
- dummy_threading — 可直接替代 threading 模塊。
- contextvars — Context Variables
- Context Variables
- Manual Context Management
- asyncio support
- 網絡和進程間通信
- asyncio — 異步 I/O
- socket — 底層網絡接口
- ssl — TLS/SSL wrapper for socket objects
- select — Waiting for I/O completion
- selectors — 高級 I/O 復用庫
- asyncore — 異步socket處理器
- asynchat — 異步 socket 指令/響應 處理器
- signal — Set handlers for asynchronous events
- mmap — Memory-mapped file support
- 互聯網數據處理
- email — 電子郵件與 MIME 處理包
- json — JSON 編碼和解碼器
- mailcap — Mailcap file handling
- mailbox — Manipulate mailboxes in various formats
- mimetypes — Map filenames to MIME types
- base64 — Base16, Base32, Base64, Base85 數據編碼
- binhex — 對binhex4文件進行編碼和解碼
- binascii — 二進制和 ASCII 碼互轉
- quopri — Encode and decode MIME quoted-printable data
- uu — Encode and decode uuencode files
- 結構化標記處理工具
- html — 超文本標記語言支持
- html.parser — 簡單的 HTML 和 XHTML 解析器
- html.entities — HTML 一般實體的定義
- XML處理模塊
- xml.etree.ElementTree — The ElementTree XML API
- xml.dom — The Document Object Model API
- xml.dom.minidom — Minimal DOM implementation
- xml.dom.pulldom — Support for building partial DOM trees
- xml.sax — Support for SAX2 parsers
- xml.sax.handler — Base classes for SAX handlers
- xml.sax.saxutils — SAX Utilities
- xml.sax.xmlreader — Interface for XML parsers
- xml.parsers.expat — Fast XML parsing using Expat
- 互聯網協議和支持
- webbrowser — 方便的Web瀏覽器控制器
- cgi — Common Gateway Interface support
- cgitb — Traceback manager for CGI scripts
- wsgiref — WSGI Utilities and Reference Implementation
- urllib — URL 處理模塊
- urllib.request — 用于打開 URL 的可擴展庫
- urllib.response — Response classes used by urllib
- urllib.parse — Parse URLs into components
- urllib.error — Exception classes raised by urllib.request
- urllib.robotparser — Parser for robots.txt
- http — HTTP 模塊
- http.client — HTTP協議客戶端
- ftplib — FTP protocol client
- poplib — POP3 protocol client
- imaplib — IMAP4 protocol client
- nntplib — NNTP protocol client
- smtplib —SMTP協議客戶端
- smtpd — SMTP Server
- telnetlib — Telnet client
- uuid — UUID objects according to RFC 4122
- socketserver — A framework for network servers
- http.server — HTTP 服務器
- http.cookies — HTTP state management
- http.cookiejar — Cookie handling for HTTP clients
- xmlrpc — XMLRPC 服務端與客戶端模塊
- xmlrpc.client — XML-RPC client access
- xmlrpc.server — Basic XML-RPC servers
- ipaddress — IPv4/IPv6 manipulation library
- 多媒體服務
- audioop — Manipulate raw audio data
- aifc — Read and write AIFF and AIFC files
- sunau — 讀寫 Sun AU 文件
- wave — 讀寫WAV格式文件
- chunk — Read IFF chunked data
- colorsys — Conversions between color systems
- imghdr — 推測圖像類型
- sndhdr — 推測聲音文件的類型
- ossaudiodev — Access to OSS-compatible audio devices
- 國際化
- gettext — 多語種國際化服務
- locale — 國際化服務
- 程序框架
- turtle — 海龜繪圖
- cmd — 支持面向行的命令解釋器
- shlex — Simple lexical analysis
- Tk圖形用戶界面(GUI)
- tkinter — Tcl/Tk的Python接口
- tkinter.ttk — Tk themed widgets
- tkinter.tix — Extension widgets for Tk
- tkinter.scrolledtext — 滾動文字控件
- IDLE
- 其他圖形用戶界面（GUI）包
- 開發工具
- typing — 類型標注支持
- pydoc — Documentation generator and online help system
- doctest — Test interactive Python examples
- unittest — 單元測試框架
- unittest.mock — mock object library
- unittest.mock 上手指南
- 2to3 - 自動將 Python 2 代碼轉為 Python 3 代碼
- test — Regression tests package for Python
- test.support — Utilities for the Python test suite
- test.support.script_helper — Utilities for the Python execution tests
- 調試和分析
- bdb — Debugger framework
- faulthandler — Dump the Python traceback
- pdb — The Python Debugger
- The Python Profilers
- timeit — 測量小代碼片段的執行時間
- trace — Trace or track Python statement execution
- tracemalloc — Trace memory allocations
- 軟件打包和分發
- distutils — 構建和安裝 Python 模塊
- ensurepip — Bootstrapping the pip installer
- venv — 創建虛擬環境
- zipapp — Manage executable Python zip archives
- Python運行時服務
- sys — 系統相關的參數和函數
- sysconfig — Provide access to Python's configuration information
- builtins — 內建對象
- main — 頂層腳本環境
- warnings — Warning control
- dataclasses — 數據類
- contextlib — Utilities for with-statement contexts
- abc — 抽象基類
- atexit — 退出處理器
- traceback — Print or retrieve a stack traceback
- future — Future 語句定義
- gc — 垃圾回收器接口
- inspect — 檢查對象
- site — Site-specific configuration hook
- 自定義 Python 解釋器
- code — Interpreter base classes
- codeop — Compile Python code
- 導入模塊
- zipimport — Import modules from Zip archives
- pkgutil — Package extension utility
- modulefinder — 查找腳本使用的模塊
- runpy — Locating and executing Python modules
- importlib — The implementation of import
- Python 語言服務
- parser — Access Python parse trees
- ast — 抽象語法樹
- symtable — Access to the compiler's symbol tables
- symbol — 與 Python 解析樹一起使用的常量
- token — 與Python解析樹一起使用的常量
- keyword — 檢驗Python關鍵字
- tokenize — Tokenizer for Python source
- tabnanny — 模糊縮進檢測
- pyclbr — Python class browser support
- py_compile — Compile Python source files
- compileall — Byte-compile Python libraries
- dis — Python 字節碼反匯編器
- pickletools — Tools for pickle developers
- 雜項服務
- formatter — Generic output formatting
- Windows系統相關模塊
- msilib — Read and write Microsoft Installer files
- msvcrt — Useful routines from the MS VC++ runtime
- winreg — Windows 注冊表訪問
- winsound — Sound-playing interface for Windows
- Unix 專有服務
- posix — The most common POSIX system calls
- pwd — 用戶密碼數據庫
- spwd — The shadow password database
- grp — The group database
- crypt — Function to check Unix passwords
- termios — POSIX style tty control
- tty — 終端控制功能
- pty — Pseudo-terminal utilities
- fcntl — The fcntl and ioctl system calls
- pipes — Interface to shell pipelines
- resource — Resource usage information
- nis — Interface to Sun's NIS (Yellow Pages)
- Unix syslog 庫例程
- 被取代的模塊
- optparse — Parser for command line options
- imp — Access the import internals
- 未創建文檔的模塊
- 平臺特定模塊
- 擴展和嵌入 Python 解釋器
- 推薦的第三方工具
- 不使用第三方工具創建擴展
- 使用 C 或 C++ 擴展 Python
- 自定義擴展類型：教程
- 定義擴展類型：已分類主題
- 構建C/C++擴展
- 在Windows平臺編譯C和C++擴展
- 在更大的應用程序中嵌入 CPython 運行時
- Embedding Python in Another Application
- Python/C API 參考手冊
- 概述
- 代碼標準
- 包含文件
- 有用的宏
- 對象、類型和引用計數
- 異常
- 嵌入Python
- 調試構建
- 穩定的應用程序二進制接口
- The Very High Level Layer
- Reference Counting
- 異常處理
- Printing and clearing
- 拋出異常
- Issuing warnings
- Querying the error indicator
- Signal Handling
- Exception Classes
- Exception Objects
- Unicode Exception Objects
- Recursion Control
- 標準異常
- 標準警告類別
- 工具
- 操作系統實用程序
- 系統功能
- 過程控制
- 導入模塊
- Data marshalling support
- 語句解釋及變量編譯
- 字符串轉換與格式化
- 反射
- 編解碼器注冊與支持功能
- 抽象對象層
- Object Protocol
- 數字協議
- Sequence Protocol
- Mapping Protocol
- 迭代器協議
- 緩沖協議
- Old Buffer Protocol
- 具體的對象層
- 基本對象
- 數值對象
- 序列對象
- 容器對象
- 函數對象
- 其他對象
- Initialization, Finalization, and Threads
- 在Python初始化之前
- 全局配置變量
- Initializing and finalizing the interpreter
- Process-wide parameters
- Thread State and the Global Interpreter Lock
- Sub-interpreter support
- Asynchronous Notifications
- Profiling and Tracing
- Advanced Debugger Support
- Thread Local Storage Support
- 內存管理
- 概述
- 原始內存接口
- Memory Interface
- 對象分配器
- 默認內存分配器
- Customize Memory Allocators
- The pymalloc allocator
- tracemalloc C API
- 示例
- 對象實現支持
- 在堆中分配對象
- Common Object Structures
- Type 對象
- Number Object Structures
- Mapping Object Structures
- Sequence Object Structures
- Buffer Object Structures
- Async Object Structures
- 使對象類型支持循環垃圾回收
- API 和 ABI 版本管理
- 分發 Python 模塊
- 關鍵術語
- 開源許可與協作
- 安裝工具
- 閱讀指南
- 我該如何...？
- ...為我的項目選擇一個名字？
- ...創建和分發二進制擴展？
- 安裝 Python 模塊
- 關鍵術語
- 基本使用
- 我應如何 ...？
- ... 在 Python 3.4 之前的 Python 版本中安裝 pip ？
- ... 只為當前用戶安裝軟件包？
- ... 安裝科學計算類 Python 軟件包？
- ... 使用并行安裝的多個 Python 版本？
- 常見的安裝問題
- 在 Linux 的系統 Python 版本上安裝
- 未安裝 pip
- 安裝二進制編譯擴展
- Python 常用指引
- 將 Python 2 代碼遷移到 Python 3
- 簡要說明
- 詳情
- 將擴展模塊移植到 Python 3
- 條件編譯
- 對象API的更改
- 模塊初始化和狀態
- CObject 替換為 Capsule
- 其他選項
- Curses Programming with Python
- What is curses?
- Starting and ending a curses application
- Windows and Pads
- Displaying Text
- User Input
- For More Information
- 實現描述器
- 摘要
- 定義和簡介
- 描述器協議
- 發起調用描述符
- 描述符示例
- Properties
- 函數和方法
- Static Methods and Class Methods
- 函數式編程指引
- 概述
- 迭代器
- 生成器表達式和列表推導式
- 生成器
- 內置函數
- itertools 模塊
- The functools module
- Small functions and the lambda expression
- Revision History and Acknowledgements
- 引用文獻
- 日志 HOWTO
- 日志基礎教程
- 進階日志教程
- 日志級別
- 有用的處理程序
- 記錄日志中引發的異常
- 使用任意對象作為消息
- 優化
- 日志操作手冊
- 在多個模塊中使用日志
- 在多線程中使用日志
- 使用多個日志處理器和多種格式化
- 在多個地方記錄日志
- 日志服務器配置示例
- 處理日志處理器的阻塞
- Sending and receiving logging events across a network
- Adding contextual information to your logging output
- Logging to a single file from multiple processes
- Using file rotation
- Use of alternative formatting styles
- Customizing LogRecord
- Subclassing QueueHandler - a ZeroMQ example
- Subclassing QueueListener - a ZeroMQ example
- An example dictionary-based configuration
- Using a rotator and namer to customize log rotation processing
- A more elaborate multiprocessing example
- Inserting a BOM into messages sent to a SysLogHandler
- Implementing structured logging
- Customizing handlers with dictConfig()
- Using particular formatting styles throughout your application
- Configuring filters with dictConfig()
- Customized exception formatting
- Speaking logging messages
- Buffering logging messages and outputting them conditionally
- Formatting times using UTC (GMT) via configuration
- Using a context manager for selective logging
- 正則表達式HOWTO
- 概述
- 簡單模式
- 使用正則表達式
- 更多模式能力
- 修改字符串
- 常見問題
- 反饋
- 套接字編程指南
- 套接字
- 創建套接字
- 使用一個套接字
- 斷開連接
- 非阻塞的套接字
- 排序指南
- 基本排序
- 關鍵函數
- Operator 模塊函數
- 升序和降序
- 排序穩定性和排序復雜度
- 使用裝飾-排序-去裝飾的舊方法
- 使用 cmp 參數的舊方法
- 其它
- Unicode 指南
- Unicode 概述
- Python's Unicode Support
- Reading and Writing Unicode Data
- Acknowledgements
- 如何使用urllib包獲取網絡資源
- 概述
- Fetching URLs
- 處理異常
- info and geturl
- Openers and Handlers
- Basic Authentication
- Proxies
- Sockets and Layers
- 腳注
- Argparse 教程
- 概念
- 基礎
- 位置參數介紹
- Introducing Optional arguments
- Combining Positional and Optional arguments
- Getting a little more advanced
- Conclusion
- ipaddress模塊介紹
- 創建 Address/Network/Interface 對象
- 審查 Address/Network/Interface 對象
- Network 作為 Address 列表
- 比較
- 將IP地址與其他模塊一起使用
- 實例創建失敗時獲取更多詳細信息
- Argument Clinic How-To
- The Goals Of Argument Clinic
- Basic Concepts And Usage
- Converting Your First Function
- Advanced Topics
- 使用 DTrace 和 SystemTap 檢測CPython
- Enabling the static markers
- Static DTrace probes
- Static SystemTap markers
- Available static markers
- SystemTap Tapsets
- 示例
- Python 常見問題
- Python常見問題
- 一般信息
- 現實世界中的 Python
- 編程常見問題
- 一般問題
- 核心語言
- 數字和字符串
- 性能
- 序列（元組/列表）
- 對象
- 模塊
- 設計和歷史常見問題
- 為什么Python使用縮進來分組語句？
- 為什么簡單的算術運算得到奇怪的結果？
- 為什么浮點計算不準確？
- 為什么Python字符串是不可變的？
- 為什么必須在方法定義和調用中顯式使用“self”？
- 為什么不能在表達式中賦值？
- 為什么Python對某些功能（例如list.index()）使用方法來實現，而其他功能（例如len(List)）使用函數實現？
- 為什么 join()是一個字符串方法而不是列表或元組方法？
- 異常有多快？
- 為什么Python中沒有switch或case語句？
- 難道不能在解釋器中模擬線程，而非得依賴特定于操作系統的線程實現嗎？
- 為什么lambda表達式不能包含語句？
- 可以將Python編譯為機器代碼，C或其他語言嗎？
- Python如何管理內存？
- 為什么CPython不使用更傳統的垃圾回收方案？
- CPython退出時為什么不釋放所有內存？
- 為什么有單獨的元組和列表數據類型？
- 列表是如何在CPython中實現的？
- 字典是如何在CPython中實現的？
- 為什么字典key必須是不可變的？
- 為什么 list.sort() 沒有返回排序列表？
- 如何在Python中指定和實施接口規范？
- 為什么沒有goto？
- 為什么原始字符串（r-strings）不能以反斜杠結尾？
- 為什么Python沒有屬性賦值的“with”語句？
- 為什么 if/while/def/class語句需要冒號？
- 為什么Python在列表和元組的末尾允許使用逗號？
- 代碼庫和插件 FAQ
- 通用的代碼庫問題
- 通用任務
- 線程相關
- 輸入輸出
- 網絡 / Internet 編程
- 數據庫
- 數學和數字
- 擴展/嵌入常見問題
- 可以使用C語言中創建自己的函數嗎？
- 可以使用C++語言中創建自己的函數嗎？
- C很難寫，有沒有其他選擇？
- 如何從C執行任意Python語句？
- 如何從C中評估任意Python表達式？
- 如何從Python對象中提取C的值？
- 如何使用Py_BuildValue()創建任意長度的元組？
- 如何從C調用對象的方法？
- 如何捕獲PyErr_Print()（或打印到stdout / stderr的任何內容）的輸出？
- 如何從C訪問用Python編寫的模塊？
- 如何從Python接口到C ++對象？
- 我使用Setup文件添加了一個模塊，為什么make失敗了？
- 如何調試擴展？
- 我想在Linux系統上編譯一個Python模塊，但是缺少一些文件。為什么?
- 如何區分“輸入不完整”和“輸入無效”？
- 如何找到未定義的g++符號__builtin_new或__pure_virtual？
- 能否創建一個對象類，其中部分方法在C中實現，而其他方法在Python中實現（例如通過繼承）？
- Python在Windows上的常見問題
- 我怎樣在Windows下運行一個Python程序？
- 我怎么讓 Python 腳本可執行？
- 為什么有時候 Python 程序會啟動緩慢？
- 我怎樣使用Python腳本制作可執行文件？
- *.pyd 文件和DLL文件相同嗎？
- 我怎樣將Python嵌入一個Windows程序？
- 如何讓編輯器不要在我的 Python 源代碼中插入 tab ？
- 如何在不阻塞的情況下檢查按鍵？
- 圖形用戶界面（GUI）常見問題
- 圖形界面常見問題
- Python 是否有平臺無關的圖形界面工具包？
- 有哪些Python的GUI工具是某個平臺專用的？
- 有關Tkinter的問題
- “為什么我的電腦上安裝了 Python ？”
- 什么是Python？
- 為什么我的電腦上安裝了 Python ？
- 我能刪除 Python 嗎？
- 術語對照表
- 文檔說明
- Python 文檔貢獻者
- 解決 Bug
- 文檔錯誤
- 使用 Python 的錯誤追蹤系統
- 開始為 Python 貢獻您的知識
- 版權
- 歷史和許可證
- 軟件歷史
- 訪問Python或以其他方式使用Python的條款和條件
- Python 3.7.3 的 PSF 許可協議
- Python 2.0 的 BeOpen.com 許可協議
- Python 1.6.1 的 CNRI 許可協議
- Python 0.9.0 至 1.2 的 CWI 許可協議
- 集成軟件的許可和認可
- Mersenne Twister
- 套接字
- Asynchronous socket services
- Cookie management
- Execution tracing
- UUencode and UUdecode functions
- XML Remote Procedure Calls
- test_epoll
- Select kqueue
- SipHash24
- strtod and dtoa
- OpenSSL
- expat
- libffi
- zlib
- cfuhash
- libmpdec