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# [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") —— Python 对象序列化
**源代码：** [Lib/pickle.py](https://github.com/python/cpython/tree/3.7/Lib/pickle.py) \[https://github.com/python/cpython/tree/3.7/Lib/pickle.py\]
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模块 [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 实现了对一个 Python 对象结构的二进制序列化和反序列化。 *"Pickling"* 是将 Python 对象和所拥有的层次结构被转化为一个字节流的过程，而 *"unpickling"* 是相反的操作，会将（来自一个 [binary file](../glossary.xhtml#term-binary-file) 或者 [bytes-like object](../glossary.xhtml#term-bytes-like-object) 的）字节流转化回一个对象层次结构。Pickling（和 unpickling）也被称为“序列化”, “编组” [1](#id6) 或者 “平面化”。而为了避免混乱，此处采用术语 “pickling” 和 “unpickling”。
警告
[`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 模块在接受被错误地构造或者被恶意地构造的数据时不安全。永远不要 unpickle 来自于不受信任的或者未经验证的来源的数据。
## 与其他 Python 模块间的关系
### 与 `marshal` 间的关系
Python 有一个更原始的序列化模块称为 [`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).")，但一般地 [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 应该是序列化 Python 对象时的首选。[`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).") 存在主要是为了支持 Python 的 `.pyc` 文件.
[`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 模块与 [`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).") 在如下几方面显著地不同：
- [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 模块会跟踪已被序列化的对象，所以该对象之后再次被引用时不会再次被序列化。[`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).") 不会这么做。
这隐含了递归对象和共享对象。递归对象指包含对自己的引用的对象。这种对象并不会被 marshal 接受，并且实际上尝试 marshal 递归对象会让你的 Python 解释器崩溃。对象共享发生在对象层级中存在多处引用同一对象时。[`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 只会存储这些对象一次，并确保其他的引用指向同一个主副本。共享对象将保持共享，这可能对可变对象非常重要。
- [`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).") 不能被用于序列化用户定义类及其实例。[`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 能够透明地存储并保存类实例，然而此时类定义必须能够从与被存储时相同的模块被引入。
- The [`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).") serialization format is not guaranteed to be portable across Python versions. Because its primary job in life is to support `.pyc` files, the Python implementers reserve the right to change the serialization format in non-backwards compatible ways should the need arise. The [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") serialization format is guaranteed to be backwards compatible across Python releases provided a compatible pickle protocol is chosen and pickling and unpickling code deals with Python 2 to Python 3 type differences if your data is crossing that unique breaking change language boundary.
### 与 `json` 模块的比较
Pickle 协议和 [JSON (JavaScript Object Notation)](http://json.org) \[http://json.org\] 间有着本质的不同：
- JSON 是一个文本序列化格式（它输出 unicode 文本，尽管在大多数时候它会接着以 `utf-8` 编码），而 pickle 是一个二进制序列化格式；
- JSON is human-readable, while pickle is not;
- JSON is interoperable and widely used outside of the Python ecosystem, while pickle is Python-specific;
- JSON, by default, can only represent a subset of the Python built-in types, and no custom classes; pickle can represent an extremely large number of Python types (many of them automatically, by clever usage of Python's introspection facilities; complex cases can be tackled by implementing [specific object APIs](#pickle-inst)).
参见
The [`json`](json.xhtml#module-json "json: Encode and decode the JSON format.") module: a standard library module allowing JSON serialization and deserialization.
## Data stream format
The data format used by [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") is Python-specific. This has the advantage that there are no restrictions imposed by external standards such as JSON or XDR (which can't represent pointer sharing); however it means that non-Python programs may not be able to reconstruct pickled Python objects.
By default, the [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") data format uses a relatively compact binary representation. If you need optimal size characteristics, you can efficiently [compress](archiving.xhtml) pickled data.
The module [`pickletools`](pickletools.xhtml#module-pickletools "pickletools: Contains extensive comments about the pickle protocols and pickle-machine opcodes, as well as some useful functions.") contains tools for analyzing data streams generated by [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back."). [`pickletools`](pickletools.xhtml#module-pickletools "pickletools: Contains extensive comments about the pickle protocols and pickle-machine opcodes, as well as some useful functions.") source code has extensive comments about opcodes used by pickle protocols.
There are currently 5 different protocols which can be used for pickling. The higher the protocol used, the more recent the version of Python needed to read the pickle produced.
- Protocol version 0 is the original "human-readable" protocol and is backwards compatible with earlier versions of Python.
- Protocol version 1 is an old binary format which is also compatible with earlier versions of Python.
- Protocol version 2 was introduced in Python 2.3. It provides much more efficient pickling of [new-style class](../glossary.xhtml#term-new-style-class)es. Refer to [**PEP 307**](https://www.python.org/dev/peps/pep-0307) \[https://www.python.org/dev/peps/pep-0307\] for information about improvements brought by protocol 2.
- Protocol version 3 was added in Python 3.0. It has explicit support for [`bytes`](stdtypes.xhtml#bytes "bytes") objects and cannot be unpickled by Python 2.x. This is the default protocol, and the recommended protocol when compatibility with other Python 3 versions is required.
- Protocol version 4 was added in Python 3.4. It adds support for very large objects, pickling more kinds of objects, and some data format optimizations. Refer to [**PEP 3154**](https://www.python.org/dev/peps/pep-3154) \[https://www.python.org/dev/peps/pep-3154\] for information about improvements brought by protocol 4.
注解
Serialization is a more primitive notion than persistence; although [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") reads and writes file objects, it does not handle the issue of naming persistent objects, nor the (even more complicated) issue of concurrent access to persistent objects. The [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") module can transform a complex object into a byte stream and it can transform the byte stream into an object with the same internal structure. Perhaps the most obvious thing to do with these byte streams is to write them onto a file, but it is also conceivable to send them across a network or store them in a database. The [`shelve`](shelve.xhtml#module-shelve "shelve: Python object persistence.")module provides a simple interface to pickle and unpickle objects on DBM-style database files.
## Module Interface
To serialize an object hierarchy, you simply call the [`dumps()`](#pickle.dumps "pickle.dumps") function. Similarly, to de-serialize a data stream, you call the [`loads()`](#pickle.loads "pickle.loads") function. However, if you want more control over serialization and de-serialization, you can create a [`Pickler`](#pickle.Pickler "pickle.Pickler") or an [`Unpickler`](#pickle.Unpickler "pickle.Unpickler") object, respectively.
The [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") module provides the following constants:
`pickle.``HIGHEST_PROTOCOL`An integer, the highest [protocol version](#pickle-protocols)available. This value can be passed as a *protocol* value to functions [`dump()`](#pickle.dump "pickle.dump") and [`dumps()`](#pickle.dumps "pickle.dumps") as well as the [`Pickler`](#pickle.Pickler "pickle.Pickler")constructor.
`pickle.``DEFAULT_PROTOCOL`An integer, the default [protocol version](#pickle-protocols) used for pickling. May be less than [`HIGHEST_PROTOCOL`](#pickle.HIGHEST_PROTOCOL "pickle.HIGHEST_PROTOCOL"). Currently the default protocol is 3, a new protocol designed for Python 3.
The [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") module provides the following functions to make the pickling process more convenient:
`pickle.``dump`(*obj*, *file*, *protocol=None*, *\**, *fix\_imports=True*)Write a pickled representation of *obj* to the open [file object](../glossary.xhtml#term-file-object) *file*. This is equivalent to `Pickler(file, protocol).dump(obj)`.
The optional *protocol* argument, an integer, tells the pickler to use the given protocol; supported protocols are 0 to [`HIGHEST_PROTOCOL`](#pickle.HIGHEST_PROTOCOL "pickle.HIGHEST_PROTOCOL"). If not specified, the default is [`DEFAULT_PROTOCOL`](#pickle.DEFAULT_PROTOCOL "pickle.DEFAULT_PROTOCOL"). If a negative number is specified, [`HIGHEST_PROTOCOL`](#pickle.HIGHEST_PROTOCOL "pickle.HIGHEST_PROTOCOL") is selected.
The *file* argument must have a write() method that accepts a single bytes argument. It can thus be an on-disk file opened for binary writing, an [`io.BytesIO`](io.xhtml#io.BytesIO "io.BytesIO") instance, or any other custom object that meets this interface.
If *fix\_imports* is true and *protocol* is less than 3, pickle will try to map the new Python 3 names to the old module names used in Python 2, so that the pickle data stream is readable with Python 2.
`pickle.``dumps`(*obj*, *protocol=None*, *\**, *fix\_imports=True*)Return the pickled representation of the object as a [`bytes`](stdtypes.xhtml#bytes "bytes") object, instead of writing it to a file.
Arguments *protocol* and *fix\_imports* have the same meaning as in [`dump()`](#pickle.dump "pickle.dump").
`pickle.``load`(*file*, *\**, *fix\_imports=True*, *encoding="ASCII"*, *errors="strict"*)Read a pickled object representation from the open [file object](../glossary.xhtml#term-file-object)*file* and return the reconstituted object hierarchy specified therein. This is equivalent to `Unpickler(file).load()`.
The protocol version of the pickle is detected automatically, so no protocol argument is needed. Bytes past the pickled object's representation are ignored.
The argument *file* must have two methods, a read() method that takes an integer argument, and a readline() method that requires no arguments. Both methods should return bytes. Thus *file* can be an on-disk file opened for binary reading, an [`io.BytesIO`](io.xhtml#io.BytesIO "io.BytesIO") object, or any other custom object that meets this interface.
Optional keyword arguments are *fix\_imports*, *encoding* and *errors*, which are used to control compatibility support for pickle stream generated by Python 2. If *fix\_imports* is true, pickle will try to map the old Python 2 names to the new names used in Python 3. The *encoding* and *errors* tell pickle how to decode 8-bit string instances pickled by Python 2; these default to 'ASCII' and 'strict', respectively. The *encoding* can be 'bytes' to read these 8-bit string instances as bytes objects. Using `encoding='latin1'` is required for unpickling NumPy arrays and instances of [`datetime`](datetime.xhtml#datetime.datetime "datetime.datetime"), [`date`](datetime.xhtml#datetime.date "datetime.date") and [`time`](datetime.xhtml#datetime.time "datetime.time") pickled by Python 2.
`pickle.``loads`(*bytes\_object*, *\**, *fix\_imports=True*, *encoding="ASCII"*, *errors="strict"*)Read a pickled object hierarchy from a [`bytes`](stdtypes.xhtml#bytes "bytes") object and return the reconstituted object hierarchy specified therein.
The protocol version of the pickle is detected automatically, so no protocol argument is needed. Bytes past the pickled object's representation are ignored.
Optional keyword arguments are *fix\_imports*, *encoding* and *errors*, which are used to control compatibility support for pickle stream generated by Python 2. If *fix\_imports* is true, pickle will try to map the old Python 2 names to the new names used in Python 3. The *encoding* and *errors* tell pickle how to decode 8-bit string instances pickled by Python 2; these default to 'ASCII' and 'strict', respectively. The *encoding* can be 'bytes' to read these 8-bit string instances as bytes objects. Using `encoding='latin1'` is required for unpickling NumPy arrays and instances of [`datetime`](datetime.xhtml#datetime.datetime "datetime.datetime"), [`date`](datetime.xhtml#datetime.date "datetime.date") and [`time`](datetime.xhtml#datetime.time "datetime.time") pickled by Python 2.
The [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") module defines three exceptions:
*exception* `pickle.``PickleError`Common base class for the other pickling exceptions. It inherits [`Exception`](exceptions.xhtml#Exception "Exception").
*exception* `pickle.``PicklingError`Error raised when an unpicklable object is encountered by [`Pickler`](#pickle.Pickler "pickle.Pickler"). It inherits [`PickleError`](#pickle.PickleError "pickle.PickleError").
Refer to [What can be pickled and unpickled?](#pickle-picklable) to learn what kinds of objects can be pickled.
*exception* `pickle.``UnpicklingError`Error raised when there is a problem unpickling an object, such as a data corruption or a security violation. It inherits [`PickleError`](#pickle.PickleError "pickle.PickleError").
Note that other exceptions may also be raised during unpickling, including (but not necessarily limited to) AttributeError, EOFError, ImportError, and IndexError.
The [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") module exports two classes, [`Pickler`](#pickle.Pickler "pickle.Pickler") and [`Unpickler`](#pickle.Unpickler "pickle.Unpickler"):
*class* `pickle.``Pickler`(*file*, *protocol=None*, *\**, *fix\_imports=True*)This takes a binary file for writing a pickle data stream.
The optional *protocol* argument, an integer, tells the pickler to use the given protocol; supported protocols are 0 to [`HIGHEST_PROTOCOL`](#pickle.HIGHEST_PROTOCOL "pickle.HIGHEST_PROTOCOL"). If not specified, the default is [`DEFAULT_PROTOCOL`](#pickle.DEFAULT_PROTOCOL "pickle.DEFAULT_PROTOCOL"). If a negative number is specified, [`HIGHEST_PROTOCOL`](#pickle.HIGHEST_PROTOCOL "pickle.HIGHEST_PROTOCOL") is selected.
The *file* argument must have a write() method that accepts a single bytes argument. It can thus be an on-disk file opened for binary writing, an [`io.BytesIO`](io.xhtml#io.BytesIO "io.BytesIO") instance, or any other custom object that meets this interface.
If *fix\_imports* is true and *protocol* is less than 3, pickle will try to map the new Python 3 names to the old module names used in Python 2, so that the pickle data stream is readable with Python 2.
`dump`(*obj*)Write a pickled representation of *obj* to the open file object given in the constructor.
`persistent_id`(*obj*)Do nothing by default. This exists so a subclass can override it.
If [`persistent_id()`](#pickle.Pickler.persistent_id "pickle.Pickler.persistent_id") returns `None`, *obj* is pickled as usual. Any other value causes [`Pickler`](#pickle.Pickler "pickle.Pickler") to emit the returned value as a persistent ID for *obj*. The meaning of this persistent ID should be defined by [`Unpickler.persistent_load()`](#pickle.Unpickler.persistent_load "pickle.Unpickler.persistent_load"). Note that the value returned by [`persistent_id()`](#pickle.Pickler.persistent_id "pickle.Pickler.persistent_id") cannot itself have a persistent ID.
See [Persistence of External Objects](#pickle-persistent) for details and examples of uses.
`dispatch_table`A pickler object's dispatch table is a registry of *reduction functions* of the kind which can be declared using [`copyreg.pickle()`](copyreg.xhtml#copyreg.pickle "copyreg.pickle"). It is a mapping whose keys are classes and whose values are reduction functions. A reduction function takes a single argument of the associated class and should conform to the same interface as a [`__reduce__()`](#object.__reduce__ "object.__reduce__")method.
By default, a pickler object will not have a [`dispatch_table`](#pickle.Pickler.dispatch_table "pickle.Pickler.dispatch_table") attribute, and it will instead use the global dispatch table managed by the [`copyreg`](copyreg.xhtml#module-copyreg "copyreg: Register pickle support functions.") module. However, to customize the pickling for a specific pickler object one can set the [`dispatch_table`](#pickle.Pickler.dispatch_table "pickle.Pickler.dispatch_table") attribute to a dict-like object. Alternatively, if a subclass of [`Pickler`](#pickle.Pickler "pickle.Pickler") has a [`dispatch_table`](#pickle.Pickler.dispatch_table "pickle.Pickler.dispatch_table") attribute then this will be used as the default dispatch table for instances of that class.
See [Dispatch Tables](#pickle-dispatch) for usage examples.
3\.3 新版功能.
`fast`Deprecated. Enable fast mode if set to a true value. The fast mode disables the usage of memo, therefore speeding the pickling process by not generating superfluous PUT opcodes. It should not be used with self-referential objects, doing otherwise will cause [`Pickler`](#pickle.Pickler "pickle.Pickler") to recurse infinitely.
Use [`pickletools.optimize()`](pickletools.xhtml#pickletools.optimize "pickletools.optimize") if you need more compact pickles.
*class* `pickle.``Unpickler`(*file*, *\**, *fix\_imports=True*, *encoding="ASCII"*, *errors="strict"*)This takes a binary file for reading a pickle data stream.
The protocol version of the pickle is detected automatically, so no protocol argument is needed.
The argument *file* must have two methods, a read() method that takes an integer argument, and a readline() method that requires no arguments. Both methods should return bytes. Thus *file* can be an on-disk file object opened for binary reading, an [`io.BytesIO`](io.xhtml#io.BytesIO "io.BytesIO") object, or any other custom object that meets this interface.
Optional keyword arguments are *fix\_imports*, *encoding* and *errors*, which are used to control compatibility support for pickle stream generated by Python 2. If *fix\_imports* is true, pickle will try to map the old Python 2 names to the new names used in Python 3. The *encoding* and *errors* tell pickle how to decode 8-bit string instances pickled by Python 2; these default to 'ASCII' and 'strict', respectively. The *encoding* can be 'bytes' to read these 8-bit string instances as bytes objects.
`load`()Read a pickled object representation from the open file object given in the constructor, and return the reconstituted object hierarchy specified therein. Bytes past the pickled object's representation are ignored.
`persistent_load`(*pid*)Raise an [`UnpicklingError`](#pickle.UnpicklingError "pickle.UnpicklingError") by default.
If defined, [`persistent_load()`](#pickle.Unpickler.persistent_load "pickle.Unpickler.persistent_load") should return the object specified by the persistent ID *pid*. If an invalid persistent ID is encountered, an [`UnpicklingError`](#pickle.UnpicklingError "pickle.UnpicklingError") should be raised.
See [Persistence of External Objects](#pickle-persistent) for details and examples of uses.
`find_class`(*module*, *name*)Import *module* if necessary and return the object called *name* from it, where the *module* and *name* arguments are [`str`](stdtypes.xhtml#str "str") objects. Note, unlike its name suggests, [`find_class()`](#pickle.Unpickler.find_class "pickle.Unpickler.find_class") is also used for finding functions.
Subclasses may override this to gain control over what type of objects and how they can be loaded, potentially reducing security risks. Refer to [Restricting Globals](#pickle-restrict) for details.
## What can be pickled and unpickled?
The following types can be pickled:
- `None`, `True`, and `False`
- integers, floating point numbers, complex numbers
- strings, bytes, bytearrays
- tuples, lists, sets, and dictionaries containing only picklable objects
- functions defined at the top level of a module (using [`def`](../reference/compound_stmts.xhtml#def), not [`lambda`](../reference/expressions.xhtml#lambda))
- built-in functions defined at the top level of a module
- classes that are defined at the top level of a module
- instances of such classes whose [`__dict__`](stdtypes.xhtml#object.__dict__ "object.__dict__") or the result of calling [`__getstate__()`](#object.__getstate__ "object.__getstate__") is picklable (see section [Pickling Class Instances](#pickle-inst) for details).
Attempts to pickle unpicklable objects will raise the [`PicklingError`](#pickle.PicklingError "pickle.PicklingError")exception; when this happens, an unspecified number of bytes may have already been written to the underlying file. Trying to pickle a highly recursive data structure may exceed the maximum recursion depth, a [`RecursionError`](exceptions.xhtml#RecursionError "RecursionError") will be raised in this case. You can carefully raise this limit with [`sys.setrecursionlimit()`](sys.xhtml#sys.setrecursionlimit "sys.setrecursionlimit").
Note that functions (built-in and user-defined) are pickled by "fully qualified" name reference, not by value. [2](#id7) This means that only the function name is pickled, along with the name of the module the function is defined in. Neither the function's code, nor any of its function attributes are pickled. Thus the defining module must be importable in the unpickling environment, and the module must contain the named object, otherwise an exception will be raised. [3](#id8)
Similarly, classes are pickled by named reference, so the same restrictions in the unpickling environment apply. Note that none of the class's code or data is pickled, so in the following example the class attribute `attr` is not restored in the unpickling environment:
```
class Foo:
attr = 'A class attribute'
picklestring = pickle.dumps(Foo)
```
These restrictions are why picklable functions and classes must be defined in the top level of a module.
Similarly, when class instances are pickled, their class's code and data are not pickled along with them. Only the instance data are pickled. This is done on purpose, so you can fix bugs in a class or add methods to the class and still load objects that were created with an earlier version of the class. If you plan to have long-lived objects that will see many versions of a class, it may be worthwhile to put a version number in the objects so that suitable conversions can be made by the class's [`__setstate__()`](#object.__setstate__ "object.__setstate__") method.
## Pickling Class Instances
In this section, we describe the general mechanisms available to you to define, customize, and control how class instances are pickled and unpickled.
In most cases, no additional code is needed to make instances picklable. By default, pickle will retrieve the class and the attributes of an instance via introspection. When a class instance is unpickled, its [`__init__()`](../reference/datamodel.xhtml#object.__init__ "object.__init__") method is usually *not* invoked. The default behaviour first creates an uninitialized instance and then restores the saved attributes. The following code shows an implementation of this behaviour:
```
def save(obj):
return (obj.__class__, obj.__dict__)
def load(cls, attributes):
obj = cls.__new__(cls)
obj.__dict__.update(attributes)
return obj
```
Classes can alter the default behaviour by providing one or several special methods:
`object.``__getnewargs_ex__`()In protocols 2 and newer, classes that implements the [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__") method can dictate the values passed to the [`__new__()`](../reference/datamodel.xhtml#object.__new__ "object.__new__") method upon unpickling. The method must return a pair `(args, kwargs)` where *args* is a tuple of positional arguments and *kwargs* a dictionary of named arguments for constructing the object. Those will be passed to the [`__new__()`](../reference/datamodel.xhtml#object.__new__ "object.__new__") method upon unpickling.
You should implement this method if the [`__new__()`](../reference/datamodel.xhtml#object.__new__ "object.__new__") method of your class requires keyword-only arguments. Otherwise, it is recommended for compatibility to implement [`__getnewargs__()`](#object.__getnewargs__ "object.__getnewargs__").
在 3.6 版更改: [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__") is now used in protocols 2 and 3.
`object.``__getnewargs__`()This method serves a similar purpose as [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__"), but supports only positional arguments. It must return a tuple of arguments `args` which will be passed to the [`__new__()`](../reference/datamodel.xhtml#object.__new__ "object.__new__") method upon unpickling.
[`__getnewargs__()`](#object.__getnewargs__ "object.__getnewargs__") will not be called if [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__") is defined.
在 3.6 版更改: Before Python 3.6, [`__getnewargs__()`](#object.__getnewargs__ "object.__getnewargs__") was called instead of [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__") in protocols 2 and 3.
`object.``__getstate__`()Classes can further influence how their instances are pickled; if the class defines the method [`__getstate__()`](#object.__getstate__ "object.__getstate__"), it is called and the returned object is pickled as the contents for the instance, instead of the contents of the instance's dictionary. If the [`__getstate__()`](#object.__getstate__ "object.__getstate__") method is absent, the instance's [`__dict__`](stdtypes.xhtml#object.__dict__ "object.__dict__") is pickled as usual.
`object.``__setstate__`(*state*)Upon unpickling, if the class defines [`__setstate__()`](#object.__setstate__ "object.__setstate__"), it is called with the unpickled state. In that case, there is no requirement for the state object to be a dictionary. Otherwise, the pickled state must be a dictionary and its items are assigned to the new instance's dictionary.
注解
If [`__getstate__()`](#object.__getstate__ "object.__getstate__") returns a false value, the [`__setstate__()`](#object.__setstate__ "object.__setstate__")method will not be called upon unpickling.
Refer to the section [Handling Stateful Objects](#pickle-state) for more information about how to use the methods [`__getstate__()`](#object.__getstate__ "object.__getstate__") and [`__setstate__()`](#object.__setstate__ "object.__setstate__").
注解
At unpickling time, some methods like [`__getattr__()`](../reference/datamodel.xhtml#object.__getattr__ "object.__getattr__"), [`__getattribute__()`](../reference/datamodel.xhtml#object.__getattribute__ "object.__getattribute__"), or [`__setattr__()`](../reference/datamodel.xhtml#object.__setattr__ "object.__setattr__") may be called upon the instance. In case those methods rely on some internal invariant being true, the type should implement [`__getnewargs__()`](#object.__getnewargs__ "object.__getnewargs__") or [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__") to establish such an invariant; otherwise, neither [`__new__()`](../reference/datamodel.xhtml#object.__new__ "object.__new__") nor [`__init__()`](../reference/datamodel.xhtml#object.__init__ "object.__init__") will be called.
As we shall see, pickle does not use directly the methods described above. In fact, these methods are part of the copy protocol which implements the [`__reduce__()`](#object.__reduce__ "object.__reduce__") special method. The copy protocol provides a unified interface for retrieving the data necessary for pickling and copying objects. [4](#id9)
Although powerful, implementing [`__reduce__()`](#object.__reduce__ "object.__reduce__") directly in your classes is error prone. For this reason, class designers should use the high-level interface (i.e., [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__"), [`__getstate__()`](#object.__getstate__ "object.__getstate__") and [`__setstate__()`](#object.__setstate__ "object.__setstate__")) whenever possible. We will show, however, cases where using [`__reduce__()`](#object.__reduce__ "object.__reduce__") is the only option or leads to more efficient pickling or both.
`object.``__reduce__`()The interface is currently defined as follows. The [`__reduce__()`](#object.__reduce__ "object.__reduce__") method takes no argument and shall return either a string or preferably a tuple (the returned object is often referred to as the "reduce value").
If a string is returned, the string should be interpreted as the name of a global variable. It should be the object's local name relative to its module; the pickle module searches the module namespace to determine the object's module. This behaviour is typically useful for singletons.
When a tuple is returned, it must be between two and five items long. Optional items can either be omitted, or `None` can be provided as their value. The semantics of each item are in order:
- A callable object that will be called to create the initial version of the object.
- A tuple of arguments for the callable object. An empty tuple must be given if the callable does not accept any argument.
- Optionally, the object's state, which will be passed to the object's [`__setstate__()`](#object.__setstate__ "object.__setstate__") method as previously described. If the object has no such method then, the value must be a dictionary and it will be added to the object's [`__dict__`](stdtypes.xhtml#object.__dict__ "object.__dict__") attribute.
- Optionally, an iterator (and not a sequence) yielding successive items. These items will be appended to the object either using `obj.append(item)` or, in batch, using `obj.extend(list_of_items)`. This is primarily used for list subclasses, but may be used by other classes as long as they have `append()` and `extend()` methods with the appropriate signature. (Whether `append()` or `extend()` is used depends on which pickle protocol version is used as well as the number of items to append, so both must be supported.)
- Optionally, an iterator (not a sequence) yielding successive key-value pairs. These items will be stored to the object using
```
obj[key] =
value
```
. This is primarily used for dictionary subclasses, but may be used by other classes as long as they implement [`__setitem__()`](../reference/datamodel.xhtml#object.__setitem__ "object.__setitem__").
`object.``__reduce_ex__`(*protocol*)Alternatively, a [`__reduce_ex__()`](#object.__reduce_ex__ "object.__reduce_ex__") method may be defined. The only difference is this method should take a single integer argument, the protocol version. When defined, pickle will prefer it over the [`__reduce__()`](#object.__reduce__ "object.__reduce__")method. In addition, [`__reduce__()`](#object.__reduce__ "object.__reduce__") automatically becomes a synonym for the extended version. The main use for this method is to provide backwards-compatible reduce values for older Python releases.
### Persistence of External Objects
For the benefit of object persistence, the [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") module supports the notion of a reference to an object outside the pickled data stream. Such objects are referenced by a persistent ID, which should be either a string of alphanumeric characters (for protocol 0) [5](#id10) or just an arbitrary object (for any newer protocol).
The resolution of such persistent IDs is not defined by the [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.")module; it will delegate this resolution to the user defined methods on the pickler and unpickler, [`persistent_id()`](#pickle.Pickler.persistent_id "pickle.Pickler.persistent_id") and [`persistent_load()`](#pickle.Unpickler.persistent_load "pickle.Unpickler.persistent_load") respectively.
To pickle objects that have an external persistent id, the pickler must have a custom [`persistent_id()`](#pickle.Pickler.persistent_id "pickle.Pickler.persistent_id") method that takes an object as an argument and returns either `None` or the persistent id for that object. When `None` is returned, the pickler simply pickles the object as normal. When a persistent ID string is returned, the pickler will pickle that object, along with a marker so that the unpickler will recognize it as a persistent ID.
To unpickle external objects, the unpickler must have a custom [`persistent_load()`](#pickle.Unpickler.persistent_load "pickle.Unpickler.persistent_load") method that takes a persistent ID object and returns the referenced object.
Here is a comprehensive example presenting how persistent ID can be used to pickle external objects by reference.
```
# Simple example presenting how persistent ID can be used to pickle
# external objects by reference.
import pickle
import sqlite3
from collections import namedtuple
# Simple class representing a record in our database.
MemoRecord = namedtuple("MemoRecord", "key, task")
class DBPickler(pickle.Pickler):
def persistent_id(self, obj):
# Instead of pickling MemoRecord as a regular class instance, we emit a
# persistent ID.
if isinstance(obj, MemoRecord):
# Here, our persistent ID is simply a tuple, containing a tag and a
# key, which refers to a specific record in the database.
return ("MemoRecord", obj.key)
else:
# If obj does not have a persistent ID, return None. This means obj
# needs to be pickled as usual.
return None
class DBUnpickler(pickle.Unpickler):
def __init__(self, file, connection):
super().__init__(file)
self.connection = connection
def persistent_load(self, pid):
# This method is invoked whenever a persistent ID is encountered.
# Here, pid is the tuple returned by DBPickler.
cursor = self.connection.cursor()
type_tag, key_id = pid
if type_tag == "MemoRecord":
# Fetch the referenced record from the database and return it.
cursor.execute("SELECT * FROM memos WHERE key=?", (str(key_id),))
key, task = cursor.fetchone()
return MemoRecord(key, task)
else:
# Always raises an error if you cannot return the correct object.
# Otherwise, the unpickler will think None is the object referenced
# by the persistent ID.
raise pickle.UnpicklingError("unsupported persistent object")
def main():
import io
import pprint
# Initialize and populate our database.
conn = sqlite3.connect(":memory:")
cursor = conn.cursor()
cursor.execute("CREATE TABLE memos(key INTEGER PRIMARY KEY, task TEXT)")
tasks = (
'give food to fish',
'prepare group meeting',
'fight with a zebra',
)
for task in tasks:
cursor.execute("INSERT INTO memos VALUES(NULL, ?)", (task,))
# Fetch the records to be pickled.
cursor.execute("SELECT * FROM memos")
memos = [MemoRecord(key, task) for key, task in cursor]
# Save the records using our custom DBPickler.
file = io.BytesIO()
DBPickler(file).dump(memos)
print("Pickled records:")
pprint.pprint(memos)
# Update a record, just for good measure.
cursor.execute("UPDATE memos SET task='learn italian' WHERE key=1")
# Load the records from the pickle data stream.
file.seek(0)
memos = DBUnpickler(file, conn).load()
print("Unpickled records:")
pprint.pprint(memos)
if __name__ == '__main__':
main()
```
### Dispatch Tables
If one wants to customize pickling of some classes without disturbing any other code which depends on pickling, then one can create a pickler with a private dispatch table.
The global dispatch table managed by the [`copyreg`](copyreg.xhtml#module-copyreg "copyreg: Register pickle support functions.") module is available as `copyreg.dispatch_table`. Therefore, one may choose to use a modified copy of `copyreg.dispatch_table` as a private dispatch table.
For example
```
f = io.BytesIO()
p = pickle.Pickler(f)
p.dispatch_table = copyreg.dispatch_table.copy()
p.dispatch_table[SomeClass] = reduce_SomeClass
```
creates an instance of [`pickle.Pickler`](#pickle.Pickler "pickle.Pickler") with a private dispatch table which handles the `SomeClass` class specially. Alternatively, the code
```
class MyPickler(pickle.Pickler):
dispatch_table = copyreg.dispatch_table.copy()
dispatch_table[SomeClass] = reduce_SomeClass
f = io.BytesIO()
p = MyPickler(f)
```
does the same, but all instances of `MyPickler` will by default share the same dispatch table. The equivalent code using the [`copyreg`](copyreg.xhtml#module-copyreg "copyreg: Register pickle support functions.") module is
```
copyreg.pickle(SomeClass, reduce_SomeClass)
f = io.BytesIO()
p = pickle.Pickler(f)
```
### Handling Stateful Objects
Here's an example that shows how to modify pickling behavior for a class. The `TextReader` class opens a text file, and returns the line number and line contents each time its `readline()` method is called. If a `TextReader` instance is pickled, all attributes *except* the file object member are saved. When the instance is unpickled, the file is reopened, and reading resumes from the last location. The [`__setstate__()`](#object.__setstate__ "object.__setstate__") and [`__getstate__()`](#object.__getstate__ "object.__getstate__") methods are used to implement this behavior.
```
class TextReader:
"""Print and number lines in a text file."""
def __init__(self, filename):
self.filename = filename
self.file = open(filename)
self.lineno = 0
def readline(self):
self.lineno += 1
line = self.file.readline()
if not line:
return None
if line.endswith('\n'):
line = line[:-1]
return "%i: %s" % (self.lineno, line)
def __getstate__(self):
# Copy the object's state from self.__dict__ which contains
# all our instance attributes. Always use the dict.copy()
# method to avoid modifying the original state.
state = self.__dict__.copy()
# Remove the unpicklable entries.
del state['file']
return state
def __setstate__(self, state):
# Restore instance attributes (i.e., filename and lineno).
self.__dict__.update(state)
# Restore the previously opened file's state. To do so, we need to
# reopen it and read from it until the line count is restored.
file = open(self.filename)
for _ in range(self.lineno):
file.readline()
# Finally, save the file.
self.file = file
```
A sample usage might be something like this:
```
>>> reader = TextReader("hello.txt")
>>> reader.readline()
'1: Hello world!'
>>> reader.readline()
'2: I am line number two.'
>>> new_reader = pickle.loads(pickle.dumps(reader))
>>> new_reader.readline()
'3: Goodbye!'
```
## Restricting Globals
By default, unpickling will import any class or function that it finds in the pickle data. For many applications, this behaviour is unacceptable as it permits the unpickler to import and invoke arbitrary code. Just consider what this hand-crafted pickle data stream does when loaded:
```
>>> import pickle
>>> pickle.loads(b"cos\nsystem\n(S'echo hello world'\ntR.")
hello world
0
```
In this example, the unpickler imports the [`os.system()`](os.xhtml#os.system "os.system") function and then apply the string argument "echo hello world". Although this example is inoffensive, it is not difficult to imagine one that could damage your system.
For this reason, you may want to control what gets unpickled by customizing [`Unpickler.find_class()`](#pickle.Unpickler.find_class "pickle.Unpickler.find_class"). Unlike its name suggests, [`Unpickler.find_class()`](#pickle.Unpickler.find_class "pickle.Unpickler.find_class") is called whenever a global (i.e., a class or a function) is requested. Thus it is possible to either completely forbid globals or restrict them to a safe subset.
Here is an example of an unpickler allowing only few safe classes from the [`builtins`](builtins.xhtml#module-builtins "builtins: The module that provides the built-in namespace.") module to be loaded:
```
import builtins
import io
import pickle
safe_builtins = {
'range',
'complex',
'set',
'frozenset',
'slice',
}
class RestrictedUnpickler(pickle.Unpickler):
def find_class(self, module, name):
# Only allow safe classes from builtins.
if module == "builtins" and name in safe_builtins:
return getattr(builtins, name)
# Forbid everything else.
raise pickle.UnpicklingError("global '%s.%s' is forbidden" %
(module, name))
def restricted_loads(s):
"""Helper function analogous to pickle.loads()."""
return RestrictedUnpickler(io.BytesIO(s)).load()
```
A sample usage of our unpickler working has intended:
```
>>> restricted_loads(pickle.dumps([1, 2, range(15)]))
[1, 2, range(0, 15)]
>>> restricted_loads(b"cos\nsystem\n(S'echo hello world'\ntR.")
Traceback (most recent call last):
...
pickle.UnpicklingError: global 'os.system' is forbidden
>>> restricted_loads(b'cbuiltins\neval\n'
... b'(S\'getattr(__import__("os"), "system")'
... b'("echo hello world")\'\ntR.')
Traceback (most recent call last):
...
pickle.UnpicklingError: global 'builtins.eval' is forbidden
```
As our examples shows, you have to be careful with what you allow to be unpickled. Therefore if security is a concern, you may want to consider alternatives such as the marshalling API in [`xmlrpc.client`](xmlrpc.client.xhtml#module-xmlrpc.client "xmlrpc.client: XML-RPC client access.") or third-party solutions.
## 性能
Recent versions of the pickle protocol (from protocol 2 and upwards) feature efficient binary encodings for several common features and built-in types. Also, the [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") module has a transparent optimizer written in C.
## 示例
For the simplest code, use the [`dump()`](#pickle.dump "pickle.dump") and [`load()`](#pickle.load "pickle.load") functions.
```
import pickle
# An arbitrary collection of objects supported by pickle.
data = {
'a': [1, 2.0, 3, 4+6j],
'b': ("character string", b"byte string"),
'c': {None, True, False}
}
with open('data.pickle', 'wb') as f:
# Pickle the 'data' dictionary using the highest protocol available.
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
```
The following example reads the resulting pickled data.
```
import pickle
with open('data.pickle', 'rb') as f:
# The protocol version used is detected automatically, so we do not
# have to specify it.
data = pickle.load(f)
```
参见
Module [`copyreg`](copyreg.xhtml#module-copyreg "copyreg: Register pickle support functions.")Pickle interface constructor registration for extension types.
Module [`pickletools`](pickletools.xhtml#module-pickletools "pickletools: Contains extensive comments about the pickle protocols and pickle-machine opcodes, as well as some useful functions.")Tools for working with and analyzing pickled data.
模块 [`shelve`](shelve.xhtml#module-shelve "shelve: Python object persistence.")Indexed databases of objects; uses [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.").
Module [`copy`](copy.xhtml#module-copy "copy: Shallow and deep copy operations.")Shallow and deep object copying.
Module [`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).")High-performance serialization of built-in types.
脚注
[1](#id1)Don't confuse this with the [`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).") module
[2](#id2)This is why [`lambda`](../reference/expressions.xhtml#lambda) functions cannot be pickled: all `lambda` functions share the same name: `<lambda>`.
[3](#id3)The exception raised will likely be an [`ImportError`](exceptions.xhtml#ImportError "ImportError") or an [`AttributeError`](exceptions.xhtml#AttributeError "AttributeError") but it could be something else.
[4](#id4)The [`copy`](copy.xhtml#module-copy "copy: Shallow and deep copy operations.") module uses this protocol for shallow and deep copying operations.
[5](#id5)The limitation on alphanumeric characters is due to the fact the persistent IDs, in protocol 0, are delimited by the newline character. Therefore if any kind of newline characters occurs in persistent IDs, the resulting pickle will become unreadable.
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- 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 最终版
- Python 3.7.3 发布候选版 1
- Python 3.7.2 最终版
- Python 3.7.2 发布候选版 1
- Python 3.7.1 最终版
- Python 3.7.1 RC 2版本
- Python 3.7.1 发布候选版 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
- 课前甜点
- 使用 Python 解释器
- 调用解释器
- 解释器的运行环境
- Python 的非正式介绍
- Python 作为计算器使用
- 走向编程的第一步
- 其他流程控制工具
- if 语句
- for 语句
- range() 函数
- break 和 continue 语句，以及循环中的 else 子句
- pass 语句
- 定义函数
- 函数定义的更多形式
- 小插曲：编码风格
- 数据结构
- 列表的更多特性
- del 语句
- 元组和序列
- 集合
- 字典
- 循环的技巧
- 深入条件控制
- 序列和其它类型的比较
- 模块
- 有关模块的更多信息
- 标准模块
- dir() 函数
- 包
- 输入输出
- 更漂亮的输出格式
- 读写文件
- 错误和异常
- 语法错误
- 异常
- 处理异常
- 抛出异常
- 用户自定义异常
- 定义清理操作
- 预定义的清理操作
- 类
- 名称和对象
- Python 作用域和命名空间
- 初探类
- 补充说明
- 继承
- 私有变量
- 杂项说明
- 迭代器
- 生成器
- 生成器表达式
- 标准库简介
- 操作系统接口
- 文件通配符
- 命令行参数
- 错误输出重定向和程序终止
- 字符串模式匹配
- 数学
- 互联网访问
- 日期和时间
- 数据压缩
- 性能测量
- 质量控制
- 自带电池
- 标准库简介 —— 第二部分
- 格式化输出
- 模板
- 使用二进制数据记录格式
- 多线程
- 日志
- 弱引用
- 用于操作列表的工具
- 十进制浮点运算
- 虚拟环境和包
- 概述
- 创建虚拟环境
- 使用pip管理包
- 接下来？
- 交互式编辑和编辑历史
- Tab 补全和编辑历史
- 默认交互式解释器的替代品
- 浮点算术：争议和限制
- 表示性错误
- 附录
- 交互模式
- 安装和使用 Python
- 命令行与环境
- 命令行
- 环境变量
- 在Unix平台中使用Python
- 获取最新版本的Python
- 构建Python
- 与Python相关的路径和文件
- 杂项
- 编辑器和集成开发环境
- 在Windows上使用 Python
- 完整安装程序
- Microsoft Store包
- nuget.org 安装包
- 可嵌入的包
- 替代捆绑包
- 配置Python
- 适用于Windows的Python启动器
- 查找模块
- 附加模块
- 在Windows上编译Python
- 其他平台
- 在苹果系统上使用 Python
- 获取和安装 MacPython
- IDE
- 安装额外的 Python 包
- Mac 上的图形界面编程
- 在 Mac 上分发 Python 应用程序
- 其他资源
- Python 语言参考
- 概述
- 其他实现
- 标注
- 词法分析
- 行结构
- 其他形符
- 标识符和关键字
- 字面值
- 运算符
- 分隔符
- 数据模型
- 对象、值与类型
- 标准类型层级结构
- 特殊方法名称
- 协程
- 执行模型
- 程序的结构
- 命名与绑定
- 异常
- 导入系统
- importlib
- 包
- 搜索
- 加载
- 基于路径的查找器
- 替换标准导入系统
- Package Relative Imports
- 有关 main 的特殊事项
- 开放问题项
- 参考文献
- 表达式
- 算术转换
- 原子
- 原型
- await 表达式
- 幂运算符
- 一元算术和位运算
- 二元算术运算符
- 移位运算
- 二元位运算
- 比较运算
- 布尔运算
- 条件表达式
- lambda 表达式
- 表达式列表
- 求值顺序
- 运算符优先级
- 简单语句
- 表达式语句
- 赋值语句
- assert 语句
- pass 语句
- del 语句
- return 语句
- yield 语句
- raise 语句
- break 语句
- continue 语句
- import 语句
- global 语句
- nonlocal 语句
- 复合语句
- if 语句
- while 语句
- for 语句
- try 语句
- with 语句
- 函数定义
- 类定义
- 协程
- 最高层级组件
- 完整的 Python 程序
- 文件输入
- 交互式输入
- 表达式输入
- 完整的语法规范
- Python 标准库
- 概述
- 可用性注释
- 内置函数
- 内置常量
- 由 site 模块添加的常量
- 内置类型
- 逻辑值检测
- 布尔运算 — and, or, not
- 比较
- 数字类型 — int, float, complex
- 迭代器类型
- 序列类型 — list, tuple, range
- 文本序列类型 — str
- 二进制序列类型 — bytes, bytearray, memoryview
- 集合类型 — set, frozenset
- 映射类型 — dict
- 上下文管理器类型
- 其他内置类型
- 特殊属性
- 内置异常
- 基类
- 具体异常
- 警告
- 异常层次结构
- 文本处理服务
- string — 常见的字符串操作
- re — 正则表达式操作
- 模块 difflib 是一个计算差异的助手
- textwrap — Text wrapping and filling
- unicodedata — Unicode 数据库
- stringprep — Internet String Preparation
- readline — GNU readline interface
- rlcompleter — GNU readline的完成函数
- 二进制数据服务
- struct — Interpret bytes as packed binary data
- codecs — Codec registry and base classes
- 数据类型
- datetime — 基础日期/时间数据类型
- calendar — General calendar-related functions
- collections — 容器数据类型
- collections.abc — 容器的抽象基类
- heapq — 堆队列算法
- 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 — 浅层 (shallow) 和深层 (deep) 复制操作
- pprint — 数据美化输出
- reprlib — Alternate repr() implementation
- enum — Support for enumerations
- 数字和数学模块
- numbers — 数字的抽象基类
- math — 数学函数
- cmath — Mathematical functions for complex numbers
- decimal — 十进制定点和浮点运算
- fractions — 分数
- random — 生成伪随机数
- statistics — Mathematical statistics functions
- 函数式编程模块
- itertools — 为高效循环而创建迭代器的函数
- functools — 高阶函数和可调用对象上的操作
- operator — 标准运算符替代函数
- 文件和目录访问
- pathlib — 面向对象的文件系统路径
- os.path — 常见路径操作
- 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 路径操作函数
- 数据持久化
- pickle —— Python 对象序列化
- copyreg — Register pickle support functions
- shelve — Python object persistence
- marshal — Internal Python object serialization
- dbm — Interfaces to Unix “databases”
- sqlite3 — SQLite 数据库 DB-API 2.0 接口模块
- 数据压缩和存档
- zlib — 与 gzip 兼容的压缩
- gzip — 对 gzip 格式的支持
- bz2 — 对 bzip2 压缩算法的支持
- lzma — 用 LZMA 算法压缩
- zipfile — 在 ZIP 归档中工作
- tarfile — Read and write tar archive files
- 文件格式
- csv — CSV 文件读写
- configparser — Configuration file parser
- netrc — netrc file processing
- xdrlib — Encode and decode XDR data
- plistlib — Generate and parse Mac OS X .plist files
- 加密服务
- hashlib — 安全哈希与消息摘要
- hmac — 基于密钥的消息验证
- secrets — Generate secure random numbers for managing secrets
- 通用操作系统服务
- os — 操作系统接口模块
- io — 处理流的核心工具
- time — 时间的访问和转换
- argparse — 命令行选项、参数和子命令解析器
- getopt — C-style parser for command line options
- 模块 logging — Python 的日志记录工具
- logging.config — 日志记录配置
- logging.handlers — Logging handlers
- getpass — 便携式密码输入工具
- curses — 终端字符单元显示的处理
- 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 的外部函数库
- 并发执行
- threading — 基于线程的并行
- multiprocessing — 基于进程的并行
- concurrent 包
- concurrent.futures — 启动并行任务
- subprocess — 子进程管理
- sched — 事件调度器
- queue — 一个同步的队列类
- _thread — 底层多线程 API
- _dummy_thread — _thread 的替代模块
- dummy_threading — 可直接替代 threading 模块。
- contextvars — Context Variables
- Context Variables
- Manual Context Management
- asyncio support
- 网络和进程间通信
- asyncio — 异步 I/O
- socket — 底层网络接口
- ssl — TLS/SSL wrapper for socket objects
- select — Waiting for I/O completion
- selectors — 高级 I/O 复用库
- asyncore — 异步socket处理器
- asynchat — 异步 socket 指令/响应 处理器
- signal — Set handlers for asynchronous events
- mmap — Memory-mapped file support
- 互联网数据处理
- email — 电子邮件与 MIME 处理包
- json — JSON 编码和解码器
- mailcap — Mailcap file handling
- mailbox — Manipulate mailboxes in various formats
- mimetypes — Map filenames to MIME types
- base64 — Base16, Base32, Base64, Base85 数据编码
- binhex — 对binhex4文件进行编码和解码
- binascii — 二进制和 ASCII 码互转
- quopri — Encode and decode MIME quoted-printable data
- uu — Encode and decode uuencode files
- 结构化标记处理工具
- html — 超文本标记语言支持
- html.parser — 简单的 HTML 和 XHTML 解析器
- html.entities — HTML 一般实体的定义
- XML处理模块
- 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
- 互联网协议和支持
- webbrowser — 方便的Web浏览器控制器
- cgi — Common Gateway Interface support
- cgitb — Traceback manager for CGI scripts
- wsgiref — WSGI Utilities and Reference Implementation
- urllib — URL 处理模块
- urllib.request — 用于打开 URL 的可扩展库
- 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 模块
- http.client — HTTP协议客户端
- ftplib — FTP protocol client
- poplib — POP3 protocol client
- imaplib — IMAP4 protocol client
- nntplib — NNTP protocol client
- smtplib —SMTP协议客户端
- smtpd — SMTP Server
- telnetlib — Telnet client
- uuid — UUID objects according to RFC 4122
- socketserver — A framework for network servers
- http.server — HTTP 服务器
- http.cookies — HTTP state management
- http.cookiejar — Cookie handling for HTTP clients
- xmlrpc — XMLRPC 服务端与客户端模块
- xmlrpc.client — XML-RPC client access
- xmlrpc.server — Basic XML-RPC servers
- ipaddress — IPv4/IPv6 manipulation library
- 多媒体服务
- audioop — Manipulate raw audio data
- aifc — Read and write AIFF and AIFC files
- sunau — 读写 Sun AU 文件
- wave — 读写WAV格式文件
- chunk — Read IFF chunked data
- colorsys — Conversions between color systems
- imghdr — 推测图像类型
- sndhdr — 推测声音文件的类型
- ossaudiodev — Access to OSS-compatible audio devices
- 国际化
- gettext — 多语种国际化服务
- locale — 国际化服务
- 程序框架
- turtle — 海龟绘图
- cmd — 支持面向行的命令解释器
- shlex — Simple lexical analysis
- Tk图形用户界面(GUI)
- tkinter — Tcl/Tk的Python接口
- tkinter.ttk — Tk themed widgets
- tkinter.tix — Extension widgets for Tk
- tkinter.scrolledtext — 滚动文字控件
- IDLE
- 其他图形用户界面（GUI）包
- 开发工具
- typing — 类型标注支持
- pydoc — Documentation generator and online help system
- doctest — Test interactive Python examples
- unittest — 单元测试框架
- unittest.mock — mock object library
- unittest.mock 上手指南
- 2to3 - 自动将 Python 2 代码转为 Python 3 代码
- test — Regression tests package for Python
- test.support — Utilities for the Python test suite
- test.support.script_helper — Utilities for the Python execution tests
- 调试和分析
- bdb — Debugger framework
- faulthandler — Dump the Python traceback
- pdb — The Python Debugger
- The Python Profilers
- timeit — 测量小代码片段的执行时间
- trace — Trace or track Python statement execution
- tracemalloc — Trace memory allocations
- 软件打包和分发
- distutils — 构建和安装 Python 模块
- ensurepip — Bootstrapping the pip installer
- venv — 创建虚拟环境
- zipapp — Manage executable Python zip archives
- Python运行时服务
- sys — 系统相关的参数和函数
- sysconfig — Provide access to Python's configuration information
- builtins — 内建对象
- main — 顶层脚本环境
- warnings — Warning control
- dataclasses — 数据类
- contextlib — Utilities for with-statement contexts
- abc — 抽象基类
- atexit — 退出处理器
- traceback — Print or retrieve a stack traceback
- future — Future 语句定义
- gc — 垃圾回收器接口
- inspect — 检查对象
- site — Site-specific configuration hook
- 自定义 Python 解释器
- code — Interpreter base classes
- codeop — Compile Python code
- 导入模块
- zipimport — Import modules from Zip archives
- pkgutil — Package extension utility
- modulefinder — 查找脚本使用的模块
- runpy — Locating and executing Python modules
- importlib — The implementation of import
- Python 语言服务
- parser — Access Python parse trees
- ast — 抽象语法树
- symtable — Access to the compiler's symbol tables
- symbol — 与 Python 解析树一起使用的常量
- token — 与Python解析树一起使用的常量
- keyword — 检验Python关键字
- tokenize — Tokenizer for Python source
- tabnanny — 模糊缩进检测
- pyclbr — Python class browser support
- py_compile — Compile Python source files
- compileall — Byte-compile Python libraries
- dis — Python 字节码反汇编器
- pickletools — Tools for pickle developers
- 杂项服务
- formatter — Generic output formatting
- Windows系统相关模块
- msilib — Read and write Microsoft Installer files
- msvcrt — Useful routines from the MS VC++ runtime
- winreg — Windows 注册表访问
- winsound — Sound-playing interface for Windows
- Unix 专有服务
- posix — The most common POSIX system calls
- pwd — 用户密码数据库
- spwd — The shadow password database
- grp — The group database
- crypt — Function to check Unix passwords
- termios — POSIX style tty control
- tty — 终端控制功能
- 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 库例程
- 被取代的模块
- optparse — Parser for command line options
- imp — Access the import internals
- 未创建文档的模块
- 平台特定模块
- 扩展和嵌入 Python 解释器
- 推荐的第三方工具
- 不使用第三方工具创建扩展
- 使用 C 或 C++ 扩展 Python
- 自定义扩展类型：教程
- 定义扩展类型：已分类主题
- 构建C/C++扩展
- 在Windows平台编译C和C++扩展
- 在更大的应用程序中嵌入 CPython 运行时
- Embedding Python in Another Application
- Python/C API 参考手册
- 概述
- 代码标准
- 包含文件
- 有用的宏
- 对象、类型和引用计数
- 异常
- 嵌入Python
- 调试构建
- 稳定的应用程序二进制接口
- The Very High Level Layer
- Reference Counting
- 异常处理
- Printing and clearing
- 抛出异常
- Issuing warnings
- Querying the error indicator
- Signal Handling
- Exception Classes
- Exception Objects
- Unicode Exception Objects
- Recursion Control
- 标准异常
- 标准警告类别
- 工具
- 操作系统实用程序
- 系统功能
- 过程控制
- 导入模块
- Data marshalling support
- 语句解释及变量编译
- 字符串转换与格式化
- 反射
- 编解码器注册与支持功能
- 抽象对象层
- Object Protocol
- 数字协议
- Sequence Protocol
- Mapping Protocol
- 迭代器协议
- 缓冲协议
- Old Buffer Protocol
- 具体的对象层
- 基本对象
- 数值对象
- 序列对象
- 容器对象
- 函数对象
- 其他对象
- Initialization, Finalization, and Threads
- 在Python初始化之前
- 全局配置变量
- 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
- 内存管理
- 概述
- 原始内存接口
- Memory Interface
- 对象分配器
- 默认内存分配器
- Customize Memory Allocators
- The pymalloc allocator
- tracemalloc C API
- 示例
- 对象实现支持
- 在堆中分配对象
- Common Object Structures
- Type 对象
- Number Object Structures
- Mapping Object Structures
- Sequence Object Structures
- Buffer Object Structures
- Async Object Structures
- 使对象类型支持循环垃圾回收
- API 和 ABI 版本管理
- 分发 Python 模块
- 关键术语
- 开源许可与协作
- 安装工具
- 阅读指南
- 我该如何...？
- ...为我的项目选择一个名字？
- ...创建和分发二进制扩展？
- 安装 Python 模块
- 关键术语
- 基本使用
- 我应如何 ...？
- ... 在 Python 3.4 之前的 Python 版本中安装 pip ？
- ... 只为当前用户安装软件包？
- ... 安装科学计算类 Python 软件包？
- ... 使用并行安装的多个 Python 版本？
- 常见的安装问题
- 在 Linux 的系统 Python 版本上安装
- 未安装 pip
- 安装二进制编译扩展
- Python 常用指引
- 将 Python 2 代码迁移到 Python 3
- 简要说明
- 详情
- 将扩展模块移植到 Python 3
- 条件编译
- 对象API的更改
- 模块初始化和状态
- CObject 替换为 Capsule
- 其他选项
- Curses Programming with Python
- What is curses?
- Starting and ending a curses application
- Windows and Pads
- Displaying Text
- User Input
- For More Information
- 实现描述器
- 摘要
- 定义和简介
- 描述器协议
- 发起调用描述符
- 描述符示例
- Properties
- 函数和方法
- Static Methods and Class Methods
- 函数式编程指引
- 概述
- 迭代器
- 生成器表达式和列表推导式
- 生成器
- 内置函数
- itertools 模块
- The functools module
- Small functions and the lambda expression
- Revision History and Acknowledgements
- 引用文献
- 日志 HOWTO
- 日志基础教程
- 进阶日志教程
- 日志级别
- 有用的处理程序
- 记录日志中引发的异常
- 使用任意对象作为消息
- 优化
- 日志操作手册
- 在多个模块中使用日志
- 在多线程中使用日志
- 使用多个日志处理器和多种格式化
- 在多个地方记录日志
- 日志服务器配置示例
- 处理日志处理器的阻塞
- 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
- 正则表达式HOWTO
- 概述
- 简单模式
- 使用正则表达式
- 更多模式能力
- 修改字符串
- 常见问题
- 反馈
- 套接字编程指南
- 套接字
- 创建套接字
- 使用一个套接字
- 断开连接
- 非阻塞的套接字
- 排序指南
- 基本排序
- 关键函数
- Operator 模块函数
- 升序和降序
- 排序稳定性和排序复杂度
- 使用装饰-排序-去装饰的旧方法
- 使用 cmp 参数的旧方法
- 其它
- Unicode 指南
- Unicode 概述
- Python's Unicode Support
- Reading and Writing Unicode Data
- Acknowledgements
- 如何使用urllib包获取网络资源
- 概述
- Fetching URLs
- 处理异常
- info and geturl
- Openers and Handlers
- Basic Authentication
- Proxies
- Sockets and Layers
- 脚注
- Argparse 教程
- 概念
- 基础
- 位置参数介绍
- Introducing Optional arguments
- Combining Positional and Optional arguments
- Getting a little more advanced
- Conclusion
- ipaddress模块介绍
- 创建 Address/Network/Interface 对象
- 审查 Address/Network/Interface 对象
- Network 作为 Address 列表
- 比较
- 将IP地址与其他模块一起使用
- 实例创建失败时获取更多详细信息
- Argument Clinic How-To
- The Goals Of Argument Clinic
- Basic Concepts And Usage
- Converting Your First Function
- Advanced Topics
- 使用 DTrace 和 SystemTap 检测CPython
- Enabling the static markers
- Static DTrace probes
- Static SystemTap markers
- Available static markers
- SystemTap Tapsets
- 示例
- Python 常见问题
- Python常见问题
- 一般信息
- 现实世界中的 Python
- 编程常见问题
- 一般问题
- 核心语言
- 数字和字符串
- 性能
- 序列（元组/列表）
- 对象
- 模块
- 设计和历史常见问题
- 为什么Python使用缩进来分组语句？
- 为什么简单的算术运算得到奇怪的结果？
- 为什么浮点计算不准确？
- 为什么Python字符串是不可变的？
- 为什么必须在方法定义和调用中显式使用“self”？
- 为什么不能在表达式中赋值？
- 为什么Python对某些功能（例如list.index()）使用方法来实现，而其他功能（例如len(List)）使用函数实现？
- 为什么 join()是一个字符串方法而不是列表或元组方法？
- 异常有多快？
- 为什么Python中没有switch或case语句？
- 难道不能在解释器中模拟线程，而非得依赖特定于操作系统的线程实现吗？
- 为什么lambda表达式不能包含语句？
- 可以将Python编译为机器代码，C或其他语言吗？
- Python如何管理内存？
- 为什么CPython不使用更传统的垃圾回收方案？
- CPython退出时为什么不释放所有内存？
- 为什么有单独的元组和列表数据类型？
- 列表是如何在CPython中实现的？
- 字典是如何在CPython中实现的？
- 为什么字典key必须是不可变的？
- 为什么 list.sort() 没有返回排序列表？
- 如何在Python中指定和实施接口规范？
- 为什么没有goto？
- 为什么原始字符串（r-strings）不能以反斜杠结尾？
- 为什么Python没有属性赋值的“with”语句？
- 为什么 if/while/def/class语句需要冒号？
- 为什么Python在列表和元组的末尾允许使用逗号？
- 代码库和插件 FAQ
- 通用的代码库问题
- 通用任务
- 线程相关
- 输入输出
- 网络 / Internet 编程
- 数据库
- 数学和数字
- 扩展/嵌入常见问题
- 可以使用C语言中创建自己的函数吗？
- 可以使用C++语言中创建自己的函数吗？
- C很难写，有没有其他选择？
- 如何从C执行任意Python语句？
- 如何从C中评估任意Python表达式？
- 如何从Python对象中提取C的值？
- 如何使用Py_BuildValue()创建任意长度的元组？
- 如何从C调用对象的方法？
- 如何捕获PyErr_Print()（或打印到stdout / stderr的任何内容）的输出？
- 如何从C访问用Python编写的模块？
- 如何从Python接口到C ++对象？
- 我使用Setup文件添加了一个模块，为什么make失败了？
- 如何调试扩展？
- 我想在Linux系统上编译一个Python模块，但是缺少一些文件。为什么?
- 如何区分“输入不完整”和“输入无效”？
- 如何找到未定义的g++符号__builtin_new或__pure_virtual？
- 能否创建一个对象类，其中部分方法在C中实现，而其他方法在Python中实现（例如通过继承）？
- Python在Windows上的常见问题
- 我怎样在Windows下运行一个Python程序？
- 我怎么让 Python 脚本可执行？
- 为什么有时候 Python 程序会启动缓慢？
- 我怎样使用Python脚本制作可执行文件？
- *.pyd 文件和DLL文件相同吗？
- 我怎样将Python嵌入一个Windows程序？
- 如何让编辑器不要在我的 Python 源代码中插入 tab ？
- 如何在不阻塞的情况下检查按键？
- 图形用户界面（GUI）常见问题
- 图形界面常见问题
- Python 是否有平台无关的图形界面工具包？
- 有哪些Python的GUI工具是某个平台专用的？
- 有关Tkinter的问题
- “为什么我的电脑上安装了 Python ？”
- 什么是Python？
- 为什么我的电脑上安装了 Python ？
- 我能删除 Python 吗？
- 术语对照表
- 文档说明
- Python 文档贡献者
- 解决 Bug
- 文档错误
- 使用 Python 的错误追踪系统
- 开始为 Python 贡献您的知识
- 版权
- 历史和许可证
- 软件历史
- 访问Python或以其他方式使用Python的条款和条件
- Python 3.7.3 的 PSF 许可协议
- Python 2.0 的 BeOpen.com 许可协议
- Python 1.6.1 的 CNRI 许可协议
- Python 0.9.0 至 1.2 的 CWI 许可协议
- 集成软件的许可和认可
- 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