描述器使用指南

作者

Raymond Hettinger

联系方式

<python at rcn dot com>

目录

• 描述器使用指南

  • Primer

    • Simple example: A descriptor that returns a constant

    • Dynamic lookups

    • Managed attributes

    • Customized names

    • Closing thoughts

  • Complete Practical Example

    • Validator class

    • Custom validators

    • Practical use

  • Technical Tutorial

    • 摘要

    • Definition and introduction

    • Descriptor protocol

    • Overview of descriptor invocation

    • Invocation from an instance

    • Invocation from a class

    • Invocation from super

    • Summary of invocation logic

    • Automatic name notification

    • ORM example

  • Pure Python Equivalents

    • 属性

    • Functions and methods

    • Static methods

    • Class methods

    • Member objects and __slots__

Descriptors let objects customize attribute lookup, storage, and deletion.

This guide has four major sections:

1. The "primer" gives a basic overview, moving gently from simple examples, adding one feature at a time. It is a great place to start.

2. The second section shows a complete, practical descriptor example. If you already know the basics, start there.

3. The third section provides a more technical tutorial that goes into the detailed mechanics of how descriptors work. Most people don't need this level of detail.

4. The last section has pure Python equivalents for built-in descriptors that are written in C. Read this if you're curious about how functions turn into bound methods or about the implementation of common tools like classmethod(), staticmethod(), property(), and __slots__.

Primer

In this primer, we start with the most basic possible example and then we'll add new capabilities one by one.

Simple example: A descriptor that returns a constant

The Ten class is a descriptor that always returns the constant 10:

class Ten:
    def __get__(self, obj, objtype=None):
        return 10

To use the descriptor, it must be stored as a class variable in another class:

class A:
    x = 5                       # Regular class attribute
    y = Ten()                   # Descriptor instance

An interactive session shows the difference between normal attribute lookup and descriptor lookup:

>>> a = A()                     # Make an instance of class A
>>> a.x                         # Normal attribute lookup
5
>>> a.y                         # Descriptor lookup
10

In the a.x attribute lookup, the dot operator finds the value 5 stored in the class dictionary. In the a.y descriptor lookup, the dot operator calls the descriptor's __get__() method. That method returns 10. Note that the value 10 is not stored in either the class dictionary or the instance dictionary. Instead, the value 10 is computed on demand.

This example shows how a simple descriptor works, but it isn't very useful. For retrieving constants, normal attribute lookup would be better.

In the next section, we'll create something more useful, a dynamic lookup.

Dynamic lookups

Interesting descriptors typically run computations instead of doing lookups:

import os

class DirectorySize:

    def __get__(self, obj, objtype=None):
        return len(os.listdir(obj.dirname))

class Directory:

    size = DirectorySize()              # Descriptor instance

    def __init__(self, dirname):
        self.dirname = dirname          # Regular instance attribute

An interactive session shows that the lookup is dynamic — it computes different, updated answers each time:

>>> g = Directory('games')
>>> s = Directory('songs')
>>> g.size                              # The games directory has three files
3
>>> os.system('touch games/newfile')    # Add a fourth file to the directory
0
>>> g.size                              # Automatically updated
4
>>> s.size                              # The songs directory has twenty files
20

Besides showing how descriptors can run computations, this example also reveals the purpose of the parameters to __get__(). The self parameter is size, an instance of DirectorySize. The obj parameter is either g or s, an instance of Directory. It is the obj parameter that lets the __get__() method learn the target directory. The objtype parameter is the class Directory.

Managed attributes

A popular use for descriptors is managing access to instance data. The descriptor is assigned to a public attribute in the class dictionary while the actual data is stored as a private attribute in the instance dictionary. The descriptor's __get__() and __set__() methods are triggered when the public attribute is accessed.

In the following example, age is the public attribute and _age is the private attribute. When the public attribute is accessed, the descriptor logs the lookup or update:

import logging

logging.basicConfig(level=logging.INFO)

class LoggedAgeAccess:

    def __get__(self, obj, objtype=None):
        value = obj._age
        logging.info('Accessing %r giving %r', 'age', value)
        return value

    def __set__(self, obj, value):
        logging.info('Updating %r to %r', 'age', value)
        obj._age = value

class Person:

    age = LoggedAgeAccess()             # Descriptor instance

    def __init__(self, name, age):
        self.name = name                # Regular instance attribute
        self.age = age                  # Calls __set__()

    def birthday(self):
        self.age += 1                   # Calls both __get__() and __set__()

An interactive session shows that all access to the managed attribute age is logged, but that the regular attribute name is not logged:

>>> mary = Person('Mary M', 30)         # The initial age update is logged
INFO:root:Updating 'age' to 30
>>> dave = Person('David D', 40)
INFO:root:Updating 'age' to 40

>>> vars(mary)                          # The actual data is in a private attribute
{'name': 'Mary M', '_age': 30}
>>> vars(dave)
{'name': 'David D', '_age': 40}

>>> mary.age                            # Access the data and log the lookup
INFO:root:Accessing 'age' giving 30
30
>>> mary.birthday()                     # Updates are logged as well
INFO:root:Accessing 'age' giving 30
INFO:root:Updating 'age' to 31

>>> dave.name                           # Regular attribute lookup isn't logged
'David D'
>>> dave.age                            # Only the managed attribute is logged
INFO:root:Accessing 'age' giving 40
40

One major issue with this example is that the private name _age is hardwired in the LoggedAgeAccess class. That means that each instance can only have one logged attribute and that its name is unchangeable. In the next example, we'll fix that problem.

Customized names

When a class uses descriptors, it can inform each descriptor about which variable name was used.

In this example, the Person class has two descriptor instances, name and age. When the Person class is defined, it makes a callback to __set_name__() in LoggedAccess so that the field names can be recorded, giving each descriptor its own public_name and private_name:

import logging

logging.basicConfig(level=logging.INFO)

class LoggedAccess:

    def __set_name__(self, owner, name):
        self.public_name = name
        self.private_name = f'_{name}'

    def __get__(self, obj, objtype=None):
        value = getattr(obj, self.private_name)
        logging.info('Accessing %r giving %r', self.public_name, value)
        return value

    def __set__(self, obj, value):
        logging.info('Updating %r to %r', self.public_name, value)
        setattr(obj, self.private_name, value)

class Person:

    name = LoggedAccess()                # First descriptor instance
    age = LoggedAccess()                 # Second descriptor instance

    def __init__(self, name, age):
        self.name = name                 # Calls the first descriptor
        self.age = age                   # Calls the second descriptor

    def birthday(self):
        self.age += 1

An interactive session shows that the Person class has called __set_name__() so that the field names would be recorded. Here we call vars() to look up the descriptor without triggering it:

>>> vars(vars(Person)['name'])
{'public_name': 'name', 'private_name': '_name'}
>>> vars(vars(Person)['age'])
{'public_name': 'age', 'private_name': '_age'}

The new class now logs access to both name and age:

>>> pete = Person('Peter P', 10)
INFO:root:Updating 'name' to 'Peter P'
INFO:root:Updating 'age' to 10
>>> kate = Person('Catherine C', 20)
INFO:root:Updating 'name' to 'Catherine C'
INFO:root:Updating 'age' to 20

The two Person instances contain only the private names:

>>> vars(pete)
{'_name': 'Peter P', '_age': 10}
>>> vars(kate)
{'_name': 'Catherine C', '_age': 20}

Closing thoughts

A descriptor is what we call any object that defines __get__(), __set__(), or __delete__().

Optionally, descriptors can have a __set_name__() method. This is only used in cases where a descriptor needs to know either the class where it was created or the name of class variable it was assigned to.

Descriptors get invoked by the dot operator during attribute lookup. If a descriptor is accessed indirectly with vars(some_class)[descriptor_name], the descriptor instance is returned without invoking it.

Descriptors only work when used as class variables. When put in instances, they have no effect.

The main motivation for descriptors is to provide a hook allowing objects stored in class variables to control what happens during dotted lookup.

Traditionally, the calling class controls what happens during lookup. Descriptors invert that relationship and allow the data being looked-up to have a say in the matter.

Descriptors are used throughout the language. It is how functions turn into bound methods. Common tools like classmethod(), staticmethod(), property(), and functools.cached_property() are all implemented as descriptors.

Complete Practical Example

In this example, we create a practical and powerful tool for locating notoriously hard to find data corruption bugs.

Validator class

A validator is a descriptor for managed attribute access. Prior to storing any data, it verifies that the new value meets various type and range restrictions. If those restrictions aren't met, it raises an exception to prevent data corruption at its source.

This Validator class is both an abstract base class and a managed attribute descriptor:

from abc import ABC, abstractmethod

class Validator(ABC):

    def __set_name__(self, owner, name):
        self.private_name = f'_{name}'

    def __get__(self, obj, objtype=None):
        return getattr(obj, self.private_name)

    def __set__(self, obj, value):
        self.validate(value)
        setattr(obj, self.private_name, value)

    @abstractmethod
    def validate(self, value):
        pass

Custom validators need to inherit from Validator and must supply a validate() method to test various restrictions as needed.

Custom validators

Here are three practical data validation utilities:

1. OneOf verifies that a value is one of a restricted set of options.

2. Number verifies that a value is either an int or float. Optionally, it verifies that a value is between a given minimum or maximum.

3. String verifies that a value is a str. Optionally, it validates a given minimum or maximum length. It can validate a user-defined predicate as well.

class OneOf(Validator):

    def __init__(self, *options):
        self.options = set(options)

    def validate(self, value):
        if value not in self.options:
            raise ValueError(f'Expected {value!r} to be one of {self.options!r}')

class Number(Validator):

    def __init__(self, minvalue=None, maxvalue=None):
        self.minvalue = minvalue
        self.maxvalue = maxvalue

    def validate(self, value):
        if not isinstance(value, (int, float)):
            raise TypeError(f'Expected {value!r} to be an int or float')
        if self.minvalue is not None and value < self.minvalue:
            raise ValueError(
                f'Expected {value!r} to be at least {self.minvalue!r}'
            )
        if self.maxvalue is not None and value > self.maxvalue:
            raise ValueError(
                f'Expected {value!r} to be no more than {self.maxvalue!r}'
            )

class String(Validator):

    def __init__(self, minsize=None, maxsize=None, predicate=None):
        self.minsize = minsize
        self.maxsize = maxsize
        self.predicate = predicate

    def validate(self, value):
        if not isinstance(value, str):
            raise TypeError(f'Expected {value!r} to be an str')
        if self.minsize is not None and len(value) < self.minsize:
            raise ValueError(
                f'Expected {value!r} to be no smaller than {self.minsize!r}'
            )
        if self.maxsize is not None and len(value) > self.maxsize:
            raise ValueError(
                f'Expected {value!r} to be no bigger than {self.maxsize!r}'
            )
        if self.predicate is not None and not self.predicate(value):
            raise ValueError(
                f'Expected {self.predicate} to be true for {value!r}'
            )

Practical use

Here's how the data validators can be used in a real class:

class Component:

    name = String(minsize=3, maxsize=10, predicate=str.isupper)
    kind = OneOf('wood', 'metal', 'plastic')
    quantity = Number(minvalue=0)

    def __init__(self, name, kind, quantity):
        self.name = name
        self.kind = kind
        self.quantity = quantity

The descriptors prevent invalid instances from being created:

Component('WIDGET', 'metal', 5)     # Allowed.
Component('Widget', 'metal', 5)     # Blocked: 'Widget' is not all uppercase
Component('WIDGET', 'metle', 5)     # Blocked: 'metle' is misspelled
Component('WIDGET', 'metal', -5)    # Blocked: -5 is negative
Component('WIDGET', 'metal', 'V')   # Blocked: 'V' isn't a number

Technical Tutorial

What follows is a more technical tutorial for the mechanics and details of how descriptors work.

摘要

Defines descriptors, summarizes the protocol, and shows how descriptors are called. Provides an example showing how object relational mappings work.

学习描述器不仅能提供接触到更多工具集的方法，还能更深地理解 Python 工作的原理并更加体会到其设计的优雅性。

Definition and introduction

In general, a descriptor is an object attribute with "binding behavior", one whose attribute access has been overridden by methods in the descriptor protocol. Those methods are __get__(), __set__(), and __delete__(). If any of those methods are defined for an object, it is said to be a descriptor.

The default behavior for attribute access is to get, set, or delete the attribute from an object's dictionary. For instance, a.x has a lookup chain starting with a.__dict__['x'], then type(a).__dict__['x'], and continuing through the base classes of type(a). If the looked-up value is an object defining one of the descriptor methods, then Python may override the default behavior and invoke the descriptor method instead. Where this occurs in the precedence chain depends on which descriptor methods were defined.

Descriptors are a powerful, general purpose protocol. They are the mechanism behind properties, methods, static methods, class methods, and super(). They are used throughout Python itself. Descriptors simplify the underlying C code and offer a flexible set of new tools for everyday Python programs.

Descriptor protocol

descr.__get__(self, obj, type=None) -> value

descr.__set__(self, obj, value) -> None

descr.__delete__(self, obj) -> None

以上就是全部。定义这些方法中的任何一个的对象被视为描述器，并在被作为属性时覆盖其默认行为。

如果一个对象定义了 __set__() 或 __delete__()，则它会被视为数据描述器。 仅定义了 __get__() 的描述器称为非数据描述器（它们通常被用于方法，但也可以有其他用途）。

数据和非数据描述器的不同之处在于，如何计算实例字典中条目的替代值。如果实例的字典具有与数据描述器同名的条目，则数据描述器优先。如果实例的字典具有与非数据描述器同名的条目，则该字典条目优先。

为了使数据描述器成为只读的，应该同时定义 __get__() 和 __set__() ，并在 __set__() 中引发 AttributeError 。用引发异常的占位符定义 __set__() 方法使其成为数据描述器。

Overview of descriptor invocation

A descriptor can be called directly with desc.__get__(obj) or desc.__get__(None, cls).

But it is more common for a descriptor to be invoked automatically from attribute access.

The expression obj.x looks up the attribute x in the chain of namespaces for obj. If the search finds a descriptor, its __get__() method is invoked according to the precedence rules listed below.

The details of invocation depend on whether obj is an object, class, or instance of super.

Invocation from an instance

Instance lookup scans through a chain of namespaces giving data descriptors the highest priority, followed by instance variables, then non-data descriptors, then class variables, and lastly __getattr__() if it is provided.

If a descriptor is found for a.x, then it is invoked with: desc.__get__(a, type(a)).

The logic for a dotted lookup is in object.__getattribute__(). Here is a pure Python equivalent:

def object_getattribute(obj, name):
    "Emulate PyObject_GenericGetAttr() in Objects/object.c"
    null = object()
    objtype = type(obj)
    value = getattr(objtype, name, null)
    if value is not null and hasattr(value, '__get__'):
        if hasattr(value, '__set__') or hasattr(value, '__delete__'):
            return value.__get__(obj, objtype)  # data descriptor
    try:
        return vars(obj)[name]                  # instance variable
    except (KeyError, TypeError):
        pass
    if hasattr(value, '__get__'):
        return value.__get__(obj, objtype)      # non-data descriptor
    if value is not null:
        return value                            # class variable
    # Emulate slot_tp_getattr_hook() in Objects/typeobject.c
    if hasattr(objtype, '__getattr__'):
        return objtype.__getattr__(obj, name)   # __getattr__ hook
    raise AttributeError(name)

The TypeError exception handler is needed because the instance dictionary doesn't exist when its class defines __slots__.

Invocation from a class

The logic for a dotted lookup such as A.x is in type.__getattribute__(). The steps are similar to those for object.__getattribute__() but the instance dictionary lookup is replaced by a search through the class's method resolution order.

If a descriptor is found, it is invoked with desc.__get__(None, A).

The full C implementation can be found in type_getattro() and _PyType_Lookup() in Objects/typeobject.c.

Invocation from super

The logic for super's dotted lookup is in the __getattribute__() method for object returned by super().

A dotted lookup such as super(A, obj).m searches obj.__class__.__mro__ for the base class B immediately following A and then returns B.__dict__['m'].__get__(obj, A). If not a descriptor, m is returned unchanged.

The full C implementation can be found in super_getattro() in Objects/typeobject.c. A pure Python equivalent can be found in Guido's Tutorial.

Summary of invocation logic

The mechanism for descriptors is embedded in the __getattribute__() methods for object, type, and super().

要记住的重要点是：

• Descriptors are invoked by the __getattribute__() method.

• Classes inherit this machinery from object, type, or super().

• Overriding __getattribute__() prevents automatic descriptor calls because all the descriptor logic is in that method.

• object.__getattribute__() and type.__getattribute__() make different calls to __get__(). The first includes the instance and may include the class. The second puts in None for the instance and always includes the class.

• Data descriptors always override instance dictionaries.

• Non-data descriptors may be overridden by instance dictionaries.

Automatic name notification

Sometimes it is desirable for a descriptor to know what class variable name it was assigned to. When a new class is created, the type metaclass scans the dictionary of the new class. If any of the entries are descriptors and if they define __set_name__(), that method is called with two arguments. The owner is the class where the descriptor is used, and the name is the class variable the descriptor was assigned to.

The implementation details are in type_new() and set_names() in Objects/typeobject.c.

Since the update logic is in type.__new__(), notifications only take place at the time of class creation. If descriptors are added to the class afterwards, __set_name__() will need to be called manually.

ORM example

The following code is simplified skeleton showing how data descriptors could be used to implement an object relational mapping.

The essential idea is that the data is stored in an external database. The Python instances only hold keys to the database's tables. Descriptors take care of lookups or updates:

class Field:

    def __set_name__(self, owner, name):
        self.fetch = f'SELECT {name} FROM {owner.table} WHERE {owner.key}=?;'
        self.store = f'UPDATE {owner.table} SET {name}=? WHERE {owner.key}=?;'

    def __get__(self, obj, objtype=None):
        return conn.execute(self.fetch, [obj.key]).fetchone()[0]

    def __set__(self, obj, value):
        conn.execute(self.store, [value, obj.key])
        conn.commit()

We can use the Field class to define "models" that describe the schema for each table in a database:

class Movie:
    table = 'Movies'                    # Table name
    key = 'title'                       # Primary key
    director = Field()
    year = Field()

    def __init__(self, key):
        self.key = key

class Song:
    table = 'Music'
    key = 'title'
    artist = Field()
    year = Field()
    genre = Field()

    def __init__(self, key):
        self.key = key

An interactive session shows how data is retrieved from the database and how it can be updated:

>>> import sqlite3
>>> conn = sqlite3.connect('entertainment.db')

>>> Movie('Star Wars').director
'George Lucas'
>>> jaws = Movie('Jaws')
>>> f'Released in {jaws.year} by {jaws.director}'
'Released in 1975 by Steven Spielberg'

>>> Song('Country Roads').artist
'John Denver'

>>> Movie('Star Wars').director = 'J.J. Abrams'
>>> Movie('Star Wars').director
'J.J. Abrams'

Pure Python Equivalents

The descriptor protocol is simple and offers exciting possibilities. Several use cases are so common that they have been prepackaged into built-in tools. Properties, bound methods, static methods, class methods, and __slots__ are all based on the descriptor protocol.

属性

Calling property() is a succinct way of building a data descriptor that triggers a function call upon access to an attribute. Its signature is:

property(fget=None, fset=None, fdel=None, doc=None) -> property

该文档显示了定义托管属性 x 的典型用法:

class C:
    def getx(self): return self.__x
    def setx(self, value): self.__x = value
    def delx(self): del self.__x
    x = property(getx, setx, delx, "I'm the 'x' property.")

要了解 property() 如何根据描述器协议实现，这里是一个纯 Python 的等价实现如下:

class Property:
    "Emulate PyProperty_Type() in Objects/descrobject.c"

    def __init__(self, fget=None, fset=None, fdel=None, doc=None):
        self.fget = fget
        self.fset = fset
        self.fdel = fdel
        if doc is None and fget is not None:
            doc = fget.__doc__
        self.__doc__ = doc

    def __get__(self, obj, objtype=None):
        if obj is None:
            return self
        if self.fget is None:
            raise AttributeError("unreadable attribute")
        return self.fget(obj)

    def __set__(self, obj, value):
        if self.fset is None:
            raise AttributeError("can't set attribute")
        self.fset(obj, value)

    def __delete__(self, obj):
        if self.fdel is None:
            raise AttributeError("can't delete attribute")
        self.fdel(obj)

    def getter(self, fget):
        return type(self)(fget, self.fset, self.fdel, self.__doc__)

    def setter(self, fset):
        return type(self)(self.fget, fset, self.fdel, self.__doc__)

    def deleter(self, fdel):
        return type(self)(self.fget, self.fset, fdel, self.__doc__)

这个内置的 property() 每当用户访问属性时生效，随后的变化需要一个方法的参与。

例如，一个电子表格类可以通过 Cell('b10').value 授予对单元格值的访问权限。对程序的后续改进要求每次访问都要重新计算单元格；但是，程序员不希望影响直接访问该属性的现有客户端代码。解决方案是将对 value 属性的访问包装在属性数据描述器中:

class Cell:
    ...

    @property
    def value(self):
        "Recalculate the cell before returning value"
        self.recalc()
        return self._value

Functions and methods

Python 的面向对象功能是在基于函数的环境构建的。通过使用非数据描述器，这两方面完成了无缝融合。

Functions stored in class dictionaries get turned into methods when invoked. Methods only differ from regular functions in that the object instance is prepended to the other arguments. By convention, the instance is called self but could be called this or any other variable name.

Methods can be created manually with types.MethodType which is roughly equivalent to:

class MethodType:
    "Emulate Py_MethodType in Objects/classobject.c"

    def __init__(self, func, obj):
        self.__func__ = func
        self.__self__ = obj

    def __call__(self, *args, **kwargs):
        func = self.__func__
        obj = self.__self__
        return func(obj, *args, **kwargs)

To support automatic creation of methods, functions include the __get__() method for binding methods during attribute access. This means that functions are non-data descriptors that return bound methods during dotted lookup from an instance. Here's how it works:

class Function:
    ...

    def __get__(self, obj, objtype=None):
        "Simulate func_descr_get() in Objects/funcobject.c"
        if obj is None:
            return self
        return MethodType(self, obj)

Running the following class in the interpreter shows how the function descriptor works in practice:

class D:
    def f(self, x):
         return x

The function has a qualified name attribute to support introspection:

>>> D.f.__qualname__
'D.f'

Accessing the function through the class dictionary does not invoke __get__(). Instead, it just returns the underlying function object:

>>> D.__dict__['f']
<function D.f at 0x00C45070>

Dotted access from a class calls __get__() which just returns the underlying function unchanged:

>>> D.f
<function D.f at 0x00C45070>

The interesting behavior occurs during dotted access from an instance. The dotted lookup calls __get__() which returns a bound method object:

>>> d = D()
>>> d.f
<bound method D.f of <__main__.D object at 0x00B18C90>>

Internally, the bound method stores the underlying function and the bound instance:

>>> d.f.__func__
<function D.f at 0x1012e5ae8>

>>> d.f.__self__
<__main__.D object at 0x1012e1f98>

If you have ever wondered where self comes from in regular methods or where cls comes from in class methods, this is it!

Static methods

非数据描述器为把函数绑定为方法的通常模式提供了一种简单的机制。

To recap, functions have a __get__() method so that they can be converted to a method when accessed as attributes. The non-data descriptor transforms an obj.f(*args) call into f(obj, *args). Calling cls.f(*args) becomes f(*args).

下表总结了绑定及其两个最有用的变体：

转换形式	Called from an object	Called from a class
函数	f(obj, *args)	f(*args)
静态方法	f(*args)	f(*args)
类方法	f(type(obj), *args)	f(cls, *args)

静态方法返回底层函数，不做任何更改。调用 c.f 或 C.f 等效于通过 object.__getattribute__(c, "f") 或 object.__getattribute__(C, "f") 查找。这样该函数就可以从对象或类中进行相同的访问。

适合于作为静态方法的是那些不引用 self 变量的方法。

例如，一个统计用的包可能包含一个实验数据的容器类。该容器类提供了用于计算数据的平均值，均值，中位数和其他描述性统计信息的常规方法。但是，可能有在概念上相关但不依赖于数据的函数。例如， erf(x) 是在统计中的便捷转换，但并不直接依赖于特定的数据集。可以从对象或类中调用它： s.erf(1.5) --> .9332 或 Sample.erf(1.5) --> .9332。

Since static methods return the underlying function with no changes, the example calls are unexciting:

class E:
    @staticmethod
    def f(x):
        print(x)

>>> E.f(3)
3
>>> E().f(3)
3

使用非数据描述器，纯 Python 版本的 staticmethod() 如下所示：

class StaticMethod:
    "Emulate PyStaticMethod_Type() in Objects/funcobject.c"

    def __init__(self, f):
        self.f = f

    def __get__(self, obj, objtype=None):
        return self.f

Class methods

与静态方法不同，类方法在调用函数之前将类引用放在参数列表的最前。无论调用方是对象还是类，此格式相同：

class F:
    @classmethod
    def f(cls, x):
        return cls.__name__, x

>>> print(F.f(3))
('F', 3)
>>> print(F().f(3))
('F', 3)

This behavior is useful whenever the method only needs to have a class reference and does rely on data stored in a specific instance. One use for class methods is to create alternate class constructors. For example, the classmethod dict.fromkeys() creates a new dictionary from a list of keys. The pure Python equivalent is:

class Dict:
    ...

    @classmethod
    def fromkeys(cls, iterable, value=None):
        "Emulate dict_fromkeys() in Objects/dictobject.c"
        d = cls()
        for key in iterable:
            d[key] = value
        return d

现在可以这样构造一个新的唯一键字典：

>>> Dict.fromkeys('abracadabra')
{'a': None, 'r': None, 'b': None, 'c': None, 'd': None}

使用非数据描述符协议，纯 Python 版本的 classmethod() 如下：

class ClassMethod:
    "Emulate PyClassMethod_Type() in Objects/funcobject.c"

    def __init__(self, f):
        self.f = f

    def __get__(self, obj, cls=None):
        if cls is None:
            cls = type(obj)
        if hasattr(obj, '__get__'):
            return self.f.__get__(cls)
        return MethodType(self.f, cls)

The code path for hasattr(obj, '__get__') was added in Python 3.9 and makes it possible for classmethod() to support chained decorators. For example, a classmethod and property could be chained together:

class G:
    @classmethod
    @property
    def __doc__(cls):
        return f'A doc for {cls.__name__!r}'

Member objects and __slots__

When a class defines __slots__, it replaces instance dictionaries with a fixed-length array of slot values. From a user point of view that has several effects:

1. Provides immediate detection of bugs due to misspelled attribute assignments. Only attribute names specified in __slots__ are allowed:

class Vehicle:
    __slots__ = ('id_number', 'make', 'model')

>>> auto = Vehicle()
>>> auto.id_nubmer = 'VYE483814LQEX'
Traceback (most recent call last):
    ...
AttributeError: 'Vehicle' object has no attribute 'id_nubmer'

2. Helps create immutable objects where descriptors manage access to private attributes stored in __slots__:

class Immutable:

    __slots__ = ('_dept', '_name')          # Replace the instance dictionary

    def __init__(self, dept, name):
        self._dept = dept                   # Store to private attribute
        self._name = name                   # Store to private attribute

    @property                               # Read-only descriptor
    def dept(self):
        return self._dept

    @property
    def name(self):                         # Read-only descriptor
        return self._name

mark = Immutable('Botany', 'Mark Watney')   # Create an immutable instance

3. Saves memory. On a 64-bit Linux build, an instance with two attributes takes 48 bytes with __slots__ and 152 bytes without. This flyweight design pattern likely only matters when a large number of instances are going to be created.

4. Blocks tools like functools.cached_property() which require an instance dictionary to function correctly:

from functools import cached_property

class CP:
    __slots__ = ()                          # Eliminates the instance dict

    @cached_property                        # Requires an instance dict
    def pi(self):
        return 4 * sum((-1.0)**n / (2.0*n + 1.0)
                       for n in reversed(range(100_000)))

>>> CP().pi
Traceback (most recent call last):
  ...
TypeError: No '__dict__' attribute on 'CP' instance to cache 'pi' property.

It's not possible to create an exact drop-in pure Python version of __slots__ because it requires direct access to C structures and control over object memory allocation. However, we can build a mostly faithful simulation where the actual C structure for slots is emulated by a private _slotvalues list. Reads and writes to that private structure are managed by member descriptors:

class Member:

    def __init__(self, name, clsname, offset):
        'Emulate PyMemberDef in Include/structmember.h'
        # Also see descr_new() in Objects/descrobject.c
        self.name = name
        self.clsname = clsname
        self.offset = offset

    def __get__(self, obj, objtype=None):
        'Emulate member_get() in Objects/descrobject.c'
        # Also see PyMember_GetOne() in Python/structmember.c
        return obj._slotvalues[self.offset]

    def __set__(self, obj, value):
        'Emulate member_set() in Objects/descrobject.c'
        obj._slotvalues[self.offset] = value

    def __repr__(self):
        'Emulate member_repr() in Objects/descrobject.c'
        return f'<Member {self.name!r} of {self.clsname!r}>'

The type.__new__() method takes care of adding member objects to class variables. The object.__new__() method takes care of creating instances that have slots instead of an instance dictionary. Here is a rough equivalent in pure Python:

class Type(type):
    'Simulate how the type metaclass adds member objects for slots'

    def __new__(mcls, clsname, bases, mapping):
        'Emuluate type_new() in Objects/typeobject.c'
        # type_new() calls PyTypeReady() which calls add_methods()
        slot_names = mapping.get('slot_names', [])
        for offset, name in enumerate(slot_names):
            mapping[name] = Member(name, clsname, offset)
        return type.__new__(mcls, clsname, bases, mapping)

class Object:
    'Simulate how object.__new__() allocates memory for __slots__'

    def __new__(cls, *args):
        'Emulate object_new() in Objects/typeobject.c'
        inst = super().__new__(cls)
        if hasattr(cls, 'slot_names'):
            inst._slotvalues = [None] * len(cls.slot_names)
        return inst

To use the simulation in a real class, just inherit from Object and set the metaclass to Type:

class H(Object, metaclass=Type):

    slot_names = ['x', 'y']

    def __init__(self, x, y):
        self.x = x
        self.y = y

At this point, the metaclass has loaded member objects for x and y:

>>> import pprint
>>> pprint.pp(dict(vars(H)))
{'__module__': '__main__',
 'slot_names': ['x', 'y'],
 '__init__': <function H.__init__ at 0x7fb5d302f9d0>,
 'x': <Member 'x' of 'H'>,
 'y': <Member 'y' of 'H'>,
 '__doc__': None}

When instances are created, they have a slot_values list where the attributes are stored:

>>> h = H(10, 20)
>>> vars(h)
{'_slotvalues': [10, 20]}
>>> h.x = 55
>>> vars(h)
{'_slotvalues': [55, 20]}

Unlike the real __slots__, this simulation does have an instance dictionary just to hold the _slotvalues array. So, unlike the real code, this simulation doesn't block assignments to misspelled attributes:

>>> h.xz = 30   # For actual __slots__ this would raise an AttributeError