为什么 Python 类会继承对象? [英] Why do Python classes inherit object?

问题描述

类声明是否有任何理由从 object 继承?

Is there any reason for a class declaration to inherit from object?

我刚刚找到了一些执行此操作的代码，但我找不到很好的理由.

I just found some code that does this and I can't find a good reason why.

class MyClass(object):
    # class code follows...

推荐答案

类声明是否有任何理由从 object 继承?

在 Python 3 中，除了 Python 2 和 3 的兼容性之外，没有理由.在 Python 2 中，原因很多.

In Python 3, apart from compatibility between Python 2 and 3, no reason. In Python 2, many reasons.

在 Python 2.x(从 2.2 开始)中，有两种样式的类，具体取决于是否存在 object 作为基类:

In Python 2.x (from 2.2 onwards) there's two styles of classes depending on the presence or absence of object as a base-class:

1. 经典"风格类:它们没有 object 作为基类:

1. "classic" style classes: they don't have object as a base class:

>>> class ClassicSpam:      # no base class
...     pass
>>> ClassicSpam.__bases__
()

新"样式类:它们直接或间接拥有(例如从内置类型)，object作为基类:

"new" style classes: they have, directly or indirectly (e.g inherit from a built-in type), object as a base class:

>>> class NewSpam(object):           # directly inherit from object
...    pass
>>> NewSpam.__bases__
(<type 'object'>,)
>>> class IntSpam(int):              # indirectly inherit from object...
...    pass
>>> IntSpam.__bases__
(<type 'int'>,) 
>>> IntSpam.__bases__[0].__bases__   # ... because int inherits from object  
(<type 'object'>,)

毫无疑问，在编写课程时，您会总是想要选择新式课程.这样做的好处很多，列举其中一些:

Without a doubt, when writing a class you'll always want to go for new-style classes. The perks of doing so are numerous, to list some of them:

• 对描述符的支持.具体来说，使用描述符可以实现以下构造:

• Support for descriptors. Specifically, the following constructs are made possible with descriptors:

1. classmethod:一种方法接收类作为隐式参数而不是实例.
2. staticmethod:一种方法不接收隐式参数 self 作为第一个参数.
3. 具有 property 的属性:创建用于管理属性的获取、设置和删除的功能.
4. __slots__:节省内存消耗一个类，也导致更快的属性访问.当然，它确实施加限制.

1. classmethod: A method that receives the class as an implicit argument instead of the instance.
2. staticmethod: A method that does not receive the implicit argument self as a first argument.
3. properties with property: Create functions for managing the getting, setting and deleting of an attribute.
4. __slots__: Saves memory consumptions of a class and also results in faster attribute access. Of course, it does impose limitations.

__new__ 静态方法:让您自定义如何创建新的类实例.

The __new__ static method: lets you customize how new class instances are created.

方法解析顺序(MRO):按什么顺序尝试解析调用哪个方法时，将搜索类的基类.

Method resolution order (MRO): in what order the base classes of a class will be searched when trying to resolve which method to call.

与 MRO 相关，super 调用.另请参阅，super() 被认为是超级的.

Related to MRO, super calls. Also see, super() considered super.

如果你不是从 object 继承的，忘记这些.可以在 这里.

If you don't inherit from object, forget these. A more exhaustive description of the previous bullet points along with other perks of "new" style classes can be found here.

新型类的缺点之一是类本身需要更多内存.但是，除非您要创建许多类对象，否则我怀疑这将是一个问题，并且它是在积极海洋中的消极沉没.

One of the downsides of new-style classes is that the class itself is more memory demanding. Unless you're creating many class objects, though, I doubt this would be an issue and it's a negative sinking in a sea of positives.

在 Python 3 中，事情被简化了.只有新样式的类存在(简单地称为类)，所以添加 object 的唯一区别是要求您再输入 8 个字符.这个:

In Python 3, things are simplified. Only new-style classes exist (referred to plainly as classes) so, the only difference in adding object is requiring you to type in 8 more characters. This:

class ClassicSpam:
    pass

完全等同于(除了他们的名字:-):

is completely equivalent (apart from their name :-) to this:

class NewSpam(object):
     pass

对此:

class Spam():
    pass

所有的 __bases__ 中都有 object.

>>> [object in cls.__bases__ for cls in {Spam, NewSpam, ClassicSpam}]
[True, True, True]

<小时>

那么，你应该怎么做?

在 Python 2 中: 总是从 object 显式继承.获得福利.

---

So, what should you do?

In Python 2: always inherit from object explicitly. Get the perks.

在 Python 3 中: 从 object 继承，如果您编写的代码试图与 Python 无关，也就是说，它需要在 Python 2 和 Python 中都可以工作3. 否则不要，真的没什么区别，因为Python会在幕后为你插入.

In Python 3: inherit from object if you are writing code that tries to be Python agnostic, that is, it needs to work both in Python 2 and in Python 3. Otherwise don't, it really makes no difference since Python inserts it for you behind the scenes.

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