为什么许多Python内置/标准库函数实际上是类 [英] Why are many Python built-in/standard library functions actually classes

问题描述

许多Python内置的函数实际上是类，尽管它们也具有直接的函数实现。甚至是非常简单的代码，例如 itertools.repeat 。这样做的动机是什么？

Many Python builtin "functions" are actually classes, although they also have a straightforward function implementation. Even very simple ones, such as itertools.repeat. What is the motivation for this? It seems like over-engineering to me.

编辑：我不是在问 itertools.repeat 的目的。或任何其他特定功能。这只是一个非常简单的函数的示例，它具有非常简单的可能的影响：

I am not asking about the purpose of itertools.repeat or any other particular function. It was just an example of a very simple function with a very simple possible impementation:

def repeat(x):
    while True: yield x

但是 itertools.repeat 实际上不是函数，而是作为类实现的。我的问题是：为什么？似乎是不必要的开销。

But itertools.repeat is not actually a function, it's implemented as a class. My question is: Why? It seems like unnecessary overhead.

我也了解类是可调用的函数，以及如何使用类来模拟类似函数的行为。但是我不明白为什么它在标准库中如此广泛地使用。

Also I understand that classes are callable functions and how you can emulate a function-like behavior using a class. But I don't understand why it's so widely used through the standard library.

推荐答案

作为<$ c $的类实现c> itertools 具有一些生成器函数没有的优点。例如：

Implementing as a class for itertools has some advantages that generator functions don't have. For example:

1. CPython在C层实现了这些内置函数，而在C层，最好实现生成器函数作为实现 __ next __ 的类，该类将状态保留为实例属性；基于 yield 的生成器是Python的一个很好的方面，实际上，它们只是 generator 类的一个实例（因此它们实际上仍然是类实例，就像Python中的其他所有对象一样）


2. 生成器不可腌制或可复制，并且没有故事来使它们支持任何一种行为（内部状态过于复杂和不透明，无法将其概括化；一个类可以定义 __ reduce __ / __ copy __ / __ deepcopy __ （和如果是Python级别的类，则可能甚至不需要这样做；它会自动工作）并使实例可腌制/可复制（因此，如果您已经从 range生成了5个元素迭代器，您可以将其复制或腌制/解开，并在迭代中获得相同的距离）

1. CPython implements these built-ins at the C layer, and at the C layer, a generator "function" is best implemented as a class implementing __next__ that preserves state as instance attributes; yield based generators are a Python layer nicety, and really, they're just an instance of the generator class (so they're actually still class instances, like everything else in Python)
2. Generators aren't pickleable or copyable, and don't have "story" for making them support either behavior (the internal state is too complex and opaque to generalize it); a class can define __reduce__/__copy__/__deepcopy__ (and if it's a Python level class, it probably doesn't even need to do that; it will work automatically) and make the instances pickleable/copyable (so if you have already generated 5 elements from a range iterator, you can copy or pickle/unpickle it, and get an iterator the same distance along in iteration)

对于非生成器工具，原因通常相似。可以为类赋予状态，而某些功能则无法提供自定义行为。它们可以继承（如果需要的话，但是C层类如果是逻辑上的函数，则可以禁止子类化。）。

For non-generator tools, the reasons are usually similar. Classes can be given state and customized behaviors that a function can't. They can be inherited from (if that's desired, but C layer classes can prohibit subclassing if they're "logically" functions).

对于动态实例创建也很有用；如果您有一个未知类的实例但一个已知原型的实例（例如，采用可迭代或 chain 之类的序列构造函数），并且您想要转换其他输入该类，您可以执行 type（unknown）（constructorarg）;如果它是一个生成器，则 type（unknown）是没有用的，则您不能用它来使自身更多，因为您不能反省它的来源（

It's also useful for dynamic instance creation; if you have an instance of an unknown class but a known prototype (say, the sequence constructors that take an iterable, or chain or whatever), and you want to convert some other type to that class, you can do type(unknown)(constructorarg); if it's a generator, type(unknown) is useless, you can't use it to make more of itself because you can't introspect to figure out where it came from (not in reasonable ways).

此外，即使您从未使用过编程逻辑功能，您还是希望在交互式解释器中进行打印调试或查看 type（myiter），< class'generator'> ，它没有任何关于起源的提示，或< class'itertools.repeat'> 可以告诉您确切的信息以及它的来源？

And beyond that, even if you never use the features for programming logic, what would you rather see in the interactive interpreter or doing print debugging of type(myiter), <class 'generator'> that gives no hints as to origin, or <class 'itertools.repeat'> that tells you exactly what you have and where it came from?

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