在Python中调用函数集的有效方法 [英] Efficient way of calling set of functions in Python

问题描述

我有一组功能：

functions=set(...)

所有函数都需要一个参数x。

All the functions need one parameter x.

什么是最有效的方法在python中做类似的事情：

What is the most efficient way in python of doing something similar to:

for function in functions:
   function(x)

推荐答案

您提供的代码，

for function in functions:
    function(x)

...似乎没有对调用 function（x）的结果做任何事情。如果确实如此，意味着这些功能被称为副作用，那么就没有更多的pythonic替代品了。 只需保留您的代码。 ^† 这里带回家的重点是

...does not appear to do anything with the result of calling function(x). If that is indeed so, meaning that these functions are called for their side-effects, then there is no more pythonic alternative. Just leave your code as it is.^† The point to take home here, specifically, is

                       &NBSP ;      
在列表推导中避免使用带副作用的函数。

至于效率：我希望使用其他任何东西而不是你的简单的循环不会改善运行时。如有疑问，请使用 timeit 。例如，以下测试似乎表明常规for循环比列表理解更快。 （我不愿意从这个测试中得出任何一般性的结论，认为）：

As for efficiency: I expect that using anything else instead of your simple loop will not improve runtime. When in doubt, use timeit. For example, the following tests seem to indicate that a regular for-loop is faster than a list-comprehension. (I would be reluctant to draw any general conclusions from this test, thought):

>>> timeit.Timer('[f(20) for f in functions]', 'functions = [lambda n: i * n for i in range(100)]').repeat()
[44.727972984313965, 44.752119779586792, 44.577917814254761]
>>> timeit.Timer('for f in functions: f(20)', 'functions = [lambda n: i * n for i in range(100)]').repeat()
[40.320928812026978, 40.491761207580566, 40.303879022598267]

但是，再次，即使这些测试表明该列表 - 为了便于阅读，理解是更快，重要的是你不应该在涉及副作用时使用它们。

But again, even if these tests would have indicated that list-comprehensions are faster, the point remains that you should not use them when side-effects are involved, for readability's sake.

  ^† ：好吧，我在函数中为f写了，所以差值为 function 和函数更加明显。但这不是这个问题的关键。

  ^†: Well, I'd write for f in functions, so that the difference beteen function and functions is more pronounced. But that's not what this question is about.

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