使用迭代器的最快(最 Pythonic)方式

问题描述

我很好奇使用迭代器的最快方式是什么，也是最 Pythonic 的方式.

I am curious what the fastest way to consume an iterator would be, and the most Pythonic way.

例如，假设我想创建一个带有 map 内置函数的迭代器，它会累积一些东西作为副作用.我实际上并不关心 map 的结果，只关心副作用，所以我想以尽可能少的开销或样板文件来完成迭代.比如:

For example, say that I want to create an iterator with the map builtin that accumulates something as a side-effect. I don't actually care about the result of the map, just the side effect, so I want to blow through the iteration with as little overhead or boilerplate as possible. Something like:

my_set = set()
my_map = map(lambda x, y: my_set.add((x, y)), my_x, my_y)

在这个例子中，我只是想通过迭代器来累积 my_set 中的东西，而 my_set 只是一个空集，直到我真正运行 我的地图.比如:

In this example, I just want to blow through the iterator to accumulate things in my_set, and my_set is just an empty set until I actually run through my_map. Something like:

for _ in my_map:
    pass

或赤身裸体

[_ for _ in my_map]

有效，但他们都觉得笨重.有没有更 Pythonic 的方法来确保迭代器快速迭代，以便您从一些副作用中受益?

works, but they both feel clunky. Is there a more Pythonic way to make sure an iterator iterates quickly so that you can benefit from some side-effect?

我在以下方面测试了上述两种方法:

I tested the two methods above on the following:

my_x = np.random.randint(100, size=int(1e6))
my_y = np.random.randint(100, size=int(1e6))

与上面定义的 my_set 和 my_map 一起使用.我用 timeit 得到了以下结果:

with my_set and my_map as defined above. I got the following results with timeit:

for _ in my_map:
    pass
468 ms ± 20.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

[_ for _ in my_map]
476 ms ± 12.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

两者之间没有真正的区别，而且都感觉很笨重.

No real difference between the two, and they both feel clunky.

注意，我使用 list(my_map) 获得了类似的性能，这是评论中的建议.

Note, I got similar performance with list(my_map), which was a suggestion in the comments.

推荐答案

虽然您不应该仅仅为了副作用而创建地图对象，但实际上在 itertools 文档:

While you shouldn't be creating a map object just for side effects, there is in fact a standard recipe for consuming iterators in the itertools docs:

def consume(iterator, n=None):
    "Advance the iterator n-steps ahead. If n is None, consume entirely."
    # Use functions that consume iterators at C speed.
    if n is None:
        # feed the entire iterator into a zero-length deque
        collections.deque(iterator, maxlen=0)
    else:
        # advance to the empty slice starting at position n
        next(islice(iterator, n, n), None)

对于完全消费"的情况，这可以简化为

For just the "consume entirely" case, this can be simplified to

def consume(iterator):
    collections.deque(iterator, maxlen=0)

以这种方式使用 collections.deque 可以避免存储所有元素(因为 maxlen=0)并以 C 速度迭代，没有字节码解释开销.双端队列中甚至还有一个专用快速路径使用 maxlen=0 双端队列来使用迭代器的实现.

Using collections.deque this way avoids storing all the elements (because maxlen=0) and iterates at C speed, without bytecode interpretation overhead. There's even a dedicated fast path in the deque implementation for using a maxlen=0 deque to consume an iterator.

时间:

In [1]: import collections

In [2]: x = range(1000)

In [3]: %%timeit
   ...: i = iter(x)
   ...: for _ in i:
   ...:     pass
   ...: 
16.5 s ± 829 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [4]: %%timeit
   ...: i = iter(x)
   ...: collections.deque(i, maxlen=0)
   ...: 
12 s ± 566 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

当然，这都是基于 CPython 的.解释器开销的整个性质在其他 Python 实现中非常不同，并且 maxlen=0 快速路径特定于 CPython.有关其他 Python 实现，请参阅 abarnert 的回答.

Of course, this is all based on CPython. The entire nature of interpreter overhead is very different on other Python implementations, and the maxlen=0 fast path is specific to CPython. See abarnert's answer for other Python implementations.

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