Python并行遍历一个列表? [英] Python iterating over a list in parallel?

问题描述

我创建的列表中包含不超过8个项目，现在我用标准的for循环用于列表中的项目:"遍历该项目，我想知道是否有一种方法可以检查所有元素在同一时间?我看过一些有关使用zip同时遍历2个列表的帖子，这可能会有所帮助.

I have a list created with no more than 8 items in it, right now I loop over the item with a standard for loop "for item in list:" I am wondering if there is a way to check all of the elements at the exact same time? I have seen some posts about using zip to iterate over 2 lists at the same time which could help.

如果没有办法同时遍历一个列表元素，我想我需要将一个单独的列表拆分为4个单独的列表，然后使用zip遍历2个，在另一个线程上同时进行另外2个，但这似乎是一个笨拙的修复程序.

If there is no way to iterate over a lists elements at the same time I would guess I need to split one single list into 4 separate lists and iterate 2 at the same time using zip and do the other 2 on a separate thread, that seems like it could be a clunky fix though.

任何帮助将不胜感激.

修改 抱歉，我有一个清单

Edit Sorry to be vague, I have a list

apples = ['green', 'green', 'green', 'red']

现在我使用一个for循环遍历每个元素，

right now I use a for loop to go over each element,

for apple in apples:
    checkAppleColour(apple)
    #Do some more stuff depending on colour....

现在，它一个接一个地循环遍历苹果中的每个元素，我想做的是在每毫秒精确计数的同时，对每个项目进行一次颜色检查(使用的示例代码，但是原则与我要达到的目标完全相同).

right now it loops over each element in apple one by one, what I would like to be able to do is check each of the items at the exact same time for a colour check as every millisecond counts (example code used, but principle is exactly the same for what I am trying to achieve).

这里的性能是关键，我不介意是否必须使用它的线程，但是我需要最大的速度/效率来检查每个元素，我的猜测是并行性是做到这一点的方法，我只是不确定如何

Performance is key here I don't mind if its threading I have to use, but I need maximum speed / efficiency for checking each element and my guess is parallelism is the way to do it, I am just not quite sure how.

推荐答案

如果可以将函数映射到列表，则并行化很容易.

If you can map a function onto the list, parallelism is easy.

def addOne (x):
    return x + 1

默认映射在一个线程上执行操作.

The default map does things on one thread.

map(addOne, range(1, 4)) #When iterated over produces [2, 3, 4]

如果您的问题是地图，则意味着它是平行的.

If your problem is a map, it means that it's trivially parallel.

from multiprocessing import Pool

pool = Pool()
pool.map(addOne, range(1, 4)) ##When iterated over produces [2, 3, 4]

这使用多个过程. multiprocessing.dummy.Pool是线程池.请参阅这篇文章，以很好地介绍两者的原理.

This uses multiple processes. multiprocessing.dummy.Pool is a thread pool. See this article for a great introduction to the rationale behind both.

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