Python:使用列表推导而不是循环来提​​高性能 [英] Python: using a List Comprehensions instead of loop in order to improve the performance

问题描述

我有字典中的类型(示例)

I have a type from a dictionary (example)

l =('1037_97',["a","b","c","d","e"])

我希望保存一个文件(las格式)，但是 Liblas 只能写一个观点.

I wish to save a file (las format) but Liblas can write only single point.

for l in Groups.iteritems():
    for p in xrange(len(l[1])):
        file_out.write(l[1][p])

如果可能，我正在尝试使用 列表理解功能，以节省代码并加快循环速度

I am trying to use if it's possible a List Comprehensions in order to save code and speed the loop

推荐答案

如果您想使用更短的解决方案，请考虑将map()用于内部循环，甚至两者都使用.但是，它不太可能获得显着的性能提升.但是，for p in l[1]:仍可能比使用xrange的构造更快.下面的示例应在一行中完成您想要的操作:

If you want a shorter solution, consider using map() for inner cycle, or even for both. But it is unlikely to get a significant performance boost. However, for p in l[1]: still may be faster than that construction with xrange. The following example should do what you wanted in a single line:

map(lambda g: map(file_out.write, g), groups.itervalues())

现在让我们比较不同实现的性能.在这里，我尝试测量一些测试数据上的时间:

Now let's compare performance of different implementations. Here I tried to measure times on some test data:

import timeit

groups = dict(('1037_%d' % i, ["a","b","c","d","e"]) for i in xrange(100))

class FOut(object):
    def write(self, v):
        #print v
        pass

file_out = FOut()

def using_map():
    map(lambda g: map(file_out.write, g), groups.itervalues())

def initial_version():
    for l in groups.iteritems():
        for p in xrange(len(l[1])):
            file_out.write(l[1][p])

def seq_iteration():
    for l in groups.iteritems():
        for p in l[1]:
            file_out.write(p)

def seq_iteration_values():
    for l in groups.itervalues():
        for p in l:
            file_out.write(p)

def list_compr():
    [[file_out.write(v) for v in g] for g in groups.itervalues()]



tests = ('initial_version', 'using_map', 'seq_iteration', 'list_compr', 'seq_iteration_values')


for test in tests:
    print test, timeit.timeit('%s()'%test, 'from __main__ import %s'%test, number=10000)

结果是:

initial_version 0.862531900406
using_map 0.703296899796
seq_iteration 0.541372060776
list_compr 0.632550954819
seq_iteration_values 0.493131160736

如您所见，您的初始版本是最慢的，修复迭代有很大帮助，map()版本很短，但不及itervalues()版本那么快.创建不需要的列表的列表理解还不错，但仍然比普通周期慢.

As you can see, your initial version is the slowest, fixing iteration helps a lot, map() version is short, but not as fast as the version with itervalues(). List comprehension that creates unneeded lists is not bad, but still slower than the plain cycle.

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