获得列表联合的最快方法 - Python

时间:2016-03-08 11:24:51

标签: python list numpy set union

有一个C ++比较,可以从列表列表中获取列表联合:The fastest way to find union of sets

还有其他一些与python相关的问题,但没有一个提出加入名单的最快方法:

从答案中,我发现至少有两种方法可以做到:

>>> from itertools import chain
>>> x = [[1,2,3], [3,4,5], [1,7,8]]
>>> list(set().union(*x))
[1, 2, 3, 4, 5, 7, 8]
>>> list(set(chain(*x)))
[1, 2, 3, 4, 5, 7, 8]

请注意,我之后将该集转换为列表,因为我需要修复列表的顺序以便进一步处理。

经过一些比较后,似乎list(set(chain(*x)))更稳定,花费的时间更短:

from itertools import chain
import time
import random

# Dry run.
x = [[random.choice(range(10000)) 
    for i in range(10)] for j in range(10)]
list(set().union(*x))
list(set(chain(*x)))

y_time = 0
z_time = 0

for _ in range(1000):
    x = [[random.choice(range(10000)) 
        for i in range(10)] for j in range(10)]
    start = time.time()
    y = list(set().union(*x))
    y_time += time.time() - start 
    #print 'list(set().union(*x)):\t', y_time
    start = time.time()
    z = list(set(chain(*x)))
    z_time += time.time() - start 
    #print 'list(set(chain(*x))):\t', z_time
    assert sorted(y) == sorted(z)
    #print 

print y_time / 1000.
print z_time / 1000. 

[OUT]:

1.39586925507e-05
1.09834671021e-05

取出铸造集的变量列表:

y_time = 0
z_time = 0

for _ in range(1000):
    x = [[random.choice(range(10000)) 
        for i in range(10)] for j in range(10)]

    start = time.time()
    y = set().union(*x)
    y_time += time.time() - start 

    start = time.time()
    z = set(chain(*x))
    z_time += time.time() - start 

    assert sorted(y) == sorted(z)

print y_time / 1000.
print z_time / 1000. 

[OUT]:

1.22241973877e-05
1.02684497833e-05

当我尝试打印中间计时(没有列表转换)时,这是完整输出:http://pastebin.com/raw/y3i6dXZ8

为什么list(set(chain(*x)))花费的时间少于list(set().union(*x))

是否有其他方法可以实现相同的列表联合?使用numpypandassframe或其他什么? 替代方案更快吗?

1 个答案:

答案 0 :(得分:7)

最快的取决于func | x | time unique_concatenate | many_uniques | 0.863 empty_set_union | many_uniques | 1.191 short_set_union_rest | many_uniques | 1.192 long_set_union_rest | many_uniques | 1.194 set_chain | many_uniques | 1.224 func | x | time long_set_union_rest | many_duplicates | 0.958 short_set_union_rest | many_duplicates | 0.969 empty_set_union | many_duplicates | 0.971 set_chain | many_duplicates | 1.128 unique_concatenate | many_duplicates | 2.411 func | x | time empty_set_union | many_small_lists | 1.023 long_set_union_rest | many_small_lists | 1.028 set_chain | many_small_lists | 1.032 short_set_union_rest | many_small_lists | 1.036 unique_concatenate | many_small_lists | 1.351 func | x | time long_set_union_rest | few_large_lists | 0.791 empty_set_union | few_large_lists | 0.813 unique_concatenate | few_large_lists | 0.814 set_chain | few_large_lists | 0.829 short_set_union_rest | few_large_lists | 0.849 的性质 - 无论是长列表还是短列表,有多个子列表或少数子列表,子列表是长还是短,以及是否有很多重复或者很少重复。

以下是比较一些替代方案的一些时间结果。有很多可能性,这绝不是一个完整的分析,但也许这将为您提供一个研究您的用例的框架。

from __future__ import print_function
import random
import timeit
from itertools import chain
import numpy as np

def unique_concatenate(x):
    return np.unique(np.concatenate(x))

def short_set_union_rest(x):
    # This assumes x[0] is the shortest list in x
    return list(set(x[0]).union(*x[1:]))

def long_set_union_rest(x):
    # This assumes x[-1] is the longest list in x
    return list(set(x[-1]).union(*x[1:]))

def empty_set_union(x):
    return list(set().union(*x))

def set_chain(x):
    return list(set(chain(*x)))


big_range = list(range(10**7))
small_range = list(range(10**5))
many_uniques = [[random.choice(big_range) for i in range(j)] 
                for j in range(10, 10000, 10)]
many_duplicates = [[random.choice(small_range) for i in range(j)] 
              for j in range(10, 10000, 10)]
many_small_lists = [[random.choice(big_range) for i in range(10)] 
                    for j in range(10, 10000, 10)]
few_large_lists = [[random.choice(big_range) for i in range(1000)] 
                    for j in range(10, 100, 10)]

if __name__=='__main__':
    for x, n in [('many_uniques', 1), ('many_duplicates', 4), 
                 ('many_small_lists', 800), ('few_large_lists', 800)]:
        timing = dict()
        for func in [
                'unique_concatenate', 'short_set_union_rest', 'long_set_union_rest',
                'empty_set_union', 'set_chain']:
            timing[func, x] = timeit.timeit(
                '{}({})'.format(func, x), number=n,
                setup='from __main__ import {}, {}'.format(func, x))
        print('{:20} | {:20} | {}'.format('func', 'x', 'time'))
        for key, t in sorted(timing.items(), key=lambda item: item[1]):
            func, x = key
            print('{:20} | {:20} | {:.3f}'.format(func, x, t))
        print(end='\n')

请务必在您自己的计算机上运行timeit基准测试,因为结果可能会有所不同。

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