Python集合类型(list tuple dict set generator)图文详解-Tutorial Python-php.cn

Python内嵌的集合类型有list、tuple、set、dict。

列表list：看似数组，但比数组强大，支持索引、切片、查找、增加等功能。

元组tuple：功能跟list差不多，但一旦生成，长度及元素都不可变（元素的元素还是可变），似乎就是一更轻量级、安全的list。

字典dict：键值对结构哈希表，跟哈希表的性质一样，key无序且不重复，增删改方便快捷。

set：无序且不重复的集合，就是一个只有键没有值的dict，Java的HashSet就是采用HashMap实现，但愿python不会是这样，毕竟set不需要value，省去了很多指针。

Generator:

称之为生成器，或者列表推导式，是python中有一个特殊的数据类型，实际上并不是一个数据结构，只包括算法和暂存的状态，并且具有迭代的功能。

先看看它们的内存使用情况，分别用生成器生成100000个元素的set, dict, generator, tuple, list。消耗的内存dict, set, list, tuple依次减少，生成的对象大小也是一样。由于generator并不生成数据表，所以不需要消耗内存：

import sys
from memory_profiler import profile

@profile
def create_data(data_size):
    data_generator = (x for x in xrange(data_size))
    data_set = {x for x in xrange(data_size)}
    data_dict = {x:None for x in xrange(data_size)}
    data_tuple = tuple(x for x in xrange(data_size))
    data_list = [x for x in xrange(data_size)]
    return data_set, data_dict, data_generator, data_tuple, data_list

data_size = 100000
for data in create_data(data_size):
    print data.__class__, sys.getsizeof(data)

Line #    Mem usage    Increment   Line Contents
================================================
    14.6 MiB      0.0 MiB   @profile
                            def create_data(data_size):
    14.7 MiB      0.0 MiB       data_generator = (x for x in xrange(data_size))
    21.4 MiB      6.7 MiB       data_set = {x for x in xrange(data_size)}
    29.8 MiB      8.5 MiB       data_dict = {x:None for x in xrange(data_size)}
    33.4 MiB      3.6 MiB       data_tuple = tuple(x for x in xrange(data_size))
    38.2 MiB      4.8 MiB       data_list = [x for x in xrange(data_size)]
    38.2 MiB      0.0 MiB       return data_set, data_dict, data_generator, data_tuple, data_list
 
<type &#39;set&#39;> 4194528
<type &#39;dict&#39;> 6291728
<type &#39;generator&#39;> 72
<type &#39;tuple&#39;> 800048
<type &#39;list&#39;> 824464

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再看看查找性能，dict，set是常数查找时间（O(1)），list、tuple是线性查找时间（O(n)），用生成器生成指定大小元素的对象，用随机生成的数字去查找：

import time
import sys
import random
from memory_profiler import profile

def create_data(data_size):
    data_set = {x for x in xrange(data_size)}
    data_dict = {x:None for x in xrange(data_size)}
    data_tuple = tuple(x for x in xrange(data_size))
    data_list = [x for x in xrange(data_size)]
    return data_set, data_dict, data_tuple, data_list

def cost_time(func):
    def cost(*args, **kwargs):
        start = time.time()
        r = func(*args, **kwargs)
        cost = time.time() - start
        print &#39;find in %s cost time %s&#39; % (r, cost)
        return r, cost  #返回数据的类型和方法执行消耗的时间
    return cost

@cost_time
def test_find(test_data, data):
    for d in test_data:
        if d in data:
            pass
    return data.__class__.__name__

data_size = 100
test_size = 10000000
test_data = [random.randint(0, data_size) for x in xrange(test_size)]
#print test_data
for data in create_data(data_size):
    test_find(test_data, data)

输出：
----------------------------------------------
find in <type &#39;set&#39;> cost time 0.47200012207
find in <type &#39;dict&#39;> cost time 0.429999828339
find in <type &#39;tuple&#39;> cost time 5.36500000954
find in <type &#39;list&#39;> cost time 5.53399991989

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100个元素的大小的集合，分别查找1000W次，差距非常明显。不过这些随机数，都是能在集合中查找得到。修改一下随机数方式，生成一半是能查找得到，一半是查找不到的。从打印信息可以看出在有一半最坏查找例子的情况下，list、tuple表现得更差了。

def randint(index, data_size):
    return random.randint(0, data_size) if (x % 2) == 0 else random.randint(data_size, data_size * 2)

test_data = [randint(x, data_size) for x in xrange(test_size)]

输出：
----------------------------------------------
find in <type &#39;set&#39;> cost time 0.450000047684
find in <type &#39;dict&#39;> cost time 0.397000074387
find in <type &#39;tuple&#39;> cost time 7.83299994469
find in <type &#39;list&#39;> cost time 8.27800011635

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元素的个数从10增长至500，统计每次查找10W次的时间，用图拟合时间消耗的曲线，结果如下图，结果证明dict, set不管元素多少，一直都是常数查找时间，dict、tuple随着元素增长，呈现线性增长时间：

import matplotlib.pyplot as plot
from numpy import *

data_size = array([x for x in xrange(10, 500, 10)])
test_size = 100000
cost_result = {}
for size in data_size:
    test_data = [randint(x, size) for x in xrange(test_size)]
    for data in create_data(size):
        name, cost = test_find(test_data, data) #装饰器函数返回函数的执行时间
        cost_result.setdefault(name, []).append(cost)

plot.figure(figsize=(10, 6))
xline = data_size
for data_type, result in cost_result.items():
    yline = array(result)
    plot.plot(xline, yline, label=data_type)

plot.ylabel(&#39;Time spend&#39;)
plot.xlabel(&#39;Find times&#39;)

plot.grid()

plot.legend()
plot.show()

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