一个快速的性能测试显示卢茨的解决方案是最好的:

import time

def speed_test(func):
    def wrapper(*args, **kwargs):
        t1 = time.time()
        for x in xrange(5000):
            results = func(*args, **kwargs)
        t2 = time.time()
        print '%s took %0.3f ms' % (func.func_name, (t2-t1)*1000.0)
        return results
    return wrapper

@speed_test
def compare_bitwise(x, y):
    set_x = frozenset(x)
    set_y = frozenset(y)
    return set_x & set_y

@speed_test
def compare_listcomp(x, y):
    return [i for i, j in zip(x, y) if i == j]

@speed_test
def compare_intersect(x, y):
    return frozenset(x).intersection(y)

# Comparing short lists
a = [1, 2, 3, 4, 5]
b = [9, 8, 7, 6, 5]
compare_bitwise(a, b)
compare_listcomp(a, b)
compare_intersect(a, b)

# Comparing longer lists
import random
a = random.sample(xrange(100000), 10000)
b = random.sample(xrange(100000), 10000)
compare_bitwise(a, b)
compare_listcomp(a, b)
compare_intersect(a, b)

这是我的机器上的结果:

# Short list:
compare_bitwise took 10.145 ms
compare_listcomp took 11.157 ms
compare_intersect took 7.461 ms

# Long list:
compare_bitwise took 11203.709 ms
compare_listcomp took 17361.736 ms
compare_intersect took 6833.768 ms

显然，任何人工性能测试都应该持保留态度，但由于set().intersection()答案至少与其他解决方案一样快，而且也是最易读的，因此它应该是这个常见问题的标准解决方案。

你可以使用

def returnMatches(a,b):
       return list(set(a) & set(b))

快捷方式:

list(set(a).intersection(set(b)))

使用__and__属性方法也可以。

>>> a = [1, 2, 3, 4, 5]
>>> b = [9, 8, 7, 6, 5]
>>> set(a).__and__(set(b))
set([5])

或者简单地

>>> set([1, 2, 3, 4, 5]).__and__(set([9, 8, 7, 6, 5]))
set([5])
>>>    

这是为那些可能返回某个字符串或输出的人准备的， 下面是代码，希望能有所帮助:

lis =[]
#convert to list
a = list(data)
b = list(data)
def make_list():
    c = "greater than"
    d = "less_than"
    e = "equal"
    for first, first_te in zip(a, b):
        if first < first_te:
            lis.append(d)
        elif first > first_te:
            lis.append(c)
        else:
            lis.append(e)
    return lis

make_list()

使用set.intersection()，它快速且可读。

>>> set(a).intersection(b)
set([5])