我更喜欢基于集合的答案，但这里有一个不管怎样都有用的答案

[x for x in a if x in b]

这不是最有效的方法，但到目前为止最明显的方法是:

>>> a = [1, 2, 3, 4, 5]
>>> b = [9, 8, 7, 6, 5]
>>> set(a) & set(b)
{5}

如果顺序很重要，你可以用这样的列表推导式来做:

>>> [i for i, j in zip(a, b) if i == j]
[5]

(仅适用于同等大小的列表，这意味着顺序重要性)。

最简单的方法是使用set:

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

>>> s = ['a','b','c']   
>>> f = ['a','b','d','c']  
>>> ss= set(s)  
>>> fs =set(f)  
>>> print ss.intersection(fs)   
   **set(['a', 'c', 'b'])**  
>>> print ss.union(fs)        
   **set(['a', 'c', 'b', 'd'])**  
>>> print ss.union(fs)  - ss.intersection(fs)   
   **set(['d'])**

如果你想要一个布尔值:

>>> a = [1, 2, 3, 4, 5]
>>> b = [9, 8, 7, 6, 5]
>>> set(b) == set(a)  & set(b) and set(a) == set(a) & set(b)
False
>>> a = [3,1,2]
>>> b = [1,2,3]
>>> set(b) == set(a)  & set(b) and set(a) == set(a) & set(b)
True

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。