下面来点简单的小例子说明把。  

>>> x = set('spam')

>>> y = set(['h','a','m'])

>>> x, y

(set(['a', 'p', 's', 'm']), set(['a', 'h', 'm']))  

再来些小应用。  

>>> x & y # 交集

set(['a', 'm'])  

>>> x | y # 并集

set(['a', 'p', 's', 'h', 'm'])  

>>> x - y # 差集

set(['p', 's'])  

a = t | s          # t 和 s的并集  

b = t & s          # t 和 s的交集  

c = t – s          # 求差集（项在t中，但不在s中）  

d = t ^ s          # 对称差集（项在t或s中，但不会同时出现在二者中）  

#Python中，使用相应的优化函数可以大大的提高系统的运行效率。比如下面的这个例子：

from time import time

lista=[1,2, 3, 4,5, 6, 7 ,8, 9, 10, 25, 50, 36, 43, 52]

listb=[2, 4, 6, 9, 36]

def noset_test():

    t = time()

    filter=[]

    for i in range(100000):

        for a in lista:

            for b in listb:

                if a==b:

                    filter.append(a)

    print 'no set total run time:'

    print time() -t

def set_test():

    t1 = time()

    for i in range(100000):

        list(set(lista)&set(listb))

    print "set total run time:"

    print time() - t1

noset_test();

set_test();

输出为：

no set total run time:

0.365000009537

set total run time:

0.15700006485

通过使用set,运行的时间明显的有缩减。

engineers = set(['John', 'Jane', 'Jack', 'Janice'])

programmers = set(['Jack', 'Sam', 'Susan', 'Janice'])

managers = set(['Jane', 'Jack', 'Susan', 'Zack'])

employees = list(engineers | programmers | managers)           # union

engineering_management = list(engineers & managers)            # intersection

fulltime_management = list(managers - engineers - programmers) # difference

print"employees", employees

print"engineering_management" ,engineering_management

print "fulltime_management", fulltime_management

输出为：

employees ['Jack', 'Sam', 'Susan', 'Jane', 'Janice', 'John', 'Zack']

engineering_management ['Jane', 'Jack']

fulltime_management ['Zack']