参考文献：http://www.cnblogs.com/wupeiqi/articles/4943406.html
set集合
集合的特点：
集合的常用方法如下：
>>> s1 = set([11,22,33,44]) >>> s1 set([33, 11, 44, 22]) >>> s1.add(66) >>> s1 set([33, 66, 11, 44, 22])
1 >>> s1 = set([11,22,33,44]) 2 >>> s1 3 {33, 11, 44, 22} 4 >>> s1.clear() 5 >>> s1 6 set()
1 >>> s1 = set([11,22,33,44]) 2 >>> s2 = set([123,22,33,543]) 3 >>> s1.difference(s2) 4 {11, 44}
1 >>> s1 = set([11,22,33,44]) 2 >>> s2 = set([123,22,33,543]) 3 >>> s1.difference_update(s2) 4 >>> s1 5 {11, 44} 6 >>> s2 7 {33, 123, 22, 543}
1 >>> s1 = set([11,22,33,44]) 2 >>> s2 = set([123,22,33,543]) 3 >>> s1.intersection(s2) 4 {33, 22}
1 >>> s1 = set([11,22,33,44]) 2 >>> s2 = set([123,22,33,543]) 3 >>> s1.intersection_update(s2) 4 >>> s1 5 {33, 22}
1 >>> s1 = set([11,22,33,44]) 2 >>> s1.pop() 3 33 4 >>> s1 5 {11, 44, 22}
1 >>> s1 = set([11,22,33,44]) 2 >>> s1.remove(11) 3 >>> s1 4 {33, 44, 22}
1 >>> s1 = set([11,22,33,44]) 2 >>> s2 = set([123,22,33,543]) 3 >>> s1.isdisjoint(s2) 4 False 5 >>> s1 = set([11,22]) 6 >>> s2 = set([33,44]) 7 >>> s1.isdisjoint(s2) 8 True
1 >>> s1 = set([11,22,33,44]) 2 >>> s2 = set([11,22]) 3 >>> s2.issubset(s1) 4 True 5 >>> s1.issubset(s2) 6 False
1 >>> s1 = set([11,22,33,44]) 2 >>> s2 = set([11,22]) 3 >>> s2.issuperset(s1) 4 False 5 >>> s1.issuperset(s2) 6 True
1 >>> s1 = set([11,22,33,44]) 2 >>> s2 = set([11,22,55]) 3 >>> s1.symmetric_difference(s2) 4 {33, 44, 55} 5 >>> s2.symmetric_difference(s1) 6 {33, 44, 55}
1 >>> s1 = set([11,22,33,44]) 2 >>> s2 = set([11,22,55]) 3 >>> s1.symmetric_difference_update(s2) 4 >>> s1 5 {33, 44, 55}
1 >>> s1 = set([11,22,33,44]) 2 >>> s2 = set([11,22,55]) 3 >>> s1.union(s2) 4 {33, 22, 55, 11, 44}
1 >>> s1 = set([11,22,33,44]) 2 >>> s2 = set([11,22,55]) 3 >>> s1.update(s2) 4 >>> s1 5 {33, 22, 55, 11, 44}
#!/usr/bin/env python # -*- coding:utf-8 -*- old_dict = { ‘1‘:{‘hostname‘:‘c1‘,‘cpu‘:2,‘mem‘:‘123‘}, ‘2‘:{‘hostname‘:‘c1‘,‘cpu‘:2,‘mem‘:‘123‘}, ‘3‘:{‘hostname‘:‘c1‘,‘cpu‘:2,‘mem‘:‘123‘} } new_dict = { ‘1‘:{‘hostname‘:‘c1‘,‘cpu‘:2,‘mem‘:‘1234‘}, ‘3‘:{‘hostname‘:‘c1‘,‘cpu‘:2,‘mem‘:‘123‘}, ‘4‘:{‘hostname‘:‘c2‘,‘cpu‘:2,‘mem‘:‘123‘} } old_set = set(old_dict.keys()) new_set = set(new_dict.keys()) update_set = old_set.intersection(new_set)#交集：需要更新的集合 # delete_set = old_set.symmetric_difference(update_set)#差集：需要删除的集合 # add_set = new_set.symmetric_difference(update_set)#差集：需要添加的集合 delete_set = old_set.difference(update_set) #与需要更新的集合对比，找出需要删除的数据 add_set = new_set.difference(update_set) #与需要更新的集合对比，找出需要添加的数据 for i in delete_set: old_dict.pop(i) for k in add_set: old_dict[k] = new_dict[k] for v in update_set: old_dict[v] = new_dict[v] print(old_dict)
collections常用方法
使用collections需要导入模块
import collections
1.Counter计数器(对字典的扩展，可以继承字典的所有方法)
>>> c2 = collections.Counter(‘abcdefgaaabbbdddedjdkfljs‘) >>> c2 Counter({‘d‘: 6, ‘a‘: 4, ‘b‘: 4, ‘e‘: 2, ‘f‘: 2, ‘j‘: 2, ‘c‘: 1, ‘g‘: 1, ‘k‘: 1, ‘l‘: 1, ‘s‘: 1}) #统计每个字符在字符串中出现的次数
常用方法有：
1 >>> c1 = collections.Counter(‘aacdabc‘) 2 >>> c2 = collections.Counter(‘aac‘) 3 >>> c1 4 Counter({‘a‘: 3, ‘c‘: 2, ‘b‘: 1, ‘d‘: 1}) 5 >>> c2 6 Counter({‘a‘: 2, ‘c‘: 1}) 7 >>> c1.update(c2) 8 >>> c1 9 Counter({‘a‘: 5, ‘c‘: 3, ‘b‘: 1, ‘d‘: 1})#更新后次数出现变化
1 >>> c1 = collections.Counter(‘aacdabc‘) 2 >>> c1 3 Counter({‘a‘: 3, ‘c‘: 2, ‘b‘: 1, ‘d‘: 1}) 4 Counter({‘a‘: 5, ‘c‘: 3, ‘b‘: 1, ‘d‘: 1}) 5 >>> c1[‘r‘] 6 0
1 >>> c2 = collections.Counter(‘aac‘) 2 >>> c2 3 Counter({‘a‘: 2, ‘c‘: 1}) 4 >>> c2.clear() 5 >>> c2 6 Counter()
1 >>> c1 = collections.Counter(‘aacdabc‘) 2 >>> c1 3 Counter({‘a‘: 3, ‘c‘: 2, ‘d‘: 1, ‘b‘: 1}) 4 >>> c1.most_common(3) 5 [(‘a‘, 3), (‘c‘, 2), (‘d‘, 1)]
1 >>> c1 = collections.Counter(‘aacdabcasdfete‘) 2 >>> c1 3 Counter({‘a‘: 4, ‘c‘: 2, ‘e‘: 2, ‘d‘: 2, ‘b‘: 1, ‘f‘: 1, ‘s‘: 1, ‘t‘: 1}) 4 >>> for i in c1.elements(): 5 ... print i 6 ... 7 a 8 a 9 a 10 a 11 c 12 c 13 b 14 e 15 e 16 d 17 d 18 f 19 s 20 t
2.有序字典OrderedDict，跟普通字典的用法一样(没有fromkeys方法)
>>> o1 = collections.OrderedDict() >>> o1[‘k1‘]=1 >>> o1[‘k2‘]=2 >>> o1[‘k3‘]=3 >>> o1 OrderedDict([(‘k1‘, 1), (‘k2‘, 2), (‘k3‘, 3)])
3. defaultdict为字典里的值（values）设置一个默认类型
>>> my_dict = collections.defaultdict(list) >>> my_dict[‘k1‘].append(123) >>> my_dict defaultdict(<type ‘list‘>, {‘k1‘: [123]})
4.namedtuple可命名元组，对元组进行扩展，给tuple元素添加一个名字，相当于自己创建了一个类
>>> mytuple = collections.namedtuple(‘mytuple‘,[‘x‘,‘y‘]) >>> tu1 = mytuple(1,2) >>> tu1 mytuple(x=1, y=2) >>> tu1.x 1 >>> tu1.y 2
5.deque双向队列：
特点：两边都可取可插，线程安全
常用方法：
1 >>> q=collections.deque([1, 11, 123, 1234]) 2 >>> q 3 deque([1, 11, 123, 1234]) 4 >>> q.pop() 5 1234 6 >>> q 7 deque([1, 11, 123]) 8 >>> q.popleft() 9 1 10 >>> q 11 deque([11, 123])
1 >>> q=collections.deque([1, 11, 123, 1234]) 2 >>> q.remove(123) 3 >>> q 4 deque([1, 11, 1234])
1 >>> q=collections.deque([1, 11, 123, 1234]) 2 >>> q.append(555) 3 >>> q 4 deque([1, 11, 123, 1234, 555]) 5 >>> q.appendleft(222) 6 >>> q 7 deque([222, 1, 11, 123, 1234, 555])
1 >>> q=collections.deque([‘a‘,‘b‘,‘c‘,‘d‘,‘e‘,‘a‘,‘b‘,]) 2 >>> q 3 deque([‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘, ‘a‘, ‘b‘]) 4 >>> q.count(‘b‘) 5 2
1 >>> q=collections.deque([‘a‘,‘b‘,‘c‘,‘d‘,‘e‘,‘a‘,‘b‘,]) 2 >>> q.extend([‘a‘,‘b‘,]) 3 >>> q 4 deque([‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘, ‘a‘, ‘b‘, ‘a‘, ‘b‘]) 5 >>> q.extendleft([‘b‘,‘b‘,‘b‘]) 6 >>> q 7 deque([‘b‘, ‘b‘, ‘b‘, ‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘, ‘a‘, ‘b‘, ‘a‘, ‘b‘])
1 >>> q=collections.deque([‘a‘,‘b‘,‘c‘,‘d‘,‘e‘,‘a‘,‘b‘,]) 2 >>> q.index(‘b‘) 3 1
1 >>> q 2 deque([‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘, ‘a‘, ‘b‘]) 3 >>> q.pop() 4 ‘b‘ 5 >>> q 6 deque([‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘, ‘a‘]) 7 >>> q.popleft() 8 ‘a‘ 9 >>> q 10 deque([‘b‘, ‘c‘, ‘d‘, ‘e‘, ‘a‘])
1 >>> q=collections.deque([‘a‘,‘b‘,‘c‘,‘d‘,‘e‘,‘a‘,‘b‘,]) 2 >>> q 3 deque([‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘, ‘a‘, ‘b‘]) 4 >>> q.remove(‘c‘) 5 >>> q 6 deque([‘a‘, ‘b‘, ‘d‘, ‘e‘, ‘a‘, ‘b‘])
1 >>> q=collections.deque([‘a‘,‘b‘,‘c‘,‘d‘,‘e‘,‘a‘,‘b‘,]) 2 >>> q 3 deque([‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘, ‘a‘, ‘b‘]) 4 >>> q.reverse() 5 >>> q 6 deque([‘b‘, ‘a‘, ‘e‘, ‘d‘, ‘c‘, ‘b‘, ‘a‘])
1 >>> q=collections.deque([‘a‘,‘b‘,‘c‘,‘d‘,‘e‘,‘a‘,‘b‘,]) 2 >>> q 3 deque([‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘, ‘a‘, ‘b‘]) 4 >>> q.rotate(1) 5 >>> q 6 deque([‘b‘, ‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘, ‘a‘]) 7 >>> q.rotate(3) 8 >>> q 9 deque([‘d‘, ‘e‘, ‘a‘, ‘b‘, ‘a‘, ‘b‘, ‘c‘])
6.单向队列
特点： 线程安全，FIFO(先进先出)
>>> import Queue #Python2.x 是Queue，3.x是queue >>> q = Queue.Queue(10)#设置长度 >>> q >>> q.put(1) #插数据 >>> q.put(2) >>> q.put(3) >>> q.qsize() #计算队列长度 3 >>> q.get() #取数据 1 >>> q.get() 2 >>> q.get() 3
深浅拷贝用法
字符串/数字深浅拷贝的结果一样，会共用内存地址空间，所以这里只讨论列表/元组/字典深浅拷贝的差别
浅拷贝：只拷贝第一层，如果拷贝的元组/列表/字典内有嵌套列表/字典时，当嵌套列表/字典修改时，原列表/字典也会被修改。
1 l1 = [11,22,33,[22,33,44]] 2 >>> l2 = l1.copy() 3 >>> l2 4 [11, 22, 33, [22, 33, 44]] 5 >>> l2[3][1] = ‘b‘ 6 >>> l2 7 [11, 22, 33, [22, ‘b‘, 44]] 8 >>> l1 9 [11, 22, 33, [22, ‘b‘, 44]] #当修改嵌套列表时，原列表也会被改变 10 >>> l2[0] = 100 11 >>> l2 12 [100, 22, 33, [22, ‘b‘, 44]] 13 >>> l1 14 [11, 22, 33, [22, ‘b‘, 44]]#修改第一层列表时，原列表不改变
1 >>> l1 = [11,22,44,{11:22}] 2 >>> l2 = l1.copy() 3 >>> l2 4 [11, 22, 44, {11: 22}] 5 >>> l2[3][11] = ‘abc‘ 6 >>> l2 7 [11, 22, 44, {11: ‘abc‘}] 8 >>> l1 9 [11, 22, 44, {11: ‘abc‘}]
1 >>> d1 = {11:11,22:{33:44}} 2 >>> d2 = d1.copy() 3 >>> d2 4 {11: 11, 22: {33: 44}} 5 >>> d2[22][33] = ‘abc‘ 6 >>> d2 7 {11: 11, 22: {33: ‘abc‘}} 8 >>> d1 9 {11: 11, 22: {33: ‘abc‘}}#可以看出字典也是一样的，只能拷贝第一层
1 >>> import copy 2 >>> t1 = (11,22,33,[11,22,33]) 3 >>> t2 = copy.copy(t1) 4 >>> t2 5 (11, 22, 33, [11, 22, 33]) 6 >>> t2[3][1] = ‘abc‘ 7 >>> t2 8 (11, 22, 33, [11, ‘abc‘, 33]) 9 >>> t1 10 (11, 22, 33, [11, ‘abc‘, 33])
深拷贝：完完全全独立的拷贝，所有层都有独立的内存地址
1 >>> l1 = [11,22,33,[11,22,33]] 2 >>> l2 = copy.deepcopy(l1) 3 >>> l2 4 [11, 22, 33, [11, 22, 33]] 5 >>> l2[3][0] = ‘abc‘ 6 >>> l2 7 [11, 22, 33, [‘abc‘, 22, 33]] 8 >>> l1 9 [11, 22, 33, [11, 22, 33]]
函数
1.内置函数
内置函数是Python里自带的，本身就可以被调用的函数
常用内置函数有：
1 >>> vars() 2 {‘__doc__‘: None, ‘d2‘: {11: 11, 22: {33: ‘abc‘}}, ‘__name__‘: ‘__main__‘, ‘t1‘: (11, 22, 33, [11, ‘abc‘, 33]), ‘__builtins__‘: <module ‘builtins‘ (built-in)>, ‘l2‘: [11, 22, 33, [‘abc‘, 22, 33]], ‘l1‘: [11, 22, 33, [11, 22, 33]], ‘t2‘: (11, 22, 33, [11, ‘abc‘, 33]), ‘__package__‘: None, ‘a1‘: 123123, ‘__spec__‘: None, ‘a2‘: 123123, ‘d1‘: {11: 11, 22: {33: ‘abc‘}}, ‘__loader__‘: <class ‘_frozen_importlib.BuiltinImporter‘>, ‘copy‘: <module ‘copy‘ from ‘/usr/local/python35/lib/python3.5/copy.py‘>}
vars()函数下需要比较注意的有以下三个：
1 >>> dir() 2 [‘__builtins__‘, ‘__doc__‘, ‘__loader__‘, ‘__name__‘, ‘__package__‘, ‘__spec__‘, ‘a1‘, ‘a2‘, ‘copy‘, ‘d1‘, ‘d2‘, ‘i‘, ‘l1‘, ‘l2‘, ‘ret‘, ‘t1‘, ‘t2‘, ‘x‘]
reload:重新导入模块
1 >>> max(11,22,33,44) 2 44
1 >>> min(22,33,66,33,22) 2 22
1 >>> sum([123,532,888]) #sum的参数是列表 2 1543
1 >>> pow(2,10) 2 1024
1 >>> bool([]) 2 False 3 >>> bool() 4 False 5 >>> bool(‘‘) 6 False 7 >>> bool(‘ ‘) 8 True 9 >>> bool(‘a‘) 10 True 11 >>> bool([11]) 12 True
1 #代码 2 li = [] 3 while True: 4 a = input(‘>>>‘) 5 if a == ‘abc‘: 6 break 7 else: 8 li.append(a) 9 print(li) 10 b = all(li) 11 print(b) 12 #执行结果 13 >>>11 14 >>>22 15 >>> 16 >>>abc 17 [‘11‘, ‘22‘, ‘‘] 18 False
1 #代码 2 li = [] 3 while True: 4 a = input(‘>>>‘) 5 if a == ‘abc‘: 6 break 7 else: 8 li.append(a) 9 print(li) 10 b = any(li) 11 print(b) 12 #执行结果 13 >>>11 14 >>>22 15 >>> 16 >>>abc 17 [‘11‘, ‘22‘, ‘‘] 18 True
1 >>> ord(‘a‘) 2 97 3 >>> ord(‘A‘) 4 65
1 >>> chr(65) 2 ‘A‘ 3 >>> chr(97) 4 ‘a‘
1 >>> hex(16) 2 ‘0x10‘
1 >>> oct(16) 2 ‘0o20‘
1 >>> bin(16) 2 ‘0b10000‘
1 >>> li = [11,22,33,44,55] 2 >>> for index,i in enumerate(li): 3 ... print(index,i) 4 ... 5 (0, 11) 6 (1, 22) 7 (2, 33) 8 (3, 44) 9 (4, 55)
1 #代码 2 def fun(x): 3 return x + 100 4 li = [11,22,33,44] 5 #li2 = tuple(map(lambda x:x+100,li)) 6 li2 = tuple(map(fun,li)) 7 print(li2) 8 #执行结果 9 (111, 122, 133, 144)
1 >>> li = [11,22,33,44] 2 >>> def func(x): 3 ... if x>33: 4 ... return True 5 ... else: 6 ... return False 7 ... 8 >>> a = filter(func,li) 9 >>> list(a) 10 [44]
1 >>> round(8.8) 2 9
1 >>> x = [1,2,3] 2 >>> y = [4,5,6,7,] 3 >>> zipped = zip(x,y) 4 >>> zipped 5 <zip object at 0x7f797511b888> 6 >>> list(zipped) 7 [(1, 4), (2, 5), (3, 6)] 8 >>> x2 , y2 = zip(*zip(x,y)) 9 >>> list(x2) == x 10 True 11 >>> list(y2) == y 12 False
2.自定义函数
def 函数名(参数): ... 函数体 ... return
>>> def show(a,b=123): #默认参数为b ... print(a,b) ... >>> show(999) #如果只有一个参数，b自动填充 999 123 >>> show(999,888)#如果有两个参数，b的默认值无效 999 888
>>> def show(a,b): ... print(a,b) ... >>> show(b=123,a=222) 222 123
1 >>> def show(*args): 2 ... print(args,type(args)) 3 ... 4 >>> show(1,2,3,) 5 (1, 2, 3) <class ‘tuple‘> 6 >>>
1 >>> def show(**args): 2 ... print(args,type(args)) 3 ... 4 >>> show(a = 11,b = 12,c = 13) 5 {‘c‘: 13, ‘b‘: 12, ‘a‘: 11} <class ‘dict‘>
def show(*args,**kwargs): print(args,type(args)) print(kwargs,type(kwargs)) l = [11,22,33,44] d = {‘n1‘:88,‘n2‘:99} show(*l,**d) show(l,d) show(11,22,33,44,n1 = 88,n2 = 99)
执行结果：
(11, 22, 33, 44) <class ‘tuple‘> {‘n2‘: 99, ‘n1‘: 88} <class ‘dict‘> ([11, 22, 33, 44], {‘n2‘: 99, ‘n1‘: 88}) <class ‘tuple‘> {} <class ‘dict‘> (11, 22, 33, 44) <class ‘tuple‘> {‘n2‘: 99, ‘n1‘: 88} <class ‘dict‘
s1 = ‘{name} is {type}‘ # s1 = ‘{0} is {1}‘ # s2 = s1.format(‘akon‘,‘goodboy‘) # l = [‘akon‘,‘goodboy‘] # s2 = s1.format(*l) d = {‘name‘:‘akon‘,‘type‘:‘goodboy‘} s2 = s1.format(**d) print(s2)
akon is goodboy
>>> func = lambda a: a+1
>>> ret = func(20)
>>> print(ret)
21
文件常用操作：
read(),可以指定从当前指针位置往后读取多少个字节，如果不指定，读取当前指针后所有内容
tell()显示当前指针位置
seek()指定当前指针位置
truncate()截取指针前面的内容，放弃指针后面的内容
原文：http://www.cnblogs.com/akon0207/p/5172099.html