Table of contents
Foreword:
The built-in containers in python include list, dict, set, and tuple, while the collections module in python introduces five additional data structures to better meet the coding requirements.
collections is a collection module inside python , with the following built-in data types:
1.namedtuple(‘name’, [list])
Tuple is an immutable tuple, such as defining coordinate points (1,2), (3,4), but it is difficult to see directly from the code that this represents coordinates. In order to increase the readability of the code, we can use * * namedtuple() ** Create a custom tuple object, which is a subclass of tuple.
For example, use (x,y) to represent a point:
from collections import namedtuple #从collections导入数据类型
Point = namedtuple('Point',['x','y'])#自定义tuple类型Point
p = Point(1,3)
print(isinstance(p,Point))#p是Point类型的实例
print(p)#Point(x=1, y=3)
print (p.x)#1
print (p.y)#3
#也可以定义其他类型
Circle = namedtuple('Circle'，['x','y','r']) #自定义tuple类型Circle
Such a tuple object has the referenceable name Point , and the tuple elements can be accessed by referring to the object properties.
2.Counter()
Counter is a counter used to keep track of the number of occurrences of a value. It is an unordered container type stored as a dictionary of key-value pairs, where the element is used as the key and its count is used as the value.
#统计字符串中各个字符出现的个数
from collections import Counter
#从可iterable数据类型（list,tuple,dict）创建Counter类型实例
#从字符串创建
s = ' programming '
c = Counter(s);
print(c)#Counter({' ': 2, 'r': 2, 'g': 2, 'm': 2, 'p': 1, 'o': 1, 'a': 1, 'i': 1, 'n': 1})
#默认按键值对从大到小输出
#从字典类型创建
cdict = Counter({'a':4,'b':5})
print (cdict)#output:Counter({'b': 5, 'a': 4})
#从键值对创建，a b指的是键值，不能加引号
ckeys = Counter(a=5,b=6)
print(ckeys)#output:Counter({'b': 6, 'a': 5})
print(type(c))#<class 'collections.Counter'>
#返回计数器TopN部分
print(c.most_common())#[(' ', 2), ('r', 2), ('g', 2), ('m', 2), ('p', 1), ('o', 1), ('a', 1), ('i', 1), ('n', 1)]
print(c.most_common(5))#返回出现频率最高的五个元素[(' ', 2), ('r', 2), ('g', 2), ('m', 2), ('p', 1)]
#注意到c.most_common()返回类型为list
print(type(c.most_common()))#<class 'list'>
#那么就可以取出现次数最少的几个元素
print(c.most_common()[:-4:-1])#出现次数最少的3个元素[('n', 1), ('i', 1), ('a', 1)]
print(c['m'])#2返回元素出现次数
#计数器内容增加 update
c.update('mmm')#参数为字符串类型
print(c['m'])#5
print(c['a'])#1
c2=Counter('aaa')
c.update(c2)#参数为Counter类型
print(c['m'])#5
print(c['a'])#4，增加了了三个'a'
print('=======')
#计数器内容减少 subtract
print(c['b'])#访问的键不存在时,输出0，而不会报错.output:0
c.subtract('aab')
print(c['a'])#,减少了两个'a' output:2
print(c['b'])#output:-1
#del 删除键值
print(c)#output:Counter({'m': 5, ' ': 2, 'r': 2, 'g': 2, 'a': 2, 'p': 1, 'o': 1, 'i': 1, 'n': 1, 'b': -1})
del c['a']
print(c)#output:Counter({'m': 5, ' ': 2, 'r': 2, 'g': 2, 'p': 1, 'o': 1, 'i': 1, 'n': 1, 'b': -1}) 没有'a'
print(c['a'])#0
#element() 返回迭代器
print(list(c.elements()))#[' ', ' ', 'p', 'r', 'r', 'o', 'g', 'g', 'm', 'm', 'm', 'm', 'm', 'i', 'n']，排列无序，不包括个数小于1的键值
#c中的键值转换为list、set、dict类型
print(list(c))#[' ', 'p', 'r', 'o', 'g', 'm', 'i', 'n', 'b']
print(set(c))#{'g', 'b', 'o', 'r', ' ', 'm', 'p', 'i', 'n'}
print(dict(c))#{' ': 2, 'p': 1, 'r': 2, 'o': 1, 'g': 2, 'm': 5, 'i': 1, 'n': 1, 'b': -1}
#浅拷贝
d = c.copy()
print(c==d)#True
#加减算术操作，没有乘除
a = Counter(a = 4,b = 5)
b = Counter(c= 7,b =3)
print(a+b)#Counter({'b': 8, 'c': 7, 'a': 4})
print(a-b)#Counter({'a': 4, 'b': 2})subtract 只保留正数计数的元素
print(a&b)#Counter({'b': 3}) 输出元素交集 min(a,b)
print(a|b)#Counter({'c': 7, 'b': 5, 'a': 4}) 输出元素并集 max(a,b)
#求键值技术的和
print(sum(a.values()))#9
#重置counter对象
a.clear()
print(a)#Counter(),没有删除
print(b.items())#dict_items([('c', 7), ('b', 3)])
Deep copy:
3.deque()
In python, the time complexity of list search is O(1), but the time complexity of inserting and deleting elements is O(n), that is, the time consumption will increase linearly with the data size.
The time complexity of deque adding and deleting elements is O(1)
from collections import deque
q = deque([1,3,4])
q.pop()
print(q)#deque([1, 3])
q.append(9)
print(q)#deque([1, 3, 9])
q.appendleft(4)#从头部添加元素
print(q)#deque([4, 1, 3, 9])
q.popleft()#删除头部元素
print(q)#deque([1, 3, 9])
4.OrderedDict()
An ordered dictionary, which considers the dictionary to be ordered, is arranged in the order of the key-value pairs entered to create an orderdict instance, not in the order of the key values themselves.
# 普通Dict类型认为字典是无序的，OrderedDict认为字典是有序的，因此对于键值对排列不同的两个字典，OrderDict会认为是不同的
from collections import OrderedDict
print ('Regular dictionary:')
d2={}
d2['a']='A'
d2['b']='B'
d2['c']='C'
d3={}
d3['c']='C'
d3['a']='A'
d3['b']='B'
print (d2 == d3)#True
print ('\nOrderedDict:')
d4=OrderedDict()
d4['a']='A'
d4['b']='B'
d4['c']='C'
d5=OrderedDict()
d5['c']='C'
d5['a']='A'
d5['b']='B'
print (d4==d5)#False
print ("Regular dictionary")
d={}
d['b']='B'
d['c']='C'
d['a']='A'
for k,v in d.items():
print (k,v)#python3中字典dict正常按序输出，从当前情况看，和orderdict没有区别
print ("\nOrder dictionary")
d1 = OrderedDict()
d1['b'] = 'B'
d1['c'] = 'C'
d1['a'] = 'A'
d1['1'] = '1'
d1['2'] = '2'
for k,v in d1.items():
print (k,v)
#b B
# c C
# a A
# 1 1
# 2 2