python itertools模块

itertools模块
1、创建属于自己的迭代器，实现遍历
2、快速&节约内存

无限迭代器：

count
cycle
repeat

count返回一系列值，以start参数开始，也可以接受step参数

无step方式：

from itertools import count

for i in count(10):
    if i>20:
        break
    else:
        print(i)

结果为：10 11 12 13 14 15 16 17 18 19 20

有step方式：

from itertools import count,islice:

for i in islice(count(10),10):
    print(i)

结果为：10 11 12 13 14 15 16 17 18 19
解析：count从10开始，迭代10次后停止

cycle迭代器可实现在序列上创建无限循环的迭代器

用计数的方式实现有限迭代：

from itertools import cycle
count =0
for item in cycle("ABCDEFG"):
    if count >20:
        break
    else:
        print(item)
        count+=1

结果为：A B C D E F G A B C D E F G A B C D E F G
解析：cycle("ABCDEFG")会生成一个由"A" "B" "C" "D" "E" "F" "G"组成的无限循环迭代器，可实现遍历

用python内置next()函数实现遍历迭代

from itertools import cycle
list1=["A","B","C","D","E","F","G"]
iterator=cycle(list1)
for i in range(20):
    print(next(iterator))

结果为：A B C D E F G A B C D E F G A B C D E F
解析：cycle("ABCDEFG")会生成一个由"A" "B" "C" "D" "E" "F" "G"组成的无限循环迭代器，
每次调用next()函数会返回迭代器中的下一个值

repeat迭代器返回同一个对象，如不设定次数会无限迭代

指定迭代次数：

from itertools import repeat
iterator=repeat("txd",5)
for i in range(6):
    print(next(iterator))

结果：txd txd txd txd txd
第六次执行迭代next()方法时报错：
Traceback (most recent call last):
File "D:\unitest\exercise\排序.py", line 4, in <module>
    print(next(iterator))
StopIteration

解析：由于指定了迭代次数为5，循环执行到第6次时会报错

不指定迭代次数：

from itertools import repeat
iterator=repeat("txd")
for i in range(20):
    print(next(iterator))
结果：txd txd txd txd txd txd txd txd txd txd txd txd txd txd txd txd txd txd txd txd txd txd txd txd txd

有限迭代器

accumulate迭代器返回累加之和或两个函数的累计结果

返回迭代器每对象被累加之和：
from itertools import accumulate
print(list(accumulate(range(20))))
结果：[0, 1, 3, 6, 10, 15, 21, 28, 36, 45, 55, 66, 78, 91, 105, 120, 136, 153, 171, 190]
解析：第一个是0,第二个是0+1,第三个是1+2，等等；用内置的list方法，为了可读性强，否则仅仅打印出迭代器对象

返回迭代器对象被operator.mul函数相乘之积：
from itertools import accumulate
import operator
print(list(accumulate(range(1,20),operator.mul)))

结果：[1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800, 479001600, 6227020800, 87178291200, 1307674368000, 20922789888000, 355687428096000, 6402373705728000, 121645100408832000]
解析：第一个是1，第二个是1*2,第三个是2*3,等等；用内置的list方法，为了可读性强，否则仅仅打印出迭代器对象

chain迭代器将一系列可迭代对象平铺为一个长的可迭代对象

使用chain完成迭代器合并:

from itertools import chain

list1=[1,2,3,4,5]
list2=['a','b','c','d','e']
list3=['ls','cd','mv','/home','/usr']
fn_list=list(chain(list1,list2,list3))
print(fn_list)
结果：[1, 2, 3, 4, 5, 'a', 'b', 'c', 'd', 'e', 'ls', 'cd', 'mv', '/home', '/usr']
    list1地址为：1921345868416；fn_list地址为1921348731392
解析：将可迭代对象传入chain迭代器中；用内置的list方法，为了可读性强，否则仅仅打印出迭代器对象

另一种方式，直接使用列表的添加：
list1=[1,2,3,4,5]
list2=['a','b','c','d','e']
list3=['ls','cd','mv','/home','/usr']
fn_list=list1+list2+list3
print(fn_list)
print("list1地址为：{0}；fn_list地址为{1}".format(id(list1),id(list2)))

结果：[1, 2, 3, 4, 5, 'a', 'b', 'c', 'd', 'e', 'ls', 'cd', 'mv', '/home', '/usr']
    list1地址为：1921348872640；fn_list地址为1921345868416

使用chain里from_iterable()方法,与chain不同的是，需要传递一个嵌套的列表

from itertools import chain

list1 = [1, 2, 3, 4, 5]
list2 = ['a', 'b', 'c', 'd', 'e']
list3 = ['ls', 'cd', 'mv', '/home', '/usr']
print(list(chain.from_iterable(list1,list2,list3)))
结果：
Traceback (most recent call last):
File "D:\unitest\exercise\排序.py", line 6, in <module>
print(chain.from_iterable(list1,list2,list3))
TypeError: chain.from_iterable() takes exactly one argument (3 given)

正确方式为：
from itertools import chain

list1 = [1, 2, 3, 4, 5]
list2 = ['a', 'b', 'c', 'd', 'e']
list3 = ['ls', 'cd', 'mv', '/home', '/usr']
print(list(chain.from_iterable([list1,list2,list3])))
结果为：[1, 2, 3, 4, 5, 'a', 'b', 'c', 'd', 'e', 'ls', 'cd', 'mv', '/home', '/usr']

compress子模块可实现迭代器过滤，传入两个迭代器，通过第二个迭代器的布尔表达式判断进行筛选

from itertools import compress
list1=[1,2,3,4,5,5]
list2=[True,False,False,True]
print(list(compress(list1,list2)))
结果：[1, 4]

dropwhile

from itertools import dropwhile
list1=[1,2,3,4,5,6,7,8,9,10,9,8,7]
print(list(dropwhile(lambda x:x < 10,list1)))

结果：[10, 9, 8, 7]

from itertools import dropwhile
list1=[1,2,3,4,5,6,7,8,9,10,11,10,9,8,7]
print(list(dropwhile(lambda x:x > 10,list1)))

结果：[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 10, 9, 8, 7]
解析：dropwhile内部判断一旦为false,则获得该迭代对象及它之后的元素

talkwhile 与dropwhile 刚好相反，遇到条件为False时，忽略可迭代对象中剩余的元素

filterfalse 只返回判断为false的值

from itertools import filterfalse
list1=[1,2,3,4,5,6,7,8,9,10,11,20,21,22,23,23,35,562,10,9,8,7]
print(list(filterfalse(lambda x:x > 10,list1)))

结果：[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 9, 8, 7]

groupby 迭代器返回迭代器对象中连续的keys和groups

传入groupby 的为排序后的迭代器
from itertools import groupby

vehicles =[('Ford','Taurus'),('Dodge','Durango'),('Chevrolet','Cobalt'),('Ford','F150'),('Dodge','Charger'),('Ford','GT')]
sorted_vehicles =sorted(vehicles)
for key ,group in groupby(sorted_vehicles,lambda make:make[0]):
    for make ,model in group:
        print("{model} is made by {make}".format(model=model,make=make))
    print("------ End of Group-----")

结果：
Cobalt is made by Chevrolet
***** End of Group *****

Charger is made by Dodge
Durango is made by Dodge
***** End of Group *****

F150 is made by Ford
GT is made by Ford
Taurus is made by Ford
***** End of Group *****

传入groupby的非排序后的迭代器：

from itertools import groupby

vehicles =[('Ford','Taurus'),('Dodge','Durango'),('Chevrolet','Cobalt'),('Ford','F150'),('Dodge','Charger'),('Ford','GT')]

for key ,group in groupby(vehicles,lambda make:make[0]):
    for make ,model in group:
        print("{model} is made by {make}".format(model=model,make=make))
    print("------ End of Group-----")

结果为：
Taurus is made by Ford
------ End of Group-----
Durango is made by Dodge
------ End of Group-----
Cobalt is made by Chevrolet
------ End of Group-----
F150 is made by Ford
------ End of Group-----
Charger is made by Dodge
------ End of Group-----
GT is made by Ford
------ End of Group-----

islice从迭代对象中返回选择元素的迭代器

itertools.islice(iterable,stop)方式

from itertools import islice
print(list(islice([1,2,3,4,5,6,7,8],4)))
结果：[1, 2, 3, 4]

itertools.islice(iterable,start,stop,[,step])方式

from itertools import islice
print(list(islice([1,2,3,4,5,6,7,8,9],4,9)))
结果：[5, 6, 7, 8, 9]

starmap会创建一个新迭代器，与map()区别是:map()传入的函数是function(*c)，starmap()传入的是function(a,b)

from itertools import starmap

def add_f(a,b):
    return a+b
for item in starmap(add_f,[(1,3),(5,8),(9,100)]):
    print(item)

结果：
4
13
109

tee 实现从一个迭代器中创建多个迭代器

from itertools import tee

list1=[1,2,3,4,5,6]
iter1,iter2=tee(list1)
print(list(iter1))
print(list(iter2))

结果：
[1, 2, 3, 4, 5, 6]
[1, 2, 3, 4, 5, 6]

zip_longest 将两个迭代对象打包在一起，如果可迭代对象长度不同，可以传入fillvalue

from itertools import zip_longest

A=['a','b','c','d']
B=[1,2,3,4,5,6,7,8,9]
new_tup=zip_longest(A,B,fillvalue='BLANK')
print(list(new_tup))

组合产生器

combinations 从一个迭代器中创建一个迭代器，迭代器元素相同(按长度进行组合的新迭代对象)，不会创建重复的组合

from itertools import combinations
A = 'txdT'
new_iter=combinations(A,2)
for item in new_iter:
    print(''.join(item))
    print(item)
结果：
tx
('t', 'x')
td
('t', 'd')
tT
('t', 'T')
xd
('x', 'd')
xT
('x', 'T')
dT
('d', 'T')

combinations_with_replacement 在combinations基础上加(tt,xx,dd,TT)

permutations 在combinations基础上加(xt,dt,Tt,dx,Tx,Td)

product 从输入中创建笛卡尔积

from itertools import  product

A=[('A','B'),(1,2),("大","小")]
new_dke=product(*A)
print(list(new_dke))

结果：[('A', 1, '大'), ('A', 1, '小'), ('A', 2, '大'), ('A', 2, '小'), ('B', 1, '大'), ('B', 1, '小'), ('B', 2, '大'), ('B', 2, '小')]