8.02_python_lx_day14

一.推导式 : 通过一行循环判断,遍历一系列数据的方式

推导式的语法:
　　val for val in Iterable
　　三种方式:
　　　　[val for val in Iterable]
　　　　{val for val in Iterable}
　　　　{k:v for k,v in Iterable}

（1）列表里面需要100条数据

①普通写法

lst = []
for i in range(1,101):
    lst.append(i)
    print(lst)

②列表推导式基本语法

lst = [i for i in range(1,101)]
print(lst)

(2)单循环推导式 [1,2,3,4,5] -> [3,6,9,12,15]

①普通写法

lst = [1,2,3,4,5]
lst_new = []
for i in lst:
    res = i * 3
    lst_new.append(res)
print(lst_new)

②改写成推导式

lst = [i*3 for i in lst]
print(lst)

(3)带有判断条件的单循环推导式 (只能是单项分支,接在for后面)

①普通写法

lst = [1,2,3,4,5,6,7,8]
lst_new = []
for i in lst:
    if i % 2 == 1:
        lst_new.append(i)
print(lst_new)

②改写成推导式

lst = [i for i in lst if i % 2 == 1]
print(lst)

(4)双循环推导式

①普通写法

lst1 = ["1","2","3","4"]
lst2 = ["a","b","c","d"]
lst_new = []
for i in lst1:
    for j in lst2:
        strvar = i + "❤" + j
        lst_new.append(strvar)
print(lst_new)

②改写成推导式

lst1 = ["1","2","3","4"]
lst2 = ["a","b","c","d"]
lst = [i + "❤" + j for i in lst1 for j in lst2]
print(lst)

(5) 带有判断条件的多循环推导式

①普通写法

lst1 = ["1","2","3","4"]
lst2 = ["a","b","c","d"]
lst_new = []
for i in lst1:
    for j in lst2:
        if lst1.index(i) == lst2.index(j):
            strvar = i + "❤" + j
            lst_new.append(strvar)            
print(lst_new)

②改写成推导式

lst1 = ["1","2","3","4"]
lst2 = ["a","b","c","d"]
lst = [ i + "❤" + j for i in lst1 for j in lst2 if lst1.index(i) == lst2.index(j) ]
print(lst)

二.集合推导式

案例:
满足年龄在18到21,存款大于等于5000 小于等于5500的人,
开卡格式为:尊贵VIP卡老x(姓氏),否则开卡格式为:抠脚大汉卡老x(姓氏)
把开卡的种类统计出来

listvar = [
    {"name":"刘11","age":18,"money":10000},
    {"name":"刘22","age":19,"money":5100},
    {"name":"刘33","age":20,"money":4800},
    {"name":"孔44","age":21,"money":2000},
    {"name":"宋55","age":18,"money":20}
]

①常规写法

setvar = set()
for i in listvar:
    if 18 <= i["age"] <= 21 and  5000 <= i["money"] <= 5500:
        res = "尊贵VIP卡老" + i["name"][0]
    else:
        res = "抠脚大汉卡老" + i["name"][0]
    setvar.add(res)
print(setvar)

②改写成三元运算符 + 集合推导式

setvar = { "尊贵VIP卡老" + i["name"][0] if 18 <= i["age"] <= 21 and  5000 <= i["money"] <= 5500 else "抠脚大汉卡老" + i["name"][0] for i in listvar }
print(setvar)

三.字典推导式

(1)enumerate

enumerate(iterable,[start=0])
　　功能:枚举 ; 将索引号和iterable中的值,一个一个拿出来配对组成元组放入迭代器中
　　参数:
　　　　iterable: 可迭代性数据 (常用:迭代器,容器类型数据,可迭代对象range)
　　　　start: 可以选择开始的索引号(默认从0开始索引)
　　返回值:迭代器

from collections import Iterator
lst = ["东邪","西毒","南帝","北丐"]

①基本使用

it = enumerate(lst)
print(isinstance(it,Iterator))

②list

#start可以指定开始值,默认是0
it = enumerate(lst,start=1)
print(list(it))

③enumerate 形成字典推导式 变成字典

dic = { k:v for k,v in enumerate(lst,start=1) }
print(dic)

④dict 强制变成字典

dic = dict(enumerate(lst,start=1))
print(dic)

(2)zip

zip(iterable, ... ...)

　　功能: 将多个iterable中的值,一个一个拿出来配对组成元组放入迭代器中

　　iterable: 可迭代性数据 (常用:迭代器,容器类型数据,可迭代对象range) 

返回: 迭代器

特征: 如果找不到对应配对的元素,当前元素会被舍弃

<1>基本使用

lst1 = ["a","b","c","d"]
lst2 = ["1","2","3"]
lst3 = ["$","%"]
it = zip(lst1,lst2)
it = zip(lst1,lst2,lst3)
print(isinstance(it,Iterator))
print(list(it))

<2>zip 形成字典推导式 变成字典

lst1 = ["a","b","c","d"]
lst2 = ["1","2","3"]
dic = { k:v for k,v in zip(lst1,lst2) }
print(dic)

<3>dict 强制变成字典

dic = dict(zip(lst1,lst2))
print(dic)

四.生成器表达式

生成器本质是迭代器，允许自定义逻辑的迭代器

迭代器和生成器区别:

　　迭代器本身是系统内置的.重写不了.而生成器是用户自定义的,可以重写迭代逻辑

生成器可以用两种方式创建:

　　(1)生成器表达式  (里面是推导式,外面用圆括号)

　　(2)生成器函数    (用def定义,里面含有yield)

from collections import Iterator,Iterable

(1)生成器表达式

gen = (i*2 for i in range(1,11))
print(isinstance(gen,Iterator))
print(list(gen))

五.生成器函数

yield 类似于 return

共同点在于:执行到这句话都会把值返回出去

不同点在于:yield每次返回时,会记住上次离开时执行的位置 , 下次在调用生成器 , 会从上次执行的位置往下走

   而return直接终止函数,每次重头调用.

yield 6 和 yield(6) 2种写法都可以 yield 6 更像 return 6 的写法 推荐使用

(1) 生成器函数的基本语法

 <1>定义一个生成器函数

def mygen():
    print(111)
    yield 1
    print(222)
    yield 2
    print(333)
    yield 3

<2> 初始化生成器函数,返回生成器,简称生成器

gen = mygen()
print(isinstance(gen,Iterator))

<3>使用next调用

res = next(gen)
print(res)

(2)优化代码

def mygen():
    for i in range(1,101):
        yield "该球衣号码是{}".format(i)

初始化生成器函数 -> 生成器

gen = mygen()

for + next 调用数据

for i in range(50):
    res = next(gen)
    print(res)

(3)send 用法

next和send区别:

　　next 只能取值

　　send 不但能取值,还能发送值

send注意点:

　　第一个 send 不能给 yield 传值 默认只能写None

　　最后一个yield 接受不到send的发送值

　　send 是给上一个yield发送值

<1>

def mygen():
    print("process start")
    res = yield 100
    print(res,"内部打印1")
    
    res = yield 200
    print(res,"内部打印2")
    
    res = yield 300
    print(res,"内部打印3")
    print("process end")

<2>初始化生成器函数 -> 生成器

gen = mygen()

<3>在使用send时,第一次调用必须传递的参数是None(硬性语法),因为第一次还没有遇到上一个yield

第一次调用

res = gen.send(None) #<=> next(gen)
print(res)

第二次调用

res = gen.send(101) #<=> next(gen)
print(res)

第三次调用

res = gen.send(201) #<=> next(gen)
print(res)

第四次调用, 因为没有更多的yield返回数据了,所以StopIteration

res = gen.send(301) #<=> next(gen)
print(res)

(4)yield from : 将一个可迭代对象变成一个迭代器返回

def mygen():
    yield from ["1","2","3","4"]
gen = mygen()
print(next(gen))    #1
print(next(gen))    #2
print(next(gen))    #3
print(next(gen))    #4

(5)用生成器描述斐波那契数列

1 1 2 3 5 8 13 21 34 ...

yield 1
a,b = b,a+b = 1,1
0 1
yield 1
a,b = b,a+b = 1,2

yield 2
a,b = b,a+b = 2,3

yield 3
a,b = b,a+b = 3,5

yield 5

def mygen(maxlen):
    a,b = 0,1
    i = 0
    while i < maxlen:
        yield b
        a,b = b,a+b
        i+=1

初始化生成器函数 -> 生成器

gen = mygen(10)
for i in range(3):
    print(next(gen))

it = enumerate(lst)print(isinstance(it,Iterator))