推导式与生成器

一、列表推导式

'''通过一行循环判断,遍历一系数据的方式'''

推导式语法
    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、向列表里插入100条数据

#列表里面需要100条数据
lst = []
for i in range(1,101):
    lst.append(i)
print(lst)

改为推导式

# 基本语法
lst = [i for i in range(1,101)]
print(lst)

2、[1,2,3,4,5] -> [3,6,9,12,15]

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

改成推导式

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)

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 )

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

lst1 = ["李博伦","高云峰","孙致和","葛龙"]
lst2 = ["李亚","刘彩霞","刘子豪","刘昕"]
# "谁"❤"谁"
lst_new = []
for i in lst1:
    for j in lst2:
        strvar = i+ '*' + j
        lst_new.append(strvar)

print(lst_new)

改写成推导式

# 改写成推导式
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

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)

改写成推导式

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":"刘鑫炜","age":18,"money":10000},
    {"name":"刘聪","age":19,"money":5100},
    {"name":"刘子豪","age":20,"money":4800},
    {"name":"孔祥群","age":21,"money":2000},
    {"name":"宋云杰","age":18,"money":20}
]

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)

改写成集合推导式

# {三元运算符 + 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

"""
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"]

# 基本使用
it = enumerate(lst)
print(isinstance(it,Iterator))

for + next

# for + next
for i in range(4):
    print(next(it))

# (0, '东邪')
# (1, '西毒')
# (2, '南帝')
# (3, '北丐')

list

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

#[(1, '东邪'), (2, '西毒'), (3, '南帝'), (4, '北丐')]

enumerate 形成字典推导式 变成字典

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

# {1: '东邪', 2: '西毒', 3: '南帝', 4: '北丐'}

dict 强制变成字典

dic = dict(enumerate(lst,start=1))
print(dic)
# {1: '东邪', 2: '西毒', 3: '南帝', 4: '北丐'}

四、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
"""

# 基本使用
lst1 = ["晏国彰","刘子涛","郭凯","宋云杰"]
lst2 = ["刘有右柳翔","冯雍","孙志新"]
lst3 = ["周鹏飞","袁伟倬"]
# it = zip(lst1,lst2)
it = zip(lst1,lst2,lst3)
print(isinstance(it,Iterator))
print(list(it))
"""
[('晏国彰', '刘有右柳翔'), ('刘子涛', '冯雍'), ('郭凯', '孙志新')]
[('晏国彰', '刘有右柳翔', '周鹏飞'), ('刘子涛', '冯雍', '袁伟倬')]
"""

1、zip 形成字典推导式 变成字典

lst1 = ["晏国彰","刘子涛","郭凯","宋云杰"]
lst2 = ["刘有右柳翔","冯雍","孙志新"]
dic = { k:v for k,v in zip(lst1,lst2) }
print(dic)

# dict 强制变成字典
dic = dict(zip(lst1,lst2))
print(dic)

五、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
# 生成器表达式
gen = (i*2 for i in range(1,11))
print(isinstance(gen,Iterator))

# next 
res = next(gen)
print(res)

# for 
for i in gen:
    print(i)

# for + next
gen = (i*2 for i in range(1,11))
for i in range(3):
    res = next(gen)
    print(res)

# list
print("<=====>")
res = list(gen)
print(res)

六、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

# 定义一个生成器函数
def mygen():
    print(111)
    yield 1
    
    print(222)
    yield 2
    
    print(333)
    yield 3

# 初始化生成器函数,返回生成器对象,简称生成器
gen = mygen()
print(isinstance(gen,Iterator))

# 使用next调用
res = next(gen)
print(res)
res = next(gen)
print(res)
res = next(gen)
print(res)
# res = next(gen) error
# print(res)

2、代码优化

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)
print("<====>")
for i in range(30):
    res = next(gen)
    print(res)

3、send用法

"""
### send
# 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    
"""

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")

# 初始化生成器函数 -> 生成器
gen = mygen()
# 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'''

4、yield from : 将一个可迭代对象变成一个迭代器返回

def mygen():
    yield from ["马生平","刘彩霞","余锐","晏国彰"]
    
gen = mygen()
print(next(gen))
print(next(gen))
print(next(gen))
print(next(gen))

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

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))