生成器

# """
# 2021/11/1
# 今日学习内容:
#     1.生成器与生成器函数
#         生成器的本质就是迭代器
#         生成器的三种生成办法:
#             1.通过生成器函数
#             2.通过生成器表达式创建表达式创建生成器
#     2.各种推导式,生成器函数
#
# """
#
#
# # def func():
# #     print("我是周建荣")
# #     yield "a"  # 函数中包含 yield ,表示当前这个函数不再是普通的函数了,是生成器函数
# #
# #
# # g = func()  # 通过函数func()来创建一个生成器
# # print(func())  # generator object func at 0x0000013FC6C52030
# # print(g.__next__())
# # # print(g.__next__())  # StopIteration
# def func1():
#     print("我是周建荣")
#     yield "a"  # 函数中包含 yield ,表示当前这个函数不再是普通的函数了,是生成器函数
#     print("我是yuxin")
#     yield 'B'
#
#
# g = func1()
# print(g.__next__())
# print(g.__next__())
#
#
# # return 是直接返回结果,结束函数的调用
# # yield 返回函数的结果,可以让函数分段执行
# def func_2():
#     print("A")
#     a = yield "11"
#     print(a)
#     b = print("B")
#     yield "22"
#     c = print("C")
#     yield "33"
#
#
# g = func_2()
# print(g.__next__())
# print(g.send(1))
# print(g.__next__())
#
#
# # send()可以给上一个yield赋值
#
#
# def eat():
#     print("我吃什么呀")
#     a = yield '馒头'
#     print("a=", a)
#     b = yield "大饼"
#     print("b=", b)
#     c = yield "韭菜盒子"
#     print("c=", c)
#     yield "GAME OVER"
#
#
# gen = eat()  # 获取生成器
# ret1 = gen.__next__()
# print(ret1)
# ret2 = gen.send("胡辣汤")
# print(ret2)
# ret3 = gen.send("狗粮")
# print(ret3)
# ret4 = gen.send("猫粮")
# print(ret4)

"""
推导式
# 列表推导式: 最终给你的是列表
语法 : [最终结果(变量) for 变量 in 可迭代对象]
"""
# lst = [i for i in range(1, 15)]
# print(lst)
#
# names = [['yuxin', 'neihaiyuan', 'yuxiaoxin'], ['yufanxiu', 'yuhuaxiu']]
# lst = [name for first in names for name in first if name.count("x") == 2]
# print(lst)
# # 列表推导式
#
# g = (i for i in range(0, 10))
# print(g)  # 打印出来的是生成器地址
# print(list(g))
#
# for e in g:
#     print(e)


def func():
    print("111")
    yield 222


g = func()  # 获取生成器
g1 = (i for i in g)
g2 = (i for i in g1)
print(list(g))  # 打印 111
print(list(g1))
print(list(g2))
# yield 与 return 的区别
"""
yield 是分段来执行一个函数
而 return 直接停止执行函数
"""
# 获取1-100内所有的偶数
lst = [i for i in range(1, 101) if i % 2 == 0]
print(lst)

gen = (i for i in range(0, 101))
print(gen)   # <generator object <genexpr> at 0x0000022918E41F50>

gen = (i for i in range(2, 55))
for i in gen:
    print(i, end=" ")
print()
# 获取1-100内能被3整除的数
gen = (i for i in range(1, 101) if i % 3 == 0)
while 1:
    try:
        print(gen.__next__(), end=" ")
    except StopIteration:
        break


# 100以内能被3整除的数的平⽅
gen = (i*i for i in range(1, 101) if i % 3 == 0)
for i in gen:
    print(i, end="\t")
print("  ")

# 寻找名字中带有两个e的⼈的名字
names = [['Tom', 'Billy', 'Jefferson', 'Andrew', 'Wesley', 'Steven', 'Joe'],
         ['Alice', 'Jill', 'Ana', 'Wendy', 'Jennifer', 'Sherry', 'Eva']]
# 普通方法
for i in names:
    for name in i:
        if name.count("e") == 2:
            print(name, end="\t")
print("  ")

# 生成器方法
gen = (name for i in names for name in i if name.count("e") == 2)
for name in gen:
    print(name, end="\t")
# ⽣成器表达式和列表推导式的区别:
# 1. 列表推导式比较耗内存. ⼀次性加载. ⽣成器表达式⼏乎不占⽤内存. 使⽤的时候才分
# 配和使⽤内存
# 2. 得到的值不⼀样. 列表推导式得到的是⼀个列表. ⽣成器表达式获取的是⼀个⽣成器.
"""
字典推导式:
 根据名字应该也能猜到. 推到出来的是字典
"""
# 把字典中的key和value互换
# dic = {'a': 1, 'b': '2'}
# dict_1 = {dic[key]: key for key in dic}
# print(dict_1)

# 在以下list中. 从lst1中获取的数据和lst2中相对应的位置的数据组成⼀个新字典
lst1 = ['jay', 'jj', 'sylar']
lst2 = ['周杰伦', '林俊杰', '邱彦涛']
# dic = {i: j for i in lst1 for j in lst2}
# print(dic)    这个写法是错误的
dic = {lst1[i]: lst2[i] for i in range(len(lst1))}
print(dic)
# dic = {}
# for i in range(len(lst1)):
#     dic[lst1[i]] = lst2[i]
# print(dic)
# 集合推导式:
#  集合推导式可以帮我们直接⽣成⼀个集合. 集合的特点: ⽆序, 不重复. 所以集合推导式⾃
# 带去重功能
lst = [1, -1, 8, -8, 12]
# 绝对值去重
s = {abs(i) for i in lst}   # abs() 函数 意思是 绝对值函数
# s = {i for i in lst if i > 0}
print(s)


def add(a, b):
    return a + b


def test():
    for r_i in range(4):
        yield r_i


g = test()
for n in [2, 10]:
    g = (add(n, i) for i in g)
print(list(g))