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))