1. 推导式（列表推导式、集合推导式、字典推导式）
# ### 推导式 : 通过一行循环判断,遍历出一系列数据的方式是推导式
"""
推导式一共三种:
列表推导式,集合推导式,字典推导式
[val for val in Iterable]
{val for val in Iterable}
{a:b for a,b in iterable}
""" # (1)单循环的推导式
"""[1,2,3,4,5,6,7,8 ... 50]"""
lst = []
for i in range(1,51):
print(i)
lst.append(i)
print(lst) # 改写成推导式
lst = [val for val in range(1,51)]
print(lst) # (2)带有判断条件的单循环推导式 [判断条件只能是单项分支,其他的不可以]
"""[1,2,3,4,5,6,7,8 ... 50] 要所有的偶数"""
lst = []
for i in range(1,51):
if i % 2 == 0:
lst.append(i)
print(lst) # 改写成推导式
lst = [i for i in range(1,51) if i % 2 == 0]
print(lst) # (3)多循环推导式 "谁❤谁"
lst1 = ["常远","皮得意","纸质红"]
lst2 = ["李德亮","林明辉","陈佳琪"]
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
lst = [i + "❤" + j for i in lst1 for j in lst2]
print(lst) # (4)带有判断条件的多循环推导式
lst1 = ["常远","皮得意","纸质红"]
lst2 = ["李德亮","林明辉","陈佳琪"]
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)
推导式 示例代码
# (1).{'x': 'A', 'y': 'B', 'z': 'C' } 把字典写成x=A,y=B,z=C的列表推导式
dic = {'x': 'A', 'y': 'B', 'z': 'C' }
lst = [k+"="+v for k,v in dic.items()]
print(lst)
# (2).把列表中所有字符变成小写 ["ADDD","dddDD","DDaa","sss"]
lst = ["ADDD","dddDD","DDaa","sss"]
lst_new = [i.lower() for i in lst]
print(lst_new)
# (3).x是0-5之间的偶数,y是0-5之间的奇数 把x,y组成一起变成元组,放到列表当中
# (0,1) (0,3) (0,5)
# (2,1) (2,3) (2,5)
# (4,1) (4,3) (4,5)
# 方法一
lst = []
for i in range(6):
for j in range(6):
if i % 2 == 0 and j % 2 == 1:
res = (i,j)
lst.append(res)
print(lst)
# 改写成推导式
lst = [(i,j) for i in range(6) for j in range(6) if i % 2 == 0 and j % 2 == 1]
print(lst)
# 方法二
lst = []
for i in range(6):
if i % 2 == 0:
for j in range(6):
if j % 2 == 1:
res = (i,j)
lst.append(res)
print(lst)
# 改写成推导式
lst = [(i,j) for i in range(6) if i % 2 == 0 for j in range(6) if j % 2 == 1 ]
print(lst)
# (4).使用列表推导式 制作所有99乘法表中的运算
for i in range(1,10):
for j in range(1,i+1):
print("%d*%d=%2d " % (i,j,i*j),end="")
print()
# 改写成推导式
lst = ["%d*%d=%2d " % (i,j,i*j) for i in range(1,10) for j in range(1,i+1) ]
print(lst)
for i in range(9,0,-1):
for j in range(1,i+1):
print("%d*%d=%2d " % (i,j,i*j),end="")
print()
# 改写成推导式
lst = ["%d*%d=%2d " % (i,j,i*j) for i in range(9,0,-1) for j in range(1,i+1) ]
print(lst)
# (5)#求M,N中矩阵和元素的乘积
# M = [ [1,2,3],
# [4,5,6],
# [7,8,9] ]
# N = [ [2,2,2],
# [3,3,3],
# [4,4,4] ]
# =>实现效果1 [2, 4, 6, 12, 15, 18, 28, 32, 36]
# =>实现效果2 [[2, 4, 6], [12, 15, 18], [28, 32, 36]]
M = [[1,2,3] , [4,5,6] , [7,8,9]]
N = [[2,2,2] , [3,3,3] , [4,4,4]]
"""
M[0][0] * N[0][0] 2
M[0][1] * N[0][1] 4
M[0][2] * N[0][2] 6
M[1][0] * N[1][0] 12
M[1][1] * N[1][1] 15
M[1][2] * N[1][2] 16
M[2][0] * N[2][0] 28
M[2][1] * N[2][1] 32
M[2][2] * N[2][2] 36
"""
# =>实现效果1 [2, 4, 6, 12, 15, 18, 28, 32, 36]
# lst = [(i,j) for i in range(3) for j in range(3)]
# print(lst)
lst = [M[i][j] * N[i][j] for i in range(3) for j in range(3)]
print(lst)
# =>实现效果2 [[2, 4, 6], [12, 15, 18], [28, 32, 36]]
# lst = [5 for i in range(3)]
# lst = [[] for i in range(3)]
# print(lst)
# lst = [ [(i,j) for j in range(3)] for i in range(3) ]
lst = [ [ M[i][j]*N[i][j] for j in range(3) ] for i in range(3) ]
print(lst)推导式练习 示例代码
# ### 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":"王家辉","age":18,"money":10000},
{"name":"王水机","age":19,"money":5100},
{"name":"王鹏","age":20,"money":4800},
{"name":"李站","age":21,"money":2000},
{"name":"李小龙","age":180,"money":20}
] 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]
# 把最后的元素添加到集合中
setvar.add(res)
print(setvar) """三目运算符 : 真区间值 if 条件表达式 else 假区间值"""
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) # ### 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,Iterable
# 基本语法
listvar = ["张龙","余泽民","众赞林"]
it = enumerate(listvar)
print(it) res = isinstance(it,Iterator)
print(res) # (1) next
"""
res = next(it)
print(res)
res = next(it)
print(res)
res = next(it)
print(res)
"""
# for
"""
for i in it:
print(i)
"""
# for + next
"""
for i in range(2):
res = next(it)
print(res)
"""
# list
"""
lst = list(it)
print(lst)
""" # 从5下标开始枚举
lst= list( enumerate(listvar,start=5) )
print(lst) # 1.转化成字典推导式变成字典
listvar = ["张龙","余泽民","众赞林"]
dic = {a:b for a,b in enumerate(listvar) }
dic = {a:b for a,b in enumerate(listvar,start = 5) }
print(dic) # 2.dict 用dict强转迭代器变成字典
dic = dict( enumerate(listvar ) )
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
多出来无人配对的元素,会自动的舍掉;
"""
# 基本语法
lst1 = ["刘守乱","马训","周冰洁"]
lst2 = ["郭少东","罗启云","尹家平"]
lst3 = ["郭少东1","罗启云2"]
it = zip(lst1,lst2,lst3)
print(isinstance(it , Iterator))
# 使用list强转迭代器
lst = list(it)
print(lst) # 用zip 形成字典推导式 变成字典
lst1 = ["a","b","c"]
lst2 = [1,2,3]
dic = { k:v for k,v in zip(lst1,lst2) }
print(dic) # 用dict 强制转换zip形成的迭代器 变成字典
dic = dict( zip(lst1,lst2) )
print(dic) # 小案例 把dic1中的键和dic2中的值 组合在一起变成新字典;
dic1 = {"cpx":"身材高大魁梧","zjc":"爱走神","zyl":"活泼好动"}
dic2 = {"a":"曹培显","b":"主进程","c":"周永玲"}
container1 = dic1.keys()
print(container1) container2 = dic2.values()
print(container2) # res = list( zip(container1,container2) )
# print(res) # 1.通过dict 强制转换变成字典
dic = dict( zip(container1,container2) )
print(dic ) # 2.使用推导式配合zip 变成字典
dic = {k:v for k,v in zip(container1,container2)}
print(dic)
集合_字典推导式 示例代码
2. 生成器与生成器函数
# ### 生成器
"""
#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 generator
gen = ( i for i in range(5) )
print(gen)
res = isinstance(gen,Iterator)
print(res) # (2) 获取生成器中的数据 # 1. next
res = next(gen)
print(res)
res = next(gen)
print(res)
res = next(gen)
print(res)
res = next(gen)
print(res)
res = next(gen)
print(res)
# res = next(gen) error
# print(res) # 2.for
gen = ( i for i in range(5) )
for i in gen:
print(i) # 3.list
gen = ( i for i in range(5) )
lst = list(gen)
print(lst) # 4 for + next
gen = ( i for i in range(5) )
for i in range(2):
res = next(gen)
print(res)
生成器 示例代码
# ### 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) 基本语法
# 生成器函数
def mygen():
print("one")
yield 1 print("two")
yield 2 print("three")
yield 3 # 初始化生成器函数 -> 生成器对象 -> 简称生成器
gen = mygen()
print(gen) # 调用生成器
res = next(gen) # 第一次调用生成器
print(res) res = next(gen) # 第二次调用生成器
print(res) res = next(gen) # 第三次调用生成器
print(res) # res = next(gen) # error # 第四次调用生成器
# print(res) """
初始化生成器函数 第一次调用生成器
res = next(gen) print(one) yield 1 记录当前代码执行的状态,将1返回,返回到调用处,阻塞等待下一次调用 第二次调用生成器
res = next(gen) 从13行,上一次记录的位置,往下执行 , print(two) yield 2 记录当前代码执行的状态,将2返回,返回到调用处,阻塞等待下一次调用 第三次调用生成器
res = next(gen ) 从16行,上一次记录的位置,往下执行 , print(three) yield 3 记录当前代码执行的状态,将3返回,返回到调用处,阻塞等待下一次调用 第四次调用生成器
从19行继续向下执行,发现没有yield 了 ,没有数据可以返回, 直接报错;
""" # (2) 改造生成器
def func():
for i in range(1,101):
yield "球衣号码{}".format(i) # 初始化生成器函数 -> 生成器对象 -> 简称生成器
gen = func()
for i in range(50):
res = next(gen)
print(res) for i in range(30):
res = next(gen)
print(res) # (3) send send是把数据发送给上一个yield
"""
### 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
"""
def mygen():
print("start")
res1 = yield 1
print(res1) res2 = yield 2
print(res2) res3 = yield 3
print(res3) print("end") # 初始化生成器函数 -> 生成器对象 -> 生成器
gen = mygen()
# 因为要发送给上一个yield , 第一次发送只能None
val1 = gen.send(None)
print(val1) print("<====>")
val2 = gen.send("one")
print(val2) print("<====>")
val3 = gen.send("two")
print(val3) # 最后一次调用,因为没有yield 直接越界报错
# val4 = gen.send("three")
# print(val4)
"""
val1 = gen.send(None)
第一次发送 因为要发送给上一个yield ,所以只能是None
print("start")
走到 81 行 ,记录当前代码执行的状态,将1返回,val1接受数据,添加阻塞,等待下一次调用 第二次发送
val2 = gen.send("one") 81行记录的状态往下执行,yield 1 接收到send 发送过来的数据"one" res1 = "one" print(pme)
走到 84 行 ,res2 = yield 2 , 记录当前代码执行的状态,将2返回,val2接受数据,添加阻塞,等待下一次调用 第三次发送
val3 = gen.send("two") 84行记录的状态往下执行, yield 2 接收到send 发送过来的数据"two" res2 = "two" print(two)
走到 87 行 ,res3 = yield 3, 记录当前代码执行的状态,将3返回,val3接受数据,添加阻塞,等待下一次调用 第四次发送
从87行往下执行
print(res3)
print("end")
因为没有yield 返回数据, StopIteration 越界错误;
""" # (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()
res = next(gen)
print(res)
res = next(gen)
print(res) res = next(gen)
print(res) res = next(gen)
print(res) # (5) 斐波那契数列
# 1 1 2 3 5 8 13 21 34 55 .... # a = 0
# b = 1
# a,b = b,a+b
# print(a,b)
# a,b = b,a+b
# print(a,b)
# a,b = b,a+b
# print(a,b)
# a,b = b,a+b
# print(a,b)
# a,b = b,a+b
# print(a,b)
# a,b = b,a+b
print("<=====>")
def myfib(maxlength):
a,b = 0,1
i = 0
while i<maxlength:
# print(b)
yield b
a,b = b,a+b
i+=1
# 生成器函数 => 生成器对象
gen = myfib(10) # for i in gen:
# print(i) for i in range(3):
res = next(gen)
print(res)
生成器函数 示例代码
day12