13.三元表达式，生成式，递归函数，匿名函数，面向过程编程

• 引子

• 三元表达式

• 生成式

• 递归函数

• 匿名函数

• 面向过程编程

  ---

• 三元表达式

def max2(x,y):
    if x > y:
        return x
    else:
        return y

x=11
y=22

res = 'ok' if x > y else 'no'
print(res)

• 生成式

• 列表生成式

# 案例1
l = []
for i in range(10):
    l.append(i)
print(l)

l = [i for i in range(10)]
print(l)

# 案例2
l = []
for i in range(10):
    if i > 5:
        l.append(i)
print(l)

l = [i for i in range(10) if i > 5]
print(l)

# 案例3：
names = ["lxx",'hxx',"wxx",'lili']
l = [name + "_sb" for name in names]
print(l)

# 案例4：
names = ["egon","lxx_sb","hxx_sb","wxx_sb"]
res = [name for name in names if name.endswith("sb")]
print(res)

• 字典生成式

字典生成式
res = {i: i ** 2 for i in range(5)}
items = [('k1',111),('k2',222),('k3',333)]
print(dict(items))
print({k:v for k,v in items})

• 集合生成式

res = {i for i in range(5)}
print(res)

• 生成器表达式

res = (i for i in range(3))
print(res)
print(next(res))
print(next(res))
print(next(res))
print(next(res))


with open('a.txt',mode='rt',encoding='utf-8') as f:
    # res = f.read()
    # print(len(res))

    # res = 0
    # for line in f:
    #     res += len(line)

    # res = sum((len(line) for line in f))
    res = sum(len(line) for line in f)

    print(res)

---

• 函数递归

• 函数的递归调用：

  在调用一个函数的内部又调用自己，所以递归调用的本质就是一个循环的过程

  大前提: 递归调用一定要在某一层结束

def func():
    print("func")
    func()

# func()

import sys
sys.setrecursionlimit(2000)
print(sys.getrecursionlimit())

​

• 递归的两个阶段：

  ​ 1、回溯：向下一层一层挖井
  ​ 2、递推：向上一层一层返回

age(5) = age(4) + 10
age(4) = age(3) + 10
age(3) = age(2) + 10
age(2) = age(1) + 10
age(1) = 18

def age(n):
    if n == 1:
        return 18
    return age(n-1) + 10

res = age(5)
print(res)


nums = [1,[2,[3,[4,[5,[6,[7,]]]]]]]

def get(l):
    for num in l:
        if type(num) is list:
            get(num)
        else:
            print(num)

get(nums)

• 二分法

nums = [-3,1,3,7,13,23,37,43,57,63,77,91,103]
find_num = 64

def find(nums,find_num):
    print(nums)
    if len(nums) == 0:
        print("not exists")
        return
    mid_index = len(nums) // 2
    if find_num > nums[mid_index]:
        # in the right
        find(nums[mid_index+1:],find_num)
    elif find_num < nums[mid_index]:
        # in the left
        find(nums[:mid_index], find_num)
    else:
        print('you got it')


find(nums,find_num)

---

• 匿名函数

  • 匿名函数：没有名字的函数

  • 特点：临时用一次、随时定义

def func():
    print('func')

func()
func()
func()

f = lambda x,y:x+y
print(f)
res = f(1,2)
print(res)

res = (lambda x,y:x+y)(1,2)
print(res)


salaries = {
    "egon":3000,
    "tom":1000000,
    "zxx":1000
}

print(max([11,22,33]))
print(max(salaries.values()))
print(max(salaries))

def func(k):
    return salaries[k]


print(max(salaries,key=func))
print(max(salaries,key=lambda k:salaries[k]))
print(min(salaries,key=lambda k:salaries[k]))
print(sorted(salaries,key=lambda k:salaries[k]))
print(sorted(salaries,key=lambda k:salaries[k],reverse=True))


map
names = ["lxx",'hxx',"wxx",'lili']
                    规则
       "lxx_sb","hxx_sb"

l = (name + "_sb" for name in names)
res = map(lambda name:name + "_sb",names)
print(list(res))

filter
names = ["lxx_sb",'egon',"wxx_sb",'lili_sb']
print([name for name in names if name.endswith('sb')])

res = filter(lambda name:name.endswith('sb'),names)
print(list(res))

reduce
from functools import reduce

res = reduce(lambda x,y:x+y,[1,2,3])
res = reduce(lambda x,y:x+y,["aa","bb","cc"])
print(res)


res = pow(10,2,3)  # 10 ** 2 % 3
print(res)

• 面向过程编程

  • 强调：

    面向过程编程绝对不是用函数编程这么简单，面向过程是一种编程思路、思想，而编程思路是不依赖于具体的
    语言或语法的。 言外之意是即使我们不依赖于函数，也可以基于面向过程的思想编写程序

  • 定义：

    核心是“过程”二字,过程就是解决问题的步骤，即先干啥、再干啥、后干啥
    所以基于该思想编写程序就好比设计一条一条的流水线

    优点：复杂的问题流程化、进而就简单化
    缺点：牵一发而动全身、扩展性差

  • 应用：

    扩展性要求不高的场景，典型案例如linux内核，git，httpd