一.有参装饰器
def auth(x,engine = "file"): def outter(func): def wrapper(*args, **kwargs): x name = input("username>: ").strip() pwd = input("password>: ").strip() if engine == "file": if name == "egon" and pwd == "123": print('login successful'.center(50,'=')) res = func(*args, **kwargs) return res else: print('username or password error') elif engine == "mysql": print("基于mysql的认证") elif engine == "ldap": print("基于ldap的认证") else: print('engine不存在') return wrapper return outter @auth(11,"file") # @outter函数的内存地址 # index=outter函数的内存地址(index函数的内存地址) def index(): print('from index') @auth(11,"mysql") def home(name): print('welcome %s to home page' %name) # index=auth(index,"file") # home=auth(index,"ldap") index() home("egon") # 模板 def outter2(x): def outter1(func): def wrapper(*args, **kwargs): res = func(*args, **kwargs) return res return wrapper return outter1 @outter2(x=1) def index(): pass
二.叠加多个装饰器
# def deco1(func1): # func1 = wrapper2的内存地址 # def wrapper1(*args, **kwargs): # print('====>wrapper1') # res1 = func1(*args, **kwargs) # return res1 # return wrapper1 # # def deco2(func2): # func2 = wrapper3的内存地址 # def wrapper2(*args, **kwargs): # print('====>wrapper2') # res2 = func2(*args, **kwargs) # return res2 # return wrapper2 # # def deco3(func3): # func3 = index函数的内存地址 # def wrapper3(*args, **kwargs): # print('====>wrapper3') # res3 = func3(*args, **kwargs) # return res3 # return wrapper3 # # # 加载/得到wrapper函数的顺序是自下而上的 # # 我们运行wrapper函数的顺序是： # # # index=wrapper1的内存地址 # @deco1 # deco1(wrapper2的内存地址) => wrapper1的内存地址 # @deco2 # deco2(wrapper3的内存地址) => wrapper2的内存地址 # @deco3 # deco3(index函数的内存地址) => wrapper3的内存地址 # def index(): # print('from index') # # # print(index) # res=index() # """ # ====>wrapper1 # ====>wrapper2 # ====>wrapper3 # from index # """ import time def timmer(func): def wrapper(*args,**kwargs): start=time.time() res=func(*args,**kwargs) stop=time.time() print("run time is : %s" %(stop - start)) return res return wrapper # 千万不要加括号 def auth(x,engine = "file"): def outter(func): def wrapper(*args, **kwargs): x name = input("username>: ").strip() pwd = input("password>: ").strip() if engine == "file": if name == "egon" and pwd == "123": print('login successful'.center(50,'=')) res = func(*args, **kwargs) return res else: print('username or password error') elif engine == "mysql": print("基于mysql的认证") elif engine == "ldap": print("基于ldap的认证") else: print('engine不存在') return wrapper return outter @auth(111,"file") @timmer def index(): print("index=====>") time.sleep(3) index()
三.迭代器
""" 1 什么是迭代器 器=>工具 迭代：迭代是一个重复的过程，但是每次重复都是基于上一次的结果而来的 count = 1 while count < 5: print(count) count+=1 迭代器：迭代取值的工具 2 为何要用迭代器 优点： 1、迭代器是一种通用的、不依赖于索引的迭代取值方式 2、节省内存 缺点: 1、取值不灵活，只能往后取，不能取指定的值 2、无法预知长度，只能取一次 3 如何用迭代器 python为一些类型内置了__iter__方法，调用了__iter__方法就会将原类型转换成一个迭代器 可迭代对象：内置有__iter__ 迭代器对象：内置有__next__,__iter__ """ # 例1 # names=["egon","alex","lxx"] # names="abcdefg" # i=0 # while i < len(names): # print(names[i]) # i+=1 # 例2： # c="hello" # d=["a",'B','C'] # e=("a",'B','C') # a={"k1":111} # g={111,222,333} # f=open("a.txt",'w') # dic={'k1':111,"k2":222,'k3':333} # dic={111,222,333} # iter_dic=dic.__iter__() # print(iter_dic.__iter__() is iter_dic) # print(iter_dic) # res=iter_dic.__next__() # print(res) # # res=iter_dic.__next__() # print(res) # # res=iter_dic.__next__() # print(res) # # res=iter_dic.__next__() # print(res) dic={111,222,333} iter_dic=dic.__iter__() while True: try: print(iter_dic.__next__()) except StopIteration: break print('='*50) iter_dic=dic.__iter__() while True: try: print(iter_dic.__next__()) except StopIteration: break # for循环的底层工作原理 # 1、dic.__iter__()拿到一个迭代器 # 2、k=迭代器.__next__()，循环往复直到抛出异常 # 3、for循环会捕捉异常，然后结束循环 # dic = {'k1': 111, "k2": 222, 'k3': 333} # # for k in dic: # # print(k,dic[k]) # # print('='*50) # # for k in dic: # # print(k,dic[k]) # for k in f: # print(k)
四.生成器
# 生成器：自定义的迭代器 # 函数内但凡出现yield关键字，再调用函数，不会触发函数体代码的运行 # 会得到一个返回值，该返回值就是一个生成器对象，也就是自定义的迭代器 # yield与return # 相同点：都能返回值 # 不同点：yield能返回多次值，而return只能返回一次值函数就立即结束 def func(): print("first") yield print("second") yield 222 return 777777777777777777 print("third") yield 333 print('fourth') g=func() # print(g) # iter(g) # g.__iter__() # next(g) # g.p__next__() # res=next(g) # print(res) # # res=next(g) # print(res) # # res=next(g) # print(res) # # next(g) #range(1,5,2) def my_range(start,stop,step=1): while start < stop: yield start start += step for i in my_range(1,5,2): print(i)
五.递归
# 递归调用：在调用一个函数的过程中又直接或者间接地调用函数自己 # 例1： # def foo(): # print('from foo') # foo() # foo() # import sys # print(sys.getrecursionlimit()) # sys.setrecursionlimit(2000) # 例2： # def foo(): # print('from foo') # bar() # # def bar(): # print('from bar') # foo() # # foo() # 递归调用有两个阶段 # 1、回溯：一层一层地递归调用下去 # 2、递推：在某一层结束递归调用，开始向上一层一层地返回 # age(5) = age(4) + 10 # age(4) = age(3) + 10 # age(3) = age(2) + 10 # age(2) = age(1) + 10 # age(1) = 18 #n = 1: age(1) = 18 #n > 1: age(n) = age(n-1) + 10 # def age(n): # if n == 1: # return 18 # return age(n-1) + 10 # # res=age(5) # print(res) # 应用示例1： # nums=[1,[2,[3,[4,[5,[6,[7,[8,[9]]]]]]]]] # # def func(list1): # for item in list1: # if type(item) is list: # func(item) # else: # print(item) # # func(nums) # 应用示例2：二分法 nums=[-3,11,13,25,37,39,43,52,77,84,91] def search(list1,find_num): print(list1) if len(list1) == 0: print('not exists') return mid_index = len(list1) // 2 if find_num > list1[mid_index]: # on the right search(list1[mid_index+1:],find_num) elif find_num < list1[mid_index]: # on the left search(list1[:mid_index],find_num) else: print('find it') search(nums,85)
六.匿名函数
# 匿名函数：没有名字的函数，意味着只能调用一次就被回收了 # 应用场景：临时调用一次的场景 # res=(lambda x,y:x+y)(1,2) # print(res) # 匿名函数应用示例 salaries = { "egon": 3000, "alex": 5000, "zhangsan": 1000, } # def func(k): # return salaries[k] # res = max(salaries,key=lambda k:salaries[k]) # print(res) # print(max([11,10,44,9])) # res = min(salaries,key=lambda k:salaries[k]) # print(res) # res=sorted([11,10,44,9],reverse=True) # print(res) # res=sorted(salaries,key=lambda k:salaries[k],reverse=True) # print(res) # 了解 # map # filter # reduce
七.面向过程编程
''' 面向过程编程 核心是过程二字，过程即做事的步骤，即先干什么、再干什么、然后干什么... 基于该思想写程序就好比是在设计一条条的流水线 优点：复杂问题流程化、进而简单化 缺点：扩展性差 '''
八.三元表达式
def max2(x,y): if x > y: return x else: return y # res =条件成立时运行的表达式 if 条件 else 条件不成立时运行的表达式 x=11 y=22 res=x*12 if x > y else y*100 print(res)
九.生成器
# l=[] # for i in range(1,6): # if i > 3: # l.append(i) # # print(l) #1、列表生成式 # res=[i for i in range(1,6) if i > 3] # print(res) #2、集合生成式 # res={i for i in range(1,6) if i > 3} # print(res,type(res)) #3、字典生成式 # items=[("name","egon"),('age',18),('gender','male')] # dict(items) # res={k:v for k,v in items if k != "age"} # print(res,type(res)) #4、生成器表达式 # res = (i for i in range(1, 6) if i > 3) # print(res) # print(next(res)) # print(next(res)) # print(next(res)) # def my_sum(nums,res=0): # for num in nums: # res+=num # return res # with open(r'D:weekend_s7day06 2 叠加多个装饰器.py',mode='rt',encoding="utf-8") as f: # # print(len(f.read())) # 2061 # # # res=0 # # for line in f: # # res+=len(line) # # res=sum(len(line) for line in f) # print(res) # # # # g=(len(line) for line in f) # # res=my_sum(g) # # # print(res) # 了解：map、filter、reduce # 1、map函数 # salaries=[1000,2000,3000,4000,5000] # res=map(lambda num:num*12,salaries) # print(res) # print(list(res)) # print((num*12 for num in salaries)) #2、filter函数 # names=['egon',"alex_dsb","liuqingzheng_dsb"] # res=filter(lambda n:n.endswith("dsb"),names) # print(res) # print([name for name in names if name.endswith('dsb')]) #3、reduce函数 # from functools import reduce # # res=reduce(lambda x,y:x+y,[1,2],100) # print(res)