十一、python沉淀之路--map函数、filter函数、reduce函数、匿名函数、内置函数

一、map函数

1、自定义函数，实现类似于map函数的功能

 num_l = [1,3,4,5,6,9]
 def power(n):
     return n ** 2
 def map_test(func,array):
     li0 = []
     for i in array:
         p = func(i)
         li0.append(p)
     return li0

 f = map_test(power,num_l)  运用自己定义的函数来计算
 print(f)
 f = map_test(lambda x: x ** 2, num_l)   #调用匿名函数实现简单的功能，减少代码量，以下几种类似
 print(f)

 def add_one(n):
     return n + 1

 f1 = map_test(add_one,num_l)
 print(f1)
 f1 = map_test(lambda x: x+1, num_l)
 print(f1)

 def reduce_one(n):
     return n - 1

 f2 = map_test(reduce_one,num_l)
 print(f2)
 f2 = map_test(lambda x: x - 1, num_l)
 print(f2)

 [1, 9, 16, 25, 36, 81]
 [1, 9, 16, 25, 36, 81]
 [2, 4, 5, 6, 7, 10]
 [2, 4, 5, 6, 7, 10]
 [0, 2, 3, 4, 5, 8]
 [0, 2, 3, 4, 5, 8]

2、map函数的运用:作用于成哥序列，让整个序列实现想要的转换

 ############n内置函数 map 的使用
 num_l = [1,3,4,5,6,9]
 f3 = map(lambda x:x + 3, num_l)  # map(func, *iterables) --> map object  这是map函数官方解释
 print(f3)
 print(list(f3))    #注意细节：map返回只是一个object，需要用list形式打印出来

 s = 'abcefg'
 f4 = map(lambda st:st.upper(),s)
 print(f4)
 print(list(f4))

 <map object at 0x000001C6AC2B7860>
 [4, 6, 7, 8, 9, 12]
 <map object at 0x000001C6AC2B7898>
 ['A', 'B', 'C', 'E', 'F', 'G']

二、filter函数

1、自定义函数，实现类似于filter的功能

例1：铺垫

 bjper = ['bj_老王','bj_老赵','bj_老李','tian an men','gugong']
 def filter_test(array):
     li0 = []
     li1 = []
     for i in array:
         if i.startswith('bj'):
             li0.append(i)
         if not i.startswith('bj'):
             li1.append(i)
     return li0,li1

 f = filter_test(bjper)
 print(f)

 (['bj_老王', 'bj_老赵', 'bj_老李'], ['tian an men', 'gugong'])

例2

 def show_bj(s):
     return s.startswith('bj')

 bjper = ['bj_老王ha','bj_老赵','bj_老李','tian an menha','gugongha']
 def filter_test(func,array):
     li0 = []
     for i in array:
         if func(i):
             li0.append(i)
     return li0

 f = filter_test(show_bj,bjper)
 print(f)
 # lambad 运用
 f = filter_test(lambda s:s.endswith('ha'),bjper)
 print(f)

 ['bj_老王ha', 'bj_老赵', 'bj_老李']
 ['bj_老王ha', 'tian an menha', 'gugongha']

2、filter函数运用：主要筛选出想要的元素

 ################ filter 应用：官方解释：filter(function or None, iterable) --> filter object
 bjper = ['bj_老王ha','bj_老赵','bj_老李','tian an menha','gugongha']
 def show_bj(s):
     return s.startswith('bj')
 f1 = filter(show_bj,bjper)
 print(f1)
 print(list(f1))                  #注意细节：filter返回只是一个object，需要用list形式打印出来

 f2 = filter(lambda st: not st.endswith('ha'),bjper)
 print(f2)
 print(list(f2))

 <filter object at 0x00000218E63A7898>
 ['bj_老王ha', 'bj_老赵', 'bj_老李']
 <filter object at 0x00000218E63A78D0>
 ['bj_老赵', 'bj_老李']

三、reduce函数：

1、

例1

 num_l = [2,4,10,100]
 init = 0
 for i in num_l:
     init += i
 print(init)

结果：116

例2

 num_l = [2,4,10,100]
 def sum_test(array):
     init = 0
     for i in array:
         init += i
     return init
 f = sum_test(num_l)
 print(f)

结果：116

例3

 num_l = [2,4,10,100]
 init = 1
 for i in num_l:
     init *= i
 print(init)

结果8000

例4

 num_l = [2,4,10,100]
 def mul(array):
     init = 1
     for i in array:
         init *= i
     return init

 f = mul(num_l)
 print(f)

结果：8000

例5

 num_l = [2,4,10,100]
 def reduce_test(func,array,init=None):
     init = array.pop(0)
     for i in array:
         init = func(init,i)
     return init

 def product(x,y):
     return x * y

 f = reduce_test(product,num_l,)
 print(f)

结果：8000

例6

 num_l = [2,4,10,100]
 def reduce_test(func,array,init=None):
     if init == None:
         res = array.pop(0)
     else:
         res = init
     for i in array:
         res = func(res,i)
     return res

 def product(x,y):
     return x * y

 f = reduce_test(product,num_l,4)
 print(f)

结果：32000

例7

 ############# reduce 函数
 #使用前需要导入reduce函数包
 from functools import reduce

 num_l = [2,4,10,100]
 def product(x,y):
     return x * y

 f1 = reduce(product,num_l,5)
 print(f1)

 f1 = reduce(lambda x,y: x + y + 1,num_l,1000)
 print(f1)

 40000
 1120