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