一、关于函数式编程的内建函数
apply()逐渐被舍弃，这里不讨论
1、filter()
#filter(func,seq)
"""纯Python描述filter函数"""
def Myfilter(bool_func,seq):
filtered_seq = []
for obj in seq:
if bool_func(obj):
filtered_seq.append(obj)
return filtered_seq
print Myfilter(lambda x:x%3==0, [1,2,3,4,5,6,7,8,9]) #[3, 6, 9]
"""Build-In function filter()"""
print filter(lambda x:x%3==0, [1,2,3,4,5,6,7,8,9]) #[3, 6, 9]
"""非函数下的实现"""
print [x for x in [1,2,3,4,5,6,7,8,9] if x%3==0] #[3, 6, 9]
#还记得上一节写的，如果能有for..in..if（或列表解析）能处理的最好用这个而放弃lambda
试验：列表解析和lambda性能比较
lambda:
import time start = time.clock() filter(lambda x:x%3==0, [i for i in range(10000000)]) end = time.clock() print end-start #耗时4.9441799282
列表解析：
import time start = time.clock() [x for x in range(10000000) if x%3==0] end = time.clock() print end-start #耗时2.95589058109
从上可见，最好使用列表解析。
2、map()
"""纯Python模拟map()"""
def Mymap(func,seq):
mapped_seq = []
for obj in seq:
mapped_seq.append(func(obj))
return mapped_seq
print Mymap(lambda x:x*10,[i for i in range(10)]) #[0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
"""内建map()函数"""
print map(lambda x:x*10,[i for i in range(10)]) #[0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
"""非函数式编程"""
print [x*10 for x in range(10)] #[0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
"""多个序列的map()"""
print map(lambda x,y:x+y,[1,2,3],[4,5,6]) #[5, 7, 9]
"""None与map()"""
print map(None,[1,2,3],[4,5,6]) #[(1, 4), (2, 5), (3, 6)]
"""None在map()中使用效果同zip()，将不相干的序列归并在一起"""
print zip([1,2,3],[4,5,6]) #[(1, 4), (2, 5), (3, 6)]
3、reduce()
"""纯Python模拟reduce()"""
def Myreduce(bin_func,seq,init=None):
Iseq = list(seq)
if init is None:
res = Iseq.pop()
else:
res = init
for obj in Iseq:
res = bin_func(res,obj)
return res
"""reduce()"""
综上，尽量用最简便的方式去实现。