Apply function of two arguments cumulatively to the items of iterable, from left to right, so as to reduce the iterable to a single value. For example, reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) calculates ((((1+2)+3)+4)+5). The left argument, x, is the accumulated value and the right argument, y, is the update value from the iterable. If the optional initializer is present, it is placed before the items of the iterable in the calculation, and serves as a default when the iterable is empty. If initializer is not given and iterable contains only one item, the first item is returned. Roughly equivalent to：

def reduce(function, iterable, initializer=None): 
  it = iter(iterable) 
  if initializer is None: 
    try: 
      initializer = next(it) 
    except StopIteration: 
      raise TypeError('reduce() of empty sequence with no initial value') 
  accum_value = initializer 
  for x in iterable: 
    accum_value = function(accum_value, x) 
  return accum_value 

sum(lst) 

def customer_sum(lst): 
  result = 0 
  for x in lst: 
    result+=x 
  return result 
 
#或者 
def customer_sum(lst): 
  result = 0 
  while lst: 
      temp = lst.pop(0) 
      result+=temp 
  return result 
 
if __name__=="__main__": 
  lst = [1,2,3,4,5] 
  print customer_sum(lst) 

def add(lst,result): 
  if lst: 
    temp = lst.pop(0) 
    temp+=result 
    return add(lst,temp) 
  else: 
    return result 
 
if __name__=="__main__": 
  lst = [1,2,3,4,5] 
  print add(lst,0) 

lst = [1,2,3,4,5] 
print reduce(lambda x,y:x+y,lst) 
#這種方式用lambda表示當(dāng)做參數(shù)，因?yàn)闆](méi)有提供reduce的第三個(gè)參數(shù)，所以第一次執(zhí)行時(shí)x=1,y=2,第二次x=1+2,y=3,即列表的第三個(gè)元素 
 
 
#或者 
lst = [1,2,3,4,5] 
print reduce(lambda x,y:x+y,lst,0) 
#這種方式用lambda表示當(dāng)做參數(shù)，因?yàn)橹付藃educe的第三個(gè)參數(shù)為0，所以第一次執(zhí)行時(shí)x=0,y=1,第二次x=0+1,y=2,即列表的第二個(gè)元素， 
假定指定reduce的第三個(gè)參數(shù)為100，那么第一次執(zhí)行x=100,y仍然是遍歷列表的元素，最后得到的結(jié)果為115 
 
 
 
#或者 
def add(x,y): 
  return x+y 
 
print reduce(add, lst) 
#與方式1相同，只不過(guò)把lambda表達(dá)式換成了自定義函數(shù) 
 
#或者 
def add(x,y): 
  return x+y 
 
print reduce(add, lst,0) 
#與方式2相同，只不過(guò)把lambda表達(dá)式換成了自定義函數(shù) 

def statistics(lst): 
  dic = {} 
  for k in lst: 
    if not k in dic: 
      dic[k] = 1 
    else: 
      dic[k] +=1 
  return dic 
 
lst = [1,1,2,3,2,3,3,5,6,7,7,6,5,5,5] 
print(statistics(lst)) 

def statistics2(lst): 
  m = set(lst) 
  dic = {} 
  for x in m: 
    dic[x] = lst.count(x) 
 
  return dic 
 
lst = [1,1,2,3,2,3,3,5,6,7,7,6,5,5,5] 
print statistics2(lst) 

def statistics(dic,k): 
  if not k in dic: 
    dic[k] = 1 
  else: 
    dic[k] +=1 
  return dic 
 
lst = [1,1,2,3,2,3,3,5,6,7,7,6,5,5,5] 
print reduce(statistics,lst,{})  
#提供第三個(gè)參數(shù)，第一次，初始字典為空，作為statistics的第一個(gè)參數(shù)，然后遍歷lst,作為第二個(gè)參數(shù)，然后將返回的字典集合作為下一次的第一個(gè)參數(shù) 
 
或者 
d = {} 
d.extend(lst) 
print reduce(statistics,d) 
#不提供第三個(gè)參數(shù)，但是要在保證集合的第一個(gè)元素是一個(gè)字典對(duì)象，作為statistics的第一個(gè)參數(shù)，遍歷集合依次作為第二個(gè)參數(shù)