Python3 higher-order functions map, reduce, filter Detailed examples

This article describes the Python3 higher-order functions map, reduce, filter the sample code in this article to tell you in great detail, has a certain value for references, a friend in need can refer to the
parameters of the function to receive variable, then a function can receive another function as a parameter, this function is called to higher order functions.

Note wherein: map and a return filter inert sequence, iterables, must be transformed to list

>>> a = 3.1415
>>> round(a,2)
3.14
>>> a_round = round
>>> a_round(a,2)
3.14
>>> def func_devide(x, y, f):
  return f(x) - f(y)
#传递参数为函数
print(func_devide(9.3, 3.2, round))

1. map function

map () function accepts two arguments, a function, a is Iterable, map the incoming function sequentially applied to each element of the sequence, and the result returned as the new Iterator.

>>> print(list(map(str, [1, 2, 3])))
['1', '2', '3']
>>> dt = map(str,[-1,2,3,4,5,-34,-45,-23.454])
>>> dt
<map object at 0x10f431dd8>
>>> list(dt)
['-1', '2', '3', '4', '5', '-34', '-45', '-23.454']
>>> dt = map(abs,[-1,2,3,4,5,-34,-45,-23.454])
>>> list(dt)
[1, 2, 3, 4, 5, 34, 45, 23.454]

Note error: TypeError: 'map' object is not callable

Reasons generally appear iterative objects (str, abs, etc.) or function (Map) is modified, the original function is no longer, resulting in non-iterative object appears

2. reduce function

reduce to a function on a sequence [x1, x2, x3, ...], the function must receive two parameters, and continue to reduce the result of the next element in the sequence is calculated as the accumulator. It returns the final result of a calculation function takes two parameters:

def add(x,y):
...   return x + y
... 
>>> reduce(add,[1,2,3,4,5,6,7,8,9,10])
55
>>> def concate(x,y):
...   return str(x)+str(y)
... 
>>> reduce(concate,[1,2,3,4,5,6,7,8,9,0])
'1234567890'

map function and reduce binding strings do revolutions integer (integer or string rpm)

>>> str = '12121212132323'
>>> dic_str_int = {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9}
>>> def str_arr(x):
...   dic_str_int = {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9}
...   return dic_str_int[x]
... 
>>> def int_dum(x,y):
...   return 10*x + y
... 
>>> reduce(int_dum,map(str_arr,str))
12121212132323

Example, converting data within the list to uppercase, initial caps

>>> names = ['jack','john','wilianmon','jobs','bill','gates']
>>> def str_upper(string):
...   return string.upper()
... 
>>> names = map(str_upper,names)
>>> list(names)
['JACK', 'JOHN', 'WILIANMON', 'JOBS', 'BILL', 'GATES']
>>> def str_capitialize(string):
...   return string.capitalize()
... 
>>> names = ['jack','john','wilianmon','jobs','bill','gates']
>>> 
>>> names = map(str_capitialize,names)
>>> list(names)
['Jack', 'John', 'Wilianmon', 'Jobs', 'Bill', 'Gates']

List all of the elements within the parameters required product:

int_li = [2,3,5,10]
>>> reduce(lambda x, y: x*y,int_li)
300
>>> def func_mult(li=None):
...   return reduce(lambda x, y: x*y,li)
... 
>>> func_mult(int_li)
300

The above function may need to switch to a more convenient calling

'123.456' turned into an integer 123.456

Method one: cut off after stitching

def string_int(strs):
  str_li = strs.split('.')
  def str_int(str):
    dic_str_int = {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9}
    return dic_str_int[str]
  int_1 = reduce(lambda x, y: x*10+y, list( map(str_int,str_li[0])))
  int_2 = reduce(lambda x,y: x*10 + y,list(map(str_int,str_li[1])))
  return int_1 + int_2/(10**(len(str_li)+1))
 
res = string_int('123.456')
print(res)
#结果：123.456

Method two: numerical string converted into pure

def string_int1(strs):
  # 记住位置，替换
  point_len = len(strs) - strs.find('.')-1
  str_li = strs.replace('.', '')
  def str_int(str):
    dic_str_int = {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9}
    return dic_str_int[str]
  int_num = reduce(lambda x,y: x*10 + y,list(map(str_int,str_li)))
  return int_num/(10**(point_len))
 
res = string_int1('123.456')
print(res)
#结果：123.456

3. filter function

filter () also receives a function and a sequence. A sequence from the sieve elements qualified. And map () is different, filter () function is passed the sequentially applied to each element, and based on the return value is True or False decided to keep or discard the element.

Differences and map functions: Note

eg: Get all of the elements in the list integer types

def only_int(x):
  try:
    if isinstance(x, int):
      return True
    else:
      return False
  except ValueError as e:
    return False
dt = filter(type_int,[1,2,3,3,'3232',-34.5,34.5])
>>> list(dt)
[1, 2, 3, 3]

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