Python | 列表的扁平化处理

一、使用sum()函数，可展开两层的嵌套列表

a = [[1, 2, 3], [ 4, 5, 6], [7], [8, 9]] out = sum(a, []) print(out)  output:[1, 2, 3, 4, 5, 6, 7, 8, 9]

 

二、使用itertools

import itertools  a = [[1, 2, 3], [4, 5, 6], [7], [8, 9]] out = list(itertools.chain.from_iterable(a)) print(out)  output:[1, 2, 3, 4, 5, 6, 7, 8, 9]

 

三、使用operator、reduce函数

import operator from functools import reduce  a = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] print(reduce(operator.add, a))  a:[1, 2, 3, 4, 5, 6, 7, 8, 9]

 

上面方法可能只对二层列表有效，如果无法确定嵌套深度，有如下的方法：

四、可以使用递归函数来解决（万能方式）

data = [[[1],[2],[3]],[4,5],[[[[[[6]]]]]]] print(data) result = [] def take_out(arr):     for item in arr:         if isinstance(item,int):             result.append(item)         else:             take_out(item) take_out(data) print(result)  out:[[[1], [2], [3]], [4, 5], [[[[[[6]]]]]]]     [1, 2, 3, 4, 5, 6]  

 

五、使用标准库itertools中的chain()函数

from itertools import chain from copy import deepcopy  data = [[[1],[2],[3]],[[4],[5],[6]]] print(data) result = deepcopy(data)  while True:     result = list(chain(*result))     if isinstance(result[0], int):         break print(result)   [[[1], [2], [3]], [[4], [5], [6]]] [1, 2, 3, 4, 5, 6]

 

六、扩展库numpy

import numpy as np  data = [[[1],[2],[3]],[[4],[5],[6]]] print(data)  temp_data = np.array(data) a = list(temp_data.reshape(temp_data.size,)) print(a)   [[[1], [2], [3]], [[4], [5], [6]]] [1, 2, 3, 4, 5, 6]