在熊猫数据框列内的嵌套列表中转换和求和元素

如何解决在熊猫数据框列内的嵌套列表中转换和求和元素

我有一个类似df的列：

col1
[[0.73,0.43,0.5,0.0],[0.39,0.5],[0.37],[0.38,0.51,0.0,0.2]]
[[0.53,0.33,0.2,[0.79,[0.96],[0.88,0.21,0.0]]

子列表可以是任意大小。 我正在尝试将子列表中的数字转换为浮点数（它们是字符串），然后创建一列以对每个子列表求和，然后除以子列表中的项目数

所以第1行的和：

(.73 + .43 + .5 + 0) / 4 =.415
(.39 + .5) / 2 = .445
(.37) / 1 = .37
(.38 + .51 + 0.0 + .2) / 4 = .272

第2行：

(.53 + .33 + .2 + 0) / 4 = .265
(.79 + .5) / 2 = .645
(.96) / 1 = .96
(.88 + .21 + 0.0 + 0.0) / 4 = .272

结果：

new_col
[[.415],[.445],[.37],[.272]]
[[.265],[.645],[.96],[.272]]

我尝试了很多东西：

#something like this where it creates a column of the number of elements in each sublist and then uses that to divide the sum of each number

# this didn't work - just grabbed the first lists size
df1['words_in_company_name'] = df1['children_org_name_sublists'].str.len()

#this doesn't really work - i mean it shows the numbers per list,just not sure where to go from here
for i in df1.func_scores:
    length = []
    for j in i:
        print(j)

A

解决方法

只需对apply做np.mean

df['new_col'] = df.col.apply(lambda x : [[np.mean(y)] for y in x ])
df
Out[17]: 
                                                 col                               new_col
0  [[0.73,0.43,0.5,0.0],[0.39,0.5],[0.37],...  [[0.415],[0.445],[0.2725]]
1  [[0.53,0.33,0.2,[0.79,[0.96],...  [[0.265],[0.645],[0.2725]]