在熊猫数据框列中的嵌套列表中转换和求和元素

Question

I have a df column like this:

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, 0.0], [0.79, 0.5], [0.96], [0.88, 0.21, 0.0, 0.0]]

The sublists can be of any size. I am trying to convert the numbers in the sublists to floats (they're strings), then create a column that sums each sublist, then divides by the number of items in the sublist

so sum for line 1:

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

for line 2:

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

result :

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

I've tried a bunch of stuff:

#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

Answer 1

Just do apply with 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], [0.37], [0.2725]]
1  [[0.53, 0.33, 0.2, 0.0], [0.79, 0.5], [0.96], ...  [[0.265], [0.645], [0.96], [0.2725]]