如何将列表中的元素逐个附加到数据框中的嵌套列表中

Question

import pandas as pd

d = {'A': [1,2,3,4], 'B': [[[1,2],[2,3]],[[3,4],[2,5]],[[5,6],[5,6],[5,6]],[7,8]]}

df = pd.DataFrame(data=d)

C = [1,2,3,4,5,6,7,8]

I have a pandas dataframe and would like to append each element of a C list into each one of the nested lists of B, maintaining the structure, so that the resulting dataframe is:

'A': [1,2,3,4]
'B': [[[1,2,1],[2,3,2]],[[3,4,3],[2,5,4]],[[5,6,5],[5,6,6],[5,6,7]],[7,8,8]]

Answer 1

Mybe there is a more elegant solution, but this works :-)

for i in d['B']:
    for j in i:
        if (isinstance(j, list)):
            j.append(C.pop(0))
        else:
            i.append(C.pop(0))
            break

A more efficient solution based on timgebs comment (thank you!):

f = iter(C)
for i in d['B']:
    for j in i:
        if (isinstance(j, list)):
            j.append(next(f))
        else:
            i.append(next(f))
            break

Answer 2

This is an alternative method using itertools .

The idea is to flatten the list of lists, append your data, then split again via information you have stored on the number of lists in each row.

from itertools import chain, accumulate
import pandas as pd

d = {'A': [1,2,3,4], 'B': [[[1,2],[2,3]],[[3,4],[2,5]],[[5,6],[5,6],[5,6]],[[7,8]]]}
df = pd.DataFrame(data=d)
C = [1,2,3,4,5,6,7,8]

acc = [0] + list(accumulate(map(len, B)))

lst = [j+[C[i]] for i, j in enumerate(chain.from_iterable(df['B']))]

df['B'] = [lst[x:y] for x, y in zip(acc, acc[1:])]

Note I have made an important change to the input: the last element of series B is a list of lists, just like all the other elements. For consistency, I would recommend this in any case.

Result

   A                                  B
0  1             [[1, 2, 1], [2, 3, 2]]
1  2             [[3, 4, 3], [2, 5, 4]]
2  3  [[5, 6, 5], [5, 6, 6], [5, 6, 7]]
3  4                        [[7, 8, 8]]

Answer 3

d = {'A': [1,2,3,4], 'B': [[[1,2],[2,3]],[[3,4],[2,5]],[[5,6],[5,6],[5,6]],[7,8]]}

df = pd.DataFrame(data=d)

C = [1,2,3,4,5,6,7,8]

df['B_len'] = df.B.apply(len)
df['B_len_cumsum']=df.B_len.cumsum()
df['C'] = df.apply(lambda row: C[row['B_len_cumsum']-row['B_len']:row['B_len_cumsum']], axis=1)
df['B'] = df.B.apply(lambda x: [x] if type(x[0])==int else x)
for x,y in zip(df.B,df.C):
        for xx,yy in zip(x,y):
            xx.append(yy)
df

Output:

   A                                  B  B_len  B_len_cumsum          C
0  1             [[1, 2, 1], [2, 3, 2]]      2             2     [1, 2]
1  2             [[3, 4, 3], [2, 5, 4]]      2             4     [3, 4]
2  3  [[5, 6, 5], [5, 6, 6], [5, 6, 7]]      3             7  [5, 6, 7]
3  4                        [[7, 8, 8]]      2             9        [8]