Using pandas groupby().apply(list) on multiple columns at once

Question

I'm trying to combine multiple rows of a dataframe into one row, with the columns with different values being combined in a list. There are multiple columns with different values.

The df.groupby('a')['b'].apply(list) works well if only 1 column ('b' in this instance) has to be made to a list, but I can't figure out how to do it for multiple columns.

Dataframe:

   a  b  c       d
0  1  b  1   first
1  1  b  2  second
2  2  c  1   third
3  2  c  2  fourth
4  2  c  3   fifth

Prefered dataframe post operation:

   a  b          c                       d
0  1  b     [1, 2]         [first, second]
1  2  c  [1, 2, 3]  [third, fourth, fifth]

Is there an easy way to do this?

@Georgy: you don't even need to specify `column`. It is `df.groupby(['a', 'b']).agg(list)` — Andy L., May 13 '19 at 09:57

score 11 · Answer 1 · answered May 13 '19 at 09:35

df = df.groupby(['a','b']).apply(lambda x: [list(x['c']), list(x['d'])]).apply(pd.Series)
df.columns =['a','b','c','d']

Output

   a  b          c                       d
0  1  b     [1, 2]         [first, second]
1  2  c  [1, 2, 3]  [third, fourth, fifth]

Using pandas groupby().apply(list) on multiple columns at once

1 Answers1