pandas convert lists in multiple columns within DataFrame to separate columns

Question

I am trying to convert a list within multiple columns of a pandas DataFrame into separate columns.

Say, I have a dataframe like this:

           0          1
0  [1, 2, 3]  [4, 5, 6]
1  [1, 2, 3]  [4, 5, 6]
2  [1, 2, 3]  [4, 5, 6]

And would like to convert it to something like this:

   0  1  2  0  1  2
0  1  2  3  4  5  6
1  1  2  3  4  5  6
2  1  2  3  4  5  6

I have managed to do this in a loop. However, I would like to do this in fewer lines. My code snippet so far is as follows:

import pandas as pd

df = pd.DataFrame([[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]]])
output1 = df[0].apply(pd.Series)
output2 = df[1].apply(pd.Series)

output = pd.concat([output1, output2], axis=1)

Are you sure you want that output? Duplicate column names are allowed but not particularly useful... — Jon Clements
– Jon Clements, Commented Nov 22, 2018 at 15:13
At this point I am not really worried about the duplicated column names. I would like to reduce it to one line instead of implementing what will be loop/concat structure once applied to a dataframe with more than two columns — Christian
– Christian, Commented Nov 22, 2018 at 15:18

w-m · Accepted Answer · 2018-11-22 15:34:41Z

2

If you don't care about the column names you could do:

>>> df.apply(np.hstack, axis=1).apply(pd.Series)
   0  1  2  3  4  5
0  1  2  3  4  5  6
1  1  2  3  4  5  6
2  1  2  3  4  5  6

answered Nov 22, 2018 at 15:34

w-m

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Comments

BENY · Accepted Answer · 2018-11-22 15:50:45Z

1

Using sum

pd.DataFrame(df.sum(1).tolist())
   0  1  2  3  4  5
0  1  2  3  4  5  6
1  1  2  3  4  5  6
2  1  2  3  4  5  6

answered Nov 22, 2018 at 15:50

BENY

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