Pandas: Create several rows from column that is a list

Question

Let's say I have something like this:

df = pd.DataFrame({'key':[1,2,3], 'type':[[1,3],[1,2,3],[1,2]], 'value':[5,1,8]})

key type        value
1   [1, 3]      5
2   [1, 2, 3]   1
3   [1]         8

Where one of the columns contains a list of items. I would like to create several rows for each row that contains multiple types.

Ontaining this:

key type  value
1   1     5
1   3     5
2   1     1
2   2     1
2   3     1
3   1     8

I've been playing with apply with axis=1 but I can't find a way to return more than 1 row per row of the DataFrame. Extracting all different 'types' and then looping-concatenating seems to be ugly.

any ideas? Thanks!!!

Loop concatenate is the way to go. If you are reluctant of doing so because it 'seems to be ugly', the real ugly thing here is to build a dataframe with list in columns in the first place. Avoid building such a column upfront is the right way to go and you problem will disappear nicely. — Zeugma, Nov 13 '16 at 01:31
I inherited the DataFrame :) I'll try the loop-concat approach. — user3635284, Nov 13 '16 at 02:15
https://stackoverflow.com/questions/53218931/how-to-unnest-explode-a-column-in-a-pandas-dataframe-into-multiple-rows — cardamom, May 19 '22 at 22:29

score 1 · Accepted Answer · answered Nov 13 '16 at 01:43

import itertools
import pandas as pd
import numpy as np

def melt_series(s):
    lengths = s.str.len().values
    flat = [i for i in itertools.chain.from_iterable(s.values.tolist())]
    idx = np.repeat(s.index.values, lengths)
    return pd.Series(flat, idx, name=s.name)


melt_series(df.type).to_frame().join(df.drop('type', 1)).reindex_axis(df.columns, 1)

setup

df = pd.DataFrame({'key':[1,2,3],
                   'type':[[1,3],[1,2,3],[1,2]],
                   'value':[5,1,8]})
df

Pandas: Create several rows from column that is a list

1 Answers1