Split a Pandas column of lists into multiple columns

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Chapters
00:00 Split A Pandas Column Of Lists Into Multiple Columns
00:26 Accepted Answer Score 539
01:12 Answer 2 Score 23
01:29 Answer 3 Score 123
01:54 Answer 4 Score 78
02:10 Thank you

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Full question
https://stackoverflow.com/questions/3549...

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Content licensed under CC BY-SA
https://meta.stackexchange.com/help/lice...

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Tags
#python #pandas #list #dataframe #split

#avk47

ACCEPTED ANSWER

Score 539

You can use the DataFrame constructor with lists created by to_list:

import pandas as pd

d1 = {'teams': [['SF', 'NYG'],['SF', 'NYG'],['SF', 'NYG'],
                ['SF', 'NYG'],['SF', 'NYG'],['SF', 'NYG'],['SF', 'NYG']]}
df2 = pd.DataFrame(d1)
print (df2)
       teams
0  [SF, NYG]
1  [SF, NYG]
2  [SF, NYG]
3  [SF, NYG]
4  [SF, NYG]
5  [SF, NYG]
6  [SF, NYG]

df2[['team1','team2']] = pd.DataFrame(df2.teams.tolist(), index= df2.index)
print (df2)
       teams team1 team2
0  [SF, NYG]    SF   NYG
1  [SF, NYG]    SF   NYG
2  [SF, NYG]    SF   NYG
3  [SF, NYG]    SF   NYG
4  [SF, NYG]    SF   NYG
5  [SF, NYG]    SF   NYG
6  [SF, NYG]    SF   NYG

And for a new DataFrame:

df3 = pd.DataFrame(df2['teams'].to_list(), columns=['team1','team2'])
print (df3)
  team1 team2
0    SF   NYG
1    SF   NYG
2    SF   NYG
3    SF   NYG
4    SF   NYG
5    SF   NYG
6    SF   NYG

A solution with apply(pd.Series) is very slow:

#7k rows
df2 = pd.concat([df2]*1000).reset_index(drop=True)

In [121]: %timeit df2['teams'].apply(pd.Series)
1.79 s ± 52.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [122]: %timeit pd.DataFrame(df2['teams'].to_list(), columns=['team1','team2'])
1.63 ms ± 54.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

ANSWER 2

Score 123

Much simpler solution:

pd.DataFrame(df2["teams"].to_list(), columns=['team1', 'team2'])

Yields,

  team1 team2
-------------
0    SF   NYG
1    SF   NYG
2    SF   NYG
3    SF   NYG
4    SF   NYG
5    SF   NYG
6    SF   NYG
7    SF   NYG

If you wanted to split a column of delimited strings rather than lists, you could similarly do:

pd.DataFrame(df["teams"].str.split('<delim>', expand=True).values,
             columns=['team1', 'team2'])

ANSWER 3

Score 78

This solution preserves the index of the df2 DataFrame, unlike any solution that uses tolist():

df3 = df2.teams.apply(pd.Series)
df3.columns = ['team1', 'team2']

Here's the result:

  team1 team2
0    SF   NYG
1    SF   NYG
2    SF   NYG
3    SF   NYG
4    SF   NYG
5    SF   NYG
6    SF   NYG

ANSWER 4

Score 23

There seems to be a syntactically simpler way, and therefore easier to remember, as opposed to the proposed solutions. I'm assuming that the column is called 'meta' in a dataframe df:

df2 = pd.DataFrame(df['meta'].str.split().values.tolist())