CODEWITHSUNDEEP
pandas
Aivar
Aivar

Reputation: 7022

How to split/expand a string value into several pandas DataFrame rows?

Let's say my DataFrame df is created like this:

df = pd.DataFrame({"title" : ["Robin Hood", "Madagaskar"],
                  "genres" : ["Action, Adventure", "Family, Animation, Comedy"]},
                 columns=["title", "genres"])

and it looks like this:

        title                     genres
0  Robin Hood          Action, Adventure
1  Madagaskar  Family, Animation, Comedy

Let's assume each movie can have any number of genres. How can I expand the DataFrame into 

        title      genre
0  Robin Hood     Action
1  Robin Hood  Adventure
2  Madagaskar     Family
3  Madagaskar  Animation
4  Madagaskar     Comedy

?

Upvotes: 6

Views: 4093

Answers (3)

Erfan
Erfan

Reputation: 42916

Since pandas >= 0.25.0 we have a native method for this called explode.

This method unnests each element in a list to a new row and repeats the other columns.

So first we have to call Series.str.split on our string value to split the string to list of elements.

>>> df.assign(genres=df['genres'].str.split(', ')).explode('genres')

        title     genres
0  Robin Hood     Action
0  Robin Hood  Adventure
1  Madagaskar     Family
1  Madagaskar  Animation
1  Madagaskar     Comedy

Upvotes: 1

MaxU - stand with Ukraine
MaxU - stand with Ukraine

Reputation: 210882

In [33]: (df.set_index('title')
            ['genres'].str.split(',\s*', expand=True)
            .stack()
            .reset_index(name='genre')
            .drop('level_1',1))
Out[33]:
        title      genre
0  Robin Hood     Action
1  Robin Hood  Adventure
2  Madagaskar     Family
3  Madagaskar  Animation
4  Madagaskar     Comedy

PS here you can find more generic approach.

Upvotes: 8

jezrael
jezrael

Reputation: 863166

You can use np.repeat with numpy.concatenate for flattening.

splitted = df['genres'].str.split(',\s*')
l = splitted.str.len()

df1 = pd.DataFrame({'title': np.repeat(df['title'].values, l),
                     'genres':np.concatenate(splitted.values)}, columns=['title','genres'])
print (df1)
        title      genres
0  Robin Hood      Action
1  Robin Hood   Adventure
2  Madagaskar      Family
3  Madagaskar   Animation
4  Madagaskar      Comedy

Timings:

df = pd.concat([df]*100000).reset_index(drop=True)

In [95]: %%timeit
    ...: splitted = df['genres'].str.split(',\s*')
    ...: l = splitted.str.len()
    ...: 
    ...: df1 = pd.DataFrame({'title': np.repeat(df['title'].values, l),
    ...:                      'genres':np.concatenate(splitted.values)}, columns=['title','genres'])
    ...: 
    ...: 
1 loop, best of 3: 709 ms per loop

In [96]: %timeit (df.set_index('title')['genres'].str.split(',\s*', expand=True).stack().reset_index(name='genre').drop('level_1',1))
1 loop, best of 3: 750 ms per loop

Upvotes: 4

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