How to Explode a Pandas DataFrame Column into Multiple Rows?

How to Unnest (Explode) a Column in a Pandas DataFrame, into Multiple Rows

In Pandas, exploding a column involves transforming data from a single row into multiple rows. This is useful when you have a column containing list-type cells and need to split them into individual rows.

Consider a DataFrame with a column 'B' containing lists:

df = pd.DataFrame({'A': [1, 2], 'B': [[1, 2], [1, 2]]})

Output:

   A       B
0  1  [1, 2]
1  2  [1, 2]

To explode this column 'B,' we present various methods:

Method 0 [Pandas >= 0.25]
Starting from Pandas 0.25, if you need to explode only one column, use the pandas.DataFrame.explode function:

df.explode('B')

Output:

   A  B
0  1  1
1  1  2
3  2  1
4  2  2

Method 1
apply pd.Series (easy to understand but not recommended for performance):

df.set_index('A').B.apply(pd.Series).stack().reset_index(level=0).rename(columns={0:'B'})

Method 2
Using repeat with DataFrame constructor:

df = pd.DataFrame({'A': df.A.repeat(df.B.str.len()), 'B': np.concatenate(df.B.values)})

Method 3
Re-create the list:

pd.DataFrame([[x] + [z] for x, y in df.values for z in y], columns=df.columns)

Method 4
Using reindex or loc:

df.reindex(df.index.repeat(df.B.str.len())).assign(B=np.concatenate(df.B.values))

Method 5
When the list contains only unique values:

from collections import ChainMap
d = dict(ChainMap(*map(dict.fromkeys, df['B'], df['A'])))
pd.DataFrame(list(d.items()), columns=df.columns[::-1])

Method 6
Using NumPy for high performance:

newvalues = np.dstack((np.repeat(df.A.values, list(map(len, df.B.values))), np.concatenate(df.B.values)))
pd.DataFrame(data=newvalues[0], columns=df.columns)

Method 7
Using itertools cycle and chain:

from itertools import cycle, chain
l = df.values.tolist()
l1 = [list(zip([x[0]], cycle(x[1])) if len([x[0]]) > len(x[1]) else list(zip(cycle([x[0]]), x[1]))) for x in l]
pd.DataFrame(list(chain.from_iterable(l1)), columns=df.columns)

Generalizing to Multiple Columns
To handle multiple exploding columns, a function can be defined:

def unnesting(df, explode):
    idx = df.index.repeat(df[explode[0]].str.len())
    df1 = pd.concat([
        pd.DataFrame({x: np.concatenate(df[x].values)}) for x in explode], axis=1)
    df1.index = idx

    return df1.join(df.drop(explode, 1), how='left')

unnesting(df, ['B', 'C'])

Column-Wise Unnesting
To expand a list horizontally, use the pd.DataFrame constructor:

df.join(pd.DataFrame(df.B.tolist(), index=df.index).add_prefix('B_'))

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