Flatten a column with value of type list while duplicating the other column's value accordingly in Pandas

I guess easies way to flatten list of lists would be a pure python code, as this object type is not well suited for pandas or numpy. So you can do it with for example

>>> b_flat = pd.DataFrame([[i, x] 
...               for i, y in input['B'].apply(list).iteritems() 
...                    for x in y], columns=list('IB'))
>>> b_flat = b_flat.set_index('I')

Having B column flattened, you can merge it back:

>>> input[['A']].merge(b_flat, left_index=True, right_index=True)
   A  B
0  1  a
0  1  b
1  2  c

[3 rows x 2 columns]

If you want the index to be recreated, as in your expected result, you can add .reset_index(drop=True) to last command.

It is surprising that there isn't a more "native" solution. Putting the answer from @alko into a function is easy enough:

def unnest(df, col, reset_index=False):
    import pandas as pd
    col_flat = pd.DataFrame([[i, x] 
                       for i, y in df[col].apply(list).iteritems() 
                           for x in y], columns=['I', col])
    col_flat = col_flat.set_index('I')
    df = df.drop(col, 1)
    df = df.merge(col_flat, left_index=True, right_index=True)
    if reset_index:
        df = df.reset_index(drop=True)
    return df

Then simply

input = pd.DataFrame({'A': [1, 2], 'B': [['a', 'b'], 'c']})
expected = unnest(input, 'B')

I guess it would be nice to allow unnesting of multiple columns at once and to handle the possibility of a nested column named I, which would break this code.

A slightly simpler / more readable solution than the ones above which worked for me.

 out = []
 for n, row in df.iterrows():
    for item in row['B']:
        row['flat_B'] = item
        out += [row.copy()]


flattened_df = pd.DataFrame(out)