How to group dataframe rows into list in pandas groupby

You can do this using groupby to group on the column of interest and then apply list to every group:

In [1]: df = pd.DataFrame( {'a':['A','A','B','B','B','C'], 'b':[1,2,5,5,4,6]})        dfOut[1]:    a  b0  A  11  A  22  B  53  B  54  B  45  C  6In [2]: df.groupby('a')['b'].apply(list)Out[2]: aA       [1, 2]B    [5, 5, 4]C          [6]Name: b, dtype: objectIn [3]: df1 = df.groupby('a')['b'].apply(list).reset_index(name='new')        df1Out[3]:    a        new0  A     [1, 2]1  B  [5, 5, 4]2  C        [6]

If performance is important go down to numpy level:

import numpy as npdf = pd.DataFrame({'a': np.random.randint(0, 60, 600), 'b': [1, 2, 5, 5, 4, 6]*100})def f(df):         keys, values = df.sort_values('a').values.T         ukeys, index = np.unique(keys, True)         arrays = np.split(values, index[1:])         df2 = pd.DataFrame({'a':ukeys, 'b':[list(a) for a in arrays]})         return df2

Tests:

In [301]: %timeit f(df)1000 loops, best of 3: 1.64 ms per loopIn [302]: %timeit df.groupby('a')['b'].apply(list)100 loops, best of 3: 5.26 ms per loop

A handy way to achieve this would be:

df.groupby('a').agg({'b':lambda x: list(x)})

Look into writing Custom Aggregations: https://www.kaggle.com/akshaysehgal/how-to-group-by-aggregate-using-py