How to group dataframe rows into list in pandas groupby

I have a pandas data frame df like:

a b
A 1
A 2
B 5
B 5
B 4
C 6

I want to group by the first column and get second column as lists in rows:

A [1,2]
B [5,5,4]
C [6]

Is it possible to do something like this using pandas groupby?

Solution 1:

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]})
        df

Out[1]: 
   a  b
0  A  1
1  A  2
2  B  5
3  B  5
4  B  4
5  C  6

In [2]: df.groupby('a')['b'].apply(list)
Out[2]: 
a
A       [1, 2]
B    [5, 5, 4]
C          [6]
Name: b, dtype: object

In [3]: df1 = df.groupby('a')['b'].apply(list).reset_index(name='new')
        df1
Out[3]: 
   a        new
0  A     [1, 2]
1  B  [5, 5, 4]
2  C        [6]

Solution 2:

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

Solution 3:

If performance is important go down to numpy level:

import numpy as np

df = 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 loop

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