pandas groupby: TOP 3 values for each group

Question

A new and more generic question has been posted in pandas groupby: TOP 3 values in each group and store in DataFrame and a working solution has been answered there.<

Answer 1

NOTE: This solution works only if each group has at least 3 rows

Try the following approach:

In [59]: x = (df.groupby(pd.Grouper(freq='H'))['VAL']
                .apply(lambda x: x.nlargest(3))
                .reset_index(level=1, drop=True)
                .to_frame('VAL'))

In [60]: x
Out[60]:
                     VAL
TIME
2017-12-08 00:00:00   82
2017-12-08 00:00:00   56
2017-12-08 00:00:00   53
2017-12-08 01:00:00   95
2017-12-08 01:00:00   87
2017-12-08 01:00:00   79
2017-12-08 02:00:00   88
2017-12-08 02:00:00   78
2017-12-08 02:00:00   41

In [61]: x.set_index(np.arange(len(x)) % 3, append=True)['VAL'].unstack().add_prefix('VAL')
Out[61]:
                     VAL0  VAL1  VAL2
TIME
2017-12-08 00:00:00    82    56    53
2017-12-08 01:00:00    95    87    79
2017-12-08 02:00:00    88    78    41

Some explanation:

In [94]: x.set_index(np.arange(len(x)) % 3, append=True)
Out[94]:
                       VAL
TIME
2017-12-08 00:00:00 0   82
                    1   56
                    2   53
2017-12-08 01:00:00 0   95
                    1   87
                    2   79
2017-12-08 02:00:00 0   88
                    1   78
                    2   41

In [95]: x.set_index(np.arange(len(x)) % 3, append=True)['VAL'].unstack()
Out[95]:
                      0   1   2
TIME
2017-12-08 00:00:00  82  56  53
2017-12-08 01:00:00  95  87  79
2017-12-08 02:00:00  88  78  41