group_keys parameter in groupby comes handy during apply operations that creates an additional index column corresponding to the grouped columns (group_keys=True) and eliminates in the case (group_keys=False) especially during the case when trying to perform operations on individual columns.
One such instance:
In [21]: gby = df.groupby('x',group_keys=True).apply(lambda row: row['x'])
In [22]: gby
Out[22]:
x
0 0 0
2 3 2
4 2
3 1 3
2 3
Name: x, dtype: int64
In [23]: gby_k = df.groupby('x', group_keys=False).apply(lambda row: row['x'])
In [24]: gby_k
Out[24]:
0 0
3 2
4 2
1 3
2 3
Name: x, dtype: int64
One of its intended applications could be to group by one of the levels of the hierarchy by converting it to a Multi-index dataframe object.
In [27]: gby.groupby(level="x").sum()
Out[27]:
x
0 0
2 4
3 6
Name: x, dtype: int64