The object type pandas.core.groupby.generic.DataFrameGroupBy is a list of tuples, where the first element is the groupby element and the second the dataframe for that group.

See the example below:

Creating test dataframe

import pandas as pd

df = pd.DataFrame({"ColA": [1,1,1,2,2,3,3,3], "ColB": [5,5,6,7,7,8,8,9], "ColC": [1,2,3,4,5,6,7,8]})

The test dataframe

>>> df
   ColA  ColB  ColC
0     1     5     1
1     1     5     2
2     1     6     3
3     2     7     4
4     2     7     5
5     3     8     6
6     3     8     7
7     3     9     8

Grouping dataframe

>>> groups = df.groupby(["ColA", "ColB"])

>>> type(groups)
<class 'pandas.core.groupby.generic.DataFrameGroupBy'>

Showing results

>>> for group in groups:
...     g, value = group
...     print(f"Key = {g}")
...     print(value)
...     print(80*"-")
...
Key = (1, 5)
   ColA  ColB  ColC
0     1     5     1
1     1     5     2
--------------------------------------------------------------------------------
Key = (1, 6)
   ColA  ColB  ColC
2     1     6     3
--------------------------------------------------------------------------------
Key = (2, 7)
   ColA  ColB  ColC
3     2     7     4
4     2     7     5
--------------------------------------------------------------------------------
Key = (3, 8)
   ColA  ColB  ColC
5     3     8     6
6     3     8     7
--------------------------------------------------------------------------------

IMPORTANT

As commented by @HenriChab, using aggregate or, for example, sum will return a dataframe type not a group type

>>> new_df = df.groupby(["ColA", "ColB"]).sum()
>>> new_df
           ColC
ColA ColB
1    5        3
     6        3
2    7        9
3    8       13
     9        8

Finally you can reset the index.

>>> new_df.reset_index(inplace=True)

>>> new_df
   ColA  ColB  ColC
0     1     5     3
1     1     6     3
2     2     7     9
3     3     8    13
4     3     9     8

The result of kl.aggregate(np.sum) is a normal DataFrame, you just have to assign it to a variable to further use it. With some random data:

>>> df = DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
>>>                         'foo', 'bar', 'foo', 'foo'],
...                  'B' : ['one', 'one', 'two', 'three',
...                         'two', 'two', 'one', 'three'],
...                  'C' : randn(8), 'D' : randn(8)})
>>> grouped = df.groupby('A')
>>> grouped
<pandas.core.groupby.DataFrameGroupBy object at 0x04E2F630>
>>> test = grouped.aggregate(np.sum)
>>> test
            C         D
A                      
bar -1.852376  2.204224
foo -3.398196 -0.045082