hstacking GroupBy MultiSet Objects

[HYPERTONE]

Given two GroupBy Objects with different row counts, is it possible to stack/hstack/concat/align them into a single object?

section1 = rt.rt_groupby.GroupBy(new_ds1, ['day'])['someLatency'].agg('mean')
section2 = rt.rt_groupby.GroupBy(new_ds2, ['day'])['someLatency'].agg('mean')

The following attempts were not successful:

rt.rt_hstack.stack_rows([section1, section2])
rt.rt_grouping.hstack_test([section1, section2])
rt.Dataset.concat_rows([section1, section2])
rt.Categorical.align([section1, section2])

>>> TypeError: Don't know how to construct categorical from values input of type <class 'riptable.rt_multiset.Multiset'>.

[HYPERTONE]

An easy work around may be to slice the GroupBy object as they are FastArray objects.

We can obtain the 'day' columns via:

section1[0][1]
section2[0][1]

And the 'someLatency' columns via:

section1[0][0]
section2[0][0]

We can stack both via:
rt.rt_hstack.stack_rows([section1[0][1], section2[0][1]])

Or simply just create a new Dataset using:

my_ted_talk = rt.Dataset({'Day' : rt.rt_hstack.stack_rows([section1[0][1], section2[0][1]]), 'Latency' : rt.rt_hstack.stack_rows([section1[0][0], section2[0][0]])})