BUG: DataFrameGroupBy.agg with lists doesn't respect as_index=False

[rhshadrach]

df = DataFrame({"a1": [0, 0, 1], "a2": [2, 3, 3], "b": [4, 5, 6]})
# df = df.astype({"a1": "category", "a2": "category"})
gb = df.groupby(by=["a1", "a2"], as_index=False)
result = gb.agg(["sum"])
print(result)
#         b
#       sum
# a1 a2
# 0  2    4
#    3    5
# 1  3    6

The expected output is:

  a1 a2   b
        sum
0  0  2   4
1  0  3   5
2  1  3   6

In particular, the index is a RangeIndex and a1 as well as a2 are columns.

[szczekulskij]

This looks a bit hard, but I'll give it a try. If I won't be able to solve this within a week, I'll re-assign

[szczekulskij]

take

[szczekulskij]

There is also missing data in a1

[szczekulskij]

The single missing number aside (from a1 column) - as this seems like a separate issue.

What should be the expected output for gb.agg(["sum", "min"]) ?
It outputs something this, which looks reasonable - since how otherwise can we output multiple agg columns ?

#         b    
#       sum min
# a1 a2        
# 0  2    4   4
#    3    5   5
# 1  3    6   6

Therefore, if I understand correctly - if we're passing only one argument to list, we want to overwrite the list behaviour & output sth similar to gb.agg("sum"), right ? My concern is that some could argue that current output for gb.agg(["sum"]) is correct - that if you're passing in a list, you do want the output df to include "sum" annotation.

[rhshadrach]

The single missing number aside (from a1 column) - as this seems like a separate issue.

Are you meaning something like the following?

#         b    
#       sum min
# a1 a2        
# 0  2    4   4
# 0  3    5   5
# 1  3    6   6

pandas prints a sparse representation for the index. A missing entry implicitly means it is repeated from above.

What should be the expected output for gb.agg(["sum", "min"]) ?

I originally had the incorrect columns in the OP; it should be a MultiIndex. The OP has been updated.

[szczekulskij]

Hey, I'm sorry but I'll need to drop this issue. Pandas is a bit too much for me right now, I'll come back to this in the future

[Charlie-XIAO]

I believe this is because returning too early. As shown below, it didn't go through the as_index check. I will postpone its return until after checking as_index.

pandas/pandas/core/groupby/generic.py

Lines 1331 to 1333 in 935244a

	result = op.agg()
	if not is_dict_like(func) and result is not None:
	return result

[Charlie-XIAO]

take

[Charlie-XIAO]

But Categorical data seems to be tricky. The current

result = self._insert_inaxis_grouper(result)
result.index = default_index(len(result))

seems unable to deal with that.