BUG: GroupBy dictionary aggregation raises ValueError when 'as_index=False'

[dchigarev]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

---

Code Sample, a copy-pastable example

import numpy as np
import pandas

# Could be any data with MultiIndex columns
data = {(f"col{i}", f"level-{i % 2}"): np.arange(5) for i in range(5)}
df = pandas.DataFrame(data)

by = df.columns[0]
agg_col = df.columns[1]

agg_dict = {agg_col: ["min", "max"]}
df.groupby(by, as_index=False).agg(func=agg_dict)  # ValueError

Traceback

Traceback (most recent call last):
  File "../exp.py", line 12, in <module>
    df.groupby(by, as_index=False).agg(func=agg_dict)  # ValueError
  File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 990, in aggregate
    self._insert_inaxis_grouper_inplace(result)
  File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 1614, in _insert_inaxis_grouper_inplace
    result.insert(0, name, lev)
  File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/frame.py", line 3760, in insert
    self._mgr.insert(loc, column, value, allow_duplicates=allow_duplicates)
  File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 1197, in insert
    new_axis = self.items.insert(loc, item)
  File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 3801, in insert
    item = self._validate_fill_value(item)
  File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 3784, in _validate_fill_value
    raise ValueError("Item must have length equal to number of levels.")
ValueError: Item must have length equal to number of levels.

Problem description

When we specify an aggregation function via list, pandas adds a new level with function name to the columns of the resulted frame:

>>> df
   col0  col1
0     0     0
>>> df.groupby("col0").agg({"col1": ["max"]})
     col1
      max
col0
0       0

In the result, the number of levels in the index which represents grouper and columns is different, which causes some problems in _insert_inaxis_grouper_inplace. When we're trying to insert MultiIndex grouper inside the resulted frame, but their nlevels is different, pandas.DataFrame.insert raises a ValueError because of it.

Filling mismatched levels with an empty string (like pandas already does when inserted value don't have MultiIndex columns) will probably solve this issue

>>> df.groupby("col0", as_index=False).agg({"col1": ["max"]}).columns
MultiIndex([('col0',    ''),
            ('col1', 'max')],
           )

Output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit           : 3e89b4c4b1580aa890023fc550774e63d499da25
python           : 3.7.7.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-50-generic
Version          : #54-Ubuntu SMP Mon May 6 18:46:08 UTC 2019
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.0
numpy            : 1.19.0
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.1.1
setuptools       : 47.3.1.post20200622
Cython           : None
pytest           : 6.0.2
hypothesis       : None
sphinx           : None
blosc            : None
feather          : 0.4.1
xlsxwriter       : None
lxml.etree       : 4.5.1
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : None
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : None
fsspec           : 0.7.4
fastparquet      : None
gcsfs            : None
matplotlib       : 3.2.2
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.4
pandas_gbq       : 0.13.2
pyarrow          : 1.0.1
pyxlsb           : None
s3fs             : 0.4.2
scipy            : 1.5.1
sqlalchemy       : 1.3.18
tables           : 3.6.1
tabulate         : None
xarray           : 0.15.1
xlrd             : 1.2.0
xlwt             : None
numba            : None