Repository: modin-project/modin

Issue:
BUG: groupby(as_index=False, by=two_internal_columns).mean with column partitioning produces index containing tuples
### Modin version checks

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the latest released version of Modin.

- [X] I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow [this guide](https://modin.readthedocs.io/en/stable/getting_started/installation.html#installing-from-the-github-master-branch).)


### Reproducible Example

```python
import modin.pandas as pd

df = pd.concat([pd.DataFrame({'col33': [51], 'index': [69]}), pd.DataFrame({'col34': [79]})], axis=1)
print(df.groupby(['col33', 'index'], as_index=False).mean())
print(df._to_pandas().groupby(['col33', 'index'], as_index=False).mean())
```


### Issue Description

pandas gives
```
   col33  index  col34
0     51     69   79.0
```
modin gives

```
   (col33, )  (index, )  col34
0         51         69   79.0
```

### Expected Behavior

should match pandas by not changing the index


### Installed Versions

<details>

UserWarning: Setuptools is replacing distutils.

INSTALLED VERSIONS
------------------
commit           : f2489f73dab0c13b6709ec3a9361ad79819aa9d0
python           : 3.8.16.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 21.5.0
Version          : Darwin Kernel Version 21.5.0: Tue Apr 26 21:08:22 PDT 2022; root:xnu-8020.121.3~4/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

Modin dependencies
------------------
modin            : 0.20.0+68.gf2489f73d
ray              : 2.3.1
dask             : 2023.4.0
distributed      : 2023.4.0
hdk              : None

pandas dependencies
-------------------
pandas           : 1.5.3
numpy            : 1.24.2
pytz             : 2023.3
dateutil         : 2.8.2
setuptools       : 65.6.3
pip              : 23.0.1
Cython           : None
pytest           : 7.3.0
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 3.1.2
IPython          : 8.12.0
pandas_datareader: None
bs4              : 4.12.2
bottleneck       : None
brotli           : None
fastparquet      : None
fsspec           : 2023.4.0
gcsfs            : None
matplotlib       : 3.7.1
numba            : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : 0.19.1
pyarrow          : 10.0.1
pyreadstat       : None
pyxlsb           : None
s3fs             : None
scipy            : 1.10.1
snappy           : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
zstandard        : 0.20.0
tzdata           : None


</details>



Discussion:
In the example here, it looks like the first partition has a multiindex with levels named `__ID_LEVEL_NAME__` and `None:

```
MultiIndex([('col33', ''),
            ('index', '')],
           names=['__ID_LEVEL_NAME__', None])
```

and the second partition has the expected index:

```
 Index(['col34'], dtype='object')
```

The `__ID_LEVEL_NAME__` originates here: https://github.com/modin-project/modin/blob/f2489f73dab0c13b6709ec3a9361ad79819aa9d0/modin/core/dataframe/algebra/groupby.py#L523

cc'ing @dchigarev since a lot of the blame for the related code points to you

Resolving patch:
diff --git a/modin/core/storage_formats/pandas/groupby.py b/modin/core/storage_formats/pandas/groupby.py
--- a/modin/core/storage_formats/pandas/groupby.py
+++ b/modin/core/storage_formats/pandas/groupby.py
@@ -141,7 +141,7 @@ def skew_map(dfgb, *args, **kwargs):
         def skew_reduce(dfgb, *args, **kwargs):
             df = dfgb.sum(*args, **kwargs)
             if df.empty:
-                return df
+                return df.droplevel(GroupByReduce.ID_LEVEL_NAME, axis=1)
 
             count = df["count"]
             s = df["sum"]
@@ -211,7 +211,7 @@ def mean_reduce(dfgb, **kwargs):
             """
             sums_counts_df = dfgb.sum(**kwargs)
             if sums_counts_df.empty:
-                return sums_counts_df
+                return sums_counts_df.droplevel(GroupByReduce.ID_LEVEL_NAME, axis=1)
 
             sum_df = sums_counts_df["sum"]
             count_df = sums_counts_df["count"]


Test patch:
diff --git a/modin/pandas/test/test_groupby.py b/modin/pandas/test/test_groupby.py
--- a/modin/pandas/test/test_groupby.py
+++ b/modin/pandas/test/test_groupby.py
@@ -17,6 +17,7 @@
 import numpy as np
 from unittest import mock
 
+from modin.config import StorageFormat
 from modin.config.envvars import Engine, ExperimentalGroupbyImpl
 from modin.core.dataframe.pandas.partitioning.axis_partition import (
     PandasDataframeAxisPartition,
@@ -2559,3 +2560,53 @@ def test_skew_corner_cases():
     # https://github.com/modin-project/modin/issues/5545
     modin_df, pandas_df = create_test_dfs({"col0": [1, 1], "col1": [171, 137]})
     eval_general(modin_df, pandas_df, lambda df: df.groupby("col0").skew())
+
+
+@pytest.mark.parametrize(
+    "method",
+    # test all aggregations from pandas.core.groupby.base.reduction_kernels except
+    # nth and corrwith, both of which require extra arguments.
+    [
+        "all",
+        "any",
+        "count",
+        "first",
+        "idxmax",
+        "idxmin",
+        "last",
+        "max",
+        "mean",
+        "median",
+        "min",
+        "nunique",
+        "prod",
+        "quantile",
+        "sem",
+        "size",
+        "skew",
+        "std",
+        "sum",
+        "var",
+    ],
+)
+@pytest.mark.skipif(
+    StorageFormat.get() != "Pandas",
+    reason="only relevant to pandas execution",
+)
+def test_groupby_agg_with_empty_column_partition_6175(method):
+    df = pd.concat(
+        [
+            pd.DataFrame({"col33": [0, 1], "index": [2, 3]}),
+            pd.DataFrame({"col34": [4, 5]}),
+        ],
+        axis=1,
+    )
+    assert df._query_compiler._modin_frame._partitions.shape == (1, 2)
+    eval_general(
+        df,
+        df._to_pandas(),
+        lambda df: getattr(df.groupby(["col33", "index"]), method)(),
+        # work around https://github.com/modin-project/modin/issues/6016: we don't
+        # expect any exceptions.
+        raising_exceptions=(Exception,),
+    )