Repository: modin-project/modin

Issue:
Bug with complex groupby on copied columns
### System information
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Linux
- **Modin version** (`modin.__version__`):
- **Python version**:
- **Code we can use to reproduce**:

```
import modin.pandas as mpd
import numpy as np

d = {'col1': [1, 2], 'col2': [3, 4], 'col3': [10,9], 'col4': [11,12], 'col5': [13,15]}

mdf = mpd.DataFrame(data=d)
def col5(x):
    return np.sum(x)

def min5_agg(x):
    return np.sum(np.abs(x))

def col6(x):
    return np.min(x)

def col7(x):
    return np.min(x)

agg_func = {
    'col5': [col5, min5_agg], 
    'col6': col6, 
    'col7': col7
}

mdf['col6'] = mdf['col2']
mdf['col7'] = mdf['col3']

res_m=mdf.groupby(['col1']).agg(agg_func)

res_m.columns.droplevel(0)   <--- Value Error: Cannot remove 1 levels from an index with 1 levels: at least one level must be left.
```

<!--
You can obtain the Modin version with

python -c "import modin; print(modin.__version__)"
-->

### Describe the problem
<!-- Describe the problem clearly here. -->

A bit weird bug found in one of use cases of [TPC-AI benchmark](https://www.tpc.org/tpcx-ai/default5.asp)
Modin dataframe after groupby is incorrect:
```
      (col5, col5)  (col5, min5_agg)  col6  col7
col1
1               13                13     3    10
2               15                15     4     9
```
Pandas gives
```
     col5          col6 col7
     col5 min5_agg col6 col7
col1
1      13       13    3   10
2      15       15    4    9
```
Weirdness is that bug is reproduced only in case 
```
mdf['col6'] = mdf['col2']
mdf['col7'] = mdf['col3']
```
persists in the code. Without copied columns and aggregating on them no error occurs.

### Source code / logs
<!-- Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem. -->



Discussion:
Seems it is also important that aggregation functions are specified as it is done in the test
@Garra1980 thanks for opening the issue! I tried running the snippet and was able to reproduce this behavior locally. Like you said, the strange coupling of the columns seems to happen if we set the columns the way you mentioned. If we do something like this things seem to be working:
```python
import modin.pandas as mpd
import numpy as np

# Initialize col6 and col7 up here instead statically
d = {'col1': [1, 2], 'col2': [3, 4], 'col3': [10,9], 'col4': [11,12], 'col5': [13,15], 'col6': [17,38], 'col7': [71,32]}

mdf = mpd.DataFrame(data=d)
def col5(x):
    return np.sum(x)

def min5_agg(x):
    return np.sum(np.abs(x))

def col6(x):
    return np.min(x)

def col7(x):
    return np.min(x)

agg_func = {
    'col5': [col5, min5_agg], 
    'col6': col6, 
    'col7': col7
}

res_m=mdf.groupby(['col1']).agg(agg_func)

res_m.columns.droplevel(0)
```

The Modin team will take a look and find a solution to the issue! cc: @modin-project/modin-contributors  @modin-project/modin-core 
yeah, something like that
Problem: when used to aggregate a list of functions, the end result must be a multicolumn. When a Modin dataframe contains several partitions, a situation may arise when a multicolumn occurs in one partition and not in another. The current implementation does not handle this case.

Solution: we can find out if the end result should be a multicolumn before executing the aggregation functions, simply by looking at the type of the variable. If this is the case, then it is possible for columns to which only one function is applied, to wrap it in `list` type, which will lead to the creation of a multicolumn in all partitions.

Code example:
```python
            func_dict = {col: try_get_str_func(fn) for col, fn in func_dict.items()}
            if any((isinstance(value, list) for value in func_dict.values())):
                # multicolumn case
                new_func_dict = {col: fn if isinstance(fn, list) else [fn] for col, fn in func_dict.items()}
                func_dict = new_func_dict
```

Resolving patch:
diff --git a/modin/pandas/groupby.py b/modin/pandas/groupby.py
--- a/modin/pandas/groupby.py
+++ b/modin/pandas/groupby.py
@@ -514,6 +514,13 @@ def try_get_str_func(fn):
                 func, **kwargs
             )
             func_dict = {col: try_get_str_func(fn) for col, fn in func_dict.items()}
+            if any(isinstance(fn, list) for fn in func_dict.values()):
+                # multicolumn case
+                # putting functions in a `list` allows to achieve multicolumn in each partition
+                func_dict = {
+                    col: fn if isinstance(fn, list) else [fn]
+                    for col, fn in func_dict.items()
+                }
             if (
                 relabeling_required
                 and not self._as_index


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
@@ -1500,6 +1500,24 @@ def test_dict_agg_rename_mi_columns(
     df_equals(md_res, pd_res)
 
 
+def test_agg_4604():
+    data = {"col1": [1, 2], "col2": [3, 4]}
+    modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)
+    # add another partition
+    modin_df["col3"] = modin_df["col1"]
+    pandas_df["col3"] = pandas_df["col1"]
+
+    # problem only with custom aggregation function
+    def col3(x):
+        return np.max(x)
+
+    by = ["col1"]
+    agg_func = {"col2": ["sum", "min"], "col3": col3}
+
+    modin_groupby, pandas_groupby = modin_df.groupby(by), pandas_df.groupby(by)
+    eval_agg(modin_groupby, pandas_groupby, agg_func)
+
+
 @pytest.mark.parametrize(
     "operation",
     [