# Buggy code
def _apply_agg_function(self, f, drop=True, **kwargs):
    """Perform aggregation and combine stages based on a given function.

    Args:
        f: The function to apply to each group.

    Returns:
         A new combined DataFrame with the result of all groups.
    """
    assert callable(f), "'{0}' object is not callable".format(type(f))
    from .dataframe import DataFrame

    if isinstance(self._by, type(self._query_compiler)):
        by = self._by.to_pandas().squeeze()
    else:
        by = self._by

    if self._is_multi_by:
        return self._default_to_pandas(f, **kwargs)
    # For aggregations, pandas behavior does this for the result.
    # For other operations it does not, so we wait until there is an aggregation to
    # actually perform this operation.
    if self._idx_name is not None and drop and self._drop:
        groupby_qc = self._query_compiler.drop(columns=[self._idx_name])
    else:
        groupby_qc = self._query_compiler
    new_manager = groupby_qc.groupby_agg(
        by, self._axis, f, self._kwargs, kwargs, drop=self._drop
    )
    if self._idx_name is not None and self._as_index:
        new_manager.index.name = self._idx_name
    return DataFrame(query_compiler=new_manager)


# Traceback (ValueError)
Traceback (most recent call last):
File "/localdisk/gashiman/omniscripts/taxi/taxibench_pandas.py", line 190, in <module>
query(query_df)
File "/localdisk/gashiman/omniscripts/taxi/taxibench_pandas.py", line 95, in q3
return transformed.groupby(['passenger_count','pickup_datetime'])[['passenger_count','pickup_datetime']].count()['passenger_count']
File "/nfs/site/proj/scripting_tools/gashiman/modin/modin/pandas/groupby.py", line 249, in __getitem__
return SeriesGroupBy(self._default_to_pandas(lambda df: df.__getitem__(key)))
File "/nfs/site/proj/scripting_tools/gashiman/modin/modin/pandas/groupby.py", line 535, in _default_to_pandas
return self._df._default_to_pandas(groupby_on_multiple_columns)
File "/nfs/site/proj/scripting_tools/gashiman/modin/modin/pandas/base.py", line 227, in _default_to_pandas
result = op(pandas_obj, *args, **kwargs)
File "/nfs/site/proj/scripting_tools/gashiman/modin/modin/pandas/groupby.py", line 533, in groupby_on_multiple_columns
return f(df.groupby(by=by, axis=self._axis, **self._kwargs), **kwargs)
File "/nfs/site/home/gashiman/.local/lib/python3.7/site-packages/pandas/core/generic.py", line 7894, in groupby
**kwargs
File "/nfs/site/home/gashiman/.local/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 2522, in groupby
return klass(obj, by, **kwds)
File "/nfs/site/home/gashiman/.local/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 391, in __init__
mutated=self.mutated,
File "/nfs/site/home/gashiman/.local/lib/python3.7/site-packages/pandas/core/groupby/grouper.py", line 652, in _get_grouper
if not isinstance(gpr, Grouping)
File "/nfs/site/home/gashiman/.local/lib/python3.7/site-packages/pandas/core/groupby/grouper.py", line 347, in __init__
raise ValueError("Grouper for '{}' not 1-dimensional".format(t))
ValueError: Grouper for '<class 'pandas.core.frame.DataFrame'>' not 1-dimensional

# Fixed code
def _apply_agg_function(self, f, drop=True, **kwargs):
    """Perform aggregation and combine stages based on a given function.

    Args:
        f: The function to apply to each group.

    Returns:
         A new combined DataFrame with the result of all groups.
    """
    assert callable(f), "'{0}' object is not callable".format(type(f))
    from .dataframe import DataFrame

    if self._is_multi_by:
        return self._default_to_pandas(f, **kwargs)

    if isinstance(self._by, type(self._query_compiler)):
        by = self._by.to_pandas().squeeze()
    else:
        by = self._by

    # For aggregations, pandas behavior does this for the result.
    # For other operations it does not, so we wait until there is an aggregation to
    # actually perform this operation.
    if self._idx_name is not None and drop and self._drop:
        groupby_qc = self._query_compiler.drop(columns=[self._idx_name])
    else:
        groupby_qc = self._query_compiler
    new_manager = groupby_qc.groupby_agg(
        by, self._axis, f, self._kwargs, kwargs, drop=self._drop
    )
    if self._idx_name is not None and self._as_index:
        new_manager.index.name = self._idx_name
    return DataFrame(query_compiler=new_manager)