# Buggy code
def groupby(
    self,
    by=None,
    axis=0,
    level=None,
    as_index=True,
    sort=True,
    group_keys=True,
    squeeze=False,
    observed=False,
):
    """Apply a groupby to this DataFrame. See _groupby() remote task.
    Args:
        by: The value to groupby.
        axis: The axis to groupby.
        level: The level of the groupby.
        as_index: Whether or not to store result as index.
        sort: Whether or not to sort the result by the index.
        group_keys: Whether or not to group the keys.
        squeeze: Whether or not to squeeze.
    Returns:
        A new DataFrame resulting from the groupby.
    """
    axis = self._get_axis_number(axis)
    idx_name = None
    drop = False
    if callable(by):
        by = self.index.map(by)
    elif isinstance(by, str):
        drop = by in self.columns
        idx_name = by
        if (
            isinstance(self.axes[axis], pandas.MultiIndex)
            and by in self.axes[axis].names
        ):
            # In this case we pass the string value of the name through to the
            # partitions. This is more efficient than broadcasting the values.
            pass
        else:
            by = self.__getitem__(by)._query_compiler
    elif isinstance(by, Series):
        idx_name = by.name
        by = by._query_compiler
    elif is_list_like(by):
        # fastpath for multi column groupby
        if not isinstance(by, Series) and axis == 0 and all(o in self for o in by):
            warnings.warn(
                "Multi-column groupby is a new feature. "
                "Please report any bugs/issues to bug_reports@modin.org."
            )
            by = self.__getitem__(by)._query_compiler
        else:
            mismatch = len(by) != len(self.axes[axis])
            if mismatch and all(
                obj in self
                or (hasattr(self.index, "names") and obj in self.index.names)
                for obj in by
            ):
                # In the future, we will need to add logic to handle this, but for now
                # we default to pandas in this case.
                pass
            elif mismatch:
                raise KeyError(next(x for x in by if x not in self))

    from .groupby import DataFrameGroupBy

    return DataFrameGroupBy(
        self,
        by,
        axis,
        level,
        as_index,
        sort,
        group_keys,
        squeeze,
        idx_name,
        observed=observed,
        drop=drop,
    )


# 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 groupby(
    self,
    by=None,
    axis=0,
    level=None,
    as_index=True,
    sort=True,
    group_keys=True,
    squeeze=False,
    observed=False,
):
    """Apply a groupby to this DataFrame. See _groupby() remote task.
    Args:
        by: The value to groupby.
        axis: The axis to groupby.
        level: The level of the groupby.
        as_index: Whether or not to store result as index.
        sort: Whether or not to sort the result by the index.
        group_keys: Whether or not to group the keys.
        squeeze: Whether or not to squeeze.
    Returns:
        A new DataFrame resulting from the groupby.
    """
    axis = self._get_axis_number(axis)
    idx_name = None
    # Drop here indicates whether or not to drop the data column before doing the
    # groupby. The typical pandas behavior is to drop when the data came from this
    # dataframe. When a string, Series directly from this dataframe, or list of
    # strings is passed in, the data used for the groupby is dropped before the
    # groupby takes place.
    drop = False
    if callable(by):
        by = self.index.map(by)
    elif isinstance(by, str):
        drop = by in self.columns
        idx_name = by
        if (
            isinstance(self.axes[axis], pandas.MultiIndex)
            and by in self.axes[axis].names
        ):
            # In this case we pass the string value of the name through to the
            # partitions. This is more efficient than broadcasting the values.
            pass
        else:
            by = self.__getitem__(by)._query_compiler
    elif isinstance(by, Series):
        drop = by._parent is self
        idx_name = by.name
        by = by._query_compiler
    elif is_list_like(by):
        # fastpath for multi column groupby
        if not isinstance(by, Series) and axis == 0 and all(o in self for o in by):
            warnings.warn(
                "Multi-column groupby is a new feature. "
                "Please report any bugs/issues to bug_reports@modin.org."
            )
            by = self.__getitem__(by)._query_compiler
            drop = True
        else:
            mismatch = len(by) != len(self.axes[axis])
            if mismatch and all(
                obj in self
                or (hasattr(self.index, "names") and obj in self.index.names)
                for obj in by
            ):
                # In the future, we will need to add logic to handle this, but for now
                # we default to pandas in this case.
                pass
            elif mismatch:
                raise KeyError(next(x for x in by if x not in self))

    from .groupby import DataFrameGroupBy

    return DataFrameGroupBy(
        self,
        by,
        axis,
        level,
        as_index,
        sort,
        group_keys,
        squeeze,
        idx_name,
        observed=observed,
        drop=drop,
    )