# Buggy code
def call(cls, map_func, reduce_func, *call_args, **call_kwds):
    def caller(
        query_compiler,
        by,
        axis,
        groupby_args,
        map_args,
        reduce_args=None,
        numeric_only=True,
        drop=False,
    ):
        assert isinstance(by, type(query_compiler)), (
            "Can only use groupby reduce with another Query Compiler"
        )
        assert axis == 0, "Can only groupby reduce with axis=0"

        if numeric_only:
            qc = query_compiler.getitem_column_array(
                query_compiler._modin_frame._numeric_columns(True)
            )
        else:
            qc = query_compiler
        as_index = groupby_args.get("as_index", True)

        def _map(df, other):
            def compute_map(df, other):
                # Set `as_index` to True to track the metadata of the grouping object
                # It is used to make sure that between phases we are constructing the
                # right index and placing columns in the correct order.
                groupby_args["as_index"] = True
                other = other.squeeze(axis=axis ^ 1)
                if isinstance(other, pandas.DataFrame):
                    df = pandas.concat(
                        [df] + [other[[o for o in other if o not in df]]], axis=1
                    )
                    other = list(other.columns)
                result = map_func(
                    df.groupby(by=other, axis=axis, **groupby_args), **map_args
                )
                # The _modin_groupby_ prefix indicates that this is the first partition,
                # and since we may need to insert the grouping data in the reduce phase
                if (
                    not isinstance(result.index, pandas.MultiIndex)
                    and result.index.name is not None
                    and result.index.name in result.columns
                ):
                    result.index.name = "{}{}".format(
                        "_modin_groupby_", result.index.name
                    )
                return result

            try:
                return compute_map(df, other)
            # This will happen with Arrow buffer read-only errors. We don't want to copy
            # all the time, so this will try to fast-path the code first.
            except ValueError:
                return compute_map(df.copy(), other.copy())

        def _reduce(df):
            def compute_reduce(df):
                other_len = len(df.index.names)
                df = df.reset_index(drop=False)
                # See note above about setting `as_index`
                groupby_args["as_index"] = as_index
                if other_len > 1:
                    by_part = list(df.columns[0:other_len])
                else:
                    by_part = df.columns[0]
                result = reduce_func(
                    df.groupby(by=by_part, axis=axis, **groupby_args), **reduce_args
                )
                if (
                    not isinstance(result.index, pandas.MultiIndex)
                    and result.index.name is not None
                    and "_modin_groupby_" in result.index.name
                ):
                    result.index.name = result.index.name[len("_modin_groupby_") :]
                if isinstance(by_part, str) and by_part in result.columns:
                    if "_modin_groupby_" in by_part and drop:
                        col_name = by_part[len("_modin_groupby_") :]
                        new_result = result.drop(columns=col_name)
                        new_result.columns = [
                            col_name if "_modin_groupby_" in c else c
                            for c in new_result.columns
                        ]
                        return new_result
                    else:
                        return result.drop(columns=by_part)
                return result

            try:
                return compute_reduce(df)
            # This will happen with Arrow buffer read-only errors. We don't want to copy
            # all the time, so this will try to fast-path the code first.
            except ValueError:
                return compute_reduce(df.copy())

        if axis == 0:
            new_columns = qc.columns
            new_index = None
        else:
            new_index = query_compiler.index
            new_columns = None
        new_modin_frame = qc._modin_frame.groupby_reduce(
            axis,
            by._modin_frame,
            _map,
            _reduce,
            new_columns=new_columns,
            new_index=new_index,
        )
        return query_compiler.__constructor__(new_modin_frame)

    return caller


# Traceback (IndexError)
pd_res:
population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Malta       434000    12011      MT

md_res_partitions:
population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Malta       434000    12011      MT

md_res:
distributed.worker - WARNING -  Compute Failed
Function:  apply_list_of_funcs
args:      ([[b"\x80\x04\x95\xb0\x03\x00\x00\x00\x00\x00\x00\x8c\x17cloudpickle.cloudpickle\x94\x8c\x0e_fill_function\x94\x93\x94(h\x00\x8c\x0f_make_skel_func\x94\x93\x94h\x00\x8c\r_builtin_type\x94\x93\x94\x8c\x08CodeType\x94\x85\x94R\x94(K\x01K\x00K\x01K\x05K\x13C\x14t\x00\xa0\x01|\x00j\x02\x88\x01\x88\x00f\x02\x19\x00\xa1\x01S\x00\x94N\x85\x94\x8c\x06pandas\x94\x8c\tDataFrame\x94\x8c\x04iloc\x94\x87\x94\x8c\x02df\x94\x85\x94\x8cZc:\\users\\dchigare\\desktop\\repos\\modin\\modin\\engines\\dask\\pandas_on_dask\\frame\\partition.py\x94\x8c\x08<lambda>\x94KcC\x00\x94\x8c\x0bcol_indices\x94\x8c\x0brow_indices\x94\x86\x94)t\x94R\x94K\x02}\x94(\x8c\x0b__package__\x94\x8c'modin.engines.dask.pandas_on_dask.frame\x94\x8c\x08__name__\x94\x8c1modin.engines.dask.pandas_on_dask.frame.partition\x94\x8c\x08__file__\x94h\x12u\x87\x94R\x94}\x94(\x8c\x07globals\x94}\x94h\x0ch\x00\x8c\tsubimport\x94\x93\x94h\x0c\x85\x94R\x94s\x8c\x08defaults\x94N\x8c\x04dict\x94}\x94\x8c\x0eclosure_values\x94]\x94(\x8c\x15numpy.c
kwargs:    {}
Exception: IndexError('positional indexers are out-of-bounds')

Traceback (most recent call last):
File "C:\Users\dchigare\Desktop\REPOS\TESTS\reprod.py", line 39, in <module>
print("\nmd_res:\n", md_res, sep="") # Exception: IndexError('positional indexers are out-of-bounds')
File "c:\users\dchigare\desktop\repos\modin\modin\pandas\base.py", line 3465, in __str__
return repr(self)
File "c:\users\dchigare\desktop\repos\modin\modin\pandas\dataframe.py", line 162, in __repr__
result = repr(self._build_repr_df(num_rows, num_cols))
File "c:\users\dchigare\desktop\repos\modin\modin\pandas\base.py", line 100, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "c:\users\dchigare\desktop\repos\modin\modin\backends\pandas\query_compiler.py", line 188, in to_pandas
return self._modin_frame.to_pandas()
File "c:\users\dchigare\desktop\repos\modin\modin\engines\base\frame\data.py", line 1350, in to_pandas
df = self._frame_mgr_cls.to_pandas(self._partitions)
File "c:\users\dchigare\desktop\repos\modin\modin\engines\base\frame\partition_manager.py", line 258, in to_pandas
retrieved_objects = [[obj.to_pandas() for obj in part] for part in partitions]
File "c:\users\dchigare\desktop\repos\modin\modin\engines\base\frame\partition_manager.py", line 258, in <listcomp>
retrieved_objects = [[obj.to_pandas() for obj in part] for part in partitions]
File "c:\users\dchigare\desktop\repos\modin\modin\engines\base\frame\partition_manager.py", line 258, in <listcomp>
retrieved_objects = [[obj.to_pandas() for obj in part] for part in partitions]
File "c:\users\dchigare\desktop\repos\modin\modin\engines\dask\pandas_on_dask\frame\partition.py", line 127, in to_pandas
dataframe = self.get()
File "c:\users\dchigare\desktop\repos\modin\modin\engines\dask\pandas_on_dask\frame\partition.py", line 63, in get
return self.future.result()
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\distributed\client.py", line 218, in result
raise exc.with_traceback(tb)
File "c:\users\dchigare\desktop\repos\modin\modin\engines\dask\pandas_on_dask\frame\partition.py", line 27, in apply_list_of_funcs
df = func(df, **kwargs)
File "c:\users\dchigare\desktop\repos\modin\modin\engines\dask\pandas_on_dask\frame\partition.py", line 99, in <lambda>
lambda df: pandas.DataFrame(df.iloc[row_indices, col_indices])
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\indexing.py", line 1762, in __getitem__
return self._getitem_tuple(key)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\indexing.py", line 2067, in _getitem_tuple
self._has_valid_tuple(tup)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\indexing.py", line 703, in _has_valid_tuple
self._validate_key(k, i)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\indexing.py", line 2009, in _validate_key
raise IndexError("positional indexers are out-of-bounds")
IndexError: positional indexers are out-of-bounds

# Fixed code
def call(cls, map_func, reduce_func, *call_args, **call_kwds):
    def caller(
        query_compiler,
        by,
        axis,
        groupby_args,
        map_args,
        reduce_args=None,
        numeric_only=True,
        drop=False,
    ):
        assert isinstance(by, type(query_compiler)), (
            "Can only use groupby reduce with another Query Compiler"
        )
        assert axis == 0, "Can only groupby reduce with axis=0"

        if numeric_only:
            qc = query_compiler.getitem_column_array(
                query_compiler._modin_frame._numeric_columns(True)
            )
        else:
            qc = query_compiler
        as_index = groupby_args.get("as_index", True)

        def _map(df, other):
            def compute_map(df, other):
                # Set `as_index` to True to track the metadata of the grouping object
                # It is used to make sure that between phases we are constructing the
                # right index and placing columns in the correct order.
                groupby_args["as_index"] = True
                other = other.squeeze(axis=axis ^ 1)
                if isinstance(other, pandas.DataFrame):
                    df = pandas.concat(
                        [df] + [other[[o for o in other if o not in df]]], axis=1
                    )
                    other = list(other.columns)
                result = map_func(
                    df.groupby(by=other, axis=axis, **groupby_args), **map_args
                )
                # The _modin_groupby_ prefix indicates that this is the first partition,
                # and since we may need to insert the grouping data in the reduce phase
                if (
                    not isinstance(result.index, pandas.MultiIndex)
                    and result.index.name is not None
                    and result.index.name in result.columns
                ):
                    result.index.name = "{}{}".format(
                        "_modin_groupby_", result.index.name
                    )
                return result

            try:
                return compute_map(df, other)
            # This will happen with Arrow buffer read-only errors. We don't want to copy
            # all the time, so this will try to fast-path the code first.
            except ValueError:
                return compute_map(df.copy(), other.copy())

        def _reduce(df):
            def compute_reduce(df):
                other_len = len(df.index.names)
                df = df.reset_index(drop=False)
                # See note above about setting `as_index`
                groupby_args["as_index"] = as_index
                if other_len > 1:
                    by_part = list(df.columns[0:other_len])
                else:
                    by_part = df.columns[0]
                result = reduce_func(
                    df.groupby(by=by_part, axis=axis, **groupby_args), **reduce_args
                )
                if (
                    not isinstance(result.index, pandas.MultiIndex)
                    and result.index.name is not None
                    and "_modin_groupby_" in result.index.name
                ):
                    result.index.name = result.index.name[len("_modin_groupby_") :]
                if isinstance(by_part, str) and by_part in result.columns:
                    if "_modin_groupby_" in by_part and drop:
                        col_name = by_part[len("_modin_groupby_") :]
                        new_result = result.drop(columns=col_name)
                        new_result.columns = [
                            col_name if "_modin_groupby_" in c else c
                            for c in new_result.columns
                        ]
                        return new_result
                    else:
                        return result.drop(columns=by_part)
                return result

            try:
                return compute_reduce(df)
            # This will happen with Arrow buffer read-only errors. We don't want to copy
            # all the time, so this will try to fast-path the code first.
            except ValueError:
                return compute_reduce(df.copy())

        # TODO: try to precompute `new_index` and `new_columns`
        new_modin_frame = qc._modin_frame.groupby_reduce(
            axis, by._modin_frame, _map, _reduce
        )
        return query_compiler.__constructor__(new_modin_frame)

    return caller