# Buggy code
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)


# 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 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)