# Buggy code
def _apply_full_axis(
    self,
    axis,
    func,
    new_index=None,
    new_columns=None,
    dtypes=None,
):
    """
    Perform a function across an entire axis.

    Parameters
    ----------
        axis : 0 or 1
            The axis to apply over (0 - rows, 1 - columns).
        func : callable
            The function to apply.
        new_index : list-like (optional)
            The index of the result. We may know this in advance,
            and if not provided it must be computed.
        new_columns : list-like (optional)
            The columns of the result. We may know this in
            advance, and if not provided it must be computed.
        dtypes : list-like (optional)
            The data types of the result. This is an optimization
            because there are functions that always result in a particular data
            type, and allows us to avoid (re)computing it.

    Returns
    -------
    BasePandasFrame
        A new dataframe.

    Notes
    -----
    The data shape may change as a result of the function.
    """
    new_partitions = self._frame_mgr_cls.map_axis_partitions(
        axis,
        self._partitions,
        self._build_mapreduce_func(axis, func),
        keep_partitioning=True,
    )
    # Index objects for new object creation. This is shorter than if..else
    if new_columns is None:
        new_columns = self._frame_mgr_cls.get_indices(
            1, new_partitions, lambda df: df.columns
        )
    if new_index is None:
        new_index = self._frame_mgr_cls.get_indices(
            0, new_partitions, lambda df: df.index
        )
    if dtypes == "copy":
        dtypes = self._dtypes
    elif dtypes is not None:
        dtypes = pandas.Series([np.dtype(dtypes)] * len(new_columns), index=new_columns)
    return self.__constructor__(
        new_partitions, new_index, new_columns, None, None, dtypes
    )


# 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 _apply_full_axis(
    self,
    axis,
    func,
    new_index=None,
    new_columns=None,
    dtypes=None,
):
    """
    Perform a function across an entire axis.

    Parameters
    ----------
        axis : 0 or 1
            The axis to apply over (0 - rows, 1 - columns).
        func : callable
            The function to apply.
        new_index : list-like (optional)
            The index of the result. We may know this in advance,
            and if not provided it must be computed.
        new_columns : list-like (optional)
            The columns of the result. We may know this in
            advance, and if not provided it must be computed.
        dtypes : list-like (optional)
            The data types of the result. This is an optimization
            because there are functions that always result in a particular data
            type, and allows us to avoid (re)computing it.

    Returns
    -------
    BasePandasFrame
        A new dataframe.

    Notes
    -----
    The data shape may change as a result of the function.
    """
    new_partitions = self._frame_mgr_cls.map_axis_partitions(
        axis,
        self._partitions,
        self._build_mapreduce_func(axis, func),
        keep_partitioning=True,
    )
    # Index objects for new object creation. This is shorter than if..else
    if new_columns is None:
        new_columns = self._frame_mgr_cls.get_indices(
            1, new_partitions, lambda df: df.columns
        )
    if new_index is None:
        new_index = self._frame_mgr_cls.get_indices(
            0, new_partitions, lambda df: df.index
        )
    if dtypes == "copy":
        dtypes = self._dtypes
    elif dtypes is not None:
        dtypes = pandas.Series([np.dtype(dtypes)] * len(new_columns), index=new_columns)
    return self.__constructor__(
        new_partitions,
        new_index,
        new_columns,
        None,
        None,
        dtypes,
        validate_axes="reduced",
    )