# Buggy code
def __getitem__(self, key):
    # When getting along a single axis,
    if not isinstance(key, tuple):
        # Try to fasttrack the code through already optimized path
        try:
            return self.df.__getitem__(key)
        # This can happen if it is a list of rows
        except KeyError:
            pass
    else:
        if len(key) > self.df.ndim:
            raise IndexingError("Too many indexers")
        if isinstance(key[0], slice) and key[0] == slice(None):
            if not isinstance(key[1], slice):
                return self.df.__getitem__(key[1])
            else:
                result_slice = self.df.columns.slice_locs(key[1].start, key[1].stop)
                return self.df.iloc[:, slice(*result_slice)]
    row_loc, col_loc, ndim, self.row_scaler, self.col_scaler = _parse_tuple(key)
    row_lookup, col_lookup = self._compute_lookup(row_loc, col_loc)
    # Check that the row_lookup/col_lookup is longer than 1 or that the
    # row_loc/col_loc is not boolean list to determine the ndim of the
    # result properly for multiindex.
    ndim = (0 if len(row_lookup) == 1 and not is_boolean_array(row_loc) else 1) + (
        0 if len(col_lookup) == 1 and not is_boolean_array(col_loc) else 1
    )
    result = super(_LocIndexer, self).__getitem__(row_lookup, col_lookup, ndim)
    # Pandas drops the levels that are in the `loc`, so we have to as well.
    if hasattr(result, "index") and isinstance(result.index, pandas.MultiIndex):
        if (
            isinstance(result, Series)
            and not isinstance(col_loc, slice)
            and all(col_loc[i] in result.index.levels[i] for i in range(len(col_loc)))
        ):
            result.index = result.index.droplevel(list(range(len(col_loc))))
        elif all(row_loc[i] in result.index.levels[i] for i in range(len(row_loc))):
            result.index = result.index.droplevel(list(range(len(row_loc))))
    if (
        hasattr(result, "columns")
        and isinstance(result.columns, pandas.MultiIndex)
        and all(col_loc[i] in result.columns.levels[i] for i in range(len(col_loc)))
    ):
        result.columns = result.columns.droplevel(list(range(len(col_loc))))
    return result


# Traceback (ValueError)
Pandas:
max_speed  shield
cobra               1       2
viper               4       5
sidewinder          7       8
UserWarning: Distributing <class 'list'> object. This may take some time.
Traceback (most recent call last):
File "modint.py", line 17, in <module>
o = mdf.loc[:, ~mdf.columns.duplicated()]
File "/opt/anaconda/lib/python3.7/site-packages/modin/pandas/indexing.py", line 216, in __getitem__
return self.df.__getitem__(key[1])
File "/opt/anaconda/lib/python3.7/site-packages/modin/pandas/base.py", line 3304, in __getitem__
return self._getitem(key)
File "/opt/anaconda/lib/python3.7/site-packages/modin/pandas/dataframe.py", line 1896, in _getitem
return self._getitem_array(key)
File "/opt/anaconda/lib/python3.7/site-packages/modin/pandas/dataframe.py", line 1926, in _getitem_array
len(key), len(self.index)
ValueError: Item wrong length 2 instead of 3.

# Fixed code
def __getitem__(self, key):
    # When getting along a single axis,
    if not isinstance(key, tuple):
        # Try to fasttrack the code through already optimized path
        try:
            return self.df.__getitem__(key)
        # This can happen if it is a list of rows
        except KeyError:
            pass
    else:
        if len(key) > self.df.ndim:
            raise IndexingError("Too many indexers")
        # If we're only slicing columns, handle the case with `__getitem__`
        if isinstance(key[0], slice) and key[0] == slice(None):
            if not isinstance(key[1], slice):
                # Boolean indexers can just be sliced into the columns object and
                # then passed to `__getitem__`
                if is_boolean_array(key[1]):
                    return self.df.__getitem__(self.df.columns[key[1]])
                return self.df.__getitem__(key[1])
            else:
                result_slice = self.df.columns.slice_locs(key[1].start, key[1].stop)
                return self.df.iloc[:, slice(*result_slice)]
    row_loc, col_loc, ndim, self.row_scaler, self.col_scaler = _parse_tuple(key)
    row_lookup, col_lookup = self._compute_lookup(row_loc, col_loc)
    # Check that the row_lookup/col_lookup is longer than 1 or that the
    # row_loc/col_loc is not boolean list to determine the ndim of the
    # result properly for multiindex.
    ndim = (0 if len(row_lookup) == 1 and not is_boolean_array(row_loc) else 1) + (
        0 if len(col_lookup) == 1 and not is_boolean_array(col_loc) else 1
    )
    result = super(_LocIndexer, self).__getitem__(row_lookup, col_lookup, ndim)
    # Pandas drops the levels that are in the `loc`, so we have to as well.
    if hasattr(result, "index") and isinstance(result.index, pandas.MultiIndex):
        if (
            isinstance(result, Series)
            and not isinstance(col_loc, slice)
            and all(col_loc[i] in result.index.levels[i] for i in range(len(col_loc)))
        ):
            result.index = result.index.droplevel(list(range(len(col_loc))))
        elif all(row_loc[i] in result.index.levels[i] for i in range(len(row_loc))):
            result.index = result.index.droplevel(list(range(len(row_loc))))
    if (
        hasattr(result, "columns")
        and isinstance(result.columns, pandas.MultiIndex)
        and all(col_loc[i] in result.columns.levels[i] for i in range(len(col_loc)))
    ):
        result.columns = result.columns.droplevel(list(range(len(col_loc))))
    return result