def _ixs(self, i, axis=0):
        """
        Parameters
        ----------
        i : int, slice, or sequence of integers
        axis : int

        Notes
        -----
        If slice passed, the resulting data will be a view.
        """
        # irow
        if axis == 0:
            if isinstance(i, slice):
                return self[i]
            else:
                label = self.index[i]
                if isinstance(label, Index):
                    # a location index by definition
                    result = self.take(i, axis=axis)
                    copy = True
                else:
                    new_values = self._data.fast_xs(i)
                    if is_scalar(new_values):
                        return new_values

                    # if we are a copy, mark as such
                    copy = (isinstance(new_values, np.ndarray) and
                            new_values.base is None)
                    result = self._constructor_sliced(new_values,
                                                      index=self.columns,
                                                      name=self.index[i],
                                                      dtype=new_values.dtype)
                result._set_is_copy(self, copy=copy)
                return result

        # icol
        else:
            label = self.columns[i]
            if isinstance(i, slice):
                # need to return view
                lab_slice = slice(label[0], label[-1])
                return self.loc[:, lab_slice]
            else:
                if isinstance(label, Index):
                    return self._take(i, axis=1)

                index_len = len(self.index)

                # if the values returned are not the same length
                # as the index (iow a not found value), iget returns
                # a 0-len ndarray. This is effectively catching
                # a numpy error (as numpy should really raise)
                values = self._data.iget(i)

                if index_len and not len(values):
                    values = np.array([np.nan] * index_len, dtype=object)
                result = self._box_col_values(values, label)

                # this is a cached value, mark it so
                result._set_as_cached(label, self)

                return result