def test_negative_scalar_index(adata, index: int, obs: bool):
    pos_index = index + (adata.n_obs if obs else adata.n_vars)

    if obs:
        adata_pos_subset = adata[pos_index]
        adata_neg_subset = adata[index]
    else:
        adata_pos_subset = adata[:, pos_index]
        adata_neg_subset = adata[:, index]

    np.testing.assert_array_equal(
        adata_pos_subset.obs_names, adata_neg_subset.obs_names
    )
    np.testing.assert_array_equal(
        adata_pos_subset.var_names, adata_neg_subset.var_names
    )
def select(cls, dataset, selection_mask=None, **selection):
        df = dataset.data
        if selection_mask is None:
            selection_mask = cls.select_mask(dataset, selection)

        indexed = cls.indexed(dataset, selection)
        if selection_mask is not None:
            df = df.loc[selection_mask]
        if indexed and len(df) == 1 and len(dataset.vdims) == 1:
            return df[dataset.vdims[0].name].iloc[0]
        return df