def test_deepcopy_subset(adata, spmat: type):
    adata.obsp["arr"] = np.zeros((adata.n_obs, adata.n_obs))
    adata.obsp["spmat"] = spmat((adata.n_obs, adata.n_obs))

    adata = deepcopy(adata[:10].copy())

    assert isinstance(adata.obsp["arr"], np.ndarray)
    assert not isinstance(adata.obsp["arr"], ArrayView)
    np.testing.assert_array_equal(adata.obsp["arr"].shape, (10, 10))

    assert isinstance(adata.obsp["spmat"], spmat)
    assert not isinstance(
        adata.obsp["spmat"],
        SparseCSRView if spmat is sparse.csr_matrix else SparseCSCView,
    )
    np.testing.assert_array_equal(adata.obsp["spmat"].shape, (10, 10))
def iloc(cls, dataset, index):
        import cudf

        rows, cols = index
        scalar = False
        columns = list(dataset.data.columns)
        if isinstance(cols, slice):
            cols = [d.name for d in dataset.dimensions()][cols]
        elif np.isscalar(cols):
            scalar = np.isscalar(rows)
            cols = [dataset.get_dimension(cols).name]
        else:
            cols = [dataset.get_dimension(d).name for d in index[1]]
        col_index = [columns.index(c) for c in cols]
        if np.isscalar(rows):
            rows = [rows]

        if scalar:
            return dataset.data[cols[0]].iloc[rows[0]]
        result = dataset.data.iloc[rows, col_index]

        # cuDF does not handle single rows and cols indexing correctly
        # as of cudf=0.10.0 so we have to convert Series back to DataFrame
        if isinstance(result, cudf.Series):
            if len(cols) == 1:
                result = result.to_frame(cols[0])
            else:
                result = result.to_frame().T
        return result