def compute_empty_cells_score(stats, columns, col_name):
    """
    # Creates a quality socre based on the percentage of empty cells (empty_percentage)

    :param stats: The stats extracted up until this point for all columns
    :param columns: All the columns
    :param col_name: The name of the column we should compute the new stats for
    :return: Dictioanry containing:
        empty_cells_score: A quality score based on the nr of empty cells, ranges from 1 to 0, where 1 is lowest quality and 0 is highest quality.
    """
    return {'empty_cells_score': round(10 * (1 - stats[col_name][
        'empty_percentage'] / 100)), 'empty_cells_score_description':
        'This score is computed as the % of empty values / 100. Empty values in a column are always bad for training correctly on that data.'
        }
