def anonymize(self, df, k, l=0):

    # Check inputs
    if df is None or len(df) == 0:
        raise Exception("Dataframe is empty")
    if self.sensitive_attribute_columns is None or len(self.sensitive_attribute_columns) == 0:
        raise Exception("Provide at least one sensitive attribute column")

    if not self.feature_columns:
        self.init_feature_colums(df)

    if self.avg_columns:
        for c in self.avg_columns:
            if not is_numeric_dtype(df[c]):
                raise Exception("Column " + c + " is not numeric and average cannot be calculated.")

    mondrian = MondrianAnonymizer(df, self.feature_columns, self.sensitive_attribute_columns)
    partitions = mondrian.partition(k, l)
    dfa = self.build_anonymized_dataframe(df, partitions)
    return dfa