def anonymize_workload()

in scripts/anonymize.py [0:0]

• 28 lines of code
• 10 McCabe index (conditional complexity)

    def anonymize_workload(self, input_dir, file_name):
        print("processing", os.path.join(input_dir, file_name))
        # header
        # abstractFingerPrint, db_name, table_name, inputDataSize, cputime
        df = pd.read_csv(os.path.join(input_dir, file_name))

        # adjust abstractfingerprint into abstractFingerPrint
        if 'abstractfingerprint' in df.columns:
            df.rename(columns={'abstractfingerprint': 'abstractFingerPrint'}, inplace=True)

        counter_a, counter_d, counter_t = self.get_counters()

        # anonymization
        for index, row in df.iterrows():
            a_string = row['abstractFingerPrint']
            d_string = row['db_name']
            t_string = row['table_name']
            if a_string not in self.abFP:
                self.abFP[a_string] = counter_a
                counter_a += 1
            if d_string not in self.db:
                self.db[d_string] = counter_d
                counter_d += 1
            if t_string not in self.table:
                self.table[t_string] = counter_t
                counter_t += 1
        print("mapping created")

        print("now", counter_a, counter_d, counter_t)

        # Apply mappings to anonymize the data
        df['abstractFingerPrint'] = df['abstractFingerPrint'].map(self.abFP)
        df['db_name'] = df['db_name'].map(self.db)
        df['table_name'] = df['table_name'].map(self.table)
        # if 'uown_names' column exists, anonymize it
        if 'uown_names' in df.columns:
            df['uown_names'] = df['uown_names'].apply(lambda x: False if pd.isna(x) or x.strip() == "" else True)

        # Generate a new CSV file with anonymized data
        anonymized_file_name = "anonymized_" + file_name
        df.to_csv(os.path.join(self.dir_path, anonymized_file_name), index=False)