split_performing_book.py

# Copyright (c) 2023 - 2024 Open Risk (https://www.openriskmanagement.com)
#
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# Script used in Step 2 of the Open Risk Academy Course
# https://www.openriskacademy.com/mod/page/view.php?id=754

import os

import pandas as pd

from config import column_names
from utils import load_file

input_directory = "./PARTS/"
output_directory = "./PERF/"

if __name__ == '__main__':

    files = os.listdir(input_directory)
    input_files = [input_directory + f for f in files if os.path.isfile(input_directory + '/' + f)]
    output_files = [output_directory + f for f in files if os.path.isfile(input_directory + '/' + f)]

    i = 1
    for in_file, out_file in zip(input_files, output_files):
        if i > 0:
            input_table = load_file(in_file, column_names)
            tmp1 = input_table.groupby('LOAN_ID')
            output_groups = []
            for name, group in tmp1:
                delinquencies = set(group['DLQ_STATUS'].values)
                modifications = set(group['MOD_FLAG'].values)
                zero_balance_codes = set(group['ZERO_BAL_CODE'].values)
                # remove loans that have prepaid, have been modified or have been delinquent
                if '01' not in zero_balance_codes and 'Y' not in modifications and len(delinquencies) == 1:
                    output_groups.append(group)
            print(in_file, len(output_groups))
            if len(output_groups) > 0:
                output_table = pd.concat(output_groups)
                output_table.to_csv(out_file, sep='|', index=False, header=False)
            i += 1