examination_step.py

# Copyright (c) 2023 - 2025 Open Risk (https://www.openriskmanagement.com)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

# Script used in Step 3 of the Open Risk Academy Course
# https://www.openriskacademy.com/mod/page/view.php?id=746


import pandas as pd

from config import column_names, column_datatypes

# Input parameters for actual data fragment (single loan)
input_directory = "./SPLIT/"
acquisition_year = '2011'
acquisition_qtr = 'Q1'
filename = input_directory + '101034254427.csv'


def load_file(filename, col_names):
    df = pd.read_csv(filename,
                     sep="|",
                     names=col_names,
                     dtype=column_datatypes
                     )
    return df


if __name__ == '__main__':
    pd.set_option('display.max_columns', 200)
    pd.set_option('display.max_rows', 1000)
    pd.set_option('display.width', 200)
    pd.set_option('mode.chained_assignment', 'raise')

    # Load Loan Performance file for a single loan id
    input_table = load_file(filename, column_names)

    # Convert the Acquisition Data Column to the Number of Monthly Periods
    input_table['ACT_PERIOD_NUM'] = input_table['ACT_PERIOD'].apply(
        lambda x: 12 * int(str(x)[1:5]) + int(str(x)[0:1]) if len(str(x)) == 5 else 12 * int(str(x)[2:6]) + int(str(x)[0:2]) if len(str(x)) == 6 else 0)

    # Group and Select Earliest Period
    acquisition_table = input_table.loc[input_table.groupby('LOAN_ID')['ACT_PERIOD_NUM'].idxmin()]

    # Reshape table for easier inspection of column values
    print(pd.melt(acquisition_table))