amort.py

#!/usr/bin/env python3

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

def amort(principal, rate, n):
    return principal * (rate * ((1 + rate) ** n)) / ((1 + rate) ** n - 1)


def compute_schedule(bal, pmt, rate):
    while bal > 0:
        new_bal = bal - (pmt - (bal * rate))
        period_interest = (bal * rate)
        period_principal = pmt - (bal * rate)
        if new_bal > 0:
            yield (new_bal, period_interest, period_principal)
        bal = new_bal


def amort_table(pmt, rate):
    asking = 349900.00
    negotiated = 0.03
    closing = negotiated * asking
    down_pmt = 12200.00
    purchase = asking + closing
    loan_amt = purchase - down_pmt
    rate = 0.025 / 12
    n = 30 * 12

    df = pd.DataFrame(
        columns=['Payment', 'Amount', 'Interest', 'Principal', 'Balance'],
        index = np.arange(1, 361, 1)
    )
    for x, (b, i, p) in enumerate(compute_schedule(loan_amt, amort(loan_amt, rate, n), rate)):
        df.loc[x + 1] = pd.Series(
            and{
                'Payment': x+1,
                'Amount': round(amort(loan_amt, rate, n), 2),
                'Interest': round(i, 2),
                'Principal': round(p, 2),
                'Balance': round(b, 2)
            }
        )

    print(df)

def main():
    asking_price = 349_900.00
    closing_costs_percent = 0.03
    closing_costs = closing_costs_percent * asking_price
    down_pmt = 12_200.00
    purchase = asking_price + closing_costs
    loan_amt = purchase - down_pmt
    rate = 0.025 / 12
    n = 30 * 12
    amort_table(amort(loan_amt, rate, n), rate)




if __name__ == '__main__':
    main()