我正在从事金融项目，我想为摊销时间表准备一个api。利息和付款运作良好，但是本金从零开始，我尝试了许多网站，但无法获得确切答案。谁能帮我？以下是我尝试过的python代码。预先感谢。
import pandas as pd
import numpy as np
from datetime import date
Interest_Rate = 10
Years = 5
Payments_Year = 12
Principal = 100000
Addl_Princ = 50
start_date = (date(2019,5,28))
pmt = np.pmt(Interest_Rate/Payments_Year, Years*Payments_Year, Principal)
per = 1
# Calculate the interest
ipmt = np.ipmt(Interest_Rate/Payments_Year, per, Years*Payments_Year, Principal)
# Calculate the principal
ppmt = np.ppmt(Interest_Rate/Payments_Year, per, Years*Payments_Year, Principal)
rng = pd.date_range(start_date, periods=Years * Payments_Year, freq='MS')
rng.name = "Payment_Date"
df = pd.DataFrame(index=rng, columns=['Payment', 'Principal', 'Interest'], dtype='float')
df.reset_index(inplace=True)
df.index += 1
df.index.name = "Period"
df["Payment"] = np.pmt(Interest_Rate/Payments_Year, Years*Payments_Year, Principal)
df["Principal"] = np.ppmt(Interest_Rate/Payments_Year, df.index, Years*Payments_Year, Principal)
df["Interest"] = np.ipmt(Interest_Rate/Payments_Year, df.index, Years*Payments_Year, Principal)
# df["Addl_Principal"] = -Addl_Principal
df = df.round(2)
print(df)
我的输出是：
Payment_Date Payment Principal Interest Balance
Period
1 2019-06-01 -83333.33 0.00 -83333.33 100000.00
2 2019-07-01 -83333.33 -0.00 -83333.33 100000.00
3 2019-08-01 -83333.33 0.00 -83333.33 100000.00
4 2019-09-01 -83333.33 -0.00 -83333.33 100000.00
5 2019-10-01 -83333.33 -0.00 -83333.33 100000.00
6 2019-11-01 -83333.33 -0.00 -83333.33 100000.00
7 2019-12-01 -83333.33 0.00 -83333.33 100000.00
8 2020-01-01 -83333.33 -0.00 -83333.33 100000.00
9 2020-02-01 -83333.33 -0.00 -83333.33 100000.00
10 2020-03-01 -83333.33 -0.00 -83333.33 100000.00
11 2020-04-01 -83333.33 0.00 -83333.33 100000.00
12 2020-05-01 -83333.33 0.00 -83333.33 100000.00
13 2020-06-01 -83333.33 0.00 -83333.33 100000.00
14 2020-07-01 -83333.33 -0.00 -83333.33 100000.00
15 2020-08-01 -83333.33 -0.00 -83333.33 100000.00
16 2020-09-01 -83333.33 0.00 -83333.33 100000.00
17 2020-10-01 -83333.33 -0.00 -83333.33 100000.00
18 2020-11-01 -83333.33 0.00 -83333.33 100000.00
19 2020-12-01 -83333.33 0.00 -83333.33 100000.00
20 2021-01-01 -83333.33 0.00 -83333.33 100000.00
21 2021-02-01 -83333.33 0.00 -83333.33 100000.00
22 2021-03-01 -83333.33 0.00 -83333.33 100000.00
23 2021-04-01 -83333.33 -0.00 -83333.33 100000.00
24 2021-05-01 -83333.33 -0.00 -83333.33 100000.00
25 2021-06-01 -83333.33 -0.00 -83333.33 100000.00
26 2021-07-01 -83333.33 -0.00 -83333.33 100000.00
27 2021-08-01 -83333.33 -0.00 -83333.33 100000.00
28 2021-09-01 -83333.33 0.00 -83333.33 100000.00
29 2021-10-01 -83333.33 -0.00 -83333.33 100000.00
30 2021-11-01 -83333.33 0.00 -83333.33 100000.00
31 2021-12-01 -83333.33 0.00 -83333.33 100000.00
32 2022-01-01 -83333.33 0.00 -83333.33 100000.00
33 2022-02-01 -83333.33 -0.00 -83333.33 100000.00
34 2022-03-01 -83333.33 -0.01 -83333.33 99999.99
35 2022-04-01 -83333.33 0.00 -83333.33 99999.99
36 2022-05-01 -83333.33 0.00 -83333.33 99999.99
37 2022-06-01 -83333.33 0.00 -83333.33 99999.99
38 2022-07-01 -83333.33 0.00 -83333.33 99999.99
39 2022-08-01 -83333.33 -0.10 -83333.23 99999.89
40 2022-09-01 -83333.33 -0.21 -83333.12 99999.68
41 2022-10-01 -83333.33 -0.42 -83332.92 99999.26
42 2022-11-01 -83333.33 -0.83 -83332.50 99998.43
43 2022-12-01 -83333.33 0.00 -83333.33 99998.43
44 2023-01-01 -83333.33 0.00 -83333.33 99998.43
45 2023-02-01 -83333.33 -6.67 -83326.67 99991.76
46 2023-03-01 -83333.33 -6.67 -83326.67 99985.09
47 2023-04-01 -83333.33 -13.33 -83320.00 99971.76
48 2023-05-01 -83333.33 -26.67 -83306.67 99945.09
49 2023-06-01 -83333.33 -26.67 -83306.67 99918.42
50 2023-07-01 -83333.33 -26.67 -83306.67 99891.75
51 2023-08-01 -83333.33 -133.33 -83200.00 99758.42
52 2023-09-01 -83333.33 -133.33 -83200.00 99625.09
53 2023-10-01 -83333.33 -560.00 -82773.33 99065.09
54 2023-11-01 -83333.33 -560.00 -82773.33 98505.09
55 2023-12-01 -83333.33 -1413.33 -81920.00 97091.76
56 2024-01-01 -83333.33 -4826.67 -78506.67 92265.09
57 2024-02-01 -83333.33 -1413.33 -81920.00 90851.76
58 2024-03-01 -83333.33 -1413.33 -81920.00 89438.43
59 2024-04-01 -83333.33 -28720.00 -54613.33 60718.43
60 2024-05-01 -83333.33 -28720.00 -54613.33 31998.43
答案 0 :(得分：0)
是的...最后我想了很多，我得到了输出。
代码：
Interest_Rate = 0.10
Years = 5
Payments_Year = 12
Principal = 200000
start_date = (date(2019, 5, 28))
pmt = np.pmt(Interest_Rate / Payments_Year, Years * Payments_Year, Principal)
per = 1
ipmt = np.ipmt(Interest_Rate / Payments_Year, per, Years * Payments_Year, Principal)
ppmt = np.ppmt(Interest_Rate / Payments_Year, per, Years * Payments_Year, Principal)
rng = pd.date_range(start_date, periods=Years * Payments_Year, freq='MS')
rng.name = "Payment_Date"
df = pd.DataFrame(index=rng, columns=['Payment', 'Principal', 'Interest', 'Balance'],
dtype='float')
df.reset_index(inplace=True)
df.index += 1
df.index.name = "Period"
print(df.index)
df["Payment"] = np.pmt(Interest_Rate / Payments_Year, Years * Payments_Year, Principal)
df["Principal"] = np.ppmt(Interest_Rate / Payments_Year, df.index, Years * Payments_Year, Principal)
df["Interest"] = np.ipmt(Interest_Rate / Payments_Year, df.index, Years * Payments_Year, Principal)
df = df.round(2)
df["Balance"] = 0
df.loc[1, "Balance"] = Principal + df.loc[1, "Principal"]
for i in range(2, len(df) + 1):
prev_balance = df.ix[i - 1, 'Balance']
principal = df.ix[i, 'Principal']
if prev_balance == 0:
df.ix[i, ['Payment', 'Principal', 'Interest', 'Balance']] = 0
continue
if abs(principal) <= prev_balance:
df.ix[i, 'Balance'] = principal + prev_balance
else:
if prev_balance <= abs(principal):
principal = -prev_balance
addl_principal = 0
else:
print('else')
df.ix[i, 'Balance'] = 0
df.ix[i, 'Principal'] = principal
df.ix[i, "Payment"] = principal + df.ix[i, "Interest"]
df = df.round(2)
print(df)