Create the evolution of exposure from inception and maturity of a list of loans in Python/Pandas

Question

I have a DataFrame with a list of loans, with inception date, maturity, amount.
I would like to create a DataFrame with the evolution of the exposure as follows:

from:

contract	amount	inception	maturity
1	100	31/12/2022	31/12/2042
2	90	31/12/2023	31/12/2052
3	50	31/12/2024	31/12/2035

to:

date	1	2	3
31/12/2022	100	0	0
31/12/2023	100	90	0
31/12/2024	100	90	50
31/12/2025	100	90	50
…	100	90	50
31/12/2042	0	90	0
…	0	90	0
31/12/2052	0	0	0

Thanks!

Asked By: Nassir Bin

||

Source

Answer 1

First convert to datetimes by to_datetime, then subtract years and repeat rows by DataFrame.loc with Index.repeat, create counter column by GroupBy.cumcount and add to years, last pivoting by DataFrame.pivot_table with aggfunc='sum' for aggregate sum if duplicated values:

df['inception'] = pd.to_datetime(df['inception'])
df['maturity'] = pd.to_datetime(df['maturity'])

df = (df.loc[df.index.repeat(df['maturity'].dt.year.sub(df['inception'].dt.year) + 1)]
        .assign(y = lambda x: x.groupby(level=0).cumcount().add(x['inception'].dt.year))
        .pivot_table(index='y',
                     columns='contract', 
                     values='amount', 
                     fill_value=0, 
                     aggfunc='sum')
        .rename(lambda x: f'31/12/{x}'))

print (df)
contract      1   2   3
y                      
31/12/2022  100   0   0
31/12/2023  100  90   0
31/12/2024  100  90  50
31/12/2025  100  90  50
31/12/2026  100  90  50
31/12/2027  100  90  50
31/12/2028  100  90  50
31/12/2029  100  90  50
31/12/2030  100  90  50
31/12/2031  100  90  50
31/12/2032  100  90  50
31/12/2033  100  90  50
31/12/2034  100  90  50
31/12/2035  100  90  50
31/12/2036  100  90   0
31/12/2037  100  90   0
31/12/2038  100  90   0
31/12/2039  100  90   0
31/12/2040  100  90   0
31/12/2041  100  90   0
31/12/2042  100  90   0
31/12/2043    0  90   0
31/12/2044    0  90   0
31/12/2045    0  90   0
31/12/2046    0  90   0
31/12/2047    0  90   0
31/12/2048    0  90   0
31/12/2049    0  90   0
31/12/2050    0  90   0
31/12/2051    0  90   0
31/12/2052    0  90   0

EDIT: You can subtract each 5 values per amount if Trues in is_instalment by subtract column by multiplied counter by 5:

print (df)
   contract  amount   inception    maturity  is_instalment
0         1     100  31/12/2022  31/12/2042           True
1         2      90  31/12/2023  31/12/2052          False
2         3      50  31/12/2024  31/12/2035          False


df['inception'] = pd.to_datetime(df['inception'])
df['maturity'] = pd.to_datetime(df['maturity'])

df['y'] = df['inception'].dt.year

df1 = df.loc[df.index.repeat(df['maturity'].dt.year.sub(df['y']) + 1)].copy()

df1['counter'] = df1.groupby(level=0).cumcount()
df1['Date'] = df1['y'].add(df1['counter'])

df1['amount'] = df1['amount'].mask(df1['is_instalment'], 
                                   df1['amount'].sub(df1['counter'].mul(5)))


df2 = df1.pivot_table(index='Date',
                      columns='contract', 
                      values='amount', 
                      fill_value=0, 
                      aggfunc='sum')

print (df2)
contract    1   2   3
Date                 
2022      100   0   0
2023       95  90   0
2024       90  90  50
2025       85  90  50
2026       80  90  50
2027       75  90  50
2028       70  90  50
2029       65  90  50
2030       60  90  50
2031       55  90  50
2032       50  90  50
2033       45  90  50
2034       40  90  50
2035       35  90  50
2036       30  90   0
2037       25  90   0
2038       20  90   0
2039       15  90   0
2040       10  90   0
2041        5  90   0
2042        0  90   0
2043        0  90   0
2044        0  90   0
2045        0  90   0
2046        0  90   0
2047        0  90   0
2048        0  90   0
2049        0  90   0
2050        0  90   0
2051        0  90   0
2052        0  90   0

Answered By: jezrael

Create the evolution of exposure from inception and maturity of a list of loans in Python/Pandas

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