Faster way to iterate in numpy / pandas?

Question

I have a big portfolio of bonds and I want to create a table with days as index, the bonds as columns and the notional of the bonds as values.

I need to put at 0 the rows before the starting date and after the terminating date of each bond.

Is there a more efficient way than this:

[[np.where( (day>=bonds.inception[i]) & 
(day + relativedelta(months=+m) >= bonds.maturity[i] ) & 
(day <= bonds.maturity[i]), 

bonds.principal[i],

0)   

for i in range(bonds.shape[0])] for day in idx_d]

input example:

id	nom	inception	maturity
38	200	22/04/2022	22/04/2032
87	100	22/04/2022	22/04/2052

output example:

day	38	87
21/04/2022	0	0
22/04/2022	100	200

Answer 1

The solution below still requires a loop. I don't know if it's faster, or whether you find it clear, but I'll offer it as an alternative.

Create an example dataframe (with a few extra bonds for demonstration purposes):

import pandas as pd

df = pd.DataFrame({'id': [38, 87, 49, 51, 89], 
                   'nom': [200, 100, 150, 50, 250],
                   'start_date': ['22/04/2022', '22/04/2022', '01/01/2022', '01/05/2022', '23/04/2012'],
                   'end_date': ['22/04/2032', '22/04/2052', '01/01/2042', '01/05/2042', '23/04/2022']})
df['start_date'] = pd.to_datetime(df['start_date'])
df['end_date'] = pd.to_datetime(df['end_date'])
df = df.set_index('id')
print(df)

This then looks like:

id	nom	start_date	end_date
38	200	2022-04-22 00:00:00	2032-04-22 00:00:00
87	100	2022-04-22 00:00:00	2052-04-22 00:00:00
49	150	2022-01-01 00:00:00	2042-01-01 00:00:00
51	50	2022-01-05 00:00:00	2042-01-05 00:00:00
89	250	2012-04-23 00:00:00	2022-04-23 00:00:00

Now, create a new blank dataframe, with 0 as the default value:

new = pd.DataFrame(data=0, columns=df.index, index=pd.date_range('2022-04-20', '2062-04-22'))
new.index.rename('day', inplace=True)

Then, iterate over the columns (or index of the original dataframe), selecting the relevant interval and set the column value to the relevant 'nom' for that selected interval:

for column in new.columns:
    sel = (new.index >= df.loc[column, 'start_date']) & (new.index <= df.loc[column, 'end_date'])
    new.loc[sel, column] = df.loc[df.index == column, 'nom'].values
print(new)

which results in:

day	38	87	49	51	89
2022-04-20 00:00:00	0	0	150	50	250
2022-04-21 00:00:00	0	0	150	50	250
2022-04-22 00:00:00	200	100	150	50	250
2022-04-23 00:00:00	200	100	150	50	250
2022-04-24 00:00:00	200	100	150	50	0
...
2062-04-21 00:00:00	0	0	0	0	0
2062-04-22 00:00:00	0	0	0	0	0

[14613 rows x 5 columns]