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sorry, if my title sounds a bit confusing. What I'm basically trying to do is adding new rows in a data frame, where I duplicate the value of each unique value of one column, while another column's new values are changing.

This is what my data frame looks like:

id year
01 2022
02 2022
03 2022
... ...
99 2022

And I want it to look like this:

id year
01 2022
01 2023
01 2024
02 2022
02 2023
02 2024
03 2022
... ...
99 2024

I.e. I want for every id to add the years 2023 and 2024 in the year column. I tried doing this with an apply function, but it always didn't work out, could you guys help me out in solving this?

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3 Answers 3

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years = [2022 + i for i in range(3)] # or years = [2022,2023, 2024] pd.DataFrame({ 'id': np.repeat((data:=df.id.to_numpy()), len(years)).reshape(-1,len(years)).flatten(), 'year': np.repeat(np.array(years), data.shape[0]).reshape(len(years), data.shape[0]).T.flatten() })
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You can simply make a list comprehension and concat all dataframe years wirh increments of your desire. For example:

pd.concat([df.assign(year=df.year+increment) for increment in range(0,3)]).sort_values(by='id').reset_index(drop=True)

This will increment your dataframe to three years as follows. You can play around with range for the desired number of extensions:

id year
1 2022
1 2023
1 2024
2 2022
2 2023
2 2024
3 2022
3 2023
3 2024
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1 Comment

I think using numpy is faster: your approach: 2.32 ms ± 742 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each), mine: 257 µs ± 67.3 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
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A quick solution would be to make two copies of your current dataframe and change accordingly the year date to 2023 and 2024. After you do that, concatenate all 3 datasets together using pd.concat.

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yes, that would work, but my original dataframe is alot bigger, i.e. there I need to add more than just two years, which would take up a lot of time

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