向数据框中添加新行，其中一列保持不变，而另一列更改值

Question

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

Ie 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?

Answer 1

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()
})

Answer 2

一个快速的解决方案是制作当前数据框的两个副本，并将年份日期相应地更改为 2023 年和 2024 年。之后，使用pd.concat将所有 3 个数据集连接在一起。

Answer 3

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