Pandas: Create A Conditional Column And Return A Value Based On The Values Of 2 Columns In Another Df.groupby

This question is an extension of another question but with a different approach. I have the following 2 dfs: (if someone can show me a more efficient way of creating the df below,i

Solution 1:

I can help with the time series, you don't need to type the data by hand, here's how you can do it.

pd.DataFrame(np.array(pd.date_range(start='1900', end='1920', freq='A').strftime('%Y')), columns=['years'])

or lose the .strftime() if you want to have months and days and full date in other words.

For running the logic you are describing, I was thinking that np.where might work fine, something like (not tested)

yrs['served'] = np.where((yrs['years'] >= dfyears['start'] | yrs['years'] <= dfyears['end']), 1, 0)

However, that wouldn't really address the fact that you want to add new rows to yrs, according to your example at least.

I know this is not a complete answer, but I hope it helps to some extent.