Hoping I can get help with a tricky transposition. I have a pandas data frame with 4 columns:
- Customer name
- Customer ID
- Time period
- Revenue
In the current structure, a customer name / ID will show up in multiple rows because they have revenue occurring in different periods.
I'd like to do a partial transposition of the data frame to where the new data frame as the following columns:
- Customer name
- Customer ID
- Period 1 Revenue
- Period 2 Revenue
- Period 3 Revenue
- ...
- Period N Revenue
In the new data structure, each customer name / ID would only show up in a single row, with all their relevant revenue data in the same row.
Example of current df structure
Example of the df I'd like to transform it into
I've tried / thought through a few ideas (e.g., using .transpose(), creating new columns using np.where and then doing some sort of sum / groupby to collapse the rows), but have struggled to find a simple solution. Any help appreciated!
