Rolling cumulative product over aggregated data in PySpark
In the following PySpark dataframe I am trying to multiply values in column top to bottom (as if I was using F.product function) but instead of row by row calculation I have subgroups of identical values for every id that I want to multiply.
Product is the name of the column with expected results based on id and values columns. Grateful for any suggestions.