In this case, we will compute ranks (based on a certain order) within groups. This is a much easier task, but can be very useful if you want to select the outliers without prior knowledge to define cut-off points. However, it can also be useful for summarizing historical data (find the three/five top hits leading the sales list the longest in different genres, for example). There is also a simplification when we do not need the rank, but just the extreme values. But, certain algorithms can use the rank values for better predictions, because we humans are biased to the best options. For example, in a 100-minute race, the difference between the first and the fifth drivers, is one minute hypothetically; that is it amounts to one percent. It's a quite small difference, although the difference in the prizes and fame are much larger.
The example workflow is in the GroupRanks.zip file.
First, we generate some sample data with the Data Generator node, just like before...