I have a few groups (by column “Iteration”) of data, each group is normalized from 100 to 0. Some rows are common by column “Name” in different groups, and I want to transfer the data as many as possible in one single group so they are comparable.
INPUT:
Name Data Iteration
A 100 1
B 80 1
C 50 2
B 100 2
D 30 3
C 20 3
E 100 4
F 85 4
Can you rephrase your question, as I’m having some difficulties understanding what your specific goals are. What do you mean by transferring data - from where to where, what kind of data etc. (Or is the term ‘manipulation/transformation’ the one you’re meant to say?)
Also, can you explain how the input becomes the output as you showed above? (i.e. What’s the logic behind it?)
The rows are only comparable in the same iteration but some Names are common in other iterations. For example, B is 100 in iteration 2 and C is 50 which means C is 50% of B, while B is 8:10 vs A in iteration 1, so if A = 100, B = 80 and C = 40… and so on, I try to find out the Names’ comparable values among different iterations.
Apologies @anguslou I’m not able to grasp what you’re trying to say, still. I just can’t see the pattern of the data in order to reproduce the output. I’ll pass this one to others who might be able to.
Actually, it is a case of Google Trends, as Google Trends is limited to 5 keywords every search, and these 5 items are normalized to 0 - 100. If I want to search for multiple (like hundreds) keywords and make them comparable, I need to rescale them through the common keywords from different searching.