rapaio VS Tablesaw

Thank you a lot for your thoughts. I have implemented some vectorization into my pet project which I do not want to advertise but I will put some links to some relevant sources. In short I wanted to introduce Vector API into the linear algebra part. I chose the following design: implement a base class to offer default standard implementations. From there I derived three vector classes, one dense, one with stride and another one with indexes. To avoid implementing in each class I made them to implement loadVector and storeVector at some given position. Those are simple methods which basically translated to code like DoubleVector.fromArray(species, array, offset_pos) etc. I tested those implementations against vanilla vector API and I see no issue and there is no memory increase at all. See some vector code here: dense vector, stride vector and some generic code in a base class. I tested with jmh in multiple ways and I see no performance difference. The only hint which I have is that the overloaded methods are inlined by jit since they are small and hot. Perhaps this could be an explanation. What do you think?

Have you tried the tablesaw library? It's data frames in Java, with stream support. https://jtablesaw.github.io/tablesaw/

Interesting. How does it compare with Tablesaw, which also claims to do grouping, joins, window functions? https://github.com/jtablesaw/tablesaw

For data frames, tablesaw or anything with apache arrow interop would be a good way to go: https://github.com/jtablesaw/tablesaw