Python is very popular. I need to explore Pandas / Numpy more, but I was under the impression that they are closely linked to the underlying C arrays to provide high performance.
In my opinion the problem with computational biology is that most biologists are not keen to improve beyond a basic level of programming.
That is a problem for a large number of biologists who are working with bioinformatics, not for computational biologists, who tend to be computer scientists working in biology. I'll admit there are a good number of crap computational biologists as well, but that's not a reason to stop the rest of us from having good tools. In fact we should be trying to propagate tools that help them do what they are trying to do with minimal friction. Like Julia.
Numpy/pandas are good. But as evidenced by the Julia benchmarks, Numpy is relatively very slow. Also, most things that slow down computational bio are to do with much broader aspects of the language than linear algebra libs. Most successful standalone sequence analysis software is written in C or C++ for this reason.
In my opinion the problem with computational biology is that most biologists are not keen to improve beyond a basic level of programming.