Speed is not a huge issue if you're happy to leave your simulation running overnight anyway, or if you have the option to just throw more and more cores at the problem (or in our case, both). The goal is to get your papers published. Code developing/interpreting/debugging time is generally far more serious an obstacle to that goal than simulation speed. I was the only developer there. Everyone else in the group coded on an almost daily basis, but none of them considered themselves programmers, and very few of them actually learn about good programming practices. They're biophysics researchers.

Having not worked in Python much before I was also pretty pleased to find that, having determined that I needed to use Dijkstra's algorithm for path-finding and then working out the smallest Standard Deviation between certain sets of data points, Python came with libraries to do both of those things off the cuff. It's just so easy, I can see why it has a favoured place in this field.