I'm working on a project that leans heavily on matrix vectorization and large dirty datasets. I'm not a scientist, though. Here's how I work. * Hack together

I'm working on a project that leans heavily on matrix vectorization and large dirty datasets. I'm not a scientist, though. Here's how I work. * Hack together a PoC with python, sed, awk, grep, cut, xsv etc. * Clean that up, run it on larger sample sets (samples made with said sed/awk/cut etc) * Attempt to run it on the full dataset. * Rewrite it in rust. Step 2 and 3 are hit-or-miss in python. I find it near impossible to do any refactoring without static types and/or tests. And quite often, I'm looking at a run for over an hour to have it crash on that one broken line. Whereas the same happens in the rust version in seconds: crucial for my trial-and-error style of building. So: Python because I must, rust, as soon as it's clear what I'm going to do.