I often wonder about that, especially having written lots and lots of lousy, unmaintainable code in my own life.

It usually starts with "oh it's just a one-off thing" and then it turns out to be useful and the rest is messy history.

But sure, within that genre I could see Python being a faster language to write in than many others.

Sometimes even for a one-shot job you dive down and write passable code then as you start to tackle the complexities of the problem at hand you realise that the amount of ropy code has just tied your hands and now it gets increasingly harder to wrap your head around your implementation and finally complete the one-shot job.

> In lots of areas, "good code" doesn't matter much, if at all.

This is the received wisdom in biological science but I’m convinced that it’s trivially wrong. I’ve seen a lot of research code, most of it bad. I have no idea how many bugs are in this code, and I know for a fact that the original authors also don’t know. And it would be truly exceptional if these pieces of code were bug-free (in fact, there’s enough software engineering know-how to categorically conclude that a very high percentage of such code has bugs). How many of these bugs affect the correctness of the results?

… since the code quality is so bad, this is impossible to quantify. So, yes, code quality does matter in science, since it affects the probability of publishing wrong results.

Incidentally, there are cases of retractions of high-impact papers due to errors in code. Of course this will also happen with better code quality; but if conventional software engineering wisdom is right then it will happen substantially less.