[biztos]

Interesting assertion re: TimeToWriteCode, but I think there's TimeToWriteCode vs. TimeToWriteGoodCode.

I'm working on my first serious Python project right now, and I find it's super easy to throw together some code that more or less works; but for solid, readable, documented, properly unit-tested code I hope is production-ready, it's not any faster than Perl or Golang.

(Sure, if you're a Python expert it's faster for you than for me, but if it's about TimeForExpertsToWriteGoodCode I'm not any more convinced.)

[guitarbill]

Production-ready is so complex, it's hard to make any comparison. E.g. for a library, writing good documentation (with diagrams and decent technical writing) takes me way longer coding anyway - probably by an order of magnitude.

Proper unit-testing is also going to take roughly the same time in any language, just because you have to think hard about sensible tests (although I still love mocking/patching in Python, so I'd give it an edge, plus pdb/ipdb for debugging tests is cool). Production-ready also includes deployment, which for anything non-trivial I'd say Golang > Python > Perl.

Finally, if we're talking "serious project", IMO tooling and how that tooling integrates into a CI pipeline are more important than development speed, because as a team or project goes, terrible CI will slow developers more than any language. Although again here I think Python does quite well with decent linting, unit test frameworks, and code coverage options, Golang's opinionated tools are simpler in this respect.

(I enjoyed C# for similar reasons, although I don't think it's kept up w.r.t. tooling - been ages since I used it though.)

[biztos]

Good points. So far I find I really like Python's mocking, "with self.some_useful_patch()" is really nice, and I like the idea of side effects especially with boto. Of course in some cases it's really difficult, but every language has its tricky unit-testing problems.

One big point I would give to Golang, about which lots of people disagree with me, is the "opinionatedness" of it. It seems to me that Python, like Perl, has a "There's More Than One Way To Do It" mentality, and after many years of that I really appreciated Golang's emphasis on the "idiomatic." That goes for the tooling too.

I have also noticed that the Python ecosystem doesn't have a strong documentation culture, which I find annoying as a relative newbie. But that presumably matters less over time, and it seems to be part of the Python Way to use libraries that "just work" and not worry about the details.

[coldtea]

>Interesting assertion re: TimeToWriteCode, but I think there's TimeToWriteCode vs. TimeToWriteGoodCode.

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

Scientific computing is full of those cases -- you write code to run a few times, and don't care for maintaining it and running it ever again (as long as the results are correct).

[biztos]

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.

[lloeki]

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.

[klmr]

> 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.