3 ms·

And most recently, tons of self styled "Data scientists". This has, IMO, diluted the rabid rhetoric of the 90's/oughties python community which is good, but re

And most recently, tons of self styled "Data scientists". This has, IMO, diluted the rabid rhetoric of the 90's/oughties python community which is good, but replaced it with people who don't know and don't care what good code even looks like, so it can be difficult to work among them.

KptMarchewa 6y ago
I feel sometimes that they pride in writing shitty, unmaintainable code; that they do it on purpose. One of the ways they enjoy life is to disregard all "best practices". And I'm not talking about some high-level OOP principles, but the basics - like keeping consistent code style, or even writing some tests. The previous crowd with love for monkeypatching everything and hate for abstraction other than dictionaries wasn't too pleasant, too.
- salixrosa 6y ago
  lol I was just sitting here thinking I miss my Python job and not having trouble with IntelliJ detecting changes in dependencies and how frustrating it is to invalidate caches and reindex the whole project multiple times before I can run tests for the dependency change. I regularly miss the testing libraries we used, too -- it was so absurdly clean and easy to build robust mocks in our DJango stack (and I set up that tooling, largely on my own iniatiative, based on my team's feedback, so it's not like I'm just like wishing other people had already done the work). Now I work with a bunch of Java devs who use to work for Amazon and somehow use that as an excuse for not caring about tests, our ci-cd tools, and all these other things I'd gotten use to thinking of as standard in my past life as a Python dev. Code style! Hah. First day on the job I asked about that and was told nobody cared and everybody had their own preferences. ... I'm very bitter today. I'm working on it.
  - ta988 6y ago
    This sounds more like a people problem than a language problem to me.
- FridgeSeal 6y ago
  I’m in this forks and I have a hard time disagreeing. Everything is jupyter notebooks, to the point where example documentation is delivered as notebooks and services run notebooks as it’s apparently easier to do that than to get people to write their code properly. “Oh but data exploration” they say, which is fine, but you shouldn’t be doing data exploration in your prod ML pipeline anyway, and you’re writing code, it’s really not that hard to follow some even rudimentary practices.

KptMarchewa 6y ago

I feel sometimes that they pride in writing shitty, unmaintainable code; that they do it on purpose. One of the ways they enjoy life is to disregard all "best practices". And I'm not talking about some high-level OOP principles, but the basics - like keeping consistent code style, or even writing some tests. The previous crowd with love for monkeypatching everything and hate for abstraction other than dictionaries wasn't too pleasant, too.

salixrosa 6y ago
lol I was just sitting here thinking I miss my Python job and not having trouble with IntelliJ detecting changes in dependencies and how frustrating it is to invalidate caches and reindex the whole project multiple times before I can run tests for the dependency change. I regularly miss the testing libraries we used, too -- it was so absurdly clean and easy to build robust mocks in our DJango stack (and I set up that tooling, largely on my own iniatiative, based on my team's feedback, so it's not like I'm just like wishing other people had already done the work). Now I work with a bunch of Java devs who use to work for Amazon and somehow use that as an excuse for not caring about tests, our ci-cd tools, and all these other things I'd gotten use to thinking of as standard in my past life as a Python dev. Code style! Hah. First day on the job I asked about that and was told nobody cared and everybody had their own preferences. ... I'm very bitter today. I'm working on it.
- ta988 6y ago
  This sounds more like a people problem than a language problem to me.
FridgeSeal 6y ago
I’m in this forks and I have a hard time disagreeing. Everything is jupyter notebooks, to the point where example documentation is delivered as notebooks and services run notebooks as it’s apparently easier to do that than to get people to write their code properly. “Oh but data exploration” they say, which is fine, but you shouldn’t be doing data exploration in your prod ML pipeline anyway, and you’re writing code, it’s really not that hard to follow some even rudimentary practices.