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I always love coming into these Python threads. It's extremely cathartic to know there are enough people who hate the language as much as I do, even if we are s

I always love coming into these Python threads. It's extremely cathartic to know there are enough people who hate the language as much as I do, even if we are stuck with it for any number of good reasons. All of the reasons we're stuck with it related to datascience and scientific computing are the exact reasons why we hate it. It's exceptionally fast to write code that does a very specific task without sparing a single solitary thought for any sort of software development best practices. It's incredibly approachable, which means it can be used by any keen scientific or mathematical minds who spend more time reading and writing whitepapers than they do writing code. The code, as an entity in and of itself, clearly shows that. But the _output_ of that code also shows that, which is why it isn't going to go away or be swapped out for anything else. This is like the ideal breeding ground for further scientific or academic pursuits, and since everyone is standing on the shoulders of numpy/scipy/sklearn/pytorch-esquian giants, you're stuck in the scenario of having to replicate all of these libraries in any other language. I can't honestly say it's worth the effort to replace the entire stack, and it's also not going to help when these same researchers continue to use python going forward and start using some other library that you didn't port yet. And so, they will continue pumping out new papers and prototypes-masquerading-as-quality-libraries, and we'll continue roping them in and duct taping fix after fix after fix on this kaleidoscope of horrors and we'll continue self medicating to dull the daily frustration of python while also accepting that it's the price we're going to need to pay if we want to work with these brilliant minds with all their neat new ideas pushing forth such a breadth of new understanding from the data _we already have_. Or at least that's what we say when we try to ignore the growing bald spot and bursts of incandescent rage every time we get a "quick script that needs a little polish" that clocks in at 2000 loc and literally doesn't have a single function definition in it and variable names that identify up to 14 different types depending on some global state and... You know, I'm going to stop typing this. It's christmas eve and I wasn't planning on being this angry today Edit: Some people weren't liking this, it may be because they think I'm being sarcastic when I talk about these brilliant people. I want to just clarify - I am not being sarcastic. They ARE brilliant. They're far more intelligent than I, and they're really pushing the boundaries of human understanding in mathematics and sciences. I acknowledge they're bring a whole hell more good to the table than they are bad, and there's a damn good reason why I am going to be doing this thing I'm doing in perpetuity, or until I can't handle it anymore. But that doesn't mean their coding practices aren't often _abysmal_, and I won't apologize for my frustrations in this and the language that just _lets_ them do it.

p1esk 6y ago
I'm a deep learning researcher. I have no idea what you're talking about. When I crank out some Pytorch code of questionable quality it's not because of Python. It's because I either don't know better, or don't care. Switching to another language won't change this.
- legerdemain 6y ago
  Right on! It's not the job of ML specialists to waste their time polishing "clean," "performant," or "correct" code.
- isbjorn16 6y ago
  Some languages force you into better practices. The extra overhead that you may not want to deal with - and that learning curve - are the exact sorts of things you want to do if you want to write code that is of less questionable quality. Good Python looks a lot like what you would get from a compiled language, and every researcher I work with looks at it and goes "holy shit this is so much extra work I don't wanna do it" And like I said, that's fair. The code is not the point, it's just a tool to get the data - that's the point. I equate it to the one time use jigs woodworkers use. Some can get pretty fancy and be awesome, but most are just slapped together and will probably get chucked in the bin. I don't mean to shame anyone - there's a reason for it, and it serves it's purpose. It's when someone hands you that shitty-ass jig, from a language that lets you do some pretty heinous things by design, the frustration builds like crazy! I've seen some pretty gnarly Java and C# in my day too, and all I know is the worst Java still is an order of magnitude easier to handle than the worst Python I see on the regular, it's wild.
  - p1esk 6y ago
    Can you please show me some examples of what good Python code looks like, and point out the overhead? Next month I will be working on a major redesign of a fairly complicated simulations code (Pytorch) with the goal of making it more flexible, and incorporating some new features. This code will be used by many others, so I want to follow good software engineering practices.

p1esk 6y ago

I'm a deep learning researcher. I have no idea what you're talking about. When I crank out some Pytorch code of questionable quality it's not because of Python. It's because I either don't know better, or don't care. Switching to another language won't change this.

legerdemain 6y ago
Right on! It's not the job of ML specialists to waste their time polishing "clean," "performant," or "correct" code.
isbjorn16 6y ago
Some languages force you into better practices. The extra overhead that you may not want to deal with - and that learning curve - are the exact sorts of things you want to do if you want to write code that is of less questionable quality. Good Python looks a lot like what you would get from a compiled language, and every researcher I work with looks at it and goes "holy shit this is so much extra work I don't wanna do it" And like I said, that's fair. The code is not the point, it's just a tool to get the data - that's the point. I equate it to the one time use jigs woodworkers use. Some can get pretty fancy and be awesome, but most are just slapped together and will probably get chucked in the bin. I don't mean to shame anyone - there's a reason for it, and it serves it's purpose. It's when someone hands you that shitty-ass jig, from a language that lets you do some pretty heinous things by design, the frustration builds like crazy! I've seen some pretty gnarly Java and C# in my day too, and all I know is the worst Java still is an order of magnitude easier to handle than the worst Python I see on the regular, it's wild.
- p1esk 6y ago
  Can you please show me some examples of what good Python code looks like, and point out the overhead? Next month I will be working on a major redesign of a fairly complicated simulations code (Pytorch) with the goal of making it more flexible, and incorporating some new features. This code will be used by many others, so I want to follow good software engineering practices.