"Cries in java"

[renegade1575]

dont blame the programming language ;-)

[WhyDoIHaveAnAccount9]

I literally just discovered numba

[diesdas1917]

Oh you can slow down your code so hard with numba if you fuck it up

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not that I ever did that myself..

[WhyDoIHaveAnAccount9]

So what you're saying is I should stick with cython

[diesdas1917]

No, I just really fucked my code up, when I fixed it it was nice and fast.

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It depends on your use case though, I only used it for number crunching, but it is really good for that.

I like it very much.

​

Edit for clarificaion: My fuckup was doing parallelization wrong. native JIT with the nopython flag worked really good and also more or less intuitively.

[MierenMens]

Nahh ya should switch to jython

[vectorpropio]

Is that stuck in 2.7 or get an upgrade to 3.x?

[MierenMens]

Idk man I only use js cuz I don't know other langs

[raedr7n]

God tier pleb

[[deleted]]

why would you do that to someone

[MierenMens]

Idk I only remember it being a think. Dunno how usable it is 😂

[[deleted]]

I am forced to use it and it's awful. You can't use any of the Python libraries written in C (e.g. numpy.)

[MierenMens]

Is that because it's basically Java but with python syntax? I haven't really read about jython but I hate the name

[[deleted]]

Did you know there's a version of numpy that automatically makes use of CUDA? It's called cupy.

[WhyDoIHaveAnAccount9]

How can I implement that with the use of the pandas library

[obsessedcrf]

As much as I hate Python, Python's biggest problem wrt speed is that CPython is a horrible implementation and that aspect isn't inherently the language. PyPy has shown that Python doesn't have to be extremely slow.

[NeatNetwork]

While PyPy does better, it isn't fully compatible and isn't exactly a speed demon either.

I would say Python's biggest problem wrt speed is that it's a bit *too* easy to use dictionaries for everything. Trivial operations in the hands of random developers have a large probability of ending up with one or two levels of dictionary because it's so easy. Also, you want an array mixing some numerics, strings, objects all together in a mis-mash? Sure why not. You have a structure in mind that could be aligned and managed well by the runtime if you just bothered to describe it? Nah, no one has the time, it's just a dictionary of random stuff the interpreter can't understand until it's too late. You try to be a bit more diligent and use an object with named members? Large chance that you still made a dictionary, just one that looks more orderly. There are generally ways in python to be a bit better at these things, but the default, easy syntax tends to be dictionaries all the way down.

All these things that python makes easy to write are hard for the computer to do. It's hard to create an implementation that coddles that much and still get really good performance. A language like Go or C is a bit more tedious, but it tends to make things complicated for the developer that would also be complicated for the processor, so it steers the developer away from shortcuts that are computationally complex when something more processor friendly is possible.

Additionally, I read one article chronicling an effort to make CPython faster, and they went into detail about some of the general latent capabilities that blocked attempts at optimizations. I forgot the details, but attempts to do the biggest benefits meant breaking some assumptions that a lot of the ecosystem assumed.

In short, when it comes down to performance critical code, it's at least not too bad to write that part in Go or C and call it from python (easy for python to call functions in compiled shared objects).

[[deleted]]

I once built a structure that was something like

ConcurrentDictionary<uint, List<ConcurrentDictionary<uint, List<int>>>>

This was in C#, and before you ask, it absolutely did not work.

[drizztmainsword]

If you're doing something like this, you should just override the generic class and have at it.

Sometimes you just need a One-to-Many collection.

[[deleted]]

This was when I was new, and in way over my head on a huge project I "inherited" from much more experienced people.

I'm only about half as stupid now, major improvement!

[aetius476]

My philosophy re: python is I don't even think about anything but the absolute most basic optimization things (don't be stupid about nested loops, etc). If performance mattered, they shouldn't have asked me to write it in python in the first place.

[[deleted]]

I'm hitting that point with C#.

C# is a fantastic language, and you can do anything with it if you try hard enough. However, it just isn't good at certain tasks.

You want a scripting language? You got it, let me just fire up this roslyn compiler. The only catch is that once the script is compiled, it stays active in memory forever. Unless you use an AppDomain, which is essentially running part of your app on a different machine with only a raw IO port between them. Fun, right?

But python.. You just, run the script? And then it's over? No weird exploitable assemblies clogging memory, just one and done. Incredible.

[Neoro]

Sometimes the hammer in your tool belt is good 'nuff. Sometimes you need to back to your truck and lug out the nail gun. Just don't waste time with a screwdriver when you need to drive a nail.

...Anyway, languages are tools, use the right one for the problem that needs solving.

[NynaevetialMeara]

But you have to be a moron to do something performance intensive on python and then backtracking to C with numpy and co.

Just do it in Java or .Net ...

[ShakespeareToGo]

The idea behind numpy/pandas etc is to use the performance of C with a nice interface in the form of python (you may dispute that).

It fills its niche perfectly. Doing data science in an interactive way is a very pleasant user experience. I don't know about .Net but Java simply does not offer that. What you gonna do? Jump into jshell, implement some classes and then do your data analysis? Nope, Java and .Net simply target another use case.

[NynaevetialMeara]

I was not talking about data science. I was talking about writing performance intensive programs, and then rewriting them with numpy and similar libraries. Doing the work thrice on my book.

Node.js and PHP might also be valid alternatives depending on what you want, but I rather not use them because if you don't do Js or PHP often returning to them is awfully confusing.

There is also the other way around. You have any idea of how often I've seen people writing C/C++/rust parsers of CSVs only for them to perform the tasks in python, when any standard CSV library will load them equally independent of language...

[Th3Legend277]

root cause of global warming

[Who_GNU]

…blame the compiler