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Comment by vegabook | Hacker News Reader

Interchangeable cookie-cutter coder availability is Finance's number one priority. Definitely no room for critical pricers or infra written in languages that they can't frictionlessly slot someone else into as people leave. So python.

Also, arguably, Julia, while fantastic, just didn't do that much that Python didn't do already. It's main argument outside of tidier ergonomics was basically "speed without leaving Julia" but with Numpy and Pandas being essentially stdlib, that just wasn't a very powerful argument. Julia was basically too incremental to be worth switching to. It seems to have found its niche elsewhere though with the optimization people?

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One of the main things I like about Julia is that all the libraries are just built on their core Array type. In Python, there’s torch tensors, numpy arrays, and I think Jax has their own array type too. Also, Julia has a really beautiful distributed computing paradigm, whereas python needs to use libraries like Ray, which have their own quirks and documentation and community

General scientific computing is pretty good across the Julia ecosystem, from optimization, to ODE and now PDE solver libraries, to various statistics and inference packages, etc. It lacks the deep NN tooling or breadth of ML libraries of Python, and nothing matches R for breadth of stats libraries, but for most other scientific computing it is really great at this point.

I am by no means an expert, but I used Flux.jl for a convolutional neural net in electromagnetics for my latest paper and it was such a breath of fresh air compared to Python and PyTorch. (I'm an EE and not great at programming, so I found a lot of frustration in PyTorch). Even though the Keras library in Python is pretty nice, even then I got myself into some odd pickles when trying to do some custom layers which used FFT processing as it relates to gradient computation. Things are much smoother in Julia, and that doesn't even count how much easier the Plots library is! I'm ashamed to admit that I have no idea how to manipulate the figures and axes in Matplotlib without extensive googling.

I am really waiting for the day Julia ML ecosystem improves and I can jump ship to it.

Scientific/Numeric/Data-Python is essentially a DSL around C-API which creates friction (try for example to map a custom function over a Pandas column). Whereas in Julia, it's just Julia. It's liberating to just extend and use a library written in the same language. It leads to surprising synergies.

numpy is only fast if the computation does not escape it. There are plenty of cases where execution ping-pongs (if that's a verb) between python and the C(++) wrapper numpy actually is. Then everything becomes quite slow.

Anyway, I see data scientists and statisticians (at least 100% of the ones I know) completely ignoring Julia, just because they only have been exposed to Python and R in their education. The quality of the programming language/ecosystem seems to be irrelevant.

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