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Comment by siproprio | Hacker News Reader

Now, all we need for crystal to succeed is numpy, scipy, something pandas-like, and of course matplotlib (not plots, matplotlib).

By the way, how are we on data-science friendly IDEs? Debugging? Automatic thorough documentation generator? Tooling in general? Is the time to first plot fast?

Where I work, we desperately need a fast python.

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Data science is a domain crystal could excel in. Having both an approachable syntax and the feedback that the compiler and type system provides makes it an excellent companion for that work.

There's some interesting things you can do with the type system too, like capturing dimensionality in the type: https://git.sr.ht/~kb/matrix/tree/main/item/src/matrix.cr

One missing item though is SIMD support: https://github.com/crystal-lang/crystal/issues/3057

Re docgen - that's built into the compiler: https://crystal-lang.org/reference/1.5/syntax_and_semantics/...

I've tried a few times now to do data science in a statically typed language, and I just haven't enjoyed the developer experience. In general I tend to think the advantages of dynamic typing are overblown, but this is one problem domain where it seems to be indispensable.

ML and data engineering is a different story, of course. Also, I wouldn't be surprised if something clever could be done with a structural type system.

What languages have you tried, and what in them did not work?

What’s wrong with casting variables to mutate their type?

Static typing is like salt, sprinkle a little bit and it enhances and gives flavor to your food. Too much of it and it completely ruins the dish.

Nicely said! Let me try... Types are like guard rails, you don't think you need them till you fly off the road.

It only "ruins the dish" if you don't want your dish to be correct.

Its either tasty or salty, never heard of a correct dish.

Why would you want "a little" static typing? What does that mean? I used to write python in vim and hated it, made tons of mistakes that were time consuming to find. Type hinting religioussly and using pylance in vscode sped me up a great deal. But I still make those mistakes sometimes, and they are still troublesome to find. Sometimes they pass unit tests because the types are off in the test too. I think a compiled strongly and statically typed language with excellent type inference is the best of both worlds, freedom with a safety net. The problem with interpreted or jit languages is that even with strong typing, inference won't save you until runtime. I'm enjoying julia, but finding type errors at runtime is a downside.

>... numpy, scipy, something pandas-like, and of course matplotlib... Where I work, we desperately need a fast python.

It sounds like Julia would be a better fit than Crystal?

Ironically they have similar problems: compile/start-up time (though crystal has working ahead of time compilation - AFAIK Julia developers are still working on speeding up "first run" in various ways).

At first I thought Julia could fill role, but it has many problems with correctness (bugs), proper documentation, tooling…

You would also need a Notebook equivalent REPL for fast iteration.

https://crystal-lang.org/2021/12/29/crystal-i.html

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