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Comment by mumblemumble | Hacker News Reader

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.

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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.

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