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I dont know about F# and ocaml, but haskell's numerical libraries really pale compared to numpy.



It's language vs libraries. If you have a library that has a function

    get_the_shit_done_quick ()
than you don't care much about the language.

When you don't have such function, you need an expressive language to write it (and a bulk of python libs are not written in python, tho mostly for the performance reasons).

So it's all about finding a sweet spot between fancy libraries which do the shit for you, and fancy language, which let you to express things, absent in libraries.

This sweet spot differs from domain to domain, from user to user. Even in numerical stuff someone could have a requirement for a better language, although this domain is indeed to well defined to have enough fancy libraries.


Language vs libraries isn't just about an expressive language to build in when you don't have a library. The likelihood of a library's availability also depends on the barrier to entry. An amazing language that isn't usable by biologists won't have many libraries that solve biologist's problems.

To your original point of being "surprised at how many data scientists and quants are ignoring more mathematically principled languages like F#, OCaml and Haskell," I'd much rather use one of those languages, but I'd have to build the foundations myself. Today, they aren't the right tool for the job. They don't have the libraries I need, which means I don't build further libraries for them, making other people less likely to build on them, so they aren't the right tool for the job tomorrow either. I'd say it's a network effects thing primarily.




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