The main reasons I see for nim in scientific computing over python are: (a) you need performance for applications outside of numpy / pandas & you want to avoid

The main reasons I see for nim in scientific computing over python are: (a) you need performance for applications outside of numpy / pandas & you want to avoid writing a C extension to do so (nim is also actually a good choice for python extensions via nimpy - if you are still using python as the main language of your project/system) (a2) you want good concurrency (some would say python's async/await is good - but it feels too little too late to me) & you at least want the option of parallelism (like this article takes advantage of) (b) you want / need the powerful macro system for dsls / custom (compile-type-checkable) language extensions. There is a good future for nim in scientific computing (frankly, there is a good present) - but for it to be better, people need to be willing to forgo some of the ecosystem (when possible) and to help build that future. https://github.com/nim-lang/needed-libraries/issues/77 https://github.com/nim-lang/needed-libraries/issues/77