Python, to me, is the all-around B+ language. It has good (but not great) performance, language semantics, runtime technical detail. As history showed us, being

Python, to me, is the all-around B+ language. It has good (but not great) performance, language semantics, runtime technical detail. As history showed us, being good at a lot of things enabled the growth of a large community (without corporate sponsorship) and an immense library ecosystem. So "all-around B+" isn't a knock against it. If anything, it's to be admired. Unfortunately, this means that most data science findings, when transmuted into permanent production programs, don't stay in Python. Often Java or C++ are used instead. Personally, that's why I think Clojure's got a real shot at being "the language of choice for data science" in 2023. It has the power of Lisp, it makes a lot of data-frame manipulations really easy, and because it sits on top of the JVM, it can be "productionized" pretty easily (you might have to write a couple functions in Java, for performance).