If I want to wrangle, explore, or visualise data I’ll always reach for R.
If I want to build ML/DL models or work with LLM’s I will usually reach for Python.
Often in the same document - nowadays this is very easy with Quarto.
Julia allows embedding both R and Python code, and has some very nice tools for drilling down into datasets:
It is the first language I've seen in decades that reduces entire paradigms into single character syntax, often outperforming both C and Numpy in many cases. =3
Not a "smear", but rather a well known limitation of the language. Perhaps your environment context works differently than mine.
It is bizarre people get emotionally invested in something so trivial and mundane. Julia is at v1.12.2 so YMMV, but Queryverse is a lot of fun =3
https://github.com/python/cpython/issues
Griefers ranting about years old _closed_ tickets on v1.0.5 versions on a blog as some sort of proof of lameness... is a poorly structured argument. Julia includes regression testing features built into even its plotting library output, and thus issues usually stay resolved due to pedantic reproducibility. Also, running sanity-checks in any llvm language code is usually wise.
Best of luck =3
Languages like Julia trivially handle conditional parallelism much more cleanly with the broadcast operator, and transparent remote host process instancing over ssh (still needs a lot of work to reach OTP like cluster functionality.)
Much like Go, library resources ported into the native language quietly moves devs away from the same polyglot issues that hit Python.
Best of luck. =3