[sanderjd]

I'm not the person you replied to, but I have experience with all of these. My background is computer science / software engineering, incorporating data analysis tools a few years into my career, rather than starting with a data analysis focus and figuring out tools to help me with that. In my experience, this seems to lead to different conclusions than the other way around.

tldr: Julia is my favorite.

I could never click with R. It is true that data.table and dplyr and ggplot are well done and I think we owe a debt of gratitude to the community that created them. But the language itself is ... not good. But that's just, like, my opinion!

Pandas I also have really never clicked with. But I like python a lot more than R, and pandas basically works. For what it's worth, the polars api style is more my thing. But most of the data scientists I work with prefer the pandas style, :shrug:.

But I really like this part of Julia. It feels more "native" to Julia than pandas does to python. More like data.table in R, but embedded in a, IMO, even better language than python. The only issue is that Julia itself remains immature in a number of ways and who knows whether it will ever overcome that. But I hope it does!

[nerdponx]

I sympathize with anyone who doesn't like R. Even as a statistics/math DSL it's really wonky.

But it's a lot more fun when you realize that it's an homoiconic array language with true lazily-evaluated F-exprs (not Rebol/Tcl strings).

[sanderjd]

I realized that (not in so many words...) pretty quickly and do not like it at all :)