Chain.jl

https://github.com/jkrumbiegel/Chain.jl would probably work there, but you're not the only one who would like to see this in the language/stdlib itself.

> and we're stuck with an inferior Lisp/ML, especially in the scientific sector. You will love Julia. Here are some links: https://julialang.org/blog/2012/02/why-we-created-julia/ Julia: Dynamism and Performance Reconciled by Design (https://dl.acm.org/doi/pdf/10.1145/3276490) <= This is great and surprisingly approachable. https://opensourc.es/blog/basics-multiple-dispatch/ And when you start finding things that you miss, Julia and the community got you with excellent Metaprogramming support. https://github.com/thautwarm/MLStyle.jl https://github.com/MikeInnes/Lazy.jl https://github.com/jkrumbiegel/Chain.jl

I have created one of the recent competitors in this space [1], and I think it's not so bad, as long as the macros are reasonably simple to understand. If you look in the readme of my project, the four syntax examples actually look quite similar so I don't anticipate they'd cause a lot of confusion. When I got annoyed in my own work that some data wrangling syntax was repetitive I was just really glad that I could easily build my optimal solution and didn't have to just accept that there's one suboptimal (for me) way. In Python and R, if you like what they offer that's good, if not - not so good. Part of the problem comes from Julia not being geared towards DataFrames like R is, but I gladly trade a bit of convenience in one domain against a lot of expressive freedom with very clean rules that apply everywhere. For example, I think it's q

I also like pipe syntax and I've found there is nice support for it in Julia. There are some nice packages to improve it over base [1]. Have you checked queryverse [2]? [1] https://github.com/jkrumbiegel/Chain.jl [2] https://www.queryverse.org