cmssw VS Chain.jl

I don’t have much insight on the scientific computing landscape in general, but here’s one notable data point: I worked on the CMS experiment of LHC (Large Hadron Collider) for a while, which is one of the highest profile experiments in experimental physics. The majority of CMS code is C++, which you can check for yourself at https://github.com/cms-sw/cmssw (yes, much/most? of the code is open source). What I worked on specifically was prototyped in Python, then ported to C++ and plugged into the massive data processing pipeline where performance is critical due to the sheer amount of data. So I probably wouldn’t put C++ in parentheses.

The [Chain.jl package](https://github.com/jkrumbiegel/Chain.jl) is becoming idiomatic for these kind of pipelines.

If you do have tabular data in a dataframe you have a few options for data manipulation, the most popular packages are probably DataFramesMeta and Query, although in my opinion the best way to manipulate dataframes is with the functions built in to DataFrames.jl and using a package like Chain.jl or Pipe.jl to pipe the functions into each other like magrittr in R.

The Julia community built a better piping method than any other language has AFAIK: Chain.jl.

@chain and @match.

Have you ever tried Infiltrator.jl and Chain.jl?

You are right. However, sometimes well used is very useful, and readable. One suggestion, in Julia I suggest Chain.jl, because it allows intercalate easily the output for debugging:

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