Yet another take on piping in Julia. Even with multiple existing implementations of the piping concept in general, I didn’t find one that is convenient enough [for my usecases] while still remains general. Additionally, I became curious in how metaprogramming works, so developing DataPipes helped m…

Funny, this is already implemented in Transducers.jl (all the more reasons to use this awesome package) using Transducers using Underscores julia> data = 5:12 5:12 julia> @_ data |> Filter(_>10) |> Map(_^2) |> collect 2-element Vector{Int64}: 121 144 a…

Yes Transducers.jl is really cool for many reasons. But sometimes you just want to do some quick data processing without any extra dependencies, which is why I kind of wish we had versions of normal map() and filter() as above.

Out of my habit, I find it easier to write/read code where the functions are embedded (# 2) rather than the form # 1. But I like the ability to shorten the syntax. The first form I tried was # 5, which doesn’t work. Then I found the other shapes that give the expected result, but I’m not sure I un…

All these five variants work for me and give exactly the same results with DataPipes.jl 0.1.7. Could you please share the error you are getting?

[image] rocco_sprmnt21: But I like the ability to shorten the syntax. Maybe you could get rid of map and broadcast, for example (respecting the number of steps): @p 1:4 .|> _^2 |> filter(exp(_)>5)

ok. I have the same version (downloaded yesterday) and now I get the same results as you. I don’t know what to think. I apologize for the wrong report. I had initially tried with the expression @p filter(exp(_)>5, map(_^2,1:4) ) which I then corrected in form # 5 and since it seemed to me (perh…

[image] rocco_sprmnt21: Could you in this case use the nested form with the placeholder _1? The _1 placeholder has a very different semantics now, don’t think it is possible to combine them somehow. And explicit delimiters (like @p) are necessary anyway with the nested function style you use…

Thanks for taking the time. Maybe I’m still missing something to understand how @p works. If I did a brutal substitution of f (_) with x-> f (x), then again the naive form I used first would work. Even using the same variable name, since the scope is different filter(x->exp(x)>5, map(x->x^2,1:4…

Unfortunately, the naive replacement you suggest doesn’t really work well in general. The main issue is to determine function boundaries: what does a(b(c(_), _), d(e(f(_) + _))) get converted to? That’s why DataPipes.jl takes the approach of converting only top-level function arguments (containing _…

In the meantime, DataPipes.jl has got a significant new feature, and the placeholder style changed to a more convenient one. You can continue using the old syntax in 0.1.x versions, or switch to the 0.2 version with the updated placeholders. Future features will only appear in 0.2 and won’t be back…

[ANN] DataPipes.jl 0.3.0

Package Announcements

rafael.guerra December 13, 2021, 11:03pm 63

See possible explanation here.

Topic		Replies	Views
ANN: Underscores.jl: Placeholder syntax for closures Package Announcements data , syntax	49	4918	April 6, 2020
[RFC] PipelessPipes.jl (now Chain.jl) Package Announcements	61	5173	March 25, 2021
Data First Pipeline General Usage piping	5	831	September 7, 2023
A confusing problem of pipe General Usage piping	10	1152	May 20, 2021
Frustrated using DataFrames New to Julia dataframes , data_structures	97	11552	April 22, 2022

[ANN] DataPipes.jl 0.3.0

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