Chain.jl
A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality. (by jkrumbiegel)
PyCall.jl
Package to call Python functions from the Julia language (by JuliaPy)
| Chain.jl | PyCall.jl | |
|---|---|---|
| 8 | 28 | |
| 348 | 1,438 | |
| - | 0.3% | |
| 4.2 | 6.1 | |
| 2 months ago | about 2 months ago | |
| Julia | Julia | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Chain.jl
Posts with mentions or reviews of Chain.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-07-22.
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Pains of Julia compared to python
The [Chain.jl package](https://github.com/jkrumbiegel/Chain.jl) is becoming idiomatic for these kind of pipelines.
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Transition from R Tidyverse to Julia (VS Code)
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.
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The (updated) history of the pipe operator in R
The Julia community built a better piping method than any other language has AFAIK: Chain.jl.
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What are some of your favourite macros?
@chain and @match.
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Why is piping so well-accepted in the R community compared to those in Julia and Python?
Have you ever tried Infiltrator.jl and Chain.jl?
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https://np.reddit.com/r/Julia/comments/nnu6if/julia_object_oriented_programming_with_dot/h0anaru/
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:
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Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
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
PyCall.jl
Posts with mentions or reviews of PyCall.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-06.
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I just started into Julia for ML
For point 3 you can use https://github.com/cjdoris/PythonCall.jl or https://github.com/JuliaPy/PyCall.jl (and their respective Python sister packages).
- The Mojo Programming Language: A Python Superset Drawing from Rust's Strengths
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Calling Chapel, Carbon, and zig code in Julia
PyCall.jl is really handy. Are there any similar projects for calling Chapel code, or Carbon/zig?
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Am I dumb in thinking I can use Rust as a Fast Python and leave it at that?
Julia and Python interop should not be a problem at all. Actually Julia has one of the best interops I’ve ever seen, so much that swift copied it. https://github.com/JuliaPy/PyCall.jl
- Which tools do you use for python + Data Science?
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I don't want to abandon Rust for Julia
One small note, julia also has great python interop via PyCall.jl
- Faster Python calculations with Numba: 2 lines of code, 13× speed-up
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Interoperability in Julia
It is possible to call Python from Julia using PyCall. Then to install PyCall, run the command in the Julia REPL.
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Why is Python so used in the machine learning?
That said, you can run python modules in Julia. So you can just export your code as a module and then use it in Julia via the PyCall package. short description here github here <— you’d just add the pacakge via the really nice package manager built into julia, but for link for more detailed documentation
- Use rust code in Python with pyo3
What are some alternatives?
When comparing Chain.jl and PyCall.jl you can also consider the following projects:
Pipe.jl - An enhancement to julia piping syntax
py2many - Transpiler of Python to many other languages
Genie.jl - 🧞The highly productive Julia web framework
Revise.jl - Automatically update function definitions in a running Julia session
julia - The Julia Programming Language
JLD2.jl - HDF5-compatible file format in pure Julia
PaddedViews.jl - Add virtual padding to the edges of an array
are-we-fast-yet - Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays
Infiltrator.jl - No-overhead breakpoints in Julia
fast-ruby - :dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.