DataFramesMeta.jl
DaemonMode.jl
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| DataFramesMeta.jl | DaemonMode.jl | |
|---|---|---|
| 4 | 22 | |
| 472 | 269 | |
| 3.2% | - | |
| 6.7 | 4.7 | |
| 11 days ago | 4 months ago | |
| Julia | Julia | |
| GNU General Public License v3.0 or later | MIT License |
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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.
DataFramesMeta.jl
- Pandas vs. Julia – cheat sheet and comparison
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Why not Julia?
A package: https://github.com/JuliaData/DataFramesMeta.jl
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Is there tidyverse/dplyr for Julia?
I'd also heartily recommend DataFramesMeta which provides really nice macros for manipulating dataframes.
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[S] Among R, Python, SQL, and SAS, which language(s) do you prefer to perform data manipulation and merge datasets?
I do get the feeling though that Python people are considered “more sophisticated” as programmers than R. But I think Julia is gaining traction now and it can handle general programming tasks better than R can, while still remaining pretty similar so its worth learning too. It has DataFramesMeta.jl: https://github.com/JuliaData/DataFramesMeta.jl. Works like dplyr.
DaemonMode.jl
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Potential of the Julia programming language for high energy physics computing
Thats for an entry point, you can search `Base.@main` to see a little summary of it. Later it will be able to be callable with `juliax` and `juliac` i.e. `~juliax test.jl` in shell.
DynamicalSystems looks like a heavy project. I don't think you can do much more on your own. There have been recent features in 1.10 that lets you just use the portion you need (just a weak dependency), and there is precompiletools.jl but these are on your side.
You can also look into https://github.com/dmolina/DaemonMode.jl for running a Julia process in the background and do your stuff in the shell without startup time until the standalone binaries are there.
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Julia 1.9.0 lives up to its promise
> If I were to use e.g. Rust with polars, load time would be virtually none.
Because you're compiling...
And if you need to do the same in Julia, you should also pre-compile or some other method like https://github.com/dmolina/DaemonMode.jl (their demo shows loading a database, with subsequent loads after the first one taking roughly ~0.2% of the first)
- Administrative Scripting with Julia
- GNU Octave 8.1
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Ask HN: Why is Julia so underrated?
Well, not nicely certainly, but:
https://github.com/dmolina/DaemonMode.jl
> portable
Neither is python - it just relies on universal availability. Over time…
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Is Julia suitable today as a scripting language?
You can get around a lot of these problems with DaemonMode.jl though.
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Julia performance, startup.jl, and sysimages
You might want DaemonMode.jl
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Can I execute code in Julia REPL if I'm connected to a remote server?
https://github.com/dmolina/DaemonMode.jl can possibly help in the future. Leaving it here so that people know this is planned.
- Ask HN: Why hasn't the Deep Learning community embraced Julia yet?
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Compile for faster execution?
If you strongly prefer to run scripts though, then you can use the package https://github.com/dmolina/DaemonMode.jl in order to re-use a Julia session between multiple scripts, saving you recompilation time.
What are some alternatives?
db-benchmark - reproducible benchmark of database-like ops
julia - The Julia Programming Language
DataFrames.jl - In-memory tabular data in Julia
Makie.jl - Interactive data visualizations and plotting in Julia
siuba - Python library for using dplyr like syntax with pandas and SQL
HTTP.jl - HTTP for Julia
TwoBasedIndexing.jl - Two-based indexing
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia
julia-numpy-fortran-test - Comparing Julia vs Numpy vs Fortran for performance and code simplicity