CSV.jl
DaemonMode.jl
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CSV.jl | DaemonMode.jl | |
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5 | 22 | |
447 | 269 | |
3.4% | - | |
6.2 | 4.7 | |
20 days ago | 4 months ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | MIT License |
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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.
CSV.jl
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Manifest.toml vs Project.toml in Julia
If you’ve used other package managers before, you may be wondering where the package versions are stored. While the Project.toml stores the IDs of the project’s direct dependencies, the Manifest.tomltracks the entire dependency tree, including both the direct and indirect dependencies and the versions of each. Because of this, the Manifest.tomlis always a larger file. For example, after adding just the CSV package to a fresh project, my Manifest.toml is already 200 lines long!
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I'm trying to install ClimateTools.jl, and failing. The problem appears to track back to CSV.jl. Can anyone please help? Thanks1
This thread seems to have different suggestions to what to do https://github.com/JuliaData/CSV.jl/issues/981 like ] add [email protected] ] pin Parsers
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Teaching Python
Julia also has the CSV.jl library for reading/writing csv files, the DataFrames.jl library for manipulating data like pandas, and Images.jl for image processing/analysis. However, since Julia is so much newer than Python, the Julia libraries are almost never as feature rich as their Python counterparts.
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CSV.File read extremely slow
Is this CSV.jl package? I think it shouldn't take that long. What Julia version is yours?
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.