JLD2.jl
DataFrames.jl
JLD2.jl | DataFrames.jl | |
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2 | 9 | |
525 | 1,696 | |
1.7% | 1.0% | |
8.1 | 7.0 | |
9 days ago | 18 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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.
JLD2.jl
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
First, you need to save the model from the notebook to a file. For this you can use JLD2.jl module. This module used to serialize Julia object to HDF5-compatible format (which is well known by Python data scientists) and save it to a file.
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Best format to save matrices to a text file? (R interop)
I didn't realize this was the Julia subreddit. HDF5 or multiple CSV files would be my suggestion. As a side note, check out the JLD2 package. It's a HDF5 compatible format where the package is written in pure Julia.
DataFrames.jl
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Julia's latency: Past, present and future
I don't think we've seen the final state of it though. v1.9 really gives people the proper tools for solving latency problems. Before, invalidations hurt a little, but it was also kind of a wash because LLVM code didn't precompile, so you could spend time setting up a Snoopprecompile and fix some invalidations and end up LLVM bound saving 1 second out of 20. But with package images it's almost always better to fix precompilation. The only thing hampering time much now is the `using` time went up, but as mentioned in the Reddit post there's a lot of ideas for what to do there. The other thing is package extensions, which cut down the amount of code to load. There's tons of PRs floating around the ecosystem turning things into extensions, and thus cutting down the overall code that is actually ran and loaded.
This means that in a few months, people will start to see some major tangible benefits from following the compilation improvement practices laid out here and https://sciml.ai/news/2022/09/21/compile_time/. I think then you'll have a lot more people start to take all of these new tools seriously and it will be standard to incorporate them into packages. Right now they are still kind of niche things for packages with known TTFX problems, but I think come v1.9 you'll see every major package use all of these methods.
> Different packages are affected differently
I think this is one of the pieces that's effected by this. I don't think "Julia has become optimised for running Plots.jl" is quite correct. Julia's compilation and runtime is much more optimized for well-inferred code, which Plots.jl is not. However, the compiler developers have been using Plots.jl as a test case for all of these new tools, and therefore its SnoopPrecompile and invalidations have gotten some dramatic improvements because those required ecosystem changes I mentioned are being done by the compiler team for this specific package. Plots.jl had things like precompilation snooping way back before there was even a package for it, the earliest I know of was around v1.0. Meanwhile, DataFrames.jl only setup its precompilation snooping 7 months ago (https://github.com/JuliaData/DataFrames.jl/pull/3182), which I would presume was just in time for the v1.8 mark on your plot and is one of the big reasons for having a sudden drop (which continues into v1.9 because of package images).
What I mean to say then is that, I think all packages will get the improvements we've seen from Plots.jl, but package authors will need to update their packages in order for that to happen. Some packages have already done this, many have not.
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IJulia: The Julia Notebook
IJulia also supports viewing and manipulating tables. To create a table, first install the DataFrames.jl package by running the following command in a new cell:
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
It were just a few percents of all possible manipulations that you can do with data using DataFrames.jl library. Read more about it in the documentation.
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Unleashing the Power of Julia: Top 5 Must-Have Packages
DataFrames.jl
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Automate the boring stuff with Julia?
DataFrames.jl and XLSX.jl for JSON, CSV, and XLSX files
- What would it take to recreate dplyr in Python?
- Dataframes.jl version 1.0: Tools for working with tabular data in Julia
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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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Polars (Rust DataFrame library) join algorithm fastest in db-benchmark
Looks like it's single threaded according to this open issue: https://github.com/JuliaData/DataFrames.jl/issues/2626
What are some alternatives?
julia - The Julia Programming Language
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
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.
Tables.jl - An interface for tables in Julia
Plots.jl - Powerful convenience for Julia visualizations and data analysis
DataFramesMeta.jl - Metaprogramming tools for DataFrames
Rocket.jl - Functional reactive programming extensions library for Julia
StatsWithJuliaBook
MPI.jl - MPI wrappers for Julia
PlotDocs.jl - Documentation for Plots.jl
Curry.jl - Currying for Julia