polars
DataFrames.jl
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polars | DataFrames.jl | |
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144 | 9 | |
26,043 | 1,690 | |
6.1% | 1.1% | |
10.0 | 7.0 | |
3 days ago | 4 days ago | |
Rust | Julia | |
MIT License | 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.
polars
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Why Python's Integer Division Floors (2010)
This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.
I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).
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Polars
https://github.com/pola-rs/polars/releases/tag/py-0.19.0
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Stuff I Learned during Hanukkah of Data 2023
That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection:
- Polars 0.20 Released
- Segunda linguagem
- Polars: Dataframes powered by a multithreaded query engine, written in Rust
- Summing columns in remote Parquet files using DuckDB
- Polars 0.34 is released. (A query engine focussing on DataFrame front ends)
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?
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
Tables.jl - An interface for tables in Julia
modin - Modin: Scale your Pandas workflows by changing a single line of code
DataFramesMeta.jl - Metaprogramming tools for DataFrames
arrow-datafusion - Apache DataFusion SQL Query Engine
Plots.jl - Powerful convenience for Julia visualizations and data analysis
datatable - A Python package for manipulating 2-dimensional tabular data structures
MPI.jl - MPI wrappers for Julia
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
Curry.jl - Currying for Julia
db-benchmark - reproducible benchmark of database-like ops
ScikitLearn.jl - Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/