polars
maturin
polars | maturin | |
---|---|---|
144 | 37 | |
26,378 | 3,275 | |
3.4% | 3.1% | |
10.0 | 9.4 | |
3 days ago | 4 days ago | |
Rust | Rust | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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)
maturin
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In Rust for Python: A Match from Heaven
This story unfolds as a captivating journey where the agile Flounder, representing the Python programming language, navigates the vast seas of coding under the wise guidance of Sebastian, symbolizing Rust. Central to their adventure are three powerful tridents: cargo, PyO3, and maturin.
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Feedback from calling Rust from Python
-- Maturin on GitHub
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Some Reasons to Avoid Cython
My new favorite way to write very fast libraries for Python is to just use Rust and Maturin:
https://github.com/PyO3/maturin
It basically automates everything for you. If you use it with Github actions, it will compile wheels for you on each release for every platform and python version you want, and even upload them to PyPi (pip) for you. Everything feels very modern and well thought out. People really care about good tooling in the Rust world.
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Which programming language to focus on for my PhD journey in bioinformatics?
Python first, you will be able to experiment quickly with the notebooks. Then maybe write (or rewrite) some modules in Rust that you can expose as python modules, with py03 and maturin. Feel free to publish useful packages on both crates.io and pypi.org, so you can contribute to Python and Rust ecosystems.
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python to rust migration
Now if you really want to use Rust, you can rewrite only the part that are slowing down your consumer. It's easy by using Py03 and maturin. Maybe also rayon to parallelize.
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Ask HN: Is it worth it for me to learn Go or Rust as a Data Engineer?
It's relatively easy to extend Python with project like Py03[0] and Maturin[1]. Polars[2] is the perfect example of that.
It's not easy to push coworkers/companies to use an unfamiliar language. Rust isn't fast to learn. You need very good arguments and a good usecase to make it works.
I doubt that learning Rust will help you more that learning more about the data engineers tools, so this isn't really "worth" your time.
[0] -- https://pyo3.rs/v0.18.3/
[1] -- https://github.com/PyO3/maturin
[2] -- https://www.pola.rs/
- Rust CLI app installable via PIP?
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Blog Post: Making Python 100x faster with less than 100 lines of Rust
In this case, PyO3/maturin does all the setup and getting the module into Python. They also have docs going into a lot more depth on this.
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Is Rust faster than Python out of the box
Lastly if you're willing to introduce Rust, I'd consider a gradual approach using native libraries built in rust with PYO3. Check the maturin guide that helps you to streamline the build process of native libraries : https://github.com/PyO3/maturin . From there you could try to find hotspots in your python app and replace those with a native implementation.
- sccache now supports GHA as backend
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 🚀
Poetry - Python packaging and dependency management made easy
modin - Modin: Scale your Pandas workflows by changing a single line of code
setuptools-rust - Setuptools plugin for Rust support
datafusion - Apache DataFusion SQL Query Engine
termux-packaging - Termux packaging tools.
DataFrames.jl - In-memory tabular data in Julia
PyOxidizer - A modern Python application packaging and distribution tool
datatable - A Python package for manipulating 2-dimensional tabular data structures
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
pybind11 - Seamless operability between C++11 and Python