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News & links about Python programming.
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7 Drop-In Replacements to Instantly Speed Up Your Python Data Science Workflows

This article explains how to use drop-in replacements like NVIDIA cuDF, cuML, and cuGraph to dramatically speed up common Python data science workflows. It provides specific examples for accelerating popular libraries such as pandas, Polars, scikit-learn, and NetworkX on a GPU with minimal to no code changes.

https://developer.nvidia.com/blog/7-drop-in-replacements-to-instantly-speed-up-your-python-data-science-workflows/
Starting with pytest’s parametrize

The article explains how pytest's parametrize feature can simplify and reduce duplication in writing tests by allowing a single test function to run multiple cases with different inputs and expected outputs. It uses a clear step-by-step example with a simple function to demonstrate how parametrize automatically runs the test for multiple data sets, making tests easier to write, read, and...

https://nedbatchelder.com/blog/202508/starting_with_pytests_parametrize.html
Litestar is worth a look

James Bennett reflects that Litestar, formerly known as Starlite, is a standout async-first, type-hint-driven Python web framework. For the past 18 months, it has been his go-to choice for new projects at work, and he believes more developers should discover this under-the-radar gem.

https://www.b-list.org/weblog/2025/aug/06/litestar/
How JIT builds of CPython actually work

You don't have to be a compiler engineer to understand how your code runs in a JIT build of CPython.

https://savannah.dev/posts/how-your-code-runs-in-a-jit-build
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