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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