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News & links about Python programming.
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20 Pandas One-Liners That Can Save You Hours of Work

A curated set of 20 concise Pandas one-liners that leverage advanced features—like Arrow-backed dtypes, vectorized eval, and efficient group-by transforms—to optimize common data preprocessing, filtering, and aggregation tasks. These snippets are designed to streamline data analysis workflows on large datasets by reducing memory usage, speeding up computations, and minimizing boilerplate...

https://www.nb-data.com/p/20-pandas-one-liners-that-can-save
How local variables work in Python bytecode

The post explains how local variables are managed in Python bytecode: they’re stored in reserved slots at the bottom of each function’s stack frame, with the stack holding references to objects on the heap. By walking through a custom Python interpreter in Rust, the author illustrates how compiled bytecode uses indices (not names) to access these slots, demystifying the stack-based stora...

https://fromscratchcode.com/blog/how-local-variables-work-in-python-bytecode/
Surprisingly Fast AI-Generated Kernels We Didn’t Mean to Publish (Yet)

Stanford researchers show that AI-generated CUDA kernels—created without relying on standard libraries—can now match or even outperform expert-optimized PyTorch kernels on specific tasks, thanks to parallel search and synthetic data generation. Their approach demonstrates that combining strong reasoning with broad exploratory search yields rapid performance gains, highlighting a promisin...

https://crfm.stanford.edu/2025/05/28/fast-kernels.html
Should You Replace Every For Loop With Map and Filter?

Think map() and filter() are always better than for loops? Not so fast. This video walks you through four situations where functional code actually makes things worse—and explain why the classic for loop still deserves a place in your toolbox.

https://www.youtube.com/watch?v=ylzo04lU9Xs