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The easiest way to speed up Python with Rust

When NumPy is too slow

Python 3.7: 30% of PyPI downloads, end of life in 3 weeks

Understanding CPUs can help speed up Numba and NumPy code

Choosing a good file format for Pandas

“Externally managed environments”: when PEP 668 breaks pip

Polars for initial data analysis, Polars for production

Staying secure by breaking Docker caching

Speeding up text processing in Python (is hard)

Python’s Multiprocessing Performance Problem

Don’t bother trying to estimate Pandas memory usage

The problem with float32: you only get 16M values

Float64 to float32: Saving memory without losing precision