Ever been burned by a mysterious slowdown in your data pipeline? In this session, we'll reveal how a stealthy performance regression in the Polars DataFrame library was hunted down and squashed. Using git bisect, Bash scripting, and uv, we automated commit compilation and benchmarking across two repos to pinpoint a commit that degraded multi-file Parquet loading. This led to challenging assumptions and rethinking performance monitoring for the Python data science library Polars.

Jeroen Janssens and Thijs Nieuwdo join me to chat about all things Polars. We discuss the evolution of the Polars library, its advantages over pandas, and their journey of writing 'Python Polars: The Definitive Guide.'