815: Polars: Faster DataFrame Ops, with Marco Gorelli
Super Data Science: ML & AI Podcast with Jon Krohn
Extend and Optimize with Polars
Polars is a lightweight data processing tool that doesn't require heavy dependencies, such as PyArrow, pandas, or NumPy. Its ability to extend functionality through custom Rust plugins allows for tailored solutions that integrate seamlessly with existing workflows. This flexibility enables users to add specific plugins or create their own, which can be distributed via PyPI for easy installation. Additionally, the efficiency of lazy execution in Polars allows for managing data constraints effectively, enabling queries to be run on massive datasets without exceeding memory limits by only reading necessary rows. This combination of extensibility and optimization makes Polars a powerful choice for data processing tasks in environments like AWS Lambda.
Remember Everything You Learn from Podcasts
