Pandas is Too Slow: Wrangling Massive Tracking Data with Polars

What you'll build

• The same analysis in pandas and Polars, timed side by side
• A Polars pipeline that handles tracking data pandas chokes on
• A reusable Polars workflow for large sports datasets

What you'll learn

• Why Polars is faster than pandas and when the difference matters
• How to translate pandas habits into idiomatic Polars
• How lazy evaluation lets you process data larger than memory
• When to reach for Polars and when pandas is still fine

About Saiprasad

Sports data scientist with 4+ years in cricket analytics, founder of BallTrack Analyzer, turning complex sports data into clear, useful stories.