Building on the introductory Polars session, this masterclass dives deeper into advanced features that unlock even greater speed, efficiency, and expressiveness in your analytics workflows. You will explore powerful capabilities such as lazy computation, expression optimisation, complex joins, window functions, and integrating Polars into larger data pipelines.
This is a highly practical session focused on writing production-ready analytical code that scales to millions of rows with minimal overhead.
Content
- Master lazy computation: Learn how Polars optimises query plans, reduces memory usage, and delivers dramatic performance gains.
- Use advanced expressions: Apply window functions, conditional logic, pivot operations, and custom aggregations using Polars’ expression-based API.
- Build end-to-end pipelines: Construct fast, maintainable analytical workflows and integrate Polars with Python tools such as Arrow, SQL engines, and machine-learning libraries.
- Optimise performance: Understand profiling techniques and best practices to get the most out of Polars on large and complex datasets.
Target Audience
This masterclass is designed for analysts, data scientists, quants, and engineers who have used Pandas or Polars before and want to take the next step toward high-performance data engineering. Participants should have basic familiarity with Python dataframes; no advanced background is required.
