Pandas works until you hit memory limits, slow joins, or null coercion bugs. Polars fixes most of these by design: columnar memory layout, strict types, lazy evaluation, and real parallelism.

We will start with familiar Pandas style analytics tasks, then showcase selected parts using Polars to show where lazy execution, query optimization, and memory-efficient execution become useful. The focus is not on replacing Pandas everywhere, but on recognizing the point where Polars gives clearer, faster, or more maintainable workflows.

From there, we look at sandboxed execution: running Polars in an isolated environment to safely execute dynamically generated transformation code that can also be used as a tool for those building in Agents for Data Analytics and Visualization Agentic workflow.