Tarun Jain

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 using Deepset Haystack Python Framework. Furthermore we will have the demo running on Python Streamlit.

The session's major focus is on Python developers, Data engineers, Data scientists, and maintainers who work with tabular data. Note: Prior experience with Pandas is helpful, since the talk uses Pandas as the starting point before comparing it with Polars. A basic understanding of filtering, grouping, joining, and aggregation operations in Pandas should be enough to get the most out of this session. The major takeaway from the session will be:
- Introducing what Polars is useful for when working with tabular data, and deciding when Polars is a better fit for a specific data analytics workload.
- Compare eager execution in Pandas with lazy execution in Polars, especially for filtering, joins, aggregations, and file scans.