A four-part series on the Polars DataFrame library — from why it matters and how it works to practical examples and running Polars on Microsoft Fabric.
Adventures in Polars
Polars: Faster Pipelines, Simpler Infrastructure, Happier Engineers
We've migrated our own IP and several customers from Pandas and Spark to Polars. The benefits go beyond raw speed: faster test suites, lower platform costs, and an API developers actually enjoy using.
Under the Hood: What Makes Polars So Scalable and Fast?
Polars gets its speed from a strict type system, lazy evaluation, and automatic parallelism. Here's how each piece works under the hood.
Practical Polars: Code Examples for Everyday Data Tasks
Unlock Python Polars with this hands-on guide featuring practical code examples for data loading, cleaning, transformation, aggregation, and advanced operations that you can apply to your own data analysis projects.
Polars Workloads on Microsoft Fabric
Polars now ships inside Microsoft Fabric by default. Here's how to use it alongside Fabric's other analytics tools and what that means for your data workflows.