After completing this guided project, you will be able to do:

Polars is a high-performance DataFrame library built in Rust, engineered for speed, scalability, and efficient memory usage. With lazy evaluation and multi-threading at its core, Polars is ideal for handling large-scale datasets—making it a compelling alternative to traditional tools like Pandas. 

In this hands-on guided project, you'll learn how to use Polars to analyze real-world weather data through a progression of techniques, starting from beginner-friendly tasks like filtering rows, selecting columns, and basic aggregations. As you move forward, you'll dive into more advanced concepts such as creating conditional columns, handling missing values and outliers, and applying rolling window functions to observe temperature trends over time. You'll also explore powerful features like chained transformations using Polars’ expression API, time-based grouping, and ranking methods to uncover insights in the data.

In the latter half of the project, you'll master operations like joining multiple DataFrames, performing anti and outer joins, and horizontally concatenating datasets to enrich your analysis. The project wraps up with exercises and mini-challenges designed to reinforce your learning, making sure you gain both conceptual clarity and practical skills.

By the end, you'll be equipped to build fast, expressive, and scalable data pipelines using Polars—ready to apply them in real-world scenarios, whether you're working on analytics, reporting, or building production-grade systems.

Let your learners know what technology and skills they'll need prior to starting this guided project. Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save them the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.