#067 - Polars - 01 | Faster Data Analysis in Engineering

Practical Steps for Handling Large Datasets More Efficiently

If you've followed my writing, you'll know that handling engineering data is a recurring theme. I often discuss strategies for dealing with the sheer volume and complexity of information our work generates – analysis outputs, site data, financials etc. I've mentioned Polars many times without really digging it!

There was a reason for that hesitation. While aware of Polars and its purported advantages, I prefer not to discuss these tools in depth until I've spent time using them – until I've "clocked up a few miles" and understood the practical nuances, the strengths, and the limitations through direct experience. I think we’re there with Polars.

This article serves as that explicit introduction to Polars. It outlines what the library is, why I've increasingly started using it, and provides a basic overview of how it works. My current workflow probably involves a roughly 50/50 split between Pandas and Polars. For many smaller, routine tasks, Pandas remains efficient due to familiarity and muscle memory. But as datasets grow, or as analysis requires chaining multiple complex operations together, performance often becomes a bottleneck. In those situations, the speed and memory efficiency of Polars make it the clear choice.

How Polars Works (The Structure)

Polars is another Python library designed to handle tabular data, but it is built differently. It achieves high performance through several key design choices:

1. Foundation in Rust: Polars is largely written in the Rust programming language. Rust allows for code that runs quickly and manages computer memory efficiently. This provides a base level of performance. Do you need to know Rust? No.

2. Parallel Operations: Polars automatically breaks down many calculations to run on multiple processor cores simultaneously. If a task can be divided, Polars attempts to do so, reducing the total time required.

3. Lazy Evaluation: This is perhaps the most significant difference from Pandas. When you write a sequence of Polars commands (like loading data, then filtering it, then calculating a new column), Polars doesn't necessarily execute each step immediately. Instead, it records the sequence of operations as an execution plan. Only when you explicitly ask for the final result does Polars look at the entire plan, optimize it to find efficiencies (like combining steps or avoiding the creation of temporary intermediate tables), and then execute it. This contrasts with Pandas, which typically performs each step as requested.

These features mean Polars can often process larger datasets much faster and using less memory than Pandas, especially when the analysis involves multiple chained operations.

Interacting with Data Using Polars