Why Polars is Faster Than Pandas (10Million Row Study)

In modern data pipelines, performance bottlenecks rarely come from algorithms alone; they often stem from the tools we rely on every day. Python libraries like Pandas are the backbone of data processing, but newer alternatives like Polars are rapidly challenging that dominance with claims of significantly better performance. Pandas have been the default choice for years. But should it still be? During a recent internal experiment, we set out to evaluate how these two libraries perform under realistic workloads. While our production datasets are protected under strict privacy and non-disclosure agreements, the performance challenges we encountered are far from unique they are shared by many teams working with large-scale data. To make this study transparent and reproducible, I reconstructed a 10-million-row dataset that closely mirrors the structure and complexity of real-world infrastructure logs. Using this dataset, we benchmark Pandas and Polars across common data engineering tasks t