This article compares the performance of Pandas and Polars, two Python libraries for data analysis, by running benchmarks on a synthetic 6-million-row dataset. While both libraries offer similar functionality for filtering, arithmetic, and string manipulation, Polars significantly outperforms Pandas in native operations, delivering up to 99% faster processing times. However, for custom Python functions or compatibility with third-party tools, Pandas still holds its ground. The piece concludes with practical guidelines on when to use each library based on speed, syntax complexity, and interoperability.