Bodo, a Python-based engine, has demonstrated superior performance compared to other contenders such as Daft and Polars when scaling Pandas workloads on multi-node clusters. Bodo's compiler optimizations and parallel execution capabilities enable it to deliver high-performance results without requiring significant changes to existing code or user-defined functions (UDFs). In contrast, Daft and Polars, although designed for scalability, introduce additional complexity and overhead, particularly when using their custom APIs. The study highlights the importance of ease of use in choosing a data processing engine, with Bodo preserving familiar Pandas APIs while providing aggressive performance gains. While other engines like Polars may offer advantages in specific scenarios, Bodo's unique approach makes it an ideal choice for easily scaling Pandas workloads without sacrificing performance or familiarity.