Polars vs Pandas (2025): Why Everyone Is Switching to Polars
Polars vs Pandas (2025): Why Everyone Is Switching to Polars For years, pandas was the default choice for working with tabular data in Python. But in 2025, a new player is taking over serious data workloads: Polars — a blazing-fast DataFrame library written in Rust. In many benchmarks, Polars is reported to be 5–10× faster on typical operations and can reach 10–100× speedups on some workloads, while also using much less memory compared to pandas. [1][2] For analysts and engineers working with large datasets, that’s a game-changer. What Is Pandas? Pandas is the most popular Python library for data analysis. It provides: DataFrame and Series data structures Easy CSV, Excel, SQL, JSON reading Powerful indexing, grouping, and joins Huge ecosystem, tutorials, and community support For small to medium datasets and exploratory analysis, pandas is still an excellent choice. But it starts struggling when: Data gets large (millions of rows) Operati...