Python

Pandas has been the default Python data analysis library for over a decade. In 2026, it’s still everywhere — but it’s no longer the obvious choice. A new generation of libraries offers dramatically better performance, lower memory usage, and more intuitive APIs. This guide compares the major options and helps determine which one fits different use cases. The Contenders Library Maturity Written In Key Advantage Pandas 2.2 Mature C/Python Ecosystem, familiarity Polars 1.x Stable Rust Speed, memory efficiency DuckDB 1.x Stable C++ SQL interface, zero-copy Modin Stable Python Drop-in Pandas replacement Vaex Maintenance C++/Python Out-of-core processing DataFusion (Python) Growing Rust Apache Arrow native Performance: What the Benchmarks Show Rather than fabricating numbers, here’s what official and third-party benchmarks demonstrate: ...