Not only that, but the benchmarks ignore the cost of translating back and forth between pandas format. By the time you've done a comprehensive benchmark, you'll realize that pandas is actually pretty well optimised and the only opportunity for speedup is writing specialised functions for a narrow set of use cases. This library is a small box of such specialised functions. It will never be able to compete with pandas in general.

cuDF is a more plausible candidate for replacing pandas in performance-critical scenarios, and even cuDF explicitly aims to supplement pandas rather than replace it.