Russ Hyde compares Pandas and Polars:
In Data Science we are often working with rectangular data structures – databases, spreadsheets, data-frames. Within Python alone, there are multiple ways to work with this type of data, and your choice is constrained by data volume, storage, fluency and so on. For datasets that could readily be held in memory on a single computer, the standard Python tool for rectangling is Pandas, which became an open-source project in 2009. Many other tools now exist though. In particular, the Polars library has become extremely popular in Python over recent years. But when Pandas works, is well-supported, and is the standard tool in your team or your domain, and if you are primarily working with in-memory datasets, is there a value in learning a new data-wrangling tool? Of course there is.
Read on for a demonstration of fairly basic data operations and how they differ in Pandas vs Polars.