To me, Polars feels like almost exactly how I would want to redesign Pandas interfaces for small - medium sized data processing, given my previous experience with Pandas and PySpark. Throw out all the custom multi index nonsense, throw out numpy and handle types properly, memory map arrow, focus on method chaining interface, do standard stuff like groupby and window functions in the standard way, and implement all the query optimizations under the hood that we know make stuff way faster.

To be fair, Polars has the benefit of hindsight and designing their interfaces and syntax from scratch. The poor choices in Pandas were made long ago, and its adoption and evolution into the most popular dataframe library for python feels like mostly about timing the market than having the best software product.