Comment by data-ottawa

Pandas deserves a ton of respect in my opinion. I built my career on knowing it well and using it daily for a decade, so I’m biased.

Pandas created the modern Python data stack when there was not really any alternatives (except R and closed source). The original split-apply-combine paradigm was well thought out, simple, and effective, and the built in tools to read pretty much anything (including all of your awful csv files and excel tables) and deal with timestamps easily made it fit into tons of workflows. It pioneered a lot, and basically still serves as the foundation and common format for the industry.

I always recommend every member of my teams read Modern Pandas by Tom Augspurger when they start, as it covers all the modern concepts you need to get data work done fast and with high quality. The concepts carry over to polars.

And I have to thank the pandas team for being a very open and collaborative bunch. They’re humble and smart people, and every PR or issue I’ve interacted with them on has been great.

Polars is undeniably great software, it’s my standard tool today. But they did benefit from the failures and hard edges of pandas, pyspark, dask, the tidyverse, and xarray. It’s an advantage pandas didn’t have, and they still pay for.

I’m not trying to take away from polars at all. It’s damn fast — the benchmarks are hard to beat. I’ve been working on my own library and basically every optimization I can think of is already implemented in polars.

I do have a concern with their VC funding/commercialization with cloud. The core library is MIT licensed, but knowing they’ll always have this feauture wall when you want to scale is not ideal. I think it limits the future of the library a lot, and I think long term someone will fill that niche and the users will leave.