Polars is an immense project, and I hope it continues to gain traction. But there's lots more factors than just speed.
The main one in my team is ubiquity- i.e. lots of people know pandas, who might not be traditional "developers". I.e. data scientists, data analysts etc. Having a data scientist put together some code, it gets optimized by an engineer, and they can talk back and forth about the same code is a massive benefit.
Shifting to polars (and keeping that ability to collaborate) would require not just training the engineers to use a new framework, but all the analysts, data scientists etc that they are adjescant to. That's a huge business cost, and in a lot of cases it might be worth it. But I wouldn't describe it as "getting 95% speed increase for free".
While that's fair, it's fairly easy to fit it in only the most intensive operations and then seamlessly convert back to a pandas data frame.
I understand why you wouldn’t do this on an organizational level for production workflows, but for personal workflows in my opinion, it’s a no-brainer to incrementally learn and adopt it.
The main one in my team is ubiquity- i.e. lots of people know pandas, who might not be traditional "developers". I.e. data scientists, data analysts etc. Having a data scientist put together some code, it gets optimized by an engineer, and they can talk back and forth about the same code is a massive benefit.
Shifting to polars (and keeping that ability to collaborate) would require not just training the engineers to use a new framework, but all the analysts, data scientists etc that they are adjescant to. That's a huge business cost, and in a lot of cases it might be worth it. But I wouldn't describe it as "getting 95% speed increase for free".