It's fast! The API is nice. But the documentation is not great. While using pandas I can just open the docs and find all functions and examples of how to use, etc. Polars felt really bare.
It's something I'd like to use in personal projects. But other data scientists would not accept using polars as it is today.
We hope to have such a clear API that once the expression language clicks, it is less needed to have the docs open constantly as it should feel natural.
I used it a month or two ago, so I don't remember specifics, but. The docs weren't unclear, they were bare compared to pandas tough. Once I found an example of how to do something I managed to do it. But a couple of times I opened a page and there was WIP message.
There's definitely a bit of unfairness in the comparison, as pandas as you say has been the standard for python for years. But that means I already know all the syntax and how the API works, so even just a method name or required params will get me a long way.
When I was fumbling around with polars I would have liked more examples of how to use each method/function. I mean, once the language clicks I wouldn't need it anymore. But at the start I really need that hand-holding. I'd have to reread other pages to remember simple syntax.
I used on an analysis that was taking long and it went way quicker.
And everyone uses it because it's what you do when your boss tells you "we're transforming the analytics team, you're all to become data scientists because everyone has data scientists now". You just grab whatever had the biggest mindshare on SO and in random yt tutorials. Can't blame them.
But hooo boy does pd get on my nerves.
Care to share what propriety stuff you were using?
Honestly I still feel like I'm missing some sort of larger story about the semantics of Pandas (like the "functions" explanation above), so if anyone knows of anything that made Pandas click, please let me know.
One big issue I see with Pandas is that it assumes that I want multidimensional keys represented via axes labels on some sort of grid (or hypercube). This is evident from the __getitem__ interface, which is frankly confusing. In reality, selection is the most important detail, and managing columns is just a nice-to-have. Pandas's MVP is the Series object, not DataFrame.
In article #1 in your link, "SettingWithCopy" is emblematic of the issue that happens when you allow mutability, so mutability delenda est. This is why FridgeSeal is confused below. One day I'll write a blog post about ways Pandas could be better, but in the meantime I will probably monkey patch a lot of the nonsense out.