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You may wish to try https://github.com/pola-rs/polars, which I believe is a Pandas-like library in Rust.
I recently started learning rust and decided to make a python library with PyO3 and NDArray as a first project. With the name Panther, the library was supposed to be an implementation of stock technical indicators (EMA, SMA, RSI, Ect). I added a few functions, and decided to do some speed tests with the pandas way of calculating these indicators. I was shocked to see that my code was about 9x faster on average than pandas calculations. I know this is expected when using a low level language like rust in python, but I'm amazed none the less. Especially as someone new to rust, the fact I could get these "advertised" results with rust in python without having to do crazy optimizations is crazy to me. Plus, something about writing low-level code and getting these results in python is very satisfying. The best part though? The process to get these results wasn't even hard! The cargo packages I used had great documentation and the compiler?! Actually helpful! With reference material on errors too! Officially done geeking out about Rust haha but love this language and love this community. Hoping to get more involved with OS stuff. What project is everyone working on? Anything cool?
I'm working on strop. It's code generation by random mutation. Evolving assembly languages basically. It has a while to go before it's useful, but it's going okay. And one reason for that is there's high-quality, open source, third party crates I can make use of.
Hopefully the last hand on https://github.com/lenscas/tealr for a new release. Mostly going over the documentation. Laying less emphasis on teal and more on "Hey, using this you can express a more typesafe api to lua and can actually easily document it.".
As for your experience with Rust: Same. The API that Rust uses works GREAT, the error messages are just so much better than anything else I've worked with. And the fact that Rust settled on 1 tool for documentation thus allowing docs.rs to exist is probably just the best thing that can happen to a language.
Similarly, also getting https://github.com/lenscas/tealr_doc_gen ready for a release. Need it to report errors nicer, a way to set the root folder to be used and making sure there are no panics left.
There is also https://github.com/lenscas/tealsql which is used as kind of showcase project for tealr and tealr_doc_gen. While at the same time (hopefully) filling a pain point in the lua/teal eco system.
If I'm understanding this documentation correctly then you may be able to pass the numpy array directly with func(df['col'].to_numpy) which may save some conversion.