tidypolars VS modin

I recommend trying tidypolars

There’s a tidypolars package that appears to be well-maintained https://github.com/markfairbanks/tidypolars

tidypolars is one I’ve seen. Still very new, but it’s built on top of polars (which is a little more like dplyr to begin with), so it’s much faster than pandas.

If I misunderstood your question - feel free to open a discussion with a small code example and we can talk through how you can do it in tidypolars.

Try out tidypolars. It's really close to tidyverse syntax and it's a lot faster than pandas as well

The interesting thing about Polars is that it does not try to be a drop-in replacement to pandas, like Dask, cuDF, or Modin, and instead has its own expressive API. Despite being a young project, it quickly got popular thanks to its easy installation process and its “lightning fast” performance.

Yeah, tried Polars a couple of times: the API seems worse than Pandas to me too. eg the decision only to support autoincrementing integer indexes seems like it would make debugging "hmmm, that answer is wrong, what exactly did I select?" bugs much more annoying. Polars docs write "blazingly fast" all over them but I doubt that is a compelling point for people using single-node dataframe libraries. It isn't for me.

Modin (https://github.com/modin-project/modin) seems more promising at this point, particularly since a migration path for standing Pandas code is highly desirable.

If anyone wants a faster version of pandas it’s not hard to find, modin for example uses multiple cores to speed it up, so if you have 4 cores it’s about 4 times faster than pandas, and has the same API as pandas.

Modin should fit. It implements Pandas APIs with e.g. Ray as backend. https://github.com/modin-project/modin

If you really want speed you should try modin.pandas which makes pandas multi-threaded.

I am guessing XGB is fairly optimised as it is. If you would want to use the sklearn libraries with pandas, look into Modin

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