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Comment by epistasis | Hacker News Reader

Have you examined siuba at all? It promises to be more similar to the R tidyverse, which IMHO has a much better API. And I personally prefer dplyr/tidyverse to Polars for exploratory analysis.

https://siuba.org

I have not yet used siuba, but would be interested in others' opinions. The activation energy to learn a new set of tools is so large that I rarely have the time to fully examine this space...

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I think the choice of using functions instead of classes + methods doesn't really fit well into Python. Either you need to do a huge amount of imports or use the awful `from siuba import *`. This feels like shoehorning the dplyr syntax into Python when method chaining would be more natural and would still retain the idea.

Also, having (already a while ago) looked at the implementation of the magic `_` object, it seemed like an awful hack that will serve only a part of use cases. Maybe someone can correct me if I'm wrong, but I get the impression you can do e.g. `summarize(x=_.x.mean())` but not `summarize(x=median(_.x))`. I'm guessing you don't get autocompletion in your editor or useful error messages and it can then get painful using this kind of a magic.

The lack of non standard evaluation still forces you to write `_.` so this might be a better Pandas but not a better tidyverse.

A pity their compares don’t have tidyverse or R’s data.table. I think R would look simpler but now it remains unclear.

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