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tidypolars uses the polars package as a backend, which might be the fastest data frame manipulation library out there. (Faster even than R's data.table, which has been the king of speed for many years.)
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CodeRabbit
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I think having a basic understanding of pandas, given how broadly it's used, is beneficial. That being said, polars seems to be matching or beating data.table in performance, so I think it'd be very worth it to take it up. Wes McKinney, creator of pandas, has been quite vocal about architecture flaws of pandas -- which is why he's been working on the Arrow project. polars is based on Arrow, so in principle it's kinda like pandas 2.0 (adopting the changes that Wes proposed).
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Apache Arrow
Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics
I think having a basic understanding of pandas, given how broadly it's used, is beneficial. That being said, polars seems to be matching or beating data.table in performance, so I think it'd be very worth it to take it up. Wes McKinney, creator of pandas, has been quite vocal about architecture flaws of pandas -- which is why he's been working on the Arrow project. polars is based on Arrow, so in principle it's kinda like pandas 2.0 (adopting the changes that Wes proposed).
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What's cool about this (and /u/GoodAboutHood's other package tidytable) is that they adopt the widely used Tidyverse syntax for high-performance packages without sacrificing speed (and, in my opinion of dtplyr, making it too complicated).