When you think about processing tabular data in Python, what library comes to mind? Pandas, I'd guess. But there are other libraries out there and Polars is one of the more exciting new ones. It's built in Rust, embraces parallelism, and can be 10-20x faster than Pandas out of the box.
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We have Polars' creator, Ritchie Vink here to give us a look at this exciting new data frame library.<br/>
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<strong>Links from the show</strong><br/>
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<div><b>Ritchie on Mastodon</b>: <a href="https://fosstodon.org/@ritchie46" target="_blank" rel="noopener">@ritchie46@fosstodon.org</a><br/>
<b>Ritchie on Twitter</b>: <a href="https://twitter.com/RitchieVink" target="_blank" rel="noopener">@RitchieVink</a><br/>
<b>Ritchie's website</b>: <a href="https://www.ritchievink.com" target="_blank" rel="noopener">ritchievink.com</a><br/>
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<b>Polars</b>: <a href="https://www.pola.rs" target="_blank" rel="noopener">pola.rs</a><br/>
<b>Apache Arrow</b>: <a href="https://arrow.apache.org" target="_blank" rel="noopener">arrow.apache.org</a><br/>
<b>Polars Benchmarks</b>: <a href="https://www.pola.rs/benchmarks.html" target="_blank" rel="noopener">pola.rs</a><br/>
<b>Coming from Pandas Guide</b>: <a href="https://pola-rs.github.io/polars-book/user-guide/coming_from_pandas.html" target="_blank" rel="noopener">github.io</a><br/>
<b>Watch this episode on YouTube</b>: <a href="https://www.youtube.com/watch?v=KdCcqWAsuRs" target="_blank" rel="noopener">youtube.com</a><br/>
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