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The blog post doesn't really make a comparison between DuckDB and data frame libraries. It mentions that the DuckDB Python bindings can interoperate with Pandas, but it doesn't really explain why you would use DuckDB instead of Pandas, or Polars (which is both faster and more portable than Pandas).

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Don't Polars and Pandas both require your entire data to fit in memory?

Pandas doesn't. Polars I think has some lazy-loading capability, but it's not the default mode of operation and I don't think it supports all features. If DuckDB doesn't, then that's a big advantage.

I think you mean that Pandas does require your entire data to fit in memory? https://pandas.pydata.org/docs/user_guide/scale.html

Yes, thank you, that was a typo.

Apparently duckdb paired with arrow lets you work performatively on bigger than memory parquet data (I haven't tried this though)

Arrow seems to be the common denominator for larger than ram. Polars is completely built on arrow, so the same advantages should apply.

https://github.com/pola-rs/polars#handles-larger-than-ram-da...

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