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A tangent, from someone still doing most of their mangling the old fashioned way in sql used to extract stuff into old fashioned pandas df in a notebook, ... but I'm wondering and HN is a wonderful place to ask :)

How does polars sql context stack up against alternatives e.g. perhaps duckdb? If I'm in a notebook and I want to suck in and process a lot of data, which has the least boilerplate, the strongest support and the most efficiency (both RAM usage and speed)?



Here is a head-to-head comparison about efficiency: https://www.youtube.com/watch?v=wKH0-zs2g_U

"Strongest support" is probably Pandas, in that it is very widely used and easy to get help with. DuckDB lets you write SQL and is very fast.


That comparison is heavily outdated as at that benchmark we are IO bound on downloading.

Since then Polars has improved downloading speeds 20x with shipping a proper async runtime in the engine.




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