Although I find pandas a bit aggravating in many ways, for myself and my equally idiotic laboratory scientist pals, seems that it is the default way you might interface with other libraries like SciPy (i.e. they expect things as NumPy arrays or pandas dataframes). Is this a real issue or will most things happily accept a polars dataframe? We don’t work with such large datasets that speed is likely a huge concern tbh.

finally long time coming

cant wait to upgrade my Quant trading bot

Spelling error in first paragraph. enthousiastic

I can use pandas to clean a dataset, but each cleaning task is usually one line of code. OTOH, With DuckDB with one SQL statement I can replace 40+ lines of polars/pandas. You may reply, SQL isn't as easy to understand! Fair point, it's a declarative language... which is why I use Malloy. Malloy is to TypeScript as Javascript is to SQL. Malloy is much easier to read and write (just as TypeScript is) because it has a built in semantic model -- all the joins, measures, and dimensions are done in one place.

Here is an example [1] of visualizing college football games. Here are all the queries, and semantic model that power all the visualizations [2] Here is the AI generated typescript/react that does the visualizations [3]. The Malloy ecosystem has Malloyyo and Publisher which are replacements for PowerBI and Tableau and Looker. Here is another example for visualizing global trade [4].

[1] - https://mrtimo.github.io/cfb-games/games-2026.html?week=Week... [2] - https://github.com/mrtimo/cfb-games/blob/main/drives.malloy [3] - https://github.com/mrtimo/cfb-games/blob/main/dashboards/gam... [4] - https://tradeexplorer.org/