This article explains it pretty well: https://dynomight.net/numpy/
This article explains it pretty well: https://dynomight.net/numpy/
The original comment I responded to was confusing Pandas with Polars, and now your blog post refers to Numpy, but Polars takes a completely different approach to dataframes/data processing than either of these tools.
Take two examples of dataframe apis, dplyr and ibis. Both can run on a range of SQL backends because dataframe apis are very similar to SQL DML apis.
Moreover, the SQL translation for tools for pivot_longer in R are a good illustration of complex dynamics dataframe apis can support, that you'd use something like dbt to implement in your SQL models. duckdb allows dynamic column selection in unpivot. But in some SQL dialects this is impossible. dataframe apis -> SQL tools (or dbt) enable them in these dialects.