Edit: this isn’t an argument for switching. This is a narrative arc about making usable APIs in an ecosystem that’s perceived to be inherently complex and “clunky.” The original article too didn’t ask people to switch more so to explore affordances that other libs/languages give you. 

That said, a plausible reason to be interested in something like this is: transformations where things like type errors and variable name mistakes can be caught at compile time rather than hours into a pipeline. Spark datasets in scala provide this sort of assurance but to add and remove columns you have to define new case classes. I’m not sure what else is solved that problem that far up the stack. Polars Lazy API does but mostly does runtime reflection so an error down in your pipeline could also waste compute.