I've had some success with polars. But: I'm not well versed in Python Pandas either, and my rust is better than my python (though Ruby is my daily language, so

I've had some success with polars. But: I'm not well versed in Python Pandas either, and my rust is better than my python (though Ruby is my daily language, so I'm very used to dynamic languages). The thing I miss most is the rapid REPL workflow in Python, where I work out the pipelines, filters, map-reduce and so forth, on the interactive REPL and then extract that into python scripts. The polars scripts that I worked with, were small enough and the builds are incremental, therefore fast: milliseconds. But the build-step inbetween is somewhat clumsy when exploring data. But the end-results are just so extremely cleaner, saner and especially faster that it certainly is worth it. I worked on a dataset where crunching it in python took upwards of 14 hours, and the polars under an hour. That feedback loop alone is worth days of fighting the borrow-checker. And that immediate feedback by the compiler/checker "this column isn't guaranteed a String, it could be missing, could be unparsable; deal with this now" is priceless. Too often did I have some ETL run for 8 hours, and then choke on some column that I swore should've been UTF8. Fix it, and re-run. Wasting 8 hours. A type-checker in ETL/data flows is priceless. Edit: to be clear: I am developer. Not datascience. But I do need to understand my users, my business, my market. Developer/devops today do need to crunch large datasets: at least I do. [1] https://www.pola.rs/ https://www.pola.rs/