chain-ops-python VS polars

You don't need pandas to do chaining. It's a one-liner in pure python: https://github.com/tpapastylianou/chain-ops-python

Not to mention, it's a lot more debuggable this way (which is generally the biggest downside to most specialised chaining approaches).

This is great. I will use it with my python chaining approach: https://github.com/tpapastylianou/chain-ops-python

My only grief is that decorator, which forces you to wrap existing functions anyway (same way I had to define lambdas in my example anyway).

Do you have any insight on that?

This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.

I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).

https://github.com/pola-rs/polars/releases/tag/py-0.19.0

That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection: