FireDucks: Pandas but Faster

So many foot guns, poorly thought through functions, 10s of keyword arguments instead of good abstractions, 1d and 2d structures being totally different objects (and no higher-order structures). I'd take 50% of the speed for a better API.

I looked at Polars, which looks neat, but seems made for a different purpose (data pipelines rather than building models semi-interactively).

To be clear, this library might be great, it's just a shame for me that there seems no effort to make a Pandas-like thing with better API. Maybe time to roll up my sleeves...

For a comparison, dplyr offers a lot of elegant functionality, and the functional approach in Pandas often feels like an afterthought. If R is cleaner than Python, it tells a lot (as a side note: the same story for ggplot2 and matplotlib).

Another surprise for friends coming from non-Python backgrounds is the lack of column-level type enforcement. You write df.loc[:, "col1"] and hope it works, with all checks happening at runtime. It would be amazing if Pandas integrated something like Pydantic out of the box.

I still remember when Pandas first came out—it was fantastic to have a tool that replaced hand-rolled data structures using NumPy arrays and column metadata. But that was quite a while ago, and the ecosystem has evolved rapidly since then, including Python’s gradual shift toward type checking.