It depends on what you do with Pandas. The semantics are different so you’ll have to rewrite stuff — that said, for me it was worth it for the most part because I work with massive columnar data in Parquet.

So no, not a drop in replacement. But not a difficult transition either.

This page explains how Polars differs from Pandas.

https://pola-rs.github.io/polars-book/user-guide/coming_from...

As someone who loathes the Pandas syntax and lusts for the relatively cleaner tidyverse code of my colleagues, the Polar syntax just feels ... off (from the link):

df.with_columns( pl.when(pl.col("c") == 2) .then(pl.col("b")) .otherwise(pl.col("a")).alias("a") )

Seeing the multiple nested pl calls within a single expression just feels odd to me. It's definitely reminiscent of Dplyr but in a much less elegant way.

Yeah, I tried to get into it but it seems very verbose.

* * *

I like the idea of Polars, and it's on my list of things to try. Unfortunately, my current codebase in heavily tied to Pandas (bioinformatics tools that use Pandas as a dependency). I also deal mostly in sparse matrices which I'm unsure if duckdb handles.

Another caveat for me is at multiple times in my workflow I drop into R for plots and rpy2 can convert pandas dataframes into equivalent R atomics. With Polars it would be just an additional step of converting to pandas df but just something I need to consider. That said, I've disliked the Pandas syntax for so long that the mental overhead might be well worth it.

Thank you, that is definitely the page for me.