I'm not following the trends closely, but has Polars become a full replacement for Pandas? Are there use cases where one is better suited than the other?
Polars is effectively a full replacement for Pandas for 99.9% of all cases. The only exception I'm really aware of is if you're working with geospatial data, as there isn't yet a "Geopolars" equivalent of the commonly used "Geopandas". However, Geopolars is still in active development and should eventually be production ready.
Imitation is the sincerest form of flattery! GeoPandas — and its underlying libraries of shapely and GEOS — is an incredible production-ready tool.
GeoPolars is nowhere near the functionality or stability of GeoPandas, but competition is good and, due to its pure-Rust core, GeoPolars will be much easier to use in WebAssembly.
my understanding is Polars is faster, scales better without using external solutions, better API, +Rust. Pandas wins if you want to use what the vast majority of folks are using and have used in the past. Probably has a more complete set of helpers / recipes for the little things you bump into when using it thoroughly, but in the age of LLMs, I think that's minor.
>Pandas wins if you want to use what the vast majority of folks are using
Vast majority of skilled developers are now using Polars, unless they are constrained by lack of Narwhals support in their third-party library of choice (e.g. Great Expectations, SHAP). That's the more important trend to follow.
Actually yes. We had already been planning to do 2.0 for a long time. We originally said we'd move on from 1.x quickly when released 1.0 but ended up staying at 1.x much longer than intended.
From a quick check our first PRs were merged to the 2.0 branch in June:
2026-06-17T21:27:51Z #27993 chore: Stop coercing `pl.col(...)` to selector ...
2026-06-18T14:19:26Z #27996 chore!: Replace multi-seed hash API with a single seed
2026-06-19T07:05:11Z #27991 chore(python!): Remove `Expr.flatten` function
Thing I care about most is whether the old eager-vs-lazy footguns got cleaned up. Half my bugs were a stray collect() in a loop killing the query plan.
It's been impressive!
> 1 Billion Row Challenge benchmark: Pandas took 4m28s vs. Polars 5.04s and DuckDB 5.19s — DuckDB also used 19x less memory
Python Vs Rust : In terms for speed - No comparison
(The above episode transcript has a link to blog post titled "Pandas should go extinct" )
from https://github.com/pola-rs/geopolars/tree/main
Comparison with GeoPandas
Imitation is the sincerest form of flattery! GeoPandas — and its underlying libraries of shapely and GEOS — is an incredible production-ready tool.
GeoPolars is nowhere near the functionality or stability of GeoPandas, but competition is good and, due to its pure-Rust core, GeoPolars will be much easier to use in WebAssembly.
Vast majority of skilled developers are now using Polars, unless they are constrained by lack of Narwhals support in their third-party library of choice (e.g. Great Expectations, SHAP). That's the more important trend to follow.
Happy that I can upgrade to 2.0 final tonight.
From a quick check our first PRs were merged to the 2.0 branch in June:
Apparently about something ))
cant wait to upgrade my Quant trading bot