Thomas Bierhance: Polars - make the switch to lightning-fast dataframes

In this talk, we will report on our experiences switching from Pandas to Polars in a real-world ML project. Polars is a new high-performance dataframe library for Python based on Apache Arrow and written in Rust. We will compare the performance of polars with the popular pandas library, and show how polars can provide significant speed improvements for data manipulation and analysis tasks. We will also discuss the unique features of polars, such as its ability to handle large datasets that do not fit into memory, and how it feels in practice to make the switch from Pandas. This talk is aimed at data scientists, analysts, and anyone interested in fast and efficient data processing in Python.
github.com/datenzauberai/PyCo...

[@zerdofish9989]

Polars changed my whole pipeline. I love it!

[@datenzauberai]

I love it too! It really makes a difference!

[@rokaskarabevicius]

glad to hear I'm not the only one who finds pandas multi-index confusing.

[@datenzauberai]

I think I've never met someone in person who is fluent in "multi-index-filtering" 😂

[@ryan_chew97]

@@datenzauberaipretty much. I just ask chatgpt and half the time it’s wrong

[@chobblegobbler6671]

Herr Schuler.. Offnen Sie die tur!

[@datenzauberai]

Ich kaufe drei Umlaute

[@rubendevroomen2637]

I cant use polars until it supports complex numbers

[@datenzauberai]

It's definitely not a replacement for numpy for this kind of scientific computations.

[@ScienceMinisterZero]

Rust is the future of data science.

[@floopybits8037]

It is good for backuend programming. Not for actual DS

[@slavikdoter]

whenever i see a new orm i try to avoid it as long as possible

[@datenzauberai]

It's not a tool for object-relational-mapping, so it would be totally fine to have look 😉