What's new in pandas 3 pandas 3.0 has just been released. This article uses a real‑world example to explain the most important differences between pandas 2 and...

pandas 3 has been released and marks the most significant evolution of pandas in over ten years.

You can read about the main features in this blog post: datapythonista.me/blog/whats-n...

What's new in pandas 3 pandas 3.0 has just been released. This article uses a real‑world example to explain the most important differences between pandas 2 and...

#pandas 3 is faster, and more intuitive. I just published an article with practical examples explaining the main improvements: datapythonista.me/blog/whats-n...

Release Python Polars 1.31.0 · pola-rs/polars 💥 Breaking changes Remove old streaming engine (#23103) ⚠️ Deprecations Deprecate allow_missing_columns in scan_parquet in favor of missing_columns (#22784) 🚀 Performance improvements Improve ...

Polars 1.31 is released. With as highlight DataType expressions✨. These allow you to get the datatype of an expression dynamically.

See the full changelog here.

github.com/pola-rs/pola...

Want something more with DataType expressions, here is the RFC:

github.com/pola-rs/polars

We just reorganized the pandas ecosystem page. Is it clearer? Anything missing or not useful? Feedback welcome

pandas.pydata.org/community/ec...

pandas 2.3 has been released, the last version before pandas 3.0.

This seems nicer than Twitter and Mastodon so far. Happy to reconnect with many people I didn't see for a while.

pixi for science
uv for everything else

A high-level summary diagram taken from the slides linked below. It shows the interplay of two main components: a probabilistic model and decision maker or planner.

Probabilistic predictions of an underfitting polynomial classifier on a noisy XOR task and the corresponding under-confident calibration curve.

Probabilistic predictions of an overfitting polynomial classifier and the resulting overconfident calibration curve on the same noisy XOR problem.

Simulation study to show the relative lack of stability of hyperparameter tuning when using hard metrics such as Accuracy or soft yet not probabilistic metrics such as ROC AUC compared to a strictly proper scoring rule such as the log-loss.

I recently shared some of my reflections on how to use probabilistic classifiers for optimal decision-making under uncertainty at @pydataparis.bsky.social 2024.

Here is the recording of the presentation:

www.youtube.com/watch?v=-gYn...

Vegafusion demo

✨ New Vegafusion release (2.0)!

🦀 Modernized Rust core

➡️ Arrow PyCapsule Interface

🌊🦄 Narwhals for agnostic Dataframe support

👉 Full release notes vegafusion.io/posts/2024/2...

Version 1.6 Legend for changelogs something big that you couldn’t do before., something that you couldn’t do before., an existing feature now may not require as much computation or memory., a miscellaneous min...

Please help us test the first release candidate for scikit-learn 1.6: pip install scikit-learn==1.6.0rc1

Changelog: scikit-learn.org/1.6/whats_ne...

In particular, if you maintain a project with a dependency on
scikit-learn, please let us know about any regression.