skrub DataOps help you construct complex and extensive hyperparameter search spaces. However, interpreting results from large grids can be challenging. To address this, skrub generates a parallel coordinate plot that visualizes all runs and the parameters used to achieve specific results.

Do you have to deal with numerical features that involve large outliers, and need to train linear models or neural networks? Then you might want to try the skrub SquashingScaler. The SquashingScaler behaves like scikit-learn RobustScaler, but smoothly clips outliers to predefined boundaries.

Our first talk tonight is from @gaelvaroquaux.bsky.social on @skrub-data.bsky.social. Real tables are too messy for sklearn - skrub preprocesses them for you.

Had a great PyData London tonight! Was a real treat to hear from @gaelvaroquaux.bsky.social on @skrub-data.bsky.social and the real world data pains its solving. (Try it if you haven’t already; super easy to get going!)

⚡Maintenance release ⚡ Release 0.6.1 fixes a bug that may happen when combining certain column-based skrub transformers with the scikit-learn ColumnTransformer. github.com/skrub-data/s...

We had a great tutorial at #EuroScipy2025! We had the opportunity of showing off the features of skrub to a wide audience, and show how they can be used in a pretty complex use case.

⚡ Release 0.6.0 is now out! ⚡ 🚀 Major update! Skrub DataOps, various improvements for the TableReport, new tools for applying transformers to the columns, and a new robust transformer for numerical features are only some of the features included in this release.

📅 The skrub API includes various functions and objects that help with dealing with datetime strings. 1/

🚀⚡ Release: 0.5.4: Maintenance release! This release makes skrub compatible with scikit-learn 1.7. Changelog: skrub-data.org/stable/CHANG...

👀 This week's post will be another sneak peek into skrub expressions, an upcoming feature that will ease the preparation and execution of machine learning pipelines on dataframes. This time we will focus on how expressions can simplify the construction of complex hyperparameter grids.

📝 The skrub TextEncoder brings the power of HuggingFace language models to embed text features in tabular machine learning, for all those use cases that involve text-based columns.

The Skrub Cleaner is a lightweight transformer that performs consistency checks on a dataframe: 🔍 It gives a uniform representation of null values, converting those represented as strings (such as "N/A") 🗑️ It drops columns that contain too many null values (according to a user-defined threshold)

👀 This week's post is a sneak peek into the next major Skrub feature, Skrub expressions 🚀 As this is a preview of an upcoming feature, we are looking for your thoughts and feedback before release.

The Skrub TableReport is a lightweight tool that allows to get a rich overview of a table quickly and easily. ✅ Filter columns 🔎 Look at each column's distribution 📊 Get a high level view of the distributions through stats and plots, including correlated columns 🌐 Export the report as html

🚀 The Skrub learning materials website is now live at: skrub-data.org/skrub-materi... Here you'll find introductory talks and tutorials about Skrub, along with notebooks and blog posts showcasing the features of the library. Bookmark it to not miss any update 👀

🚀⚡ Release: 0.5.3 Check out the release notes: skrub-data.org/stable/CHANG... Highlights below ⤵️

🗒️ Do you need to prepare a ML model, and you are working with text and strings? Skrub provides four encoders to convert strings into numerical features. 🤗 models included! What's the best? Check out our blog post to find out 👀 skrub-data.org/skrub-materi...

The code can be found here github.com/GaelVaroquau...

A recent talk, fully in a vscode: 100% code on data wrangling for machine learning with @skrub-data.bsky.social www.youtube.com/watch?v=hdWW... super powerful to easily assemble production-ready pipelines in easy syntax

🚀 The Skrub workshop at Campus Cyber in La Défense was a great success! Connecting with professionals from both startups and large companies has given us valuable insights for Skrub's next steps. Stay tuned for more!

🎉⚡️Release 0.5.1: ◼ Encode strings faster and better with StringEncoder! StringEncoder applies a tf-idf vectorization followed by SVD to produce high quality and FAST embeddings of textual and categorical features.

Dealing with unseen categories requires modeling "surface form", ie strings, as we do in @skrub-data.bsky.social's GapEncoder: skrub-data.org/stable/refer...

**@skrub-data.bsky.social Repository Updates - December 11, 2024** • Version update to 0.4.1 (#1198) • Bug fix for object columns in table reports (#1196) • Updated contributing documentation (#1185) • Nightly builds stability fix (#1193)

A new version of skrub is out! skrub 0.4.1 fixes some issues with the TableReport and the tabular_learner, fills some gaps in the documentation, and ensures compatibility with the latest scikit-learn version (1.6). Changelog: skrub-data.org/stable/CHANG...

We had a great time at the @pyladiescon.bsky.social #PyLadiesCon 2024! Over the course of the mentored sprint we were able to merge five challenging PRs that will be added to the next release, and welcomed three new contributors 🎉🎉🎉 See you there next year!

🎉⚡️Release 0.4: ◼ Easily use deep learning for text entries ◼ TableVectorizer can remove columns with too many missing values ◼ TableReport more robust and prettier ... 1/5

Please let us know if you're on here! 👋

We're always updating the pydata & scipy project starter pack: go.bsky.app/6HkrMcp Hello @scikit-learn.bsky.social , @networkx.bsky.social , @scipyconf.bsky.social

👀 Explore your dataframes interactively with TableReport. AKA: 📈 we heard you liked plots so we put plots in your tables 📈

🐍📰 NumPy Practical Examples: Useful Techniques

Great video explaining a clever vectorization for learning on strings and dirty categories: the MinHashEncoder is fast, stateless, and excellent with tree-based learners. It's in @skrub-data.bsky.social youtu.be/ZMQrNFef8fg

A starter pack of pydata-related projects on bluesky. go.bsky.app/6HkrMcp Which handles are we missing? Help us make it grow

@bsky.app is the new cool. So is tabular learning? skrub is a library that eases preprocessing and feature engineering for tabular machine learning. skrub-data.org/stable/ Main features: - scikit-learn compatible - handles Pandas and Polars dataframes - works on heterogeneous types