Some biggies in this release: ■ The CatEncoder can be super useful ■ Caching makes exploratory work so much more productive And many places got faster (always nice to have) or easier to use. Enjoy more powerful and easier learning with dataframes :)

🧠 TextEncoder is now LLMEncoder -- clearer name, same transformer embeddings

📊 DataOp reports now link to your source code and show docstrings -- can be generated without executing calculations

⚡ The SessionEncoder is now up to 15x faster

🔍 describe_transformations() -- can now print a plain text explanation of what TableVectorizer and Cleaner did to each column

💾 Persistent caching for DataOps -- pipeline steps can now be cached across runs

🆕 CatEncoder -- OneHotEncoder + TargetEncoder, two encoders combined in a single column transformer to deal with columns with many infrequent classes

🫂13 new contributors helped with this release!

Also in this release: faster construction of deep DataOps, ToCategorical for numeric columns, and more!

Mitigating this problem is needed to unlock the full value of free-threading Python, especially for @scikit-learn.org workloads that often nest BLAS calls (via NumPy, SciPy or PyTorch) and OpenMP calls (via Cython) under Python level threads (typically via joblib).

Oversubscription problems typically happen when nesting BLAS or OpenMP calls under Python threads: naively spawning 10 Python threads that themselves spawn 10 BLAS threads each results in 100 starving threads on a 10 cores CPU.