Feature-engine
Feature-engine is a free Python library for preparing and selecting features for machine-learning models.
What Feature-engine is
Feature-engine is a free Python library for preparing and selecting features for machine-learning models. Its transformers handle imputation, categorical encoding, discretisation, outlier treatment, data transformation, feature creation, feature selection, and extraction from dates, text, and time series. The library works with dataframes and keeps transformed column names and order. Its scikit-learn-style fit() and transform() methods support scikit-learn pipelines, cross-validation, and hyperparameter searches. You can choose which variables a transformer processes, with support for a broad set of feature-selection techniques. Feature-engine sits between pandas and scikit-learn by wrapping pandas operations in scikit-learn-like transformers. It requires Python 3.9 or later and installs through pip or conda. Source code is available on GitHub under the BSD 3-Clause License. It is self-hosted and intended for machine-learning work centered on pandas and scikit-learn.
Good
- Free plan with no trial requirement
- Covers broad feature-engineering operations
- Keeps dataframe column names and order
- Works with scikit-learn pipelines
- Installs through pip or conda
Watch out
- Requires Python 3.9 or later
- Linux platform listed
- Self-hosted deployment required
Who it suits
Feature-engine suits Python users whose machine-learning workflows rely mainly on pandas and scikit-learn. It is also suitable for teams wanting a self-hosted, open-source library.
Verdict
Feature-engine brings many preparation and selection tasks into dataframe-friendly transformers. Its pandas and scikit-learn integration makes it a practical fit for those ecosystems.
The facts
Each one read from Feature-engine's own pages, with the date it was checked.
What it does
- Dataframes
- The library is designed to work with dataframes and says transformed dataframes retain their column names and order.checked 4 Oct 2026
- Distribution
- The documentation provides pip and conda installation commands, and the repository offers source code on GitHub.checked 4 Oct 2026
- Feature operations
- Its transformers cover imputation, categorical encoding, discretisation, outlier handling, transformation, feature creation and selection, and date, text and time-series feature extraction.checked 4 Oct 2026
- Governance
- The project documentation names Soledad Galli as founder and says she has final decision authority, while noting that in practice she defers to community discussion and core contributors.checked 4 Oct 2026
- Installation
- The documentation says Feature-engine is a Python 3 package for Python 3.9 or later and can be installed with pip or conda.checked 4 Oct 2026
- Integration
- The project describes itself as sitting at the interface of pandas and scikit-learn, wrapping pandas functionality in scikit-learn-like transformers.checked 4 Oct 2026
- Intended users
- The documentation says the library is suited to people whose machine-learning data work primarily uses pandas and scikit-learn.checked 4 Oct 2026
- License
- The repository provides Feature-engine under the BSD 3-Clause License.checked 4 Oct 2026
- Pipelines
- Transformers use scikit-learn-style fit() and transform() methods and are compatible with scikit-learn pipelines, cross-validation and hyperparameter search.checked 4 Oct 2026
- Purpose
- Feature-engine is a Python library with transformers to engineer and select features for machine-learning models.checked 4 Oct 2026
- Security reporting
- The project’s security policy supports version 1.9.x, directs vulnerability reports to GitHub security advisories or email, and says accepted issues are patched privately for a dedicated bug-fix release.checked 4 Oct 2026
- Support and community
- The governance page directs contributors to GitHub issues and the Gitter community.checked 4 Oct 2026
- Variable selection
- Transformers let users choose which variables to transform, and the project says it supports a wide range of feature-selection techniques.checked 4 Oct 2026
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