Feature-engine

Open-source library for feature engineering and selection based on pandas and scikit-learn.

EstablishedOpen SourceLow lock-in

Pricing

Free tier

Flat rate

Adoption

Stable

License

Open Source

Data freshness

Aging · Jun 8, 2026

Overview

What is Feature-engine?

Feature-engine is an open-source Python library that provides a wide range of methods for feature engineering and selection, built on top of pandas and scikit-learn. It simplifies the process of preparing data for machine learning models by offering robust preprocessing techniques.

Key differentiator

Feature-engine stands out with its extensive range of feature engineering methods and seamless integration with pandas and scikit-learn, making it an essential tool for data preprocessing in machine learning workflows.

Capability profile

Capability Radar

Ease of StartEcosystemValueMaturityFlexibilityScale Ready

Honest assessment

Strengths & Weaknesses

↑ Strengths

Wide range of feature engineering methodsmedium

Integration with pandas and scikit-learn for seamless data manipulationmedium

Support for both numerical and categorical featuresmedium

Extensive documentation and examplesmedium

↓ Weaknesses

Steep learning curve for non-Python developershigh

Feature-engine's API is tightly integrated with Python-specific patterns and idioms, which can be challenging for developers not familiar with the language.

Limited documentation for advanced use casesmedium

While basic usage is well-documented, more complex scenarios such as custom transformers or integration with other libraries may lack detailed guidance.

Performance issues with large datasetshigh

Feature-engine relies heavily on pandas for data manipulation, which can lead to performance bottlenecks when processing very large datasets due to memory constraints and slower operations compared to more optimized libraries like Dask.

Limited support for non-tabular data formatsmedium

Feature-engine is primarily designed for tabular data (pandas DataFrames). It lacks native support for other common data structures such as time series, images, or text, which limits its applicability in certain domains.

Frequent breaking changes between versionsmedium

The library has undergone significant API changes across major releases (e.g., v0.1 to v0.2), requiring users to update their codebases frequently and potentially causing disruptions in ongoing projects.

Fit analysis

Who is it for?

✓ Best for

Teams working with complex datasets requiring extensive preprocessing before model training

Projects that need to handle both numerical and categorical data efficiently

Developers looking for a comprehensive library for feature engineering

✕ Not a fit for

Applications where real-time data processing is critical, as Feature-engine is designed for batch processing

Users who prefer a graphical user interface (GUI) over command-line tools

Cost structure

Pricing

Free Tier

Available

Open source — free to use

Starts at

$0

Model

Flat rate

Enterprise

None

Performance benchmarks

How Fast Is It?

Ecosystem

Relationships

Next step

Get Started with Feature-engine

Step-by-step setup guide with code examples and common gotchas.

View Setup Guide →