Feature-engine


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Feature-engine is a Python library with multiple transformers to engineer and select features for use in machine learning models. Feature-engine's transformers follow Scikit-learn's functionality with fit() and transform() methods to learn the transforming parameters from the data and then transform it.

Feature-engine features in the following resources

Blogs about Feature-engine

Documentation

Documentation

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Current Feature-engine's transformers include functionality for:

Missing Data Imputation
Categorical Encoding
Discretisation
Outlier Capping or Removal
Variable Transformation
Variable Creation
Variable Selection
Datetime Features
Text Features
Time Series
Preprocessing
Scaling
Scikit-learn Wrappers

Imputation Methods

MeanMedianImputer
ArbitraryNumberImputer
RandomSampleImputer
EndTailImputer
CategoricalImputer
AddMissingIndicator
DropMissingData

Encoding Methods

OneHotEncoder
OrdinalEncoder
CountFrequencyEncoder
MeanEncoder
WoEEncoder
RareLabelEncoder
DecisionTreeEncoder
StringSimilarityEncoder

Discretisation methods

EqualFrequencyDiscretiser
EqualWidthDiscretiser
GeometricWidthDiscretiser
DecisionTreeDiscretiser
ArbitraryDiscreriser

Outlier Handling methods

Winsorizer
ArbitraryOutlierCapper
OutlierTrimmer

Variable Transformation methods

LogTransformer
LogCpTransformer
ReciprocalTransformer
ArcsinTransformer
PowerTransformer
BoxCoxTransformer
YeoJohnsonTransformer
ArcSinhTransformer

Variable Scaling methods

MeanNormalizationScaler

Variable Creation:

MathFeatures
RelativeFeatures
CyclicalFeatures
DecisionTreeFeatures
GeoDistanceFeatures

Feature Selection:

DropFeatures
DropConstantFeatures
DropDuplicateFeatures
DropCorrelatedFeatures
SmartCorrelationSelection
ShuffleFeaturesSelector
SelectBySingleFeaturePerformance
SelectByTargetMeanPerformance
RecursiveFeatureElimination
RecursiveFeatureAddition
DropHighPSIFeatures
SelectByInformationValue
ProbeFeatureSelection
MRMR

Datetime

DatetimeFeatures
DatetimeSubtraction
DatetimeOrdinal

Text Features

TextFeatures

Time Series

LagFeatures
WindowFeatures
ExpandingWindowFeatures

Pipelines

Pipeline
make_pipeline

Preprocessing

MatchCategories
MatchVariables

Wrappers:

SklearnTransformerWrapper

Installation

From PyPI using pip:

pip install feature_engine

From Anaconda:

conda install -c conda-forge feature_engine

Or simply clone it:

git clone https://github.com/feature-engine/feature_engine.git

Example Usage

>>> import pandas as pd
>>> from feature_engine.encoding import RareLabelEncoder

>>> data = {'var_A': ['A'] * 10 + ['B'] * 10 + ['C'] * 2 + ['D'] * 1}
>>> data = pd.DataFrame(data)
>>> data['var_A'].value_counts()

Out[1]:
A    10
B    10
C     2
D     1
Name: var_A, dtype: int64

>>> rare_encoder = RareLabelEncoder(tol=0.10, n_categories=3)
>>> data_encoded = rare_encoder.fit_transform(data)
>>> data_encoded['var_A'].value_counts()

Out[2]:
A       10
B       10
Rare     3
Name: var_A, dtype: int64

Find more examples in our Jupyter Notebook Gallery or in the documentation.

Contribute

Details about how to contribute can be found in the Contribute Page

Briefly:

Fork the repo
Clone your fork into your local computer:

git clone https://github.com/<YOURUSERNAME>/feature_engine.git

navigate into the repo folder

cd feature_engine

Install Feature-engine as a developer:

pip install -e .

Optional: Create and activate a virtual environment with any tool of choice
Install Feature-engine developer dependencies:

pip install -e ".[tests]"

Create a feature branch with a meaningful name for your feature:

git checkout -b myfeaturebranch

Develop your feature, tests and documentation
Make sure the tests pass
Make a PR

Thank you!!

Documentation

Feature-engine documentation is built using Sphinx and is hosted on Read the Docs.

To build the documentation make sure you have the dependencies installed: from the root directory:

pip install -r docs/requirements.txt

Now you can build the docs using:

sphinx-build -b html docs build

License

The content of this repository is licensed under a BSD 3-Clause license.

Sponsor

Feature-engine is made possible with the support of Train in Data.

Metadata

Release files for feature-engine 1.9.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for feature-engine 1.9.4
File	Size	Uploaded
feature_engine-1.9.4.tar.gz	150.9 kB	Feb 27, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for feature-engine 1.9.4
File	Interpreter	ABI	Platform	Reset
feature_engine-1.9.4-py3-none-any.whl 243.5 kB Feb 27, 2026	Python 3	none	any	Details

Total release size: 394.5 kB

Release files / feature_engine-1.9.4.tar.gz

Download URL	feature_engine-1.9.4.tar.gz
Size	150.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`4ee817b9a557f9670636deb417689645e54fc2391c2f14fbe0ef4f185fd1ab17`
BLAKE2b-256 checksum How to use checksums	`2b9d802c7c4a3f1b8a988e7370b377f426eba7e5ed51a42019973a8063ea5532`
Upload date	Feb 27, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.10.0`

Release files / feature_engine-1.9.4-py3-none-any.whl

Download URL	feature_engine-1.9.4-py3-none-any.whl
Size	243.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`a1181dcb25fc906cc74dd4f2947c49a71012a727d30a4505f2418419c83ace9e`
BLAKE2b-256 checksum How to use checksums	`5c92698f823c1e0a4affe75d8dc6685639b82c65241bdda1f04f6a69fd162119`
Upload date	Feb 27, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.10.0`