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Feature engineering package with Scikit-learn's fit transform functionality

Project description

Feature Engine

Python 3.6 Python 3.7 Python 3.8 License CircleCI Documentation Status

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

Feature-engine features in the following resources:

Blogs about Feature-engine:


En Español:

More resources will be added as they appear online!

Current Feature-engine's transformers include functionality for:

  • Missing Data Imputation
  • Categorical Variable Encoding
  • Outlier Capping or Removal
  • Discretisation
  • Numerical Variable Transformation
  • Scikit-learn Wrappers
  • Variables Combination
  • Variable Selection

Imputing Methods

  • MeanMedianImputer
  • RandomSampleImputer
  • EndTailImputer
  • AddNaNBinaryImputer
  • CategoricalVariableImputer
  • FrequentCategoryImputer
  • ArbitraryNumberImputer

Encoding Methods

  • CountFrequencyCategoricalEncoder
  • OrdinalCategoricalEncoder
  • MeanCategoricalEncoder
  • WoERatioCategoricalEncoder
  • OneHotCategoricalEncoder
  • RareLabelCategoricalEncoder

Outlier Handling methods

  • Winsorizer
  • ArbitraryOutlierCapper
  • OutlierTrimmer

Discretisation methods

  • EqualFrequencyDiscretiser
  • EqualWidthDiscretiser
  • DecisionTreeDiscretiser
  • UserInputDiscreriser

Variable Transformation methods

  • LogTransformer
  • ReciprocalTransformer
  • PowerTransformer
  • BoxCoxTransformer
  • YeoJohnsonTransformer

Scikit-learn Wrapper:

  • SklearnTransformerWrapper

Variable Combinations:

  • MathematicalCombinator

Feature Selection:

  • DropFeatures


From PyPI using pip:

pip install feature_engine

From Anaconda:

conda install -c conda-forge feature_engine

Or simply clone it:

git clone


>>> from feature_engine.categorical_encoders import RareLabelCategoricalEncoder
>>> import pandas as pd

>>> data = {'var_A': ['A'] * 10 + ['B'] * 10 + ['C'] * 2 + ['D'] * 1}
>>> data = pd.DataFrame(data)
>>> data['var_A'].value_counts()
A    10
B    10
C     2
D     1
Name: var_A, dtype: int64
>>> rare_encoder = RareLabelCategoricalEncoder(tol=0.10, n_categories=3)
>>> data_encoded = rare_encoder.fit_transform(data)
>>> data_encoded['var_A'].value_counts()
A       10
B       10
Rare     3
Name: var_A, dtype: int64

See more usage examples in the Jupyter Notebooks in the example folder of this repository, or in the documentation.


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

In short:

Local Setup Steps

  • Fork the repo
  • Clone your fork into your local computer: git clone<YOURUSERNAME>/feature_engine.git
  • cd into the repo cd feature_engine
  • Install as a developer: pip install -e .
  • Create and activate a virtual environment with any tool of choice
  • Install the dependencies as explained in the Contributing Page
  • 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!!

Opening Pull Requests

PR's are welcome! Please make sure the CI tests pass on your branch.


We prefer tox. In your environment:

  • Run pip install tox
  • cd into the root directory of the repo: cd feature_engine
  • Run tox

If the tests pass, the code is functional.

You can also run the tests in your environment (without tox). For guidelines on how to do so, check the Contributing Page.


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: sphinx-build -b html docs build


BSD 3-Clause


Many of the engineering and encoding functionalities are inspired by this series of articles from the 2009 KDD Competition.

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