A Python library for Feature Engineering and Selection#


Feature-engine rocks!#

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

Feature-engine includes transformers for:

  • Missing data imputation

  • Categorical encoding

  • Discretisation

  • Outlier capping or removal

  • Variable transformation

  • Variable creation

  • Variable selection

  • Datetime features

  • Time series

  • Preprocessing

Feature-engine allows you to select the variables you want to transform within each transformer. This way, different engineering procedures can be easily applied to different feature subsets.

Feature-engine transformers can be assembled within the Scikit-learn pipeline, therefore making it possible to save and deploy one single object (.pkl) with the entire machine learning pipeline. Check **Quick Start** for an example.

Pst! How did you find us?#

We want to share Feature-engine with more people. It’d help us loads if you tell us how you discovered us.

Then we’d know what we are doing right and which channels to use to share the love.

Please share your story by answering 1 quick question at this link . 😃

What is unique about Feature-engine?#

The following characteristics make Feature-engine unique:

  • Feature-engine contains the most exhaustive collection of feature engineering transformations.

  • Feature-engine can transform a specific group of variables in the dataframe.

  • Feature-engine returns dataframes, hence suitable for data exploration and model deployment.

  • Feature-engine is compatible with the Scikit-learn pipeline, Grid and Random search and cross validation.

  • Feature-engine automatically recognizes numerical, categorical and datetime variables.

  • Feature-engine alerts you if a transformation is not possible, e.g., if applying logarithm to negative variables or divisions by 0.

If you want to know more about what makes Feature-engine unique, check this article.


Feature-engine is a Python 3 package and works well with 3.7 or later. Earlier versions are not compatible with the latest versions of Python numerical computing libraries.

The simplest way to install Feature-engine is from PyPI with pip:

$ pip install feature-engine

Note, you can also install it with a _ as follows:

$ pip install feature_engine

Feature-engine is an active project and routinely publishes new releases. To upgrade Feature-engine to the latest version, use pip like this:

$ pip install -U feature-engine

If you’re using Anaconda, you can install the Anaconda Feature-engine package:

$ conda install -c conda-forge feature_engine

Feature-engine’s Transformers#

Feature-engine hosts the following groups of transformers:

Missing Data Imputation: Imputers#

  • MeanMedianImputer: replaces missing data in numerical variables by the mean or median

  • ArbitraryNumberImputer: replaces missing data in numerical variables by an arbitrary number

  • EndTailImputer: replaces missing data in numerical variables by numbers at the distribution tails

  • CategoricalImputer: replaces missing data with an arbitrary string or by the most frequent category

  • RandomSampleImputer: replaces missing data by random sampling observations from the variable

  • AddMissingIndicator: adds a binary missing indicator to flag observations with missing data

  • DropMissingData: removes observations (rows) containing missing values from dataframe

Categorical Encoders: Encoders#

Variable Discretisation: Discretisers#

Outlier Capping or Removal#

Numerical Transformation: Transformers#

Feature Creation:#

  • MathFeatures: creates new variables by combining features with mathematical operations

  • RelativeFeatures: combines variables with reference features

  • CyclicalFeatures: creates variables using sine and cosine, suitable for cyclical features


Feature Selection:#



  • MatchCategories: ensures categorical variables are of type ‘category’

  • MatchVariables: ensures that columns in test set match those in train set

Scikit-learn Wrapper:#

Getting Help#

Can’t get something to work? Here are places where you can find help.

  1. The **User Guide** in the docs.

  2. Stack Overflow. If you ask a question, please mention “feature_engine” in it.

  3. If you are enrolled in the Feature Engineering for Machine Learning course , post a question in a relevant section.

  4. If you are enrolled in the Feature Selection for Machine Learning course , post a question in a relevant section.

  5. Join our gitter community. You an ask questions here as well.

  6. Ask a question in the repo by filing an issue (check before if there is already a similar issue created :) ).


Interested in contributing to Feature-engine? That is great news!

Feature-engine is a welcoming and inclusive project and we would be delighted to have you on board. We follow the Python Software Foundation Code of Conduct.

Regardless of your skill level you can help us. We appreciate bug reports, user testing, feature requests, bug fixes, addition of tests, product enhancements, and documentation improvements. We also appreciate blogs about Feature-engine. If you happen to have one, let us know!

For more details on how to contribute check the contributing page. Click on the **Contribute** guide.

Open Source#

Feature-engine’s license is an open source BSD 3-Clause.

Feature-engine is hosted on GitHub. The issues and pull requests are tracked there.