Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (
Help us improve Python packaging - Donate today!

A collection sklearn transformers to encode categorical variables as numeric

Project Description

[![Travis Status](]( [![Coveralls Status](]( [![CircleCI Status](](

A set of scikit-learn-style transformers for encoding categorical variables into numeric by means of different techniques.

Encoding Methods

  • Ordinal [2][3]
  • One-Hot [2][3]
  • Binary [5]
  • Helmert Contrast [2][3]
  • Sum Contrast [2][3]
  • Polynomial Contrast [2][3]
  • Backward Difference Contrast [2][3]
  • Hashing [1]
  • BaseN [6]
  • LeaveOneOut [4]
  • Target Encoding [7]


The package by itself comes with a single module and an estimator. Before installing the module you will need numpy, statsmodels, and scipy.

To install the module execute:

`shell $ python install `


` pip install category_encoders `


` conda install -c conda-forge category_encoders `

To use:

import category_encoders as ce

encoder = ce.BackwardDifferenceEncoder(cols=[…]) encoder = ce.BinaryEncoder(cols=[…]) encoder = ce.HashingEncoder(cols=[…]) encoder = ce.HelmertEncoder(cols=[…]) encoder = ce.OneHotEncoder(cols=[…]) encoder = ce.OrdinalEncoder(cols=[…]) encoder = ce.SumEncoder(cols=[…]) encoder = ce.PolynomialEncoder(cols=[…]) encoder = ce.BaseNEncoder(cols=[…]) encoder = ce.TargetEncoder(cols=[…]) encoder = ce.LeaveOneOutEncoder(cols=[…])

All of these are fully compatible sklearn transformers, so they can be used in pipelines or in your existing scripts. If the cols parameter isn’t passed, every non-numeric column will be encoded. Please see the docs for transformer-specific configuration options.


from category_encoders import * import pandas as pd from sklearn.datasets import load_boston

# prepare some data bunch = load_boston() y = X = pd.DataFrame(, columns=bunch.feature_names)

# use binary encoding to encode two categorical features enc = BinaryEncoder(cols=[‘CHAS’, ‘RAD’]).fit(X, y)

# transform the dataset numeric_dataset = enc.transform(X)

In the examples directory, there is an example script used to benchmark different encoding techniques on various datasets.

The datasets used in the examples are car, mushroom, and splice datasets from the UCI dataset repository, found here:



Category encoders is under active development, if you’d like to be involved, we’d love to have you. Check out the file or open an issue on the github project to get started.


BSD 3-Clause


  1. Kilian Weinberger; Anirban Dasgupta; John Langford; Alex Smola; Josh Attenberg (2009). Feature Hashing for Large Scale Multitask Learning. Proc. ICML.
  2. Contrast Coding Systems for categorical variables. UCLA: Statistical Consulting Group. from
  3. Gregory Carey (2003). Coding Categorical Variables, from
  4. Strategies to encode categorical variables with many categories. from
  5. Beyond One-Hot: an exploration of categorical variables. from
  6. BaseN Encoding and Grid Search in categorical variables. from
  7. A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems. from

Release History

This version
History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, Size & Hash SHA256 Hash Help File Type Python Version Upload Date
(20.2 kB) Copy SHA256 Hash SHA256
Source None Jan 21, 2018

Supported By

Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Google Google Cloud Servers