• noarch v2.6.4
• linux-64 v1.2.5
• win-32 v1.2.5
• win-64 v1.2.5
• osx-64 v1.2.4

conda install

To install this package run one of the following:
conda install conda-forge::category_encoders
conda install conda-forge/label/cf201901::category_encoders
conda install conda-forge/label/cf202003::category_encoders
conda install conda-forge/label/gcc7::category_encoders

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A set of scikit-learn-style transformers for encoding categorical variables into numeric with different techniques. While ordinal, one-hot, and hashing encoders have similar equivalents in the existing scikit-learn version, the transformers in this library all share a few useful properties:

• First-class support for pandas dataframes as an input (and optionally as output)

• Can explicitly configure which columns in the data are encoded by name or index, or infer non-numeric columns regardless of input type

• Can drop any columns with very low variance based on training set optionally

• Portability: train a transformer on data, pickle it, reuse it later and get the same thing out.

• Full compatibility with sklearn pipelines, input an array-like dataset like any other transformer