Metadata-Version: 1.1
Name: category-encoders
Version: 1.1.2
Summary: A collection sklearn transformers to encode categorical variables as numeric
Home-page: https://github.com/wdm0006/categorical_encoding
Author: Will McGinnis
Author-email: will@pedalwrencher.com
License: BSD
Download-URL: https://github.com/wdm0006/categorical_encoding/tarball/1.1.2
Description: Categorical Encoding Methods
        ============================
        
        A set of example problems examining different encoding methods for categorical variables for the purpose of 
        classification. Optionally, install the library of encoders as a package and use them in your projects directly.  They 
        are all available as methods or as scikit-learn compatible transformers. 
        
        Docs [here](http://wdm0006.github.io/categorical_encoding/)
        
        Encoding Methods
        ----------------
        
         * Ordinal
         * One-Hot
         * Binary
         * Helmert Contrast
         * Sum Contrast
         * Polynomial Contrast
         * Backward Difference Contrast
         * Simple Hashing
        
        Usage
        -----
        
        Either run the examples in encoding_examples.py, or install as:
        
            pip install 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=[...])
        
        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 column will be encoded, so be careful with that.
        
        Datasets
        --------
        
        The datasets used in the examples are car, mushroom, and splice datasets from the UCI dataset repository, found here:
        
        [datasets](https://archive.ics.uci.edu/ml/datasets)
        
        License
        -------
        
        BSD
Keywords: python data science machine learning pandas sklearn
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
