Metadata-Version: 2.1
Name: feature_engine
Version: 1.5.1
Summary: Feature engineering package with Scikit-learn's fit transform functionality
Home-page: http://github.com/feature-engine/feature_engine
Author: Soledad Galli
Author-email: solegalli@protonmail.com
License: BSD 3 clause
Description: # Feature Engine
        
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        [<img src="https://raw.githubusercontent.com/feature-engine/feature_engine/main/docs/images/logo/FeatureEngine.png" width="248">](http://feature-engine.readthedocs.io)
        
        Feature-engine is a Python library with multiple transformers to engineer and select features for use in machine learning models. 
        Feature-engine's transformers follow Scikit-learn's functionality with fit() and transform() methods to learn the 
        transforming parameters from the data and then transform it.
        
        
        ## Feature-engine features in the following resources
        
        * [Feature Engineering for Machine Learning, Online Course](https://courses.trainindata.com/p/feature-engineering-for-machine-learning)
        
        * [Feature Selection for Machine Learning, Online Course](https://courses.trainindata.com/p/feature-selection-for-machine-learning)
        
        * [Feature Engineering for Time Series Forecasting, Online Course](https://www.courses.trainindata.com/p/feature-engineering-for-forecasting)
        
        * [Python Feature Engineering Cookbook](https://packt.link/0ewSo)
        
        * [Feature Selection in Machine Learning with Python Book](https://leanpub.com/feature-selection-in-machine-learning)
        
        
        ## Blogs about Feature-engine
        
        * [Feature-engine: A new open-source Python package for feature engineering](https://trainindata.medium.com/feature-engine-a-new-open-source-python-package-for-feature-engineering-29a0ab88ea7c)
        
        * [Practical Code Implementations of Feature Engineering for Machine Learning with Python](https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd)
        
        
        ## Documentation
        
        * [Documentation](http://feature-engine.readthedocs.io)
        
        
        ## Current Feature-engine's transformers include functionality for:
        
        * Missing Data Imputation
        * Categorical Encoding
        * Discretisation
        * Outlier Capping or Removal
        * Variable Transformation
        * Variable Creation
        * Variable Selection
        * Datetime Features
        * Time Series
        * Preprocessing
        * Scikit-learn Wrappers
        
        ### Imputation Methods
        * MeanMedianImputer
        * RandomSampleImputer
        * EndTailImputer
        * AddMissingIndicator
        * CategoricalImputer
        * ArbitraryNumberImputer
        * DropMissingData
        
        ### Encoding Methods
        * OneHotEncoder
        * OrdinalEncoder
        * CountFrequencyEncoder
        * MeanEncoder
        * WoEEncoder
        * PRatioEncoder
        * RareLabelEncoder
        * DecisionTreeEncoder
        * StringSimilarityEncoder
        
        ### Discretisation methods
        * EqualFrequencyDiscretiser
        * EqualWidthDiscretiser
        * DecisionTreeDiscretiser
        * ArbitraryDiscreriser
        
        ### Outlier Handling methods
        * Winsorizer
        * ArbitraryOutlierCapper
        * OutlierTrimmer
        
        ### Variable Transformation methods
        * LogTransformer
        * LogCpTransformer
        * ReciprocalTransformer
        * ArcsinTransformer
        * PowerTransformer
        * BoxCoxTransformer
        * YeoJohnsonTransformer
        
        ### Variable Creation:
         * MathFeatures
         * RelativeFeatures
         * CyclicalFeatures
        
        ### Feature Selection:
         * DropFeatures
         * DropConstantFeatures
         * DropDuplicateFeatures
         * DropCorrelatedFeatures
         * SmartCorrelationSelection
         * ShuffleFeaturesSelector
         * SelectBySingleFeaturePerformance
         * SelectByTargetMeanPerformance
         * RecursiveFeatureElimination
         * RecursiveFeatureAddition
         * DropHighPSIFeatures
         * SelectByInformationValue
        
        ### Datetime
         * DatetimeFeatures
         
        ### Time Series
         * LagFeatures
         * WindowFeatures
         * ExpandingWindowFeatures
         
        ### Preprocessing
         * MatchCategories
         * MatchVariables
         
        ### Wrappers:
         * SklearnTransformerWrapper
        
        ## Installation
        
        From PyPI using pip:
        
        ```
        pip install feature_engine
        ```
        
        From Anaconda:
        
        ```
        conda install -c conda-forge feature_engine
        ```
        
        Or simply clone it:
        
        ```
        git clone https://github.com/feature-engine/feature_engine.git
        ```
        
        ## Example Usage
        
        ```python
        >>> import pandas as pd
        >>> from feature_engine.encoding import RareLabelEncoder
        
        >>> data = {'var_A': ['A'] * 10 + ['B'] * 10 + ['C'] * 2 + ['D'] * 1}
        >>> data = pd.DataFrame(data)
        >>> data['var_A'].value_counts()
        ```
        
        ```
        Out[1]:
        A    10
        B    10
        C     2
        D     1
        Name: var_A, dtype: int64
        ```
            
        ```python 
        >>> rare_encoder = RareLabelEncoder(tol=0.10, n_categories=3)
        >>> data_encoded = rare_encoder.fit_transform(data)
        >>> data_encoded['var_A'].value_counts()
        ```
        
        ```
        Out[2]:
        A       10
        B       10
        Rare     3
        Name: var_A, dtype: int64
        ```
        
        Find more examples in our [Jupyter Notebook Gallery](https://nbviewer.org/github/feature-engine/feature-engine-examples/tree/main/) 
        or in the [documentation](http://feature-engine.readthedocs.io).
        
        ## Contribute
        
        Details about how to contribute can be found in the [Contribute Page](https://feature-engine.readthedocs.io/en/latest/contribute/index.html)
        
        Briefly:
        
        - Fork the repo
        - Clone your fork into your local computer: ``git clone https://github.com/<YOURUSERNAME>/feature_engine.git``
        - navigate into the repo folder ``cd feature_engine``
        - Install Feature-engine as a developer: ``pip install -e .``
        - Optional: Create and activate a virtual environment with any tool of choice
        - Install Feature-engine dependencies: ``pip install -r requirements.txt`` and ``pip install -r test_requirements.txt``
        - 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!!
        
        
        ### Documentation
        
        Feature-engine documentation is built using [Sphinx](https://www.sphinx-doc.org) and is hosted on [Read the Docs](https://readthedocs.org/).
        
        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 using: ``sphinx-build -b html docs build``
        
        
        ## License
        
        BSD 3-Clause
        
        ## Sponsor us
        
        [Sponsor us](https://github.com/sponsors/feature-engine) and support further our 
        mission to democratize machine learning and programming tools through open-source 
        software.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.8.0
Description-Content-Type: text/markdown
