Feature engineering and selection package with Scikit-learn's fit transform functionality
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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: A new open-source Python package for feature engineering
Practical Code Implementations of Feature Engineering for Machine Learning with Python
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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
>>> 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
>>> 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 or in the documentation.
Details about how to contribute can be found in the Contribute Page
Briefly:
git clone https://github.com/<YOURUSERNAME>/feature_engine.git
cd feature_engine
pip install -e .
pip install -r requirements.txt
and
pip install -r test_requirements.txt
git checkout -b myfeaturebranch
Thank you!!
Feature-engine documentation is built using Sphinx and is hosted on Read the Docs.
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
The content of this repository is licensed under a BSD 3-Clause license.
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FAQs
Feature engineering and selection package with Scikit-learn's fit transform functionality
We found that feature-engine demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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