dirty_cat

.. image:: https://dirty-cat.github.io/stable/_static/dirty_cat.svg :align: center :alt: dirty_cat logo

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dirty_cat is a Python module for machine-learning on dirty categorical variables.

Website: https://dirty-cat.github.io/

dirty_cat's SuperVectorizer automatically turns pandas data frames into numerical arrays suitable for learning.

For a detailed description of the problem of encoding dirty categorical data, see Similarity encoding for learning with dirty categorical variables <https://hal.inria.fr/hal-01806175>_ [1]_ and Encoding high-cardinality string categorical variables <https://hal.inria.fr/hal-02171256v4>_ [2]_.

Installation

Dependencies

dirty_cat requires:

- Python (>= 3.8)
- NumPy (>= 1.17.3)
- SciPy (>= 1.4.0)
- scikit-learn (>= 0.21.0)
- pandas (>= 1.1.5)

Optional dependency:

- python-Levenshtein for faster edit distances (not used for the n-gram
  distance)

User installation

If you already have a working installation of NumPy and SciPy, the easiest way to install dirty_cat is using pip ::

pip install -U --user dirty_cat

Other implementations

-  Spark ML: https://github.com/rakutentech/spark-dirty-cat


References
~~~~~~~~~~

.. [1] Patricio Cerda, Gaël Varoquaux, Balázs Kégl. Similarity encoding for learning with dirty categorical variables. 2018. Machine Learning journal, Springer.
.. [2] Patricio Cerda, Gaël Varoquaux. Encoding high-cardinality string categorical variables. 2020. IEEE Transactions on Knowledge & Data Engineering.