A data-science library built for testing cleaning, schema validation and model robustness. It messes up your data so you can test your data engineering and data science code (before it breaks in production).
.. image:: https://img.shields.io/pypi/v/datafuzz.svg
.. image:: https://img.shields.io/travis/kjam/datafuzz.svg
.. image:: https://readthedocs.org/projects/datafuzz/badge/?version=latest
:alt: Documentation Status
.. image:: https://pyup.io/repos/github/kjam/datafuzz/shield.svg
A data-science library built for testing cleaning, schema validation and model robustness. Datafuzz messes up your data so you can test things before they go wrong in production.
* Free software: BSD license
* Documentation: https://datafuzz.readthedocs.io.
* Transform your data by adding noise to a subset of your rows
* Duplicate data to test your duplication handling
* Generate synthetic data for use in your testing suite
* Insert random "dumb" fuzzing strategies to see how your tools cope with bad data
* Seamlessly handle normal input and output types including CSVs, JSON, SQL, numpy and pandas
Install datafuzz by running::
$ pip install datafuzz
Recommended use is with a proper Virtual Environment (learn more about `virtual environments <http://docs.python-guide.org/en/latest/dev/virtualenvs/>`).
For more details see :doc:`/installation`
- Issue Tracker: https://github.com/kjam/datafuzz/issues
- Source Code: https://github.com/kjam/datafuzz
If you are having issues, please let reach out via the Repository issues.
The project is licensed under the BSD license.
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
* First release on PyPI.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.