https://github.com/kjam/datafuzz

A data science Python library aimed at adding fuzz, noise and other issues to your data for testing purposes.
https://github.com/kjam/datafuzz

Last synced: 4 months ago
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A data science Python library aimed at adding fuzz, noise and other issues to your data for testing purposes.

• Host: GitHub
• URL: https://github.com/kjam/datafuzz
• Owner: kjam
• License: other
• Created: 2017-09-22T07:39:43.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2023-03-30T15:25:12.000Z (about 3 years ago)
• Last Synced: 2026-01-08T08:30:40.086Z (5 months ago)
• Language: Python
• Size: 164 KB
• Stars: 30
• Watchers: 3
• Forks: 4
• Open Issues: 3
• Metadata Files:
  • Readme: README.rst
  • Changelog: HISTORY.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE

Awesome Lists containing this project

README

          
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datafuzz

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.. image:: https://img.shields.io/pypi/v/datafuzz.svg

        :target: https://pypi.python.org/pypi/datafuzz

.. image:: https://img.shields.io/travis/kjam/datafuzz.svg

        :target: https://travis-ci.org/kjam/datafuzz

.. image:: https://readthedocs.org/projects/datafuzz/badge/?version=latest

        :target: https://datafuzz.readthedocs.io/en/latest/?badge=latest

        :alt: Documentation Status

.. image:: https://pyup.io/repos/github/kjam/datafuzz/shield.svg

     :target: https://pyup.io/repos/github/kjam/datafuzz/

     :alt: Updates

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.

Features

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* 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

Installation

------------

Install datafuzz by running::

    $ pip install datafuzz

Recommended use is with a proper Virtual Environment (learn more about `virtual environments `).

For more details see Installation Instructions.

Contribute

----------

- Issue Tracker: https://github.com/kjam/datafuzz/issues

- Source Code: https://github.com/kjam/datafuzz

Support

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If you are having issues, please let reach out via the Repository issues.

License

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The project is licensed under the BSD license.

Credits

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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