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 datafuzzRecommended 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 Installation Instructions.
- 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.