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).
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
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 Installation Instructions.
If you are having issues, please let reach out via the Repository issues.
The project is licensed under the BSD license.
Update python versions
Fix several nondeterministic bugs
First release on PyPI.
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