AutoDQ is an open-source Python framework that automates data quality checks, cleaning, visualization, and reporting for analytics and machine learning projects. It helps data scientists and analysts quickly identify issues, clean datasets, generate visualizations, and produce reports without writing extensive custom code. The project is available on PyPI and hosted on GitHub under the MIT license.
autodq sits in PulseGate's Data science & ML workbench category. Manually inspecting, cleaning and validating data quality in data science and machine learning workflows. autodq is an open-source project aimed at data scientists. The project is open source (MIT). autodq is available on the command line and API, and it can be self-hosted.
Behind autodq is Joseph Ubani, and the product first shipped in 2026. The project is developed in the open on GitHub with 84 commits in the last 90 days. Among its 5 catalogued features are Automated Data Quality, Data Cleaning, and Data Visualization.
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