Create Streamlit Data Validation App With Deepchecks Test Suites and Pandas-Profiling Reports

Learn how to build a Streamlit data validation app using Deepchecks test suites and pandas-profiling reports in this hands-on tutorial. Explore the process of integrating Deepchecks, an open-source Python library for data scientists and ML engineers, into a Streamlit application. Discover how to render test suites, implement file uploading, create profile and validation choice menus, and integrate pandas-profiling. Follow along as the instructor demonstrates Deepchecks test integration, test validation, and JSON filtering of test results. By the end of this 52-minute tutorial, gain the skills to create a comprehensive Python application for displaying DeepChecks reports and enhancing your data validation workflows.

- Hands-on Lab Starts
- Content Intro
- Complete Application Demo
- Deepchecks and previous Tutorial
- Pandas and streamlit-pandas profiling
- Streamlit coding starts
- Deepchecks sample script
- Streamlit file selected/uploader
- Profile and Validation choice menu
- Pandas-profiling implemented
- Deepchecks test integration
- Deepchecks test validation added
- Test results JSON filtering
- Code and Functionality Recap
- Code push to GitHub
- Credits