Developing a Streamlit App for ydata-profiling: Automated EDA in Python
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Description
This lecture walks through building a simple Streamlit application that generates ydata-profiling reports from any CSV file. You will learn how to structure the app, handle file uploads, manage large reports, and present clean, shareable EDA results with minimal code. The session is theory-first with a guided build so students understand each step.
What you will learn
• What automated EDA is and when to use it
• How ydata-profiling summarizes data quality, distributions, and correlations
• Streamlit basics: layout, widgets, file upload, caching
• Generating and displaying profiling reports inside a Streamlit app
• Exporting HTML/PDF reports for sharing
Topics covered
1. Why automated EDA saves time
2. Installing and importing: streamlit, pandas, ydata_profiling
3. App layout: title, sidebar, file uploader, options
4. Reading CSV safely and handling errors
5. Creating the ProfileReport with basic settings
6. Embedding the report (HTML) and offering downloads
7. Tips for larger files: sampling and cache
8. Next steps: adding charts, profiling options, and simple data cleaning
Assignment for students
• Build the app, test with a small public CSV (e.g., Iris or Titanic), and export the HTML report.
• Extend the app with two options: row sampling and a checkbox to include correlations.
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