How to Build an Advanced, Interactive Exploratory Data Analysis Workflow Using PyGWalker and Feature-Engineered Data

This tutorial demonstrates building an interactive exploratory data analysis workflow using PyGWalker and feature engineering. Moving beyond static code-heavy visualizations, it shows how to prepare the Titanic dataset for large-scale interactive queries. Feature-engineered data reveals underlying patterns, enabling both detailed row-level exploration and high-level aggregation analysis through an interactive interface.