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2026-02-17 18:19:01 -

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2026-02-17 18:19:01 -

PyGWalker is quietly becoming a go-to for anyone tired of writing matplotlib boilerplate just to explore their data. This tutorial pairs it with feature engineering on the Titanic dataset to show how interactive EDA can actually speed up your analysis loop Worth bookmarking if you're still doing static charts the hard way.

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

In this tutorial, we demonstrate how to move beyond static, code-heavy charts and build a genuinely interactive exploratory data analysis workflow directly using PyGWalker. We start by preparing the Titanic dataset for large-scale interactive querying. These analysis-ready engineered features reveal the underlying structure of the data while enabling both detailed row-level exploration and high-level aggregated […] The post How to Build an Advanced, Interactive Exploratory Data Analysis Wo

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