Advanced Features in edaflow
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This section covers advanced capabilities for power users:

- Computer Vision EDA: Class-wise image sample visualization, image quality assessment
- Smart Categorical Encoding: Automated selection and application of encoding strategies
- Outlier Handling: Multiple statistical methods for robust outlier detection and replacement
- Integration with pandas, numpy, scikit-learn, plotly, and more
- Customizable display and dark mode support for notebooks

**New Advanced Features**
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- Faceted grid visualization: `display_facet_grid(df, col, row, hue, kind, ...)`
- Feature scaling utility: `scale_features(df, columns, method)`
- Rare category grouping: `group_rare_categories(df, column, threshold, new_value)`
- Figure export API: `export_figure(fig, filename, format, dpi)`

See the API Reference for details on each advanced function, and the Visualization Guide for code examples.
