Advanced Visualization

This example showcases advanced plotting and dashboard creation with edaflow.

1. Customizing Plots

eda.display_boxplot(df, column='score', color='skyblue', title='Score Distribution')
eda.display_histogram(df, column='income', bins=30, color='orange')

2. Interactive Dashboards

eda.display_interactive_scatter(df, x='age', y='income', color='score')
eda.display_interactive_boxplot(df, column='score', group_by='category')

3. Publication-Ready Visuals

eda.display_correlation_matrix(df, cmap='coolwarm', annot=True)
eda.display_scatter_matrix(df, columns=['age', 'income', 'score'], figsize=(10,8))

4. Multi-Panel and Faceted Plots

eda.display_facet_grid(df, row='gender', col='region', plot_type='boxplot', column='score')

5. Custom Themes and Styles

eda.display_boxplot(df, column='score', style='seaborn-darkgrid')

6. Exporting Figures

fig = eda.display_boxplot(df, column='score')
fig.savefig('score_boxplot.png')

Tips: - Use color and grouping to highlight key insights - Adjust plot parameters for clarity and aesthetics - Export figures for presentations and reports - Try multi-panel plots to compare subgroups - Apply custom styles for consistent branding

See the User Guide for more visualization options and best practices.