Advanced Visualization
======================

This example showcases advanced plotting and dashboard creation with edaflow.

**1. Customizing Plots**
------------------------
.. code-block:: python

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

**2. Interactive Dashboards**
-----------------------------
.. code-block:: python

	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**
--------------------------------
.. code-block:: python

	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**
-------------------------------------
.. code-block:: python

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

**5. Custom Themes and Styles**
-------------------------------
.. code-block:: python

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

**6. Exporting Figures**
------------------------
.. code-block:: python

	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.
