Interactive Plots with Plotly

Purpose

Interactive plots enable users to explore data dynamically via hover tooltips, zooming, panning, and legend toggling. Plotly is a popular library for building web-based interactive charts.

Example in the Notebook

The notebook creates two Plotly Express charts:

1. Scatter plot: total bill vs. tip, colored by day, with hover data (sex, party size).
2. Line plot: monthly passenger counts over time.

Both are exported to HTML files that can be opened in any web browser.

Key Code Snippet

import plotly.express as px
fig = px.scatter(tips, x='total_bill', y='tip', color='day', 
                 hover_data=['sex', 'size'], title='Tips: total bill vs tip')
fig.write_html('interactive_tips.html', include_plotlyjs='cdn')

Features

• Hover tooltips: Show data values and custom labels.
• Zoom & pan: Click and drag to zoom; double-click to reset.
• Legend toggling: Click legend items to show/hide traces.
• Download: Camera icon to save as PNG.
• Export: Save as HTML for sharing or embedding in reports.

Customization Tips

• Markers: Change marker size with size='size' column or fixed marker_size=10.
• Colors: Use color='day' for categorical or color_continuous_scale='Viridis' for continuous.
• Faceting: Add facet_row='day' or facet_col='day' for subplots.
• Annotations: Use fig.add_annotation() to add text or arrows.
• Custom styling: Modify fig.update_layout() and fig.update_traces().

When to Use

• Always: for exploratory data analysis and interactive dashboards.
• Consider: exporting to HTML for stakeholder sharing (no Python environment needed).
• Avoid: Plotly for very large datasets (>50k points) without aggregation; consider datashader instead.

See Also

• Plotly Express: scatter(), line()
• Plotly documentation: plotly.com