Plotly (Python)
What it is: Python library for interactive charts. Hover tooltips, zoom, pan built-in. Exports to HTML.
What It Does Best
Interactive by default. Every chart gets zoom, pan, hover. No extra code needed.
Export to HTML. Share visualizations as standalone files. Recipients don't need Python installed.
Dash framework. Build web dashboards entirely in Python. No JavaScript required.
Key Features
40+ chart types: From basic bar charts to 3D surface plots and maps
HTML export: Share interactive charts as standalone HTML files
Dash integration: Build full web applications in pure Python
Plotly Express: High-level API for quick visualizations
Plotly Graph Objects: Low-level API for fine control
Pricing
Open source: Free for all features
Dash Enterprise: $950+/month (commercial deployment)
When to Use It
✅ Need to share interactive charts with non-technical users
✅ Want interactivity without learning D3.js
✅ Building data apps in Python only
✅ Jupyter notebooks that need interactivity
✅ Presenting insights to stakeholders
When NOT to Use It
❌ Publication-quality static images (Matplotlib better)
❌ Extreme customization needs (D3.js better)
❌ Very large datasets (performance degrades above 100k points)
❌ Need declarative syntax (Altair cleaner)
❌ Primarily print media
Common Use Cases
Interactive presentations: Share findings with stakeholders via HTML files
Data science reporting: Jupyter notebooks with interactive exploration
Web dashboards: Full applications with Dash framework
Geographic visualizations: Interactive maps and choropleth charts
3D visualizations: Surface plots, scatter plots, network graphs
Plotly vs Alternatives
vs Matplotlib: Plotly interactive by default; Matplotlib better static output, more control
vs Altair: Plotly more chart types; Altair cleaner, declarative syntax
vs Bokeh: Plotly easier API; Bokeh better for server-side apps
Unique Strengths
HTML export: Share interactive visualizations without Python
Dash ecosystem: Build entire apps without JavaScript
Plotly Express: Simplest API for common interactive charts
3D capabilities: Best Python library for 3D visualization
Bottom line: Perfect middle ground between static Matplotlib and complex D3.js. Interactive, shareable, and Python-native.