Useful Data Tips

Bokeh

⏱️ 8 sec read 📊 Data Visualization

What it is: Interactive visualization for modern browsers. Python library with Bokeh Server for real-time updates.

What It Does Best

Streaming data. Bokeh Server enables live, real-time dashboard updates. Data changes, charts update automatically.

Large datasets. Handles 100k+ points efficiently. Server-side downsampling for performance.

Web-native. Outputs modern HTML5/JavaScript. No plugins, works everywhere.

Key Features

Bokeh Server: Real-time streaming and interactive apps

Large data handling: Efficiently render 100k+ points

Interactive widgets: Sliders, dropdowns, buttons for dashboards

Linked interactions: Connect multiple plots, selections sync

Custom extensions: Write JavaScript for advanced features

Pricing

Free. Open source, BSD license.

When to Use It

✅ Real-time dashboards

✅ Streaming data visualization

✅ Large datasets (100k+ points)

✅ Web applications with Python backend

✅ Need custom interactive widgets and controls

When NOT to Use It

❌ Quick exploratory analysis (Plotly simpler)

❌ Static images for papers (Matplotlib better)

❌ Don't need server features (simpler tools available)

❌ Simple one-off charts (overhead too high)

❌ Mobile-first applications (performance varies)

Common Use Cases

Real-time monitoring: Live system metrics, sensor data, financial tickers

Interactive dashboards: Business intelligence with filters and controls

Large dataset exploration: Scatter plots with millions of points

Scientific visualization: Complex multi-panel interactive plots

Web applications: Embedded visualizations in Flask/Django apps

Bokeh vs Alternatives

vs Plotly: Bokeh better for streaming/server apps; Plotly simpler API, easier to start

vs Dash: Bokeh more lightweight; Dash better ecosystem, easier deployment

vs Matplotlib: Bokeh interactive and web-native; Matplotlib better static output

Unique Strengths

Bokeh Server: Built-in server for real-time Python-JavaScript communication

Performance: Optimized for large datasets with WebGL support

Flexibility: Low-level API for complete control

No JavaScript required: Build complex web apps in pure Python

Bottom line: Built for real-time web applications. If your data updates live and you need interactive dashboards, Bokeh is the answer.

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