Bokeh vs D3.js

Struggling to choose between Bokeh and D3.js? Both products offer unique advantages, making it a tough decision.

Bokeh is a Development solution with tags like python, data-visualization, interactive, graphics, web-browser.

It boasts features such as Interactive data visualization, Supports streaming data, Python library, Targets modern web browsers, Elegant and concise graphics, High-performance interactivity, Can handle large datasets and pros including Very flexible and customizable visualizations, Integrates well with other Python data tools like NumPy and Pandas, Open source and free, Good performance even with large datasets, Nice web-based interface for sharing visualizations.

On the other hand, D3.js is a Development product tagged with javascript, data-binding, svg, graphs, charts.

Its standout features include Data-Driven DOM Manipulation, Powerful Visualization Components, Animated Transitions, Highly Customizable and Flexible, Wide Browser Support, and it shines with pros like Open source and free, Large and active community support, Integrates well with other JS libraries, High performance with canvas rendering, Supports large datasets and real-time updates.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Bokeh

Bokeh

Bokeh is an interactive data visualization library for Python that targets modern web browsers for presentation. It offers elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.

Categories:
Development Data Visualization
python data-visualization interactive graphics web-browser

Bokeh Features

  1. Interactive data visualization
  2. Supports streaming data
  3. Python library
  4. Targets modern web browsers
  5. Elegant and concise graphics
  6. High-performance interactivity
  7. Can handle large datasets

Pricing

  • Open Source

Pros

Very flexible and customizable visualizations

Integrates well with other Python data tools like NumPy and Pandas

Open source and free

Good performance even with large datasets

Nice web-based interface for sharing visualizations

Cons

Steeper learning curve than some visualization libraries

Visualizations can be more complex to build

Limited built-in statistical analysis features

Requires knowledge of Python and web development

Not as simple as drag-and-drop visualization builders

D3.js

D3.js

D3.js is a JavaScript library for visualizing data with HTML, SVG, and CSS. It allows developers to bind arbitrary data to DOM elements and apply data-driven transformations to the document. Common uses are for creating interactive graphs, charts, maps, and data visualizations.

Categories:
Development Data Visualization
javascript data-binding svg graphs charts

D3.js Features

  1. Data-Driven DOM Manipulation
  2. Powerful Visualization Components
  3. Animated Transitions
  4. Highly Customizable and Flexible
  5. Wide Browser Support

Pricing

  • Open Source

Pros

Open source and free

Large and active community support

Integrates well with other JS libraries

High performance with canvas rendering

Supports large datasets and real-time updates

Cons

Steep learning curve

Complex documentation

Requires knowledge of SVG

Not optimized for mobile platforms

Version 3.0 has breaking changes from 2.0