Vis.js vs Bokeh

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

Vis.js is a Data Visualization solution with tags like data-visualization, graphs, networks, timelines.

It boasts features such as Network graphs, Timelines, Graph2d, Graph3d and pros including Open source, Good documentation, Active community, Integrates well with web apps.

On the other hand, Bokeh is a Development product tagged with python, data-visualization, interactive, graphics, web-browser.

Its standout features include Interactive data visualization, Supports streaming data, Python library, Targets modern web browsers, Elegant and concise graphics, High-performance interactivity, Can handle large datasets, and it shines with pros like 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.

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.

Vis.js

Vis.js

Vis.js is a dynamic, browser based visualization library. It offers utilities for data visualizations like timelines, networks and graphs out of the box. It's easy to get started with and integrates well with web applications.

Categories:
Data Visualization Javascript Libraries
data-visualization graphs networks timelines

Vis.js Features

  1. Network graphs
  2. Timelines
  3. Graph2d
  4. Graph3d

Pricing

  • Open Source

Pros

Open source

Good documentation

Active community

Integrates well with web apps

Cons

Steep learning curve

Not suitable for large datasets

Limited customization options

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