EJSCharts vs Bokeh

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

EJSCharts is a Development solution with tags like charts, data-visualization, graphing.

It boasts features such as Supports various chart types like line, bar, pie, scatter, radar, candlestick etc, Interactive and customizable charts, Animated transitions and interactions, Touch support for mobile devices, Canvas rendering for better performance, Client-side data processing, Export charts as images, Open source and free and pros including Easy to use API, Good documentation, Lightweight and fast, Many customization options, Supports large datasets, Works across devices and browsers.

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.

EJSCharts

EJSCharts

EJSCharts is a feature-rich JavaScript charting library that allows you to create interactive charts and graphs in the browser. It has support for a wide variety of chart types including line, bar, pie, scatter, radar, candlestick and more.

Categories:
Development Javascript Libraries
charts data-visualization graphing

EJSCharts Features

  1. Supports various chart types like line, bar, pie, scatter, radar, candlestick etc
  2. Interactive and customizable charts
  3. Animated transitions and interactions
  4. Touch support for mobile devices
  5. Canvas rendering for better performance
  6. Client-side data processing
  7. Export charts as images
  8. Open source and free

Pricing

  • Open Source

Pros

Easy to use API

Good documentation

Lightweight and fast

Many customization options

Supports large datasets

Works across devices and browsers

Cons

Less chart types than some competitors

Lacks some advanced features like 3D charts

Relies on Canvas, so no SVG output

Requires learning JavaScript to use

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