AnyChart vs Bokeh

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

AnyChart is a Data Visualization solution with tags like charts, dashboards, data-visualization, javascript.

It boasts features such as Over 80 chart types including line, area, bar, pie, scatter, stock, maps, heatmaps, gauges, etc, Interactive and customizable dashboards, Drill down and data filtering capabilities, Robust API for advanced customization, Works across devices and screen sizes, Export charts as images and PDFs, Animate charts and add interactivity, Real-time and streaming data support, Integrates with popular frameworks like React, Angular, Vue.js, Can be embedded into web apps or work as a standalone library and pros including Large variety of chart types, Highly customizable and interactive charts, Powerful data visualization capabilities, Lightweight and fast, Integrates well with many frameworks and libraries, Good documentation and samples, Free version available.

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.

AnyChart

AnyChart

AnyChart is a lightweight and robust JavaScript charting library that allows developers to quickly create interactive charts and dashboards. With over 80 chart types and 1,000 customizable charting features, AnyChart enables building dashboards with advanced analytical capabilities for both web and mobile applications.

Categories:
charts dashboards data-visualization javascript

AnyChart Features

  1. Over 80 chart types including line, area, bar, pie, scatter, stock, maps, heatmaps, gauges, etc
  2. Interactive and customizable dashboards
  3. Drill down and data filtering capabilities
  4. Robust API for advanced customization
  5. Works across devices and screen sizes
  6. Export charts as images and PDFs
  7. Animate charts and add interactivity
  8. Real-time and streaming data support
  9. Integrates with popular frameworks like React, Angular, Vue.js
  10. Can be embedded into web apps or work as a standalone library

Pricing

  • Free
  • Commercial License

Pros

Large variety of chart types

Highly customizable and interactive charts

Powerful data visualization capabilities

Lightweight and fast

Integrates well with many frameworks and libraries

Good documentation and samples

Free version available

Cons

Steep learning curve

Advanced features require complex coding

Limited support options on free version

Not as feature rich as some commercial competitors


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:
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