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Head-to-head · Data Visualization Libraries Software

Apache ECharts vs Bokeh

  • Updated Oct 2026
  • Both researched from official sources
  • 1 check side by side
Higher score Apache ECharts #3 in Data Visualization Libraries Software 9.0/10 Free plan Free plan Visit Apache ECharts
Bokeh #6 in Data Visualization Libraries Software 8.6/10 Free plan Free plan Visit Bokeh

Apache ECharts leads on 0 checks, Bokeh on 0, and 1 is even. Who comes out ahead on the 1 yes/no, price and count check where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreApache ECharts · 9.0/10
  • Free planboth

Apache ECharts scores higher on our rubric for data visualization libraries software: 9.0 against 8.6 out of 10; our editors rank them #3 and #6.

Apache ECharts is the better fit for free, flexible JavaScript visualizations. Bokeh is the better fit for python developers building interactive web plots.

  • Apache ECharts fits best

    Free, flexible JavaScript visualizations

  • Bokeh fits best

    Python developers building interactive web plots

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Side by side

Feature Apache ECharts 9.0/10 Visit ↗ Bokeh 8.6/10 Visit ↗
At a glance
Editor score 9.0 8.6
Ranking #3 in Data Visualization Libraries Software #6 in Data Visualization Libraries Software
Best for Free, flexible JavaScript visualizations Python developers building interactive web plots
Pricing model Free Free
Starting price Free plan Free plan
Free plan ✓ ✓
Free trial — —
Deployment Cloud Self-hosted
Platforms Web Web
Support Docs, Community Community, Docs
Built for Solo, Small business, Mid-market, Enterprise Solo, Small business, Mid-market, Enterprise
Specs
Supported frameworks JavaScript; usable with web frameworks Not checked yet
Chart types Line, bar, pie, scatter, map Not checked yet
Rendering modes Not checked yet Not checked yet
Interactive features Tooltips, zooming, panning, legend interaction Not checked yet
Export formats PNG, JPEG, SVG Not checked yet
License model Open-source Not checked yet
Our review
Pros
  • Offers line, bar, pie, scatter, and map charts.
  • Supports Canvas and SVG rendering with responsive resizing.
  • Includes tooltips, zooming, panning, and PNG, JPEG, or SVG export.
  • Offers pan, zoom, hover, tap, and selection tools for interactive plots
  • Supports standalone HTML, embeddable components, and Bokeh server apps
  • Works with Jupyter and a range of Python data sources
Cons
  • Focused on web applications rather than a range of deployment platforms.
  • Requires JavaScript configuration and developer involvement.
  • Support is centered on documentation and community channels.
  • Its workflow centers on Python rather than general JavaScript charting
  • Server applications require self-hosted deployment
  • Support is through community resources and documentation
Our verdict

Apache ECharts is an open-source JavaScript charting library for developers building interactive visualizations in web applications. Its chart options include line, bar, pie, scatter, and map charts, making it suitable for teams that want…

Read the review →

Bokeh is an open-source Python library for creating interactive plots, dashboards, and data applications in web browsers. It suits Python developers who want to explore data in notebooks, publish plots on web pages, or build browser…

Read the review →
  1. Apache EChartsData Visualization Libraries Software 9.0Free plan
  2. BokehData Visualization Libraries Software 8.6Free plan

Strengths and trade-offs

  • Apache ECharts — where it wins

    • Offers line, bar, pie, scatter, and map charts.
    • Supports Canvas and SVG rendering with responsive resizing.
    • Includes tooltips, zooming, panning, and PNG, JPEG, or SVG export.

    Where it doesn't

    • Focused on web applications rather than a range of deployment platforms.
    • Requires JavaScript configuration and developer involvement.
    • Support is centered on documentation and community channels.

  • Bokeh — where it wins

    • Offers pan, zoom, hover, tap, and selection tools for interactive plots
    • Supports standalone HTML, embeddable components, and Bokeh server apps
    • Works with Jupyter and a range of Python data sources

    Where it doesn't

    • Its workflow centers on Python rather than general JavaScript charting
    • Server applications require self-hosted deployment
    • Support is through community resources and documentation

More comparisons

All data visualization libraries software comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update