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Head-to-head · Data Visualization Libraries Software

Matplotlib vs Bokeh

  • Updated Oct 2026
  • Both researched from official sources
  • 1 check side by side
Higher score Matplotlib #2 in Data Visualization Libraries Software 9.1/10 Free plan Free plan Visit Matplotlib
Bokeh #6 in Data Visualization Libraries Software 8.6/10 Free plan Free plan Visit Bokeh

Matplotlib 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 scoreMatplotlib · 9.1/10
  • Free planboth

Matplotlib scores higher on our rubric for data visualization libraries software: 9.1 against 8.6 out of 10; our editors rank them #2 and #6.

Matplotlib is the better fit for python users making publication-quality plots. Bokeh is the better fit for python developers building interactive web plots.

  • Matplotlib fits best

    Python users making publication-quality plots

  • Bokeh fits best

    Python developers building interactive web plots

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Side by side

Feature Matplotlib 9.1/10 Visit ↗ Bokeh 8.6/10 Visit ↗
At a glance
Editor score 9.1 8.6
Ranking #2 in Data Visualization Libraries Software #6 in Data Visualization Libraries Software
Best for Python users making publication-quality plots Python developers building interactive web plots
Pricing model Free Free
Starting price Free plan Free plan
Free plan ✓ ✓
Free trial — —
Deployment Self-hosted, Desktop Self-hosted
Platforms Web, Windows, macOS, Linux Web
Support Community, Docs Community, Docs
Built for Solo, Small business, Mid-market, Enterprise Solo, Small business, Mid-market, Enterprise
Specs
Supported frameworks Not checked yet Not checked yet
Chart types Not checked yet Not checked yet
Rendering modes Not checked yet Not checked yet
Interactive features Not checked yet Not checked yet
Export formats Not checked yet Not checked yet
License model Not checked yet Not checked yet
Our review
Pros
  • Publication-quality plots with customizable styles and layouts
  • Static, animated, and interactive figures with pan and zoom
  • Exports to PNG, SVG, PDF, and PS formats
  • 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
  • Requires Python programming rather than no-code dashboard building
  • Browser-native interactive dashboards are not its primary focus
  • 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

Matplotlib is an open-source Python library for creating static, animated, and interactive visualizations. It suits Python developers, researchers, analysts, and scientific computing teams that need programmable charting and plotting. The…

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. MatplotlibData Visualization Libraries Software 9.1Free plan
  2. BokehData Visualization Libraries Software 8.6Free plan

Strengths and trade-offs

  • Matplotlib — where it wins

    • Publication-quality plots with customizable styles and layouts
    • Static, animated, and interactive figures with pan and zoom
    • Exports to PNG, SVG, PDF, and PS formats

    Where it doesn't

    • Requires Python programming rather than no-code dashboard building
    • Browser-native interactive dashboards are not its primary focus
    • 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
  • Matplotlib9.1/10 · Free plan

    A free, mature Python plotting library for publication-quality charts and figures.

    Visit MatplotlibFull verdict →
  • Bokeh8.6/10 · Free plan

    A free, open-source Python library for interactive plots, embedded visuals, and server-backed apps.

    Visit BokehFull verdict →

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