bokeh python
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What is bokeh python
Bokeh is an open-source Python visualization library used to build interactive charts and dashboards for web browsers. It targets Python developers and data teams that need programmatic plotting with interactivity such as zooming, panning, tooltips, and linked selections. Bokeh can render to standalone HTML/JavaScript, integrate with Jupyter notebooks, and run server-backed applications for streaming or callback-driven interactions.
Interactive web-ready visualizations
Bokeh produces interactive plots that run in modern browsers using HTML and JavaScript outputs. It supports common interaction patterns such as hover tooltips, selection, zoom/pan, and linked brushing across multiple plots. This makes it suitable for embedding interactive visuals into internal web apps and reports without requiring users to install Python.
Multiple integration options
Bokeh works in Jupyter environments, can export standalone HTML documents, and can be deployed as a Bokeh Server application for richer interactivity. The server mode enables Python callbacks, periodic updates, and streaming data scenarios. These options allow teams to choose between static deployment and application-style deployments depending on governance and infrastructure constraints.
Extensible plotting and models
Bokeh exposes a model-based architecture (plots, glyphs, data sources, tools, and layouts) that can be composed into custom visual applications. It supports custom extensions via JavaScript/TypeScript for specialized behaviors and rendering. This extensibility can help teams implement domain-specific visualization components when built-in primitives are insufficient.
Not a general UI component suite
Bokeh focuses on data visualization and dashboard-style layouts rather than providing a broad set of enterprise UI widgets. Teams building full business applications often need additional front-end frameworks for navigation, forms, authentication, and complex UI patterns. As a result, Bokeh typically serves as the visualization layer within a larger application stack.
Server deployment adds complexity
Advanced interactivity that relies on Python callbacks generally requires running and operating Bokeh Server. This introduces operational considerations such as scaling, session management, security controls, and integration with existing web infrastructure. For some organizations, these requirements can be heavier than using purely client-side visualization components.
Learning curve for fine control
Achieving highly customized visuals often requires understanding Bokeh’s model system, data sources, and callback mechanisms. Debugging interactions can involve both Python and browser-side JavaScript behavior, especially when custom extensions are used. Teams may need additional time to standardize patterns and build reusable components for consistent results.
Plan & Pricing
Pricing model: Open-source / Free (BSD license) Free tier/trial: Permanently free tier available (open-source) Example costs: None — Bokeh is free to download and use Discount options: Not applicable
Notes: Bokeh is BSD-licensed open-source software; the project accepts donations (fiscal sponsorship via NumFOCUS) to support development and hosting.
Seller details
Anaconda, Inc.
Austin, Texas, USA
2012
Private
https://www.anaconda.com/
https://x.com/anacondainc
https://www.linkedin.com/company/anaconda-inc/
