If you are choosing a version today, understand these critical differences:

You can use environment variables like BOKEH_LOG_LEVEL=debug to troubleshoot complex serialization issues. 5. Resources for Further Deep Dives

If your logic exceeds 20 lines of JavaScript, consider running a bokeh serve app instead. The CustomJS debugger in 2.3.3 is less friendly than modern devtools.

Added support for multi-line axis labels and TeX-style superscripts for log axes. 3. Implementation Guide

Users can define their own "properties-only" subclasses to share data across different parts of an application more efficiently.

While not a feature-packed release (that was 2.3.0), version 2.3.3 introduced subtle yet critical refinements:

Bokeh 2.3.3 inherits the major capabilities introduced earlier in the 2.3.x cycle, which significantly upgraded the library's aesthetic and functional depth:

Improved the extension system to ensure it fetches the exact version of resources from the Bokeh Content Delivery Network (CDN), preventing version mismatches. Defining Features of the Bokeh 2.3 Series

import bokeh print(bokeh.__version__) # Should output: 2.3.3 print(bokeh.__version__) # Should output: 2.3.3

Standardizes the "look and feel" (e.g., fonts, colors) across all your dashboards. Appearance

Even a stable version has pitfalls. Follow these guidelines for Bokeh 2.3.3.

Bokeh is a powerful Python library for creating interactive and scalable visualizations for modern web browsers. Unlike static plotting libraries (like Matplotlib), Bokeh generates visualizations as HTML/JavaScript applications. This means plots come with native panning, zooming, hovering, and linking capabilities without any JavaScript code from the user.

The "brain" of your plot; holds the data and enables automatic synchronization between Python and the browser. Basic Plotting

Bokeh 2.3.3

If you are choosing a version today, understand these critical differences:

You can use environment variables like BOKEH_LOG_LEVEL=debug to troubleshoot complex serialization issues. 5. Resources for Further Deep Dives

If your logic exceeds 20 lines of JavaScript, consider running a bokeh serve app instead. The CustomJS debugger in 2.3.3 is less friendly than modern devtools.

Added support for multi-line axis labels and TeX-style superscripts for log axes. 3. Implementation Guide

Users can define their own "properties-only" subclasses to share data across different parts of an application more efficiently.

While not a feature-packed release (that was 2.3.0), version 2.3.3 introduced subtle yet critical refinements:

Bokeh 2.3.3 inherits the major capabilities introduced earlier in the 2.3.x cycle, which significantly upgraded the library's aesthetic and functional depth:

Improved the extension system to ensure it fetches the exact version of resources from the Bokeh Content Delivery Network (CDN), preventing version mismatches. Defining Features of the Bokeh 2.3 Series

import bokeh print(bokeh.__version__) # Should output: 2.3.3 print(bokeh.__version__) # Should output: 2.3.3

Standardizes the "look and feel" (e.g., fonts, colors) across all your dashboards. Appearance

Even a stable version has pitfalls. Follow these guidelines for Bokeh 2.3.3.

Bokeh is a powerful Python library for creating interactive and scalable visualizations for modern web browsers. Unlike static plotting libraries (like Matplotlib), Bokeh generates visualizations as HTML/JavaScript applications. This means plots come with native panning, zooming, hovering, and linking capabilities without any JavaScript code from the user.

The "brain" of your plot; holds the data and enables automatic synchronization between Python and the browser. Basic Plotting