Bokeh 2.3.3 =link= -

Configured custom extensions to fetch the exact matching version directly from the Bokeh CDN. This prevents major security and compatibility issues resulting from mismatched server and client environments. 💻 Sample Code: Creating a Basic Plot in Bokeh 2.3.3

Addressed a formatting issue with y-axis labels when applying custom styles or themes.

Patched a regression affecting downstream dashboard frameworks like Panel, ensuring seamless integration and layout rendering for advanced multi-page data applications. bokeh 2.3.3

Fixed an issue where the Column layout model ignored the scrollable CSS class, preventing the correct behavior of long lists and overflow UI elements.

Ensured that the active tab in a layout component is forced directly into view when rendering. This creates a smoother initial load state for multi-tab analytical interfaces. Configured custom extensions to fetch the exact matching

Corrected specific styling differences in the Div model, preventing unwanted CSS shifts between different views or parent containers.

Python developers utilize Bokeh to build high-performance, interactive visualizations directly for modern web browsers without needing to write client-side JavaScript. Version 2.3.3 secures this workflow by ensuring that the browser-based client ( BokehJS ) interprets Python commands predictably and uniformly. 📈 Key Bug Fixes & Improvements This creates a smoother initial load state for

Creating a scatter plot with panning, zooming, and hover tools is straightforward in Bokeh 2.3.3. Below is a complete standalone example utilizing the bokeh.plotting interface:

If your system relies on Python 3.6 or early Python 3.7 configurations, Bokeh 2.3.3 provides a compatible and reliable backend.