Bokeh 2.3.3

Bokeh 2.3.3 offers a robust suite of tools for data scientists and engineers looking to build scalable web plots:

: This version still relied on older WebGL code, which some users found buggy, leading many to later upgrade to version 2.4.x for better performance. Working with Text in Bokeh 2.3.3

Fixed an issue where dropdown menus in the MultiChoice input widget were erroneously hidden or cut off by parent overflow rules. 3. Build & Sub-Resource Integrity bokeh 2.3.3

| Problem | Fix | |---------|-----| | Jupyter plot not showing | Use output_notebook() before show() . | | HoverTool shows ??? | Ensure tooltips use field names matching ColumnDataSource columns. | | Layout breaks | Use sizing_mode="stretch_width" or "stretch_both" on top-level layout. | | Widgets not updating | Check that CustomJS uses source.change.emit() . |

# --- 4. The Reveal --- p.legend.location = "top_left" p.title.text_font_size = "14px" Bokeh 2

The figure object serves as the canvas for your visualization. It manages the plot's global properties, including titles, axis labels, scales, grid lines, and tool configurations.

While 2.3.3 was an essential update for stability in its time, it has since been succeeded by the , which introduced major architectural changes, including improved CSS-based theming and performance upgrades. Releases — Bokeh 2.3.3 Documentation Build & Sub-Resource Integrity | Problem | Fix

Bokeh offers pre-configured aesthetic themes to transform your plots from a default grey background to sleek layouts like dark_minimal , light_minimal , or caliber . To apply a global theme in version 2.3.3:

Version 3.x completely swapped out the internal styling engine to use web-standard CSS variables and shadow DOM configurations. Custom theme files written for 2.3.3 will require rewriting.

data = dict(x=[1,2,3], y=[4,5,6], color=["red","green","blue"]) source = ColumnDataSource(data)