Bokeh 2.3.3 __link__ Today

The fix for the 600px height limitation ( #11344 ) was crucial for users creating compact, top-to-bottom dashboards or embedding small plot elements. Prior to 2.3.3, setting height constraints below this threshold led to unexpected rendering behaviors. Why Bokeh 2.3.3 Matters for Dashboard Design

: You can add line breaks to labels using the \n string.

Addressed a panel layout regression and ensured that dropdown menus within multi-choice components are not incorrectly hidden.

pip install bokeh==2.3.3

For developers, data scientists, and analysts utilizing the Bokeh library to build dashboards, 2.3.3 ensured smoother rendering and more predictable layouts across various browser environments. What’s New in Bokeh 2.3.3?

Working with categorical axes (e.g., bar charts with string categories) became more intuitive. The factor_cmap and factor_mark functions saw internal fixes, ensuring that color mapping and marker shapes apply correctly even when categories have long names or special characters.

Bokeh 2.3.3, released in , was a critical patch release that prioritized stability and visual consistency within the Bokeh interactive visualization library . While minor versions like 2.3 introduced heavy-hitting features like multi-line axis labels and improved log-axis rendering, the 2.3.3 update focused on refining the user experience through precise layout and extension fixes. The Role of Patch Stability bokeh 2.3.3

Fixed a bug where y-axis labels were incorrectly formatted when using specific Bokeh themes.

Fixed an explicit bug that prevented plot heights from dropping below 600px . Developers regained the flexibility to customize compact visualizations for mobile views or compressed grids. 2. UI and Widget Enhancements

When you run this script, a new browser tab will open, displaying your fully interactive plot with pan, zoom, save, and hover functionality. The fix for the 600px height limitation (

Some users complain that hover tooltips show ??? for special characters. : Explicitly set a css_classes property and define font-family in your own CSS file.

from bokeh.plotting import figure, output_file, show from bokeh.models import HoverTool # Step 1: Configure output to a standalone HTML file output_file("bokeh_233_demo.html") # Step 2: Initialize your figure with specific dimensions and tools p = figure( title="Bokeh 2.3.3 Maintenance Release Demo", x_axis_label="X Axis", y_axis_label="Y Axis", plot_width=700, # Below the 600px restriction bug fixed in 2.3.3 plot_height=450, tools="pan,box_zoom,reset,save" ) # Step 3: Populate sample data x_data = [1, 2, 3, 4, 5] y_data = [6, 7, 2, 4, 5] # Step 4: Render your visual elements (glyphs) p.circle(x_data, y_data, size=15, color="navy", alpha=0.6) # Step 5: Inject custom interactivity hover = HoverTool(tooltips=[("Value (X, Y)", "(@x, @y)")]) p.add_tools(hover) # Step 6: Generate the visualization show(p) Use code with caution. ⚖️ When to Use Bokeh 2.3.3 Today

from bokeh.plotting import figure, output_file, show # Prepare the data x = [1, 2, 3, 4, 5] y = [6, 7, 2, 4, 5] # Specify the output file destination output_file("line_chart.html") # Create a new plot with a title and axis labels p = figure(title="Simple Line Plot in Bokeh 2.3.3", x_axis_label='X-Axis', y_axis_label='Y-Axis') # Add a line glyph p.line(x, y, legend_label="Temp.", line_width=2, color="blue") # Display the results in a browser browser tab show(p) Use code with caution. Advanced Implementations 1. Utilizing the ColumnDataSource Addressed a panel layout regression and ensured that

For real-time dashboards, this version fixed a critical bug where stream() and patch() methods on ColumnDataSource would sometimes update the wrong indices, leading to visual artifacts.