Addressed a formatting issue with y-axis labels when applying custom styles or themes.
For older enterprise architectures that cache specific Sub-Resource Integrity (SRI) hashes, Bokeh 2.3.3 supplies vetted script hashes for stable deployment. bokeh 2.3.3
The official Bokeh 2.3.3 release notes highlight several fundamental corrections that address how components adapt to their containing layouts: 1. Layout and Panel Adjustments Addressed a formatting issue with y-axis labels when
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 Layout and Panel Adjustments from bokeh
If your system relies on Python 3.6 or early Python 3.7 configurations, Bokeh 2.3.3 provides a compatible and reliable backend.