Bokeh 2.3.3 Online

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

Legacy versions of analytics packages like HoloViews or older iterations of Panel rely heavily on the DOM and layout architecture of Bokeh 2.x. bokeh 2.3.3

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 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. from bokeh

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.

While the Bokeh project has since moved to 3.x, certain situations still mandate using the legacy 2.3.3 version: Recommendation

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