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Bokeh 2.3.3 [work] -

Released in July 2021, Bokeh 2.3.3 represents a vital maintenance milestone in the 2.x lifecycle of the Bokeh data visualization ecosystem . This release continues to be widely used in enterprise legacy systems, specific LTS Python environments, and production pipelines where stability and backwards compatibility are absolute priorities. 🛠️ The Purpose of Bokeh 2.3.3

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

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

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. Released in July 2021, Bokeh 2

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

Addressed a formatting issue with y-axis labels when applying custom styles or themes. 5] y_data = [6

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