Bokeh: 2.3.3

: Updated extensions to fetch exact versions from the CDN to prevent version mismatch issues. Major Features from the 2.3.x Series

In 2.3.3, export_png may need manual setup. Use save() for reliable sharing.

One of the most important fixes in 2.3.3 was addressing how Bokeh handles plot sizing, particularly with the panel and div models. Panel Layout Regression

: Built-in panning, zooming, hovering, and selecting capabilities. bokeh 2.3.3

p = figure() p.circle(x="x", y="y", color="color", size=10, source=source) show(p)

: It works seamlessly with the broader PyData stack, including Pandas dataframes and NumPy arrays. The Role of Version 2.3.3

Here's a summary of the major changes in Bokeh 2.3.3: : Updated extensions to fetch exact versions from

It supports streaming data in real-time, making it suitable for live monitoring dashboards.

slider = Slider(start=0, end=10, step=1, value=1, title="Multiplier")

In the software lifecycle, version 2.3.3 served as a critical patch and refinement release. It addressed minor regressions and bugs found in previous 2.3 sub-versions, ensuring compatibility with evolving dependencies like Tornado and Jinja2 . For developers at the time, it represented a stable environment for production-level dashboards before the eventual transition to the 3.0 release branch. Conclusion One of the most important fixes in 2

# Add a line renderer with legend and line thickness p.line(x, y, legend_label="sin(x)", line_width=2)

This comprehensive guide explores the core architecture, installation, specific features, and optimization strategies for Bokeh 2.3.3. 1. Introduction to Bokeh 2.3.3