p.line('date', 'price', source=source, legend_label="Price", color="navy", alpha=0.7) p.line('date', 'moving_avg', source=source, legend_label="10-day MA", color="firebrick", line_width=2)
# Create some data x = np.linspace(0, 4*np.pi, 100) y = np.sin(x) bokeh 2.3.3
Using Bokeh 2.3.3, they wrote a script to visualize the intensity. They decided to use a combination of box plots and scatter points to show not just the average noise, but the outliers—those moments when the crowd truly erupted. Its goal is to provide elegant, concise construction
Bokeh is an interactive visualization library in Python that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications. It addressed minor regressions and bugs found in previous 2
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
—a complete, self-contained script used to demonstrate a feature or bug. For example, version 2.3.3 users often share "full pieces" of code to troubleshoot layout regressions in the model or panels. Bokeh documentation 3. Misleading "Apk" or Video Content
# Show the results show(p)