Bokeh 2.3.3

pip install bokeh==2.3.3

Or via conda:

conda install bokeh=2.3.3

Archived docs:
https://docs.bokeh.org/en/2.3.3/

Unlocking Stunning Visuals: A Comprehensive Guide to Bokeh 2.3.3

Bokeh is a popular Python library used for creating interactive visualizations and dashboards. With its latest release, Bokeh 2.3.3, users can now enjoy a wide range of features and improvements that make data visualization even more powerful and intuitive. In this article, we'll explore the key features, enhancements, and use cases of Bokeh 2.3.3, providing you with a comprehensive guide to unlocking stunning visuals.

What is Bokeh?

Bokeh is an open-source Python library designed to help data scientists and developers create interactive visualizations and dashboards. It provides a high-level interface for drawing plots, charts, and other graphical elements, making it easy to create web-based interactive plots. Bokeh's primary goal is to provide a simple and elegant way to create interactive visualizations that can be easily shared and deployed.

Key Features of Bokeh 2.3.3

Bokeh 2.3.3 comes with a range of exciting features and improvements. Some of the key highlights include:

New Features in Bokeh 2.3.3

In addition to the key features mentioned above, Bokeh 2.3.3 also introduces several new features, including:

Enhancements and Bug Fixes

Bokeh 2.3.3 also includes several enhancements and bug fixes. Some of the notable enhancements include:

Use Cases for Bokeh 2.3.3

Bokeh 2.3.3 can be used in a variety of scenarios, including:

Example Code: Getting Started with Bokeh 2.3.3

To get started with Bokeh 2.3.3, you can use the following example code:

import numpy as np
from bokeh.plotting import figure, show
# Create some data
x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)
# Create a new plot
p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')
# Add a line to the plot
p.line(x, y, legend_label="sin(x)", line_width=2)
# Show the results
show(p)

This code creates a simple line plot using Bokeh 2.3.3.

Conclusion

Bokeh 2.3.3 is a powerful and feature-rich library for creating interactive visualizations and dashboards. With its improved performance, enhanced HoverTool, and new color palette, Bokeh 2.3.3 provides a comprehensive platform for data scientists and developers to create stunning visuals. Whether you're working with big data, creating dashboards, or simply exploring data, Bokeh 2.3.3 is an ideal choice. Try it out today and unlock the full potential of your data! bokeh 2.3.3

output_file("bokeh233_stock_demo.html") show(layout)

Run this script:

python stock_viewer.py

You will see a fully interactive HTML document open in your browser. Notice how the hover tool works reliably, the table updates smoothly, and the WebGL backend (if you had thousands more points) would handle it gracefully—all thanks to the refinements of Bokeh 2.3.3.

Bokeh 2.3.3 automatically tries to load BokehJS (the client-side library) from a CDN. If you're working in an air-gapped or offline environment, you can download the BokehJS static files separately and serve them locally.

Even with a stable release, users occasionally encounter issues. Here are common ones and how to solve them:

For scatter plots with tens or hundreds of thousands of points, Bokeh 2.3.3 includes refinements to the WebGL rendering backend. Markers no longer flicker when panning and zooming, and performance degradation over time (memory leaks) was significantly reduced. pip install bokeh==2