Bokeh 2.3.3 〈2026 Release〉

# Create a new plot p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')

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

# Add a line to the plot p.line(x, y, legend_label="sin(x)", line_width=2) # Create a new plot p = figure(title="simple

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. Bokeh 2

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 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.

import numpy as np from bokeh.plotting import figure, show