Bokeh 2.3.3 -

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

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.

# Create a new plot p = figure(title="simple line example", x_axis_label='x', y_axis_label='y') bokeh 2.3.3

# Create some data x = np.linspace(0, 4*np.pi, 100) y = np.sin(x)

# Show the results show(p) This code creates a simple line plot using Bokeh 2.3.3. import numpy as np from bokeh

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.

To get started with Bokeh 2.3.3, you can use the following example code: # Create a new plot p = figure(title="simple

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