Bokeh is a popular data visualization library in Python that provides a high-level interface for creating interactive plots, dashboards, and data applications. The latest version, Bokeh 2.3.3, has been released with several new features, improvements, and bug fixes. In this article, we will explore the key features and updates in Bokeh 2.3.3, and discuss how it can be used for data visualization and analysis.
Bokeh is an open-source library that allows users to create web-based interactive plots, charts, and dashboards. It provides a simple and intuitive API for creating a wide range of visualizations, from simple line plots to complex dashboards. Bokeh is designed to be highly customizable and extensible, making it a popular choice among data scientists, analysts, and developers. bokeh 2.3.3
python Copy Code Copied import numpy as np from bokeh . plotting import figure , show x = np . linspace ( 0 , 4 np . pi , 100 ) y = np . sin ( x ) p = figure ( title = “simple line example” , x_axis_label = ‘x’ , y_axis_label = ‘y’ ) p . line ( x , y , legend label = “sin(x)” ) show ( p ) This code creates a simple line plot of the sine function. Bokeh is a popular data visualization library in