David Landup
Dan Nelson

Bokeh is a JavaScript-based data visualization library that specializes in the creation of interactive plots. While Matplotlib and libraries based around it are the most popular data visualization libraries in Python, the JavaScript counterparts (Bokeh and Plotly) are quickly catching up. While Plotly has been starting to "steal the spotlight" in the JS-based ecosystem, Bokeh is still very well worth covering. We'll cover Plotly in Lesson 8.

The advantage of using Matplotlib is that the plots produced with it are consistent and easily reproducible by others. It's widely-used and many are familiar with it, but Bokeh is able to create visualizations that are much more interactive and optimized for display on the web.

In the following sections of this lesson, we’ll cover the most notable features of Bokeh, explore how interactive plots in Bokeh are created, and then explore some different examples of the plots you can create with Bokeh. Finally, we'll dive into another mini project!

Features of Bokeh

As alluded to above, Bokeh was designed to create interactive visualizations, and these visualizations can be embedded in websites. The visualizations are created with “glyphs”, which can be individually stylized and customized. Bokeh visualizations can be formatted and exported using simple HTML formatting, allowing you to ensure that your visualizations mesh nicely with any web pages you are going to display them on. This makes it a great companion for data-driven applications that want to display visualizations to its users! Matplotlib's and Seaborn's output would have to be saved as a file and then added as an image (or SVG), which is a hassle.

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