In this chapter, we will cover the following topics:

• 6.1. Using matplotlib styles
• 6.2. Creating statistical plots easily with seaborn
• 6.3. Creating interactive Web visualizations with Bokeh and HoloViews
• 6.4. Visualizing a NetworkX graph in the Notebook with D3.js
• 6.5. Discovering interactive visualization libraries in the Notebook *
• 6.6. Creating plots with Altair and the Vega-Lite specification

While matplotlib is the main visualization library in Python, it is not the only one. In this chapter, we will introduce some of the many other visualization libraries that cover more domain-specific use-cases, or that offer specific interactivity features in the Jupyter Notebook.