Interactive Visualization in Python

Up to this point, we have been generic static image plots in Python using a combination of pandas, matplotlib, and seaborn. But in many cases we may want to generate interactive plots that can exist on the web.

This chapter provides an introduction to interactive visualization in Python, focusing on plotly workflows.

Acknowledgements

The author consulted the following resources when writing this chapter:

• plotly documentation and tutorials

• dash documentation and tutorials

Chapter Contents

• Introduction to Plotly
• Plotly Mechanics
• Plotly Express Workflows
• Other Plot Types
  • Line Plots
  • Bar Charts
  • Pie Charts
  • Bubble Charts
  • Tables
  • Additional Resources
• Working With Categorical Data
• Saving & Exporting
• User Interactivity
• Application

Data

We’ll use a few different datasets in this chapter.

• Sample datasets from the plotly library. Code to load these datasets is included.

  • iris

  • gapminder

  • us-states

  • tips

Application

Click here for a Jupyter Notebook template for this chapter’s application problems.