11 Publishing the data story

Throughout this book, we have built our data story. Now, in this final chapter of the book, it is time to show it to our audience. This chapter is all about taking your data story and getting it to your audience—and doing so in an ethical manner. In the first part of this chapter, we’ll focus on the different techniques to export a data story. Next, we’ll describe Streamlit, a Python library fully integrated with Altair. Streamlit helps you build a complete standalone website hosting your Python code. Then, we’ll see some alternative ways to publish your story, including techniques to integrate it into some popular tools for data analytics and visualization: Tableau and Power BI. Afterward, we’ll describe how to integrate your data story in Comet, an experimentation platform for machine learning (ML). Finally, we’ll see how to present your data story through slides. Let’s start with the first point: exporting the story.

11.1 Exporting the story

Altair provides different formats to export your data story. Throughout this book, we have used chart.save('chart.html'), but Altair also supports other formats, including the following:

11.2 Publishing the story over the web: Streamlit

11.3 Tableau

11.4 Power BI

11.5 Comet

11.6 Presenting through slides

11.7 Final thoughts

Summary

References