Summary
In this book, we learned about the benefits of creating interactive data visualizations and how to build on static data visualizations to make them interactive. Simply incorporating features such as sliders, hover tools, and checkboxes can have an immensely positive impact on the way data is understood and how insights are gained.
We looked at different Python libraries and what visualizations and situations they are best suited for. For example, bokeh is preferred when creating visualizations for web-based applications.
Data and what you wish to show can be classified into four broad categories – comparisons, relationships, geo-spatial, and temporal. Each category has a wide array of graphs that suit that type of data best, but interactive features can help when data or what you want to show fall under more than one category – that's why interactive data visualizations are so great!
We also created context-based visualizations for different types...