In this chapter, we covered a great deal of material:
- A quick review of the evolution of high-level plotting
- An examination of third-party libraries, which wrap matplotlib functionality for high-level plotting results
- An overview of the grammar of graphics and the implementations available in the Python world
- A tour of a one town's data climate over a century, and the ways in which this might be rendered in various high-level plots
Our goal was to not only provide more context into the world of data visualization where each layer builds upon one before it, but to also demonstrate practical usage on a real-world dataset, identifying the ways in which one might need to modify the collected or supplied data, and then apply various methods to gain deeper insights about the data. Sometimes those insights come as a result of simply highlighting different relationships within a dataset; other times they come when supplementing a dataset with new calculations.
It is our hope that having walked with us through this valley of high-level plotting and data analysis, you are better prepared to strike out on your own adventures in the high-plains of the vast datasets and analysis that await you.