Introduction

Data analysis is more of an art than a science. Creating attractive visualizations is an integral part of this art. Obviously, what one person finds attractive, other people may find completely unacceptable. Just as in art, in the rapidly evolving world of data analysis, opinions, and taste change over time; however, in principle, nobody is absolutely right or wrong. As data artists and Pythonistas, we can choose from among several libraries of which I will cover matplotlib, seaborn, Bokeh, and ggplot. Installation instructions for some of the packages we use in this chapter were already covered in Chapter 1, Laying the Foundation for Reproducible Data Analysis, so I will not repeat them. I will provide an installation script (which uses pip only) for this chapter; you can even use the Docker image I described in the previous chapter. I decided to not include the Proj cartography library and the R-related libraries in the image because of their size. So for the two recipes involved in this chapter, you may have to do extra work.