In this recipe, we are going to look at some of the fuel efficiency metrics over time and in relation to other data points. To do so, we are going to have to replicate the functionality of two very popular R libraries, which are
ggplot2, in Python. The split-apply-combine data analysis capabilities that are so handily covered by the
plyr R library are handled equally well but in a slightly different fashion by pandas right out of the box. The data visualization abilities of
ggplot2—an R library implementation of the grammar of graphics—are not handled as readily, as we shall see in this recipe.
If you've completed the previous recipe, you should have almost everything ...