Visualize This, 2nd Edition

Ch.5Visualizing Categories

What is the best? What is the worst? How does one choice compare to another? How are totals distributed across categories? The stories we tell with categorical data are based on comparisons. This chapter describes the charts that help make such comparisons easier.

When analyzing categorical data, you usually look at amounts to figure out scale and magnitude. Together, the categories might form a total, and you'll want to know how the parts of the whole are spread out. Then rank and order categories in a way that makes sense for your dataset and purpose, which draws focus to the highs and lows.

In a poll, people might be asked if they approve, disapprove, or have no opinion on an issue. Each category represents an answer, and the sum of the parts represent a population. We compare metrics across demographics, such as age, sex, and race. We have food groups. We shop different departments. We watch and listen to various forms of entertainment.

In the sections that follow, you learn to highlight the differences and similarities in categorical data. You use what you learned in the previous chapter and get your first taste of making charts with Python. Then you take a step back from code and try a couple of streamlined point-and-click tools. See how code and point-and-click can be used together.

AMOUNTS

How much? How many? These questions are probably why numbers and data exist in the first place, so there better be ways to visualize the answers. There ...