After you’ve collected the ingredients you want for your recipe, you have to prepare them before you start cooking. This is the prep step, in which you wash, chop, and otherwise get your ingredients ready. Many of us dislike this step because it isn’t much fun, but if you’ve ever had a dish with dirty vegetables, you know firsthand how much the end result can suffer if this step is done poorly or skipped altogether!

As in cooking, many also dislike the prep step when working with data, but it is necessary. You can’t just grab the data, throw it into a visualization, and expect it to come out right. If you want to create the best graphics you can, you need to make sure your data is prepared correctly. Some of your data may need to be separated into different groups, some may need converting, and some may need to be cleaned, like the vegetables.

In this section, we’ll talk about some of the common preparation and cleaning tasks encountered when working with data, and about ways that you can potentially decrease the amount of time you’ll need to spend doing them. No matter how you slice it, if you get your data set up the right way, it will make everything a go a lot smoother when you get to the visualization stage.