Data Preparation Using Clean, Groups, and Split

Cleaning is a very important part of data preparation, because having the right data leads to proper and efficient data analysis.

For example, imagine the sales amount for an order in a dataset is blank, but an order is processed anyway. This cannot be right, and requires some action. The order in question should either not be included, or the sales amount should be replaced with an average.

Another example would be the same customer having multiple names, or more than one customer ID. You may need to combine the names into one to correctly analyze information. All such tasks can be done using data cleaning. Prep provides a variety of options to clean data. In this section, you will learn about them.

Refer to the Orders_South dataset workflow that was created earlier:

Figure 3.25: Orders_South workflow

Right-click on the Clean 1 step to open the additional properties, as shown in the following screenshot...