Beyond numeric features, which have been the main topic of this section so far, a great part of your data will also comprise qualitative variables. Databases especially tend to record data readable and understandable by human beings; consequently, they are quite crowded by qualitative data, which can appear in data fields in the form of text or just single labels explicating information, such as telling you the class of an observation or some of its characteristics.
For a better understanding of qualitative variables, a working example could be a weather dataset. Such a dataset describes conditions under which you would want to play tennis because of weather information such as outlook, temperature, humidity, and wind, which are all kinds of information that can be rendered by numeric measurements. However, you will easily find such data online and recorded in datasets with their qualitative translations such as sunny or overcast, rather than numeric satellite...