Data science is founded upon making observations of the world around us. But what are observations and how do we record them in tabular data? An observation is a recording of the qualities and quantities of an observable phenomenon in the natural world. This includes what we can see, hear, feel, or measure with sensors. In data science, we record observations on the rows of a table. The rows are the horizontal groups of data that are contained within the table. For example, imagine that we are recording the vital signs of a patient at a hospital. For each observation, we would record: the date of the observation, the patient’s heart rate, their temperature, and other vitals. Each of these observations would be recorded on a separate row. What is important to note is that all of the elements in a row of data belong to the same observation. For example, a row of data can be an observation of a person, a place, a thing, or a set of sensor readings at a specific time. In data science, we want each row to contain one and only one observation. Essentially, each row should record one, and only one, person, place, or thing begin observed at a given time. Outside of data science, the rows of a table of data go by various names. First, you may hear them simply referred to as “rows”, for, well, obvious reasons, I guess. In computer science, they are often referred to as a tuples (or a tupples), which is a mathematical term for a list of data. Or you may often hear them referred to as “records”, because they store a recording of an observation, an entity, or a transaction of some kind. No matter what they are called, observations should always be represented as rows in tabular data.