The world is in a constant state of change;
things vary from one observation to the next.
But how do we record these variations across
observations in data science?
A variable is placeholder for a value that
We call them “variables” because their values
“vary” across each observation.
In data science, we store variables on the
columns of a table.
Columns are the vertical groups of data that
are contained within the table.
For example, imagine we’re recording vital
signs for a patient at a hospital.
Our variables might be:
the date and time of the observation,
the patients heart rate (measured by their
and their body temperature at the time of
What is most important, is that all of the
elements in a specific column must be of the
same data type, scale, and unit of measure.
We don’t want our dates to be stored using date formats.
We don’t want our heart rate data to be stored
using two different data types
And we don’t want our temperature to use both
Celsius and Fahrenheit units of measure.
Instead, we want all of the data in the column
to use the same data type, same scale, and
same units of measure.
Finally, we want one and only one variable
per column of data.
We don’t want to try placing multiple variables
in a single column.
For example, if we’re recording blood pressure,
we record two numbers:
the systolic blood pressure
and the diastolic blood pressure.
We don’t want to record both of these measures
in a single column, like we commonly see it
written in our medical history.
Instead, we would prefer to have a single
column for systolic blood pressure and a single
column for diastolic blood pressure.
Storing each variable in a separate column
allows us to store, process, and analyze the
data more efficiently.
Outside of data science, the columns of a
table go by various names.
First, you may simply hear them referred to
In addition, you may also hear them referred
to as “attributes”.
Or in some cases, as “properties”.
No matter what they are called, variables
should always be represented as columns in