Visualizing data is the most intuitive way to interpret it, so it’s an invaluable skill. It is much easier to visualize data if you know its type and measurement level.

For the type of data we have categorical and numerical. For categorical data we can visualize the data in these ways.

• frequency distribution tables
• bar charts
• pie charts
• Pareto diagrams

What about numerical data? The most common graph to represent numerical data is the histogram. It looks like a bar chart. The vertical axis shows the frequency. The frequency is the counts of the observations. The x-axis shows the observations themselves. If the data is discrete, you will plot each possible value. If the data is continuous, you will create bins (ranges or intervals). The bars on the histogram touch each other.

Two Numerical Variables – Scatterplot

The chart we would use here is a scatterplot. These plots are a great way to spot correlations or relationships between two series of numbers. For example, is there a relationship between ice cream sales and average temperature? If you are working in Python, have a look at the post we have showing a scatterplot of some of the Palmer Penguins data in the post

Two Categorical Variables and One Numeric Variable

Here we could use a side-by-side bar chart. As an example, consider the numeric variable Sales. For the two categories, consider location and product type. The y-axis has Sales. If we have three locations (North, South and West) and we have three types of products we sell (Sports, Hardware and Camping) we could show three groups of three bars on the x-axis.