Introduction to Box Plots and how to interpret them

An implementation with Python

Box Plots are very useful graphs used in descriptive statistics. Box plots visually show many features of numerical data through displaying their statistics, like means, averages, and so forth.

Visually speaking, a Box Plot looks like the following:

Let’s examine all the information displayed:

• Box: the box embraces the portion of data included between the 25 and 75 percentiles (also known as first and third quartiles). In statistics, percentiles indicate values in data below which fall a given percentage of all values. Namely, the 25 percentile (or first quartile) of a given sample of numerical data indicates the value below which 25% of all sample data are located. The range between these two quartiles is called Interquartile Range (IQR).
• Median: within the box, we can also see the value of median. Note that the median is nothing but the 50 percentile of the underlying numerical data.
• Whisker: they account for all the values that fall outside the central 50% of data (the portion contained into the IQR).