Visualisation: Box Plot in Python Matplotlib

Box plots are very effective in summarising the spread of large data into a visual representation. They take the help of percentiles to divide the data range. 
 

The percentile value gives the proportion of the data range that falls below a chosen data point when all the data points are arranged in descending order. For example, if a data point with a value of 700 has a percentile value of 99% in a data set, then it means that 99% of the values in the data set are less than 700.
 

Let’s hear more about box plots in the video below.

You can use the following command to create a box plot in Python using Matplotlib:

plt.boxplot([ list_1, list_2])

Box plots divide the data range into three important categories:

• Median value: This is the value that divides the data range into two equal halves, that is, the 50th percentile.
• Interquartile range (IQR): These are data points between the 25th and 75th percentile values.
• Outliers: These are data points that differ significantly from other observations and lie beyond the whiskers.

In the next segment, you will learn about some additional Matplotlib features that can help make your visualisations more effective.