In this article, I am going to show you how to remove outliers from Seaborn boxplots. First, I am going to plot a boxplot without modifications. Then, I will remove all of the outliers. In the end, I am going to restore outliers, but this time I am going to make them less prominent.
Table of Contents
Boxplot with outliers
Let’s start with plotting the data I already have.
import seaborn as sb
sb.boxplot(x = 'Value', data = with_merged)

Want to build AI systems that actually work?
Download my expert-crafted GenAI Transformation Guide for Data Teams and discover how to properly measure AI performance, set up guardrails, and continuously improve your AI solutions like the pros.
Boxplot without outliers
To remove the outliers from the chart, I have to specify the “showfliers” parameter and set it to false.
sb.boxplot(x = 'Value', data = with_merged, showfliers = False)

Change the outliers style
In the next example, I am going to change the size of the outliers markers to make them less distracting for people who look at the chart.
sb.boxplot(x = 'Value', data = with_merged, flierprops = dict(markerfacecolor = '0.50', markersize = 2))
