Libraries
First, we need to load a few libraries:
- matplotlib: for displaying the chart
- seaborn: for creating the chart
import seaborn as sns
import matplotlib.pyplot as pltDataset
Since boxplot is used to display the distribution of a numerical variable, we need a dataset that contains at least one numerical variable.
In this example, we will use the iris dataset that we can easily load:
df = sns.load_dataset('iris')One single numerical variable
The simplest form of boxplot: analysis of the overall distribution of a single numerical variable with the boxplot() function.
sns.set_theme(style="darkgrid")
sns.boxplot(y=df["sepal_length"])
plt.show()
One numerical variable and several groups
Depending on your data, you may want to have a better understanding of the distribution of a given variable between two or more groups.
You can do so by specifying the x parameter in the boxplot() function.
sns.set_theme(style="darkgrid")
sns.boxplot(x=df["species"], y=df["sepal_length"])
plt.show()
Going further
This post explains how to create and customize a boxplot with the seaborn library.
You might be interested in how adding individual data points in boxplot and how to create a raincloud plot with the ptitprince library.
