Let’s learn together how to create violin plot using seaborn Python library.
Seaborn’s violinplot function allows us to create violin plots, which are used to visualize the distribution of a continuous variable across different categories. In this section, we will discuss how to create a violin plot using Seaborn.
Example: Creating a Violin Plot Using Seaborn
Suppose you have a dataset containing the heights of students from three different classes. You want to create a violin plot that shows the distribution of heights across the three classes. Here’s how you can create the violin plot using Seaborn:
import seaborn as sns
import pandas as pd
# create a sample dataset
data = {'Class': ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C'],
'Height': [165, 170, 172, 168, 172, 174, 169, 176, 178]}
df = pd.DataFrame(data)
# create a violin plot using Seaborn
sns.violinplot(x='Class', y='Height', data=df)
# show the plot
plt.show()
In this example, we first create a sample dataset that contains the heights of students from three different classes. We then create a violin plot using the violinplot function in Seaborn. We set the x parameter to the categorical variable (Class) and the y parameter to the continuous variable (Height). We pass in the dataset using the data parameter.
The resulting violin plot shows the distribution of heights across the three classes. The shape of each violin represents the density of data at each height value, with the width of the violin indicating the density of data at each value. The dashed line inside each violin represents the median height value for each class.
Seaborn’s violinplot function provides a convenient way to create informative and visually appealing violin plots to explore the distribution of a continuous variable across different categories. By choosing an appropriate dataset and customizing the plot’s parameters, you can create a violin plot that effectively communicates the insights and trends in your data.