Visualizing Violin Plots Using the factorplot Function

A violin plot combines a box plot and a kernel density plot, effectively showing the distribution of a numeric variable for different categories. In earlier versions of Seaborn, the factorplot() function was widely used to create various types of categorical plots, including violin plots. However, starting from Seaborn version 0.9.0, factorplot() was replaced with the more versatile catplot() function, while still supporting various plot kinds like violin plots, box plots, and strip plots.

Why Use Violin Plots?

Violin plots are used when you want to compare the distribution of data across different categories. Unlike a box plot, which only shows summary statistics (like quartiles), violin plots display the full distribution of the data, allowing for a deeper understanding of how data varies within and across groups. Key features of a violin plot:

• Displays both the probability density of the data and summary statistics.
• Useful for visualizing the distribution of data points across multiple categories.
• Can be split to show comparisons between two groups for each category.

Transition from factorplot() to catplot()

In earlier versions of Seaborn, factorplot() was commonly used to create categorical plots, including violin plots. However, since Seaborn version 0.9.0, factorplot() was replaced by catplot(). The transition from factorplot() to catplot() is straightforward, as they share a similar syntax, but catplot() provides more flexibility and options for creating different types of categorical plots.

To follow this article, you need to have Seaborn and Matplotlib installed in your Python environment:

pip install seaborn matplotlib pandas

Now we will discuss step by step How a Violin Plot Can Be Visualized Using factorplot Function in Python.

Step 1: Importing Libraries

First, let’s import the necessary libraries. We'll use Seaborn for the plotting and Matplotlib for showing the figures.

import seaborn as sns
import matplotlib.pyplot as plt

# Load a sample dataset
tips = sns.load_dataset("tips")

Step 2: Creating a Violin Plot Using catplot()

Let’s create a basic violin plot using catplot(). We'll visualize the distribution of the total_bill variable based on different days of the week, with the data split by gender.

# Creating a violin plot using catplot
sns.catplot(x="day", y="total_bill", hue="sex", data=tips, kind="violin", split=True)
plt.title("Violin Plot of Total Bill by Day and Gender")
plt.show()

Output:

• x="day": Specifies the categorical variable for the x-axis, which represents the days of the week.
• y="total_bill": Specifies the numeric variable for the y-axis, which represents the total bill amount.
• hue="sex": Adds another categorical variable (gender), allowing the violin plot to be split by this factor.
• data=tips: Refers to the dataset from which the variables will be drawn.
• kind="violin": Indicates that we want to create a violin plot.
• split=True: Splits the violins into two halves based on the hue variable (gender).

The generated plot shows the distribution of total_bill amounts for each day, split by gender (male vs. female). The width of the violin plot reflects the density of the data at different values.

Step 3: Customizing the Violin Plot

Seaborn’s catplot() function allows for various customizations to enhance the readability and aesthetics of your violin plot.

# Adding inner quartiles to the violin plot
sns.catplot(x="day", y="total_bill", hue="sex", data=tips, kind="violin", 
                                           split=True, inner="quartile")
plt.title("Violin Plot with Quartiles")
plt.show()

Output:

Step 4: Faceting the Plot

Seaborn’s catplot() also allows faceting, which enables you to create multiple violin plots for different subgroups of the data. You can create faceted plots by specifying the col or row arguments.

# Faceting the violin plot by time (Lunch or Dinner)
sns.catplot(x="day", y="total_bill", hue="sex", data=tips, kind="violin", split=True, col="time")
plt.suptitle("Violin Plots Faceted by Time (Lunch or Dinner)", y=1.03)
plt.show()

Output:

Here, we facet the plot by the time variable, which indicates whether the data is from lunch or dinner. Each plot corresponds to a different subset of the data.

Conclusion

Violin plots are an excellent way to visualize data distributions and compare them across multiple categories. In Seaborn, catplot() is the preferred function for creating violin plots when you need flexibility, such as faceting or adding multiple layers of information with hue.