A violin plot is used to visualize and compare the distribution of quantitative data over several levels of categorical features. It is very useful to visualize several distributions in a dataset at a time. If you don’t know how to visualize violin plots using the Python programming language, this article is for you. In this article, I’ll take you through a tutorial on how to visualize a violin plot using Python.

## Violin Plot

A violin plot is a combination of a boxplot and a probability density function plot. It allows us to analyze:

- data distribution
- its degree of dispersion
- type of probability distribution
- the shape of the probability distribution

Violin plots are often compared to boxplots because they both have many similarities. Violin plots are preferred over box plots because they can also visualize the probability density function of a dataset at different values. So the violin plots are more useful and efficient than any other data visualization technique used for visualizing the distribution of a dataset, but they are still not very popular.

You can use any **data visualization library** in Python to visualize the violin plots such as Matplotlib, Seaborn, and Plotly. In the section below, I’ll take you through a tutorial on how to use the Seaborn library to visualize violin plots using Python.

## Violin Plot using Python

Hope you now understand what violin plots are and why they are used. Now let’s see how we can visualize a violin plot using Python:

The figure above is a visualization of a single violin plot. Now here is how we can visualize multiple violin plots grouped based on the categorical features of the dataset:

### Summary

So this is how you can visualize the violin plots using the Seaborn library in Python. They are used to visualize and compare the distribution of quantitative data over several levels of categorical characteristics. They are also very useful for viewing multiple distributions in a dataset at a time. Hope you liked this article on how to visualize violin plots using Python. Please feel free to ask your valuable questions in the comments section below.