Violin Plot – Frequently Asked Questions (FAQs)

What is the difference between a bar plot and a violin plot?

A bar plot represents categorical data with rectangular bars, typically showing the mean or count of each category. In contrast, a violin plot displays the distribution of numeric data across different categories, providing insight into the data’s spread and density.

What is the difference between violin plot and swarm plot?

ViolinPlot is a statistical visualization that shows the distribution of data across categories using kernel density estimation and box plots. SwarmPlot, on the other hand, displays individual data points along a categorical axis, avoiding overlap by jittering or spreading them out. While ViolinPlot emphasizes the distribution, SwarmPlot focuses on showing each data point.

What is the difference between a histogram and a violin plot?

A histogram represents the distribution of numeric data by dividing it into intervals (bins) and plotting the frequency or density of observations within each bin. In contrast, a violin plot displays the distribution of data across different categories, often using kernel density estimation to show the shape of the distribution along with summary statistics like quartiles.

When should you use a violin plot?

You should use a violin plot when you want to visualize the distribution of numeric data across different categories or groups, especially when you’re interested in comparing the shapes of distributions between groups and identifying potential differences in central tendency, spread, and skewness. It’s particularly useful when you have multiple groups or categories and want to display their distributions simultaneously.

Data visualization is instrumental in understanding and interpreting data trends. Various visualization charts aid in comprehending data, with the violin plot standing out as a powerful tool for visualizing data distribution. This article aims to explore the fundamentals, implementation, and interpretation of violin plots.

Before applying any transformations to the features of a dataset, it is often necessary to seek answers to questions like the following: 

• Are the values primarily clustered around the median?
• Alternatively, do they exhibit clustering at the extremes with a dearth of values in the middle range? 

These inquiries go beyond median and mean values alone and are essential for obtaining a comprehensive understanding of the dataset. We can use a Violin plot for answering these questions.