How to read a Violin Plot?

The violin plot uses a kernel density estimation technique for deciding the boundary of the plot. A Kernel density estimation (KDE) is a statistical technique that is used to estimate the probability density function (PDF) of a random variable based on a set of observed data points. It provides a smooth and continuous estimate of the underlying distribution from which the data is assumed to be generated. 

A violin plot consists of four components. 

• A white Centered Dot at the middle of the graph – The white dot point at the middle is the median of the distribution. 
• A thin gray bar inside the plot – The bar in the plot represents the Quartile range of the distribution. 
• A long thin line coming outside from the bar – The thin line represents the rest of the distribution which is calculated by the formulae Q1-1.5 IQR for the lower range and Q3+1.5 IQR for the upper range. The point lying beyond this line are considered as outliers.   
• A line boundary separating the plot- A KDE plot is used for defining the boundary of the violin plot it represents the distribution of data points.

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.