We’ll print the head of the dataset to ensure that the data has been properly loaded and to take a look at the names of the columns/features. We’ll start by importing Seaborn, the PyPlot module from Matplotlib, and Pandas: import pandas as pd The shape of the “Violin” in a Violin Plot is a Kernel Density Estimation that represents the shape/distribution of the data.įor this tutorial, we will be working with the Gapminder dataset.
Violin Plots essentially show the same summary statistics as box plots, but they also include additional information. They display the range, median, and distribution of the data. This is because Violin Plots are used to visualize distributions of continuous data. The dataset should have continuous, numerical features. To start with, we’ll want to choose a dataset that is suited to the creation of violin plots. Violin plots show the same summary statistics as box plots, but they also include Kernel Density Estimations that represent the shape/distribution of the data. Violin plots are used to visualize data distributions, displaying the range, median, and distribution of the data.
In this tutorial, we'll take a look at how to plot a Violin Plot in Seaborn. It offers a simple, intuitive, yet highly customizable API for data visualization. Seaborn is one of the most widely used data visualization libraries in Python, as an extension to Matplotlib.
