Matplotlib Violin Plot - violinplot() Function

In this tutorial, we will cover the Violin Plot and how to create a violin plot using the violinplot() function in the Matplotlib library.

The Violin Plot is used to indicate the probability density of data at different values and it is quite similar to the Matplotlib Box Plot.

• These plots are mainly a combination of Box Plots and Histograms.

• The violin plot usually portrays the distribution, median, interquartile range of data.

• In this, the interquartile and median are statistical information that is provided by the box plot whereas the distribution is being provided by the histogram.

• The violin plots are also used to represent the comparison of a variable distribution across different "categories"; like the Box plots.

• The Violin plots are more informative as they show the full distribution of the data.

Here is a figure showing common components of the Box Plot and Violin Plot:

Creation of the Violin Plot

The violinplot() method is used for the creation of the violin plot.

The syntax required for the method is as follows:

violinplot(dataset, positions, vert, widths, showmeans, showextrema,showmedians,quantiles,points=1, bw_method, *, data)

Parameters

The description of the Parameters of this function is as follows:

• dataset

  This parameter denotes the array or sequence of vectors. It is the input data.

• positions

  This parameter is used to set the positions of the violins. In this, the ticks and limits are set automatically in order to match the positions. It is an array-like structured data with the default as = [1, 2, …, n].

• vert

  This parameter contains the boolean value. If the value of this parameter is set to true then it will create a vertical plot, otherwise, it will create a horizontal plot.

• showmeans

  This parameter contains a boolean value with false as its default value. If the value of this parameter is True, then it will toggle the rendering of the means.

• showextrema

  This parameter contains the boolean values with false as its default value. If the value of this parameter is True, then it will toggle the rendering of the extrema.

• showmedians

  This parameter contains the boolean values with false as its default value.If the value of this parameter is True, then it will toggle the rendering of the medians.

• quantiles

  This is an array-like data structure having None as its default value.If value of this parameter is not None then,it set a list of floats in interval [0, 1] for each violin,which then stands for the quantiles that will be rendered for that violin.

• points

  It is scalar in nature and is used to define the number of points to evaluate each of the Gaussian kernel density estimations.

• bw_method

  This method is used to calculate the estimator bandwidth, for which there are many different ways of calculation. The default rule used is Scott's Rule, but you can choose ‘silverman’, a scalar constant, or a callable.

Now its time to dive into some examples in order to clear the concepts:

Violin Plot Basic Example:

Below we have a simple example where we will create violin plots for a different collection of data.

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(10)
collectn_1 = np.random.normal(120, 10, 200)
collectn_2 = np.random.normal(150, 30, 200)
collectn_3 = np.random.normal(50, 20, 200)
collectn_4 = np.random.normal(100, 25, 200)

data_to_plot = [collectn_1, collectn_2, collectn_3, collectn_4]

fig = plt.figure()

ax = fig.add_axes([0,0,1,1])

bp = ax.violinplot(data_to_plot)
plt.show()

The output will be as follows:

Time For Live Example!

Let us take a look at the Live example of the Violin Plot:

Summary:

In this tutorial we covered how to create a Violin plot, various parameters of the violinplot() method with a few examples.