Violin plots in matplotlib

I recently stumbled over some very useful but little know function provided by Matplotlib, called violinplot() and I wanted to share this with you. The function creates a so-called violin plot that shows the distribution of data together with some other statistical like median and standard deviation . Unlike other types of plots, such as histograms or box plots, violin plots show the shape of the distribution, making it easier to identify patterns and outliers in the data. In this article, we'll discuss how to use Matplotlib's violinplot() function to create effective visualizations.

A violin plot is a type of plot that shows the distribution of data by using a kernel density estimate to show the probability density of the data at different values. The shape of the violin indicates the distribution of the data, with wider sections indicating higher density and narrower sections indicating lower density. The bar in the middle of the violin indicates the median of the data, while the bars on either side of the violin indicate the interquartile range.

Here is an example code demonstrating the usage:

import matplotlib.pyplot as plt
import numpy as np

# Generate some sample data
data = [np.random.normal(0, std, 100) for std in range(1, 4)]

# Create a violin plot
fig, ax = plt.subplots()
ax.violinplot(data, showmeans=False, showmedians=True)
ax.set_title('Distribution of Data')
ax.set_xlabel('Variable')
ax.set_ylabel('Value')

plt.show()

Here, data is a list or array of data to be plotted. The showmeans parameter controls whether or not to show the mean of the data, while the showmedians parameter controls whether or not to show the median. Also, we use NumPy's random.normal() function to generate three sets of data with means of 0 and standard deviations of 1, 2, and 3, respectively. Quite easy and practical, isn't it?