Violin Plot for Data Analysis

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.  

What is a Violin Plot?

Violin Plot is a method to visualize the distribution of numerical data of different variables. It is quite similar to Box Plot but with a rotated plot on each side, giving more information about the density estimate on the y-axis. The density is mirrored and flipped over, and the resulting shape is filled in, creating an image resembling a violin. The advantage of a violin plot is that it can show nuances in the distribution that aren’t perceptible in a boxplot. On the other hand, the boxplot more clearly shows the outliers in the data. Violin Plots hold more information than box plots, they are less popular. Because of their unpopularity, their meaning can be harder to grasp for many readers not familiar with the violin plot representation.

Tools to create Violin Plot

There are many tools and libraries available to create Violin Plot:

1. Alteryx: Alteryx is a data analytics platform that analyze the data to uncover insights and make data-driven decisions.
2. Python Libraries:
   • Matplotlib: Matplotlib is a widely used plotting library in Python that offers support for creating violin plots. It provides a high level of customization and flexibility in plot design.
   • Seaborn: Seaborn is built on top of Matplotlib and offers a higher-level interface for creating statistical visualizations, including violin plots. It provides a simple and concise syntax for generating complex plots with minimal code.
   • Plotly: Plotly is a versatile plotting library that supports interactive and dynamic visualizations. It offers an easy-to-use API for creating violin plots and allows for embedding plots in web applications and notebooks.
3. ggplot2 (R): If you're working with R, ggplot2 is a powerful plotting library that supports a wide range of visualization types, including violin plots. It follows a grammar of graphics approach, making it easy to create complex plots with simple commands.

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.

Types of Violin Plot  

Violin plots can be used for univariate and bivariate analysis.

Univariate Analysis

In univariate analysis, violin plots are used to visualize the distribution of a single continuous variable. The plot displays the density estimation of the variable's values, typically with a combination of a kernel density plot and a mirrored histogram. The width of the violin represents the density of data points at different values, with wider sections indicating higher density.

import matplotlib.pyplot as plt
import numpy as np

# Generate random data
np.random.seed(1)
data = np.random.randn(100)

# Create a violin plot
plt.figure()
plt.violinplot(data, showmedians=True)

# Set plot labels and title
plt.xlabel('Variable')
plt.ylabel('Value')
plt.title('Univariate Violin Plot')

# Show the plot
plt.show()

Output:

Bivariate Analysis

In bivariate analysis, violin plots are utilized to examine the relationship between a continuous variable and a categorical variable. The categorical variable is represented on the x-axis, while the y-axis represents the values of the continuous variable. By creating separate violins for each category, the plot visualizes the distribution of the continuous variable for different categories.

import matplotlib.pyplot as plt
import numpy as np

# Generate random data
np.random.seed(2)
data1 = np.random.normal(0, 1, 100)
data2 = np.random.normal(2, 1.5, 100)
data3 = np.random.normal(-2, 0.5, 100)
categories = ['Category 1', 'Category 2', 'Category 3']
all_data = [data1, data2, data3]

# Create a violin plot
plt.figure()
plt.violinplot(all_data, showmedians=True)

# Set plot labels and title
plt.xlabel('Category')
plt.ylabel('Value')
plt.title('Bivariate Violin Plot')

# Set x-axis tick labels
plt.xticks(np.arange(1, len(categories) + 1), categories)

# Show the plot
plt.show()

Output:

Python Implementation of Volin Plot on Custom Dataset

Importing required libraries

import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot
from sklearn.datasets import load_iris

Loading Data 

# Load the Iris dataset
iris = load_iris()

# Create a DataFrame from the 
# features (X) with column names
df = pd.DataFrame(data=iris.data,\
                  columns=iris.feature_names)

# Add the target variable (y) to the DataFrame
df['target'] = iris.target

# Display the first five rows of the DataFrame
print(df.head(5))

Output: 

   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  target 
0                5.1               3.5                1.4               0.2   0
1                4.9               3.0                1.4               0.2   0
2                4.7               3.2                1.3               0.2   0
3                4.6               3.1                1.5               0.2   1
4                5.0               3.6                1.4               0.2   0

Description of the dataset 

df.describe()

Output: 

       sepal length (cm)  sepal width (cm)  petal length (cm)  \
count         150.000000        150.000000         150.000000   
mean            5.843333          3.057333           3.758000   
std             0.828066          0.435866           1.765298   
min             4.300000          2.000000           1.000000   
25%             5.100000          2.800000           1.600000   
50%             5.800000          3.000000           4.350000   
75%             6.400000          3.300000           5.100000   
max             7.900000          4.400000           6.900000   

       petal width (cm)      target  
count        150.000000  150.000000  
mean           1.199333    1.000000  
std            0.762238    0.819232  
min            0.100000    0.000000  
25%            0.300000    0.000000  
50%            1.300000    1.000000  
75%            1.800000    2.000000  
max            2.500000    2.000000  

 Information About the Dataset 

df.info()

Output: 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   sepal length (cm)  150 non-null    float64
 1   sepal width (cm)   150 non-null    float64
 2   petal length (cm)  150 non-null    float64
 3   petal width (cm)   150 non-null    float64
 4   target             150 non-null    int64  
dtypes: float64(4), int64(1)
memory usage: 6.0 KB

Describing the 'sepal length (cm)' feature of the Iris dataset. 

df["sepal length (cm)"].describe()

Output: 

count    150.000000
mean       5.843333
std        0.828066
min        4.300000
25%        5.100000
50%        5.800000
75%        6.400000
max        7.900000
Name: SepalLengthCm, dtype: float64

Univariate Violin Plot for 'sepal length (cm)' Feature. 

fig, ax = pyplot.subplots(figsize =(9, 7))
sns.violinplot(ax = ax,  y = df["sepal length (cm)"] )

Output: 

As you can see, we have a higher density between 5 and 6. That is very significant because as in the sepal length (cm) description, a mean value is at 5.43.

Univariate Violin Plot for the 'sepal width (cm)' feature. 

fig, ax = pyplot.subplots(figsize =(9, 7))
sns.violinplot(ax = ax,  y = df["sepal width (cm)"] )

Output:

Here also, Higher density is at the mean = 3.05.

Bivariate Violin Plot comparing 'SepalLengthCm' and 'SepalWidthCm'. 

fig, ax = pyplot.subplots(figsize =(9, 7))
sns.violinplot(ax = ax, data = df.iloc[:, :2])

Output: 

Bivariate Violin Plot comparing 'sepal length (cm)' species-wise. 

fig, ax = pyplot.subplots(figsize =(9, 7))
sns.violinplot(ax = ax, x = df["target"], y = df["sepal length (cm)"], palette = 'Set1' )

Output: 

Also Check:

• Violinplot using Seaborn in Python
• Violin plot using Plotly in Python
• Make a violin plot in Python using Matplotlib
• Sinaplot vs Violin plot – Why Sinaplot is better than Violinplot in R
• How To Make Violinplot with Data Points in R?

mohit gupta_omg :)

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Article Tags :

• Data Visualization
• Data Analysis
• AI-ML-DS
• AI-ML-DS With Python