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.
Python3
import matplotlib.pyplot as pltimport numpy as np# Generate random datanp.random.seed(1)data = np.random.randn(100)# Create a violin plotplt.figure()plt.violinplot(data, showmedians=True)# Set plot labels and titleplt.xlabel('Variable')plt.ylabel('Value')plt.title('Univariate Violin Plot')# Show the plotplt.show() |
Output:
Univariate Violin plot
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.
Python3
import matplotlib.pyplot as pltimport numpy as np# Generate random datanp.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 plotplt.figure()plt.violinplot(all_data, showmedians=True)# Set plot labels and titleplt.xlabel('Category')plt.ylabel('Value')plt.title('Bivariate Violin Plot')# Set x-axis tick labelsplt.xticks(np.arange(1, len(categories) + 1), categories)# Show the plotplt.show() |
Output:
Bivariate Violin plot
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.
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