Violinplot – Python(matplot)

A Violin Plot in Matplotlib visualizes data distribution by combining a Box Plot and Kernel Density Estimation (KDE).

The graph widens where data is dense and narrows where it’s sparse, making distribution patterns easy to interpret. While similar to a Box Plot, it uniquely represents data with a symmetrical density distribution.

It uses Gaussian Kernel Density Estimation (KDE) to visualize the Probability Density Function (PDF) of the data.

Benefits

• Intuitive visualization of data distribution
  • Violin plots reveal the overall shape and density distribution better than box plots.
  • You can easily detect asymmetry, skewness, and multimodal distributions (multiple peaks).
• Natural presentation without outlier removal
  • Unlike box plots, violin plots do not isolate outliers but incorporate them into the density distribution.
  • This allows for a more natural and holistic view of the data without artificial filtering.
• More informative than box plots
  • While box plots only display key statistics (min, quartiles, median, max), violin plots show the entire density distribution, making them more insightful for deeper data analysis.

Cons

• Hard to see individual values
  • Violin plots effectively show data distribution but make it difficult to identify individual data points.
  • Using a Swarm Plot alongside a Violin Plot can help compensate for this.
• Kernel Density Estimation (KDE) can be inaccurate
  • KDE smooths the data, which may lead to inaccuracies, especially with small sample sizes.
• Requires relatively complex interpretation
  • Box plots are easier to interpret since they focus on simple statistics like the median and quartiles.
  • Violin plots, however, require understanding the shape of the distribution, making them more complex to analyze.

Violinplot Code

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(10)
data = [np.random.normal(0, std, 100) for std in range(1, 5)]

plt.figure(figsize=(10, 6))
plt.violinplot(data, showmeans=True, showmedians=True)
"""
showmeans : mean
showmedians : median
"""
plt.xlabel('Group')
plt.ylabel('Value')
plt.title('Violin Plot Example')
plt.grid(True)  # Add grid for better visualization
plt.show()

Where Violin Plots Are Most Used

• Comparing Data Distributions
  • Useful for comparing the shape of distributions across multiple groups.
  • Examples:
    • Comparing data between experimental groups
    • Analyzing temperature distributions by region
    • Evaluating customer satisfaction across different products
• When a More Detailed Analysis Is Needed
  • Box plots provide a simple summary, but violin plots offer deeper insights into data distribution.
  • Examples:
    • Analyzing results from a biological experiment
    • Comparing stock market volatility across different assets
• Visualizing Multimodal Distributions
  • If data has multiple peaks (modes) instead of a single center, box plots may not capture this.
  • Violin plots make multimodal distributions easy to identify and interpret.

Violin plots are often used for comparing data distributions, identifying multimodal distributions, and conducting more detailed analysis than box plots. However, they can produce inaccurate density distributions with small samples and make it difficult to check individual data points, so use them with caution.