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A distribution plot combining box plot summaries with mirrored kernel density estimates.
DESCRIPTION
A violin plot displays the full distribution of a variable via a smoothed density mirrored on both sides, with optional box plot elements. It reveals multimodality, skewness, and outliers more clearly than box plots alone. Bandwidth selection affects smoothness and interpretability. Violin plots are effective for comparing distributions across categories. They are available in libraries like Seaborn and ggplot2.
ALTERNATIVE DEFINITIONS
Wikipedia
A violin plot is a method of plotting numeric data. It is similar to a box plot, with the addition of a rotated kernel density plot on each side.
Contrasts with box plots and highlights density component.
Seaborn
A violin plot plays a similar role as a box-and-whisker plot. It shows the distribution of quantitative data across several levels by showing density curves.
Focuses on categorical comparisons and density curves.
ggplot2
Violin plots are a compact way of comparing distributions. They are similar to boxplots, except that they also show the probability density of the data.
Emphasizes compact comparison and probability density.
RELATED TERMS
Kernel Density Estimate Ridgeline Plot Beeswarm Plot Outliers Quartile Probability Distribution Bandwidth Seaborn
COMPETING TERMS
KNOWLEDGE AREAS
Data Visualization Data Science Mathematics
TARGET AUDIENCE
Data Scientist Data Analyst BI Specialist Analytics Engineer UI-UX Designer
EXTERNAL SOURCES
- Wikipedia - Violin plot
- Seaborn - API docs and examples
- ggplot2 - R implementation and options
- Data to Viz - Use cases and pitfalls
TOP QUESTIONS
When should I use a Violin Plot?
Use violin plots to compare full distributions across categories, especially to reveal multimodal or skewed data that box plots may hide.
Category: Best Practices
How does a Violin Plot differ from a Box Plot?
Both summarize distributions, but violin plots add smoothed density to show distribution shape beyond quartiles and median.
Category: Comparison
What affects the smoothness in a Violin Plot?
Kernel bandwidth controls smoothness; too high oversmooths, too low shows noise. Choose based on sample size and variability.
Category: Technique
Can I show summary statistics on a Violin Plot?
Yes, you can overlay medians, quartiles, and even raw data points (e.g., swarm) for context.
Category: Design
What are common mistakes with Violin Plots?
Using default bandwidth blindly or comparing vastly different sample sizes without annotations can mislead interpretation.
Category: Mistakes
How many categories are suitable for Violin Plots?
A modest number, often fewer than 10, works best to avoid clutter and overlapping shapes.
Category: Guidance
Should I log-transform data before a Violin Plot?
For highly skewed data, log transformation can clarify density shape. Indicate transformations in labels.
Category: Technique
Can Violin Plots handle large datasets?
Yes, they summarize distribution regardless of size, but ensure computation of density is efficient and bandwidth appropriate.
Category: Capabilities
How do I compare groups with different sizes?
Annotate sample size and consider width scaling by counts or show jittered points to reflect data density.
Category: Best Practices