AI Violin Plot Maker

AI-powered violin plot with KDE density estimation. Upload or generate data to visualize distributions.

Violin Plot AI

Hi! I'm your Violin Plot analysis assistant.

• Analyze your distribution — I'll check for multi-modality, skewness, and density patterns • Generate sample data — e.g. "generate exam scores showing a bimodal distribution, 25 points" • Explain concepts — ask me "what is KDE?" or "how to read a violin plot"

Try: "Analyze the shape of my data" or "Generate 30 income values with right skew"

How to Use This AI Violin Plot Maker

  1. Enter your data. Type or paste numeric values separated by commas, spaces, or line breaks into the input field. Use Samples to try a built-in dataset.
  2. Generate the violin plot. Click Generate to compute quartiles, KDE density, and render the violin plot as a crisp SVG graphic.
  3. Customize the visualization. Use the color picker to change the chart color. Toggle notch and jitter overlays for enhanced detail.
  4. Analyze with AI. The right panel's AI assistant can interpret distribution shape, detect multiple modes, and explain what the density curves reveal.
  5. Export your result. Download as PNG (for presentations), SVG (for publications), CSV/XLSX (for further analysis), or Markdown (for documentation).

What is a Violin Plot?

A violin plot combines a box plot with a kernel density estimation (KDE) mirrored on each side, showing the full distribution shape — not just summary statistics. The width of the violin at any point represents the relative density of data values, revealing patterns a box plot alone cannot show.

Violin plots are especially valuable when your data may be multi-modal (having multiple peaks). A box plot would look perfectly symmetric for a bimodal distribution, but a violin plot would clearly show two humps. They are widely used in bioinformatics, social sciences, and any field where distribution shape matters as much as summary statistics.

Why Use Violin Plots?

  • Reveal multi-modal distributions. Unlike box plots, violin plots show when your data has multiple peaks — critical information that summary statistics alone hide.
  • Compare shape, spread, and density. Place violin plots side by side to compare not just medians and IQRs, but the entire distributional shape across groups.
  • Identify clusters and gaps. Narrow regions in the violin indicate sparse data areas; wide regions indicate high density clusters.
  • Combine with box plot elements. This tool overlays the five-number summary inside the violin so you get both statistical precision and distributional richness.

Frequently Asked Questions

How is a violin plot different from a box plot?
A violin plot shows the full probability density of the data using a KDE curve on each side of the box, revealing multi-modal distributions and clustering that a box plot's summary statistics alone cannot show. Both display the five-number summary, but violin plots add shape information.
What does the width of the violin represent?
The width at any y-value represents the estimated density (relative frequency) of data points at that value. Wider sections mean more observations near that value, while narrow sections indicate fewer data points.
How many data points are needed for a violin plot?
A minimum of 10–15 data points is recommended for the KDE to produce a meaningful density curve. With fewer points, the bandwidth estimation becomes unreliable and the violin shape may be misleading.

Key Terms

KDE (Kernel Density Estimation)
A non-parametric way to estimate the probability density function of a random variable. Used to draw the smooth shape of violin plots.
Bandwidth
Controls the smoothness of the KDE curve. Smaller bandwidths reveal fine detail; larger bandwidths produce smoother curves.
Minimum (Min)
The smallest data point excluding outliers.
First Quartile (Q1)
The median of the lower half. 25% of data falls below Q1.
Median (Q2)
The middle value. 50% of data falls below the median.
Third Quartile (Q3)
The median of the upper half. 75% of data falls below Q3.
Maximum (Max)
The largest data point excluding outliers.
Interquartile Range
Q3 − Q1. Measures statistical spread; the width of the box.
Mean
The arithmetic average of all data points.
Outliers
Data points below Q1−1.5×IQR or above Q3+1.5×IQR (Tukey fences).

Computation Method

  • Quartiles are calculated using linear interpolation (method 7 from Hyndman & Fan, 1996), consistent with Python's NumPy and pandas defaults.
  • Outlier detection uses the Tukey fences method: lower fence = Q1 − 1.5 × IQR, upper fence = Q3 + 1.5 × IQR.
  • Chart scaling caps the display axis at the upper fence value so the visualization remains clear even when extreme outliers are present.

References

  • Hyndman, R. J. & Fan, Y. (1996). “Sample Quantiles in Statistical Packages.” The American Statistician, 50(4), 361–365.
  • Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley.