Free Violin Plot Generator – Create Density Distribution Charts Online

Visualize data density and distribution shape with kernel density estimation overlay.

KDE Parameters
BandwidthSmoothness
Appearance
Box Plot AI

Hi! I'm your Box Plot analysis assistant.

• Analyze the data currently on your chart — I'll interpret min, Q1, median, Q3, max, IQR, and outliers • Generate sample data — e.g. "generate 10 datasets of monthly temperatures, 20 points each" • Explain statistics — ask me "what does IQR tell me?" or "how to spot skewness in a box plot"

Try: "Analyze my current data" or "Generate temperature data for 12 cities, 15 points each"

How to Use This Free Violin Plot Generator – Create Density Distribution Charts Online

  1. Enter your data. Paste comma-separated or space-separated numbers into the text area.
  2. Click Generate. The tool will compute the five-number summary and kernel density estimation.
  3. Analyze the shape. Look at the width of the violin — wider sections indicate higher data density.

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.

Why Use Violin Plots?

  • Reveal multi-modal distributions. Unlike box plots, violin plots show when your data has multiple peaks.
  • Compare shape, spread, and density. Place violin plots side by side to compare the entire distributional shape across groups.
  • Combine with box plot elements. This tool overlays the five-number summary inside the violin.

Frequently Asked Questions

What does a violin plot show?
A violin plot combines a box plot with a kernel density estimation on each side, showing the full distribution shape — including multiple peaks and density clusters.
How is a violin plot different from a box plot?
A box plot shows only the five-number summary (min, Q1, median, Q3, max). A violin plot shows the full probability density, revealing bimodal distributions and other patterns that box plots miss.

Key Terms

KDE
Kernel Density Estimation — a non-parametric way to estimate the probability density function.
Bandwidth
Controls the smoothness of the KDE curve. Smaller = finer detail; larger = smoother.
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.

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