Free Online Violin Plot Maker

Show data distribution shape with mirrored density curves. More detail than box plots.

Built & maintained by the ChartLoad editorial team

What Is a Violin Plot?

A violin plot combines a box plot with a mirrored density curve. The wider sections represent higher density of data points. This reveals distribution shape — bimodal, skewed, or uniform — that box plots hide.

The width of the violin at any given value represents the density of data points there. This dual representation shows both key summary statistics and detailed distribution shape, including bimodality, skewness, and multimodal peaks. Key components include the outer density curves forming the violin shape, an embedded box plot or median marker inside, and symmetric mirroring on both sides. Violin plots are particularly valuable when distribution shape carries important analytical information.

What Data You Need for a Violin Plot

You need one numeric measurement column plus an optional category column that splits the data into groups (one violin per group). Example: a "Group" column with values like A, B, C and a "Value" column holding the raw measurements — e.g. A,4.2 / A,4.9 / B,7.1 / B,2.3 — each row is one observation, not a pre-aggregated summary.

When to Use a Violin Plot

Use violin plots when you need to see the full distribution shape, not just summary statistics. Great for comparing multimodal distributions across groups. They are ideal for clinical research treatment comparisons, score distributions with clustering in education, salary distributions across departments, and response time patterns across server configurations.

Violin plots are used in biostatistics, data science, and academic research. They work best with datasets of 50 or more values per group. Choose violin plots over box plots when you suspect bimodal or non-normal distributions. Choose them over histograms when comparing multiple groups side by side. Pair them with overlaid strip plots to show individual data points alongside the density shape.

Where a Violin Plot Works Best

Comparing the same metric across several groups (e.g. exam scores by class, salaries by department) when you suspect some groups are bimodal or skewed rather than bell-shaped.
Quality control where a box plot's median looks on-target but you need to catch a hidden second cluster of off-spec parts.
A/B test analysis: checking whether a treatment widened, split, or shifted the whole response distribution instead of just nudging the average.
Showing reviewers the full shape of model residuals or measurement error so a clean median doesn't hide fat tails or asymmetry.

Violin Plot Example: Two-peak commute times

A transit analyst plots morning trip durations for a single bus route across 300 weekday riders. A box plot showed a tidy median near 28 minutes, so the route looked fine. The violin plot was built to check the actual shape of the spread.

Sample data: Minutes: 18,19,20,21,22,22,23,24,24,25,38,39,40,41,42,42,43,44,45,46 (full set n=300)

The violin bulges at TWO widths — a fat hump around 22 min and a second around 42 min — exposing a bimodal split (express vs. all-stops trips) that the single-median box plot completely flattened.

How to Create a Violin Plot Online

Step 1: Open ChartLoad and select the Violin Plot chart type from the chart picker on the main page
Step 2: Enter your raw data values for each group or category in separate columns of the data editor
Step 3: The kernel density estimation calculates automatically, rendering the mirrored violin shape for each group
Step 4: Add multiple groups by creating additional data columns to enable side-by-side distribution comparison
Step 5: Customize the violin colors, border styles, embedded box plot visibility, axis labels, and title
Step 6: Click Export and download your violin plot as PNG, PDF, SVG, JPEG, or WebP

Violin Plot Best Practices & Tips

Violin plots reveal multimodal distributions that box plots hide — use them when shape matters.
Add a mini box plot inside each violin so readers also see quartile statistics alongside the density curve.
Use violins when sample sizes are large enough (50+) for the kernel density estimate to be reliable.
Mirror the density symmetrically for a traditional violin shape, or show one-sided density for a simpler look.
Compare violins side by side to spot group differences in spread, skewness, and modality at a glance.
Truncate the density at the data range to avoid implying the distribution extends beyond observed values.
Use violins to compare distributions across groups in clinical trials, A/B tests, and survey analysis.

Common Violin Plot Mistakes to Avoid

Feeding it tiny samples: with under ~30 points per group the kernel density estimate invents smooth curves that aren't real, making noise look like a meaningful second peak.
Reading width as count — a fatter violin means higher relative density at that value, not more total observations; two violins of different sample sizes can look equally wide.
Ignoring the bandwidth setting: too smooth erases a genuine bimodal dip, too rough turns every cluster into a spiky false peak. Always sanity-check against the raw points.
Letting the density tails spill past your real min and max, suggesting impossible negative or out-of-range values that never occurred in the data.

Frequently Asked Questions

When should I use violin vs box plots?

Use violin plots when the distribution shape matters (bimodal, skewed). Use box plots for simpler comparisons of medians and quartiles.

Does it include box plot elements?

Yes, the box plot (median line and quartile box) is shown inside the violin shape.

How is a violin different from a box plot?

A violin plot shows the full density curve shape, revealing multimodal distributions that box plots hide. Box plots are simpler and show quartile statistics more explicitly.

Related Chart Types

Ready-Made Violin Plot Templates

Skip the blank canvas — open a free, editable Violin Plot template, swap in your own data, and export. Browse all templates.

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