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Sign in to get your referral linkViolin plots reveal what box plots hide - the full shape of your distribution. Bimodality, skewness, and density are instantly visible. Plotivy creates them with overlaid box plots, jitter, and significance brackets.
A box plot with identical quartiles can represent wildly different distributions. Violin plots show the full probability density - revealing bimodal patterns, heavy tails, and gaps that box plots completely miss.
Two peaks in the violin body reveal subpopulations - invisible in a box plot
Asymmetric violin shape shows whether data leans left or right
Wider sections = more data points at that value - intuitive reading
The best practice is to overlay a thin box plot and individual data points on top of the violin. This code demonstrates all three layers plus summary statistics.
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Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
The raincloud plot is the most informative distribution chart - combining a half-violin, box plot, and individual points in a clean layout that avoids symmetry redundancy.
Preparing preview
Running once automatically on first load
This is a safe playground for learning! Try changing:
Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Automatically combines violin + box + jitter + significance brackets in a single prompt.
Compare two conditions within each group using side-by-side half-violins.
KDE bandwidth is automatically tuned for your sample size to avoid over-smoothing.
300+ DPI output with proper fonts and sizing matching Nature, Science, and Cell guidelines.
Chart gallery
More ways to visualize distributions
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Displays data distribution using quartiles, median, and outliers in a standardized format.
Sample code / prompt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
# Generate gene expression data for 4 genotypes
np.random.seed(42)
genotypes = ['WT', 'KO1', 'KO2', 'Mutant']
n_per_group = 20
Displays the distribution of numerical data by grouping values into bins.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde, skewnorm
# Generate age data with slight right skew
np.random.seed(42)
ages = skewnorm.rvs(a=2, loc=42, scale=15, size=500)
ages = np.clip(ages, 18, 80) # Clip to realistic range
fig, ax = plt.subplots(figsize=(12, 7))
Displays values for two variables as points on a Cartesian coordinate system.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import pandas as pd
# Generate sample data
np.random.seed(42)
n_samples = 200
height = np.random.normal(170, 8, n_samples)
weight = height * 0.6 + np.random.normal(0, 8, n_samples) - 50
Compares categorical data using rectangular bars with heights proportional to values.
Sample code / prompt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
# Generate performance scores for 5 treatment groups
np.random.seed(42)
groups = ['Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Treatment D']
n_samples = 30
Represents data values as colors in a two-dimensional matrix format.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# Create correlation matrix for financial metrics
metrics = ['Revenue', 'Profit', 'Expenses', 'ROI', 'Customers', 'AOV', 'Marketing', 'Employees']
correlation_data = np.array([
[1.00, 0.85, -0.45, 0.72, 0.88, 0.65, 0.72, 0.55],
[0.85, 1.00, -0.78, 0.92, 0.75, 0.58, 0.63, 0.48],Upload your data and get publication-ready violin plots with overlaid statistics in seconds.