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Sign in to get your referral linkBox plots are the gold standard for comparing distributions across groups. Plotivy generates them with automatic statistical tests, significance brackets, individual data point overlays, and journal-compliant styling.
A box plot (box-and-whisker plot) summarizes a distribution using five key statistics. Understanding each element is crucial for interpreting your data correctly.
Middle value (Q2) - the line inside the box
Bottom of box - 25% of data falls below
Top of box - 75% of data falls below
Extend to 1.5x IQR or data extremes
Points beyond whiskers - always show them
This code creates a publication-ready box plot with ANOVA, pairwise t-tests, significance brackets (*, **, ***), and individual data point overlay. Edit anything and see results instantly.
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.
data and labels arraysttest_ind for mannwhitneyu for non-parametric datavert=False to the boxplot call| Plot Type | Best For | Shows | Limitation |
|---|---|---|---|
| Box plot | Comparing 2-8 groups | Median, IQR, outliers | Hides distribution shape |
| Violin plot | Distribution shape matters | Full density + quartiles | Needs more space |
| Strip/jitter plot | Small sample sizes (n<30) | Every individual point | Cluttered for large n |
| Bar + error bars | Simple group means | Mean +/- SEM/SD | Hides distribution; avoid if possible |
Pro tip: many journals now require showing individual data points on box plots. The code above already includes this via the jitter overlay.
Track biomarker changes across timepoints in a clinical trial or longitudinal study. Side-by-side panels show opposing trends clearly.
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.
Plotivy runs ANOVA, t-tests, or Mann-Whitney automatically and adds the correct significance brackets to your plot.
Every data point is shown as a jittered dot overlaid on the box - meeting modern journal requirements.
Upload grouped data and Plotivy detects categories, creating color-coded grouped box plots automatically.
300 DPI output with proper fonts, axis labels, and figure sizing ready for Nature, Science, or PLOS ONE.
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Explore more ways to visualize distributions
Combines box plots with kernel density to show distribution shape across groups.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from scipy.stats import f_oneway
# Generate exam score data for 3 groups
np.random.seed(42)
control = np.random.normal(72, 12, 50)
treatment_a = np.random.normal(78, 10, 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
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
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))
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 experimental data and get a publication-ready box plot with automatic statistics in under 30 seconds.