Box and Whisker Plot
Chart overview
Box and whisker plots (boxplots) provide a standardized way of displaying a distribution from five summary statistics: minimum, first quartile (Q1), median, third quartile (Q3), and maximum.
Key points
- The box spans the interquartile range (IQR = Q3 - Q1), the line inside marks the median, and the whiskers typically extend to the most extreme points within 1.
- 5 x IQR, with anything beyond plotted as individual outliers.
- In Python they are usually drawn with Seaborn's boxplot or Matplotlib, and pair well with an overlaid strip or swarm plot to show the raw data points.
Practical guidance
Boxplots are essential for comparing distributions across groups, spotting skew, and detecting outliers - reach for a violin plot instead when the shape of the distribution (for example multiple peaks) matters more than its quartiles.
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Python Tutorial
How to create a box and whisker plot in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
How to Make a Box Plot in PythonExample Visualization
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"Create a publication-ready box plot comparing 'Gene Expression Levels' (normalized counts) across 4 genotypes: WT (Wild Type), KO1 (Knockout 1), KO2 (Knockout 2), and Mutant. Generate a realistic dataset with n=20 biological replicates per group, with KO1 showing upregulation (~1.5x WT), KO2 showing downregulation (~0.8x WT), and Mutant showing moderate increase (~1.2x WT). Overlay jittered individual data points with transparency. Perform pairwise t-tests against WT control and add significance brackets with stars (* p<0.05, ** p<0.01, *** p<0.001, ns for non-significant). Use a colorblind-friendly palette, add y-axis label with units, and include sample size (n=) in x-axis labels."
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Python Code Example
Console Output
Generated dataset: 80 samples across 4 genotypes
count mean std min 25% 50% 75% max
Genotype
KO1 20.0 1.433506 0.242010 1.010082 1.229742 1.435316 1.549965 1.963070
KO2 20.0 0.794662 0.164170 0.447392 0.697058 0.805572 0.928675 1.011424
Mutant 20.0 1.190581 0.333626 0.414076 1.043802 1.207683 1.444461 1.669393
WT 20.0 0.965740 0.192006 0.617344 0.870256 0.953171 1.101635 1.315843
Pairwise t-test p-values against WT:
KO1: t=-6.772, p=6.439e-08
KO2: t=3.029, p=4.454e-03
Mutant: t=-2.612, p=1.386e-02
Significant genotypes (p<0.05): ['KO1', 'KO2', 'Mutant']Common Use Cases
- 1Comparing experimental groups in scientific research
- 2Detecting outliers in datasets
- 3Analyzing test score distributions
- 4Quality control in manufacturing
Pro Tips
Overlay individual data points for small datasets
Use notched boxplots to compare medians visually
Add statistical significance annotations when comparing groups
Frequently asked questions
When should you use a box and whisker plot?
Box and whisker plots (boxplots) provide a standardized way of displaying a distribution from five summary statistics: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. The box spans the interquartile range (IQR = Q3 - Q1), the line inside marks the median, and the whiskers typically extend to the most extreme points within 1. Common applications include comparing experimental groups in scientific research, detecting outliers in datasets, and analyzing test score distributions.
Which Python libraries can create a box and whisker plot?
A box and whisker plot can be built in Python with seaborn, matplotlib, and plotly — seaborn for statistically-aware defaults on tidy data, matplotlib for precise control over axes, annotations, and journal styling, and Plotly for interactive hover, zoom, and web sharing. In Plotivy you describe the figure and it writes the seaborn code for you.
Can I make a box and whisker plot without writing Python code?
Yes. Describe the box and whisker plot you need in plain language and upload your dataset — Plotivy's AI writes the Python code and renders a publication-ready figure. You still get the full, editable seaborn source, so nothing is locked in a black box.
What are best practices for a clear box and whisker plot?
Overlay individual data points for small datasets. Use notched boxplots to compare medians visually.
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
seaborn
Fastest path to statistically-aware defaults and tidy-data workflows, especially for grouped and distribution-focused box-and-whisker-plot views.
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for box-and-whisker-plot.
plotly
Best for interactive hover, zoom, and web sharing when collaborators need to inspect values directly from box-and-whisker-plot figures.
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.