Proportion Plot
Chart overview
A proportion plot normalises each bar to 100% and stacks category segments, so the reader compares relative compositions across groups rather than absolute counts.
Key points
- Ecologists use it to display community composition across sites, clinical researchers to show categorical outcome distributions across treatment arms, and geologists to display stratigraphic mineral proportions.
- It is the standard tool for compositional data where part-to-whole structure is the primary message.
Example Visualization
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Generate publication-ready proportion plots with AI in seconds. No coding required – just describe your data and let AI do the work.
View example prompt
"Create a 100% stacked proportion plot from my compositional data. Normalise each bar to 100%, stack categories with distinct colours, add percentage labels inside segments wider than 5%, sort categories by dominant proportion, and apply a publication-quality journal style."
How to create this chart in 30 seconds
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AI Generation
Our AI analyzes your data and generates the Proportion Plot code automatically.
Customize & Export
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Python Code Example
Console Output
Figure saved: plotivy-proportion-plot.png
Common Use Cases
- 1Displaying microbial community composition across environmental sample sites
- 2Comparing categorical treatment outcome distributions across clinical trial arms
- 3Showing stratigraphic facies proportions across geological drill core sections
- 4Visualising land-use category proportions across spatial regions or time periods
Pro Tips
Sort stacked categories consistently across all bars so the eye can track one segment
Label percentage values directly inside segments larger than approximately 5%
Use a colourblind-safe palette with sufficient contrast between adjacent segments
Consider a side-by-side bar chart if precise comparison of one category is the primary goal
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.