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.
Python Tutorial
How to create a proportion plot in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
Python Scatter Plot TutorialExample Visualization
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"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."
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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
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for proportion-plot.
numpy
Useful in specialized workflows that complement core Python plotting libraries for proportion-plot analysis tasks.
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.