Alluvial Diagram
Chart overview
An alluvial diagram visualizes the flow and redistribution of observations between categorical states across multiple time points or stages.
Key points
- Unlike a Sankey diagram that emphasizes quantities of flow, an alluvial diagram focuses on grouping and tracking how entities transition between categories: patients progressing through clinical stages, cell populations changing phenotype over a time course, or species composition shifting across ecological sites.
- Each vertical column represents a categorical variable at one stage, and the alluvia (ribbons) connecting columns are proportional in width to the number of observations flowing between states.
- The color of each ribbon typically encodes the source or destination category.
Python Tutorial
How to create a alluvial diagram in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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"Create an alluvial diagram from my longitudinal clinical data showing patient transitions between response categories (CR, PR, SD, PD) at baseline, 3 months, and 6 months. Width of ribbons proportional to patient counts. Color ribbons by baseline category. Label each category block with the count and percentage. Use a white background and distinct categorical colors. Format for a clinical journal at 300 DPI."
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Python Code Example
Console Output
Figure saved: plotivy-alluvial-diagram.png
Common Use Cases
- 1Tracking patient clinical response category transitions over serial treatment assessments
- 2Single-cell RNA-seq trajectory: showing cell state redistribution between timepoints after stimulation
- 3Gut microbiome study: visualizing community state transitions across dietary intervention phases
- 4Epidemiology: displaying population movement between disease severity grades over a surveillance period
Pro Tips
Sort categories consistently at each column to minimize ribbon crossings and improve readability
Use color to encode one meaningful variable - source category or a biologically relevant grouping
Keep the number of stages (columns) to three or four maximum before the diagram becomes uninterpretable
Label the width of each block with the absolute count so proportions are quantitatively grounded
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 alluvial-diagram.
pandas
Good for quick exploratory drafts directly from DataFrame operations before polishing in matplotlib or plotly.
numpy
Useful in specialized workflows that complement core Python plotting libraries for alluvial-diagram 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.