Cleveland Dot Plot
Chart overview
The Cleveland dot plot arranges categories on the y-axis sorted by their quantitative value and places a single dot for each value, optionally connecting it to the axis with a thin reference line (creating a lollipop chart).
Key points
- Statistician William Cleveland demonstrated that dot plots enable more accurate magnitude judgements than bar charts because the eye compares positions rather than lengths.
- Scientists use them to rank species abundance, country-level metrics, gene expression values, or any ordered categorical comparison.
Example Visualization
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"Create a Cleveland dot plot from my data. Sort categories by value, draw filled circles at each value, connect each dot to the y-axis with a thin grey line, label the top and bottom items, and format as a minimal publication-quality figure."
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Python Code Example
Console Output
Figure saved: plotivy-dot-plot-cleveland.png
Common Use Cases
- 1Ranking species richness or abundance across ecological survey sites
- 2Comparing standardised effect sizes across studies in a systematic review
- 3Displaying country-level prevalence rates for a disease in a single year
- 4Showing gene expression log fold-change values ranked by magnitude
Pro Tips
Sort categories by the plotted value to make ranking immediately readable
Use a thin grey reference line from axis to dot to guide the eye without adding ink weight
For two-group comparisons draw two dot colours on the same row for a paired Cleveland plot
Keep horizontal axes starting near the minimum value, not at zero, to maximise resolution
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.