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.
Create a Cleveland Dot Plot with your data using AI — no coding required.
Further reading
Python Scatter Plot Tutorial
Build scatter plots with regression overlays, color encodings, and uncertainty bars.
Read the guideExample Visualization
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Generate publication-ready cleveland dot plots with AI in seconds. No coding required – just describe your data and let AI do the work.
View example prompt
"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."
How to create this chart with your data
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AI Generation
Our AI analyzes your data and generates the Cleveland Dot Plot code automatically.
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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 so readers can scan the ranking quickly
Label the horizontal axis with the measurement and unit used for every category
For paired comparisons, use two distinct markers on each row and connect the corresponding values
Keep reference lines subtle so the dots remain the primary visual encoding
Frequently asked questions
When should you use a cleveland dot plot?
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). Statistician William Cleveland demonstrated that dot plots enable more accurate magnitude judgements than bar charts because the eye compares positions rather than lengths. Common applications include ranking species richness or abundance across ecological survey sites, comparing standardised effect sizes across studies in a systematic review, and displaying country-level prevalence rates for a disease in a single year.
Which Python libraries can create a cleveland dot plot?
A cleveland dot plot can be built in Python with matplotlib and numpy — matplotlib for precise control over axes, annotations, and journal styling and numpy. In Plotivy you describe the figure and it writes the matplotlib code for you.
Can I make a cleveland dot plot without writing Python code?
Yes. Describe the cleveland dot 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 matplotlib source, so nothing is locked in a black box.
What are best practices for a clear cleveland dot plot?
Sort categories by the plotted value so readers can scan the ranking quickly. Label the horizontal axis with the measurement and unit used for every category.
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for dot-plot-cleveland.
numpy
Useful in specialized workflows that complement core Python plotting libraries for dot-plot-cleveland analysis tasks.
Scientific Chart Selection Cheat Sheet
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