Bee Colony Chart
Chart overview
A bee colony chart is a jittered categorical scatter plot specifically designed for small-to-medium sample sizes in biology (n = 3-50 per group).
Key points
- Each data point is plotted at the correct y-value for its measurement while being horizontally jittered by a small random offset within the column width to prevent exact overlap.
- Unlike the beeswarm algorithm which produces deterministic placement, jitter-based approaches are simpler to implement and more familiar in biological literature.
- The result resembles bees hovering around a colony center.
Python Tutorial
How to create a bee colony chart in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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"Create a bee colony chart from my in vivo experimental data with treatment groups on the x-axis and measured outcome on the y-axis. Jitter individual data points horizontally within each group column. Overlay group mean as a horizontal line and standard error bars. Color points by treatment group. Add pairwise significance brackets with p-values for each comparison. Show N per group below x-axis labels. Use a clean white background and format for a Journal of Experimental Medicine figure at 300 DPI."
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Python Code Example
Console Output
Figure saved: plotivy-bee-colony-chart.png
Common Use Cases
- 1In vivo pharmacology: individual animal tumor volume or body weight measurements per drug treatment group
- 2Immunology: per-well or per-mouse cytokine concentrations from ELISA or multiplex immunoassay
- 3Cell biology: individual cell viability or proliferation index values per siRNA knockdown condition
- 4Neuroscience: behavioral assay outcomes (open field distance, latency, rotarod time) per genotype or condition
Pro Tips
Set jitter width to 20-30% of the column spacing so points cluster visibly within their group
Use a fixed random seed for reproducible jitter positions between figure revisions
Add the mean +/- SEM as a crosshair overlay so summary statistics are available alongside individual points
For paired data (e.g., same animal before and after), connect paired points with thin lines to show individual changes
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 bee-colony-chart.
numpy
Useful in specialized workflows that complement core Python plotting libraries for bee-colony-chart analysis tasks.
pandas
Good for quick exploratory drafts directly from DataFrame operations before polishing in matplotlib or plotly.
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.