Hexbin Plot
Chart overview
A hexbin plot divides the two-dimensional space into a regular grid of hexagonal bins and colors each hexagon according to the number of data points (or a summary statistic) that fall within it.
Key points
- Hexagons tile the plane more efficiently than squares: they are equidistant from the center to all six neighbors, producing less visual distortion at bin edges and making density gradients appear smoother.
- This makes hexbin plots the preferred solution for visualizing tens of thousands to millions of points where a conventional scatter plot would be an opaque blob of overplotted marks.
- In single-cell genomics, hexbin plots display UMAP or t-SNE embeddings density when large atlas-scale datasets are involved.
Python Tutorial
How to create a hexbin 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 hexbin plot from my large single-cell RNA-seq dataset showing the joint distribution of two gene expression values. Use gridsize=50 and viridis colormap from white (low density) to dark purple (high density). Add a colorbar labeled 'Cell Count'. Overlay contour lines for the 25th, 50th, and 75th density percentiles. Label axes with gene names. Output at 300 DPI for publication."
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Python Code Example
Console Output
Figure saved: plotivy-hexbin-plot.png
Common Use Cases
- 1Single-cell RNA-seq: visualizing co-expression of two marker genes across hundreds of thousands of cells
- 2Mass spectrometry: displaying m/z vs. retention time feature density for untargeted metabolomics
- 3High-content imaging phenomics: correlating two morphological features measured from millions of cells
- 4GWAS: joint density of effect size estimates from two independent GWAS cohorts for genetic correlation
Pro Tips
Choose gridsize based on data density: too coarse loses detail, too fine produces sparse empty hexagons
Use a colormap that starts at white or light yellow for empty areas so background is distinguishable
For log-distributed data, log-transform both axes before binning so density is not dominated by outliers
Add marginal histograms or KDE curves along each axis for additional distributional context
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 hexbin-plot.
numpy
Useful in specialized workflows that complement core Python plotting libraries for hexbin-plot 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.