Bubble Map
Chart overview
Bubble maps combine geographic mapping with proportional symbols to display quantitative data across locations.
Key points
- Each bubble is positioned at specific coordinates and sized according to the value it represents.
- This visualization is excellent for showing regional comparisons, identifying geographic clusters, and communicating location-based metrics like sales by city or population by region.
Python Tutorial
How to create a bubble map in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
How to Create a Heatmap in PythonExample Visualization
.png&w=1280&q=70)
Create This Chart Now
Generate publication-ready bubble maps with AI in seconds. No coding required – just describe your data and let AI do the work.
View example prompt
"Create a bubble map of the United States displaying 'Regional Sales Performance' by major metropolitan area. Generate data for 15+ cities including New York, Los Angeles, Chicago, Houston, Phoenix, etc. Size bubbles proportionally to 'Annual Sales Volume' ($500K to $50M range) using sqrt scaling. Color bubbles by 'Year-over-Year Growth' using a diverging colorscale (red for negative, green for positive). Show city names and include a legend for both size and color."
How to create this chart in 30 seconds
Upload Data
Drag & drop your Excel or CSV file. Plotivy securely processes it in your browser.
AI Generation
Our AI analyzes your data and generates the Bubble Map code automatically.
Customize & Export
Tweak the design with natural language, then export as high-res PNG, SVG or PDF.
Newsletter
Get one weekly tip for better bubble maps
Join researchers receiving concise Python plotting techniques to improve chart clarity and reduce revision cycles.
Python Code Example
Console Output
Total cities: 15 Total sales: $330M Average growth: 3.8%
Common Use Cases
- 1Visualizing sales performance by location
- 2Showing population distribution across cities
- 3Mapping earthquake magnitudes
- 4Displaying store locations with revenue
Pro Tips
Use sqrt scaling for bubble sizes to prevent large values from dominating
Include both size and color legends for multi-variable encoding
Set appropriate map boundaries for your region
Add city labels for major bubbles to improve readability
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 bubble-map.
numpy
Useful in specialized workflows that complement core Python plotting libraries for bubble-map 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.