Statistical
Static
Heatmap
Heatmaps use color intensity to represent values in a two-dimensional matrix. They're excellent for visualizing correlation matrices, pivot tables, and any data that can be organized in a grid. The color gradient immediately highlights patterns, clusters, and outliers in your data. Heatmaps are widely used in genomics, finance, marketing, and data science for pattern recognition.
Example Visualization
Try this prompt
"Create a heatmap showing the correlation matrix for 8 financial metrics: 'Revenue', 'Profit', 'Expenses', 'ROI', 'Customer Count', 'Avg Order Value', 'Marketing Spend', 'Employee Count'. Generate realistic correlation data where logically related metrics are positively correlated (Revenue-Profit: 0.85, Marketing-Revenue: 0.72) and others have weak or negative correlations (Expenses-Profit: -0.45). Use a diverging RdBu colorscale centered at zero (-1 to +1 range). Display correlation coefficients inside each cell with 2 decimal places. Mask the upper triangle to avoid redundancy. Add clear axis labels, a color bar, and title 'Financial Metrics Correlation Matrix'."
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Common Use Cases
- 1Correlation analysis between variables
- 2Website user behavior patterns
- 3Gene expression analysis
- 4Sales by region and time
Pro Tips
Use diverging color scales for data with meaningful center
Annotate cells with values for precise reading
Consider clustering rows/columns for pattern discovery