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.

Try this prompt

"Use seaborn to create a heatmap of a correlation matrix. Use a diverging color scale and display the correlation coefficients. Generate a proper example dataset to demonstrate this visualization."
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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