Heatmap
Explore how to use Python Altair's mark_rect() function to create heatmaps that reveal patterns and relationships in numeric data. Understand when heatmaps are appropriate, how to prepare data by binning variables, and discover alternatives like bubble charts and hex bin plots to effectively visualize complex datasets.
A heatmap represents the density of data as colors. We use heatmaps to visualize patterns in data and find outliers or areas of interest.
An example of a heatmap
When are heatmaps useful?
Heatmaps are useful when:
We want to find patterns or trends in data.
We have a large amount of data.
We want to understand how different variables relate to each other. ...