Heatmaps are a handy tool for showing complicated and big datasets to uncover data patterns, trends, and connections between points. Here’s a simple explanation of heatmaps:
- Color Codes: In a heatmap, each square in a grid gets a color based on its value. Darker colors mean higher values, while lighter ones represent lower values. Typically, a color scale goes from cool colors like blue for low values to warm colors like red for high values.
- Applications: Heatmaps are versatile and can be used in various fields like data analysis, biology, finance, and more. They’re used for things like:
- Displaying relationships between data points, like which variables are related.
- Showing temperature changes, often on weather maps or in engineering.
- Highlighting areas of high or low risk in a dataset.
- Interactive Options: Some heatmaps allow users to interact with them. This means you can hover over squares to see exact values, zoom in, or filter the data.
- Sorting with Hierarchy: Heatmaps can be made even more informative by arranging rows and columns based on data similarities using a method called hierarchical clustering. This helps reveal hidden patterns in the data.