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I recently implemented a custom extension to Seaborn's heatmap function to visualize hierarchical data with multi-level indices. This approach reshapes and formats hierarchical data into a dimensionally-stacked heatmap, enabling clear representation of multi-dimensional relationships (e.g., parameter sweeps or policy comparisons).
Here’s an example visualization:
Note the custom formatting of axis ticks to display hierarchical levels and dividing lines to visually separate higher-level categories (these could be thicker or a color though).
This type of plot is particularly useful for multi-dimensional data analysis, like parameter sweeps or experimental results. Practically anything you want to visualize with more than two dimensions. It was originally inspired from the EMA workbench.
@mwaskom Would you find such functionality to be useful for Seaborn, or should it remain an external utility built on top of it? If it’s a potential fit, I’d be happy to refine the design and open an PR.
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I recently implemented a custom extension to Seaborn's
heatmap
function to visualize hierarchical data with multi-level indices. This approach reshapes and formats hierarchical data into a dimensionally-stacked heatmap, enabling clear representation of multi-dimensional relationships (e.g., parameter sweeps or policy comparisons).Here’s an example visualization:
Note the custom formatting of axis ticks to display hierarchical levels and dividing lines to visually separate higher-level categories (these could be thicker or a color though).
This type of plot is particularly useful for multi-dimensional data analysis, like parameter sweeps or experimental results. Practically anything you want to visualize with more than two dimensions. It was originally inspired from the EMA workbench.
@mwaskom Would you find such functionality to be useful for Seaborn, or should it remain an external utility built on top of it? If it’s a potential fit, I’d be happy to refine the design and open an PR.
Thanks for maintaining such an excellent library!
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