How can you create a custom discrete colorbar for a scatterplot in Matplotlib?

Discretizing a Matplotlib Colorbar for Scatterplot

In scatterplots, a continuous colorbar may not always suffice in representing discrete data. Creating a discrete colorbar is crucial for effectively visualizing the underlying patterns.

To achieve this, the BoundaryNorm class in Matplotlib can be leveraged as a normalizer for the scatterplot. This ensures that distinct values are represented by unique colors.

One challenge in customizing the colorbar arises when attempting to set a specific value to display as gray. To tackle this issue, the existing color palette can be modified by extracting and overriding color entries.

<code class="python">cmaplist = [cmap(i) for i in range(cmap.N)]
# force the first color entry to be grey
cmaplist[0] = (.5, .5, .5, 1.0)</code>

After modifying the color palette, a custom colormap can be created. The BoundaryNorm is then utilized to define the binning and normalize the data accordingly.

<code class="python">cmap = mpl.colors.LinearSegmentedColormap.from_list(
    'Custom cmap', cmaplist, cmap.N)
bounds = np.linspace(0, 20, 21)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)</code>

With the modified color palette and normalization in place, the scatterplot can be rendered. A separate axes is added to accommodate the colorbar, which is customized to display the discrete bounds and their corresponding colors.

<code class="python">scat = ax.scatter(x, y, c=tag, cmap=cmap, norm=norm)
ax2 = fig.add_axes([0.95, 0.1, 0.03, 0.8])
cb = plt.colorbar.ColorbarBase(ax2, cmap=cmap, norm=norm,
    spacing='proportional', ticks=bounds, boundaries=bounds, format='%1i')</code>

This approach enables the creation of custom discrete colorbars that effectively communicate discrete data within a scatterplot, providing a clearer visual representation of the underlying patterns.

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