How to Create a Discrete Colorbar for Scatterplots in Matplotlib?

Creating a Discrete Colorbar for Matplotlib Scatterplots

Generating a scatterplot with discrete color representation is a common task in data visualization. This allows each data point to be assigned a unique color based on a specific value.

In Matplotlib, creating a discrete colorbar can be achieved using a BoundaryNorm as the normalizer for the scatterplot. This is particularly useful when working with integer tag values, such as in the example provided:

plt.scatter(x, y, c=tag)

where tag represents the integer tag value for each data point.

The default settings, plt.colorbar(), typically display a continuous range of colors. To create a discrete colorbar, it is necessary to specify a set of discrete colors. For instance, to create a colorbar with 20 colors, the LinearSegmentedColormap.from_list function can be employed:

cmaplist = [cmap(i) for i in range(cmap.N)]
cmaplist[0] = (.5, .5, .5, 1.0)
cmap = mpl.colors.LinearSegmentedColormap.from_list('Custom cmap', cmaplist, cmap.N)

Here, the first color entry is set to gray, which can be used to represent the tag value of 0. The other entries in the cmaplist define the colors for the remaining tag values.

To create a discrete colorbar, it is also necessary to define the bins and normalize the data. The scipy.stats.binned_statistic function can be used to compute the individual ranks of each data point and assign them to discrete groups. Then, the colors.BoundaryNorm can be used to normalize the data and map it to the desired colorbar range:

bounds = np.linspace(0, 20, 21)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)

Finally, a second axes can be added to the figure to create the colorbar itself:

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')

This will create a discrete colorbar with the specified color scheme. The number of discrete colors can be adjusted by modifying the bounds and boundaries parameters.

By following these steps, it is possible to generate a discrete colorbar in Matplotlib that accurately represents the underlying integer tag values of the data points.

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