How to use the same colorbar for seaborn heatmaps?

Question

I have three subplots which have slightly different colorbars when plotted individually

I plotted the heatmaps as follows:

fig, axes = plt.subplots(nrows=1, ncols=3, figsize = (20,10),sharey=True)

sns.heatmap(df,cmap=colormap,ax = axes[0])
sns.heatmap(df2,cmap=colormap,ax = axes[1])
sns.heatmap(df3,cmap=colormap,ax = axes[2])

I know that I can just set cbar = False for the first two plots, however as they are slightly different colorbars, the third colour bar will not represent all subplots. My first subplot ranges from 0-35, the second 0-36 and the third 0-37. I want a colorbar that encompasses the 0-37 range but obviously the colors will correspond to the wrong values for the first two subplots if I just do cbar = False .

How would I set up my subplots to contain just one colorbar which applies to all subplots, instead of three? Sorry about the lack of figures, I am unable to share them at this stage.

Answer 1

I want a colorbar that encompasses the 0-37 range but obviously the colors will correspond to the wrong values for the first two subplots if I just do cbar=False .

Either set vmin and vmax on each heatmap:

sns.heatmap(df, cmap=colormap, ax=axes[0], vmin=0, vmax=37, cbar=False)
sns.heatmap(df2, cmap=colormap, ax=axes[1], vmin=0, vmax=37, cbar=False)
sns.heatmap(df3, cmap=colormap, ax=axes[2], vmin=0, vmax=37)

Or create a min/max norm using matplotlib.colors.Normalize :

from matplotlib import colors as mcolors
norm = mcolors.Normalize(0, 37)

sns.heatmap(df, cmap=colormap, ax=axes[0], norm=norm, cbar=False)
sns.heatmap(df2, cmap=colormap, ax=axes[1], norm=norm, cbar=False)
sns.heatmap(df3, cmap=colormap, ax=axes[2], norm=norm)

---

Note that if you want to extract vmin and vmax automatically, ravel and stack the data:

values = np.hstack([d.values.ravel() for d in [df, df2, df3]])
norm = mcolors.Normalize(values.min(), values.max())