["python","matplotlib","nan","colormap","imshow"]
How can I plot NaN values as a special color with imshow?

I am trying to use imshow in matplotlib to plot data as a heatmap, but some of the values are NaNs. I'd like the NaNs to be rendered as a special color not found in the colormap. example: import numpy as np import matplotlib.pyplot as plt f = plt.figure() ax = f.add_subplot(111) a = np.arange(25).reshape((5,5)).astype(float) a[3,:] = np.nan ax.imshow(a, interpolation='nearest') f.canvas.draw() The resultant image is unexpectedly all blue (the lowest color in the jet colormap). However, if I do the plotting like this: ax.imshow(a, interpolation='nearest', vmin=0, vmax=24) --then I get something better, but the NaN values are drawn the same color as vmin... Is there a graceful way that I can set NaNs to be drawn with a special color (eg: gray or transparent)?
With newer versions of Matplotlib, it is not necessary to use a masked array anymore. For example, let’s generate an array where every 7th value is a NaN: arr = np.arange(100, dtype=float).reshape(10, 10) arr[~(arr % 7).astype(bool)] = np.nan .cm.get_cmap() is replaced by .colormaps.get_cmap('viridis') in matplotlib v3.7.0 Set the color with .set_bad. import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np arr = np.arange(100, dtype=float).reshape(10, 10) arr[~(arr % 7).astype(bool)] = np.nan cmap = mpl.colormaps.get_cmap('viridis') # viridis is the default colormap for imshow cmap.set_bad(color='red') plt.imshow(arr, cmap=cmap) .cm.get_cmap() is deprecated We can modify the current colormap and plot the array with the following lines: current_cmap = mpl.cm.get_cmap() current_cmap.set_bad(color='red') plt.imshow(arr)