How can I mask the lower triangle while hierarchical clustering with seaborn's clustermap?
import numpy as np import seaborn as sns import matplotlib.pyplot as plt #pearson coefficients corr = np.corrcoef(np.random.randn(10, 200)) #lower triangle mask = np.tril(np.ones_like(corr)) fig, ax = plt.subplots(figsize=(6,6)) #heatmap works as expected sns.heatmap(corr, cmap="Blues", mask=mask, cbar=False) #clustermap not so much sns.clustermap(corr, cmap="Blues", mask=mask, figsize=(6,6)) plt.show() 
1 Answer
Well, the clustermap clusters the values according to similarity. This changes the order of the rows and the columns.
You could create a regular clustermap, and in a second step apply the mask:
import numpy as np import seaborn as sns import matplotlib.pyplot as plt corr = np.corrcoef(np.random.randn(10, 200)) g = sns.clustermap(corr, cmap="Blues", figsize=(6, 6)) mask = np.tril(np.ones_like(corr)) values = g.ax_heatmap.collections[0].get_array().reshape(corr.shape) new_values = np.ma.array(values, mask=mask) g.ax_heatmap.collections[0].set_array(new_values) plt.show() 
