In the example, mtcars dataset that shows the features of cars through numerical variables is used. While clustering cars, a color sheme is added to the left part of the plot. The 3 colours represent the 3 possible values of the ‘cyl’ column. By using this feature, you can evaluate whether samples within a group are clustered together.
# Libraries import seaborn as sns import pandas as pd from matplotlib import pyplot as plt # Data set url = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/mtcars.csv' df = pd.read_csv(url) df = df.set_index('model') # Prepare a vector of color mapped to the 'cyl' column my_palette = dict(zip(df.cyl.unique(), ["orange","yellow","brown"])) row_colors = df.cyl.map(my_palette) # plot sns.clustermap(df, metric="correlation", method="single", cmap="Blues", standard_scale=1, row_colors=row_colors) plt.show()