For example, in your code, you could consider, for xs, ys in zip(icoord, dcoord): Just choose a colormap, and get a color from that colormap depending upon some fraction.

So you no longer need to be restricted to values provided to you. For example, import matplotlib.pyplot as plt Most matplotlib colormaps will give you a value given a value between 0 and 1. Len(set(D_dendro)), '^ # of colors from dendrogram',

Plt.ylim( y_min, y_max + 0.1*abs(y_max) )ДЇig,ax = plotTree(D_dendro) #wrapper I made Y_min, y_max = dcoord.min(), dcoord.max()ДЇor xs, ys, color in zip(icoord, dcoord, color_list): X_min, x_max = icoord.min(), icoord.max()

Z = linkage(squareform(DF_dist.as_matrix()),method='average') #metric='euclidead' necessary since the input is a dissimilarity measure?Д¬olor_list = np.array( D_dendro ) #DataFrame: rows = Samples, cols = AttributesДЄttributes = ДF_data = pd.DataFrame(np.random.randn(n, m),#ДЄ_dist = distance.cdist(DF_data.as_matrix().T, DF_data.as_matrix().T)ДF_dist = pd.DataFrame(A_dist, index = attributes, columns = attributes)

Scipy cdist