How to express classes on the axis of a heatmap in Seaborn

Question

I created a very simple heatmap chart with Seaborn displaying a similarity square matrix. Here is the one line of code I used:

sns.heatmap(sim_mat, linewidth         


        

Answer 1

Building on the above answer, I think it's worth noting the possibility of multiple colour levels for labels - as noted in the clustermap docs ({row,col}_colors). I couldn't find an example of multiple levels, so I thought I'd share an example here.

networks = sns.load_dataset("brain_networks", index_col=0, header=[0, 1, 2])

network level

network_labels = networks.columns.get_level_values("network")
network_pal = sns.cubehelix_palette(network_labels.unique().size, light=.9, dark=.1, reverse=True, start=1, rot=-2)
network_lut = dict(zip(map(str, network_labels.unique()), network_pal))

Create index using the columns for networks

network_colors = pd.Series(network_labels, index=networks.columns).map(network_lut)

node level

node_labels = networks.columns.get_level_values("node")
node_pal = sns.cubehelix_palette(node_labels.unique().size)
node_lut = dict(zip(map(str, node_labels.unique()), node_pal))

Create index using the columns for nodes

node_colors = pd.Series(node_labels, index=networks.columns).map(node_lut)

Create dataframe for row and column color levels

network_node_colors = pd.DataFrame(network_colors).join(pd.DataFrame(node_colors))

create clustermap

g = sns.clustermap(networks.corr(),
# Turn off the clustering
row_cluster=False, col_cluster=False,
# Add colored class labels using data frame created from node and network colors
row_colors = network_node_colors,
col_colors = network_node_colors,
# Make the plot look better when many rows/cols
linewidths=0,
xticklabels=False, yticklabels=False,
center=0, cmap="vlag")

create two legends - one for each level by creating invisible column and row barplots (as per above)

network legend

from matplotlib.pyplot import gcf

for label in network_labels.unique():
    g.ax_col_dendrogram.bar(0, 0, color=network_lut[label], label=label, linewidth=0)

l1 = g.ax_col_dendrogram.legend(title='Network', loc="center", ncol=5, bbox_to_anchor=(0.47, 0.8), bbox_transform=gcf().transFigure)

node legend

for label in node_labels.unique():
    g.ax_row_dendrogram.bar(0, 0, color=node_lut[label], label=label, linewidth=0)

l2 = g.ax_row_dendrogram.legend(title='Node', loc="center", ncol=2, bbox_to_anchor=(0.8, 0.8), bbox_transform=gcf().transFigure)

plt.show()