To draw a dendrogram, you first need to have a numeric matrix. Each line represents an entity (here a car). Each column is a variable that describes the cars. The objective is to cluster the entities to show who shares similarities with whom. The dendrogram will draw the similar entities closer to each other in the tree.
Let’s start by loading a dataset and the requested libraries:
# Libraries import pandas as pd from matplotlib import pyplot as plt from scipy.cluster.hierarchy import dendrogram, linkage import numpy as np # Import the mtcars dataset from the web + keep only numeric variables 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') df = df.reset_index(drop=True) df.head()
All right, now that we have our numeric matrix, we can calculate the distance between each car, and draw the hierarchical clustering. Distance calculation can be done by the
linkage() function. I strongly advise you to visit the next page for more details concerning this crucial step.
# Calculate the distance between each sample # You have to think about the metric you use (how to measure similarity) + about the method of clusterization you use (How to group cars) Z = linkage(df, 'ward')
Last but not least, you can easily plot this object as a dendrogram using the
dendrogram() function of scipy library. These parameters are passed to the function:
Z: The linkage matrix
labels: Labels to put under the leaf node
leaf_rotation: Specifies the angle (in degrees) to rotate the leaf labels
See post #401 for possible customisations to a dendrogram.
# Plot title plt.title('Hierarchical Clustering Dendrogram') # Plot axis labels plt.xlabel('sample index') plt.ylabel('distance (Ward)') # Make the dendrogram dendrogram(Z, labels=df.index, leaf_rotation=90) # Show the graph plt.show()