Learn Performing Hierarchical Clustering

A clustermap is a matrix plot that combines a heatmap with hierarchical clustering.

While a standard heatmap displays data in a fixed grid, a clustermap reorders the rows and columns to place similar values next to each other. The tree-like diagrams on the axes are called dendrograms, and they show how the data points are grouped.

Key Parameters

To control how the clustering works, you can use these parameters:

standard_scale: standardizes the data (0 for rows, 1 for columns) so that each feature has a mean of 0 and variance of 1. This is crucial when variables have different units;
metric: the distance measure to use (e.g., 'euclidean', 'correlation'). It determines what "similar" means;
method: the linkage algorithm to use (e.g., 'single', 'complete', 'average'). It determines how to group the clusters.

Example

Here is a clustermap of the Iris dataset. Notice how the species (rows) are automatically grouped together because they have similar measurements.


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import seaborn as sns
import matplotlib.pyplot as plt

# Load dataset
df = sns.load_dataset('iris')
# Prepare matrix (drop non-numeric column for calculation)
species = df.pop("species")

# Create a clustermap
sns.clustermap(
    data=df,
    standard_scale=1,    # Normalize columns
    metric='euclidean',  # Measure distance
    method='average',    # clustering method
    cmap='viridis',
    figsize=(6, 6)
)

plt.show()

Task

Swipe to start coding

Analyze the flight passengers data to find similarities between years.

Set the style to 'ticks'. Change the background color to 'seagreen' ('figure.facecolor').
Create a clustermap using the reshaped upd_df DataFrame:
- Pass upd_df as the data.
- Normalize the columns by setting standard_scale to 1.
- Use the 'single' clustering method.
- Use 'correlation' as the distance metric.
- Display values in cells (annot=True).
- Set the value limits: vmin=0 and vmax=10.
- Use the 'vlag' color map.
Display the plot.

Solution

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Section 1. Chapter 17

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