Create a Clustermap with Seaborn

Clustermaps offer a compelling method to visualize complex datasets, highlighting patterns and correlations effectively. I walk you through creating a clustermap using the Seaborn library in Python.

Prerequisites

Before beginning, verify that the Seaborn, Matplotlib, and SciPy libraries are installed on your system:

pip install seaborn matplotlib scipy

Step by Step Guide

1. Import Libraries

import seaborn as sns
import matplotlib.pyplot as plt

2. Prepare Data

flights = sns.load_dataset("flights")
flights = flights.pivot("month", "year", "passengers")

3. Create Cluster Map

sns.clustermap(flights, cmap="coolwarm", standard_scale=1)
plt.show()

Customizing Your Clustermap

The clustermap function in Seaborn provides a variety of parameters for customization, enabling adjustments to the clustering method, metric, and color palette for tailored visualizations. Experimenting with these can help you better visualize and interpret your dataset’s underlying structure and insights.