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Introduction to Data Science with Python

Matrix Plots

Explore how to visualize data as color-coded matrices using Seaborn's heatmap and clustermap functions. Learn to prepare data in matrix form, create pivot tables, and interpret clusters to uncover patterns and trends in datasets such as flight passenger counts over years and months.

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Seaborn’s matrix plots, such as heatmap(), allow us to plot the data as color-encoded matrices, while clustermap() can be used to indicate clusters within the data.

Let’s learn this with examples:

C++
# Importing required libraries and loading datasets
import seaborn as sns
# Another trick to avoid warnings and to have a cleaner notebook 
import warnings
warnings.filterwarnings('ignore')
tips = sns.load_dataset('tips')
flights = sns.load_dataset('flights')
print("Tips Data")
print(tips.head())
print("\nFlights Data")
print(flights.head())

The heatmap() plot

The heatmap() function represents data as a color-encoded matrix. It is commonly used to visualize matrix data.

For a heatmap() function to work properly, our data should already be in a matrix form. The sns.heatmap function just colors it in for us.

Being in the matrix form also means that the index name and the column name match, so the ...