Pair Plots and Heatmaps in Seaborn
Seaborn includes two powerful tools for exploring relationships across multiple variables — pair plots and heatmaps.
Pair Plots
A pair plot automatically generates scatter plots for every combination of numeric variables in a dataset, with histograms or KDE plots shown along the diagonal for each feature.
Use pair plots to:
- Visualize relationships among several numeric features
- Identify correlations and clusters
- Detect outliers or unusual patterns
For example, you can compare numerical columns like total_bill, tip, and size in the tips dataset using sns.pairplot().
Heatmaps
A heatmap visualizes data as a color-coded matrix — often used to display correlation coefficients.
Use heatmaps to:
- Visualize correlation matrices
- Highlight strong positive or negative relationships
- Assist in feature selection for machine learning
A common example is plotting the correlation matrix of your DataFrame with sns.heatmap(), applying color gradients to show relationship strength.
Summary
- Pair plots – Compare multiple numeric variables using scatter and distribution plots.
- Heatmaps – Show the strength of variable relationships through color intensity.
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