Pair Plots and Heatmaps in Seaborn

Seaborn provides two powerful tools for exploring relationships between variables: pair plots and heatmaps.

A pair plot automatically creates scatter plots for every combination of numeric variables in your dataset.
It also shows histograms (or KDE plots) along the diagonal for each individual variable.

Use pair plots to:

• Spot relationships between multiple features at once
• Identify correlations visually
• Detect outliers and unusual patterns

For example, you can compare numerical columns like total_bill, tip, and size in the tips dataset using sns.pairplot().

A heatmap uses colors to represent values in a matrix, often based on 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.

• Pair plots: Compare multiple numeric variables visually using scatter plots and histograms.
• Heatmaps: Show the strength of relationships between variables in a color-coded matrix.