Pair Plots and Heatmaps in Seaborn

Seaborn offers two powerful tools for exploring relationships in your dataset: pair plots and heatmaps.

A pair plot automatically creates scatterplots for every numeric variable combination in your dataset, and histograms (or KDE plots) along the diagonal.

This makes them perfect for:

• Quickly spotting relationships between features
• Identifying correlations visually
• Detecting outliers or unusual patterns

Common usage example:

• Comparing numerical columns like total_bill, tip, and size in the tips dataset.

A heatmap uses colors to represent values in a matrix or table, often based on correlation coefficients.

Why use them:

• Great for visualizing correlation matrices
• Quickly highlight strong positive or negative relationships
• Help in feature selection for machine learning

Typical use:

• Plotting the correlation matrix of your DataFrame using sns.heatmap() with color gradients.

• Pair plots → Quick multi-variable visual comparison.
• Heatmaps → Color-coded relationship strength in matrix form.

In the next step, explore how they work in the Jupyter Notebook on the right side of the screen.