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Practice

Pair Plots and Heatmaps in Seaborn

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


Pair Plots​

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.

Heatmaps​

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.

Summary​

  • 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.

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