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Practice

Visualizing Relationships with Seaborn

Seaborn makes it easy to explore and visualize relationships between variables.

Whether you’re comparing continuous values, identifying category-based trends, or examining correlations across multiple features, Seaborn provides a clean and efficient workflow.

Unlike Matplotlib, which often requires manual styling, Seaborn automatically applies attractive defaults — letting you focus on what to visualize rather than how to style it.


Common Relationship Plot Types in Seaborn​

  • Relational plots – Display relationships between two continuous variables using points or lines (scatterplot, lineplot).
  • Categorical plots – Compare numerical values across categories (barplot, countplot).
  • Matrix plots – Visualize correlations or pairwise variable relationships (heatmap, pairplot).

Why These Plots Are Useful​

  • Quick insights – Identify trends, relationships, and outliers at a glance.
  • Built-in grouping – Use the hue parameter to separate data by category automatically.
  • Automatic styling – Clean, professional visuals with minimal setup.

To explore these plots visually, open the slide deck for this lesson, which includes examples of each relationship type.

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