Visualizing Relationships with Seaborn

Seaborn makes it easy to explore relationships between variables — whether you are comparing two continuous variables, looking for patterns across categories, or visualizing correlations between multiple features.

Instead of manually setting styles and axes like in Matplotlib, Seaborn’s plotting functions handle much of the formatting automatically, letting you focus on what you want to show rather than how to style it.

• Relational plots: Show how two continuous variables interact over points or time (scatterplot, lineplot).
• Categorical plots: Compare numerical values across categories (barplot, countplot).
• Matrix-based plots: Visualize pairwise relationships or correlations (heatmap, pairplot).

• Quick insights – spot trends, patterns, and anomalies in your data.
• Built-in grouping – easily split data by categories using the hue parameter.
• Automatic styling – Seaborn applies clean, professional themes by default.

To deepen your understanding of these concepts, explore the slide deck for this lesson and see visual examples of each relationship plot type.