📊 Comparison Plots with pairplot() and jointplot()

Use these plots to explore relationships between multiple variables in a dataset, especially during EDA (Exploratory Data Analysis).

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🔍 Exploring Two Variables with jointplot()

sns.jointplot(x='math score', y='reading score', data=df)

🔹 Scatterplot with histograms

• Shows relationship between math and reading scores.
• Marginal histograms give insight into individual distributions.

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sns.jointplot(x='math score', y='reading score', data=df, kind='hex')

🔹 Hexbin plot

• Replaces scatterplot with a hexagon heatmap.
• Useful for large datasets where points overlap.

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sns.jointplot(x='math score', y='reading score', data=df, kind='kde')

🔹 KDE plot

• Shows smooth contour lines representing data density.
• Great for visualizing continuous distributions and overall trends.

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🧩 Exploring Multiple Variables with pairplot()

sns.pairplot(df)

🔹 Basic pairplot

• Automatically draws scatterplots for every numeric pair.
• Diagonal shows univariate histograms (default is KDE if not specified).

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sns.pairplot(df, hue='gender', palette='viridis')

🔹 Colored by category

• Uses the gender column to color data points.
• Helps compare variable interactions across groups.

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sns.pairplot(df, hue='gender', palette='viridis', corner=True)

🔹 Lower-triangle only

• corner=True avoids redundant mirrored plots.
• More compact and readable.

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sns.pairplot(df, hue='gender', palette='viridis', diag_kind='hist')

🔹 Diagonal uses histograms

• Replaces KDE with classic histograms for univariate plots.
• Better when data isn’t smoothly distributed.

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✅ Summary Table

Function	Use Case	Supports Category Highlight (hue)
jointplot()	Visualizing 2 variables	❌ No hue support
pairplot()	Visualizing multiple variables	✅ Yes, with hue