Relational Plots in Seaborn – Scatter and Line Plots

Relational plots allow you to explore how two variables interact or correlate.

In Seaborn, the two key functions for relational data are:

• scatterplot() – Visualizes the relationship between two continuous variables using points.
• lineplot() – Illustrates trends or patterns between variables using lines.

Both belong to Seaborn’s relational plotting family.

• Scatter plot – Best for examining how one variable changes with another without assuming continuity (e.g., height vs. weight).
• Line plot – Best for showing trends or progressions across an ordered variable, such as time or sequence.

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import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.scatterplot(data=tips, x="total_bill", y="tip")
plt.title("Scatter Plot of Total Bill vs Tip")
plt.show()

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fmri = sns.load_dataset("fmri")

sns.lineplot(data=fmri, x="timepoint", y="signal")
plt.title("Line Plot of Signal over Time")
plt.show()

ou can further enhance relational plots using parameters such as hue, style, and size — adding categories, visual patterns, or size variations to represent extra dimensions of data.