Relational Plots in Seaborn – Scatter and Line Plots

Relational plots help you understand how two variables relate to each other.
In Seaborn, the two main functions for this are:

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

Both functions are part of Seaborn’s relational plotting category.

• 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 over an ordered sequence (e.g., time series data).

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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.show()

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fmri = sns.load_dataset("fmri")

sns.lineplot(data=fmri, x="timepoint", y="signal")
plt.show()

Relational plots can be further customized with parameters like hue, style, and size to add categories, patterns, or variable-sized points.

You’ll explore these enhancements in the Jupyter Notebook on the right side of the screen.