Relational plots in Seaborn - Part I

Relational plots are used to visualise relationships between variables in a dataset. They help identify trends, patterns and correlations between data points. Seaborn provides several functions for creating relational plots, including relplot(), scatterplot() and lineplot().

Implementation

1. Import Required Libraries

• Seaborn for creating relational plots.
• Matplotlib for displaying the plots.

import seaborn as sns
import matplotlib.pyplot as plt

2. Load the Dataset

The built-in tips dataset is loaded.

tips = sns.load_dataset("tips")
tips.head()

Output:

3. Create a Basic Relational Plot

This creates a simple relational (scatter) plot.

• Shows the relationship between total bill and tip.
• Each point represents one observation.
• Helps identify trends between the two variables.

sns.set(style="ticks")

sns.relplot(
    x="total_bill",
    y="tip",
    data=tips
)

plt.show()

Output:

4. Group Data Using Hue

The hue parameter separates data points using different colors.

• Distinguishes between Lunch and Dinner customers.
• Makes category wise comparisons easier.

sns.relplot(
    x="total_bill",
    y="tip",
    hue="time",
    data=tips
)

plt.show()

Output

5. Create Faceted Relational Plots

This creates multiple plots based on categories.

• row="sex" creates separate rows for males and females.
• col="time" creates separate columns for lunch and dinner.
• Allows comparison across multiple categories simultaneously.

sns.relplot(
    x="total_bill",
    y="tip",
    hue="day",
    col="time",
    row="sex",
    data=tips
)

plt.show()

Output:

6. Use Different Point Sizes

The size parameter changes the marker size according to the group size.

• Larger points represent larger groups.
• Adds an extra dimension to the visualization.

sns.relplot(
    x="total_bill",
    y="tip",
    hue="day",
    size="size",
    data=tips
)

plt.show()

Output:

7. Create a Line Relational Plot

This creates a line plot instead of a scatter plot.

• Shows trends and patterns in the data.
• Useful for analyzing changes between variables.

sns.relplot(
    x="total_bill",
    y="tip",
    kind="line",
    data=tips
)

plt.show()

Output:

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