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Relational Plot

  • This module teaches how to create relational plots in Seaborn for multi-variable visualization. You will learn to use hue, style, and size parameters, switch between scatter and line modes, and utilize Facet Grid for structured comparison in Python.

  • What is a Relational Plot?

    Theory

    A Relational Plot is used to:

    ✔ Visualize relationship between two numerical variables
    ✔ Add multiple dimensions (color, size, style)
    ✔ Create Scatter or Line plots
    ✔ Support Facet Grid

    It is mainly used for:

    • Trend analysis

    • Correlation visualization

    • Multi-variable analysis


    Multi-variable Visualization

    Why Use relplot()?

    Basic scatter plot shows:

    • X variable

    • Y variable

    But relplot allows adding:

    • hue → category color

    • size → numeric scaling

    • style → marker style

    • row / col → facet grid

    Basic Example (Scatter Mode)

Relationship between Total Bill and Tip (Relational Plot)

This visualization uses Seaborn’s relplot to display the relationship between total bill amounts and tips. 

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.relplot(x="total_bill", y="tip", data=tips)
plt.show()
Lesson image

  • Output Explanation

    • X-axis → total_bill

    • Y-axis → tip

    • Each point → One observation

    You can analyze:

    ✔ Positive relationship
    ✔ Negative relationship
    ✔ No relationship


    hue, style, size Parameters

    hue Parameter

    Adds color based on category.

Total Bill vs Tip Colored by Gender (Relational Plot with Hue)

This visualization uses Seaborn’s relplot with the hue parameter to show the relationship between total bill and tip, separated by gender. 

sns.relplot(x="total_bill",
           y="tip",
           hue="sex",
           data=tips)
Lesson image

  • Different colors for Male & Female.

    size Parameter

    Changes marker size based on numeric variable.

Total Bill vs Tip (Bubble Plot with Size Parameter)

This visualization uses Seaborn’s relplot with the size parameter to show the relationship between total bill and tip, where the marker size represents the number of people (size column). 

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

  • Bigger marker = Larger group size.

    style Parameter

    Changes marker style based on category.

Total Bill vs Tip (Marker Style by Smoking Status)

This visualization uses Seaborn’s relplot with the style parameter to change marker shapes based on the smoking status of customers. 

sns.relplot(x="total_bill",
           y="tip",
           style="smoker",
           data=tips)
Lesson image

  • Different shapes for Smoker & Non-Smoker.

    Combine All Together

Multi-Variable Scatter Plot (Total Bill vs Tip)

This visualization combines multiple dimensions in a Seaborn relplot: x="total_bill" → Total bill on X-axis y="tip" → Tip amount on Y-axis hue="sex" → Color differentiates Male and Female customers style="smoker" → Marker shape indicates Smoker vs Non-Smoker size="size" → Marker size represents the number of people in the party 

sns.relplot(x="total_bill",
           y="tip",
           hue="sex",
           style="smoker",
           size="size",
           data=tips)
Lesson image

  • This creates multi-dimensional visualization.


    Scatter vs Line Mode

    Relplot supports two modes:

    Mode

    Description

    scatter (default)

    Shows individual points

    line

    Shows trend over time


    Scatter Mode (Default)

Total Bill vs Tip (Scatter Plot)

This visualization uses Seaborn’s relplot in scatter mode (default) to show the relationship between total bill and tip amounts. 

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

  • Used for:

    • Correlation

    • Clusters

    • Distribution

    Line Mode

Total Bill Trend by Day (Line Plot)

This visualization uses Seaborn’s relplot in line mode to show the trend of total bill amounts across different days of the week. 

sns.relplot(x="day",
           y="total_bill",
           kind="line",
           data=tips)
Lesson image

  • Used for:

    • Trend analysis

    • Time series data

    Line Mode with Hue

Total Bill Trend by Day and Gender (Line Plot with Hue)

This visualization uses Seaborn’s relplot in line mode with the hue parameter to show total bill trends across days, separated by gender. 

sns.relplot(x="day",
           y="total_bill",
           hue="sex",
           kind="line",
           data=tips)
Lesson image

  • Compare trends by category.


    Facet Grid Support

    Relplot supports:

    • row

    • col

    For creating multiple subplots.

    Example — Column Facet

Total Bill vs Tip by Gender (Column Facet)

This visualization uses Seaborn’s relplot with the col parameter to create separate scatter plots for each gender. 

sns.relplot(x="total_bill",
           y="tip",
           col="sex",
           data=tips)
Lesson image

  • Separate scatter plots for:

    • Male

    • Female

    Example — Row + Column

Total Bill vs Tip by Gender and Smoking Status (Facet Grid)

This visualization uses Seaborn’s relplot with both row and col parameters to create a grid of scatter plots: 

sns.relplot(x="total_bill",
           y="tip",
           row="smoker",
           col="sex",
           data=tips)
Lesson image

  • Creates grid:

    • Male Smoker

    • Male Non-Smoker

    • Female Smoker

    • Female Non-Smoker

    This is powerful for multi-category analysis.

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