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Strip Plot

  • This module explains how to create strip plots in Seaborn to visualize individual data points. You will learn about the jitter concept to reduce overlap and how to combine strip plots with boxplots for better categorical data analysis in Python.

  • What is a Strip Plot?

    A Strip Plot is used to display:

    Individual data points
    Distribution of categorical data
    Spread of observations

    Unlike box plot or violin plot, it shows actual raw data points.


    Individual Data Points

    Theory

    Strip plot:

    • Plots each observation separately

    • Useful when dataset is small to medium

    • Helps identify:

      • Clusters

      • Spread

      • Outliers

      • Overlapping points

    Example Code

Total Bill by Day (Strip Plot)

This visualization displays individual total bill values for each day of the week using a strip plot. 

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.stripplot(x="day", y="total_bill", data=tips)
plt.title("Total Bill by Day (Strip Plot)")
plt.show()
Lesson image

  • Output Explanation

    • X-axis → Days

    • Y-axis → Total Bill

    • Each dot → One customer bill

    If many dots are near 20 → Most bills are around 20.
    If some dots are very high → Possible outliers.


    Jitter Concept

    Problem Without Jitter

    When multiple data points have same value:

    • They overlap

    • Hard to see distribution

    What is Jitter?

    Jitter adds small random noise horizontally to spread points.

    Prevents overlapping
    Makes visualization clearer

    Example — With Jitter

Strip Plot with Jitter

This visualization shows individual total bill values for each day, with jitter enabled to improve clarity. 

sns.stripplot(x="day", y="total_bill", data=tips, jitter=True)
plt.title("Strip Plot with Jitter")
plt.show()
Lesson image

  • Output Explanation

    • Points slightly spread horizontally

    • Easier to see density

    • No overlapping confusion

    Adjust Jitter Size

    sns.stripplot(x="day", y="total_bill", data=tips, jitter=0.3)

    Higher value → More spread
    Lower value → Less spread


    Category Comparison

    Strip plot works very well for comparing:

    • Spending by gender

    • Sales by region

    • Scores by class

    Example — Using Hue

Total Bill by Day and Gender (Strip Plot with Hue)

This visualization displays individual total bill values for each day of the week, separated by gender using the hue parameter. 

sns.stripplot(x="day", y="total_bill",
             hue="sex",
             data=tips,
             jitter=True)
plt.title("Total Bill by Day and Gender")
plt.show()
Lesson image

  • Output Explanation

    • Different colors → Male & Female

    • Compare distribution visually

    • See which group spends more


    Combining with Boxplot

    Why Combine?

    • Box plot → Shows summary (median, quartiles)

    • Strip plot → Shows actual data points

    Together → Complete visualization.

    Example — Boxplot + Stripplot

Boxplot with Stripplot (Combined Visualization)

This visualization combines a box plot and a strip plot to give both statistical summary and individual data points for total bills across different days. 

sns.boxplot(x="day", y="total_bill", data=tips)
sns.stripplot(x="day", y="total_bill",
             data=tips,
             color="black",
             jitter=True)
plt.title("Boxplot with Stripplot")
plt.show()
Lesson image

  • Output Explanation

    • Box → Statistical summary

    • Dots → Individual observations

    • Very useful for presentations

    This gives both:

    Distribution summary
    Raw data visibility


    Custom Styling

    Horizontal Strip Plot

sns.stripplot(y="day", x="total_bill", data=tips, jitter=True)

  • Adjust Point Size

sns.stripplot(x="day", y="total_bill", data=tips, size=6)

  • Adjust Transparency

sns.stripplot(x="day", y="total_bill", data=tips, alpha=0.5)
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