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Box Plot

  • This module explains how to create box plots in Seaborn to understand quartiles, detect outliers, compare categories, and apply custom styling for better data analysis and visualization in Python.

  • What is a Box Plot?

    A Box Plot (also called Box and Whisker Plot) is used to visualize:

    • Data distribution

    • Quartiles

    • Median

    • Spread of data

    • Outliers

    It is very important in Exploratory Data Analysis (EDA).

    Understanding Quartiles

    Theory

    Box plot is based on five-number summary:

    1. Minimum

    2. Q1 (First Quartile – 25%)

    3. Median (Q2 – 50%)

    4. Q3 (Third Quartile – 75%)

    5. Maximum

    Visual Structure of Box Plot


    Min ────|──── Q1 ─── [ Median ] ─── Q3 ───|──── Max

    Important Concept

    • IQR (Interquartile Range)
       IQR = Q3 – Q1
       It shows the middle 50% of the data.

    Example Code

Distribution of Total Bill – Box Plot

This code creates a box plot to visualize the distribution of the total_bill column from the tips dataset. 

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.boxplot(y="total_bill", data=tips)
plt.title("Box Plot of Total Bill")
plt.show()
Lesson image

  • 🖼 Output Explanation

    • Middle line → Median

    • Box → Q1 to Q3

    • Whiskers → Min & Max (within range)

    • Dots outside → Outliers

    If box is large → High variation
     If median is not centered → Data is skewed


    Detecting Outliers

    Theory

    Outliers are values that are:

    • Extremely high

    • Extremely low

    • Far from other observations

    Box plot automatically detects outliers using:

    Lower Limit = Q1 - 1.5 * IQR

    Upper Limit = Q3 + 1.5 * IQR

    Values outside this range are shown as dots.

    Example

sns.boxplot(y="tip", data=tips)
plt.title("Detecting Outliers in Tips")
plt.show()

  • Output Explanation

    • Small dots above whisker → High tip outliers

    • Helps identify unusual customer behavior

    Comparing Categories

    Theory

    Box plot is very powerful for:

    • Comparing distribution across groups

    • Checking variation between categories

    Example — Compare Total Bill by Day

Total Bill Distribution by Day – Box Plot

This code creates a grouped box plot to compare the distribution of total bills across different days of the week. 

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

  • Output Explanation

    • X-axis → Days

    • Y-axis → Total Bill

    • Each box → Distribution for each day

    Insights Example:

    • Saturday may have higher median

    • Sunday may have more spread

    • Some days may have more outliers

    Example — Using Hue (Multiple Category Comparison)

Total Bill by Day and Gender – Grouped Box Plot

This code creates a grouped box plot to compare the distribution of total bills across different days, separated by gender. 

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

  • Output Explanation:

    • Two colored boxes per day

    • Compare Male vs Female spending


    Custom Styling in Box Plot

    Change Color

sns.boxplot(x="day", y="total_bill", data=tips, palette="Set2")

  • Horizontal Box Plot

sns.boxplot(y="day", x="total_bill", data=tips)

  • Remove Outliers

sns.boxplot(x="day", y="total_bill", data=tips, showfliers=False)

  • Add Mean Line

sns.boxplot(x="day", y="total_bill", data=tips, showmeans=True)
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