Box Plot in Data Science (Whisker Plot) : Meaning, Examples & Interpretation

Did you know? Box plots were first introduced in the 1970s by John Tukey, a renowned statistician, as part of his exploratory data analysis (EDA) techniques. It is one of the few visualization tools that can simultaneously highlight the presence of outliers while also showing the central tendency and variability of the data.

Box Plot, also known as a Whisker Plot, is a powerful data visualization tool that provides a clear summary of a dataset’s distribution. It displays key statistical values such as the median, quartiles, and potential outliers, helping to highlight the spread and skewness of the data.

Unlike other charts, Box Plot in data science allows you to compare multiple groups simultaneously, revealing insights about variations within and between datasets.

In this tutorial, we’ll explore data visualization using Box Plot in data science, discuss key features, and walk through how to interpret and implement them effectively to gain deeper insights.

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What is a Box Plot in Data Science? Simple Explanation

A Box Plot, or Whisker Plot, is a powerful tool for visualizing the distribution of a dataset, revealing key insights like skewness, kurtosis, and the presence of outliers. Beyond the basic five-number summary, it highlights data points that fall outside 1.5 times the interquartile range (IQR). 

The Interquartile Range (IQR) is a measure of statistical spread that describes the range between the first quartile (Q1) and the third quartile (Q3) of a dataset. The IQR represents the middle 50% of the data. 

To detect potential outliers, a common rule is to look for data points that fall outside of 1.5 times the IQR from Q1 and Q3. This is known as the "1.5 * IQR rule," where any data point more than 1.5 times the IQR above Q3 or below Q1 is considered an outlier. This could indicate errors or rare, yet significant, observations during data mining.

Box plot in data science is especially useful for comparing multiple distributions side by side, allowing analysts to quickly detect trends and variations across different categories or datasets. They are primarily designed for continuous or numerical data, making them excellent for visualizing the distribution and spread of such data. 

However, they can also be used to compare distributions within categorical data when the categories are ordered or represent a logical progression (e.g., low, medium, high). In these cases, Box Plots can help illustrate trends or variations in the data across different categories.

Machine learning professionals use Box Plot in data mining to ensure data quality and consistency before feeding data into algorithms. 

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For example, imagine you have a dataset of student exam scores. A Box Plot in data mining would show you not only the middle 50% of the scores but also any outliers (extremely high or low scores) and give you a clear view of the overall range and central value.

A Box Plot in data science consists of several key components:

• Box: Represents the interquartile range (IQR), where 50% of the data falls between Q1 and Q3.
• Whiskers: The whiskers extend from the box (which represents the interquartile range) to the minimum and maximum values within 1.5 times the IQR from the first and third quartiles.  These whiskers represent the range of "normal" data points that fall within the accepted range of variation. It’s important to note that the whiskers do not necessarily represent the absolute minimum or maximum values in the dataset—only those within the non-outlier range.
• Outliers: Any data points outside the whiskers are considered outliers.
• Median: The line inside the box represents the median, showing the central value of the data.

This method is widely used in data science for its ability to quickly identify trends and anomalies in datasets, making it useful for exploratory data analysis (EDA). 

Box Plot in data science is often used in finance to compare stock returns, in healthcare to analyze patient data, and in machine learning to detect outliers in feature distributions.

It provides a clear, concise visual summary of the dataset, helping you understand its distribution and spot any unusual data points.

If you want to learn more about statistical analysis, upGrad’s free Basics of Inferential Statistics course can help you. You will learn probability, distributions, and sampling techniques to draw accurate conclusions from random data samples.

Also Read: Statistics for Machine Learning: Everything You Need to Know

Now, let’s understand how Box Plot in data science works in more detail.

How Does Box Plot in Data Science Work? Step-by-Step Guide

The Box Plot in data science consists of a rectangular box representing the interquartile range (IQR), with "whiskers" extending from the box to the data points within a set range. Any data points outside this range are considered outliers and are displayed individually. This visual representation allows you to see the distribution's symmetry, whether it’s skewed, and identify the presence of anomalies that could affect data analysis.

This makes Box Plot in data science particularly useful in detecting outliers, comparing different datasets, or analyzing the spread of data across various categories. For example, in finance, Box Plots can help you analyze the volatility of stock returns, while in healthcare, they are helpful for comparing patient data distributions and identifying outliers.

Let’s explore how a Box Plot in machine learning works using a real-life example: analyzing the monthly income of a group of individuals. This example will help you understand how to visualize the distribution, detect outliers, and interpret data spread in a simple way.

1. Organize the Data into Quartiles

Imagine you have data on the monthly income of 20 individuals:

[25,000, 30,000, 35,000, 40,000, 45,000, 50,000, 55,000, 60,000, 65,000, 70,000, 75,000, 80,000, 85,000, 90,000, 100,000, 110,000, 120,000, 130,000, 150,000, 200,000]

Here’s how to create a Box Plot with this data:

1. Sort the data in ascending order (already sorted).

2. Divide the data into quartiles (Q1, Q2, Q3):

• Q1 (First Quartile): The 25th percentile of the data.
• Q2 (Median): The middle value (50th percentile).
• Q3 (Third Quartile): The 75th percentile of the data.

3. For this data:

• Q1 = 40,000
• Q2 (Median) = 75,000
• Q3 = 110,000

2. Calculate the Interquartile Range (IQR)

The Interquartile Range (IQR) is the difference between the third quartile (Q3) and the first quartile (Q1), representing the spread of the middle 50% of the data.

It provides insight into the variability and central tendency of the dataset by measuring the range in which most data points are concentrated.

IQR = Q3 - Q1 = 110,000 - 40,000 = 70,000

The IQR represents the range where the middle 50% of the data lies.

3. Plot the Box

Draw a box from Q1 (40,000) to Q3 (110,000). The box represents where most of the data lies.

Draw a line at the median (75,000), which divides the box into two parts, showing the middle 50% of the data.

Also Read: What is Cluster Analysis in Data Mining? Methods, Benefits, and More

4. Draw the Whiskers

The whiskers extend from the box to the minimum and maximum values within 1.5 * IQR from Q1 and Q3.

• 1.5 * IQR = 1.5 * 70,000 = 105,000.
• Lower whisker: The smallest value within 105,000 below Q1, which is 25,000 (since 25,000 is within 105,000 below Q1).
• Upper whisker: The largest value within 105,000 above Q3, which is 200,000 (since 200,000 is within 105,000 above Q3).

Here’s a box plot summary:

• Q1 = 40,000, Median = 75,000, Q3 = 110,000
• IQR = 70,000
• Whiskers: The whiskers extend from 25,000 (min) to 200,000 (max), with 150,000 and 200,000 being outliers.

5. Identify Outliers

Any data points beyond the whiskers are considered outliers. In this case, 150,000 and 200,000 fall outside the upper whisker range, so these are considered outliers.

Why Does This Help?

• Outliers (150,000, 200,000): Box plots highlight these values, showing that a few people earn significantly more than the others. This could be due to a few high-income individuals, or it might indicate errors in the data.
• Data Spread: You can see that most people have monthly incomes between 40,000 and 110,000 INR, with a concentration around the 75,000 INR mark.
• Insights for Modeling: By detecting these outliers, you might decide whether to remove or keep them based on the problem at hand, ensuring better quality input for machine learning models.

This simplified example helps you understand the process of creating and interpreting a Box Plot with monthly income data, making it easier to spot trends, detect anomalies, and prepare the data for machine learning models.

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Also Read: Clustering vs Classification: What is Clustering & Classification

Now that you know how to use a box plot in data mining, let’s look at how you can implement it using Python.

Implementing Box Plot in Machine Learning (Whisker Plot) With Python

Python is an excellent tool for creating box plot in data science due to its powerful data visualization libraries, such as Matplotlib and Seaborn. These are highly customizable and capable of generating complex plots with ease. 

They make it simple to create a box plot in data mining so it clearly displays the distribution of data, including quartiles, median, and outliers, without requiring significant coding effort. 

Python’s wide array of data manipulation tools, such as Pandas, enables easy data cleaning and preparation, ensuring that your data is ready for visualization. Moreover, Python’s flexibility and wide usage in data science and machine learning make it a go-to language for generating box plot in data science as part of exploratory data analysis.

Here’s how you can implement a box plot in Python:

1. Import Required Libraries

First, you'll need to install and import the necessary Python libraries, matplotlib and seaborn, which are commonly used for plotting in data science.

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

You can get a better understanding of Python libraries with upGrad’s Learn Python Libraries: NumPy, Matplotlib & Pandas. Learn how to manipulate data using NumPy, visualize insights with Matplotlib, and analyze datasets with Pandas.

Also Read: Python Modules: Explore 20+ Essential Modules and Best Practices

2. Prepare the Data

For this example, we will work with plant heights (in centimeters) measured in a greenhouse experiment over several weeks. The data includes plant heights from different experimental groups.

# Plant heights in centimeters (sample data)
plant_heights = np.array([45, 47, 50, 48, 49, 52, 53, 54, 60, 65, 72, 75, 78, 80, 85, 90, 92, 100, 110, 120])

Also Read: Steps in Data Preprocessing: What You Need to Know?

3. Create the Box Plot

Now, we can use matplotlib or seaborn to create the box plot. In this case, we will use seaborn for simplicity, but both libraries provide excellent functionality for box plots.

# Create the box plot using seaborn
plt.figure(figsize=(8, 6))
sns.boxplot(data=plant_heights, color='skyblue', width=0.5)

# Add titles and labels
plt.title('Box Plot of Plant Heights in Greenhouse', fontsize=14)
plt.xlabel('Plant Heights (cm)', fontsize=12)
plt.ylabel('Height (cm)', fontsize=12)

# Display the plot
plt.show()

Output:

Also Read: 15+ Advanced Data Visualization Techniques for Data Engineers

4. Customize the Box Plot

You can customize the plot to better suit your needs. For example:

• Change the color of the box and whiskers.
• Adjust the axis labels and title.
• Add more details like gridlines or a mean line.

# Create a customized box plot
plt.figure(figsize=(8, 6))
sns.boxplot(data=plant_heights, color='lightgreen', width=0.6, fliersize=10)
# Add a mean line
sns.boxplot(data=plant_heights, color='lightgreen', width=0.6, fliersize=10)
plt.axhline(np.mean(plant_heights), color='red', linestyle='--', label='Mean Height')
# Add titles and labels
plt.title('Box Plot of Plant Heights with Mean Line', fontsize=14)
plt.xlabel('Plant Heights (cm)', fontsize=12)
plt.ylabel('Height (cm)', fontsize=12)
# Display the plot
plt.legend()
plt.show()

Output:

Also Read: Data Analysis Using Python: Everything You Need to Know

5. Understand the Output

Box: The box shows the interquartile range (IQR), which contains the middle 50% of the data. The line inside the box represents the median height of the plants.

Whiskers: The whiskers extend from the box to the smallest and largest values within 1.5 times the IQR. They help you understand the spread of the data.

Outliers: Points beyond the whiskers (marked as dots) are considered outliers. In this case, any plant height significantly higher than 120 cm may be considered an outlier.

6. Analyze the Data

Looking at the box plot, you can interpret the following:

• The median plant height lies around 70 cm.
• The interquartile range (IQR) is from about 50 cm to 90 cm, meaning that most of the plants' heights fall within this range.
• Plant heights greater than 100 cm, like those at 110 cm and 120 cm, are outliers and could indicate unique growth factors, errors, or exceptional cases in the experiment.

Box plots are widely used in data exploration to get a quick sense of data spread, detect anomalies, and summarize key statistics.

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Also Read: Matplotlib in Python: Explained Various Plots with Examples 

Now that you have a better understanding of how to implement box plots in data science with Python, let’s look at some of its advantages and drawbacks. 

Benefits and Limitations of Box Plot in Data Science

Box plots are highly effective when dealing with continuous data, as they provide a clear view of the central tendency, spread, and potential anomalies in the data. For example, in financial analysis, box plots can be used to visualize the distribution of stock prices or monthly expenses, highlighting any outliers that might indicate unusual transactions.

However, box plots do have limitations. They are not well-suited for categorical data, where distributions don't have a natural ranking or continuous range. Additionally, while box plots are great for visualizing the spread of data, they may not be the best choice for very small datasets or when the underlying distribution is highly skewed. The box plot might oversimplify the data and hide important details. 

In such cases, other visualizations like histograms or density plots may provide a clearer understanding of the data.

Here’s a breakdown of the benefits and limitations of Box Plots in data science:

To make the most out of box plots in your data analysis, here are some best practices:

1. Handle Outliers Appropriately: Use box plots to detect outliers, but be sure to investigate whether these outliers are genuine data points or errors before making decisions.

2. Compare Multiple Categories: When comparing distributions across different categories, use side-by-side box plots to visually assess differences in spread, central tendency, and outliers.

3. Add Additional Visual Elements: Enhance the box plot by adding a mean line (e.g., using axhline) or plotting notches to indicate confidence intervals for the median. This provides more insights into your data distribution.

4. Use Box Plots with Large Datasets: Box plots work best with large datasets where distribution patterns and outliers are more apparent. Ensure your dataset is large enough to accurately represent trends.

5. Pair with Other Visualizations: While box plots are great for summarizing distribution, use them alongside other visualizations (like histograms or violin plots) for more detailed understanding of the data distribution.

By following these best practices, you can leverage box plots effectively to gain meaningful insights, detect anomalies, and make data-driven decisions.

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Also Read: Top 10 Dimensionality Reduction Techniques for Machine Learning(ML) in 2025

Next, let’s look at some of the real-life applications of box plots in data mining.

What are the Use Cases of Box Plot in Data Mining? 5 Real-Life Examples

Box plots are widely used across industries like finance, healthcare, retail, and manufacturing for tasks such as fraud detection, patient health analysis, customer segmentation, and quality control. 

In finance, they help identify outliers in transaction data, while in healthcare, they can reveal abnormal patient measurements. Retailers use box plots to analyze customer spending patterns, and manufacturers use them to monitor product quality and detect defects. 

Their ability to summarize data distribution and highlight outliers makes them invaluable for decision-making across these sectors.

Below are five real-life examples where box plots can help solve real-world problems in data mining.

1. Detecting Fraudulent Transactions in Credit Card Data

You are working with a dataset containing credit card transactions, and you need to identify potential fraudulent transactions. The dataset has thousands of transactions, and manually reviewing each one is impractical.

You use a box plot to visualize the distribution of transaction amounts. By plotting the transaction amounts for each cardholder or transaction type, you can easily spot extreme values that fall outside the expected range. Outliers in the plot indicate unusual transactions, which may be flagged for further investigation.

Outcome: The box plot reveals several outliers where transaction amounts are much higher than normal, suggesting fraudulent activity. These transactions can then be flagged for further manual review or further automated fraud detection processes.

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Also Read: Anomaly Detection With Machine Learning: What You Need To Know?

2. Analyzing Employee Salaries in an Organization

You are tasked with analyzing salary data for employees in an organization to ensure that salaries are fairly distributed across departments. The challenge is that the dataset includes a few high-paid employees, which may skew your analysis.

By using a box plot, you can visualize the spread of salaries within each department. The box plot shows the median salary, interquartile range, and outliers, helping you identify departments where salaries may be uneven or where a few outlier salaries are distorting the average.

Outcome: The box plot helps you identify departments with higher-than-normal salaries, allowing you to investigate discrepancies and ensure fair pay across the organization. It also shows the spread of salaries, helping you spot outliers who might need further review.

3. Comparing Customer Age Distributions for Marketing Segmentation

You're working on customer segmentation for a marketing campaign and need to identify age groups that are most likely to respond to a new product. The age data is skewed, and you need to visualize the distribution for better segmentation.

You use a box plot to analyze the age distribution of your customer base. The box plot clearly shows the median age, the range of ages, and any outliers. By comparing box plots for different customer segments (e.g., by region, product preference), you can identify age groups with a higher concentration of customers and target them more effectively.

Outcome: The box plot reveals that a particular age group, typically in the 30–40 range, has a larger concentration of customers in specific regions. This insight allows you to tailor your marketing campaign to focus on this group, improving engagement and conversion rates.

Also Read: Segmentation in Marketing: Get Started with Effective Strategies

4. Evaluating Quality Control in Manufacturing

You’re analyzing the manufacturing quality of products at different stages of production. You need to assess if product measurements, such as weight or size, are within acceptable limits. A few defective products might be skewing the results, but you need to visually confirm.

A box plot can be used to visualize the distribution of product measurements across different production batches. By analyzing the box plots, you can easily identify whether the measurements fall within the acceptable range or if any batch has unusually high or low values.

Outcome: The box plot shows that one particular batch has several outliers where product measurements are significantly outside the acceptable range. These can be flagged for quality control and further investigation to identify the cause of the defects.

Also Read: What is Quality Control (QC)? How Does QC Works?

5. Analyzing Customer Review Scores for a Product

You're analyzing customer reviews for a product to determine how well it is being received. While you know most reviews are positive, you're concerned about a small number of negative reviews that might affect the overall perception of the product.

Solution:
You create a box plot to visualize the distribution of review scores. The box plot will help you see the spread of ratings, the median review score, and any outliers. By focusing on the lower whiskers and outliers, you can identify customers who have given unusually low ratings and explore the reasons behind these reviews.

Outcome: The box plot confirms that most reviews are clustered around high ratings, with only a few low-outliers. By investigating the negative reviews, you identify a recurring issue with the product’s usability, leading to targeted improvements and addressing customer concerns.

These case studies show how box plots can be applied to various data mining tasks, from fraud detection to quality control and marketing segmentation. 

If you’re wondering how to extract insights from datasets, the free Excel for Data Analysis Course is a perfect starting point. The certification is an add-on that will enhance your portfolio.

Also Read: Role of Data Visualization in Predictive Analytics: A Comprehensive Guide

To solidify your understanding of the box plots in data mining, test your knowledge with a quiz. It’ll help reinforce the concepts discussed throughout the tutorial and ensure you're ready to apply them in your projects.

Quiz to Test Your Knowledge on Box Plots

Assess your understanding of Box Plots, their components, uses, and limitations by answering the following multiple-choice questions.

Test your knowledge now!

1. What does a Box Plot primarily show?
a) The exact distribution shape of data
b) The spread, central tendency, and outliers of the data
c) The correlation between two variables
d) The linear relationship between data points

2. What does the "whisker" in a Box Plot represent?
a) The distance from the minimum to the median
b) The range of data within the interquartile range (IQR)
c) The highest and lowest data points that are not outliers
d) The upper and lower quartiles

3. What does the "box" in a Box Plot represent?
a) The data range
b) The interquartile range (IQR), covering 50% of the data
c) The median of the data
d) The average value of the dataset

4. What does an outlier in a Box Plot indicate?
a) A data point that is unusually high or low compared to the rest of the dataset
b) A data point that is within the IQR
c) A data point that is very close to the median
d) A data point that is identical to other data points in the dataset

5. Which of the following is NOT a feature represented by a Box Plot?
a) Minimum
b) Maximum
c) Mode
d) Median

6. In what type of datasets are Box Plots most useful?
a) Only for numerical data with no outliers
b) For visualizing the distribution and identifying outliers in continuous numerical data
c) For categorical data with few categories
d) For small datasets with few data points

7. What is a common method to handle outliers identified by a Box Plot?
a) Remove them from the dataset
b) Change the scale of the data
c) Use median or mode imputation
d) Leave them as they are without modification

8. How does a Box Plot help when comparing multiple groups or categories?
a) It highlights the highest value of each group
b) It shows the overall average for each group
c) It provides a visual comparison of the distribution of multiple datasets
d) It calculates correlations between groups

9. Why is the Box Plot considered a better alternative than a histogram in some cases?
a) It is better at showing the data's exact shape
b) It shows both summary statistics and outliers, making it efficient for large datasets
c) It works well for both categorical and continuous data
d) It shows more data points than a histogram

10. How can Box Plots be used in identifying trends or shifts in data?
a) By comparing the positions of the median and IQR across different groups
b) By plotting the mean and median against each other
c) By showing changes in the distribution with respect to the frequency of data points
d) By visualizing data in chronological order

This quiz will help you evaluate your understanding of Box Plots, their components, and how they can be effectively used in data science and machine learning to analyze and visualize data distributions.

Also Read: 5 Breakthrough Applications of Machine Learning

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