Ecommerce Purchases Data Analysis Exercises (Pandas Practice)

 

Ecommerce Purchases Data Analysis Exercises (Pandas Practice)

Are you learning Python Pandas for Data Analysis? 
This hands-on exercise will help you practice real-world data analysis using an Ecommerce Purchases dataset.

Follow the questions step-by-step and try solving them using Pandas.


Dataset Setup

First, import Pandas and load the dataset:

import pandas as pd
ecom = pd.read_csv('Ecommerce Purchases')

You can download the dataset from any dataset websites like kaggle and UCI
or comment us and we will share you the data


Basic Data Exploration

1. Check the first few rows of the dataset

 Use .head() to view the dataset.

2. How many rows and columns are there?

Use .info() to get dataset structure.


Purchase Price Analysis

3. What is the average Purchase Price?

4. What are the highest and lowest purchase prices?


User Insights

5. How many people have English ('en') as their language?

6. How many people have the job title "Lawyer"?


Time-Based Analysis

7. How many purchases were made during:

  • AM

  • PM

(Hint: Use value_counts())


Job Analysis

8. What are the top 5 most common job titles?


Transaction-Specific Queries

9. A purchase was made from Lot "90 WT"

What was the Purchase Price?

10. Find the email of the person with this Credit Card Number:

4926535242672853


Payment & Purchase Conditions

11. How many people:

  • Use American Express

  • AND made a purchase above $95


Advanced Questions (Hard Level)

12. How many people have a credit card that expires in 2025?

13. What are the top 5 most popular email providers?

(Example: gmail.com, yahoo.com, etc.)


Conclusion

This exercise helps you practice:

  • Data filtering

  • Aggregations

  • Conditional queries

  • String operations

Perfect for beginners and intermediate learners in Data Analysis with Python.


If you want more such practical exercises on:

  • Python 

  • Data Analysis 

  • Web Development 

Follow AGN HUB and stay updated!

Tip: Try solving each question using a single line of code for better mastery!


Location: Vaishali Marg W, near warrior gym, in front of nanag paradise and marriage garden, Panchyawala, Jaipur, Rajasthan 302034, India

Comments

  1. ASINFOApril 2, 2026 at 8:48 AM

    Great post! You’ve explained the importance of data and analytics in today’s digital world really well. With businesses relying more on data-driven decisions, learning skills in Data Science and Data Analysis has become essential for career growth.

    For anyone looking to get started, I’d highly recommend exploring a professional Data Science course and Data Analysis training at Aptech Learning Janakpuri. They offer practical training, live projects, and placement support, which really helps in building real-world skills and becoming job-ready. Definitely worth checking out!

    ReplyDelete
  2. preethi.torch.1991May 14, 2026 at 1:14 AM

    practice with ecommerce purchases data analysis is an excellent way to improve data analysis skills using real-world datasets. These exercises typically involve analyzing customer purchase records, product categories, payment methods, and transaction details to gain useful business insights. By working with ecommerce data, learners can practice essential Pandas operations such as filtering rows, selecting columns, grouping data, calculating statistics, and handling missing values.

    ReplyDelete
  3. preethi.torch.1991May 14, 2026 at 1:15 AM

    Common exercises include finding the highest purchase amount, identifying the most popular products, calculating average spending by customers, and analyzing purchase trends over time. These tasks help build practical experience in data manipulation and problem-solving using Python. Machine Learning Projects for Final YearEcommerce data analysis exercises are highly valuable for understanding how data-driven decision-making works in online businesses and help learners strengthen their knowledge of Pandas for data science and machine learning applications.

    ReplyDelete

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