The Complete Guide to Python Pandas Groupby

Video Description

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-automations-4579 Today we are going over many different Python Pandas GroupBy() examples. These include aggregate calculations, transform, lambda, apply, and much more. We also go over single series calculations vs multiple columns. Code: https://ryanandmattdatascience.com/python-pandas-groupby/ 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/ 👨‍💻 Mentorships: https://ryanandmattdatascience.com/mentorship/ 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord: https://discord.com/invite/F7dxbvHUhg 📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT Python Pandas Playlist: https://www.youtube.com/playlist?list=PLcQVY5V2UY4KvHRJ-awaxAPzFGdZ8yN6D Python Pandas Query: https://youtu.be/stTxgSn3O10 Python Classes: https://youtu.be/XXJdZD0SCTI Python Data Cleaning: https://youtu.be/iaZQF8SLHJs In this comprehensive tutorial, I break down everything you need to know about group by operations in Python Pandas. Group by is one of the most important and frequently used functions when working with data in Python, especially if you're dealing with data analysis or preparing for coding interviews. I start with the basics of group by syntax and gradually progress through increasingly complex examples. We cover essential aggregation functions like mean, count, sum, min, and max, then move into more advanced territory with multiple column grouping, custom aggregations, and the powerful transform function. You'll learn how to use Lambda functions with apply and transform methods to create sophisticated data transformations. Throughout the video, I use real-world datasets from Seaborn including restaurant tips data and NYC taxi data to demonstrate practical applications. I also include several practice questions at different difficulty levels so you can test your understanding and reinforce what you've learned. By the end of this tutorial, you'll be able to confidently use group by operations for data analysis, answer common interview questions, and understand how to combine group by with other Pandas methods like reset_index, sort_values, and fillna. Whether you're a beginner learning Pandas or preparing for data analyst interviews on platforms like StrataScratch, this video provides the foundation you need to master group by operations in Python. TIMESTAMPS 00:00 Introduction to Group By in Python Pandas 01:00 Importing Libraries and Loading Data 03:07 Basic Group By Example 05:17 Using to_frame() and reset_index() 06:48 Sorting Values 08:15 Different Aggregation Functions (count, size, n_unique, etc.) 10:40 Group By Practice Questions 12:23 Multiple Aggregations with .agg() 15:00 Advanced Aggregations and Describe 16:40 Group By with Multiple Columns 19:20 Renaming Columns in Group By 22:00 Group By with Multiple Series 24:30 Transform Function Introduction 27:30 Transform with Custom Calculations 29:20 Lambda Functions with Transform 31:40 Lambda Functions with Apply 34:40 Using Functions in Group By (datetime example) 37:00 Final Practice Questions 39:00 Question 3 Solution (Transform) 40:20 Question 2 Solution (Aggregate) 41:45 Question 1 Solution (Interquartile Range with Lambda) OTHER SOCIALS: Ryan’s LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/ Matt’s LinkedIn: https://www.linkedin.com/in/matt-payne-ceo/ Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.