Pandas Apply Function: Simplify Data Transformations

🧠 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 Want to clean, manipulate, or transform your data more efficiently? Learn how to use the powerful apply() function in Pandas to simplify row-wise and column-wise operations in your DataFrame. This tutorial breaks it down with clear, real-world examples! Code: https://ryanandmattdatascience.com/pandas-apply/ 🚀 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 Pivot Table: https://youtu.be/NViVYXtihMc Python Pandas Lambda: https://youtu.be/7AIEzPfC0kI Python Pandas Concat: https://youtu.be/qT-RJfVCBfI In this comprehensive Python Pandas tutorial, I walk you through eight practical examples of using the apply function to transform data in rows and columns. We start with basic if-else statements to classify states by region, then move into Lambda functions for temperature conversions from Fahrenheit to Celsius. I demonstrate how to use built-in NumPy functions like logarithms, work with multiple columns simultaneously, and apply functions across entire rows using the axis parameter. The video covers advanced techniques including args and kwargs for passing custom parameters, creating descriptive text columns by combining multiple data points, and generating summary statistics like medians across your dataset. Each example includes line-by-line code explanations so you can follow along easily, whether you're working with temperature data, state classifications, or any other pandas DataFrame. By the end, you'll confidently use apply to avoid slow loops and write cleaner, more efficient pandas code for your data analysis projects. All code examples are available on my website for quick reference. TIMESTAMPS 00:00 Introduction & Setup 01:30 Example 1: If-Else Statements with Apply 06:02 Example 2: Lambda Functions for Temperature Conversion 09:53 Example 3: Applying to Multiple Columns 11:41 Example 4: Built-in Functions with NumPy 12:39 Example 5: Working with Rows (axis=1) 15:17 Example 6 & 7: Using Args and Kwargs 19:53 Example 8: Summary Statistics with Apply 23:08 Recap & Conclusion 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.