How to Use Python Pandas Concat to Combine DataFrames (Step-by-Step)
🧠 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-aut... Want to combine multiple DataFrames in Pandas with ease? In this tutorial, you’ll learn how to use pandas.concat() step-by-step to stack, join, and merge datasets efficiently. Great for beginners working with real-world data! Code: https://ryanandmattdatascience.com/pa... 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord: / discord 📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT Python Pandas Playlist: • Python Pandas for Beginners Python Pandas Merge: • Master Python Pandas Merge: The Ultimate G... Pandas Value Counts: • How to Use Python Pandas Value Counts for ... Pandas iloc: • Mastering Python Pandas iloc: Indexing, Sl... In this Python Pandas tutorial, we dive deep into the concat function and show you exactly how to combine multiple DataFrames efficiently. Whether you need to stack DataFrames vertically like a SQL UNION or add new columns horizontally, concat is your go-to tool. We walk through four practical examples using real runner data, starting with basic row concatenation, then showing you how to reset indexes to avoid duplicates, adding keys to label your source DataFrames, and finally demonstrating column concatenation with axis=1. You'll learn the critical difference between concat and merge, understand when to use each method, and see why proper index alignment matters when combining data. We cover essential parameters like keys for data labeling and axis for controlling direction, plus important tips about index management that can save you from data quality issues. By the end of this tutorial, you'll confidently know how to concatenate DataFrames in any scenario, whether you're stacking datasets from different sources or adding calculated columns to existing data. All code from this video is available on our website - link in the description below. Perfect for data analysts, data scientists, and anyone working with Pandas who wants to master DataFrame manipulation. TIMESTAMPS 00:00 Introduction to Concatenation 00:27 Setup and Importing Pandas 01:02 Creating Ultra Runners DataFrame 02:17 Creating Marathon Runners DataFrame 03:02 Example 1: Basic Concatenation 06:00 Example 2: Resetting Index 08:08 Example 3: Using Keys Parameter 10:00 Example 4: Concatenating Columns 13:00 Concat vs Merge Explained 14:20 Final Review and Best Practices OTHER SOCIALS: Ryan’s LinkedIn: / ryan-p-nolan Matt’s LinkedIn: / 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.
