Mastering JSON in Pandas | Read, Normalize, and Manipulate JSON Data in Python
🧠 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... In this tutorial, you'll learn how to work with JSON data using pandas, one of Python's most powerful data analysis libraries. Whether you're dealing with nested JSON from APIs or large datasets stored in .json files, this video will walk you through everything you need to know — step by step. 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 Working with JSON and Python Pandas is critical for anyone building AI systems or integrating APIs. In this comprehensive tutorial, I walk you through seven practical examples of converting between JSON and DataFrames, including real API integration and handling nested JSON structures. We start with the basics—converting JSON strings to DataFrames and vice versa—then progress to reading and writing JSON files. I demonstrate different orient options (records, index, split, columns, and values) so you can choose the right format for your use case. You'll also learn how to use the explode function for cleaning up nested data, a technique that's incredibly useful when working with API responses. The tutorial includes a hands-on example using the Market Stack API (free to use) where we fetch stock data and convert it into a DataFrame. Finally, I tackle the more complex scenario of flattening deeply nested JSON structures, showing you how to use json_normalize effectively with real baseball card data examples. Whether you're building AI workflows, analyzing API data, or just need to master JSON manipulation in Python, this video covers everything you need. All code examples are available on my website for easy reference. By the end, you'll confidently handle any JSON-to-DataFrame conversion challenge that comes your way. TIMESTAMPS 00:00 Introduction to JSON with Python Pandas 00:32 Getting Started with Imports 01:00 Example 1: Create DataFrame from JSON String 02:10 Example 2: DataFrame to JSON Output 03:02 Example 3: Read JSON File to DataFrame 04:00 Example 4: Different Orient Options 06:02 Example 5: Using Explode with JSON 08:00 Example 6: Getting JSON from an API 10:00 Setting Up API Request 12:00 Converting API Response to DataFrame 12:52 Example 7: Handling Nested JSON 14:30 Normalizing and Flattening JSON Data 16:30 Working with JSON Variations 18:20 Recap and Conclusion 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.
