19 Must-Know Techniques to Create Pandas DataFrames (with Examples!)

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 In this Python Pandas tutorial we will go over different approaches to creating dataframes in Pandas. This covers lists, to excel or csv files to JSON. The Python code will be down below Code: https://ryanandmattdatascience.com/pandas-create-dataframe/ 🚀 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 19 Ways to Create a Pandas DataFrame in Python | Complete Tutorial In this video, I walk through 19 different ways to create a pandas DataFrame in Python, covering everything from basic methods to more advanced techniques. We start with simple approaches like creating DataFrames from lists and dictionaries, then move into working with tuples, series, and multiple data structures combined. I show you how to read in data from external files including CSV, Excel, and JSON formats, which are some of the most common ways you'll work with DataFrames in real projects. We also explore more advanced methods like creating DataFrames row by row, using numpy arrays, working with custom indexes, and even pulling data from SQL queries. Throughout the video, I use practical examples with real code that you can follow along with, and I explain why certain methods are more useful than others based on my experience. By the end of this tutorial, you'll understand all the main ways to create pandas DataFrames and know which approach to use for different situations. Whether you're just starting with pandas or looking to expand your data manipulation skills, this comprehensive guide covers the essential techniques you need. All the code from this video is available on my website—link in the description below. TIMESTAMPS 00:00 Introduction to Creating Pandas DataFrames 01:02 Creating DataFrame from a Simple List 02:01 Creating DataFrame from Multiple Lists 03:17 Creating DataFrame from Dictionary 05:00 Creating DataFrame from Series in Dictionary 06:05 Creating DataFrame from Tuples 07:19 Creating DataFrame from List of Dictionaries 08:09 Creating DataFrame from Two Lists 09:20 Creating DataFrame from Series 10:20 Creating DataFrame from Multiple Series 12:28 Reading CSV and Excel Files 13:40 Reading JSON Files and JSON Strings 16:02 Creating DataFrame with Custom Index 18:20 Creating DataFrame from Columns of Another DataFrame 19:40 Creating DataFrame Row by Row (Method 1) 21:00 Creating DataFrame Row by Row (Method 2) 22:40 Creating DataFrame from Numpy Array 24:05 Creating DataFrame from SQL Query 25:39 Most Common Methods Recap 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.