Pandas Query Filter Function Guide [Beginner Friendly]

🧠 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 looking at how you can use Query() within Python Pandas to filter data quickly in a dataframe. Get the full code here: https://ryanandmattdatascience.com/pandas-query/ 🚀 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 Explode: https://youtu.be/NNz6yOA_G1A Python Split Columns: https://youtu.be/d9Tn8rxiki8 Python Data Cleaning: https://youtu.be/iaZQF8SLHJs In this video, I break down how to use the query method in pandas to filter and manipulate DataFrames faster and more efficiently. Query is one of the most powerful yet underutilized features in pandas, and it's commonly tested in data analyst and data science interviews. We start with basic numerical queries using comparison operators, then move into string filtering, combining multiple conditions with AND/OR/NOT operators, and working with the IN clause. I show you how to use string methods like contains() within queries, how to filter dates by extracting month information, and how to handle null values with isna(). Throughout the tutorial, I demonstrate real examples using the Seaborn taxis dataset so you can see exactly how each query works in practice. You'll learn how to select specific columns from query results, use the inplace parameter to overwrite DataFrames, and even define variables outside your queries for cleaner, more maintainable code. By the end of this video, you'll be able to write complex pandas queries confidently and apply them to real-world data analysis problems and interview questions. TIMESTAMPS 00:00 Introduction to Pandas Queries 00:23 Importing Libraries & Loading Data 01:42 Basic Number Queries with Equal To 02:58 Greater Than & Less Than Operators 03:42 Working with String Queries 05:04 Using AND & OR Operators 06:27 Using IN and NOT IN 07:24 Defining Variables Outside Queries 08:40 Math Operators in Queries 09:42 String Functions with Contains 10:51 Working with Dates 11:21 Finding Null Values 12:00 Selecting Specific Columns 13:00 Using InPlace to Modify DataFrames 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.