How to Filter Data in Python Pandas with Multiple Conditions (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-automations-4579 Need to slice and dice your DataFrame with precision? In this hands-on tutorial, you’ll learn how to filter rows in Pandas using multiple conditions—step by step! Whether you're working on real-world data projects or prepping for interviews, mastering this technique is a must. Code: https://ryanandmattdatascience.com/pandas-dataframe-filter-with-multiple-conditions/ 🚀 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 Pandas iloc: https://youtu.be/LI2w1xLyr3w Pandas Value Counts: https://youtu.be/QB9lepDjUjM Python Pandas Merge: https://youtu.be/Fl3VGL3BuAA Learn how to filter pandas DataFrames using multiple conditions in Python with five powerful approaches. This tutorial covers loc, NumPy where, query, boolean indexing, and eval methods for filtering data. Whether you're working with AND or OR conditions, I'll show you the best practices for each method, including when to use parentheses, how to handle string conditions, and how to select specific columns from your filtered results. We walk through real examples using a sample dataset, comparing the readability and efficiency of each filtering technique. By the end of this video, you'll understand the differences between loc indexing and boolean indexing, know when to use query for cleaner code, and be able to confidently choose the right filtering method for your pandas data analysis projects. Perfect for data analysts and Python developers working with pandas DataFrames who want to level up their data manipulation skills. All code examples are available in the article linked below, so you can practice these pandas filtering techniques in your own projects. Whether you're filtering by age, salary, categories, or any other conditions, these methods will make your data analysis workflow more efficient. TIMESTAMPS 00:00 Introduction - Filtering by Multiple Conditions 00:24 Setting Up the DataFrame 01:47 Method 1: Using .loc() 03:22 Using OR Statements with .loc() 05:10 Method 2: Using NumPy np.where() 07:27 Method 3: Using Pandas .query() 09:32 Method 4: Boolean Indexing 11:40 Selecting Specific Columns with Boolean Indexing 13:03 Method 5: Using .eval() 15:05 Summary & Recap of All Methods 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.