Pandas loc: Simple Guide with Tons of Examples скачать в хорошем качестве

Pandas loc: Simple Guide with Tons of Examples

🧠 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... Confused by how .loc[] works in Pandas? You're not alone! In this hands-on tutorial, you'll learn how to use the loc indexer in Pandas to select, filter, and update data with tons of practical examples. Whether you're a beginner or just need a refresher, this guide is for you. Code: https://ryanandmattdatascience.com/pa... 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: ryannolandata@gmail.com 🌐 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   Pandas iloc:    • Mastering Python Pandas iloc: Indexing, Sl...   Pandas Indexes:    • Mastering Python Pandas Indexes: Everythin...   Python Pandas Filter Multiple Conditions:    • How to Filter Data in Python Pandas with M...   In this Python Pandas tutorial video, we dive deep into the loc (location) function with 15 practical examples that will transform how you work with DataFrames. The loc function is one of the most powerful tools in Pandas for selecting, filtering, and modifying data using label-based indexing. We start by creating a sample DataFrame with merchant data including merchant IDs, states, cities, and volumes. Throughout the video, we progressively build complexity, starting with selecting single rows and columns, then moving to multiple selections, filtering with conditions, and advanced slicing techniques. You'll learn the critical distinction between rows and columns in loc syntax, how to use AND and OR operators for complex filtering, and how to modify data values directly. We cover real-world scenarios like filtering merchants by state and volume thresholds, selecting specific columns from filtered results, and using the colon operator for slicing and skipping rows. The most practical example demonstrates filtering data based on conditions (like merchant volume greater than 50,000) while only displaying relevant columns, which is essential for data analysis interviews and professional work. By the end of this tutorial, you'll have complete confidence using pandas loc for any data selection task. All code examples are available on our website linked in the description below, so you can practice alongside the video or reference later. TIMESTAMPS 00:00 Introduction to .loc in Pandas 01:10 Setting Up Data Frame and Index 02:30 Example 1: Select a Specific Row 03:02 Example 2: Select Multiple Rows 03:55 Example 3: Select Row and Specific Column 04:40 Example 4: Select Multiple Columns 05:30 Example 5: Multiple Rows and Multiple Columns 06:10 Example 6: Filtering with Strings 07:15 Example 7: Filtering with Integers and Selecting Columns 08:40 Example 8: Multiple Conditions with AND 10:20 Using OR Conditions for Filtering 12:20 Example 10: Modifying Data Values 13:50 Example 11: Slicing Single Column 15:00 Example 12-13: Slicing Rows and Columns 16:30 Example 14-15: Skipping Rows and Columns 18:00 Recap and Most Important Use Cases 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.