Pandas where() Explained with Multiple Examples скачать в хорошем качестве

Pandas where() Explained with Multiple 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... Struggling with conditional data filtering in Pandas? In this tutorial, you'll learn how to use the powerful pandas.DataFrame.where() method to apply conditional logic to your datasets—with multiple clear examples and real-world use cases! 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 Replace:    • Clean Your Data FAST with Pandas replace()   Pandas Rank:    • How to Rank Data in Pandas — Explained wit...   Pandas Multindex:    • Pandas MultiIndex Tutorial: Work with Mult...   In this Python Pandas tutorial, we dive deep into the where() method through seven practical examples that will transform how you filter and manipulate DataFrames. The pandas where() method is essentially the opposite of mask(), but it handles null values better, making it my preferred choice for conditional data filtering. We start with basic filtering to keep only values under a threshold, then progress to replacing values with custom alternatives, filling null values, and working with specific columns. You'll learn how to implement multiple conditions using AND logic, use OR conditions with filters defined outside the where() method, and even create brand new columns based on conditional logic. Throughout the video, I show you real working code using salary data and NFL running back statistics, demonstrating exactly how where() keeps values that meet your conditions while replacing everything else with null values or custom replacements. The key difference from mask() is that where() impacts null values in your filtering logic, which often makes data transformations cleaner and more intuitive. By the end of this tutorial, you'll confidently use pandas where() for data filtering, conditional replacements, and creating derived columns. All code examples are available in the article linked below, so grab your Python notebook and start coding along to master this essential pandas method. TIMESTAMPS 00:00 Introduction to Pandas Where 00:26 Setup and Creating First DataFrame 01:31 Example 1: Keep Values Under $1,000 02:21 Example 2: Replace with Another Value 03:07 Example 3: Filling Null Values 04:22 New DataFrame: NFL Running Backs 05:17 Example 4: Replacing a Specific Column 06:01 Example 5: Multiple Conditions with AND 07:32 Example 6: OR Conditions and External Filters 09:02 Example 7: Creating a New Column 10:40 Wrap Up and Channel Information 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.