LeetCode 511- Game Play Analysis 1 (Python and SQL) [EASY] скачать в хорошем качестве

LeetCode 511- Game Play Analysis 1 (Python and SQL) [EASY]

🧠 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... In this video, we break down LeetCode 511 - Game Play Analysis 1 using both SQL and Python (Pandas). Learn how to analyze player login data, solve time-based queries, and understand how to approach real-world database problems—perfect for beginners! Leetcode 511- Game Play Analysis 1: https://leetcode.com/problems/game-pl... 🚀 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 LeetCode Playlist:    • Leetcode Data Science Interview Questions   LeetCode 183:    • Leetcode 183 - Customers Who Never Order (...   LeetCode 180:    • LeetCode 180 - Consecutive Numbers (Python...   LeetCode 175:    • Leetcode 175 Combine Two Tables (Python an...   In this video, I solve LeetCode 511 Game Play Analysis I in both SQL and Python Pandas, taking you line by line through each solution. This easy-level SQL problem asks you to find the first login date for each player from an activity table. I break down the problem step by step, showing you how to use window functions like RANK() with PARTITION BY in SQL, and how to replicate that logic in Python Pandas using groupby and rank methods. For the SQL solution, I walk through creating a CTE that ranks login dates for each player using PARTITION BY, then filtering for the first login by selecting where the rank equals one. I also show you how to rename columns with AS to match the expected output format. On the Python Pandas side, I demonstrate how to use groupby to partition data by player_id, rank the event_date column with method='min', and filter for the first login before renaming columns with the rename function. Whether you're preparing for data analyst interviews, practicing SQL window functions, or learning Pandas data manipulation techniques, this tutorial covers essential concepts like partitioning, ranking, and column renaming. By the end, you'll understand how to solve similar first-occurrence problems in both SQL and Python Pandas. If you found this helpful, subscribe for more LeetCode SQL and Python tutorials every week as I continue building my data skills! TIMESTAMPS 00:00 Problem Introduction & Overview 00:47 SQL Solution - Building the SELECT Statement 01:42 Adding Rank with Partition By 02:45 Creating the CTE 03:37 Final Query & Testing Solution 04:29 Python Pandas Solution 05:22 Renaming Columns & Return Statement 06:58 Code Explanation & Wrap Up 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.