Power Query vs. Python - Soccer Data Cleaning Challenge ⚽🥅

Master this data cleaning challenge using real World Cup soccer statistics. Learn how to fix messy, multi-line goal entries to a neat table (or data frame). This tutorial walks you through a practical data cleaning challenge where I address a common issue: multiple goals recorded in a single cell separated by new line characters. This is essential knowledge for anyone looking to clean data efficiently. I am showing both Python & Power Query approaches for cleaning the data. Which one is your favourite? Comment and let me know. 📁 Try it yourself: Grab the raw data here and try it yourself - https://chandoo.org/wp/wp-content/uploads/2026/06/fifa-worldcup-goals-cleanup.xlsx 👩‍💻🧑‍💻 Completed code: See my github repo for the python notebook and PQ workbook. Python notebook: https://github.com/chandoo-org/python/blob/ba747ec6c5373c34ee35826d6a1c6822c42bae0f/fifa%20goals%20data%20cleanup.ipynb Power Query File: https://chandoo.org/wp/wp-content/uploads/2026/06/fifa-goals-pq.xlsx 🕐 Video Topics: 0:00 - Introduction to the data cleaning challenge 1:32 - Data Cleaning with Python (loading data to Pandas Dataframe) 2:30 - Splitting and combining all data to one DF 4:15 - Splitting goals by new line and exploding the Data Frame 5:08 - Dealing with multiple goals by same person 8:55 - Removing un-needed columns and tagging each goal by type (penalty, own goal etc.) 10:36 - Possible next steps for this data... 11:13 - Loading data into Power Query 12:00 - Splitting and appending tables in PQ 13:20 - Separating each line to new record 14:25 - Separating scorer and goal time details to two columns 18:33 - Splitting multiple goals by same person to different rows 19:02 - Tagging goals by their type (penalty, own goal etc.) 19:50 - Loading data into Excel 20:13 - Closing & bloops ~ #Powerquery #python #fifa