How to Clean FBref Data with Python | Full Data Pipeline Explained

Description:
Welcome back to our in-depth data scraping tutorial! In this video, we build on our previous session, diving deeper into scraping player stats from FBref using Python and Pandas. Learn to clean, merge, and export football data for analytics and visualization projects.

What You’ll Learn:

Scraping player performance data from FBref.
Cleaning and merging multiple data frames.
Removing duplicates and fillinging missing values.
Renaming columns for easier analysis.
Exporting the final dataset to Excel for future use.
Key Highlights:

🏟 Scraping stats like passing, shooting, and defensive actions.
⚙️ Using Pandas to automate data cleaning and transformation.
📊 Preparing data for visualization projects like fullback rankings.
🧹 Handling null values, renaming leagues, and fixing formatting issues.
Useful Links:

Previous Video: https://rumble.com/v5w9itw-fbref-soccer-python-scraping-tutorial.html
Data Visualization Tutorial (Coming Soon!)
Next Up: We’ll visualize percentile rankings for fullbacks using Matplotlib and Seaborn. Stay tuned for more football data analysis!

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