Clean Data - Python vs. SQL amzn.to/4lfLa60 When it comes to data analysis, knowing how to clean data using Pandas or SQL can be a game-changer! If you’ve ever wrangled with messy data, you know the importance of cleaning it before analysis. But whether you're using Pandas in Python or writing SQL queries, the techniques can look very different. So I put together this quick reference comparing common data cleaning tasks in both Pandas and SQL side by side. 🧹 Here’s how they stack up: 🔹 Detect & handle missing values 🔹 Remove duplicates 🔹 Convert data types 🔹 Standardize casing & trim spaces 🔹 Filter outliers & unwanted records 🔹 Derive new columns 🔹 Rename or drop columns 🔹 Fix typos and inconsistencies 🔹 Encode categorical values Whether you're cleaning CSVs with Pandas or querying data warehouses using SQL, having both skillsets can dramatically improve your data workflow. 💡 Tip: Mastering data cleaning is one of the most underrated skills in data science. It sets the stage for meaningful insights. #sql #python