Today, let's move to the next topic in the Data Science Learning Series: 🔹 *Renaming Columns & Reindexing* 🧠 *Why Rename Columns?* - To make column names more readable - To standardize names (e.g., remove spaces, use lowercase) - To match expected input format for ML models or APIs ✅ *How to Rename Columns in Pandas* *1. Rename one or more columns* df.rename(columns={'OldName': 'NewName'}, inplace=True) *2. Rename all columns at once* df.columns = ['col1', 'col2', 'col3', ...] *3. Clean column names using a loop* df.columns = [col.strip().lower().replace(" ", "_") for col in df.columns] 🔁 *What Is Reindexing*? Reindexing means changing the row labels (index) of your DataFrame. ✅ *How to Reindex* *1. Reset index (commonly used)* df.reset_index(drop=True, inplace=True) *2. Set a specific column as index* df.set_index('CustomerID', inplace=True) *3. Custom reindexing* df.reindex([2, 0, 1]) # Reorders the rows 📊 *Real-Life Example* In a dataset: - Column "Customer ID" → rename to "customer_id" for consistency - Reset index after cleaning and filtering for neat presentation - Set "Date" as index when working with time-series data *React with ❤️ once you're ready for the quiz* Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998 Python Cheatsheet: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1660