Now, let's move to the next topic in the data science learning series *Data Cleaning & Preparation:* 🔹 *Topic: Combining Datasets* In real-world projects, data often comes from multiple sources: – Sales from one CSV – Customer info from another – Product data from a third To build a full picture, you need to combine them. ✅ *3 Ways to Combine Datasets in Pandas* *1. Concatenation (Stacking Data)* Used when datasets have same columns but are split across multiple files or timeframes. df_combined = pd.concat([df1, df2]) Use axis=0 for stacking rows (default) Use axis=1 for combining side by side (columns) *2. Merging (Joining on Keys)* Used when datasets share a common key/column, like customer_id. df_merged = pd.merge(df1, df2, on='customer_id', how='inner') *Merge types:* inner: only matching rows left: keep all rows from df1 right: keep all rows from df2 outer: keep all rows from both *3. Join Method (simplified merge)* df1.join(df2, how='left') Only works when indexes are aligned or you set the index before. 📊 *Real-Life Example:* You have: orders.csv → order_id, product_id, customer_id customers.csv → customer_id, name, age products.csv → product_id, name, price You’ll use merge to link them all using common columns. *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