Lesson 10: Merging & Joining Datasets

1. Why Merge Data?

Real ML projects combine multiple sources (users + purchases + logs).

Pandas merge = SQL JOIN

Types:

• inner: only matching rows
• left: all left + matching right
• right: all right + matching left
• outer: all rows from both

2. Merge Examples

# Inner merge on id
pd.merge(users, orders, on="user_id", how="inner")

# Left merge (keep all users)
pd.merge(users, orders, on="user_id", how="left")

# Different column names
pd.merge(df1, df2, left_on="id", right_on="user_id")

# Concat vertically (same columns)
pd.concat([df1, df2], ignore_index=True)

Used in ML: merge features from different tables.