Joining and Merging Datasets in Pandas | Python Pandas Tutorial for Data Engineering

Welcome to this module on merging and joining datasets! In this lecture, we introduce key concepts of merging and joining in Pandas, discuss different types of joins, and explain how datasets relate to each other. By the end of this session, you’ll have a clear understanding of when and why to use joins in your data workflows.

What You’ll Learn in This Lecture:
**1. Why Merge or Join Datasets?**
Data Enrichment: Add extra fields by pulling from related datasets (e.g., linking sales reps' names to sales data).
Data Consolidation: Combine datasets for a unified view (e.g., matching sales records with customer details).
Advanced Analysis: Merge datasets for computations like grouping and aggregations.
**2. Understanding Relationships Between Tables**
Parent Table (Primary Key): Unique identifiers, such as rep_id in the sales reps dataset.
Child Table (Foreign Key): References a parent table, such as sales_rep_id in the sales transactions dataset.
Example: sales_rep_id in Toyota sales data links to rep_id in sales reps data.
**3. Types of Joins in Pandas**
**Inner Join:** Keeps only rows with matching keys in both tables. (Example: Finding sales reps who made sales.)
**Left Join:** Retains all rows from the left (parent) table and matching rows from the right. (Example: List all sales reps, including those with no sales.)
**Right Join:** Retains all rows from the right (child) table and matching rows from the left. (Example: Ensure all sales transactions are retained, even if a rep is missing.)
**Outer Join (Full Join):** Includes all rows from both tables, filling unmatched rows with NaN. (Example: Reconciling sales reps and sales records to identify missing data.)
**4. SQL vs. Pandas Join Syntax**
SQL Equivalent:
LEFT JOIN → how="left"
RIGHT JOIN → how="right"
FULL OUTER JOIN → how="outer"
Important Note: Some databases default OUTER JOIN to LEFT JOIN, so always verify behavior when transitioning between SQL and Pandas.
Why This Lesson Matters:
Most real-world data analysis tasks require working with multiple sources. Whether it’s linking customer profiles to sales transactions, combining multi-year financial data, or matching product details to inventory records, merging datasets allows for more comprehensive and meaningful analysis.

**Key Highlights of the Lecture:**
✅ Clear explanation of joins with parent-child relationships.
✅ Step-by-step breakdown of inner, left, right, and full outer joins.
✅ Comparison of SQL joins and Pandas joins for better understanding.
✅ Real-world examples to demonstrate use cases.
✅ Best practices for ensuring clean and accurate joins.

🚀 In the next lecture, we’ll dive into practical examples of inner joins using custom DataFrames. See you there!

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