How to Use Pandas Joins using CSV Files | Python Pandas Tutorial for Data Engineering

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Welcome back to the module of Joining and Merging Dataframes in Pandas. In this lecture, we apply what we’ve learned about joins to a real-world scenario using CSV files. We'll focus on performing an inner join to combine sales reps data with sales data and then calculate aggregated sales totals grouped by sales reps.

**What You’ll Learn in This Lecture:**
**1. Merging Sales Data with Sales Reps Data**
* Work with two CSV files: Sales Reps data and Toyota Sales data.
* Perform an inner join to match sales records with their respective sales reps.
* Ensure the join keys are correctly defined to avoid errors.
**2. Aggregating Sales Data by Sales Rep**
* After merging, calculate total sales for each sales rep by grouping the data.
* Extract key details like Rep ID, First Name, Last Name, Region, and Total Sales Amount.
* Convert the result into a structured DataFrame for further analysis.
**3. Adding a Calculated Column for Commission**
* Compute Commission Earned per Sales Rep using the Commission Percentage field.
* Ensure missing commission percentages are filled with default values before performing calculations.
* Round off commission values for better readability.
**4. Handling Missing Data After Joins**
* Fill missing values in key columns to avoid inconsistencies in reports.
* Use appropriate defaults, such as "Unknown" for missing names/regions and 0 for missing numeric fields like commission percentage.

**Why This Lesson Matters:**
Real-world data analysis often involves working with multiple datasets, ensuring data quality, and deriving meaningful insights. This example demonstrates:

How to merge and process large datasets efficiently.
Techniques for grouping and summarizing financial data.
Best practices for handling missing data to maintain integrity.

**Key Highlights of the Lecture:**
✅ Step-by-step implementation of merging sales reps and sales data.
✅ Aggregating sales figures for sales reps and computing commissions.
✅ Handling missing values in sales and commission data.
✅ Practical applications of inner joins in a business context.

🚀 In the next module, we’ll dive into advanced data processing techniques like custom transformations and aggregations to take our analysis to the next level. See you there!

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In upcoming videos, we’ll explore additional file formats and advanced data manipulation techniques. Stay tuned to master the full capabilities of Python Pandas!

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