Welcome back to the module of Joining and Merging Dataframes in Pandas. In this lecture, we explore left joins and right joins in Pandas. These join types allow us to retain unmatched rows, depending on whether the focus is on the left or right table. Understanding these joins is crucial for handling parent-child relationships, data integrity, and missing values in real-world datasets.
**What You’ll Learn in This Lecture:**
**1. Understanding Left and Right Joins**
* Left Join:
* Retains all rows from the left table (parent dataset).
* Matches records from the right table (child dataset).
* Unmatched rows in the right table are filled with NaN.
* Right Join:
* Retains all rows from the right table (child dataset).
* Matches records from the left table (parent dataset).
* Unmatched rows in the left table are filled with NaN.
**2. Practical Use Cases**
*Identifying Sales Reps Without Sales (Left Join Use Case)
* Extract sales reps who have no sales by identifying missing records in the sales dataset.
* Identifying Orphaned Sales Records (Right Join Use Case)
* Find sales transactions with no assigned sales rep, ensuring data quality and consistency.
**3. Handling Missing Data After Joins**
* Fill missing values in critical fields to avoid reporting inconsistencies.
* Replace missing sales reps or categories with default placeholders like “Unknown” for better readability.
**Why This Lesson Matters:**
Data from multiple sources often contains missing relationships. Left and right joins help ensure data completeness and integrity, whether you’re:
Analyzing all employees, including those with no sales.
Ensuring all sales records are accounted for, even if they lack a sales rep.
Performing data quality checks to identify missing relationships.
Key Highlights of the Lecture:
✅ Step-by-step explanation of left and right joins and when to use them.
✅ Filtering techniques to identify missing relationships in datasets.
✅ Handling NaN values effectively for cleaner reports.
✅ Practical examples of sales reps without sales and orphaned sales records.
🚀 In the next lecture, we’ll explore Outer Joins, which include all rows from both tables for a complete dataset reconciliation. See you there!
### *Continue Your Spark Learning*
Enroll in our Guided Program to learn *Apache Spark* and get hands-on experience using Databricks Community Edition:
https://forms.gle/3LtJ13iNdDCv7cxY6
Resources:
Ready to kickstart your coding journey? Join Python for Beginners: Learn Python with Hands-on Projects and master Python by building real-world projects from day one!
https://www.udemy.com/course/python-for-beginners-hands-on/?referralCode=BADB34312470BFA1A886
Continue Your Learning Journey with Pandas! 🚀
✅ Previous Video: https://youtu.be/ntDxKsO26Cs
✅ Next Video: https://youtu.be/bi4Y1L17E6U
✅ Full Course: https://youtube.com/playlist?list=PLf0swTFhTI8oIrBWtKkNiU6yE0eeVI-jn&si=1gaYZcODglyM9q-6
Connect with Us:
* Newsletter: http://notifyme.itversity.com
* LinkedIn: https://www.linkedin.com/company/itversity/
* Facebook: https://www.facebook.com/itversity
* Twitter: https://twitter.com/itversity
* Instagram: https://www.instagram.com/itversity/
What’s Next?
In upcoming videos, we’ll explore additional file formats and advanced data manipulation techniques. Stay tuned to master the full capabilities of Python Pandas!
#DataEngineering #Pandas #Python #Analytics #DataAnalysis #programming
コメント