Welcome back! In this lecture, we explore how to use custom aggregation functions in Pandas. While built-in functions like sum() and mean() are powerful, custom functions allow us to perform advanced and unique calculations that fit specific business needs.
What You’ll Learn in This Lecture
1. Why Use Custom Aggregation Functions?
Built-in functions may not always meet analysis requirements.
Some business rules require custom calculations.
Advanced metrics such as ranges, percentages, or weighted averages demand flexibility.
2. Creating a Custom Aggregation Function
Define a custom function to calculate the range (difference between max and min values) of Sale Amount.
Apply this function using the agg() method to group data by Sales Rep ID.
Combine custom and built-in functions in a single aggregation step.
3. Combining Custom and Built-in Aggregations
Compute total sales, min, max, and range of sales per Sales Rep ID.
Ensure column names are clear and easy to interpret.
4. Applying Custom Aggregations to Multiple Columns
Perform aggregations across multiple columns like Sale Amount and Sale ID.
Example: Calculate total sales, count of sales, and sales range per Car Model.
Use tuples inside agg() to specify which column each function should apply to.
5. Real-World Example: Sales Performance Summary
Aggregate Total Sales, Sales Count, and Sales Range per Car Model.
Implement a custom aggregation function alongside built-in methods for detailed analysis.
Why This Lesson Matters
Custom aggregation functions are essential when working with real-world data that requires complex transformations beyond standard functions. These techniques are widely used in:
🔹 Sales Analytics – Computing sales ranges, discount percentages, and trend calculations.
🔹 Financial Reporting – Customizing profit margins, tax computations, and financial ratios.
🔹 Data Science – Feature engineering for machine learning models.
Key Highlights of the Lecture
✅ Creating and applying custom aggregation functions.
✅ Combining built-in and custom functions in one aggregation step.
✅ Aggregating multiple columns simultaneously.
✅ Real-world example: Analyzing sales data with custom functions.
### *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/ESD4kzxtPtU
✅ Next Video:
✅ 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