Welcome back! In this lecture, we take a deeper dive into advanced aggregations using the agg() method in Pandas. While basic aggregations like sum() and mean() are useful, the agg() method allows us to perform multiple aggregations simultaneously, rename output columns for clarity, and even apply custom aggregation functions to summarize data more effectively.
What You’ll Learn in This Lecture
1. Performing Multiple Aggregations Simultaneously
Use the agg() method to apply multiple aggregation functions on grouped data.
Compute both total and average sales per car model in a single operation.
Understand how agg() improves efficiency compared to separate aggregation calls.
2. Renaming Aggregated Columns for Better Readability
By default, agg() names columns after the aggregation functions (sum, mean, count), which can be unclear.
Learn how to rename columns dynamically using a dictionary to improve interpretability.
See how renaming helps when presenting data to non-technical audiences.
3. Applying Different Aggregations to Multiple Columns
Perform different types of aggregations on multiple columns within the same group.
Example:
Get total and average sales per car model using sum and mean.
Count commission records per sales status using count.
4. Handling Aggregations on Specific Columns
Learn how to apply multiple aggregations to a single column.
Example: On Sale Amount, calculate min, max, and total sales in one step.
See how different aggregation functions help answer diverse business questions.
5. Real-World Example: Sales Performance Summary
Aggregate total sales, average sales, and commission count per car model.
Count sales transactions per category using Sale ID.
Ensure missing values are handled properly to maintain accurate summaries.
Why This Lesson Matters
Aggregations are at the heart of data analysis, enabling us to derive meaningful insights from raw data. These techniques are widely used in:
📊 Business Intelligence – Summarizing key metrics like revenue, sales count, and performance trends.
📈 Operational Reporting – Monitoring regional or product-based performance.
📉 Data Science & Machine Learning – Feature engineering, where aggregated statistics enhance model accuracy.
Key Highlights of the Lecture
✅ Using agg() for simultaneous multi-column aggregations.
✅ Renaming aggregated columns for better clarity.
✅ Applying different aggregation functions to multiple columns.
✅ Real-world application: Summarizing car sales data with total, average, and commission counts.
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