Advanced Data Aggregation in Pandas: Mastering Multi-Level Insights with agg()

The Essential Guide to Using agg() for Multi-Level Data Analysis in Pandas

Aggregation is a core technique in data processing, allowing us to compute meaningful insights from large datasets. In this article, we will explore advanced aggregation techniques using the agg() method in Pandas.

In this guide, you will learn:

• How to apply multiple aggregation functions using agg()
• How to rename aggregated columns for better readability
• How to apply aggregations to multiple columns simultaneously
• Practical examples using a Toyota Sales dataset

By the end, you will master advanced aggregation techniques in Pandas, making your data analysis workflows more efficient.

Why This Module is Important

Advanced aggregations help to:

• Summarize large datasets using multiple metrics.
• Optimize reporting and visualization by structuring data meaningfully.
• Perform multiple computations in a single step, improving efficiency.
• Enhance data clarity by renaming aggregated columns.

These techniques allow business analysts, data scientists, and engineers to extract precise insights from their datasets.

Introduction to agg() for Multi-Level Aggregations

The agg() method allows applying multiple aggregation functions to a grouped dataset simultaneously. Unlike single-function aggregations (sum(), mean()), agg() enables custom calculations, renaming results, and multi-column aggregations.

You can download the datasets from the following GitHub link: GitHub Datasets

Loading the Data

import pandas as pd

# Load the Toyota sales dataset
toyota_sales_data = pd.read_csv("data/car_sales/toyota_sales_data.csv")        

This will display the first five rows of the dataset.

Applying Multiple Aggregations Using agg()

1. Calculating Total and Average Sales Per Car Model

We use agg() to compute total and average sales per car model:

aggregations = toyota_sales_data.groupby("car_model")["sale_amount"] \
    .agg(["sum", "mean"])


print(aggregations.head())        

2. Renaming Aggregated Columns for Better Readability

By default, Pandas assigns function names (sum, mean) as column headers, which may be unclear. To rename these columns, we use a dictionary with agg():

renamed_columns = {
    "total_sales": "sum",
    "average_sales": "mean"
}

toyota_sales_data.groupby("car_model")["sale_amount"] \
    .agg(**renamed_columns)        

This makes the table more readable for stakeholders.

3. Applying Different Aggregations to Multiple Columns

We can apply different functions to multiple columns using a dictionary:

toyota_sales_data.groupby("sale_status").agg({
    "sale_amount": "min",
    "commission_pct": "count"
})        

?? Note: The count() function ignores NaN values, counting only non-null records.

4. Performing Aggregations on Multiple Columns

We can perform multiple aggregations on a single column or apply different functions to multiple columns.

Scenario:

Compute total sales, average sales, commission count, and total number of sales per car model.

toyota_sales_data.groupby("car_model").agg(
    total_sales=("sale_amount", "sum"),
    average_sales=("sale_amount", "mean"),
    commission_pct_count=("commission_pct", "count"),
    sales_count=("sale_id", "count")
)        

This example showcases how to aggregate multiple columns in one function call.

Exercise for You

Try this:

1. Group the dataset by sales status.
2. Calculate the total sale amount, minimum sale amount, and count of commission percentage for each status.

Here’s a hint:

sales_status_summary = toyota_sales_data.groupby("sale_status").agg(
    total_sales=("sale_amount", "sum"),
    min_sales=("sale_amount", "min"),
    commission_count=("commission_percentage", "count")
)

print(sales_status_summary)        

Practice Assignment

?? Want to practice? Attempt the Advanced Aggregations Using Pandas Assignment

?? Click Here.

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What’s Next?

In the next lecture, we will explore How to Create Custom Aggregation Functions in Pandas. Aggregation is a powerful tool in data analysis that allows us to summarize and derive insights from large datasets. While Pandas provides built-in aggregation functions, custom aggregation functions give us the flexibility to perform more complex and tailored calculations.

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Conclusion

In this guide, we covered:

• The power of using agg() for multiple aggregations.
• How to rename aggregated columns for readability.
• Performing different aggregations on multiple columns simultaneously.
• Real-world applications for business intelligence and analytics.

Advanced aggregations are key to optimizing data analysis workflows, allowing businesses to gain deeper insights, automate reporting, and improve data-driven decision-making.

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? Authored by Siva Kalyan Geddada , Abhinav Sai Penmetsa

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