Data Grouping and Aggregations in Pandas: Unlock Actionable Insights from Your Data

Grouping and aggregations are essential techniques in data analysis, enabling us to segment and summarize datasets efficiently. With Pandas, you can calculate total sales per representative, average commission per model, and more, making it a powerful tool for data-driven decision-making.

In this guide, we will cover:

• Why grouping and aggregations are important.
• How to use the groupby() method to segment data effectively.
• Performing aggregations such as sum, mean, and count.
• Using agg() to apply multiple metrics simultaneously.
• Real-world examples using a Toyota Sales dataset.

By the end, you’ll be able to use these techniques to analyze large datasets with ease.

Why This Module is Important

Grouping and aggregations allow you to:

• Summarize large datasets into actionable insights.
• Segment data by key attributes such as sales reps, car models, or sales status.
• Generate business reports for sales performance and trend analysis.
• Prepare datasets for visualization in dashboards and reports.
• Optimize data processing workflows for advanced analytics.

Without proper grouping and aggregation, analyzing large datasets becomes complex and inefficient.

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

Understanding Grouping and Aggregations in Pandas

Why Do We Need Grouping?

Raw data consists of thousands of records that need segmentation and summarization for analysis. Grouping enables us to:

• Calculate total sales by sales representative.
• Analyze average commission by car model.
• Compute minimum and maximum sales per region.

How to Perform Grouping and Aggregations

1. Grouping Data by Sales Representative

To analyze total sales per representative, we use groupby() and sum():

import pandas as pd

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

# Group by sales rep ID and sum the sale amount
toyota_sales_data.groupby("sale_rep_id")["sale_amount"].sum()        

Explanation:

• The dataset is grouped by sale_rep_id, summarizing sale_amount using sum().
• This gives the total sales per representative.

2. Getting Unique Sales Rep IDs

Before performing a group-by operation, it's useful to check unique values in the dataset:

unique_sales_reps = toyota_sales_data["sale_rep_id"].unique()

print(unique_sales_reps)        

If the dataset contains 20 sales reps and 5,000 sales transactions, the grouped results will contain 20 records, one per representative.

3. Calculating the Average Sale Amount per Car Model

To analyze average sales per car model, use mean():

toyota_sales_data.groupby("car_model")["sale_amount"].mean()

toyota_sales_data["sale_status"].unique()        

Explanation:

• The dataset is grouped by car_model, and mean() computes the average sale amount per model.

4. Applying Multiple Aggregations with agg()

To calculate both total and average sales per sales rep, use agg():

toyota_sales_data.groupby("sale_rep_id")["sale_amount"]. \
         agg(["sum", "mean"])        

5. Grouping Data by Sale Status

To analyze total and average sales by sale status (Completed, Pending, Canceled):

sales_by_status = toyota_sales_data.groupby("sale_status")["sale_amount"].agg(["sum", "mean"])

print(sales_by_status)        

Explanation:

• The dataset is grouped by sale_status, and we compute total and average sales per category.

Exercise for You

Try this:

1. Group the dataset by car model.
2. Calculate the total, minimum, and maximum sale amount for each car model.

Here’s a hint:

car_model_summary = toyota_sales_data.groupby("car_model")["sale_amount"].agg(["sum", "min", "max"])

print(car_model_summary)        

Practice Assignment

?? Want to practice? Attempt the Grouping and Aggregations in Pandas Assignment

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What’s Next?

Now that we understand the basics of grouping and aggregations, we’ll move on to Advanced Aggregations using Pandas. We'll also explore renaming aggregated columns for better clarity and handling missing data in grouped results. By the end of this module, you will master data summarization techniques for business reporting and analytics.

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Conclusion

In this guide, we covered:

• The importance of grouping and aggregations in data transformation.
• How to use groupby() to segment data efficiently.
• Performing single and multiple aggregations using sum() and mean().
• Real-world applications for sales analytics and reporting.

Grouping and aggregation are fundamental data processing techniques that enable businesses to analyze trends, make data-driven decisions, and automate reporting workflows.

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? Authored by Siva Kalyan Geddada , Abhinav Sai Penmetsa

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