Summary  
This chapter explains how to group tabular data by a categorical field and apply aggregation functions (such as count and mean) to generate summary statistics per group.  

General domain of usage  
Business analytics

Customer segmentation is the process of dividing a customer base into distinct groups that share similar characteristics or behaviors. In business analytics, segmentation enables you to tailor marketing strategies, personalize communications, and optimize resource allocation. By understanding the unique needs and value of each segment—such as `New`, `Returning`, or `VIP` customers—you can deliver more targeted services and drive better business outcomes. Automating this process ensures that your reports stay up to date and actionable as your customer data evolves.

import pandas as pd

# Sample customer data
data = {
    "customer_id": [1, 2, 3, 4, 5, 6],
    "segment": ["New", "Returning", "VIP", "New", "VIP", "Returning"],
    "spend": [50, 120, 350, 45, 400, 110]
}

df = pd.DataFrame(data)

# Calculate average spend per segment
avg_spend = df.groupby("segment")["spend"].mean()

print("Average spend per customer segment:")
print(avg_spend)

## Grouping and Aggregating Data for Segment-Based Reporting

Grouping and aggregating data are essential techniques for segment-based reporting. By grouping data according to customer segment, you can compute summary statistics—such as counts, averages, or totals—for each group. This approach allows you to quickly compare the performance, value, or behavior of different segments, making it easier to identify trends and opportunities. Aggregation functions, such as **mean**, **count**, or **sum**, are commonly used to produce concise and informative reports that support business decisions.

import pandas as pd

# Sample customer data
data = {
    "customer_id": [1, 2, 3, 4, 5, 6],
    "segment": ["New", "Returning", "VIP", "New", "VIP", "Returning"],
    "spend": [50, 120, 350, 45, 400, 110]
}

df = pd.DataFrame(data)

# Generate a summary report: count and average spend per segment
summary = df.groupby("segment").agg(
    customer_count=("customer_id", "count"),
    average_spend=("spend", "mean")
)

print("Customer segment summary report:")
print(summary)

You can extend segmentation automation by incorporating more complex criteria, such as purchase frequency, geographic region, or customer lifetime value. Combining multiple attributes allows you to create multi-dimensional segments that reflect nuanced customer behaviors. Automating these advanced segmentations helps you keep pace with changing business needs and ensures that your reports remain relevant as your data grows in complexity.

What pandas method allows you to group data by a categorical column for summary statistics?

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Automating Customer Segmentation Summaries

Grouping and Aggregating Data for Segment-Based Reporting