Summary  
This chapter covers using group-by operations and aggregation functions (such as sum, mean, and count) in code to summarize data by category.

General domain of usage  
Operations management

Understanding how to summarize and group operational data is essential for making informed decisions in operations management. Grouping and aggregating data allows you to quickly answer questions such as which products are top sellers, which teams are meeting their targets, or how different branches are performing. For example, you might want to know the total sales by product or the average order value per day. These insights help you identify trends, measure performance, and allocate resources more effectively.

import pandas as pd

# Sample sales data
data = {
    "item": ["Widget", "Gadget", "Widget", "Widget", "Gadget"],
    "quantity": [10, 5, 7, 3, 2]
}
df = pd.DataFrame(data)

# Group by item and sum the quantity sold for each
total_quantity_per_item = df.groupby("item")["quantity"].sum()
print(total_quantity_per_item)

Aggregation functions like `sum`, `mean`, and `count` are powerful tools for operations managers. The `sum` function helps you find totals, such as total sales or total hours worked. The `mean` function is used to calculate averages, such as average sales per day or average delivery time. The `count` function helps you determine how many times a certain event occurred, such as the number of completed orders. By applying these functions to grouped data, you can quickly generate key performance indicators and spot operational issues or successes.

import pandas as pd

# Sample order data
orders = {
    "order_id": [101, 102, 103, 104, 105, 106],
    "status": ["Completed", "Pending", "Completed", "Pending", "Completed", "Completed"]
}
orders_df = pd.DataFrame(orders)

# Group by status and count the number of orders in each category
order_counts = orders_df.groupby("status")["order_id"].count()
print(order_counts)

What does the groupby function do in pandas?

Which aggregation function would you use to find the average sales per day?

How can grouping data help operations managers make better decisions?

This course empowers operations managers to leverage Python for streamlining workflows, analyzing operational data, and making data-driven decisions. Through practical examples and real-world challenges, you'll learn how to automate repetitive tasks, visualize key metrics, and optimize processes using Python's powerful libraries.

Learn how to use Python to automate repetitive tasks, streamline data handling, and improve efficiency in daily operations management.

Master the use of Python for analyzing operational data, identifying trends, and supporting data-driven decision-making.

Apply Python to solve classic operations management problems such as process optimization, demand forecasting, and resource allocation.

Summarizing and Grouping Operational Data

1. What does the groupby function do in pandas?

2. Which aggregation function would you use to find the average sales per day?

3. How can grouping data help operations managers make better decisions?