Aggregation-based features in Data Analysis Python - Mini Project: Build & Apply

Aggregation-based features

📖 Scenario: You work in a small online store. You have a list of orders with customer names and order amounts. You want to learn how to create new features by grouping data and calculating summary numbers like total spending per customer.

🎯 Goal: Build a small program that groups orders by customer and calculates the total amount each customer spent.

📋 What You'll Learn

Create a list of orders with customer names and amounts

Create a variable to hold the minimum amount to consider

Use a dictionary comprehension to sum amounts per customer only if the total is above the minimum

Print the resulting dictionary of customers and their total spending

💡 Why This Matters

🌍 Real World

Stores and businesses often need to summarize customer purchases to understand buying habits and target marketing.

💼 Career

Data analysts and data scientists frequently create aggregation features to prepare data for machine learning or reporting.

1

Create the orders list

Create a list called orders with these exact tuples: ("Alice", 50), ("Bob", 20), ("Alice", 30), ("Bob", 40), ("Charlie", 70).

Data Analysis Python

# Create the list called orders with the given tuples
# Your code here

Hint

Use square brackets to create a list. Each order is a tuple with a name and an amount.

2

Set the minimum total amount

Create a variable called min_total and set it to 60.

Data Analysis Python

orders = [("Alice", 50), ("Bob", 20), ("Alice", 30), ("Bob", 40), ("Charlie", 70)]
# Create min_total and set it to 60
# Your code here

Hint

Just assign the number 60 to the variable min_total.

3

Calculate total spending per customer above minimum

Use a dictionary comprehension to create a dictionary called total_per_customer. It should sum all amounts per customer from orders and include only customers whose total is greater than or equal to min_total. Use customer and amount as variable names in your comprehension.

Data Analysis Python

orders = [("Alice", 50), ("Bob", 20), ("Alice", 30), ("Bob", 40), ("Charlie", 70)]
min_total = 60
# Create total_per_customer using a dictionary comprehension
# Your code here

Hint

First sum amounts per customer using a loop and a dictionary. Then use a dictionary comprehension to keep only those with total >= min_total.

4

Print the total spending per customer

Write a print statement to display the total_per_customer dictionary.

Data Analysis Python

orders = [("Alice", 50), ("Bob", 20), ("Alice", 30), ("Bob", 40), ("Charlie", 70)]
min_total = 60

# First, sum amounts per customer
sums = {}
for customer, amount in orders:
    sums[customer] = sums.get(customer, 0) + amount

# Then, filter customers with total >= min_total

total_per_customer = {customer: total for customer, total in sums.items() if total >= min_total}

# Print the total_per_customer dictionary
# Your code here

Hint

Use print(total_per_customer) to show the result.