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.

Practice

1. What is the main purpose of aggregation-based features in data analysis?

easy

A. To summarize data by groups using functions like sum or mean

B. To visualize data using charts and graphs

C. To clean missing values from the dataset

D. To split data into training and testing sets

Solution

1. Step 1: Understand aggregation-based features

   Aggregation-based features summarize data by groups using functions like sum, mean, or count.

2. Step 2: Compare with other options

   Visualization, cleaning, and splitting data are different tasks not related to aggregation features.

3. Final Answer:

   To summarize data by groups using functions like sum or mean -> Option A

4. Quick Check:

   Aggregation = summary by groups [OK]

Hint: Aggregation means grouping and summarizing data [OK]

Common Mistakes:

• Confusing aggregation with visualization
• Thinking aggregation cleans data
• Mixing aggregation with data splitting

2. Which of the following is the correct syntax to calculate the mean of column 'sales' grouped by 'region' in pandas?

easy

A. df.mean('sales').groupby('region')

B. df['sales'].groupby('region').mean()

C. df.groupby('region')['sales'].mean()

D. df.groupby('sales')['region'].mean()

Solution

1. Step 1: Identify correct groupby syntax

   The correct syntax groups by 'region' then selects 'sales' column and applies mean: df.groupby('region')['sales'].mean()

2. Step 2: Check incorrect options

   df.mean('sales').groupby('region') reverses order, df['sales'].groupby('region').mean() tries to group on a column series, df.groupby('sales')['region'].mean() groups by 'sales' instead of 'region'.

3. Final Answer:

   df.groupby('region')['sales'].mean() -> Option C

4. Quick Check:

   Group by region, then mean sales [OK]

Hint: Use df.groupby('col')['col2'].agg_func() [OK]

Common Mistakes:

• Calling mean before groupby
• Grouping on wrong column
• Applying groupby on a Series instead of DataFrame

3. Given the DataFrame:

import pandas as pd
data = {'team': ['A', 'A', 'B', 'B', 'B'], 'points': [10, 15, 10, 5, 20]}
df = pd.DataFrame(data)
result = df.groupby('team').agg({'points': 'sum'})
print(result)

What is the output?

medium

A. team\nA 25\nB 10

B. team\nA 10\nB 20

C. team\nA 15\nB 10

D. team\nA 25\nB 35

Solution

1. Step 1: Group data by 'team'

   Teams are 'A' and 'B'. 'A' has points 10 and 15, 'B' has 10, 5, and 20.

2. Step 2: Sum points per team

   Sum for 'A' = 10 + 15 = 25; Sum for 'B' = 10 + 5 + 20 = 35.

3. Final Answer:

   team A 25 B 35 -> Option D

4. Quick Check:

   Sum points by team = 25 and 35 [OK]

Hint: Sum points after groupby to get totals per team [OK]

Common Mistakes:

• Adding only first value per group
• Confusing sum with mean
• Misreading group labels

4. What is wrong with this code snippet to get the average 'score' per 'class' in pandas?

df.groupby('class').score.agg('average')

medium

A. The groupby column 'class' is misspelled

B. The aggregation function name 'average' is invalid; should be 'mean'

C. The code should use double brackets: df.groupby(['class'])

D. The code is correct and will run without error

Solution

1. Step 1: Check aggregation function name

   'average' is not a valid pandas aggregation function; the correct function for average is 'mean'.

2. Step 2: Verify other parts

   Grouping by 'class' and accessing 'score' column is correct syntax; double brackets are optional here.

3. Final Answer:

   The aggregation function name 'average' is invalid; should be 'mean' -> Option B

4. Quick Check:

   Use 'mean' not 'average' for average calculation [OK]

Hint: Use 'mean' for average in agg() [OK]

Common Mistakes:

• Using 'average' instead of 'mean'
• Thinking double brackets are mandatory
• Assuming code runs without error

5. You have a sales DataFrame with columns 'store', 'product', and 'revenue'. You want to create a new feature showing the average revenue per store and product combined. Which pandas code correctly creates this aggregation-based feature and adds it as a new column 'avg_rev' to the original DataFrame?

hard

A. df['avg_rev'] = df.groupby(['store', 'product'])['revenue'].transform('mean')

B. df['avg_rev'] = df.groupby('store')['revenue'].mean()

C. df['avg_rev'] = df.groupby('product')['revenue'].sum()

D. df['avg_rev'] = df.groupby(['store', 'product']).agg({'revenue': 'mean'})

Solution

1. Step 1: Understand the goal

   We want average revenue per store and product, and add it as a new column matching original rows.

2. Step 2: Use groupby with transform

   Using groupby on ['store', 'product'] and transform('mean') returns a Series aligned with original DataFrame, perfect for new column.

3. Step 3: Check other options

   df['avg_rev'] = df.groupby('store')['revenue'].mean() groups only by store, not product; df['avg_rev'] = df.groupby('product')['revenue'].sum() sums revenue, not average; df['avg_rev'] = df.groupby(['store', 'product']).agg({'revenue': 'mean'}) returns a DataFrame, not a Series aligned for assignment.

4. Final Answer:

   df['avg_rev'] = df.groupby(['store', 'product'])['revenue'].transform('mean') -> Option A

5. Quick Check:

   Use transform to add group average as new column [OK]

Hint: Use transform for group stats as new column [OK]

Common Mistakes:

• Using agg() which returns DataFrame, not Series
• Grouping by only one column instead of both
• Using sum instead of mean