Creating interaction features in Data Analysis Python - Try It Yourself

Creating Interaction Features

📖 Scenario: You work as a data analyst for an online store. You have a small dataset with information about products: their price and quantity sold. You want to create a new feature that shows the total sales value for each product by multiplying price and quantity.

🎯 Goal: Build a small program that creates a dictionary with product prices and quantities, then creates a new dictionary with the total sales value for each product by multiplying price and quantity.

📋 What You'll Learn

Create a dictionary called prices with product names as keys and their prices as values.

Create a dictionary called quantities with product names as keys and quantities sold as values.

Create a new dictionary called total_sales where each product's value is the product of its price and quantity.

Print the total_sales dictionary.

💡 Why This Matters

🌍 Real World

Creating interaction features like total sales helps businesses understand product performance and make better decisions.

💼 Career

Data analysts and data scientists often create new features by combining existing data to improve models and insights.

1

Create the product price dictionary

Create a dictionary called prices with these exact entries: 'apple': 2, 'banana': 1, 'cherry': 3.

Data Analysis Python

# Create the prices dictionary
# Your code here

Hint

Use curly braces {} to create a dictionary with keys and values separated by colons.

2

Create the product quantity dictionary

Create a dictionary called quantities with these exact entries: 'apple': 5, 'banana': 7, 'cherry': 3.

Data Analysis Python

prices = {'apple': 2, 'banana': 1, 'cherry': 3}
# Create the quantities dictionary
# Your code here

Hint

Use the same dictionary syntax as before but with the quantities values.

3

Create the total sales dictionary using interaction features

Create a new dictionary called total_sales using a dictionary comprehension. For each product in prices, multiply its price by its quantity from quantities.

Data Analysis Python

prices = {'apple': 2, 'banana': 1, 'cherry': 3}
quantities = {'apple': 5, 'banana': 7, 'cherry': 3}
# Create total_sales dictionary by multiplying price and quantity
# Your code here

Hint

Use a dictionary comprehension with {key: value for key in iterable} syntax.

4

Print the total sales dictionary

Print the total_sales dictionary to see the total sales value for each product.

Data Analysis Python

prices = {'apple': 2, 'banana': 1, 'cherry': 3}
quantities = {'apple': 5, 'banana': 7, 'cherry': 3}
total_sales = {product: prices[product] * quantities[product] for product in prices}
# Print the total_sales dictionary
# Your code here

Hint

Use print(total_sales) to display the dictionary.

Practice

1. What is the main purpose of creating interaction features in data analysis?

easy

A. To capture the combined effect of two or more features on the target

B. To remove irrelevant features from the dataset

C. To reduce the size of the dataset by merging columns

D. To convert categorical data into numerical data

Solution

1. Step 1: Understand interaction features

   Interaction features combine two or more features to capture their joint effect on the target variable.

2. Step 2: Compare options

   Only To capture the combined effect of two or more features on the target describes capturing combined effects, which is the purpose of interaction features.

3. Final Answer:

   To capture the combined effect of two or more features on the target -> Option A

4. Quick Check:

   Interaction features capture combined effects = A [OK]

Hint: Interaction features combine columns to show joint effects [OK]

Common Mistakes:

• Confusing interaction features with feature selection
• Thinking interaction features reduce dataset size
• Mixing interaction features with encoding categorical data

2. Which of the following Python code snippets correctly creates an interaction feature df['A_B'] by multiplying columns 'A' and 'B' in a DataFrame df?

easy

A. df['A_B'] = df['A'] * df['B']

B. df['A_B'] = df['A'].concat(df['B'])

C. df['A_B'] = df['A'].append(df['B'])

D. df['A_B'] = df['A'] + df['B']

Solution

1. Step 1: Identify correct operation for interaction

   Interaction features are often created by multiplying columns to capture joint effects.

2. Step 2: Check code syntax

   df['A_B'] = df['A'] * df['B'] uses multiplication (*) between columns, which is correct syntax in pandas.

3. Final Answer:

   df['A_B'] = df['A'] * df['B'] -> Option A

4. Quick Check:

   Multiplying columns creates interaction feature = D [OK]

Hint: Multiply columns with * to create interaction features [OK]

Common Mistakes:

• Using + instead of * for interaction
• Using append or concat which are for combining rows or DataFrames
• Syntax errors from wrong method calls

3. Given the DataFrame df below:

   A  B
0  2  3
1  4  5
2  6  7

What will be the output of df['A_B'] = df['A'] * df['B']; df['A_B'].tolist()?

medium

A. [2, 4, 6]

B. [5, 9, 13]

C. [6, 20, 42]

D. [3, 5, 7]

Solution

1. Step 1: Multiply each row's values in columns A and B

   Row 0: 2*3=6, Row 1: 4*5=20, Row 2: 6*7=42

2. Step 2: Convert the new column to list

   The list is [6, 20, 42]

3. Final Answer:

   [6, 20, 42] -> Option C

4. Quick Check:

   Row-wise multiplication = [6, 20, 42] [OK]

Hint: Multiply row values, then list() to see results [OK]

Common Mistakes:

• Adding instead of multiplying values
• Confusing column names or indices
• Returning original columns instead of interaction

4. Consider this code snippet to create an interaction feature:

df['A_B'] = df['A'] * df['B']
df['A_B'] = df['A_B'].apply(lambda x: x if x > 10 else 0)

What is the error in this code if df['A'] and df['B'] contain numeric values?

medium

A. The apply function should be used on rows, not on a column

B. No error; the code correctly creates and modifies the interaction feature

C. The lambda function syntax is incorrect

D. Multiplying columns directly causes a runtime error

Solution

1. Step 1: Check multiplication of columns

   Multiplying two numeric columns in pandas works correctly and creates a new column.

2. Step 2: Check apply with lambda on the new column

   Applying a lambda function to modify values conditionally is valid syntax and works on a Series.

3. Final Answer:

   No error; the code correctly creates and modifies the interaction feature -> Option B

4. Quick Check:

   Column multiplication and apply lambda are valid = A [OK]

Hint: apply with lambda on Series is valid for conditional changes [OK]

Common Mistakes:

• Thinking apply only works on DataFrame rows
• Assuming multiplication causes errors with numeric columns
• Misreading lambda function syntax

5. You have a dataset with columns age and income. You want to create an interaction feature to capture how age modifies the effect of income on spending. Which code snippet correctly creates this interaction feature and then normalizes it by dividing by the maximum value?

hard

A. df['age_income'] = (df['age'] * df['income']) / df['age'].max()

B. df['age_income'] = df['age'] * df['income'] / df['income'].min()

C. df['age_income'] = (df['age'] + df['income']) / df['income'].max()

D. df['age_income'] = (df['age'] * df['income']) / (df['age'] * df['income']).max()

Solution

1. Step 1: Create interaction by multiplying age and income

   Multiplying captures how age modifies income effect.

2. Step 2: Normalize by dividing by max of the interaction feature

   Dividing by (df['age'] * df['income']).max() scales the new feature between 0 and 1.

3. Final Answer:

   df['age_income'] = (df['age'] * df['income']) / (df['age'] * df['income']).max() -> Option D

4. Quick Check:

   Multiply then normalize by max interaction = B [OK]

Hint: Multiply columns then divide by max of product to normalize [OK]

Common Mistakes:

• Normalizing by max of only one column instead of product
• Adding columns instead of multiplying for interaction
• Using min instead of max for normalization