Creating interaction features in Data Analysis Python - Visual Walkthrough

Concept Flow - Creating interaction features

Start with dataset

↓

Select two or more features

↓

Multiply or combine features

↓

Create new interaction feature

↓

Add new feature to dataset

↓

Use enhanced dataset for modeling

We start with data, pick features to combine, multiply or combine them to create new features, then add these to the dataset for better analysis.

Execution Sample

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df['A_B'] = df['A'] * df['B']
print(df)

This code creates a new feature by multiplying columns A and B, then adds it to the dataframe.

Execution Table

Step	Action	DataFrame State	New Feature 'A_B'
1	Create DataFrame with columns A and B	{'A': [1, 2], 'B': [3, 4]}	Not created
2	Multiply A and B to create 'A_B'	{'A': [1, 2], 'B': [3, 4], 'A_B': [3, 8]}	3, 8
3	Print DataFrame	{'A': [1, 2], 'B': [3, 4], 'A_B': [3, 8]}	3, 8
4	End of execution	Final DataFrame with interaction feature	3, 8

💡 All steps completed, interaction feature 'A_B' added successfully.

Variable Tracker

Variable	Start	After Step 1	After Step 2	Final
df	None	{'A': [1, 2], 'B': [3, 4]}	{'A': [1, 2], 'B': [3, 4], 'A_B': [3, 8]}	{'A': [1, 2], 'B': [3, 4], 'A_B': [3, 8]}

Key Moments - 2 Insights

Why do we multiply columns A and B to create the new feature?

Multiplying columns A and B captures the combined effect of both features, which might reveal patterns not visible when using them separately. See execution_table step 2 where 'A_B' is created.

Is the original data changed when we add the interaction feature?

No, the original columns A and B remain unchanged. The new feature 'A_B' is added as an extra column, as shown in execution_table step 2.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what is the value of 'A_B' for the second row?

A 8

B 3

C 6

D 4

💡 Hint

Check the 'New Feature A_B' column in execution_table row for step 2.

At which step is the new interaction feature added to the DataFrame?

A Step 3

B Step 1

C Step 2

D Step 4

💡 Hint

Look at the 'Action' column in execution_table to see when 'A_B' is created.

If we changed the operation from multiplication to addition, how would the 'A_B' values change?

A They would be the difference of A and B values

B They would be the sum of A and B values

C They would be the product of A and B values

D They would remain the same

💡 Hint

Think about what addition does compared to multiplication on the columns.

Concept Snapshot

Creating interaction features:
- Combine two or more features (e.g., multiply)
- Add the result as a new column
- Helps capture combined effects
- Use: df['new'] = df['A'] * df['B']
- Enhances model input data

Full Transcript

Creating interaction features means combining existing data columns to make new ones that show combined effects. For example, multiplying two columns creates a new feature that can help models find patterns better. We start with a dataset, pick columns, multiply them, and add the result as a new column. This new column does not replace the old ones but adds more information. The example code creates a new column 'A_B' by multiplying columns 'A' and 'B'. The execution table shows each step, including creating the DataFrame, adding the new feature, and printing the result. Variables like the DataFrame change only when the new feature is added. Common confusions include why multiply and whether original data changes. The quizzes check understanding of values and steps.

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