Feature engineering basics in Pandas - Mini Project: Build & Apply

Feature engineering basics

📖 Scenario: You work as a data analyst for a retail company. You have sales data with product prices and quantities sold. You want to create new features to better understand the total sales value for each product.

🎯 Goal: Create a new feature called total_sales by multiplying price and quantity for each product in the sales data.

📋 What You'll Learn

Use pandas to create and manipulate data.

Create a DataFrame with product sales data.

Add a new column total_sales by multiplying price and quantity.

Print the updated DataFrame to see the new feature.

💡 Why This Matters

🌍 Real World

Feature engineering helps improve data quality and model performance by creating meaningful new columns from existing data.

💼 Career

Data scientists and analysts often create new features to help machine learning models understand data better and make better predictions.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with these exact columns and values: product with ['A', 'B', 'C'], price with [10, 20, 15], and quantity with [5, 3, 8].

Pandas

import pandas as pd
# Create the sales_data DataFrame with product, price, and quantity columns
# Your code here

Hint

Use pd.DataFrame with a dictionary containing the columns and their values.

2

Set up a variable for the new feature name

Create a variable called new_feature and set it to the string 'total_sales'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'product': ['A', 'B', 'C'],
    'price': [10, 20, 15],
    'quantity': [5, 3, 8]
})
# Create a variable new_feature and set it to 'total_sales'
# Your code here

Hint

Just assign the string 'total_sales' to the variable new_feature.

3

Create the new feature by multiplying price and quantity

Add a new column to sales_data with the name stored in new_feature. Set this column to the product of the price and quantity columns.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'product': ['A', 'B', 'C'],
    'price': [10, 20, 15],
    'quantity': [5, 3, 8]
})
new_feature = 'total_sales'
# Create the new_feature column by multiplying price and quantity
# Your code here

Hint

Use sales_data[new_feature] to create the new column and multiply the two existing columns.

4

Print the updated DataFrame

Print the sales_data DataFrame to display the new total_sales feature.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'product': ['A', 'B', 'C'],
    'price': [10, 20, 15],
    'quantity': [5, 3, 8]
})
new_feature = 'total_sales'
sales_data[new_feature] = sales_data['price'] * sales_data['quantity']
# Print the sales_data DataFrame
# Your code here

Hint

Use print(sales_data) to show the DataFrame with the new column.

Practice

1. What is the main purpose of feature engineering in pandas?

easy

A. To create new useful columns from existing data

B. To delete unnecessary rows from the dataset

C. To change the data type of columns

D. To visualize data using charts

Solution

1. Step 1: Understand feature engineering definition

   Feature engineering means creating new columns that help models learn better.

2. Step 2: Compare options with definition

   Only To create new useful columns from existing data matches the purpose of creating new useful columns from existing data.

3. Final Answer:

   To create new useful columns from existing data -> Option A

4. Quick Check:

   Feature engineering = create new columns [OK]

Hint: Feature engineering = making new columns from old data [OK]

Common Mistakes:

• Confusing feature engineering with data cleaning
• Thinking it only changes data types
• Mixing it up with data visualization

2. Which of the following is the correct way to create a new column total as sum of columns 2a and 2b in a pandas DataFrame df?

easy

A. df['total'] <- df['2a'] + df['2b']

B. df.total = df.2a + df.2b

C. df['total'] == df['2a'] + df['2b']

D. df['total'] = df['2a'] + df['2b']

Solution

1. Step 1: Recall pandas syntax for new column

   Use df['new_col'] = expression to create new columns.

2. Step 2: Check each option

   df['total'] = df['2a'] + df['2b'] uses correct syntax. df.total = df.2a + df.2b misses brackets, A uses wrong operator, D uses comparison operator.

3. Final Answer:

   df['total'] = df['2a'] + df['2b'] -> Option D

4. Quick Check:

   New column = df['col'] = ... [OK]

Hint: Use df['col'] = ... to add new columns [OK]

Common Mistakes:

• Using == instead of = for assignment
• Using <- which is not Python syntax
• Accessing columns without brackets

3. Given the DataFrame df below, what will be the output of df['age_group'] after this code?

import pandas as pd
df = pd.DataFrame({'age': [15, 22, 37, 45]})
df['age_group'] = pd.cut(df['age'], bins=[0, 18, 35, 60], labels=['child', 'young_adult', 'adult'])

medium

A. [child, young_adult, young_adult, adult]

B. [child, child, young_adult, adult]

C. [child, young_adult, adult, adult]

D. [child, young_adult, adult, young_adult]

Solution

1. Step 1: Understand pd.cut bins and labels

   Bins: 0-18 = child, 18-35 = young_adult, 35-60 = adult.

2. Step 2: Assign each age to bin

   15 -> child, 22 -> young_adult, 37 -> adult, 45 -> adult.

3. Final Answer:

   [child, young_adult, adult, adult] -> Option C

4. Quick Check:

   Age 37 and 45 fall in adult bin [OK]

Hint: Match values to bins to find labels [OK]

Common Mistakes:

• Misunderstanding bin edges (inclusive/exclusive)
• Assigning wrong labels to ages
• Confusing order of labels

4. What is wrong with this code to create a new column year from a datetime column date in DataFrame df?

df['year'] = df['date'].year

medium

A. There is no error; the code is correct

B. You must use dt.year to access year from datetime column

C. You should use df.year = df.date.year instead

D. The column date must be converted to string first

Solution

1. Step 1: Recall how to extract year from datetime column

   Use df['date'].dt.year to get year from pandas datetime column.

2. Step 2: Identify error in code

   Code uses df['date'].year which is invalid; .year is not attribute of Series.

3. Final Answer:

   You must use dt.year to access year from datetime column -> Option B

4. Quick Check:

   Use df['date'].dt.year for datetime year [OK]

Hint: Use .dt accessor for datetime column parts [OK]

Common Mistakes:

• Using .year directly on Series instead of .dt.year
• Converting datetime to string unnecessarily
• Assigning to df.year instead of df['year']

5. You have a DataFrame df with columns height_cm and weight_kg. You want to create a new feature bmi using the formula bmi = weight_kg / (height_m)^2, where height in meters is height_cm divided by 100. Which code correctly creates the bmi column?

hard

A. df['bmi'] = df['weight_kg'] / (df['height_cm'] / 100) ** 2

B. df['bmi'] = df['weight_kg'] / df['height_cm'] ** 2 / 10000

C. df['bmi'] = df['weight_kg'] * (df['height_cm'] / 100) ** 2

D. df['bmi'] = df['weight_kg'] / (df['height_cm'] * 100) ** 2

Solution

1. Step 1: Convert height from cm to meters

   Height in meters = height_cm / 100.

2. Step 2: Apply BMI formula correctly

   BMI = weight_kg / (height_m)^2 = weight_kg / (height_cm / 100)^2.

3. Step 3: Check each option

   df['bmi'] = df['weight_kg'] / (df['height_cm'] / 100) ** 2 matches formula exactly. Others have wrong operations or units.

4. Final Answer:

   df['bmi'] = df['weight_kg'] / (df['height_cm'] / 100) ** 2 -> Option A

5. Quick Check:

   BMI formula with height in meters = weight / height² [OK]

Hint: Divide height_cm by 100 before squaring for BMI [OK]

Common Mistakes:

• Forgetting to convert cm to meters
• Multiplying instead of dividing
• Incorrect order of operations