Feature engineering basics in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of creating a new feature by combining columns

What is the output DataFrame after running this code that creates a new feature by combining two columns?

Pandas

import pandas as pd

df = pd.DataFrame({
    'height_cm': [170, 180, 160],
    'weight_kg': [70, 80, 60]
})
df['bmi'] = df['weight_kg'] / ((df['height_cm'] / 100) ** 2)
print(df)

A {'height_cm': [170, 180, 160], 'weight_kg': [70, 80, 60], 'bmi': [24.22, 24.69, 23.44]}

B {'height_cm': [170, 180, 160], 'weight_kg': [70, 80, 60], 'bmi': [24.22, 24.69, 23.44, 25.0]}

C {'height_cm': [170, 180, 160], 'weight_kg': [70, 80, 60]}

D SyntaxError

💡 Hint

Calculate BMI as weight divided by height in meters squared.

The new column 'bmi' is calculated by dividing weight in kg by the square of height in meters. The result is a new column added to the DataFrame.

❓ data_output

intermediate

Number of unique categories after encoding

After applying one-hot encoding to the 'color' column, how many new columns are created?

Pandas

import pandas as pd

df = pd.DataFrame({'color': ['red', 'blue', 'green', 'blue', 'red']})
df_encoded = pd.get_dummies(df, columns=['color'])
print(df_encoded.columns.tolist())

A 2

B 4

C 5

D 3

💡 Hint

Count unique values in the 'color' column.

There are three unique colors: red, blue, and green. One-hot encoding creates one column per unique category.

🔧 Debug

advanced

Identify the error in feature scaling code

What error does this code raise when trying to scale a feature using Min-Max scaling?

Pandas

import pandas as pd

df = pd.DataFrame({'score': [10, 20, 30, 40, 50]})
min_val = df['score'].min()
max_val = df['score'].max()
df['scaled'] = (df['score'] - min_val) / (max_val - min_val)
print(df)

A TypeError

B No error, but scaling is incorrect

C KeyError

D ZeroDivisionError

💡 Hint

Check the formula for Min-Max scaling carefully.

The code divides by max_val instead of (max_val - min_val), so the scaling is incorrect but no error occurs.

❓ visualization

advanced

Interpret the histogram of a newly created feature

Given this code that creates a new feature 'age_group' and plots its histogram, what will the histogram show?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'age': [15, 22, 37, 45, 52, 67, 70]})
df['age_group'] = pd.cut(df['age'], bins=[0, 18, 35, 60, 100], labels=['Child', 'Young Adult', 'Adult', 'Senior'])
df['age_group'].value_counts().plot(kind='bar')
plt.show()

A Bar chart with counts: Child=1, Young Adult=2, Adult=2, Senior=2

B Bar chart with counts: Child=0, Young Adult=2, Adult=3, Senior=2

C Bar chart with counts: Child=1, Young Adult=1, Adult=2, Senior=3

D Bar chart with counts: Child=1, Young Adult=1, Adult=3, Senior=2

💡 Hint

Check which ages fall into each bin range.

The bins split ages into four groups. Counting how many ages fall into each group gives the bar heights.

🚀 Application

expert

Choosing the best feature transformation for skewed data

You have a feature with a highly skewed distribution. Which transformation is most appropriate to reduce skewness before modeling?

A Apply a logarithmic transformation (log(x + 1))

B Apply a Min-Max scaling

C Apply one-hot encoding

D Apply standardization (z-score scaling)

💡 Hint

Think about transformations that reduce skewness, not just scale data.

Log transformation reduces right skewness by compressing large values. Scaling methods do not reduce skewness.

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