Feature engineering basics in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a new column 'age_squared' by squaring the 'age' column.

Pandas

df['age_squared'] = df['age'] [1] 2

Drag options to blanks, or click blank then click option'

A**

B+

C*

D//

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' instead of '**' will add 2 instead of squaring.

Using '*' will multiply by 2, not square.

We use the ** operator to raise numbers to a power in pandas.

2fill in blank

medium

Complete the code to create a new column 'is_adult' that is True if 'age' is 18 or more, else False.

Pandas

df['is_adult'] = df['age'] [1] 18

Drag options to blanks, or click blank then click option'

A<=

B>=

C==

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' will select ages less than or equal to 18, which is opposite.

Using '==' will only select age exactly 18.

The operator >= checks if age is greater than or equal to 18.

3fill in blank

hard

Fix the error in the code to create a new column 'bmi' as weight divided by height squared.

Pandas

df['bmi'] = df['weight'] / (df['height'] [1] 2)

Drag options to blanks, or click blank then click option'

A+

B*

C**

D//

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' multiplies height by 2, not squares it.

Using '//' does integer division, which is incorrect here.

To square height, we use the power operator **.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Pandas

lengths = {word: [1] for word in words if len(word) [2] 3}

Drag options to blanks, or click blank then click option'

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' will select words shorter than 3, opposite of requirement.

Using 'word' as value stores the word itself, not its length.

We want the length of each word as the value, so use len(word). We only want words longer than 3, so use >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values only for items with positive values.

Pandas

result = [1]: [2] for k, v in data.items() if v [3] 0

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'k' instead of 'k.upper()' keeps keys lowercase.

Using '<' or '<=' filters wrong items.

Keys are converted to uppercase with k.upper(). Values are v. We filter for values greater than zero using >.