Multiple conditions with & and | in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select rows where the 'age' column is greater than 30.

Pandas

filtered = df[df['age'] [1] 30]

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

A>

B<

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' will select wrong rows.

Using '==' will select only rows where age is exactly 30.

We use the greater than operator > to select rows where 'age' is more than 30.

2fill in blank

medium

Complete the code to select rows where 'age' is greater than 30 and 'score' is at least 80.

Pandas

filtered = df[(df['age'] > 30) [1] (df['score'] >= 80)]

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

A|

B&

Cor

Dand

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'and' or 'or' directly causes errors in pandas.

Using '|' instead of '&' changes the logic to 'or'.

Use & to combine conditions with 'and' in pandas.

3fill in blank

hard

Fix the error in the code to select rows where 'age' is less than 25 or 'score' is greater than 90.

Pandas

filtered = df[(df['age'] [1] 25) | (df['score'] > 90)]

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

A<

B<=

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' selects ages 25 and below, which is not the requirement.

Using '>' or '==' will select wrong rows.

The condition should check if 'age' is less than 25 using <.

4fill in blank

hard

Fill both blanks to create a dictionary of words and their lengths for words longer than 4 characters.

Pandas

lengths = {word: [1] for word in words if len(word) [2] 4}

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

Alen(word)

B<=

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' includes words of length 4 or less.

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

The dictionary maps each word to its length using len(word). The condition keeps words longer than 4 characters using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their scores for scores above 50.

Pandas

result = { [1]: [2] for word, score in data.items() if score [3] 50 }

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

Aword.upper()

Bscore

C>

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() for keys instead of uppercase.

Using '<' or '<=' in condition selects wrong scores.

Keys are uppercase words using word.upper(). Values are scores. The condition selects scores greater than 50.