Boolean indexing 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[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 rows with age less than 30.

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

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

2fill in blank

medium

Complete the code to select rows where the 'score' column is equal to 100.

Pandas

perfect_scores = df[df['score'] [1] 100]

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

A==

B>=

C<

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of '==' causes syntax errors.

Using '>=' selects scores greater than or equal to 100, not just 100.

The equality operator == checks if 'score' is exactly 100.

3fill in blank

hard

Fix the error in the code to select rows where 'height' is less than 170.

Pandas

short_people = df[df['height'] [1] 170]

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

A>

B=

C==

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of '<' causes syntax errors.

Using '==' selects rows where height equals 170, not less than 170.

The less than operator < selects rows where 'height' is below 170.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

Pandas

lengths = {word: [1] for word in words if [2]

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Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the word in the condition instead of its length.

The dictionary maps each word to its length using len(word). The condition len(word) > 3 filters words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their scores only if the score is greater than 50.

Pandas

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

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Aword.upper()

Bscore

Cscore > 50

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase words as keys instead of uppercase.

Not filtering scores greater than 50.

The dictionary keys are uppercase words using word.upper(). The values are the scores. The condition score > 50 filters scores above 50.