query() for fast filtering in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to filter rows where the 'age' column is greater than 30 using query().

Pandas

filtered = df.query('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 equals 30 only.

The query() method uses a string expression to filter rows. To select rows where 'age' is greater than 30, use 'age > 30'.

2fill in blank

medium

Complete the code to filter rows where the 'score' column is equal to 100 using query().

Pandas

perfect_scores = df.query('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 rows where score is not 100.

To filter rows where 'score' equals 100, use the equality operator '=='.

3fill in blank

hard

Fix the error in the query to select rows where 'height' is less than or equal to 170.

Pandas

short_people = df.query('height [1] 170')

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

A==

B<

C=>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=>' causes syntax errors.

Using '<' excludes rows where height equals 170.

The correct operator for 'less than or equal to' is '<='. The option '=>' is invalid syntax.

4fill in blank

hard

Fill both blanks to filter rows where 'age' is greater than 25 and 'score' is less than 80 using query().

Pandas

filtered = df.query('age [1] 25 and score [2] 80')

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

A>

B<

C>=

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the operators changes the filter logic.

Using '>=' or '<=' includes boundary values which may not be intended.

To filter rows where 'age' is greater than 25 and 'score' is less than 80, use 'age > 25 and score < 80'.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each 'name' to their 'score' only if 'score' is greater than 50 using query() filtering.

Pandas

result = {row['[1]']: row['[2]'] for _, row in df.query('[3]').iterrows()}

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

Aname

Bscore

Cscore > 50

Dage > 50

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'age > 50' filters wrong rows.

Swapping keys and values reverses the dictionary mapping.

The dictionary comprehension uses 'name' as keys and 'score' as values. The query filters rows where 'score' is greater than 50.