query() for fast filtering in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of query() with multiple conditions

What is the output DataFrame after running this code?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [5, 4, 3, 2, 1],
    'C': ['x', 'y', 'x', 'y', 'x']
})

result = df.query('A > 2 and B < 4')
print(result)

A

   A  B  C
2  3  3  x
3  4  2  y

B

   A  B  C
0  1  5  x
1  2  4  y

C Empty DataFrame\nColumns: [A, B, C]\nIndex: []

D

   A  B  C
2  3  3  x
3  4  2  y
4  5  1  x

💡 Hint

Remember that query() filters rows where both conditions are true.

The query filters rows where A > 2 and B < 4. Rows with indices 2 and 3 satisfy both conditions.

❓ data_output

intermediate

Number of rows after query filtering

How many rows remain after applying this query filter?

Pandas

import pandas as pd

df = pd.DataFrame({
    'score': [10, 20, 30, 40, 50],
    'passed': [True, False, True, False, True]
})

filtered = df.query('score >= 30 and passed == True')
print(len(filtered))

A 1

B 2

C 3

D 0

💡 Hint

Check which rows have score >= 30 and passed is True.

Rows with indices 2 and 4 have score >= 30 and passed True, so 2 rows remain.

🔧 Debug

advanced

Identify the error in query() usage

What error does this code raise when run?

Pandas

import pandas as pd

df = pd.DataFrame({'x': [1, 2, 3]})

result = df.query('x => 2')
print(result)

A SyntaxError

B ValueError

C KeyError

D No error, outputs filtered DataFrame

💡 Hint

Check the comparison operator syntax inside the query string.

The operator '=>' is invalid syntax in query expressions; it should be '>='.

🚀 Application

advanced

Using query() with variables

Given variable threshold = 3, which option correctly filters rows where column 'val' is greater than threshold using query()?

Pandas

import pandas as pd

df = pd.DataFrame({'val': [1, 2, 3, 4, 5]})
threshold = 3

A df.query('val > $threshold')

B df.query('val > threshold')

C df.query('val > @threshold')

D df.query('val > {threshold}')

💡 Hint

Use @ to refer to Python variables inside query strings.

In pandas query(), variables from Python scope are referenced with @ prefix.

🧠 Conceptual

expert

Why use query() over boolean indexing?

Which is the main advantage of using pandas query() method instead of boolean indexing for filtering DataFrames?

A query() is always faster than boolean indexing in all cases

B query() supports filtering on columns with spaces without any special syntax

C query() automatically modifies the original DataFrame without creating a copy

D query() allows filtering using a string expression which can be more readable and concise

💡 Hint

Think about code readability and expression style.

query() lets you write filtering conditions as strings, making code cleaner and easier to read compared to complex boolean indexing.