Boolean filtering in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Boolean filtering with pandas

What is the output of this code snippet filtering a DataFrame by a Boolean condition?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]})
filtered = df[df['A'] > 2]
print(filtered)

A

   A  B
2  3  7
3  4  8

B

   A  B
0  1  5
1  2  6

C

   A  B
1  2  6
2  3  7

D

Empty DataFrame
Columns: [A, B]
Index: []

💡 Hint

Remember that filtering keeps rows where the condition is True.

The condition df['A'] > 2 is True for rows where A is 3 or 4, so only those rows remain.

❓ data_output

intermediate

Count of rows after Boolean filtering

After applying this Boolean filter, how many rows remain in the DataFrame?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'score': [55, 70, 65, 80, 90]})
filtered = df[df['score'] >= 70]
print(len(filtered))

A 5

B 2

C 4

D 3

💡 Hint

Count how many scores are 70 or above.

Scores 70, 80, and 90 meet the condition, so 3 rows remain.

🔧 Debug

advanced

Identify the error in Boolean filtering code

What error does this code raise when trying to filter a DataFrame?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'X': [10, 20, 30]})
filtered = df[df['X' > 15]]
print(filtered)

A No error, outputs filtered DataFrame

B KeyError: True

C SyntaxError: invalid syntax

D TypeError: '>' not supported between instances of 'str' and 'int'

💡 Hint

Look carefully at the condition inside the brackets.

The expression 'X' > 15 compares a string to an integer, which is invalid and raises a TypeError.

🚀 Application

advanced

Filter DataFrame rows with multiple Boolean conditions

Which option correctly filters rows where column 'age' is over 30 and 'income' is at least 50000?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'age': [25, 35, 40, 28], 'income': [40000, 60000, 50000, 45000]})

A df[(df['age'] > 30) & (df['income'] >= 50000)]

B df[df['age'] > 30 and df['income'] >= 50000]

C df[(df['age'] > 30) | (df['income'] >= 50000)]

D df[df['age'] > 30 or df['income'] >= 50000]

💡 Hint

Use bitwise operators (&, |) for combining conditions in pandas.

In pandas, use & for AND and | for OR with conditions inside parentheses.

🧠 Conceptual

expert

Understanding Boolean indexing with missing values

Given a DataFrame with missing values, what does this Boolean filter return?

Data Analysis Python

import pandas as pd
import numpy as np

df = pd.DataFrame({'val': [1, np.nan, 3, np.nan, 5]})
filtered = df[df['val'] > 2]
print(filtered)

A All rows including NaNs

B Rows where 'val' is greater than 2, including NaNs

C Rows where 'val' is greater than 2, excluding NaNs

D Empty DataFrame because NaNs cause errors in filtering

💡 Hint

NaN compared with any number returns False in Boolean filtering.

NaN values do not satisfy the condition and are excluded from the filtered result.