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.

Practice

1. What does boolean filtering do in data analysis with Python?

easy

A. Changes the data type of columns

B. Removes duplicate rows from the data

C. Sorts the data in ascending order

D. Selects rows where a condition is true

Solution

1. Step 1: Understand boolean filtering purpose

   Boolean filtering is used to pick rows that meet a true condition.

2. Step 2: Compare options to definition

   Only Selects rows where a condition is true describes selecting rows based on true conditions.

3. Final Answer:

   Selects rows where a condition is true -> Option D

4. Quick Check:

   Boolean filtering = Select rows true condition [OK]

Hint: Boolean filtering picks rows where condition is true [OK]

Common Mistakes:

• Confusing filtering with sorting
• Thinking it changes data types
• Assuming it removes duplicates

2. Which of the following is the correct syntax to filter rows where column age is greater than 30 in a DataFrame df?

easy

A. df['age' > 30]

B. df[df['age'] > 30]

C. df.age[df.age > 30]

D. df[df['age'] < 30]

Solution

1. Step 1: Identify correct boolean filtering syntax

   The correct way is to put the condition inside square brackets after the DataFrame.

2. Step 2: Check each option

   df[df['age'] > 30] uses df[df['age'] > 30], which is correct syntax. df['age' > 30] is invalid syntax. df.age[df.age > 30] incorrectly filters only the age column, returning a Series not DataFrame. df[df['age'] < 30] filters less than 30, not greater.

3. Final Answer:

   df[df['age'] > 30] -> Option B

4. Quick Check:

   Correct syntax = df[df['age'] > 30] [OK]

Hint: Use df[condition] with column in quotes for filtering [OK]

Common Mistakes:

• Missing brackets around condition
• Using wrong comparison operator
• Not quoting column name

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({
  'name': ['Anna', 'Bob', 'Cara', 'Dan'],
  'score': [85, 92, 78, 90],
  'passed': [True, True, False, True]
})

What will df[df['score'] > 80 and df['passed']] return?

medium

A. ValueError due to missing parentheses

B. Rows where score > 80 or passed is true

C. Rows where score > 80 and passed is true

D. Empty DataFrame

Solution

1. Step 1: Analyze the boolean expression

   The expression uses and without parentheses around each condition, which causes a ValueError in pandas.

2. Step 2: Understand pandas boolean filtering rules

   Each condition must be in parentheses when combined with & or |. So df['score'] > 80 and df['passed'] raises ValueError.

3. Final Answer:

   ValueError due to missing parentheses -> Option A

4. Quick Check:

   Use parentheses around conditions with & or | [OK]

Hint: Always wrap each condition in parentheses when using & or | [OK]

Common Mistakes:

• Not using parentheses around conditions
• Confusing & with and
• Expecting output without fixing syntax

4. Consider this code snippet:

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3, 4]})
filtered = df[df['A'] > 2 or df['A'] < 4]

What is the error and how to fix it?

medium

A. ValueError: Use parentheses around conditions with or

B. SyntaxError: Missing colon after if

C. No error, code runs fine

D. ValueError: Cannot compare integers

Solution

1. Step 1: Identify the error cause

   The expression df['A'] > 2 or df['A'] < 4 lacks parentheses around each condition, causing a ValueError.

2. Step 2: Fix the error by adding parentheses

   Correct syntax is df[(df['A'] > 2) | (df['A'] < 4)] to ensure proper evaluation.

3. Final Answer:

   ValueError: Use parentheses around conditions with or -> Option A

4. Quick Check:

   Parentheses needed around each condition with | or & [OK]

Hint: Wrap each condition in parentheses when using | or & [OK]

Common Mistakes:

• Forgetting parentheses around conditions
• Using Python 'or' instead of |
• Assuming no error with missing parentheses

5. You have a DataFrame df with columns age and income. You want to filter rows where age is between 25 and 40 inclusive, and income is above 50000. Which code correctly applies boolean filtering?

hard

A. df[(df['age'] > 25) | (df['age'] < 40) & (df['income'] > 50000)]

B. df[df['age'] >= 25 & df['age'] <= 40 & df['income'] > 50000]

C. df[(df['age'] >= 25) & (df['age'] <= 40) & (df['income'] > 50000)]

D. df[df['age'] >= 25 and df['age'] <= 40 and df['income'] > 50000]

Solution

1. Step 1: Understand the filtering conditions

   We want age between 25 and 40 inclusive, so age >= 25 and age <= 40, and income > 50000.

2. Step 2: Check correct syntax for combining conditions

   Conditions must be in parentheses and combined with &. df[(df['age'] >= 25) & (df['age'] <= 40) & (df['income'] > 50000)] correctly uses parentheses and & operators.

3. Step 3: Identify errors in other options

   df[df['age'] >= 25 & df['age'] <= 40 & df['income'] > 50000] lacks parentheses around each condition, causing errors. df[(df['age'] > 25) | (df['age'] < 40) & (df['income'] > 50000)] uses | instead of & incorrectly. df[df['age'] >= 25 and df['age'] <= 40 and df['income'] > 50000] uses Python 'and' which does not work with pandas Series.

4. Final Answer:

   df[(df['age'] >= 25) & (df['age'] <= 40) & (df['income'] > 50000)] -> Option C

5. Quick Check:

   Use parentheses and & for multiple conditions [OK]

Hint: Use parentheses and & to combine multiple conditions [OK]

Common Mistakes:

• Using Python 'and' instead of &
• Missing parentheses around conditions
• Mixing & and | incorrectly