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

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Boolean Indexing on DataFrame

What is the output of this code snippet using Boolean indexing on a DataFrame?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'Name': ['Anna', 'Bob', 'Cara', 'Dave'],
    'Age': [23, 35, 22, 40],
    'Score': [88, 92, 85, 95]
})

result = df[df['Age'] > 30]
print(result)

A

   Name  Age  Score
0  Anna   23     88
2  Cara   22     85

B {'Name': ['Bob', 'Dave'], 'Age': [35, 40], 'Score': [92, 95]}

C

   Name  Age  Score
1   Bob   35     92
3  Dave   40     95

D

Empty DataFrame
Columns: [Name, Age, Score]
Index: []

💡 Hint

Look for rows where Age is greater than 30.

Boolean indexing filters rows where the condition is True. Here, only Bob and Dave have Age > 30.

❓ data_output

intermediate

Count of Rows After Boolean Indexing

How many rows remain after applying this Boolean indexing?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'City': ['NY', 'LA', 'NY', 'SF', 'LA'],
    'Temperature': [75, 85, 70, 65, 90]
})

filtered = df[df['City'] == 'LA']
print(len(filtered))

A 3

B 1

C 0

D 2

💡 Hint

Count how many times 'LA' appears in the City column.

There are exactly two rows where City is 'LA'.

🔧 Debug

advanced

Identify the Error in Boolean Indexing

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

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'Product': ['A', 'B', 'C'],
    'Price': [100, 200, 300]
})

filtered = df[df.Price > 150]
filtered = df[df['Price' > 150]]
print(filtered)

A SyntaxError: invalid syntax

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

C KeyError: True

D No error, prints filtered DataFrame

💡 Hint

Look carefully at the second filtering line syntax.

The second line evaluates 'Price' > 150, which compares a string to an int, raising TypeError: '>' not supported between instances of 'str' and 'int'.

❓ visualization

advanced

Visualize Filtered Data Using Boolean Indexing

Which option shows the correct plot of scores for students older than 20?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Student': ['Tom', 'Lucy', 'John', 'Anna'],
    'Age': [19, 22, 21, 18],
    'Score': [88, 95, 90, 85]
})

filtered = df[df['Age'] > 20]
plt.bar(filtered['Student'], filtered['Score'])
plt.show()

A Bar chart with bars for Lucy and John only

B Bar chart with bars for Tom, Lucy, John, and Anna

C Line chart with scores for Lucy and John

D Empty plot with no bars

💡 Hint

Check which students have Age > 20.

Only Lucy and John have Age greater than 20, so only their scores appear in the bar chart.

🧠 Conceptual

expert

Effect of Boolean Indexing on DataFrame Copy vs View

After applying Boolean indexing on a DataFrame, what is true about the resulting DataFrame?

A It may be a copy or a view depending on the operation; changes might or might not affect the original

B It is always a view; changes to it affect the original DataFrame

C It is a reference to the original DataFrame; changes always affect the original

D It is always a copy; changes to it do not affect the original DataFrame

💡 Hint

Think about how pandas handles filtering and memory.

Boolean indexing sometimes returns a copy and sometimes a view; this depends on pandas internals and can affect whether changes propagate.

Practice

1. What does Boolean indexing do in data analysis?

easy

A. Selects data based on true or false conditions

B. Sorts data in ascending order

C. Converts data types automatically

D. Merges two datasets together

Solution

1. Step 1: Understand the purpose of Boolean indexing

   Boolean indexing uses true/false values to filter data.

2. Step 2: Compare with other options

   Sorting, converting types, and merging are different operations.

3. Final Answer:

   Selects data based on true or false conditions -> Option A

4. Quick Check:

   Boolean indexing = filter by true/false [OK]

Hint: Boolean indexing filters data by true/false values [OK]

Common Mistakes:

• Confusing Boolean indexing with sorting
• Thinking it changes data types
• Mixing it up with merging datasets

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

easy

A. df['age' > 30]

B. df[df.age = 30]

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

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

Solution

1. Step 1: Identify correct Boolean indexing syntax

   Use df[condition], where condition is df['age'] > 30.

2. Step 2: Check other options for errors

   df['age' > 30] has wrong brackets, C uses assignment (=) instead of comparison, D uses wrong comparison sign.

3. Final Answer:

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

4. Quick Check:

   Correct syntax uses df[condition] with comparison [OK]

Hint: Use df[condition] with comparison operators for filtering [OK]

Common Mistakes:

• Using assignment '=' instead of comparison '>'
• Wrong bracket placement
• Using incorrect comparison signs

3. Given the DataFrame df below:

import pandas as pd
data = {'score': [85, 42, 78, 90, 55]}
df = pd.DataFrame(data)

What is the output of df[df['score'] >= 78]?

medium

A. Rows with scores 85, 78, and 90

B. Rows with scores 42 and 55

C. All rows because condition is ignored

D. Empty DataFrame

Solution

1. Step 1: Apply the condition df['score'] >= 78

   This selects rows where score is 78 or more: 85, 78, 90.

2. Step 2: Check which rows match

   Rows with scores 42 and 55 do not match, so they are excluded.

3. Final Answer:

   Rows with scores 85, 78, and 90 -> Option A

4. Quick Check:

   Filter keeps scores >= 78 [OK]

Hint: Filter keeps rows where condition is true [OK]

Common Mistakes:

• Including rows that don't meet the condition
• Thinking condition is ignored
• Expecting empty output incorrectly

4. What is wrong with this code snippet?

import pandas as pd
data = {'value': [10, 20, 30, 40]}
df = pd.DataFrame(data)
filtered = df[df.value > 25]

medium

A. It will raise a syntax error

B. It will raise an AttributeError

C. It returns an empty DataFrame

D. It works correctly and filters rows with value > 25

Solution

1. Step 1: Check attribute access for column 'value'

   Using df.value is valid if column name has no spaces or special chars.

2. Step 2: Verify filtering condition

   df.value > 25 selects rows with values 30 and 40 correctly.

3. Final Answer:

   It works correctly and filters rows with value > 25 -> Option D

4. Quick Check:

   Attribute access works for simple column names [OK]

Hint: Use df.column_name if no spaces or special chars [OK]

Common Mistakes:

• Assuming df.value always causes error
• Expecting syntax error
• Thinking it returns empty DataFrame

5. You have a DataFrame df with a column 'status' containing values 'pass', 'fail', and 'pending'. How do you select only rows where 'status' is either 'pass' or 'pending' using Boolean indexing?

hard

A. df[df['status'] == 'pass' and 'pending']

B. df[(df['status'] == 'pass') | (df['status'] == 'pending')]

C. df[df['status'] in ['pass', 'pending']]

D. df[df['status'] == 'pass' or df['status'] == 'pending']

Solution

1. Step 1: Use element-wise OR for multiple conditions

   Use | between conditions inside parentheses for correct Boolean indexing.

2. Step 2: Avoid Python keywords and wrong syntax

   'and' and 'or' do not work element-wise; 'in' is not vectorized here.

3. Final Answer:

   df[(df['status'] == 'pass') | (df['status'] == 'pending')] -> Option B

4. Quick Check:

   Use | with parentheses for multiple conditions [OK]

Hint: Use | with parentheses for multiple conditions [OK]

Common Mistakes:

• Using 'and' or 'or' instead of | and &
• Trying to use 'in' directly on Series
• Missing parentheses around conditions