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.