Boolean indexing in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Boolean Indexing with Multiple Conditions

What is the output of this code snippet using pandas Boolean indexing?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30, 40, 50],
    'B': [5, 15, 25, 35, 45]
})

result = df[(df['A'] > 20) & (df['B'] < 40)]
print(result)

A

    A   B
2  30  25
4  50  45

B

    A   B
1  20  15
2  30  25
3  40  35

C

    A   B
2  30  25
3  40  35

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

💡 Hint

Remember that both conditions must be true for each row to be included.

The code filters rows where column 'A' is greater than 20 and column 'B' is less than 40. Rows with index 2 and 3 satisfy both conditions.

❓ data_output

intermediate

Number of Rows After Boolean Filtering

Given the DataFrame below, how many rows remain after applying the Boolean filter?

Pandas

import pandas as pd

df = pd.DataFrame({
    'score': [88, 92, 79, 93, 85],
    'passed': [True, True, False, False, False]
})

filtered = df[df['passed'] & (df['score'] > 90)]
print(len(filtered))

A 1

B 2

C 3

D 0

💡 Hint

Count rows where 'passed' is true and 'score' is greater than 90.

Only one row (index 1 with score 92 and passed true) meets both conditions.

🔧 Debug

advanced

Identify the Error in Boolean Indexing Code

What error does this code raise when executed?

Pandas

import pandas as pd

df = pd.DataFrame({'X': [1, 2, 3], 'Y': [4, 5, 6]})

result = df[(df['X'] > 1) & (df['Y'] < 6)]
print(result)

A TypeError: 'and' operator cannot be used with Series

B SyntaxError: invalid syntax

C KeyError: 'X'

D No error, prints filtered DataFrame

💡 Hint

Check how to combine multiple Boolean conditions in pandas.

The 'and' operator cannot be used with pandas Series; use '&' instead for element-wise logical AND.

🚀 Application

advanced

Filter DataFrame Rows Based on String Condition

Which option correctly filters rows where the 'city' column contains the substring 'York'?

Pandas

import pandas as pd

df = pd.DataFrame({
    'city': ['New York', 'Los Angeles', 'Yorkshire', 'Boston'],
    'population': [8000000, 4000000, 500000, 700000]
})

A df[df['city'].str.startswith('York')]

B df[df['city'] == 'York']

C df[df['city'].str.match('York')]

D df[df['city'].str.contains('York')]

💡 Hint

Use a method that checks if the substring appears anywhere in the string.

str.contains('York') returns true for any string containing 'York', including 'New York' and 'Yorkshire'.

🧠 Conceptual

expert

Understanding Boolean Indexing with Missing Values

Given this DataFrame with missing values, what is the output of the Boolean indexing operation?

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, 2, np.nan, 4],
    'B': [np.nan, 2, 3, 4]
})

result = df[df['A'] > 1]
print(result)

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

B

     A    B
1  2.0  2.0
3  4.0  4.0

C

     A    B
1  2.0  2.0
2  NaN  3.0
3  4.0  4.0

D

     A    B
0  1.0  NaN
1  2.0  2.0
3  4.0  4.0

💡 Hint

Remember how comparisons with NaN behave in Boolean indexing.

Comparisons with NaN return false, so rows with NaN in 'A' are excluded. Only rows where 'A' > 1 and not NaN are included.

Practice

1. What does boolean indexing in pandas do?

easy

A. Changes the data type of columns

B. Sorts the DataFrame by column values

C. Selects rows based on True/False conditions

D. Merges two DataFrames together

Solution

1. Step 1: Understand boolean indexing

   Boolean indexing uses True/False values to filter rows in a DataFrame.

2. Step 2: Identify its purpose

   It selects only rows where the condition is True, filtering data easily.

3. Final Answer:

   Selects rows based on True/False conditions -> Option C

4. Quick Check:

   Boolean indexing = row filtering [OK]

Hint: Boolean indexing filters rows with True conditions [OK]

Common Mistakes:

• Confusing boolean indexing with sorting
• Thinking it changes data types
• Mixing it up with DataFrame merging

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: Check correct boolean indexing syntax

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

2. Step 2: Identify syntax errors in other options

   df['age' > 30] misplaces brackets, C uses assignment (=) instead of comparison, D uses invalid operator =>.

3. Final Answer:

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

4. Quick Check:

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

Hint: Use df[condition] with correct comparison operators [OK]

Common Mistakes:

• Using = instead of > for comparison
• Wrong bracket placement
• Using invalid operators like =>

3. Given the DataFrame df below:

   name  age  score
0  Anna   25     88
1  Bob    32     75
2  Cara   29     95
3  Dave   35     60
4  Ella   28     82

What will df[(df['age'] > 28) & (df['score'] > 70)] return?

medium

A. Rows with age > 28 and score > 70: Anna and Ella

B. Rows with age > 28 and score > 70: Bob and Dave

C. Rows with age > 28 and score > 70: Dave only

D. Rows with age > 28 and score > 70: Bob and Cara

Solution

1. Step 1: Identify rows where age > 28

   Rows: Bob (32), Cara (29), Dave (35) satisfy age > 28.

2. Step 2: From those, select rows with score > 70

   Bob (75), Cara (95), Dave (60). Dave fails score > 70, so only Bob and Cara remain.

3. Final Answer:

   Rows with age > 28 and score > 70: Bob and Cara -> Option D

4. Quick Check:

   Filter by both conditions = Bob and Cara [OK]

Hint: Check each condition separately, then combine results [OK]

Common Mistakes:

• Including Dave who has score 60
• Mixing up & and | operators
• Ignoring parentheses around conditions

4. What is wrong with this code snippet for filtering rows where score is above 80?

df[df['score'] > 80 & df['age'] < 30]

medium

A. Missing parentheses around conditions causing operator precedence error

B. Using & instead of | for combining conditions

C. Incorrect column name 'score'

D. DataFrame df is not defined

Solution

1. Step 1: Understand operator precedence in pandas boolean indexing

   Bitwise operators (&, |) require parentheses around each condition to avoid errors.

2. Step 2: Identify missing parentheses

   Code lacks parentheses around df['score'] > 80 and df['age'] < 30, causing wrong evaluation.

3. Final Answer:

   Missing parentheses around conditions causing operator precedence error -> Option A

4. Quick Check:

   Use parentheses with & and | [OK]

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

Common Mistakes:

• Forgetting parentheses around each condition
• Confusing & with logical and (and)
• Assuming no error without parentheses

5. You have a DataFrame df with columns 'city', 'temperature', and 'humidity'. You want to select rows where city is either 'Paris' or 'London' and temperature is above 20. Which code correctly applies boolean indexing?

hard

A. df[((df['city'] == 'Paris') | (df['city'] == 'London')) & (df['temperature'] > 20)]

B. df[df['city'] == 'Paris' | df['city'] == 'London' & (df['temperature'] > 20)]

C. df[df['city'] == ('Paris' or 'London') & df['temperature'] > 20]

D. df[(df['city'] == 'Paris') | (df['city'] == 'London') & (df['temperature'] > 20)]

Solution

1. Step 1: Understand condition grouping with parentheses

   Conditions combined with | must be wrapped in parentheses to group city checks correctly.

2. Step 2: Check each option for correct syntax and logic

   df[((df['city'] == 'Paris') | (df['city'] == 'London')) & (df['temperature'] > 20)] correctly groups city conditions and combines with temperature condition using &.

3. Final Answer:

   df[((df['city'] == 'Paris') | (df['city'] == 'London')) & (df['temperature'] > 20)] -> Option A

4. Quick Check:

   Group OR conditions with parentheses before AND [OK]

Hint: Group OR conditions in parentheses before AND conditions [OK]

Common Mistakes:

• Missing parentheses causing wrong condition order
• Using Python 'or' instead of |
• Trying to compare city to ('Paris' or 'London') directly