Boolean indexing in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select rows where the 'age' column is greater than 30.

Data Analysis Python

filtered = df[df['age'] [1] 30]

Drag options to blanks, or click blank then click option'

A<

B>

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' will select wrong rows.

Using '==' will select only rows where age equals 30.

We use the '>' operator to select rows where 'age' is greater than 30.

2fill in blank

medium

Complete the code to select rows where the 'score' column is equal to 100.

Data Analysis Python

perfect_scores = df[df['score'] [1] 100]

Drag options to blanks, or click blank then click option'

A!=

B>=

C<

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of '==' causes syntax errors.

Using '!=' selects rows where score is not 100.

The '==' operator checks for equality, so it selects rows where 'score' is exactly 100.

3fill in blank

hard

Fix the error in the code to select rows where 'height' is less than or equal to 170.

Data Analysis Python

short_people = df[df['height'] [1] 170]

Drag options to blanks, or click blank then click option'

A<=

B<

C=>

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=>' is invalid syntax.

Using '<' excludes values equal to 170.

The correct operator for 'less than or equal to' is '<='.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

Data Analysis Python

{word: [1] for word in words if [2]

Drag options to blanks, or click blank then click option'

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' as value instead of length.

Using 'word > 3' which compares string to number.

The dictionary maps each word to its length (len(word)) only if the length is greater than 3 (len(word) > 3).

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their scores only if the score is greater than 50.

Data Analysis Python

{ [1]: [2] for word, score in data.items() if [3] }

Drag options to blanks, or click blank then click option'

Aword.upper()

Bscore

Cscore > 50

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of word.upper().

Not filtering scores correctly.

The dictionary keys are uppercase words (word.upper()), values are scores, and only scores greater than 50 are included.

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