Boolean indexing in Data Analysis Python - Mini Project: Build & Apply

Boolean Indexing

📖 Scenario: You work in a small store and have a list of products with their prices. You want to find which products cost more than $20 to decide what to promote.

🎯 Goal: Use boolean indexing on a pandas DataFrame to select products with prices greater than $20.

📋 What You'll Learn

Create a pandas DataFrame with product names and prices

Create a boolean condition to select products priced above $20

Use boolean indexing to filter the DataFrame

Print the filtered DataFrame

💡 Why This Matters

🌍 Real World

Stores often need to filter products by price to create promotions or discounts.

💼 Career

Data analysts use boolean indexing to quickly select data subsets for reports and decision-making.

1

Create the products DataFrame

Import pandas as pd and create a DataFrame called products with these exact entries: 'Product' column with values 'Pen', 'Notebook', 'Backpack', 'Calculator', and 'Water Bottle', and 'Price' column with values 5, 15, 45, 30, and 10 respectively.

Data Analysis Python

# Import pandas and create the products DataFrame
# Your code here

Hint

Use pd.DataFrame with a dictionary containing two lists for columns.

2

Create a boolean condition for prices above 20

Create a variable called expensive that stores a boolean Series by checking which products['Price'] values are greater than 20.

Data Analysis Python

import pandas as pd
products = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Backpack', 'Calculator', 'Water Bottle'],
    'Price': [5, 15, 45, 30, 10]
})
# Create the boolean condition for prices > 20
# Your code here

Hint

Use the comparison operator > on the Price column.

3

Filter products using boolean indexing

Create a new DataFrame called expensive_products by selecting rows from products where expensive is True using boolean indexing.

Data Analysis Python

import pandas as pd
products = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Backpack', 'Calculator', 'Water Bottle'],
    'Price': [5, 15, 45, 30, 10]
})
expensive = products['Price'] > 20
# Use boolean indexing to create expensive_products
# Your code here

Hint

Use products[expensive] to filter rows.

4

Print the filtered DataFrame

Print the expensive_products DataFrame to see the products with prices greater than $20.

Data Analysis Python

import pandas as pd
products = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Backpack', 'Calculator', 'Water Bottle'],
    'Price': [5, 15, 45, 30, 10]
})
expensive = products['Price'] > 20
expensive_products = products[expensive]
# Print the expensive_products DataFrame
# Your code here

Hint

Use print(expensive_products) to display the filtered DataFrame.

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