Boolean indexing in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Boolean indexing in data analysis?

Boolean indexing is a way to select data from a dataset using True or False values. It helps to filter rows or elements that meet a condition.

beginner

How do you create a Boolean mask in Python with pandas?

You create a Boolean mask by writing a condition on a DataFrame or Series, like df['age'] > 30. This returns True for rows where age is over 30, and False otherwise.

beginner

Example: What does df[df['score'] >= 70] do?

It selects all rows from df where the 'score' column is 70 or higher. This filters the data to only include those rows.

intermediate

Can Boolean indexing be combined with multiple conditions?

Yes! You can combine conditions using & (and), | (or), and ~ (not) with parentheses. For example, df[(df['age'] > 20) & (df['score'] > 50)] selects rows meeting both conditions.

beginner

Why is Boolean indexing useful in data analysis?

It helps quickly find and work with specific parts of data without changing the original dataset. This makes data cleaning and exploration easier and faster.

What does Boolean indexing return when applied to a DataFrame?

A The original DataFrame unchanged

B A list of column names

C A filtered DataFrame with rows where the condition is True

D A single value from the DataFrame

Boolean indexing filters rows based on True/False conditions, returning only rows where the condition is True.

Which symbol is used for the logical AND in Boolean indexing conditions?

A &

B &&

C and

D |

In pandas Boolean indexing, & is used for logical AND between conditions.

How do you select rows where the 'age' column is NOT greater than 30?

A df[df['age'] > 30]

B df[~(df['age'] > 30)]

C df[df['age'] < 30]

D df[df['age'] == 30]

The ~ operator negates the condition, so ~(df['age'] > 30) selects rows where age is NOT greater than 30.

What will df[(df['score'] > 50) | (df['age'] < 20)] select?

A Rows where score > 50 OR age < 20

B Rows where score > 50 AND age < 20

C Rows where score <= 50 AND age >= 20

D Rows where score <= 50 OR age >= 20

The | operator means OR, so it selects rows where either condition is true.

What type of object is returned when you write df['age'] > 30?

A A DataFrame

B A list of ages

C An integer count

D A Series of True/False values

df['age'] > 30 returns a Series of Boolean values indicating which rows meet the condition.

Explain how Boolean indexing works in pandas and give a simple example.

Think about how True/False values help select data.

Describe how to combine multiple conditions in Boolean indexing and why parentheses are important.

Remember operator precedence and grouping.

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