Boolean indexing in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Boolean indexing in pandas?

Boolean indexing is a way to select rows or columns in a DataFrame using a condition that returns True or False for each element. It filters data based on these True/False values.

beginner

How do you create a Boolean mask to select rows where a column 'age' is greater than 30?

You write: df['age'] > 30. This creates a Series of True/False values for each row, True if age is greater than 30, else False.

beginner

How do you apply a Boolean mask to a DataFrame to get filtered rows?

Use the mask inside square brackets: df[df['age'] > 30]. This returns only rows where the condition is True.

intermediate

Can Boolean indexing be combined with multiple conditions? How?

Yes. Use & for AND, | for OR, and wrap each condition in parentheses. Example: df[(df['age'] > 30) & (df['city'] == 'NY')]

beginner

What happens if you use Boolean indexing with a condition that has no True values?

The result is an empty DataFrame with the same columns but no rows, because no rows meet the condition.

What does df[df['score'] > 50] return?

A Rows where 'score' is greater than 50

B Rows where 'score' is less than 50

C All rows with 'score' equal to 50

D All rows regardless of 'score'

The condition df['score'] > 50 creates a Boolean mask selecting rows where 'score' is greater than 50.

Which operator is used for AND in multiple Boolean conditions in pandas?

A |

B &&

C and

D &

In pandas, & is used for element-wise AND between Boolean Series. Conditions must be wrapped in parentheses.

What type of object does a Boolean condition like df['age'] > 25 return?

A DataFrame

B List

C Boolean Series

D Integer

The condition returns a Series of True/False values, one for each row.

What will df[df['name'] == 'Alice'] return if no rows have name 'Alice'?

A Error

B Empty DataFrame

C DataFrame with all rows

D DataFrame with one row

If no rows match the condition, the result is an empty DataFrame with the same columns.

How do you select rows where 'age' is greater than 20 OR 'city' is 'LA'?

A df[(df['age'] > 20) | (df['city'] == 'LA')]

B df[(df['age'] > 20) & (df['city'] == 'LA')]

C df[df['age'] > 20 or df['city'] == 'LA']

D df[df['age'] > 20 and df['city'] == 'LA']

Use | for OR between conditions, each wrapped in parentheses.

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

Think about how you pick items from a list using True/False values.

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

Remember operator precedence and how Python evaluates expressions.

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