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.