Boolean indexing for filtering in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Boolean indexing in numpy?

Boolean indexing is a way to select elements from a numpy array using a condition that returns True or False for each element. Only elements where the condition is True are selected.

beginner

How do you create a Boolean mask to filter values greater than 5 in a numpy array named arr?

You write mask = arr > 5. This creates an array of True/False values where True means the element is greater than 5.

beginner

What happens when you use a Boolean mask to index a numpy array?

The array returns only the elements where the mask is True, effectively filtering the array based on the condition.

beginner

Example: Given arr = np.array([1, 4, 6, 8]), what does arr[arr > 5] return?

It returns array([6, 8]) because only 6 and 8 are greater than 5.

intermediate

Can Boolean indexing be used with multi-dimensional numpy arrays?

Yes, Boolean indexing works with multi-dimensional arrays. The mask must have the same shape as the array, and it filters elements accordingly.

What does Boolean indexing return when applied to a numpy array?

A Only elements where the condition is False

B Only elements where the condition is True

C All elements regardless of condition

D The original array unchanged

Boolean indexing filters the array and returns only elements where the condition evaluates to True.

How do you create a Boolean mask for elements equal to 10 in a numpy array arr?

A mask = arr < 10

B mask = arr = 10

C mask = arr != 10

D mask = arr == 10

The correct syntax for equality comparison is arr == 10, which creates a Boolean mask.

If arr = np.array([2, 5, 7]), what is the result of arr[arr < 5]?

A array([2])

B array([5, 7])

C array([2, 5])

D array([7])

Only 2 is less than 5, so the filtered array contains just 2.

Can Boolean indexing be combined with multiple conditions in numpy?

A No, only one condition is allowed

B Yes, but only with or operator

C Yes, using & and | operators with parentheses

D No, Boolean indexing does not support conditions

Multiple conditions can be combined using & (and) and | (or) with parentheses to group conditions.

What shape must a Boolean mask have to filter a numpy array?

A The same shape as the array

B Any shape smaller than the array

C Only 1D shape

D Shape does not matter

The Boolean mask must have the same shape as the array to correctly filter elements.

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

Think about how True/False values select elements.

Describe how to filter a numpy array with multiple conditions using Boolean indexing.

Remember to use parentheses around each condition.