Boolean indexing 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 array for indexing?

You create a Boolean array by applying a condition to a numpy array, for example: arr > 5 returns True for elements greater than 5 and False otherwise.

beginner

Example: Select all elements greater than 10 from array arr = np.array([5, 12, 7, 15])

Use Boolean indexing: arr[arr > 10] returns array([12, 15]) because only 12 and 15 are greater than 10.

intermediate

Can Boolean indexing be used to modify elements in a numpy array?

Yes, you can use Boolean indexing to select elements and assign new values. For example, arr[arr < 5] = 0 sets all elements less than 5 to zero.

intermediate

What happens if the Boolean array has a different shape than the original array?

The Boolean array must have the same shape as the original array for Boolean indexing to work. Otherwise, numpy will raise a ValueError.

What does arr[arr > 3] do if arr = np.array([1, 4, 3, 6])?

A Returns the original array

B Selects elements greater than 3

C Selects elements equal to 3

D Selects elements less than 3

arr > 3 creates a Boolean array where elements greater than 3 are True. Using this to index arr selects those elements.

Which of these is a valid Boolean indexing expression?

A arr[arr == 5]

B arr[5]

C arr["5"]

D arr[5 > arr]

Both A (arr == 5) and D (5 > arr) return Boolean arrays, which can be used to index arr. B is integer indexing, C is invalid.

What type of array is needed for Boolean indexing?

A Boolean array

B Float array

C Integer array

D String array

Boolean indexing requires a Boolean array where each element is True or False.

If arr = np.array([2, 8, 5]), what does arr[arr < 5] = 0 do?

A Deletes elements less than 5

B Sets elements greater than 5 to zero

C Raises an error

D Sets elements less than 5 to zero

Boolean indexing selects elements less than 5 and assigns zero to them.

What error occurs if Boolean array shape does not match original array?

A IndexError

B TypeError

C ValueError

D KeyError

Numpy raises a ValueError if the Boolean array shape is different from the original array shape.

Explain how Boolean indexing works in numpy with an example.

Think about how True/False values select elements.

Describe how you can use Boolean indexing to modify elements in a numpy array.

Consider changing values where condition is True.

Practice

1. What does boolean indexing in numpy allow you to do?

easy

A. Create a new array filled with zeros

B. Change the data type of an array

C. Sort the array elements in ascending order

D. Select elements from an array using a True/False mask

Solution

1. Step 1: Understand boolean indexing concept

   Boolean indexing uses a True/False array to pick elements from another array.

2. Step 2: Identify the correct operation

   Only Select elements from an array using a True/False mask describes selecting elements based on True/False values.

3. Final Answer:

   Select elements from an array using a True/False mask -> Option D

4. Quick Check:

   Boolean indexing = Select elements [OK]

Hint: Boolean mask picks elements where True appears [OK]

Common Mistakes:

• Confusing boolean indexing with sorting
• Thinking it changes data types
• Assuming it creates new arrays with zeros

2. Which of the following is the correct syntax to select elements greater than 5 from a numpy array arr using boolean indexing?

easy

A. arr[arr < 5]

B. arr > 5[arr]

C. arr[arr > 5]

D. arr.select(arr > 5)

Solution

1. Step 1: Understand boolean indexing syntax

   The correct syntax is to use the array with a boolean mask inside square brackets: arr[condition].

2. Step 2: Check each option

   arr[arr > 5] uses arr[arr > 5], which is correct. Others have wrong order or invalid methods.

3. Final Answer:

   arr[arr > 5] -> Option C

4. Quick Check:

   Correct syntax = arr[condition] [OK]

Hint: Put condition inside brackets after array name [OK]

Common Mistakes:

• Placing condition before array name
• Using invalid methods like select()
• Using wrong comparison operators

3. What is the output of the following code?

import numpy as np
arr = np.array([2, 7, 4, 9, 1])
result = arr[arr % 2 == 1]

medium

A. [7 9 1]

B. [2 4]

C. [7 4 9]

D. [2 7 4 9 1]

Solution

1. Step 1: Understand the condition arr % 2 == 1

   This condition selects elements where the remainder when divided by 2 is 1, i.e., odd numbers.

2. Step 2: Apply condition to array elements

   Elements 7, 9, and 1 are odd, so they are selected.

3. Final Answer:

   [7 9 1] -> Option A

4. Quick Check:

   Odd elements = [7 9 1] [OK]

Hint: Filter with modulo to get odd numbers [OK]

Common Mistakes:

• Selecting even numbers instead
• Confusing modulo operator
• Returning full array instead of filtered

4. The following code has a problem. What is the problem?

import numpy as np
arr = np.array([1, 2, 3, 4])
mask = arr > 2
print(arr[mask == True])

medium

A. mask == True is unnecessary and redundant

B. arr should be converted to list first

C. Comparison operator > is invalid for numpy arrays

D. Square brackets cannot be used for indexing

Solution

1. Step 1: Analyze the boolean mask creation

   mask = arr > 2 creates a boolean array correctly.

2. Step 2: Check the indexing expression

   Using mask == True is redundant since mask is already boolean. It's unnecessary and bad practice.

3. Step 3: Correct usage

   Use arr[mask] directly without comparing mask to True.

4. Final Answer:

   mask == True is unnecessary and redundant -> Option A

5. Quick Check:

   Use boolean mask directly [OK]

Hint: Use mask directly, no need for '== True' [OK]

Common Mistakes:

• Adding '== True' to boolean mask
• Trying to convert numpy array to list for indexing
• Misunderstanding comparison operators

5. Given a numpy array data = np.array([10, 15, 20, 25, 30]), how can you select elements greater than 12 and less than 28 using boolean indexing?

hard

A. data[data > 12 and data < 28]

B. data[(data > 12) & (data < 28)]

C. data[data > 12 | data < 28]

D. data[(data > 12) or (data < 28)]

Solution

1. Step 1: Understand combining conditions with & and parentheses

   In numpy, use & for element-wise AND and wrap each condition in parentheses.

2. Step 2: Check each option

   data[(data > 12) & (data < 28)] correctly uses & (element-wise AND) with parentheses around each condition. Using Python keywords and/or cause errors because they don't work element-wise on arrays. Using | (bitwise OR) selects elements greater than 12 or less than 28, which is wrong here.

3. Final Answer:

   data[(data > 12) & (data < 28)] -> Option B

4. Quick Check:

   Use & with parentheses for AND conditions [OK]

Hint: Use & with parentheses for multiple conditions [OK]

Common Mistakes:

• Using 'and' or 'or' instead of & or |
• Not using parentheses around conditions
• Using | (OR) instead of & (AND) for filtering