Scalar and array broadcasting in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add a scalar value 5 to each element of the array.

NumPy

import numpy as np
arr = np.array([1, 2, 3])
result = arr [1] 5
print(result)

Drag options to blanks, or click blank then click option'

A+

B/

C-

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication instead of addition.

Using division or subtraction which changes values differently.

The plus sign + adds the scalar 5 to each element of the array using broadcasting.

2fill in blank

medium

Complete the code to multiply each element of the array by 10 using broadcasting.

NumPy

import numpy as np
arr = np.array([4, 5, 6])
result = arr [1] 10
print(result)

Drag options to blanks, or click blank then click option'

A//

B+

C-

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication.

Using floor division which truncates the result.

The multiplication operator * multiplies each element by 10 using broadcasting.

3fill in blank

hard

Fix the error in the code to subtract 3 from each element of the array using broadcasting.

NumPy

import numpy as np
arr = np.array([7, 8, 9])
result = arr [1] 3
print(result)

Drag options to blanks, or click blank then click option'

A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of subtraction.

Using multiplication or division which changes values differently.

The subtraction operator - subtracts 3 from each element using broadcasting.

4fill in blank

hard

Fill both blanks to create a new array where each element is the original element divided by 2 and then added to 4.

NumPy

import numpy as np
arr = np.array([2, 4, 6])
result = (arr [1] 2) [2] 4
print(result)

Drag options to blanks, or click blank then click option'

A/

B+

C*

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up addition and subtraction operators.

Forgetting parentheses causing wrong order of operations.

First, divide each element by 2 using /, then add 4 using + with broadcasting.

5fill in blank

hard

Fill all three blanks to create a dictionary where keys are the original array elements squared, values are the elements multiplied by 3, but only include elements greater than 2.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4])
result = { [1]: [2] for x in arr if x [3] 2 }
print(result)

Drag options to blanks, or click blank then click option'

Ax**2

Bx*3

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using less than instead of greater than in the condition.

Mixing up keys and values in the dictionary comprehension.

The dictionary comprehension uses x**2 as keys, x*3 as values, and includes only elements where x > 2.

Practice

1. What happens when you add a scalar to a NumPy array using broadcasting?

easy

A. The scalar is added to each element of the array.

B. The scalar is ignored and the array remains unchanged.

C. An error occurs because shapes do not match.

D. The scalar replaces all elements in the array.

Solution

1. Step 1: Understand scalar and array addition

   A scalar is treated as an array with no dimensions and can be added to each element of the array.

2. Step 2: Apply broadcasting rules

   Broadcasting repeats the scalar value to match the shape of the array, adding it element-wise.

3. Final Answer:

   The scalar is added to each element of the array. -> Option A

4. Quick Check:

   Scalar + array = element-wise addition [OK]

Hint: Scalar acts like a single value added to all elements [OK]

Common Mistakes:

• Thinking scalar is ignored
• Expecting shape mismatch error
• Assuming scalar replaces array

2. Which of the following is the correct syntax to add scalar 5 to a NumPy array arr?

easy

A. arr.add(5)

B. arr + 5

C. add(arr, 5)

D. arr + [5]

Solution

1. Step 1: Recall NumPy scalar addition syntax

   Adding a scalar to an array is done simply by using the plus operator: arr + scalar.

2. Step 2: Evaluate options

   arr + 5 uses arr + 5, which is correct. arr.add(5) is invalid syntax. add(arr, 5) is not a NumPy function. arr + [5] tries to add a list, which changes behavior.

3. Final Answer:

   arr + 5 -> Option B

4. Quick Check:

   Use arr + scalar for scalar addition [OK]

Hint: Use plus operator directly for scalar addition [OK]

Common Mistakes:

• Using method .add() which doesn't exist
• Passing scalar inside a list
• Using non-existent add() function

3. What is the output of the following code?

import numpy as np
arr = np.array([1, 2, 3])
result = arr * 3
print(result)

medium

A. [3 3 3]

B. [1 2 3 3 6 9]

C. Error due to shape mismatch

D. [3 6 9]

Solution

1. Step 1: Understand scalar multiplication with array

   Multiplying an array by a scalar multiplies each element by that scalar.

2. Step 2: Calculate element-wise multiplication

   Each element in [1, 2, 3] is multiplied by 3, resulting in [3, 6, 9].

3. Final Answer:

   [3 6 9] -> Option D

4. Quick Check:

   Array * scalar = element-wise multiplication [OK]

Hint: Multiply each element by scalar directly [OK]

Common Mistakes:

• Expecting concatenation instead of multiplication
• Thinking output is repeated elements
• Assuming error due to shape mismatch

4. Identify the error in this code snippet:

import numpy as np
arr = np.array([1, 2, 3])
scalar = np.array([5])
result = arr + scalar
print(result)

medium

A. Output is [1 2 3 5]

B. Error because scalar is an array, not a number

C. No error; output is [6 7 8]

D. Error due to shape mismatch

Solution

1. Step 1: Check shapes of arrays

   arr shape is (3,), scalar shape is (1,). Broadcasting allows (1,) to expand to (3,).

2. Step 2: Apply broadcasting addition

   Each element of arr is added to 5, resulting in [6, 7, 8].

3. Final Answer:

   No error; output is [6 7 8] -> Option C

4. Quick Check:

   Broadcasting works with (3,) + (1,) arrays [OK]

Hint: Arrays with shape (n,) and (1,) broadcast correctly [OK]

Common Mistakes:

• Assuming scalar must be a plain number
• Thinking shape mismatch causes error
• Expecting concatenation instead of addition

5. Given a 2D array arr = np.array([[1, 2, 3], [4, 5, 6]]), what is the result of arr + np.array([10, 20, 30])?

hard

A. [[11 22 33]
[14 25 36]]

B. [[11 12 13]
[14 15 16]]

C. Error due to shape mismatch

D. [[10 20 30]
[10 20 30]]

Solution

1. Step 1: Analyze shapes for broadcasting

   arr shape is (2,3), and np.array([10, 20, 30]) shape is (3,). The second array broadcasts across rows.

2. Step 2: Perform element-wise addition row-wise

   Each row of arr adds the vector [10, 20, 30] element-wise, resulting in [[11, 22, 33], [14, 25, 36]].

3. Final Answer:

   [[11 22 33] [14 25 36]] -> Option A

4. Quick Check:

   Broadcast vector added to each row [OK]

Hint: Vector added to each row in 2D array [OK]

Common Mistakes:

• Expecting error due to different shapes
• Adding vector only to first row
• Confusing addition with concatenation