Scalar operations on arrays in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of scalar multiplication on a NumPy array

What is the output of this code?

import numpy as np
arr = np.array([1, 2, 3])
result = arr * 4
print(result)

NumPy

import numpy as np
arr = np.array([1, 2, 3])
result = arr * 4
print(result)

A [4 8 12]

B [1 2 3 4]

C [5 6 7]

D TypeError

💡 Hint

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

Each element in the array is multiplied by 4, so [1*4, 2*4, 3*4] = [4, 8, 12].

❓ data_output

intermediate

Result of adding a scalar to a 2D NumPy array

What is the resulting array after adding 10 to each element?

import numpy as np
arr = np.array([[1, 2], [3, 4]])
result = arr + 10
print(result)

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4]])
result = arr + 10
print(result)

A

[[1 2]
 [3 4]
 10]

B

[[10 10]
 [10 10]]

C

[[11 12]
 [13 14]]

D ValueError

💡 Hint

Adding a scalar adds it to every element in the array.

Adding 10 to each element results in [[1+10, 2+10], [3+10, 4+10]] = [[11, 12], [13, 14]].

🔧 Debug

advanced

Identify the error in scalar division of a NumPy array

What error does this code produce?

import numpy as np
arr = np.array([10, 20, 30])
result = arr / 0
print(result)

NumPy

import numpy as np
arr = np.array([10, 20, 30])
result = arr / 0
print(result)

A TypeError

B ValueError

C ZeroDivisionError

D RuntimeWarning: divide by zero encountered in true_divide

💡 Hint

Dividing by zero in NumPy arrays triggers a warning, not an exception.

NumPy issues a RuntimeWarning and returns 'inf' or 'nan' values instead of raising an error.

🧠 Conceptual

advanced

Effect of scalar subtraction on a NumPy array

If you subtract 5 from each element of the array np.array([7, 8, 9]), what is the resulting array?

A [12 13 14]

B [2 3 4]

C [7 8 9 5]

D TypeError

💡 Hint

Subtracting a scalar subtracts it from every element.

Subtracting 5 from each element gives [7-5, 8-5, 9-5] = [2, 3, 4].

🚀 Application

expert

Calculate the mean after scalar multiplication

Given the array np.array([2, 4, 6, 8]), what is the mean of the array after multiplying each element by 3?

NumPy

import numpy as np
arr = np.array([2, 4, 6, 8])
result = arr * 3
mean_val = result.mean()
print(mean_val)

A 15.0

B 10.0

C 30.0

D 20.0

💡 Hint

Multiply first, then find the average of the new array.

Multiplying by 3 gives [6, 12, 18, 24]. The mean is (6+12+18+24)/4 = 60/4 = 15.0.

Practice

1. What happens when you add a scalar value to a NumPy array?
arr = np.array([1, 2, 3])
result = arr + 5

easy

A. 5 is added only to the first element

B. The array becomes a scalar value 5

C. 5 is added to each element of the array

D. An error occurs because you cannot add a scalar to an array

Solution

1. Step 1: Understand scalar addition on arrays

   Adding a scalar to a NumPy array adds that scalar to every element individually.

2. Step 2: Apply addition to each element

   Each element in [1, 2, 3] gets 5 added, resulting in [6, 7, 8].

3. Final Answer:

   5 is added to each element of the array -> Option C

4. Quick Check:

   Scalar + array = element-wise addition [OK]

Hint: Scalar operations apply to every element individually [OK]

Common Mistakes:

• Thinking scalar adds only to first element
• Expecting an error when adding scalar
• Confusing scalar with array addition

2. Which of the following is the correct syntax to multiply every element of a NumPy array arr by 3?

easy

A. arr * 3

B. arr * [3]

C. arr * arr[3]

D. arr * (3,)

Solution

1. Step 1: Identify scalar multiplication syntax

   Multiplying an array by a scalar uses the * operator with a number, like arr * 3.

2. Step 2: Check other options

   arr * [3] tries to multiply by a list, arr * arr[3] accesses an element (may cause error if index out of range), arr * (3,) is a tuple, not scalar.

3. Final Answer:

   arr * 3 -> Option A

4. Quick Check:

   Scalar multiplication uses * scalar [OK]

Hint: Use * with a number, not list or tuple [OK]

Common Mistakes:

• Using list or tuple instead of scalar
• Accessing array element incorrectly
• Confusing scalar with array

3. What is the output of the following code?

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

medium

A. [3 5 7]

B. [1 3 5]

C. [2 4 6]

D. Error: unsupported operand

Solution

1. Step 1: Understand scalar subtraction on array

   Subtracting 1 from each element in [2, 4, 6] results in [1, 3, 5].

2. Step 2: Confirm output matches expected array

   The print statement outputs the array as [1 3 5] without commas.

3. Final Answer:

   [1 3 5] -> Option B

4. Quick Check:

   Array - scalar subtracts from each element [OK]

Hint: Subtract scalar from each element for result [OK]

Common Mistakes:

• Expecting commas in printed array
• Adding instead of subtracting
• Thinking operation causes error

4. The following code throws an error. What is the cause?

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

medium

A. Cannot add string scalar to numeric array

B. Array is not defined

C. Missing import statement

D. Syntax error in addition

Solution

1. Step 1: Identify data types involved in addition

   arr is numeric array, '5' is a string scalar. NumPy cannot add string to numbers.

2. Step 2: Understand error cause

   Adding string to numeric array causes TypeError because types mismatch.

3. Final Answer:

   Cannot add string scalar to numeric array -> Option A

4. Quick Check:

   Scalar type must match array type [OK]

Hint: Scalar must be number, not string, for numeric arrays [OK]

Common Mistakes:

• Thinking import is missing
• Confusing syntax error with type error
• Assuming array is undefined

5. You have a NumPy array arr = np.array([10, 20, 30, 40]). You want to reduce each element by 10% using scalar operations. Which code correctly does this?

hard

A. arr + 0.9

B. arr - 0.1

C. arr / 10

D. arr * 0.9

Solution

1. Step 1: Understand reducing by 10%

   Reducing by 10% means multiplying by 90% or 0.9.

2. Step 2: Apply scalar multiplication

   Multiplying arr by 0.9 scales each element down by 10%.

3. Step 3: Check other options

   Subtracting 0.1 reduces by fixed amount, dividing by 10 reduces by 90%, adding 0.9 increases values.

4. Final Answer:

   arr * 0.9 -> Option D

5. Quick Check:

   Multiply by 0.9 to reduce by 10% [OK]

Hint: Multiply by 0.9 to reduce by 10% [OK]

Common Mistakes:

• Subtracting 0.1 instead of 10%
• Dividing instead of multiplying
• Adding instead of reducing