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.