Element-wise arithmetic in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of element-wise addition with broadcasting

What is the output of this code snippet using NumPy arrays?

NumPy

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([[10], [20], [30]])
result = arr1 + arr2
print(result)

A

[[1 2 3]
 [10 20 30]
 [11 22 33]]

B

[[10 20 30]
 [11 21 31]
 [12 22 32]]

C

[[11 12 13]
 [21 22 23]
 [31 32 33]]

D

[[11 22 33]
 [12 23 34]
 [13 24 35]]

💡 Hint

Remember how broadcasting works when adding a 1D array to a 2D array.

The 1D array arr1 is broadcast across each row of arr2, so each element of arr1 is added to each row element of arr2, resulting in a 3x3 array where each row is arr1 plus the corresponding element of arr2.

❓ data_output

intermediate

Result of element-wise multiplication with different shapes

Given these NumPy arrays, what is the shape and content of the result after element-wise multiplication?

NumPy

import numpy as np

x = np.array([[1, 2, 3], [4, 5, 6]])
y = np.array([2, 3, 4])
result = x * y
print(result)
print(result.shape)

A

Result:
[[ 2  4  6]
 [ 8 10 12]]
Shape: (2, 3)

B

Result:
[[ 2  6 12]
 [ 8 15 24]]
Shape: (2, 3)

C

Result:
[[ 2  6 12  8 15 24]]
Shape: (1, 6)

D

Result:
[[ 2  6]
 [ 8 15]
 [12 24]]
Shape: (3, 2)

💡 Hint

Check how the 1D array y is broadcast across the rows of x.

The array y with shape (3,) is broadcast to match the shape of x (2,3). Each element in y multiplies the corresponding column in x, resulting in element-wise multiplication with shape (2,3).

🔧 Debug

advanced

Identify the error in element-wise division

What error will this code raise when executed?

NumPy

import numpy as np

arr1 = np.array([1, 2, 0])
arr2 = np.array([0, 2, 3])
result = arr1 / arr2
print(result)

A RuntimeWarning: divide by zero encountered in true_divide and result: [inf 1. 0.]

B TypeError: unsupported operand type(s) for /: 'int' and 'int'

C ValueError: operands could not be broadcast together

D ZeroDivisionError

💡 Hint

Consider how NumPy handles division by zero in arrays.

NumPy does not raise a ZeroDivisionError for division by zero in arrays. Instead, it issues a RuntimeWarning and returns 'inf' or 'nan' where division by zero occurs.

❓ visualization

advanced

Visualizing element-wise subtraction result

Which plot correctly shows the element-wise subtraction of these two arrays?

NumPy

import numpy as np
import matplotlib.pyplot as plt

x = np.array([5, 10, 15, 20])
y = np.array([2, 8, 12, 18])
result = x - y
plt.bar(range(len(result)), result)
plt.xlabel('Index')
plt.ylabel('Difference')
plt.title('Element-wise subtraction result')
plt.show()

A Bar heights: [3, 2, 3, 2]

B Bar heights: [-3, -2, -3, -2]

C Bar heights: [7, 18, 27, 38]

D Bar heights: [2, 8, 12, 18]

💡 Hint

Subtract each element of y from the corresponding element of x.

Element-wise subtraction subtracts each element in y from x, resulting in [3, 2, 3, 2]. The bar chart shows these values as bar heights.

🧠 Conceptual

expert

Understanding element-wise arithmetic with masked arrays

Given two masked arrays, what is the output of their element-wise multiplication?

NumPy

import numpy as np
import numpy.ma as ma

arr1 = ma.array([1, 2, 3, 4], mask=[False, True, False, False])
arr2 = ma.array([10, 20, 30, 40], mask=[False, False, True, False])
result = arr1 * arr2
print(result)
print(result.mask)

A

Data: [10 40 -- 160]
Mask: [False False True False]

B

Data: [10 40 90 160]
Mask: [False False False False]

C

Data: [10 -- 90 160]
Mask: [False True True False]

D

Data: [10 -- -- 160]
Mask: [False True True False]

💡 Hint

Remember that the mask in the result is the logical OR of the input masks.

The multiplication masks elements where either input is masked. The mask is True where either arr1 or arr2 is masked, so positions 1 and 2 are masked. The data multiplies unmasked elements only.