Array arithmetic (element-wise) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Element-wise addition of NumPy arrays

What is the output of this code snippet that adds two NumPy arrays element-wise?

Data Analysis Python

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result = arr1 + arr2
print(result)

A [1 2 3 4 5 6]

B TypeError

C [4 7 9]

D [5 7 9]

💡 Hint

Think about how NumPy adds arrays of the same shape.

NumPy adds arrays element-wise when they have the same shape. So each element in arr1 is added to the corresponding element in arr2.

❓ data_output

intermediate

Element-wise multiplication with broadcasting

Given the arrays below, what is the output of the element-wise multiplication?

Data Analysis Python

import numpy as np

arr1 = np.array([[1, 2, 3], [4, 5, 6]])
arr2 = np.array([10, 20, 30])
result = arr1 * arr2
print(result)

A

[[10 20 30]
 [40 50 60]]

B

[[10 40 90]
 [40 100 180]]

C

[[10 20 30 10 20 30]
 [40 50 60 40 50 60]]

D ValueError

💡 Hint

Remember how broadcasting works when multiplying arrays of different shapes.

The 1D array arr2 is broadcast across the rows of arr1, so each element in arr2 multiplies the corresponding column in arr1.

🔧 Debug

advanced

Identify the error in element-wise division

What error does this code raise when performing element-wise division?

Data Analysis Python

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

B ValueError

C TypeError

D ZeroDivisionError

💡 Hint

Look carefully at division by zero in NumPy arrays.

NumPy raises a RuntimeWarning when dividing by zero but does not stop execution. It returns 'inf' or 'nan' in those positions.

❓ visualization

advanced

Plotting element-wise addition result

Which option correctly plots the element-wise addition of two arrays as a line chart?

Data Analysis Python

import numpy as np
import matplotlib.pyplot as plt

arr1 = np.array([1, 3, 5, 7])
arr2 = np.array([2, 4, 6, 8])
result = arr1 + arr2
plt.plot(result)
plt.show()

A A scatter plot with points [1, 3, 5, 7]

B A bar chart with bars at heights [3, 7, 11, 15]

C A line chart with points [3, 7, 11, 15] connected by lines

D An empty plot with no data

💡 Hint

plt.plot draws a line chart by default connecting the points.

plt.plot draws a line chart connecting the values in the array. The result array is [3, 7, 11, 15].

🧠 Conceptual

expert

Effect of element-wise operations on array shape

If you multiply a (3,1) shaped NumPy array by a (1,4) shaped array element-wise, what is the shape of the result?

A (3, 4)

B (3, 1)

C (1, 4)

D ValueError

💡 Hint

Think about how broadcasting expands dimensions to match.

Broadcasting expands the (3,1) array across columns and the (1,4) array across rows, resulting in a (3,4) array.