Scalar and array broadcasting in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of scalar and array addition

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

NumPy

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

A [1 2 3 5]

B [6 7 8]

C [5 7 9]

D TypeError

💡 Hint

Adding a scalar to an array adds the scalar to each element.

NumPy broadcasts the scalar 5 to each element of the array, so each element is increased by 5.

❓ data_output

intermediate

Shape after broadcasting with different dimensions

Given the arrays below, what is the shape of the result after broadcasting and addition?

NumPy

import numpy as np
arr1 = np.array([[1, 2, 3], [4, 5, 6]])
arr2 = np.array([10, 20, 30])
result = arr1 + arr2
print(result.shape)

A (2, 3)

B (3, 2)

C (2,)

D (3,)

💡 Hint

Broadcasting matches dimensions from the right.

arr2 is broadcast across the rows of arr1, so the result keeps arr1's shape (2,3).

🔧 Debug

advanced

Identify the error in broadcasting

What error does this code raise when trying to add these arrays?

NumPy

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([[1, 2], [3, 4]])
result = arr1 + arr2

A TypeError: unsupported operand type(s) for +: 'int' and 'list'

B No error, outputs a (2,3) array

C ValueError: operands could not be broadcast together with shapes (3,) (2,2)

D IndexError: index out of bounds

💡 Hint

Broadcasting requires compatible shapes from the right.

Shapes (3,) and (2,2) are incompatible for broadcasting, causing a ValueError.

🚀 Application

advanced

Using broadcasting to normalize data

You have a 2D array of data where each column needs to be normalized by subtracting the column mean. Which code correctly uses broadcasting to do this?

NumPy

import numpy as np
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# Normalize columns by subtracting mean

A data - data.mean(axis=0, keepdims=True)

B data - data.mean(axis=0)

C data - data.mean(axis=1)

D data - data.mean()

💡 Hint

Keep the mean shape compatible for broadcasting.

Using keepdims=True keeps the mean shape (1, n) so it broadcasts correctly across rows.

🧠 Conceptual

expert

Broadcasting rules and shape compatibility

Which of these pairs of shapes can be broadcast together in NumPy without error?

A (4, 1, 3) and (3, 1)

B (5, 4) and (1, 4, 5)

C (2, 3, 4) and (3, 1)

D (3, 1, 5) and (1, 5)

💡 Hint

Broadcasting compares shapes from the right and dimensions must be equal or 1.

Option D shapes (3,1,5) and (1,5) broadcast to (3,1,5) because trailing dims match or are 1.