Broadcasting errors and debugging in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this NumPy broadcasting code?

Consider the following code snippet using NumPy arrays. What will be the output?

NumPy

import numpy as np

arr1 = np.array([[1, 2, 3], [4, 5, 6]])
arr2 = np.array([1, 0, 1])
result = arr1 + arr2
print(result)

A

[[2 2 4]
 [5 5 7]]

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

C

[[2 3 4]
 [5 6 7]]

D

[[1 2 3]
 [4 5 6]]

💡 Hint

Remember how NumPy broadcasts arrays with compatible shapes by stretching dimensions.

The array arr2 with shape (3,) is broadcast across the rows of arr1 with shape (2,3). So each row of arr1 adds the arr2 vector element-wise.

❓ Predict Output

intermediate

What error does this broadcasting code raise?

What error will this code produce when run?

NumPy

import numpy as np

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

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

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

C IndexError: index 3 is out of bounds for axis 0 with size 2

D

[[2 4 4]
 [4 6 7]]

💡 Hint

Check if the shapes of the arrays can be broadcast together.

The shapes (2,2) and (3,) are incompatible for broadcasting because the last dimensions differ and cannot be stretched to match.

🔧 Debug

advanced

Identify the broadcasting bug in this code

This code tries to add two arrays but raises an error. Which line causes the broadcasting error?

NumPy

import numpy as np

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

A Line 5: addition operation arr1 + arr2 causes error

B Line 3: arr1 definition shape (2,3) incompatible with arr2 (3,1)

C No error, code runs fine

D Line 4: arr2 definition shape (3,1) incompatible with arr1 (2,3)

💡 Hint

Check the shapes of arr1 and arr2 before addition.

The addition fails because arr1 has shape (2,3) and arr2 has shape (3,1). These shapes cannot be broadcast together.

❓ data_output

advanced

What is the shape of the result after broadcasting?

Given these arrays, what is the shape of the result after adding arr1 and arr2?

NumPy

import numpy as np

arr1 = np.ones((4,1,3))
arr2 = np.ones((1,5,1))
result = arr1 + arr2
print(result.shape)

A (1, 5, 3)

B (4, 1, 5, 3)

C ValueError: operands could not be broadcast together

D (4, 5, 3)

💡 Hint

Broadcasting works by matching dimensions from the right and stretching size 1 dimensions.

The shapes (4,1,3) and (1,5,1) broadcast to (4,5,3) by stretching the 1s to match the other array's size.

🚀 Application

expert

Fix the broadcasting error to compute weighted sums

You want to compute weighted sums of rows in a 2D array using a 1D weights array. The code below raises an error. Which option fixes it correctly?

NumPy

import numpy as np

values = np.array([[10, 20, 30], [40, 50, 60]])
weights = np.array([0.1, 0.2])
weighted_sum = (values * weights).sum(axis=1)
print(weighted_sum)

A weights = weights[:, np.newaxis]

B weights = weights[np.newaxis, :]

C weights = weights.reshape(3, 1)

D weights = weights.flatten()

💡 Hint

Make weights shape compatible with values shape (2,3) for element-wise multiplication.

values shape is (2,3), weights shape is (2,). To multiply element-wise, weights must be reshaped to (1,2) to broadcast along rows.