np.power() and np.square() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.power() with array and scalar

What is the output of this code snippet?

NumPy

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

A [1 6 9]

B [1 8 27]

C [3 6 9]

D [1 4 9]

💡 Hint

np.power raises each element to the given power.

np.power(arr, 3) raises each element of arr to the power 3, so 1^3=1, 2^3=8, 3^3=27.

❓ Predict Output

intermediate

Difference between np.square() and np.power()

What is the output of this code?

NumPy

import numpy as np
arr = np.array([2, 4, 6])
result = np.square(arr)
print(result)

A [1 16 36]

B [8 64 216]

C [2 4 6]

D [4 16 36]

💡 Hint

np.square is like np.power with exponent 2.

np.square(arr) squares each element: 2^2=4, 4^2=16, 6^2=36.

❓ data_output

advanced

Result shape after np.power with broadcasting

What is the shape of the result array after this operation?

NumPy

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

A (2, 2)

B (2,)

C (2, 3)

D (3, 2)

💡 Hint

Broadcasting matches shapes to perform element-wise operations.

arr2 shape (2,) broadcasts to arr1 shape (2,2), so result shape is (2,2).

🔧 Debug

advanced

Identify the error in np.power usage

What error does this code raise?

NumPy

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

A SyntaxError

B ValueError

C TypeError

D No error, outputs [1 4 9]

💡 Hint

The exponent must be a number, not a string.

np.power expects numeric types; passing a string causes TypeError.

🚀 Application

expert

Using np.square to compute Euclidean distances

Given two 2D points, which code correctly computes the squared Euclidean distance using np.square?

A np.sum(np.square(point1 - point2))

B np.square(np.sum(point1 - point2))

C np.square(point1) - np.square(point2)

D np.sum(point1 - point2)**2

💡 Hint

Squared distance is sum of squared differences of coordinates.

Option A squares element-wise differences then sums them, correctly computing squared distance.