np.power() and np.square() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to square each element in the array using np.square().

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4])
squared = np.[1](arr)
print(squared)

Drag options to blanks, or click blank then click option'

Aabs

Bpower

Csqrt

Dsquare

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.sqrt() which computes the square root instead.

Using np.abs() which computes absolute values.

np.square() computes the square of each element in the array.

2fill in blank

medium

Complete the code to raise each element in the array to the power of 3 using np.power().

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4])
cubed = np.power(arr, [1])
print(cubed)

Drag options to blanks, or click blank then click option'

A3

B4

C2

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 2 which squares instead of cubes.

Using 0 which results in all ones.

np.power(arr, 3) raises each element of arr to the power of 3 (cube).

3fill in blank

hard

Fix the error in the code to correctly square the array elements using np.power().

NumPy

import numpy as np
arr = np.array([2, 3, 4])
squared = np.power(arr, [1])
print(squared)

Drag options to blanks, or click blank then click option'

A1

Barr

C2

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 which returns the original array.

Using 0 which returns ones.

The second argument to np.power() is the exponent. To square, use 2.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their squared lengths as values, only for words longer than 3 characters.

NumPy

import numpy as np
words = ['data', 'science', 'ai', 'ml']
squared_lengths = {word: [1] for word in words if len(word) [2] 3}
print(squared_lengths)

Drag options to blanks, or click blank then click option'

Anp.square(len(word))

Bnp.power(len(word), 3)

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.power(len(word), 3) which cubes instead of squares.

Using <= which selects shorter words.

We square the length using np.square(len(word)) and filter words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their squared lengths as values, only for words longer than 2 characters.

NumPy

import numpy as np
words = ['cat', 'dog', 'a', 'elephant']
squared_lengths = { [1]: [2] for word in words if len(word) [3] 2}
print(squared_lengths)

Drag options to blanks, or click blank then click option'

Aword.upper()

Bnp.square(len(word))

C>

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of upper().

Using < instead of > for filtering.

Using len(word) without np.square().

Keys are uppercase words using word.upper(), values are squared lengths using np.square(len(word)), and filter words longer than 2 using >.

Practice

1. What does the np.square() function do in NumPy?

easy

A. It raises each element of an array to the power of 2.

B. It calculates the square root of each element in an array.

C. It multiplies two arrays element-wise.

D. It returns the sum of squares of array elements.

Solution

1. Step 1: Understand the purpose of np.square()

   The function np.square() takes each element in an array and raises it to the power of 2.

2. Step 2: Compare with other options

   It does not calculate square roots, multiply arrays, or sum squares; it only squares each element.

3. Final Answer:

   It raises each element of an array to the power of 2. -> Option A

4. Quick Check:

   np.square(x) = x² [OK]

Hint: Remember: square means power of 2, not root or sum [OK]

Common Mistakes:

• Confusing square with square root
• Thinking it sums squares instead of element-wise operation
• Mixing with multiplication of arrays

2. Which of the following is the correct syntax to square all elements in a NumPy array arr using np.power()?

easy

A. np.power(arr)

B. np.power(2, arr)

C. np.power(arr, 2)

D. np.power(arr, arr)

Solution

1. Step 1: Understand np.power() syntax

   The function np.power(base, exponent) raises each element in base to the exponent.

2. Step 2: Apply to square elements

   To square elements of arr, use np.power(arr, 2). Other options misuse the order or miss the exponent.

3. Final Answer:

   np.power(arr, 2) -> Option C

4. Quick Check:

   np.power(arr, 2) = arr squared [OK]

Hint: np.power(base, exponent) order matters: base first [OK]

Common Mistakes:

• Swapping base and exponent
• Omitting the exponent argument
• Using the array as exponent incorrectly

3. What is the output of the following code?

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

medium

A. [1 6 9]

B. [1 8 27]

C. [3 6 9]

D. [1 4 9]

Solution

1. Step 1: Understand np.power(arr, 3)

   This raises each element of arr to the power of 3.

2. Step 2: Calculate each element

   1³=1, 2³=8, 3³=27, so the result is [1, 8, 27].

3. Final Answer:

   [1 8 27] -> Option B

4. Quick Check:

   np.power([1,2,3],3) = [1,8,27] [OK]

Hint: Power 3 means cube each element [OK]

Common Mistakes:

• Calculating square instead of cube
• Adding elements instead of powering
• Confusing element-wise with sum

4. Identify the error in the following code snippet:

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

medium

A. np.square() takes only one argument, but two were given.

B. np.square() cannot be used on arrays.

C. The array must be converted to a list before squaring.

D. The code should use np.power(arr, 2) instead.

Solution

1. Step 1: Check np.square() function signature

   np.square() accepts only one argument: the array or number to square.

2. Step 2: Analyze the code error

   The code passes two arguments, which causes a TypeError.

3. Final Answer:

   np.square() takes only one argument, but two were given. -> Option A

4. Quick Check:

   np.square(x) needs 1 argument [OK]

Hint: np.square() needs exactly one argument [OK]

Common Mistakes:

• Passing extra arguments to np.square()
• Thinking np.square() needs base and exponent
• Confusing np.square() with np.power()

5. You have a NumPy array data = np.array([1, -2, 3, -4]). You want to create a new array where each element is squared, but negative values should be squared and then multiplied by -1 to keep their sign effect. Which code correctly achieves this?

hard

A. np.square(np.abs(data))

B. np.square(data)

C. np.power(data, 2)

D. np.where(data < 0, -np.square(data), np.square(data))

Solution

1. Step 1: Understand the requirement

   Negative values should be squared and then multiplied by -1; positive values just squared.

2. Step 2: Analyze each option

   np.where(data < 0, -np.square(data), np.square(data)) uses np.where to apply -np.square() for negatives and np.square() for positives, matching the requirement exactly.

3. Final Answer:

   np.where(data < 0, -np.square(data), np.square(data)) -> Option D

4. Quick Check:

   Conditional square with sign handled = np.where(data < 0, -np.square(data), np.square(data)) [OK]

Hint: Use np.where for conditionally applying functions [OK]

Common Mistakes:

• Multiplying sign before squaring
• Using np.sign(data) directly without condition
• Confusing absolute value with sign handling