np.sqrt() for square roots in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the square root of 16 using numpy.

NumPy

import numpy as np
result = np.[1](16)
print(result)

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

Apower

Bsquare

Croot

Dsqrt

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.square() which squares the number instead of finding the root.

Using np.power() without the correct exponent.

The function np.sqrt() calculates the square root of a number.

2fill in blank

medium

Complete the code to calculate the square root of each element in the numpy array.

NumPy

import numpy as np
arr = np.array([4, 9, 25])
roots = np.[1](arr)
print(roots)

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

Asqrt

Broot

Csquare

Dpower

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to use np.square() which squares elements instead.

Using a non-existent function like np.root().

np.sqrt() works element-wise on numpy arrays to find square roots.

3fill in blank

hard

Fix the error in the code to correctly compute the square root of 49.

NumPy

import numpy as np
value = 49
result = np.[1](value)
print(result)

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

Asquare

Bsqrt

Croot

Dpow

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.square() which squares the number instead of finding the root.

Using np.pow() without the correct exponent.

The correct function to compute square root is np.sqrt().

4fill in blank

hard

Fill both blanks to create a dictionary with numbers as keys and their square roots as values.

NumPy

import numpy as np
numbers = [1, 4, 9, 16]
sqrt_dict = { [1]: np.[2](num) for num in numbers }
print(sqrt_dict)

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

Anum

Bsqrt

Cnumber

Dsquare

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong variable name for the key.

Using np.square() instead of np.sqrt().

Use num as the key and np.sqrt() to get the square root values.

5fill in blank

hard

Fill all three blanks to create a list of square roots for numbers greater than 10.

NumPy

import numpy as np
nums = [4, 16, 25, 9, 36]
sqrt_list = [np.[1](n) for n in nums if n [2] [3]]
print(sqrt_list)

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

Asquare

B>

C10

Dsqrt

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.square() instead of np.sqrt().

Using wrong comparison operators like '<' or '==' instead of '>'.

Using a wrong number for filtering.

Use np.sqrt() to get square roots, and filter numbers greater than 10 with n > 10.

Practice

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

easy

A. Calculates the square root of a number or each element in an array

B. Calculates the square of a number or each element in an array

C. Calculates the logarithm of a number or each element in an array

D. Calculates the exponential of a number or each element in an array

Solution

1. Step 1: Understand the function purpose

   np.sqrt() is designed to find the square root of numbers or arrays element-wise.

2. Step 2: Compare with other options

   Options B, C, and D describe different mathematical operations (square, logarithm, exponential) which are not what np.sqrt() does.

3. Final Answer:

   Calculates the square root of a number or each element in an array -> Option A

4. Quick Check:

   Square root = np.sqrt() [OK]

Hint: Remember: sqrt means square root, not square or log [OK]

Common Mistakes:

• Confusing square root with square
• Thinking it calculates logarithm
• Assuming it calculates exponential

2. Which of the following is the correct syntax to find the square root of 16 using NumPy?

easy

A. np.square(16)

B. np.sqrt(16)

C. np.sqrt16()

D. sqrt(np.16)

Solution

1. Step 1: Recall correct function usage

   The correct syntax to find the square root of a number is np.sqrt(number).

2. Step 2: Check each option

   np.sqrt(16) uses np.sqrt(16) which is correct. np.square(16) uses np.square(16) which squares the number, not square root. Options C and D have invalid syntax.

3. Final Answer:

   np.sqrt(16) -> Option B

4. Quick Check:

   Correct syntax = np.sqrt(value) [OK]

Hint: Use np.sqrt() with parentheses around the number [OK]

Common Mistakes:

• Using np.square() instead of np.sqrt()
• Missing parentheses after sqrt
• Incorrect function name or syntax

3. What is the output of the following code?

import numpy as np
arr = np.array([4, 9, 16])
result = np.sqrt(arr)
print(result)

medium

A. Error: np.sqrt() cannot take arrays

B. [16. 81. 256.]

C. [2. 3. 4.]

D. [1.414 3.0 4.0]

Solution

1. Step 1: Understand input array and function

   The array contains [4, 9, 16]. Applying np.sqrt() computes the square root of each element.

2. Step 2: Calculate square roots element-wise

   Square roots are sqrt(4)=2, sqrt(9)=3, sqrt(16)=4, so the result is [2. 3. 4.]

3. Final Answer:

   [2. 3. 4.] -> Option C

4. Quick Check:

   Square roots of [4,9,16] = [2,3,4] [OK]

Hint: np.sqrt() works element-wise on arrays [OK]

Common Mistakes:

• Confusing square root with square
• Expecting a single number output
• Thinking np.sqrt() cannot handle arrays

4. The following code raises a RuntimeWarning. What is the problem?

import numpy as np
arr = np.array([-4, 9, 16])
result = np.sqrt(arr)
print(result)

medium

A. np.sqrt() cannot handle negative numbers and raises a RuntimeWarning

B. The array syntax is incorrect

C. np.sqrt() requires a list, not a NumPy array

D. The print statement is missing parentheses

Solution

1. Step 1: Identify the input causing error

   The array contains a negative number -4. Square root of negative numbers is not defined for real numbers.

2. Step 2: Understand np.sqrt() behavior on negatives

   By default, np.sqrt() raises a RuntimeWarning and returns NaN for negative numbers. This causes unexpected output.

3. Final Answer:

   np.sqrt() cannot handle negative numbers and raises a RuntimeWarning -> Option A

4. Quick Check:

   Negative input to sqrt causes RuntimeWarning [OK]

Hint: Square root of negative numbers causes RuntimeWarnings unless complex dtype is used [OK]

Common Mistakes:

• Thinking array syntax is wrong
• Assuming np.sqrt() works on negatives by default
• Ignoring warning messages

5. You have a NumPy array arr = np.array([1, 4, 9, 16, 25]). You want to create a new array that contains the square roots of only the elements greater than 10. Which code correctly does this?

hard

A. np.sqrt(arr[arr < 10])

B. np.sqrt(arr) > 10

C. np.sqrt(arr) if arr > 10 else arr

D. np.sqrt(arr[arr > 10])

Solution

1. Step 1: Filter elements greater than 10

   Use boolean indexing arr > 10 to select elements 16 and 25.

2. Step 2: Apply np.sqrt() on filtered elements

   Apply np.sqrt() on the filtered array arr[arr > 10] to get square roots of 16 and 25, which are 4 and 5.

3. Final Answer:

   np.sqrt(arr[arr > 10]) -> Option D

4. Quick Check:

   Filter first, then sqrt on filtered [OK]

Hint: Filter array first, then apply np.sqrt() [OK]

Common Mistakes:

• Applying sqrt before filtering
• Using incorrect conditional syntax
• Filtering with wrong comparison operator