np.abs() for absolute values in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the absolute values of the array elements.

NumPy

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

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

Aabs

Babsval

Cabsolute

Dabs_values

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'absval' or 'abs_values' which do not exist.

The function np.abs() returns the absolute values of each element in the array.

2fill in blank

medium

Complete the code to find the absolute values of a 2D numpy array.

NumPy

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

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

Aabsval

Babs

Cabs_values

Dabsolute

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to use 'absval' or 'abs_values' which are not valid numpy functions.

np.abs() works for arrays of any shape, returning the absolute values element-wise.

3fill in blank

hard

Fix the error in the code to correctly compute absolute values of the array.

NumPy

import numpy as np
arr = np.array([-5, 6, -7])
abs_values = np.[1](arr)
print(abs_values)

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

Aabsolute

Babsval

Cabs_values

Dabs

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'absval' or 'abs_values' which are not numpy functions.

The correct numpy function to get absolute values is np.abs().

4fill in blank

hard

Fill both blanks to create a dictionary of words and their absolute length if length is greater than 3.

NumPy

words = ['data', 'ai', 'science', 'ml']
lengths = {word: [1] for word in words if [2] > 3}
print(lengths)

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

Alen(word)

Bword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' instead of 'len(word)' for length.

Not filtering words by length correctly.

The dictionary comprehension uses len(word) to get length and filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and absolute length values for words longer than 2.

NumPy

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

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

Aword.upper()

Babsolute

Cword

Dabs

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' instead of 'word.upper()' for keys.

The dictionary comprehension uses uppercase words as keys, and absolute values of their lengths as values.

Practice

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

easy

A. Calculates the square of a number

B. Computes the negative of a number

C. Finds the maximum value in an array

D. Returns the absolute value of a number or each element in an array

Solution

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

   The function np.abs() returns the absolute value, which means it removes any negative sign from numbers.

2. Step 2: Apply to numbers or arrays

   It works on single numbers or arrays, returning the size without sign for each element.

3. Final Answer:

   Returns the absolute value of a number or each element in an array -> Option D

4. Quick Check:

   np.abs(-5) = 5 [OK]

Hint: Think: absolute means distance from zero, no minus sign [OK]

Common Mistakes:

• Confusing absolute value with square
• Thinking it finds max value
• Assuming it negates numbers

2. Which of the following is the correct syntax to get absolute values of a NumPy array arr?

easy

A. np.absolutevalue(arr)

B. abs(np.arr)

C. np.abs(arr)

D. arr.abs()

Solution

1. Step 1: Recall the correct function name

   The correct NumPy function to get absolute values is np.abs().

2. Step 2: Check syntax correctness

   Calling np.abs(arr) applies the function to the array correctly. Other options have wrong function names or syntax.

3. Final Answer:

   np.abs(arr) -> Option C

4. Quick Check:

   np.abs(array) is correct syntax [OK]

Hint: Use np.abs() exactly, no extra words or dot on array [OK]

Common Mistakes:

• Using abs(np.arr) which is invalid
• Writing np.absolutevalue instead of np.abs
• Trying to call abs() as a method on array

3. What is the output of the following code?

import numpy as np
arr = np.array([-3, 0, 4, -7])
print(np.abs(arr))

medium

A. [0 3 4 7]

B. [3 0 4 7]

C. [-3 0 4 -7]

D. [3 0 -4 7]

Solution

1. Step 1: Understand np.abs on array elements

   np.abs() converts each element to its absolute value, removing negative signs.

2. Step 2: Apply to each element in arr

   Elements: -3 -> 3, 0 -> 0, 4 -> 4, -7 -> 7.

3. Final Answer:

   [3 0 4 7] -> Option B

4. Quick Check:

   Absolute values remove negatives [OK]

Hint: Replace negatives with positive, keep zeros and positives same [OK]

Common Mistakes:

• Leaving negative signs unchanged
• Mixing element order
• Confusing zero with negative

4. The code below throws an error. What is the mistake?

import numpy as np
arr = [-1, -2, 3]
print(np.abs[arr])

medium

A. Using square brackets [] instead of parentheses () with np.abs

B. Array must be a NumPy array, not a list

C. np.abs cannot handle negative numbers

D. Missing import statement

Solution

1. Step 1: Identify function call syntax

   Functions in Python require parentheses () to call, not square brackets [].

2. Step 2: Check np.abs usage

   np.abs[arr] tries to index np.abs, causing an error. Correct is np.abs(arr).

3. Final Answer:

   Using square brackets [] instead of parentheses () with np.abs -> Option A

4. Quick Check:

   Function calls need () not [] [OK]

Hint: Remember: functions use () to call, [] is for indexing [OK]

Common Mistakes:

• Using [] instead of () for function calls
• Thinking np.abs only works on NumPy arrays
• Ignoring import statement errors

5. You have an array of temperature changes: temps = np.array([-5, 3, -2, 0, 4]). You want to find the total magnitude of change ignoring direction. Which code correctly calculates this?

hard

A. total_change = np.sum(np.abs(temps))

B. total_change = np.abs(np.sum(temps))

C. total_change = np.sum(temps)

D. total_change = np.abs(temps.sum())

Solution

1. Step 1: Understand the goal

   We want the total magnitude, so sum of absolute values of each change.

2. Step 2: Compare options

   total_change = np.sum(np.abs(temps)) sums absolute values element-wise, correct. total_change = np.abs(np.sum(temps)) sums first then abs, losing individual magnitudes. Options C and D ignore absolute values properly.

3. Final Answer:

   total_change = np.sum(np.abs(temps)) -> Option A

4. Quick Check:

   Sum of absolute values gives total magnitude [OK]

Hint: Sum absolute values, not absolute of sum [OK]

Common Mistakes:

• Taking absolute after summing, losing individual sizes
• Summing without absolute, cancelling negatives
• Using wrong function names