np.abs() for absolute values in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.abs() on mixed array

What is the output of this code?

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

NumPy

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

A [3 0 4 7]

B [-3 0 4 -7]

C [3 0 -4 7]

D [0 3 4 7]

💡 Hint

np.abs() returns the absolute value of each element in the array.

np.abs() changes each number to its positive value without changing zero.

❓ data_output

intermediate

Absolute values of a 2D numpy array

Given this 2D array, what is the output of np.abs() applied to it?

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

NumPy

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

A

[[-1 2]
 [3 -4]]

B

[[1 -2]
 [-3 4]]

C

[[1 2]
 [3 4]]

D

[[1 2]
 [-3 4]]

💡 Hint

np.abs() works element-wise on arrays of any shape.

Each element is converted to its positive value, keeping the shape intact.

🧠 Conceptual

advanced

Effect of np.abs() on complex numbers

What does np.abs() return when applied to a numpy array of complex numbers?

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

NumPy

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

A [5. 1.41421356]

B [3.+4.j 1.-1.j]

C [7.+0.j 2.+0.j]

D [-3.-4.j -1.+1.j]

💡 Hint

np.abs() returns the magnitude (distance from zero) for complex numbers.

For complex numbers, np.abs() returns the magnitude calculated as sqrt(real^2 + imag^2).

🔧 Debug

advanced

Identify the error in np.abs() usage

What error will this code raise?

import numpy as np
arr = np.array(['-3', '4', '-5'])
result = np.abs(arr)
print(result)

NumPy

import numpy as np
arr = np.array(['-3', '4', '-5'])
result = np.abs(arr)
print(result)

A SyntaxError: invalid syntax

B ValueError: invalid literal for int() with base 10

C No error, output: [3 4 5]

D TypeError: bad operand type for abs(): 'str'

💡 Hint

np.abs() expects numeric types, not strings.

np.abs() cannot process string arrays directly and raises a TypeError.

🚀 Application

expert

Using np.abs() to find max absolute difference

Given two numpy arrays, which option correctly finds the maximum absolute difference between their elements?

import numpy as np
a = np.array([1, -3, 5])
b = np.array([4, -1, -6])
max_diff = ???
print(max_diff)

NumPy

import numpy as np
a = np.array([1, -3, 5])
b = np.array([4, -1, -6])
max_diff = ???
print(max_diff)

A np.abs(np.max(a) - np.max(b))

B np.max(np.abs(a - b))

C np.max(a) - np.min(b)

D np.abs(np.min(a) - np.min(b))

💡 Hint

Find the absolute difference element-wise, then get the max.

Subtract arrays element-wise, take absolute values, then find the max value.

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