np.argmin() and np.argmax() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to find the index of the smallest value in the array.

NumPy

import numpy as np
arr = np.array([3, 1, 4, 1, 5])
index_min = np.[1](arr)
print(index_min)

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

Amin

Bargmax

Cargmin

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.min() which returns the minimum value, not the index.

Using np.argmax() which finds the index of the maximum value.

The np.argmin() function returns the index of the smallest value in the array.

2fill in blank

medium

Complete the code to find the index of the largest value in the array.

NumPy

import numpy as np
arr = np.array([10, 20, 30, 20, 10])
index_max = np.[1](arr)
print(index_max)

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

Aargmax

Bargmin

Cmin

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.max() which returns the maximum value, not the index.

Using np.argmin() which finds the index of the minimum value.

The np.argmax() function returns the index of the largest value in the array.

3fill in blank

hard

Fix the error in the code to correctly find the index of the smallest value.

NumPy

import numpy as np
arr = np.array([7, 2, 5, 3])
index = np.argmin[1](arr)
print(index)

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

A<

B(

C{

D[

Attempts:

3 left

💡 Hint

Common Mistakes

Using square brackets instead of parentheses for function calls.

Missing the opening parenthesis.

The function call must use parentheses () to pass the array as an argument.

4fill in blank

hard

Fill both blanks to create a dictionary of indices of min and max values from the array.

NumPy

import numpy as np
arr = np.array([8, 6, 7, 5, 3, 0, 9])
result = {'min_index': np.[1](arr), 'max_index': np.[2](arr)}
print(result)

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

Aargmin

Bmin

Cargmax

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.min() or np.max() which return values, not indices.

Mixing up argmin and argmax.

np.argmin() and np.argmax() return the indices of the smallest and largest values respectively.

5fill in blank

hard

Fill both blanks to create a dictionary with keys as uppercase words and values as their indices if the index is greater than 2.

NumPy

words = ['apple', 'banana', 'cherry', 'date', 'fig']
result = {word[1]: i for i, word in enumerate(words) if i [2] 2}
print(result)

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

A.upper()

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to call the upper() method with parentheses.

Using the wrong comparison operator in the if condition.

Trying to modify the index value unnecessarily.

The dictionary comprehension uses word.upper() as the key, the index i as the value, and filters indices greater than 2.

Practice

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

easy

A. Finds the index of the smallest value in an array

B. Finds the index of the largest value in an array

C. Returns the smallest value in an array

D. Returns the largest value in an array

Solution

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

   The function np.argmin() returns the index (position) of the smallest value in a NumPy array.

2. Step 2: Differentiate from similar functions

   Unlike np.min() which returns the smallest value itself, np.argmin() returns where that value is located.

3. Final Answer:

   Finds the index of the smallest value in an array -> Option A

4. Quick Check:

   np.argmin() = index of smallest value [OK]

Hint: Remember: argmin = position of minimum value [OK]

Common Mistakes:

• Confusing np.argmin() with np.min()
• Thinking it returns the smallest value, not the index
• Mixing up np.argmin() with np.argmax()

2. Which of the following is the correct NumPy function syntax to find the index of the largest value in a NumPy array arr?

easy

A. np.argmin(arr)

B. arr.argmax()

C. np.argmax(arr)

D. arr.argmin()

Solution

1. Step 1: Recall the function to find largest value index

   np.argmax(arr) returns the index of the largest value in the array arr.

2. Step 2: Check other options

   arr.argmax() is valid but not the function syntax asked; np.argmin() and arr.argmin() find smallest value index, not largest.

3. Final Answer:

   np.argmax(arr) -> Option C

4. Quick Check:

   np.argmax(arr) = index of max value [OK]

Hint: Use np.argmax() to get largest value index [OK]

Common Mistakes:

• Using np.argmin() instead of np.argmax()
• Confusing method call arr.argmax() with function np.argmax()
• Mixing up largest and smallest value functions

3. Given the array arr = np.array([3, 1, 4, 1, 5]), what is the output of np.argmin(arr)?

medium

A. 3

B. 1

C. 0

D. 4

Solution

1. Step 1: Identify the smallest value in the array

   The array is [3, 1, 4, 1, 5]. The smallest value is 1.

2. Step 2: Find the index of the first occurrence of the smallest value

   The first 1 appears at index 1 (0-based indexing).

3. Final Answer:

   1 -> Option B

4. Quick Check:

   np.argmin([3,1,4,1,5]) = 1 [OK]

Hint: np.argmin() returns first smallest value index [OK]

Common Mistakes:

• Choosing the last occurrence index instead of first
• Confusing values with indices
• Using np.argmax() by mistake

4. Consider the code:

import numpy as np
arr = np.array([[2, 5, 1], [7, 3, 9]])
index = np.argmax(arr, axis=1)

What is the correct output of index?

medium

A. [1 2]

B. [0 2]

C. [2 1]

D. [2 2]

Solution

1. Step 1: Understand np.argmax with axis=1

   axis=1 means find the index of the largest value in each row.

2. Step 2: Find largest values per row and their indices

   Row 0: [2, 5, 1], max is 5 at index 1; Row 1: [7, 3, 9], max is 9 at index 2.

3. Step 3: Check options carefully

   [2 2] shows [2 2], which is incorrect because first row max index is 1, not 2. [1 2] is [1 2], which matches the correct indices.

4. Final Answer:

   [1 2] -> Option A

5. Quick Check:

   np.argmax(arr, axis=1) = [1 2] [OK]

Hint: axis=1 finds max index per row [OK]

Common Mistakes:

• Mixing up axis parameter
• Confusing row and column indices
• Assuming global max index instead of per row

5. You have a 2D NumPy array representing exam scores of 4 students in 3 subjects:

scores = np.array([[88, 92, 79], [95, 85, 91], [70, 78, 88], [90, 91, 85]])

How do you find the subject index where each student scored the lowest?

hard

A. np.argmax(scores, axis=0)

B. np.argmin(scores, axis=0)

C. np.argmax(scores, axis=1)

D. np.argmin(scores, axis=1)

Solution

1. Step 1: Understand the array and axis meaning

   Each row is a student, columns are subjects. axis=1 means operate across columns (subjects) per student.

2. Step 2: Use np.argmin with axis=1 to find lowest score subject per student

   np.argmin(scores, axis=1) returns the index of the smallest score in each student's row.

3. Final Answer:

   np.argmin(scores, axis=1) -> Option D

4. Quick Check:

   Lowest subject per student = np.argmin(scores, axis=1) [OK]

Hint: Use axis=1 to find min per row (student) [OK]

Common Mistakes:

• Using axis=0 which finds min per subject, not per student
• Using np.argmax instead of np.argmin
• Confusing rows and columns in axis parameter