np.argmin() and np.argmax() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Find the index of the smallest element in a 1D array

What is the output of the following code?

NumPy

import numpy as np
arr = np.array([4, 2, 9, 1, 7])
result = np.argmin(arr)
print(result)

A 3

B 0

C 1

D 4

💡 Hint

Look for the position of the smallest number in the array.

The smallest number is 1, which is at index 3 in the array.

❓ Predict Output

intermediate

Find the index of the largest element in a 2D array flattened

What is the output of this code?

NumPy

import numpy as np
arr = np.array([[5, 12, 7], [3, 9, 15]])
result = np.argmax(arr)
print(result)

A 2

B 4

C 5

D 3

💡 Hint

np.argmax returns the index as if the array was flattened.

The largest number is 15, which is at index 5 when the array is flattened.

❓ data_output

advanced

Find indices of minimum values along axis 0

Given the array below, what is the output of np.argmin(arr, axis=0)?

NumPy

import numpy as np
arr = np.array([[8, 3, 5], [2, 7, 1], [6, 4, 9]])
result = np.argmin(arr, axis=0)
print(result)

A [2 0 2]

B [1 0 1]

C [0 1 0]

D [1 2 1]

💡 Hint

Look for the smallest value in each column.

For each column, the smallest values are at rows 1, 0, and 1 respectively.

❓ data_output

advanced

Find indices of maximum values along axis 1

What does np.argmax(arr, axis=1) return for the array below?

NumPy

import numpy as np
arr = np.array([[1, 4, 2], [9, 3, 5], [7, 8, 6]])
result = np.argmax(arr, axis=1)
print(result)

A [1 0 1]

B [2 1 0]

C [0 2 1]

D [1 2 2]

💡 Hint

Find the largest value in each row and note its index.

The largest values in each row are at indices 1, 0, and 1 respectively.

🧠 Conceptual

expert

Understanding behavior of np.argmin with multiple minimum values

Consider the array arr = np.array([3, 1, 2, 1, 4]). What index does np.argmin(arr) return and why?

A 3, because np.argmin returns the last occurrence of the minimum value

B Raises an error because there are multiple minimum values

C 2, because np.argmin returns the index of the second smallest value

D 1, because np.argmin returns the first occurrence of the minimum value

💡 Hint

Think about how np.argmin handles ties.

np.argmin returns the index of the first minimum value it finds, which is at index 1.

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