np.argmin() and np.argmax() in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.argmin() do in numpy?

np.argmin() returns the index of the smallest value in a numpy array.

beginner

What is the difference between np.argmin() and np.min()?

np.argmin() gives the position (index) of the smallest value, while np.min() gives the smallest value itself.

beginner

How does np.argmax() help in data analysis?

np.argmax() finds the index of the largest value, helping to quickly locate maximum points in data.

intermediate

What happens if there are multiple minimum values in the array when using np.argmin()?

np.argmin() returns the index of the first occurrence of the minimum value.

intermediate

Can np.argmin() and np.argmax() be used on multi-dimensional arrays?

Yes, they can. You can specify an axis to find the index of min or max values along that axis.

What does np.argmax() return?

A Index of the smallest value in the array

B The largest value in the array

C Index of the largest value in the array

D The smallest value in the array

np.argmax() returns the index of the largest value, not the value itself.

If arr = np.array([3, 1, 2, 1]), what does np.argmin(arr) return?

A 1

B 3

C 0

D 2

The smallest value is 1, which appears first at index 1.

Which function gives the smallest value in a numpy array?

A np.min()

B np.argmax()

C np.argmin()

D np.max()

np.min() returns the smallest value itself, not the index.

How to find the index of the maximum value along columns in a 2D array?

A np.argmax(arr, axis=1)

B np.min(arr, axis=1)

C np.argmin(arr, axis=0)

D np.argmax(arr, axis=0)

Axis 0 means along columns, so np.argmax(arr, axis=0) finds max indices per column.

If multiple max values exist, which index does np.argmax() return?

A Last occurrence index

B First occurrence index

C Random occurrence index

D Sum of all indices

np.argmax() returns the index of the first maximum value found.

Explain how np.argmin() and np.argmax() work and give a simple example.

Think about finding positions of smallest and largest values.

Describe how to use np.argmin() or np.argmax() on a 2D array to find indices along a specific axis.

Axis 0 is columns, axis 1 is rows.

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