Solution:Step 1: Understand np.argmax with axis=1axis=1 means find the index of the largest value in each row.Step 2: Find largest values per row and their indicesRow 0: [2, 5, 1], max is 5 at index 1; Row 1: [7, 3, 9], max is 9 at index 2.Step 3: Check options carefully[2 2] shows [2 2], which is i

[Solved] 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? | NumPy

NumPy - Aggregation Functions

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?

A[1 2]

B[0 2]

C[2 1]

D[2 2]

Step-by-Step Solution

Solution:

Step 1: Understand np.argmax with axis=1
axis=1 means find the index of the largest value in each row.
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.
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.
Final Answer:
[1 2] -> Option A
Quick Check:
np.argmax(arr, axis=1) = [1 2] [OK]

Quick Trick: 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

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Consider the code:

Step 1: Understand np.argmax with axis=1

Step 2: Find largest values per row and their indices

Step 3: Check options carefully

Final Answer:

Quick Check:

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