[Solved] What will be the output of this code?import numpy as np from scipy.sparse import coo_matrix row = np.array([0, 1, 2]) col = np.array([1, 2, 0]) data = np.... | SciPy

SciPy - Sparse Matrices (scipy.sparse)

What will be the output of this code?

import numpy as np
from scipy.sparse import coo_matrix
row = np.array([0, 1, 2])
col = np.array([1, 2, 0])
data = np.array([4, 5, 6])
sparse_mat = coo_matrix((data, (row, col)), shape=(3, 3))
print(sparse_mat.toarray())

A[[0 4 0] [0 0 5] [6 0 0]]

B[[4 0 0] [0 5 0] [0 0 6]]

C[[0 0 6] [4 0 0] [0 5 0]]

D[[0 6 0] [5 0 0] [0 0 4]]

Step-by-Step Solution

Solution:

Step 1: Map data to positions
Data 4 at (0,1), 5 at (1,2), 6 at (2,0) in a 3x3 matrix.
Step 2: Construct full matrix
Positions without data are zero, so matrix is [[0 4 0], [0 0 5], [6 0 0]].
Final Answer:
[[0 4 0] [0 0 5] [6 0 0]] -> Option A
Quick Check:
Data at (row,col) = matrix entries [OK]

Quick Trick: Match data with (row, col) indices to build matrix [OK]

Common Mistakes:

MISTAKES

Mixing row and column indices
Assuming data fills diagonal
Confusing CSR/CSC formats with COO

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What will be the output of this code?

Step 1: Map data to positions

Step 2: Construct full matrix

Final Answer:

Quick Check:

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