[Solved] Identify the bug in this code:import numpy as np from scipy.sparse import csr_matrix arr = np.array([[0, 0], [0, 0]]) sparse = csr_matrix(arr) print(sparse.data[0]) | SciPy

SciPy - Sparse Matrices (scipy.sparse)

Identify the bug in this code:

import numpy as np
from scipy.sparse import csr_matrix

arr = np.array([[0, 0], [0, 0]])
sparse = csr_matrix(arr)
print(sparse.data[0])

ATypeError due to wrong input type

BValueError due to zero matrix

CNo error, prints 0

DIndexError because sparse.data is empty

Step-by-Step Solution

Solution:

Step 1: Analyze input matrix
Matrix contains only zeros, so sparse.data is an empty array.
Step 2: Understand indexing on empty data
Accessing sparse.data[0] causes IndexError because there is no element.
Final Answer:
IndexError because sparse.data is empty -> Option D
Quick Check:
Accessing sparse.data[0] on empty data causes IndexError [OK]

Quick Trick: Check if sparse.data is empty before indexing [OK]

Common Mistakes:

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Identify the bug in this code: