[Solved] What is wrong with this code?from scipy.sparse import csr_matrix row = [0, 1] col = [1, 2] data = [3, 4] sparse = csr_matrix((data, (row, col))) print(sparse.shape) | SciPy

SciPy - Sparse Matrices (scipy.sparse)

What is wrong with this code?

from scipy.sparse import csr_matrix
row = [0, 1]
col = [1, 2]
data = [3, 4]
sparse = csr_matrix((data, (row, col)))
print(sparse.shape)

Arow and col must be numpy arrays

Bcsr_matrix cannot be created from data and indices

CShape parameter is missing, so shape is ambiguous

DData values must be integers

Step-by-Step Solution

Solution:

Step 1: Check csr_matrix constructor usage
When shape is not provided, csr_matrix infers shape from max indices.
Step 2: Understand shape inference
Since col has index 2, shape should be at least (2, 3), but not explicitly set, so shape may be ambiguous.
Final Answer:
Shape parameter is missing, so shape is ambiguous -> Option C
Quick Check:
Missing shape can cause ambiguity [OK]

Quick Trick: Always specify shape to avoid ambiguity [OK]

Common Mistakes:

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What is wrong with this code?