[Solved] Consider the following code snippet:from scipy.sparse import csr_matrix, save_npz, load_npz import numpy as np arr = np.array([[0, 0, 1], [1, 0, 0], [0, 2, 0]]) sp = csr_matrix(arr) save_npz('matrix.... | SciPy

SciPy - Integration with Scientific Ecosystem

Consider the following code snippet:

from scipy.sparse import csr_matrix, save_npz, load_npz
import numpy as np

arr = np.array([[0, 0, 1], [1, 0, 0], [0, 2, 0]])
sp = csr_matrix(arr)
save_npz('matrix.npz', sp)
loaded_sp = load_npz('matrix.npz')
print(loaded_sp.toarray())

What will be the output printed?

AError: cannot convert sparse matrix to array

B[[0 0 0] [0 0 0] [0 0 0]]

C[[0 0 1] [1 0 0] [0 2 0]]

D[[1 0 0] [0 1 0] [0 0 1]]

Step-by-Step Solution

Solution:

Step 1: Create sparse matrix and save it
The code creates a sparse matrix from the numpy array, saves it to 'matrix.npz', then loads it back.
Step 2: Convert loaded sparse matrix to dense array and print
Using toarray() converts the sparse matrix back to the original dense numpy array, so the printed output matches the original array.
Final Answer:
[[0 0 1] [1 0 0] [0 2 0]] -> Option C
Quick Check:
load_npz + toarray() = original array [OK]

Quick Trick: load_npz returns sparse; use toarray() to see full matrix [OK]

Common Mistakes:

Expecting zeros after loading
Forgetting to convert sparse to dense before printing
Confusing save_npz and load_npz usage

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Consider the following code snippet:

Step 1: Create sparse matrix and save it

Step 2: Convert loaded sparse matrix to dense array and print

Final Answer:

Quick Check:

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