You have a large sparse matrix stored in large_matrix.npz. You want to load it, add 5 to all non-zero elements, and save it back without converting to a dense matrix (to save memory). Which code snippet correctly does this?
Step 1: Load sparse matrix and modify non-zero elements
Using mat.data accesses the non-zero values directly. Adding 5 to mat.data updates only those values without converting to dense.
Step 2: Save the updated sparse matrix back
Using save_npz saves the modified sparse matrix efficiently.
Step 3: Check other options for correctness
from scipy.sparse import load_npz, save_npz
mat = load_npz('large_matrix.npz')
mat = mat.toarray() + 5
save_npz('large_matrix.npz', mat) converts to dense explicitly, adds 5 to all elements including zeros, wastes memory, and save_npz fails on dense array. import numpy as np
mat = np.load('large_matrix.npz')
mat += 5
np.save('large_matrix.npz', mat) uses numpy load/save which does not handle sparse matrices. from scipy.sparse import load_npz, save_npz
mat = load_npz('large_matrix.npz')
mat.toarray() += 5
save_npz('large_matrix.npz', mat) tries to add 5 to a dense array but does not assign back, and wastes memory.
Final Answer:
from scipy.sparse import load_npz, save_npz
mat = load_npz('large_matrix.npz')
mat.data += 5
save_npz('large_matrix.npz', mat) -> Option A
Quick Check:
Modify mat.data for sparse update [OK]
Quick Trick:Change mat.data to update non-zero sparse values [OK]
Common Mistakes:
Adding scalar directly to sparse matrix (converts to dense)
Using numpy load/save for sparse matrices
Modifying dense array without saving changes
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