Why is it advantageous to use save_npz and load_npz for sparse matrices instead of standard NumPy save/load functions?

easy📝 Conceptual Q1 of 15

SciPy - Integration with Scientific Ecosystem

Why is it advantageous to use save_npz and load_npz for sparse matrices instead of standard NumPy save/load functions?

AThey only work with dense matrices

BThey convert sparse matrices to dense arrays automatically

CThey preserve the sparse matrix format and save disk space

DThey compress data using lossy compression

Step-by-Step Solution

Solution:

Step 1: Understand sparse matrix storage
Sparse matrices store mostly zeros efficiently, unlike dense arrays.
Step 2: Role of save_npz/load_npz
These functions save and load sparse matrices in a compressed format without converting to dense, saving disk space.
Final Answer:
They preserve the sparse matrix format and save disk space -> Option C
Quick Check:
Check if sparse format is preserved and file size is small [OK]

Quick Trick: Use save_npz/load_npz to keep sparse format and save space [OK]

Common Mistakes:

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