[Solved] You have a large 10000x10000 matrix with 99% zeros. Which approach saves the most memory and why?Option A: Store as a dense numpy arrayOption B: Store as a scipy sparse matrixOption C:... | SciPy

SciPy - Sparse Matrices (scipy.sparse)

You have a large 10000x10000 matrix with 99% zeros. Which approach saves the most memory and why?

Option A: Store as a dense numpy array
Option B: Store as a scipy sparse matrix
Option C: Store as a list of lists including zeros
Option D: Store as a dictionary with all elements

AOption A, because numpy arrays are always memory efficient

BOption B, because sparse matrices store only non-zero values and positions

COption C, because lists are flexible and fast

DOption D, because dictionaries store keys and values explicitly

Step-by-Step Solution

Solution:

Step 1: Analyze memory use of dense numpy arrays
Dense arrays store every element, so 10000x10000 elements use a lot of memory, including zeros.
Step 2: Analyze sparse matrix memory use
Sparse matrices store only non-zero elements and their positions, greatly reducing memory when zeros dominate.
Step 3: Compare lists and dictionaries
Lists of lists store all elements including zeros, using much memory. Dictionaries store keys and values, but overhead is high and all elements must be stored.
Final Answer:
Option B, because sparse matrices store only non-zero values and positions -> Option B
Quick Check:
Sparse matrices save memory with many zeros [OK]

Quick Trick: Sparse matrices save memory by ignoring zeros [OK]

Common Mistakes:

MISTAKES

Assuming numpy arrays are always memory efficient
Thinking lists or dictionaries save memory with many zeros
Ignoring overhead of storing keys in dictionaries

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More SciPy Quizzes

You have a large 10000x10000 matrix with 99% zeros. Which approach saves the most memory and why?

Step 1: Analyze memory use of dense numpy arrays

Step 2: Analyze sparse matrix memory use

Step 3: Compare lists and dictionaries

Final Answer:

Quick Check:

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