Why sparse matrices save memory in SciPy - See It in Action

Why sparse matrices save memory

📖 Scenario: Imagine you have a large table of numbers where most of the values are zero. This happens often in real life, like when you track which movies people watched but most people watch only a few movies.

🎯 Goal: You will create a normal matrix and a sparse matrix with the same data. Then you will compare their memory sizes to see why sparse matrices save memory.

📋 What You'll Learn

Create a dense matrix using numpy with mostly zeros

Create a sparse matrix using scipy.sparse.csr_matrix from the dense matrix

Calculate the memory size of both matrices

Print the memory sizes to compare

💡 Why This Matters

🌍 Real World

Sparse matrices are used in recommendation systems, natural language processing, and network analysis where data is mostly zeros.

💼 Career

Understanding sparse matrices helps in optimizing memory and speed in data science and machine learning jobs.

1

Create a dense matrix with mostly zeros

Import numpy as np and create a 5x5 dense matrix called dense_matrix with these exact values: all zeros except dense_matrix[1, 2] = 10 and dense_matrix[3, 4] = 20.

SciPy

# Import numpy and create the dense matrix
# Your code here

Need a hint?

Use np.zeros((5, 5), dtype=np.int32) to create the matrix. Then assign values to the specific positions.

2

Create a sparse matrix from the dense matrix

Import csr_matrix from scipy.sparse and create a sparse matrix called sparse_matrix from dense_matrix.

SciPy

import numpy as np

dense_matrix = np.zeros((5, 5), dtype=np.int32)
dense_matrix[1, 2] = 10
dense_matrix[3, 4] = 20

# Import csr_matrix and create sparse_matrix from dense_matrix
# Your code here

Need a hint?

Use csr_matrix(dense_matrix) to convert the dense matrix to sparse.

3

Calculate memory sizes of both matrices

Calculate the memory size of dense_matrix using dense_matrix.nbytes and store it in dense_size. Calculate the memory size of sparse_matrix using sparse_matrix.data.nbytes and store it in sparse_size.

SciPy

import numpy as np
from scipy.sparse import csr_matrix

dense_matrix = np.zeros((5, 5), dtype=np.int32)
dense_matrix[1, 2] = 10
dense_matrix[3, 4] = 20

sparse_matrix = csr_matrix(dense_matrix)

# Calculate dense_size and sparse_size
# Your code here

Need a hint?

Use .nbytes to get the memory size in bytes.

4

Print the memory sizes to compare

Print the text "Dense matrix size:" followed by dense_size. Then print the text "Sparse matrix size:" followed by sparse_size.

SciPy

import numpy as np
from scipy.sparse import csr_matrix

dense_matrix = np.zeros((5, 5), dtype=np.int32)
dense_matrix[1, 2] = 10
dense_matrix[3, 4] = 20

sparse_matrix = csr_matrix(dense_matrix)

dense_size = dense_matrix.nbytes
sparse_size = sparse_matrix.data.nbytes

# Print the sizes
# Your code here

Need a hint?

Use two print statements to show the sizes with the exact text.