Sparse matrix operations in SciPy - Mini Project: Build & Apply

Sparse matrix operations

📖 Scenario: Imagine you work with a large dataset where most values are zero, like a survey with many questions but few answers. Using sparse matrices helps save memory and speed up calculations.

🎯 Goal: You will create a sparse matrix, set a threshold to filter values, perform a matrix operation, and display the result.

📋 What You'll Learn

Use scipy.sparse to create and manipulate sparse matrices

Create a sparse matrix with exact values

Set a threshold variable to filter matrix values

Perform element-wise filtering using the threshold

Print the final filtered sparse matrix

💡 Why This Matters

🌍 Real World

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

💼 Career

Data scientists and engineers use sparse matrix operations to efficiently handle large datasets and speed up computations.

1

Create a sparse matrix

Import csr_matrix from scipy.sparse and create a sparse matrix called matrix with these exact values: [[0, 0, 3], [4, 0, 0], [0, 5, 0]].

SciPy

# Import csr_matrix and create the sparse matrix named matrix
# Your code here

Need a hint?

Use csr_matrix to create a sparse matrix from a list of lists.

2

Set a threshold value

Create a variable called threshold and set it to 3.

SciPy

from scipy.sparse import csr_matrix

matrix = csr_matrix([[0, 0, 3], [4, 0, 0], [0, 5, 0]])

# Create threshold variable and set it to 3
# Your code here

Need a hint?

Just assign the number 3 to a variable named threshold.

3

Filter matrix values using threshold

Create a new sparse matrix called filtered_matrix that contains only the values from matrix greater than or equal to threshold. Use element-wise comparison and multiplication.

SciPy

from scipy.sparse import csr_matrix

matrix = csr_matrix([[0, 0, 3], [4, 0, 0], [0, 5, 0]])

threshold = 3

# Create filtered_matrix with values >= threshold
# Your code here

Need a hint?

Use matrix >= threshold to get a mask, then multiply it element-wise with matrix.

4

Print the filtered sparse matrix

Print the filtered_matrix converted to a dense array using the .toarray() method.

SciPy

from scipy.sparse import csr_matrix

matrix = csr_matrix([[0, 0, 3], [4, 0, 0], [0, 5, 0]])

threshold = 3

filtered_matrix = matrix.multiply(matrix >= threshold)

# Print the filtered_matrix as a dense array
# Your code here

Need a hint?

Use print(filtered_matrix.toarray()) to see the full matrix.