CSR format (Compressed Sparse Row) in SciPy - Mini Project: Build & Apply

Working with CSR format (Compressed Sparse Row) using scipy

📖 Scenario: Imagine you have a large table of numbers mostly filled with zeros. This is common in real-world data like user ratings or connections in social networks. Storing all those zeros wastes space. The CSR format helps us store only the important numbers efficiently.

🎯 Goal: You will create a sparse matrix using CSR format with scipy, configure a threshold to filter values, extract the filtered data, and finally print the filtered matrix data.

📋 What You'll Learn

Create a sparse matrix in CSR format using scipy

Define a threshold value to filter matrix data

Use CSR matrix attributes to filter data based on the threshold

Print the filtered data arrays

💡 Why This Matters

🌍 Real World

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

💼 Career

Understanding CSR format helps in efficiently storing and processing large datasets in data science and machine learning jobs.

1

Create a CSR sparse matrix

Import csr_matrix from scipy.sparse and create a CSR matrix called matrix from this 3x4 dense array: [[0, 0, 1, 0], [2, 0, 0, 3], [0, 4, 0, 0]]

SciPy

# Your code here

Need a hint?

Use csr_matrix() to convert a list of lists into a sparse matrix.

2

Set a threshold value

Create a variable called threshold and set it to 2 to filter matrix values greater than this number.

SciPy

from scipy.sparse import csr_matrix

matrix = csr_matrix([[0, 0, 1, 0], [2, 0, 0, 3], [0, 4, 0, 0]])
# Your code here

Need a hint?

Just assign the number 2 to a variable named threshold.

3

Filter matrix data using the threshold

Create a new variable called filtered_data that contains only the values from matrix.data which are greater than threshold using a list comprehension.

SciPy

from scipy.sparse import csr_matrix

matrix = csr_matrix([[0, 0, 1, 0], [2, 0, 0, 3], [0, 4, 0, 0]])

threshold = 2
# Your code here

Need a hint?

Use a list comprehension to pick values from matrix.data that are greater than threshold.

4

Print the filtered data

Print the variable filtered_data to show the filtered values.

SciPy

from scipy.sparse import csr_matrix

matrix = csr_matrix([[0, 0, 1, 0], [2, 0, 0, 3], [0, 4, 0, 0]])

threshold = 2

filtered_data = [value for value in matrix.data if value > threshold]
# Your code here

Need a hint?

Use print(filtered_data) to display the filtered values.