Sparse matrix file I/O in SciPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of saving and loading a sparse matrix

What will be the output shape of the sparse matrix after saving and loading it using scipy's save_npz and load_npz?

SciPy

import numpy as np
from scipy.sparse import csr_matrix, save_npz, load_npz

matrix = csr_matrix(np.array([[0, 0, 1], [1, 0, 0], [0, 2, 0]]))
save_npz('test_matrix.npz', matrix)
loaded_matrix = load_npz('test_matrix.npz')
print(loaded_matrix.shape)

A (3, 2)

B (2, 3)

C (3, 3)

D (2, 2)

💡 Hint

The shape of the matrix does not change when saving and loading with scipy sparse functions.

The save_npz and load_npz functions preserve the sparse matrix structure and shape exactly. The original matrix is 3 rows by 3 columns, so the loaded matrix shape is (3, 3).

❓ data_output

intermediate

Data content after loading sparse matrix

After saving and loading the sparse matrix below, what will be the value at position (1, 0) in the loaded matrix?

SciPy

import numpy as np
from scipy.sparse import csr_matrix, save_npz, load_npz

matrix = csr_matrix(np.array([[0, 0, 1], [1, 0, 0], [0, 2, 0]]))
save_npz('test_matrix.npz', matrix)
loaded_matrix = load_npz('test_matrix.npz')
print(loaded_matrix[1, 0])

A None

B 1

C 0

D 2

💡 Hint

Check the original matrix value at row 1, column 0.

The original matrix has value 1 at position (1, 0). Saving and loading does not change values, so the loaded matrix at (1, 0) is 1.

🔧 Debug

advanced

Identify the error when loading a sparse matrix

What error will occur when trying to load a sparse matrix from a file that does not exist using load_npz?

SciPy

from scipy.sparse import load_npz
loaded_matrix = load_npz('nonexistent_file.npz')

A TypeError

B ValueError

C KeyError

D FileNotFoundError

💡 Hint

Think about what happens when you try to open a file that does not exist.

Trying to load a file that does not exist raises a FileNotFoundError because the file system cannot find the specified file.

🚀 Application

advanced

Choosing the best file format for sparse matrix storage

You have a large sparse matrix that you want to save and share with others. Which file format is best to use for saving and loading sparse matrices efficiently with scipy?

A NPZ format using save_npz and load_npz

B CSV format using numpy.savetxt and numpy.loadtxt

C TXT format using Python's open and write

D JSON format using json.dump and json.load

💡 Hint

Consider which format preserves sparsity and is optimized for scipy sparse matrices.

NPZ format with save_npz and load_npz is designed for sparse matrices. It preserves sparsity and is efficient in storage and speed. CSV, TXT, and JSON do not preserve sparsity and are inefficient for large sparse data.

🧠 Conceptual

expert

Understanding sparse matrix file I/O internals

Which of the following best describes what save_npz stores when saving a scipy CSR sparse matrix?

A It stores the data array, indices array, indptr array, and shape tuple in a compressed NPZ file

B It stores the full dense matrix as a numpy array in the NPZ file

C It stores only the nonzero values in a plain text file

D It stores the matrix as a JSON object with keys for rows and columns

💡 Hint

Think about how CSR format represents sparse matrices internally.

The CSR format stores three arrays: data (nonzero values), indices (column indices), and indptr (index pointers for row starts), plus the shape. save_npz saves these arrays and shape in a compressed NPZ file for efficient storage.