COO format (Coordinate) in SciPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of COO matrix data arrays

Given the following code creating a COO sparse matrix, what will be the output of the data, row, and col arrays?

SciPy

from scipy.sparse import coo_matrix
import numpy as np
row = np.array([0, 3, 1, 0])
col = np.array([0, 3, 1, 2])
data = np.array([4, 5, 7, 9])
coo = coo_matrix((data, (row, col)), shape=(4, 4))
print(coo.data)
print(coo.row)
print(coo.col)

A

[4 9 7 5]
[0 0 1 3]
[0 2 1 3]

B

[9 7 5 4]
[0 1 3 0]
[2 1 3 0]

C

[4 5 7 9]
[0 3 1 0]
[0 3 1 2]

D

[4 7 5 9]
[0 1 3 0]
[0 1 3 2]

💡 Hint

The COO format stores data in the order you provide the row and column indices.

The coo_matrix stores the data, row, and col arrays exactly as given during creation. So the output matches the input arrays.

❓ data_output

intermediate

Number of non-zero elements in COO matrix

How many non-zero elements does the following COO matrix have?

SciPy

from scipy.sparse import coo_matrix
row = [0, 1, 1, 2, 3]
col = [0, 0, 2, 3, 3]
data = [1, 2, 3, 4, 5]
coo = coo_matrix((data, (row, col)), shape=(4, 4))
print(coo.nnz)

A 6

B 5

C 3

D 4

💡 Hint

nnz means number of stored non-zero elements.

The nnz attribute counts the number of non-zero entries stored, which is the length of the data array.

🔧 Debug

advanced

Identify the error in COO matrix creation

What error will this code raise when creating a COO matrix?

SciPy

from scipy.sparse import coo_matrix
row = [0, 1, 2]
col = [0, 1]
data = [1, 2, 3]
coo = coo_matrix((data, (row, col)), shape=(3, 3))

A IndexError: index out of bounds

B TypeError: data must be a 1-dimensional array

C No error, matrix created successfully

D ValueError: shape mismatch: objects cannot be broadcast to a single shape

💡 Hint

Check if the lengths of row, col, and data arrays match.

The row and data arrays have length 3, but col has length 2, causing a shape mismatch error.

🚀 Application

advanced

Convert COO matrix to dense and find sum

What is the sum of all elements in the dense matrix converted from this COO matrix?

SciPy

from scipy.sparse import coo_matrix
row = [0, 1, 2, 2]
col = [0, 1, 1, 2]
data = [10, 20, 30, 40]
coo = coo_matrix((data, (row, col)), shape=(3, 3))
dense = coo.toarray()
print(dense.sum())

A 100

B 90

C 80

D 120

💡 Hint

Sum all the data values since zeros do not add.

Sum of data array is 10+20+30+40=100, which is the sum of all elements in the dense matrix.

🧠 Conceptual

expert

Effect of duplicate entries in COO matrix

If a COO matrix has duplicate entries at the same row and column indices, what happens when converting it to CSR format?

A The duplicate entries are summed together in the CSR matrix.

B The duplicate entries cause an error during conversion.

C Only the first duplicate entry is kept; others are discarded.

D The duplicates remain separate and increase the nnz count.

💡 Hint

Think about how sparse formats handle duplicates when compressed.

When converting COO to CSR, duplicate entries at the same position are summed into a single value.