COO format (Coordinate) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a COO sparse matrix from data, row, and col arrays.

SciPy

from scipy.sparse import [1]
data = [4, 5, 7]
row = [0, 1, 2]
col = [1, 2, 0]
sparse_matrix = [1]((data, (row, col)), shape=(3, 3))

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Adok_matrix

Bcsr_matrix

Ccsc_matrix

Dcoo_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using CSR or CSC format constructors instead of COO.

Not passing the data and (row, col) tuple correctly.

The coo_matrix function creates a sparse matrix in COO format using data, row, and column arrays.

2fill in blank

medium

Complete the code to access the row indices of the COO sparse matrix.

SciPy

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

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Arow

Bshape

Ccol

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using col instead of row.

Trying to access row indices via shape or data.

The row attribute of a COO matrix gives the array of row indices for nonzero elements.

3fill in blank

hard

Fix the error in the code to correctly create a COO matrix from data and indices.

SciPy

from scipy.sparse import coo_matrix
data = [10, 20]
row = [0, 1]
col = [1, 0]
sparse = coo_matrix([1], shape=(2, 2))

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A(data, (row, col))

Bdata, (row, col)

C[data, (row, col)]

D(data, row, col)

Attempts:

3 left

💡 Hint

Common Mistakes

Passing data, row, col as separate arguments.

Using a list instead of a tuple for the first argument.

The COO matrix constructor requires a tuple of (data, (row, col)) as the first argument.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each nonzero element's (row, col) to its value.

SciPy

from scipy.sparse import coo_matrix
data = [3, 6, 9]
row = [0, 1, 2]
col = [2, 0, 1]
sparse = coo_matrix((data, (row, col)), shape=(3, 3))
coord_dict = { [1]: sparse.data[i] for i in range(len(sparse.data)) }

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A(sparse.row[i], sparse.col[i])

B(sparse.row[i], sparse.data[i])

D(sparse.col[i], sparse.row[i])

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping row and col in the key tuple.

Using data instead of col in the key.

The dictionary comprehension uses the tuple (row, col) as keys and the data values as values.

5fill in blank

hard

Fill all three blanks to create a COO matrix and convert it to a dense array.

SciPy

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

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Acoo_matrix

Ctoarray

Dtodense

Attempts:

3 left

💡 Hint

Common Mistakes

Using todense() which returns a matrix, not an array.

Forgetting to import coo_matrix.

Use coo_matrix to create the sparse matrix and toarray() to convert it to a dense NumPy array.