CSR format (Compressed Sparse Row) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a CSR matrix from data, row indices, and column indices.

SciPy

from scipy.sparse import csr_matrix

data = [1, 2, 3]
rows = [0, 0, 1]
cols = [0, 2, 1]

matrix = csr_matrix(([1], (rows, cols)), shape=(2, 3))
print(matrix.toarray())

Drag options to blanks, or click blank then click option'

Ashape

Brows

Ccols

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using row or column indices instead of data as the first argument.

The first argument to csr_matrix is the data values, so we use data.

2fill in blank

medium

Complete the code to get the number of non-zero elements in the CSR matrix.

SciPy

from scipy.sparse import csr_matrix

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

non_zero_count = matrix.[1]
print(non_zero_count)

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Asize

Bnnz

Ccount_nonzero

Dshape

Attempts:

3 left

💡 Hint

Common Mistakes

Using shape or size which give matrix dimensions, not count of non-zero elements.

The nnz attribute gives the number of non-zero elements in a CSR matrix.

3fill in blank

hard

Fix the error in the code to convert a dense numpy array to CSR format.

SciPy

import numpy as np
from scipy.sparse import csr_matrix

arr = np.array([[0, 4, 0], [3, 0, 0]])
csr = csr_matrix([1])
print(csr.toarray())

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Aarr.flatten()

Barr.tolist()

Carr

Dlist(arr)

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list or flattened array instead of the numpy array.

The csr_matrix constructor accepts a numpy array directly to convert it to CSR format.

4fill in blank

hard

Fill both blanks to create a CSR matrix and then access its data array.

SciPy

from scipy.sparse import csr_matrix

data = [10, 20, 30]
rows = [0, 1, 2]
cols = [1, 2, 0]

matrix = csr_matrix(([1], (rows, cols)), shape=(3, 3))
data_array = matrix.[2]
print(data_array)

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Adata

Brows

Dindices

Attempts:

3 left

💡 Hint

Common Mistakes

Using row or column indices instead of data for the first blank.

Using 'indices' instead of 'data' to access values.

The first blank is the data values to build the matrix. The second blank accesses the data attribute of the CSR matrix which stores non-zero values.

5fill in blank

hard

Fill all three blanks to create a CSR matrix, convert it to dense, and get its shape.

SciPy

from scipy.sparse import csr_matrix

data = [5, 8, 3, 6]
rows = [0, 0, 1, 2]
cols = [0, 2, 2, 1]

matrix = csr_matrix(([1], ([2], [3])), shape=(3, 3))
dense = matrix.toarray()
print(dense)
print(matrix.shape)

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Adata

Brows

Ccols

Dshape

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up rows and columns in the tuple.

Using shape as part of the tuple instead of a separate argument.

The first blank is the data values, the second is the row indices, and the third is the column indices to build the CSR matrix.