Why sparse matrices save memory in SciPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a sparse matrix from a dense matrix using SciPy.

SciPy

from scipy.sparse import [1]
dense_matrix = [[0, 0, 3], [4, 0, 0], [0, 0, 0]]
sparse_matrix = [1](dense_matrix)

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Acsr_matrix

Blist

Cmatrix

Darray

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'array' or 'list' instead of a sparse matrix constructor.

Trying to create a sparse matrix without importing the correct function.

The csr_matrix function from SciPy creates a sparse matrix from a dense matrix efficiently.

2fill in blank

medium

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

SciPy

from scipy.sparse import csr_matrix
sparse_matrix = csr_matrix([[0, 0, 3], [4, 0, 0], [0, 0, 0]])
non_zero_count = sparse_matrix.[1]

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Ashape

Bsize

Ccount

Dnnz

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'size' which gives total elements, not just non-zero.

Using 'shape' which gives matrix dimensions.

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

3fill in blank

hard

Fix the error in the code to convert a sparse matrix back to a dense array.

SciPy

from scipy.sparse import csr_matrix
sparse_matrix = csr_matrix([[0, 0, 3], [4, 0, 0], [0, 0, 0]])
dense_array = sparse_matrix.[1]()

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Atoarray

Btodense

Cto_list

Dto_numpy

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'todense' which returns a matrix, not an array.

Using non-existent methods like 'to_list' or 'to_numpy'.

The toarray() method converts a sparse matrix to a dense NumPy array.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

SciPy

{word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 3

Cword

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the word in the condition instead of its length.

The dictionary comprehension maps each word to its length using len(word) and filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts only if count is greater than 0.

SciPy

{ [1]: [2] for [3], [2] in data.items() if [2] > 0 }

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Ak.upper()

Bv

Ck

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'item' instead of 'k' and 'v'.

Not converting the key to uppercase.

The comprehension maps uppercase keys k.upper() to their values v for each key k and value v in data.items(), filtering values greater than 0.