Creating sparse matrices in SciPy - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the sparse matrix module from scipy.

SciPy

from scipy import [1]

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Astats

Barray

Csparse

Doptimize

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated modules like scipy.array or scipy.stats.

Forgetting to import the sparse module before using it.

The sparse module in scipy is used to create and work with sparse matrices efficiently.

2fill in blank

medium

Complete the code to create a 3x3 sparse matrix with data [1, 2, 3] at positions (0,0), (1,1), and (2,2) using the COO format.

SciPy

from scipy.sparse import coo_matrix

row = [0, 1, 2]
col = [0, 1, 2]
data = [1, 2, 3]
sparse_matrix = coo_matrix(([1], (row, col)), shape=(3, 3))

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Adata

Brow

Ccol

D[1, 2, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing row or col instead of data as the first argument.

Passing a list literal instead of the variable holding data.

The first argument to coo_matrix is the data array containing the values to place in the sparse matrix.

3fill in blank

hard

Fix the error in the code to create a CSR sparse matrix from dense data.

SciPy

import numpy as np
from scipy.sparse import csr_matrix

dense = np.array([[0, 0, 1], [1, 0, 0], [0, 2, 0]])
sparse = csr_matrix([1])

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Adense.tolist()

Bdense.shape

Cdense.flatten()

Ddense

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list instead of a numpy array causes errors.

Passing the shape instead of the data.

The csr_matrix constructor accepts a dense numpy array directly to convert it into a sparse matrix.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

SciPy

words = ['data', 'science', 'is', 'fun']
lengths = {word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 3

Clen(word) < 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using the wrong condition like less than 3.

The dictionary comprehension maps each word to its length using len(word). The condition 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 the count is greater than 1.

SciPy

word_counts = {'data': 2, 'science': 1, 'fun': 3}
result = [1]: [2] for [3], [2] in word_counts.items() if [2] > 1}

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Aword.upper()

Bcount

Cword

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong loop variable like 'item' instead of 'word'.

Not converting words to uppercase.

Using the wrong variable in the condition.

The comprehension maps the uppercase version of each word (word.upper()) to its count (count). The loop variable is word and count from word_counts.items(). The condition filters counts greater than 1.