Sparse data handling in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Data Analysis Python

from scipy.sparse import [1]

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Acsr_matrix

Barray

Cmatrix

Dsparse_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'array' which is a dense structure.

Using 'matrix' which is deprecated in SciPy.

Using 'sparse_matrix' which is not a valid class.

The csr_matrix is a common sparse matrix format in SciPy used for efficient storage.

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).

Data Analysis Python

from scipy.sparse import csr_matrix
import numpy as np
rows = np.array([0, 1, 2])
cols = np.array([0, 1, 2])
data = np.array([1, 2, 3])
sparse_mat = csr_matrix(([1], (rows, cols)), shape=(3, 3))

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Arows

Bcols

Cdata

D[1, 2, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'rows' or 'cols' instead of the actual data values.

Using a list literal instead of the data variable.

The first argument to csr_matrix is the data array containing the non-zero values.

3fill in blank

hard

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

Data Analysis Python

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 the shape instead of the array.

Passing a list instead of a NumPy array.

Passing a flattened array which loses 2D structure.

The csr_matrix constructor accepts a dense NumPy array directly to convert it to sparse format.

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.

Data Analysis Python

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

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Alen(word)

Blen(word) > 3

Cword == 'data'

Dword in words

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of length.

Using a condition unrelated to length.

Using 'word in words' which is always true.

The comprehension maps each word to its length only if the length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of word counts keeping only words with count greater than 1.

Data Analysis Python

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

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Aword

Bcount

Cword_counts

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'item' instead of unpacking into word and count.

Using 'word_counts' as loop variable instead of items.

Not filtering counts greater than 1.

The comprehension iterates over word_counts.items() unpacking into word and count. It keeps only words with count > 1.