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.

Practice

1. What is the main advantage of using sparse data structures in data analysis?

easy

A. They make data harder to read

B. They increase the number of zeros in the data

C. They convert data into images

D. They save memory by storing only non-zero values

Solution

1. Step 1: Understand sparse data concept

   Sparse data structures store only the important non-zero values instead of all values.

2. Step 2: Identify the benefit

   By storing only non-zero values, sparse structures save memory and improve efficiency.

3. Final Answer:

   They save memory by storing only non-zero values -> Option D

4. Quick Check:

   Sparse data saves memory = A [OK]

Hint: Sparse means store only non-zero values to save memory [OK]

Common Mistakes:

• Thinking sparse data increases zeros
• Confusing sparse with data visualization
• Assuming sparse data stores all values

2. Which of the following is the correct way to import the sparse matrix module from SciPy in Python?

easy

A. import scipy.sparse as sp

B. import sparse from scipy

C. from scipy import sparse_matrix

D. import scipy.sparse_matrix

Solution

1. Step 1: Recall correct import syntax

   The SciPy sparse module is imported as import scipy.sparse as sp to use sparse matrix functions.

2. Step 2: Check other options

   Options A, B, and C use incorrect syntax or module names that do not exist.

3. Final Answer:

   import scipy.sparse as sp -> Option A

4. Quick Check:

   Correct import = D [OK]

Hint: Use 'import scipy.sparse as sp' to access sparse matrices [OK]

Common Mistakes:

• Using wrong import syntax
• Trying to import non-existent modules
• Confusing module names

3. What will be the output of the following Python code?

import numpy as np
from scipy.sparse import csr_matrix

arr = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
sparse_mat = csr_matrix(arr)
print(sparse_mat.nnz)

medium

A. 6

B. 9

C. 3

D. 0

Solution

1. Step 1: Understand the input array

   The array has three non-zero elements: 1 at (0,2), 1 at (1,0), 1 at (2,1), and zeros elsewhere.

2. Step 2: Use csr_matrix and check nnz

   The csr_matrix stores only non-zero elements. The nnz attribute counts these non-zero values, which is 3.

3. Final Answer:

   3 -> Option C

4. Quick Check:

   Count non-zero elements = 3 [OK]

Hint: nnz counts non-zero elements in sparse matrix [OK]

Common Mistakes:

• Counting total elements instead of non-zero
• Confusing nnz with shape
• Assuming zeros are counted

4. Identify the error in this code snippet that tries to create a sparse matrix:

from scipy.sparse import csr_matrix

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

medium

A. The input should be a 2D array, not a 1D list

B. csr_matrix does not accept lists

C. Missing import for numpy

D. The print statement is incorrect

Solution

1. Step 1: Check input format for csr_matrix

   The csr_matrix expects a 2D array or matrix-like input, but a 1D list is given.

2. Step 2: Understand error cause

   Passing a 1D list causes shape ambiguity. The input should be reshaped or given as 2D, e.g., [[1,0,0,2,0,3]].

3. Final Answer:

   The input should be a 2D array, not a 1D list -> Option A

4. Quick Check:

   csr_matrix needs 2D input = C [OK]

Hint: csr_matrix input must be 2D array or matrix [OK]

Common Mistakes:

• Passing 1D list directly
• Ignoring input shape requirements
• Assuming csr_matrix accepts any iterable

5. You have a large dataset with many zero values stored as a dense NumPy array. Which approach best converts it to a sparse matrix and then back to a dense array without losing data?

hard

A. Use csc_matrix to convert, then .todense() to convert back

B. Use csr_matrix to convert, then .toarray() to convert back

C. Use dok_matrix to convert, then .to_list() to convert back

D. Use lil_matrix to convert, then .to_numpy() to convert back

Solution

1. Step 1: Choose correct sparse format for conversion

   csr_matrix is efficient for large sparse data and supports easy conversion back to dense.

2. Step 2: Convert back to dense array

   Use .toarray() method on the sparse matrix to get the original dense NumPy array without data loss.

3. Final Answer:

   Use csr_matrix to convert, then .toarray() to convert back -> Option B

4. Quick Check:

   csr_matrix + toarray() preserves data = A [OK]

Hint: csr_matrix and toarray() convert sparse to dense safely [OK]

Common Mistakes:

• Using .todense() which returns matrix, not array
• Using .to_list() which does not exist
• Assuming .to_numpy() works on sparse matrices