Sparse matrix operations in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a 3x3 sparse matrix with scipy.

SciPy

from scipy.sparse import [1]

matrix = [1]([[0, 0, 1], [1, 0, 0], [0, 0, 0]])
print(matrix)

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

Adok_matrix

Bcsc_matrix

Ccsr_matrix

Dlil_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using dense matrix instead of sparse format.

Choosing a sparse format not suitable for arithmetic.

The csr_matrix is a common sparse matrix format for efficient arithmetic and matrix vector operations.

2fill in blank

medium

Complete the code to convert a dense numpy array to a sparse matrix.

SciPy

import numpy as np
from scipy.sparse import csr_matrix

dense = np.array([[0, 2, 0], [3, 0, 0], [0, 0, 4]])
sparse = csr_matrix([1])
print(sparse)

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

Adense

Bsparse

Cnp.array

Dnp.zeros

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the sparse matrix variable to the constructor again.

Passing a function instead of the array.

To convert a dense numpy array to a sparse matrix, pass the dense array to csr_matrix.

3fill in blank

hard

Fix the error in the code to add two sparse matrices correctly.

SciPy

from scipy.sparse import csr_matrix

A = csr_matrix([[1, 0], [0, 2]])
B = csr_matrix([[0, 3], [4, 0]])

result = A [1] B
print(result.toarray())

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A*

B-

C/

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication instead of addition.

Using subtraction or division which are not intended here.

To add two sparse matrices, use the + operator.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

SciPy

words = ['data', 'science', 'ai', 'ml', 'python']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length.

Using < instead of > in the condition.

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

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 2.

SciPy

data = {'a': 1, 'b': 3, 'c': 5, 'd': 2}
filtered = { [1]: [2] for k, v in data.items() if v [3] 2 }
print(filtered)

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Not converting keys to uppercase.

Using wrong comparison operator.

Using key instead of value in the dictionary values.

This dictionary comprehension creates keys as uppercase letters and includes only items where the value is greater than 2.

Practice

1. What is the main advantage of using a sparse matrix in data science?

easy

A. It duplicates all elements to increase accuracy.

B. It automatically sorts data in ascending order.

C. It converts data into a dense format for faster processing.

D. It saves memory by storing only non-zero elements.

Solution

1. Step 1: Understand sparse matrix storage

   Sparse matrices store only non-zero values to save memory space compared to dense matrices.

2. Step 2: Compare options

   Only It saves memory by storing only non-zero elements. correctly describes this key advantage. Other options describe unrelated or incorrect features.

3. Final Answer:

   It saves memory by storing only non-zero elements. -> Option D

4. Quick Check:

   Sparse matrix = memory saving [OK]

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

Common Mistakes:

• Thinking sparse matrices sort data
• Confusing sparse with dense matrix operations
• Assuming sparse matrices duplicate data

2. Which of the following is the correct way to create a CSR sparse matrix from a dense NumPy array arr?

easy

A. csr = scipy.sparse.csr(arr)

B. csr = scipy.sparse.csr_matrix(arr)

C. csr = scipy.sparse.array(arr)

D. csr = scipy.sparse.dense_matrix(arr)

Solution

1. Step 1: Recall CSR matrix creation syntax

   The correct function to create a CSR sparse matrix is csr_matrix() from scipy.sparse.

2. Step 2: Check options

   Only csr = scipy.sparse.csr_matrix(arr) uses the correct function name and syntax. Others are invalid or do not exist.

3. Final Answer:

   csr = scipy.sparse.csr_matrix(arr) -> Option B

4. Quick Check:

   Use csr_matrix() to create CSR sparse matrix [OK]

Hint: Use csr_matrix() to create CSR sparse matrix from array [OK]

Common Mistakes:

• Using non-existent functions like dense_matrix or csr
• Confusing array() with csr_matrix()
• Missing scipy.sparse prefix

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

import numpy as np
from scipy.sparse import csr_matrix

arr = np.array([[0, 1], [2, 0]])
sparse_mat = csr_matrix(arr)
result = sparse_mat.dot(np.array([1, 2]))
print(result.toarray())

medium

A. [[2]
[2]]

B. [[2 4]
[1 2]]

C. [[2 0]
[0 1]]

D. [[2]
[1]]

Solution

1. Step 1: Understand matrix and vector multiplication

   The sparse matrix is [[0,1],[2,0]]. Multiplying by vector [1,2] means:
   Row 1: 0*1 + 1*2 = 2
   Row 2: 2*1 + 0*2 = 2

2. Step 2: Check the printed output

   The result is a 2x1 matrix with values [[2],[2]]. But the code prints result.toarray(), which shows a 2D array.
   So output is:
   [[2]
   [2]]

3. Final Answer:

   [[2] [2]] -> Option A

4. Quick Check:

   Dot product result = [[2],[2]] [OK]

Hint: Multiply rows by vector elements and sum for dot product [OK]

Common Mistakes:

• Mixing element-wise and dot product multiplication
• Confusing shape of output array
• Incorrectly summing elements

4. The following code throws an error. What is the most likely cause?

from scipy.sparse import csr_matrix

mat1 = csr_matrix([[1, 0], [0, 2]])
mat2 = csr_matrix([[3, 4]])
result = mat1 + mat2

medium

A. Shapes of mat1 and mat2 do not match for addition.

B. csr_matrix cannot be added using + operator.

C. mat2 must be converted to dense before addition.

D. mat1 contains zeros which cause errors in addition.

Solution

1. Step 1: Check shapes of matrices

   mat1 shape is (2,2), mat2 shape is (1,2). Addition requires same shape.

2. Step 2: Understand addition rules for sparse matrices

   Adding matrices with different shapes causes a ValueError. The + operator works fine if shapes match.

3. Final Answer:

   Shapes of mat1 and mat2 do not match for addition. -> Option A

4. Quick Check:

   Matrix addition requires matching shapes [OK]

Hint: Check matrix shapes before adding sparse matrices [OK]

Common Mistakes:

• Assuming + operator is invalid for sparse matrices
• Ignoring shape mismatch errors
• Thinking zeros cause addition errors

5. You have two CSR sparse matrices A and B representing user-item ratings. You want to compute the element-wise product to find items rated by both users. Which method is the most efficient and correct way to do this?

hard

A. Convert both to dense arrays, multiply, then convert back to sparse.

B. Use A.dot(B) for element-wise multiplication.

C. Use A.multiply(B) to get element-wise multiplication.

D. Use A + B and then subtract zeros.

Solution

1. Step 1: Identify element-wise multiplication method

   For sparse matrices, multiply() performs element-wise multiplication efficiently without converting to dense.

2. Step 2: Evaluate other options

   dot() is matrix multiplication, not element-wise.
   Converting to dense wastes memory.
   Addition and subtracting zeros is incorrect for element-wise product.

3. Final Answer:

   Use A.multiply(B) to get element-wise multiplication. -> Option C

4. Quick Check:

   Element-wise sparse multiply = multiply() method [OK]

Hint: Use multiply() for element-wise sparse matrix product [OK]

Common Mistakes:

• Using dot() instead of multiply()
• Converting sparse to dense unnecessarily
• Trying to add and subtract for element-wise product