Converting between formats in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert a dense matrix to a CSR sparse matrix.

SciPy

from scipy.sparse import csr_matrix
import numpy as np

dense_matrix = np.array([[1, 0, 0], [0, 2, 0], [0, 0, 3]])
sparse_matrix = csr_matrix([1])
print(sparse_matrix)

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

Adense_matrix

Bcsr_matrix

Cnp.array

Dsparse_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the function name instead of the variable.

Passing the sparse matrix variable to itself.

Using np.array inside csr_matrix instead of the variable.

The csr_matrix function converts a dense numpy array into a CSR sparse matrix format.

2fill in blank

medium

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

SciPy

from scipy.sparse import csr_matrix
import numpy as np

sparse_matrix = csr_matrix([[0, 0, 1], [1, 0, 0], [0, 2, 0]])
dense_matrix = sparse_matrix.[1]()
print(dense_matrix)

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

Ato_numpy

Btodense

Ctoarray

Dtodense_array

Attempts:

3 left

💡 Hint

Common Mistakes

Using todense() which returns a matrix, not a numpy array.

Using a non-existent method like to_numpy().

Forgetting the parentheses after the method.

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

3fill in blank

hard

Fix the error in the code to convert a COO sparse matrix to CSC format.

SciPy

from scipy.sparse import coo_matrix

row = [0, 3, 1, 0]
col = [0, 3, 1, 2]
data = [4, 5, 7, 9]
coo = coo_matrix((data, (row, col)), shape=(4, 4))
csc = coo.[1]()
print(csc)

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

Atocsc

Btocsr

Ctoarray

Dtodense

Attempts:

3 left

💡 Hint

Common Mistakes

Using tocsr() which converts to CSR format instead.

Using toarray() or todense() which convert to dense formats.

Forgetting parentheses after the method.

The tocsc() method converts a COO sparse matrix to CSC format.

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', 'is', 'fun', 'and', 'powerful']
lengths = {word: [1] for word in words if [2]
print(lengths)

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

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

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

Checking if the word is greater than 3 instead of its length.

Omitting the condition or using incorrect syntax.

The dictionary comprehension maps each word to its length using len(word) only if the length is greater than 3, checked by len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each word in uppercase to its length only if the length is greater than 2.

SciPy

words = ['cat', 'a', 'dog', 'elephant']
result = { [1]: [2] for word in words if [3] }
print(result)

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

Aword.upper()

Blen(word)

Clen(word) > 2

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the key instead of uppercase.

Using the word instead of length as the value.

Incorrect condition syntax or missing condition.

The dictionary comprehension maps each word in uppercase (word.upper()) to its length (len(word)) only if the length is greater than 2 (len(word) > 2).

Practice

1. What is the main purpose of converting sparse matrices between different formats in SciPy?

easy

A. To change the data values inside the matrix

B. To convert the matrix into a dense array automatically

C. To reduce the size of the matrix by removing zeros

D. To use the most efficient format for a specific operation or tool

Solution

1. Step 1: Understand sparse matrix formats

   Sparse matrices have different formats like CSR, CSC, COO, each optimized for certain operations.

2. Step 2: Purpose of conversion

   Converting between formats helps pick the best one for speed or compatibility without changing data.

3. Final Answer:

   To use the most efficient format for a specific operation or tool -> Option D

4. Quick Check:

   Conversion optimizes format use = B [OK]

Hint: Conversion changes format, not data values [OK]

Common Mistakes:

• Thinking conversion changes matrix values
• Confusing conversion with data compression
• Assuming conversion always makes matrix dense

2. Which of the following is the correct syntax to convert a sparse matrix mat to Compressed Sparse Row (CSR) format in SciPy?

easy

A. mat.to_csr()

B. mat.convert('csr')

C. mat.tocsr()

D. csr_matrix(mat)

Solution

1. Step 1: Recall SciPy sparse matrix conversion methods

   SciPy sparse matrices use methods like tocsr() to convert formats.

2. Step 2: Identify correct method name

   The correct method to convert to CSR is tocsr(), not to_csr() or others.

3. Final Answer:

   mat.tocsr() -> Option C

4. Quick Check:

   Use tocsr() method for CSR conversion = C [OK]

Hint: Use method names starting with 'to' plus format name [OK]

Common Mistakes:

• Using incorrect method name like to_csr()
• Trying to convert with convert('csr') which doesn't exist
• Confusing constructor csr_matrix() with conversion method

3. Given the code below, what will be the output of print(type(mat_csc))?

from scipy.sparse import coo_matrix
row = [0, 1, 2]
col = [1, 2, 0]
data = [4, 5, 6]
mat_coo = coo_matrix((data, (row, col)), shape=(3, 3))
mat_csc = mat_coo.tocsc()

medium

A.

B.

C.

D.

Solution

1. Step 1: Understand initial matrix format

   The matrix mat_coo is created as COO format using coo_matrix.

2. Step 2: Conversion to CSC format

   Calling mat_coo.tocsc() converts it to CSC format, so mat_csc is a CSC matrix object.

3. Final Answer:

   <class 'scipy.sparse.csc.csc_matrix'> -> Option A

4. Quick Check:

   tocsc() returns CSC matrix = D [OK]

Hint: tocsc() returns CSC matrix type [OK]

Common Mistakes:

• Assuming type stays COO after conversion
• Confusing CSR and CSC formats
• Expecting numpy.ndarray instead of sparse matrix

4. Identify the error in the following code that attempts to convert a sparse matrix mat to COO format:

mat_coo = mat.to_coo()

medium

A. The method name should be tocoo() instead of to_coo()

B. The matrix mat must be dense before conversion

C. The method to_coo() requires extra parameters

D. The conversion to COO format is not supported in SciPy

Solution

1. Step 1: Check method naming conventions

   SciPy sparse matrices use method names like tocoo() without underscore to convert formats.

2. Step 2: Identify the typo in method name

   The code uses to_coo() which is incorrect; correct is tocoo().

3. Final Answer:

   The method name should be tocoo() instead of to_coo() -> Option A

4. Quick Check:

   Use tocoo() method name = A [OK]

Hint: Method names have no underscores, e.g., tocsr(), tocoo() [OK]

Common Mistakes:

• Using underscores in method names
• Thinking matrix must be dense first
• Believing conversion to COO is unsupported

5. You have a large sparse matrix in COO format and want to perform fast row slicing operations. Which conversion should you apply and why?

from scipy.sparse import coo_matrix
# mat_coo is your COO matrix

hard

A. Convert to CSC format using mat_coo.tocsc() because CSC is efficient for row slicing

B. Convert to CSR format using mat_coo.tocsr() because CSR is efficient for row slicing

C. Convert to dense array using mat_coo.toarray() for faster slicing

D. No conversion needed; COO is best for row slicing

Solution

1. Step 1: Understand COO format limitations

   COO format is simple but inefficient for slicing rows or columns.

2. Step 2: Identify best format for row slicing

   CSR format stores data by rows, making row slicing fast and efficient.

3. Step 3: Choose correct conversion

   Use mat_coo.tocsr() to convert COO to CSR for efficient row slicing.

4. Final Answer:

   Convert to CSR format using mat_coo.tocsr() because CSR is efficient for row slicing -> Option B

5. Quick Check:

   CSR format = fast row slicing = A [OK]

Hint: CSR format is best for row slicing [OK]

Common Mistakes:

• Choosing CSC for row slicing (CSC is column-based)
• Converting to dense wastes memory
• Assuming COO is efficient for slicing