Converting between formats in SciPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of converting dense to CSR format

What is the output of this code that converts a dense matrix to CSR format and prints its data array?

SciPy

import numpy as np
from scipy.sparse import csr_matrix

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

A [0 0 1 2 0 0 0 3 0]

B [1 2 3]

C [1 2 3 0]

D [1 2]

💡 Hint

CSR format stores only non-zero values in the data array.

The csr_matrix.data attribute contains only the non-zero elements of the matrix in row-major order. Here, the non-zero elements are 1, 2, and 3.

❓ data_output

intermediate

Shape after converting COO to CSC format

Given a sparse matrix in COO format with shape (4, 5), what is the shape of the matrix after converting it 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, 5))
csc = coo.tocsc()
print(csc.shape)

A (4, 4)

B (5, 4)

C (4, 5)

D (5, 5)

💡 Hint

Converting between sparse formats does not change the matrix shape.

The shape of the matrix remains the same when converting between sparse formats like COO to CSC.

🔧 Debug

advanced

Identify the error when converting dense to LIL format

What error does this code raise when converting a list of lists to a LIL sparse matrix?

SciPy

from scipy.sparse import lil_matrix

dense_list = [[1, 0], [0, 2]]
sparse_lil = lil_matrix(dense_list)
print(sparse_lil)

A TypeError: expected sequence of sequences, got list of lists

B ValueError: object too deep for desired array

C AttributeError: 'list' object has no attribute 'tocsc'

D No error, prints the LIL matrix correctly

💡 Hint

Check if scipy.sparse.lil_matrix accepts list of lists as input.

scipy.sparse.lil_matrix can accept a list of lists as input and convert it to a sparse matrix without error.

🚀 Application

advanced

Best sparse format for fast row slicing

Which sparse matrix format is best suited for fast row slicing operations on large sparse matrices?

A CSR (Compressed Sparse Row)

B CSC (Compressed Sparse Column)

C COO (Coordinate)

D DOK (Dictionary of Keys)

💡 Hint

Think about how data is stored internally for efficient row access.

CSR format stores data by rows, making row slicing very efficient compared to other formats.

🧠 Conceptual

expert

Memory difference between COO and CSR formats

Which statement correctly describes the memory usage difference between COO and CSR sparse matrix formats for the same data?

A COO uses more memory because it stores row and column indices separately for each non-zero element.

B CSR uses more memory because it stores an additional index pointer array for rows.

C Both COO and CSR use the same amount of memory for the same data.

D COO uses less memory because it stores data in a compressed form unlike CSR.

💡 Hint

Consider how indices are stored in COO vs CSR formats.

COO format stores row and column indices for each non-zero element, which can use more memory than CSR that compresses row indices into a pointer array.

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