Sparse matrix operations in SciPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a sparse matrix?

A sparse matrix is a matrix mostly filled with zeros. It saves memory by storing only the non-zero values and their positions.

beginner

Name two common formats for storing sparse matrices in SciPy.

Two common formats are CSR (Compressed Sparse Row) and CSC (Compressed Sparse Column). They store data efficiently for different operations.

beginner

How do you convert a dense NumPy array to a sparse CSR matrix in SciPy?

Use `scipy.sparse.csr_matrix(your_dense_array)` to convert a dense array to a CSR sparse matrix.

intermediate

What is the advantage of using sparse matrix multiplication over dense matrix multiplication?

Sparse matrix multiplication is faster and uses less memory because it only processes non-zero elements, unlike dense multiplication which processes all elements.

beginner

How can you find the number of non-zero elements in a sparse matrix?

Use the `.nnz` attribute of a sparse matrix to get the count of non-zero elements.

Which SciPy sparse matrix format is best for fast row slicing?

A LIL (List of Lists)

B CSC (Compressed Sparse Column)

C CSR (Compressed Sparse Row)

D DOK (Dictionary of Keys)

CSR format is optimized for fast row slicing and matrix-vector products.

What does the `.tocsc()` method do on a sparse matrix?

A Converts the matrix to CSC format

B Converts the matrix to CSR format

C Converts the matrix to dense format

D Returns the transpose of the matrix

The `.tocsc()` method converts a sparse matrix to Compressed Sparse Column format.

How do you create a sparse identity matrix of size 4x4 in SciPy?

A scipy.sparse.identity(4)

B All of the above

C scipy.sparse.eye(4)

D scipy.sparse.diags([1,1,1,1])

All these methods create a sparse identity matrix in SciPy.

Which attribute gives the number of non-zero elements in a sparse matrix?

A .count_nonzero()

B .shape

C .size

D .nnz

The `.nnz` attribute returns the count of non-zero elements.

Why use sparse matrices instead of dense matrices?

A To save memory and speed up calculations when many elements are zero

B To make matrices look nicer

C Because dense matrices are deprecated

D To increase matrix size

Sparse matrices save memory and speed up calculations when most elements are zero.

Explain what a sparse matrix is and why it is useful in data science.

Think about storing mostly zeros efficiently.

Describe how to convert a dense matrix to a sparse matrix and perform multiplication using SciPy.

Use SciPy functions and explain benefits.