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Scipy Sparse Arrays

Learn about the scipy sparse arrays or csc matrix in Python.
By Pranit Sharma Last updated : December 22, 2023

NumPy is an abbreviated form of Numerical Python. It is used for different types of scientific operations in python. Numpy is a vast library in python which is used for almost every kind of scientific or mathematical operation. It is itself an array which is a collection of various methods and functions for processing the arrays.

Scipy sparse csc matrix

Scipy sparse csc matrix is a Compressed Sparse Column matrix. This can be initiated in several ways:

• csc_matrix(D)- uses a dense matrix
• csc_matrix(S)- uses another sparse matrix
• csc_matrix((M, N), [dtype])- to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'.
• csc_matrix((data, (row_ind, col_ind)), [shape=(M, N)])-where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[k]] = data[k].

Let's understand with the help of an example,

Python code to demonstrate the example of scipy sparse arrays

# Import numpy
import numpy as np

# Importing scipy sparse car matrix
from scipy.sparse import csc_matrix

# Creating a csc matrix
res = csc_matrix((3, 4), dtype=np.int8).toarray()

# Display result
print("CSC matrix:\n",res,"\n")

# Assigning elements
row = np.array([0, 2, 2, 0, 1, 2])
col = np.array([0, 0, 1, 2, 2, 2])
data = np.array([1, 2, 3, 4, 5, 6])
res = csc_matrix((data, (row, col)), shape=(3, 3)).toarray()

# Display matrix
print("CSC matrix:\n",res)

Output

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