A matrix containing a large number of zero elements is called a sparse matrix. In this article, I will introduce you to the implementation of Sparse Matrix with Python using classes and OOP in Python.
Sparse matrices are very common in scientific applications, especially those dealing with systems of linear equations. A sparse matrix is formally defined as an m × n matrix containing k non-zero elements such that k << m × n.
Sparse Matrix with Python
Here I will define and implement a class to store and work with sparse matrices in which non-zero elements are stored in a list. The operations of a sparse matrix are the same as those of a general matrix and many of them can be implemented in the same way.
This would be enough if our only goal was to reduce the cost of storage, but we can take advantage of storing only non-zero elements to improve the efficiency of many of the sparse array operations. Let’s see how to implement a Sparse Matrix with Python by using classes and OOP:
The constructor defines three attributes to store data related to the sparse matrix. The _elementList field stores _MatrixElement objects representing non-zero elements. Instances of the storage class contain not only the value of a specific item but also row and column indices indicating its location in the array. The _numRows and _numCols fields are used to store the dimensions of the matrix.
I hope you liked this article on the implementation of Sparse matrix with Python. Feel free to ask your valuable questions in the comments section below.
