Sparse Matrices
In this lesson, we will learn about different sparse matrices in Python and the conversion between sparse and dense matrices.
We'll cover the following
Sparse matrices in Python #
The procedure we have used so far to construct the matrix for a is not very efficient. A full matrix is created, which consists mostly of zeros with non-zero values only appearing on diagonals. There are more efficient routines that store what are called sparse matrices. In a sparse matrix, only the value and location of non-zero values in a matrix are stored. Functionality for sparse matrices is available in the scipy
submodule sparse
.
We will import the
scipy.sparse
submodule assp
.
import scipy.sparse as sp
Dense to sparse #
When we create a sparse matrix, we have to choose which format it should be stored in. There are many ways to store a sparse matrix. Two most commonly used are:
- compressed sparse column using the
csc_matrix(A)
method. - compressed sparse row using the
csr_matrix(A)
method.
The following matrix is an example of this:
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