How to create a sparse Matrix in Python?
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# How to create a sparse Matrix in Python?

This recipe helps you create a sparse Matrix in Python

## Recipe Objective

Many a times we work on matrices in python and making Sparse Matrix manually is quite a hectic process but we know how to use python and using we can do this very well for us.

There are two popular kinds of matrices: dense and sparse. Sparse matrices have lots of 'zero' values. In machine learning projects, the learning algorithms require the data to be in-memory. If the data needed for the learning (dataframe) is not in the RAM, then the algorithm does not work. By converting a dense matrix into a sparse matrix it can be made to fit in the RAM.

There are many data structures that can be used to construct a sparse matrix in python. Python Scipy provides the following ways to represent a sparse matrix:

- Block Sparse Row matrix (BSR)
- Coordinate list matrix (COO)
- Compressed Sparse Column matrix (CSC)
- Compressed Sparse Row matrix (CSR)
- Sparse matrix with DIAgonal storage (DIA)
- Dictionary Of Keys based sparse matrix (DOK)
- Row-based linked list sparse matrix (LIL)

The recipe above takes a dense matrix and displays the various formats of sparse matrix that scipy supports.

So this is the recipe on how we can create a sparse Matrix in Python.

## Step 1 - Import the library

``` import numpy as np from scipy import sparse ```

We have imported numpy and sparse modules which will be requied.

## Step 2 - Setting up the Matrix

We have created a matrix of which we will calculate sparse Matrix. ``` matrix = np.array([[9, 8, 7], [6, 5, 4], [3, 2, 1]]) print() print("Original Matrix: \n", matrix) ```

## Step 3 - Creating Sparse Matrices

We have created various sparse matrices by passing the original matix from the required functions.

• Creating Dictionary Of Keys based sparse matrix (DOK).
• ``` print(sparse.dok_matrix(matrix)) ```
• Creating Block Sparse Row matrix (BSR).
• ``` print(sparse.bsr_matrix(matrix)) ```
• Creating Coordinate list matrix (COO)
• ``` print(sparse.coo_matrix(matrix)) ```
• Creating Compressed Sparse Column matrix (CSC)
• ``` print(sparse.csc_matrix(matrix)) ```
• Creating Compressed Sparse Row matrix (CSR)
• ``` print(sparse.csr_matrix(matrix)) ```
• Creating Sparse matrix with DIAgonal storage (DIA)
• ``` print(sparse.dia_matrix(matrix)) ```
• Creating Row-based linked list sparse matrix (LIL)
• ``` print(sparse.lil_matrix(matrix)) ```
Now we are printing the final matrices and the output comes as:

```Original Matrix:
[[9 8 7]
[6 5 4]
[3 2 1]]

Sparse Matrices:

(0, 0)	9
(0, 1)	8
(0, 2)	7
(1, 0)	6
(1, 1)	5
(1, 2)	4
(2, 0)	3
(2, 1)	2
(2, 2)	1

(0, 0)	9
(0, 1)	8
(0, 2)	7
(1, 0)	6
(1, 1)	5
(1, 2)	4
(2, 0)	3
(2, 1)	2
(2, 2)	1

(0, 0)	9
(0, 1)	8
(0, 2)	7
(1, 0)	6
(1, 1)	5
(1, 2)	4
(2, 0)	3
(2, 1)	2
(2, 2)	1

(0, 0)	9
(1, 0)	6
(2, 0)	3
(0, 1)	8
(1, 1)	5
(2, 1)	2
(0, 2)	7
(1, 2)	4
(2, 2)	1

(0, 0)	9
(0, 1)	8
(0, 2)	7
(1, 0)	6
(1, 1)	5
(1, 2)	4
(2, 0)	3
(2, 1)	2
(2, 2)	1

(2, 0)	3
(1, 0)	6
(2, 1)	2
(0, 0)	9
(1, 1)	5
(2, 2)	1
(0, 1)	8
(1, 2)	4
(0, 2)	7

(0, 0)	9
(0, 1)	8
(0, 2)	7
(1, 0)	6
(1, 1)	5
(1, 2)	4
(2, 0)	3
(2, 1)	2
(2, 2)	1

```

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