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# How to Create a Sparse Matrix in Python

• Last Updated : 18 Aug, 2020

If most of the elements of the matrix have 0 value, then it is called a sparse matrix. The two major benefits of using sparse matrix instead of a simple matrix are:

• Storage: There are lesser non-zero elements than zeros and thus lesser memory can be used to store only those elements.
• Computing time: Computing time can be saved by logically designing a data structure traversing only non-zero elements.

Sparse matrices are generally utilized in applied machine learning such as in data containing data-encodings that map categories to count and also in entire subfields of machine learning such as natural language processing (NLP).

Example:

```0 0 3 0 4
0 0 5 7 0
0 0 0 0 0
0 2 6 0 0```

Representing a sparse matrix by a 2D array leads to wastage of lots of memory as zeroes in the matrix are of no use in most of the cases. So, instead of storing zeroes with non-zero elements, we only store non-zero elements. This means storing non-zero elements with triples- (Row, Column, value).

### Create a Sparse Matrix in Python

Python’s SciPy gives tools for creating sparse matrices using multiple data structures, as well as tools for converting a dense matrix to a sparse matrix. The function csr_matrix() is used to create a sparse matrix of compressed sparse row format whereas csc_matrix() is used to create a sparse matrix of compressed sparse column format.

#### # Using csr_matrix()

Syntax:

scipy.sparse.csr_matrix(shape=None, dtype=None)

Parameters:

shape: Get shape of a matrix

dtype: Data type of the matrix

Example 1:

## Python

 `# Python program to create``# sparse matrix using csr_matrix()`` ` `# Import required package``import` `numpy as np``from` `scipy.sparse ``import` `csr_matrix`` ` `# Creating a 3 * 4 sparse matrix``sparseMatrix ``=` `csr_matrix((``3``, ``4``), ``                          ``dtype ``=` `np.int8).toarray()`` ` `# Print the sparse matrix``print``(sparseMatrix)`

Output:

```[[0 0 0 0]
[0 0 0 0]
[0 0 0 0]]
```

Example 2:

## Python

 `# Python program to create``# sparse matrix using csr_matrix()`` ` `# Import required package``import` `numpy as np``from` `scipy.sparse ``import` `csr_matrix`` ` `row ``=` `np.array([``0``, ``0``, ``1``, ``1``, ``2``, ``1``])``col ``=` `np.array([``0``, ``1``, ``2``, ``0``, ``2``, ``2``])`` ` `# taking data``data ``=` `np.array([``1``, ``4``, ``5``, ``8``, ``9``, ``6``])`` ` `# creating sparse matrix``sparseMatrix ``=` `csr_matrix((data, (row, col)), ``                          ``shape ``=` `(``3``, ``3``)).toarray()`` ` `# print the sparse matrix``print``(sparseMatrix)`

Output:

```[[ 1  4  0]
[ 8  0 11]
[ 0  0  9]]
```

#### # Using csc_matrix()

Syntax:

scipy.sparse.csc_matrix(shape=None, dtype=None)

Parameters:

shape: Get shape of a matrix

dtype: Data type of the matrix

Example 1:

## Python

 `# Python program to create``# sparse matrix using csc_matrix()`` ` `# Import required package``import` `numpy as np``from` `scipy.sparse ``import` `csc_matrix`` ` `# Creating a 3 * 4 sparse matrix``sparseMatrix ``=` `csc_matrix((``3``, ``4``), ``                          ``dtype ``=` `np.int8).toarray()`` ` `# Print the sparse matrix``print``(sparseMatrix)`

Output:

```[[0 0 0 0]
[0 0 0 0]
[0 0 0 0]]
```

Example 2:

## Python

 `# Python program to create``# sparse matrix using csc_matrix()`` ` `# Import required package``import` `numpy as np``from` `scipy.sparse ``import` `csc_matrix`` ` `row ``=` `np.array([``0``, ``0``, ``1``, ``1``, ``2``, ``1``])``col ``=` `np.array([``0``, ``1``, ``2``, ``0``, ``2``, ``2``])`` ` `# taking data``data ``=` `np.array([``1``, ``4``, ``5``, ``8``, ``9``, ``6``])`` ` `# creating sparse matrix``sparseMatrix ``=` `csc_matrix((data, (row, col)),``                          ``shape ``=` `(``3``, ``3``)).toarray()`` ` `# print the sparse matrix``print``(sparseMatrix)`

Output:

```[[ 1  4  0]
[ 8  0 11]
[ 0  0  9]]
```

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