We will learn about the “** Python Scipy Sparse Csr_matrix**” in this tutorial so that we may generate a CSR matrix and use various techniques including multiplication, dot, and transposition.

The following concepts will be covered, along with locating the index of the max and min items in the CSR matrix.

- What is Scipy Sparse Csr_matrix
- Python Scipy Sparse Csr_matrix
- Python Scipy Sparse Csr_matrix Multiply
- Python Scipy Sparse Csr_matrix Indptr
- Python Scipy Sparse Csr_matrix Dot
- Python Scipy Sparse Csr_matrix Shape
- Python Scipy Sparse Csr_matrix Sort indices
- Python Scipy Sparse Csr_matrix Nnz
- Python Scipy Sparse Csr_matrix Transpose
- Python Scipy Sparse Csr_matrix Argmax
- Python Scipy Sparse Csr_matrix Argmin

## What is Scipy Sparse Csr_matrix?

There are two common types of matrices: sparse and dense. In contrast to dense matrices, which have a majority of non-zero elements, sparse matrices have a majority of zeros.

When a large matrix is available, it is typical for the majority of the elements to be zeros. Therefore, makes it reasonable to conduct operations using only non-zero numbers because zero times anything always results in zero.

- Numerous sparse matrix functions in Scipy save only non-zero elements. By doing this, the amount of memory needed for data storage can be reduced. Data frames need to be stored in memory frequently for machine learning processes.
- The data frame is divided so that it can fit in RAM. Data can fit in RAM with ease by compressing. The algorithm’s execution performance can be significantly accelerated by performing operations on the sparse matrix’s only non-zero values.

One of the algorithms offered by Scipy is called Compressed Sparse Row (CSR). Here’s how it functions.

Suppose that a text document contains words or texts like those given below.

This is a Tsinfo

Not Tsinfo. It is Tsinfotechnologies, Is it?

The first step is indexing: The words are given numbers. Assign the same numbers if words are repeated. We can determine the total number of words in the document from this step.

```
This is a Tsinfo
0 1 2 3
This is not Tsinfo. It is Tsinfotechnologies, Is it?
0 1 4 3 5 1 6 1 5
```

Indexing begins at zero. The first word is “This,” which has an index of “0.” Every other word that is distinct will also have an index. Because the word “This” appears twice in the document, it receives the identical index value of “0” each time.

The second step is the document vector representation: There is a vector representation built for each line in the document.

How many distinct indices are there? Since there are 7 indices in this situation, ranging from 0 to 6, each document (or line) is represented by 7 values, where each value indicates the frequency with which a particular term associated with each index occurs.

`(word-index): (This-0), (is-1), (a-2), (Tsinfo-3), (not-4), (It-5), (Tsinfotechnologies-6)`

`[1111000],[1111000],[2210000]`

The third step is creating a sparse vector for every document: The sparse matrix representation of each document is shown below. It eliminates all values that are 0 and stores only non-zero values.

```
Doc<01,11>
Doc<01,11,21,31>
```

Read: Python Scipy Stats Fit

## Python Scipy Sparse Csr_matrix

The function in the Scipy subpackage

allows us to generate a CSR matrix, which stands for a compressed sparse row matrix.*scipy.sparse*

The CSR matrix is appropriate if what we need is a matrix that can execute addition, multiplication, subtraction, matrix power, and division.

As demonstrated below, there are numerous approaches to generating the CSR matrix, but here we will use one of the ways to create a matrix. To know more, please visit the official documentation of Python Scipy.

We are going to use the method **csr_matrix(D)** or also called the rank_2 ndarray or dense matrix.

Import the necessary libraries using the below code.

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

Create a rank-2 matrix using the below code.

```
D = np.array([[1, 0, 1, 0, 0, 0], [2, 0, 2, 0, 0, 1],\
[0, 0, 0, 2, 0, 1]])
```

Check the crated matrix using the below code.

`print(D)`

Pass the created matrix to function ** csr_matrix()**, to create

*csr matrix*

and view it using the below code.```
# Creating csr matrix
csr_m = csr_matrix(D)
csr_m.toarray()
```

This is how to create a CSR matrix using the method

of Python Scipy.*csr_matrix()*

Read: Python Scipy Load Mat File

## Python Scipy Sparse Csr_matrix Multiply

A sparse matrix is one in which the majority of its elements are zeros. SciPy’s 2-D sparse matrix package for numerical data is called **“scipy.sparse”**. It offers us, various classes, various classes to build sparse matrices. These two classes are **csc_matrix** and **csr_matrix**.

In contrast to **csr_matrix()**, which is used to build a compressed sparse row matrix, the **csc_matrix()** creates a compressed sparse column matrix.

To multiply two sparse matrices, we use the

method offered by the CSR matrix classes. Let’s take an example for the demonstration by following the below code.*multiply()*

Import the required libraries using the below python code.

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

Create a first CSR matrix using the below code.

```
row_1 = np.array([0, 1, 2, 0 ])
col_1 = np.array([0, 3, 0, 1])
data_1 = np.array([3, 4, 9, 8])
csr_matrix_A = sparse.csr_matrix((data_1,
(row_1, col_1)),
shape = (3, 4))
print("first CSR matrix: \n",
csr_matrix_A.toarray())
```

Create a second CSR matrix using the below code.

```
row_2 = np.array([1, 2, 0, 0 ])
col_2 = np.array([3, 0, 0, 1])
data_2 = np.array([8, 3, 4, 9])
csr_matrix_B = sparse.csr_matrix((data_2,
(row_2, col_2)),
shape = (3, 4))
print("Second CSR matrix: \n",
csr_matrix_B.toarray())
```

Multiply both matrices using the method

.*mulitply()*

```
sparse_matrix_AB = csr_matrix_A.multiply(csr_matrix_B)
print("Multiplication of Sparse Matrix:\n",
sparse_matrix_AB.toarray())
```

The output matrix contains the multiplication of the

and *csr_matrix_A*

.*csr_matrix_B*

This is how to apply the method

on CSR matrices to get the product of two CSR matrices.*multiply()*

## Python Scipy Sparse Csr_matrix Indptr

The

the attribute of the method *Indptr*

is the matrix’s index pointer array in CSR format.*csr_matrix()*

The syntax is given below.

`csr_matrix((data, indices, indptr), [shape=(M, N)])`

Where parameters are:

**data:**The matrix’s CSR format data array**indices:**The matrix’s index array in CSR format**indptr:**The matrix’s CSR-format index pointer array**shape:**It is used to specify the shape of the matrix.

Let’s create a CSR matrix using the Indptr by following the below steps:

Import the required libraries or methods using the below python code.

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

Create a sparse CSR matrix using **indptr**, **indices** and **data** values.

```
indptr_ = np.array([0, 6, 2, 3])
indices_ = np.array([0, 5, 4, 0, 4, 2])
data_ = np.array([1, 2, 3, 4, 5, 6])
matrix_csr = sparse.csr_matrix((data_, indices_, indptr_), shape=(3, 3))
matrix_csr.toarray()
```

To check the index pointer use the attribute `indptr`

on the above-created matrix.

`matrix_csr.indptr`

This is how to get the matrix’s index pointer array in CSR format using the attribute

on the CSR matrix.*indptr*

Read: Python Scipy Stats Norm

## Python Scipy Sparse Csr_matrix Dot

The Python Scipy sparse has a method **dot()** to find the ordinary dot product of the CSR matrix.

Let’s take an example by following the below steps:

Import the required libraries or methods using the below python code.

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

Create an array for the dot product using the below code.

`array_v = np.array([-1, 0, 1])`

Create a CSR matrix using the below code.

`matrix_A = sparse.csr_matrix([[2, 1, 0], [0, 3, 0], [5, 0, 4]])`

Compute the dot of the above-created matrix by applying the `dot()`

method on the matrix using the below code.

`matrix_A.dot(array_v)`

This is how to find the dot product of any CSR matrix using the method

of Python Scipy.*dot()*

Read: Python Scipy Normal Test

## Python Scipy Sparse Csr_matrix Shape

The method

can be applied to the CSR matrix to get the shape. The syntax is given below.*get_shape()*

`csr_matrix.get_shape()`

Let’s take an example by following the below steps:

Import the required libraries or methods using the below python code.

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

Create a sparse CSR matrix using the below code.

`matrx = sparse.csr_matrix((4, 4), dtype=np.int8)`

Now use the function `get_shape()`

on the above-created CSR matrix.

`matrx.get_shape()`

This is how to get the shape of any CSR matrix using the method get_shape() that returns the result in the tuple.

Read: Python Scipy Stats Poisson

## Python Scipy Sparse Csr_matrix Sort indices

The method `sort_indices()`

of Python Scipy is applied to the CSR matrix to sort the matrix’s indexes.

Import the required libraries or methods using the below python code.

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

Create a sparse CSR matrix using indptr, indices, and data values.

```
indptr_ = np.array([0, 3, 2, 6])
indices_ = np.array([0, 3, 2, 0, 2, 1])
data_ = np.array([1, 2, 3, 4, 5, 6])
matrix_csr = sparse.csr_matrix((data_, indices_, indptr_), shape=(3, 3))
matrix_csr.toarray()
```

Now apply the method

on the above matrix using the below code.*sort_indices()*

`matrix_csr.sort_indices`

This is how to apply the attribute

on the CSR matrix to sort the index of the matrix.*sort_indices*

Read: Python Scipy Eigenvalues

## Python Scipy Sparse Csr_matrix Nnz

“Non-zero” elements are kept in several arrays via a sparse matrix. Basically, nnz reports the size of these arrays.

Let’s understand with an example and find the size of the non-zero elements within the CSR matrix by following the below steps:

Import the required libraries or methods using the below python code.

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

Create a sparse CSR matrix using the below code.

```
matrix_A = sparse.csr_matrix([[2, 1, 0], [0, 3, 0], [5, 0, 4]])
matrix_A.toarray()
```

Now apply the method `nnz`

on the matrix using the below code.

```
matrix_nnz = matrix_A.nnz
matrix_nnz
```

This is how to know the size or the number of the non-zero elements in the CSR matrix using the attribute

of Python Scipy.*nnz*

Read: Python Scipy Stats Mode

## Python Scipy Sparse Csr_matrix Transpose

By flipping rows into columns or columns into rows, you can find a matrix’s transpose. Python Scipy has a method transpose() that can be applied to the CSR matrix to reverse the sparse matrix’s dimensions.

The syntax is given below.

`csr_matrix.transpose(axes=None, copy=False)`

Where parameters are:

**axes:**The only reason this argument is in the signature is to ensure NumPy compatibility. Nothing else should be entered beside the default value.**copy(boolean):**Whether or not self-attributes should be imitated wherever possible. Depending on the kind of sparse matrix being utilized, different attributes are duplicated to varying degrees.

Let’s understand with an example and compute the transpose of the CSR matrix by following the below steps:

Import the required libraries or methods using the below python code.

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

Create a sparse CSR matrix using the below code.

```
matrix_A = sparse.csr_matrix([[2, 1, 0], [0, 3, 0], [5, 0, 4]])
matrix_A.toarray()
```

Now apply the method `transpose()`

on the matrix `matrix_A`

using the below code.

```
matrix_trans = matrix_A.transpose()
matrix_trans.toarray()
```

This is how to flip the rows into columns or columns into rows using the method

of Python Scipy on the CSR matrix.*transpose() *

Read: Python Scipy Freqz

## Python Scipy Sparse Csr_matrix Argmax

The method

Python Scipy of *argmax()*

is maximum elements along an axis that are returned as indexes.*csr_matrix*

The syntax is given below.

`csr_matrix.argmax(axis=None)`

Where parameters are:

**axis(0,1,-1,-2):** Along this axis, the argmax is calculated. If None (the default), the flatten data’s maximum element’s index is returned.

Import the required libraries or methods using the below python code.

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

Create a sparse CSR matrix using indptr, indices, and data values.

```
indptr_ = np.array([0, 3, 2, 6])
indices_ = np.array([0, 2, 1, 0, 1, 1])
data_ = np.array([1, 2, 3, 4, 5, 6])
matrix_csr = sparse.csr_matrix((data_, indices_, indptr_), shape=(3, 3))
print(matrix_csr.toarray())
```

Now pass the CSR matrix to the method `argmax()`

using the below code.

`print("Index of the maximum (14) element is :",sparse.csr_matrix.argmax(matrix_csr))`

This is how to find the index of the maximum element in the CSR matrix using the method `argman()`

of Python Scipy.

Read: Python Scipy Distance Matrix

## Python Scipy Sparse Csr_matrix Argmin

The method

Python Scipy of *argmin()*

is minimum elements along an axis that are returned as indexes.*csr_matrix*

The syntax is given below.

`csr_matrix.argmin(axis=None)`

Where parameters are:

**axis(0,1,-1,-2):** Along this axis, the argmin is calculated. If None (the default), the flattened data’s maximum element’s index is returned.

Import the required libraries or methods using the below python code.

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

Create a sparse CSR matrix using indptr, indices, and data values.

```
indptr_ = np.array([0, 3, 2, 6])
indices_ = np.array([0, 2, 1, 0, 1, 1])
data_ = np.array([11, 21, 6, 8, 15, 16])
matrix_csr = sparse.csr_matrix((data_, indices_, indptr_), shape=(3, 3))
print(matrix_csr.toarray())
print("Index of the minimum element is :",sparse.csr_matrix.argmin(matrix_csr))
```

Now pass the CSR matrix to the method `argmin()`

using the below code.

`print("Index of the minimum element is :",sparse.csr_matrix.argmin(matrix_csr))`

This is how to find the index of the element minimum in the CSR matrix using the method

of Python Scipy.*argmin()*

You may also like to read the following Python SciPy tutorials.

- Python Scipy Confidence Interval
- Python Scipy LognormalÂ
- Python Scipy Smoothing
- Python Scipy Chi-Square Test
- Python Scipy Special Module
- Python Scipy Matrix + Examples

In this tutorial, we have learned how to create a CSR matrix and also explored sorting, finding the index of maximum and minimum elements with the shape of the matrix, etc. Additionally covered the following topics.

- What is Scipy Sparse Csr_matrix
- Python Scipy Sparse Csr_matrix
- Python Scipy Sparse Csr_matrix Multiply
- Python Scipy Sparse Csr_matrix Indptr
- Python Scipy Sparse Csr_matrix Dot
- Python Scipy Sparse Csr_matrix Shape
- Python Scipy Sparse Csr_matrix Sort indices
- Python Scipy Sparse Csr_matrix Nnz
- Python Scipy Sparse Csr_matrix Transpose
- Python Scipy Sparse Csr_matrix Argmax
- Python Scipy Sparse Csr_matrix Argmin

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etcâ€¦ for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.