Introduction of Python SciPy Sparse Data

In This Article, You Will Learn About Python SciPy Sparse Data.

Python SciPy Sparse Data – Before moving ahead, let’s know a bit about SciPy Optimizers

Table of Contents

SciPy Sparse Data

What is Sparse Data

Spare Data is a data that contains most of the elements as zero.

It can be any array such as:

[1, 0, 2, 0, 0, 3, 0, 10, 0, 20, 0, 15]

In other words, Spare Data is a data, where most of the values are zero.

Dense array – Dense Data is a data, where most of the values are not zero.

Note: We will discuss in detail about Spare Data, when we will go throughout partial derivatives in linear algebra.

Get started with Spare Data

To deal with Spare Data, SciPy has a function called scipy.sparse.

It includes two types of sparse matrices; we can use –

Compressed Sparse Column(CSC) – For efficient arithmetic, fast column slicing.

Compressed Sparse Row(CSR) – For fast row slicing, faster matrix vector products.

Here, we will go throughout CSR matrix.

CSR Matrix

CSR matrix can be created by passing an arrray into function scipy.sparse.csr_matrix().

Example – Using an array to create a CSR matrix

				
					import numpy as np
from scipy.sparse import csr_matrix

array = np.array([0, 0, 10, 0, 10, 1, 1, 0, 2])

print(csr_matrix(array))
				
			
python-scipy-sparse-data

As a result, it returned value after classified them in row. 

From above data:

Item is in row 0 position 2 and has the value 1.

Item is in row 0 position 4 and has the value 10.

Item is in row 0 position 5 and has the value 1.

Item is in row 0 position 6 and has the value 1.

Item is in row 0 position 8 and has the value 2.

Sparse Matrix Methods

Viewing only stored data (not included zero) with the data property.

Example – Creating data only with non-empty numbers.

				
					import numpy as np
from scipy.sparse import csr_matrix

array = np.array([[0, 50, 0], [10, 0, 1], [1, 0, 2]])

print(csr_matrix(array).data)

				
			
python-scipy-sparse-data

As a result, it returned value after classified them in non-zeros. 

Now, counting non-zeros with the count_nonzero() method.

Example – Counting number of elements that are not zero.

				
					import numpy as np
from scipy.sparse import csr_matrix

array = np.array([[0, 10, 0], [0, 50, 1], [1, 0, 2]])

print(csr_matrix(array).count_nonzero())

				
			

As shown above, it returned the number of elements, i.e., 5 (not including zero).

Now, removing zero-entries from the matrix by using method eliminate_zeros().

Example – Removing zero-entries from the matrix

				
					import numpy as np
from scipy.sparse import csr_matrix

array = np.array([[0, 8, 0], [0, 10, 1], [1, 0, 2]])

matrix = csr_matrix(array)
matrix.eliminate_zeros()

print(matrix)
				
			
python-scipy-sparse-data

As has been noted, it returned data after elementaing zeros.

Now, by using method sum_duplicates(), we are eliminating duplicates values. 

Example – Eliminating duplicates by using sum_duplicates().

				
					import numpy as np
from scipy.sparse import csr_matrix

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

matrix = csr_matrix(array)
matrix.sum_duplicates()

print(matrix)

				
			
python-scipy-sparse-data

As can be seen, it returned data after removing duplicates numbers.

Now, to convert matrix from CSR to CSC, using tocsc() method.

Example – Converting matrix from CSR to CSC.

				
					import numpy as np
from scipy.sparse import csr_matrix

array = np.array([[1, 0, 3], [0, 0, 1], [1, 0, 2]])

new_array = csr_matrix(array).tocsc()

print(new_array)
				
			

As a result, it finally converted matrix from CSR to CSC.

If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.

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