How to Create a Sparse Matrix in Python (original) (raw)
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
import numpy as np
from scipy.sparse import csr_matrix
sparseMatrix = csr_matrix(( 3 , 4 ),
`` dtype = np.int8).toarray()
print (sparseMatrix)
Output:
[[0 0 0 0] [0 0 0 0] [0 0 0 0]]
Example 2:
Python
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 ])
data = np.array([ 1 , 4 , 5 , 8 , 9 , 6 ])
sparseMatrix = csr_matrix((data, (row, col)),
`` shape = ( 3 , 3 )).toarray()
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
import numpy as np
from scipy.sparse import csc_matrix
sparseMatrix = csc_matrix(( 3 , 4 ),
`` dtype = np.int8).toarray()
print (sparseMatrix)
Output:
[[0 0 0 0] [0 0 0 0] [0 0 0 0]]
Example 2:
Python
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 ])
data = np.array([ 1 , 4 , 5 , 8 , 9 , 6 ])
sparseMatrix = csc_matrix((data, (row, col)),
`` shape = ( 3 , 3 )).toarray()
print (sparseMatrix)
Output:
[[ 1 4 0] [ 8 0 11] [ 0 0 9]]