NumPy Matrix Library

NumPy includes a matrix library numpy.matlib, which returns a matrix instead of an ndarray object.

A matrix can contain numbers, symbols, or mathematical expressions as elements. Below is a 2-row by 3-column matrix composed of 6 numeric elements:

Transpose Matrix

In NumPy, you can use the numpy.transpose function to swap the dimensions of an array, or you can use the T attribute.

For example, given a matrix with m rows and n columns, using the t() function will convert it into a matrix with n rows and m columns.

Example

import numpy as np

a = np.arange(12).reshape(3,4)

print ('Original array:')
print (a)
print ('\n')

print ('Transposed array:')
print (a.T)

Output result:

Original array:
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]

Transposed array:
[[ 0  4  8]
 [ 1  5  9]
 [ 2  6 10]
 [ 3  7 11]]

matlib.empty()

The matlib.empty() function returns a new matrix, with the syntax:

numpy.matlib.empty(shape, dtype, order)

Parameter Description:

• shape: Integer or tuple of integers defining the new matrix's shape.
• dtype: Optional, data type.
• order: C (row-major) or F (column-major).

Example

import numpy.matlib 
import numpy as np

print (np.matlib.empty((2,2)))
# Filled with random data

Output result:

[[-1.49166815e-154 -1.49166815e-154]
 [ 2.17371491e-313  2.52720790e-212]]

numpy.matlib.zeros()

The numpy.matlib.zeros() function creates a matrix filled with zeros.

Example

import numpy.matlib 
import numpy as np 

print (np.matlib.zeros((2,2)))

Output result:

[[0. 0.]
 [0. 0.]]

numpy.matlib.ones()

The numpy.matlib.ones() function creates a matrix filled with ones.

Example

import numpy.matlib 
import numpy as np 

print (np.matlib.ones((2,2)))

Output result:

[[1. 1.]
 [1. 1.]]

numpy.matlib.eye()

The numpy.matlib.eye() function returns a matrix with ones on the diagonal and zeros elsewhere.

numpy.matlib.eye(n, M, k, dtype)

Parameter Description:

• n: Number of rows in the output matrix.
• M: Number of columns in the output matrix, default is n.
• k: Index of the diagonal.
• dtype: Data type.

Example

import numpy.matlib 
import numpy as np 

print (np.matlib.eye(n =  3, M =  4, k =  0, dtype =  float))

Output result:

[[1. 0. 0. 0.]
 [0. 1. 0. 0.]
 [0. 0. 1. 0.]]

numpy.matlib.identity()

The numpy.matlib.identity() function returns the identity matrix of the given size.

The identity matrix is a square matrix with ones on the main diagonal (from the top left to the bottom right) and zeros elsewhere.

Example

import numpy.matlib 
import numpy as np 

# Size 5, type float
print (np.matlib.identity(5, dtype =  float))

Output result:

[[ 1.  0.  0.  0.  0.] 
 [ 0.  1.  0.  0.  0.] 
 [ 0.  0.  1.  0.  0.] 
 [ 0.  0.  0.  1.  0.] 
 [ 0.  0.  0.  0.  1.]]

numpy.matlib.rand()

The numpy.matlib.rand() function creates a matrix of the given size filled with random data.

Example

import numpy.matlib 
import numpy as np 

print (np.matlib.rand(2,2))

Output result:

[[0.82779383 0.00623151]
 [0.23265945 0.79915854]]

print(np.matlib.rand(3,3))

Output:

[[0.23966718 0.16147628 0.14162   ]
 [0.28379085 0.59934741 0.62985825]
 [0.99527238 0.11137883 0.41105367]]

A matrix is always two-dimensional, while an ndarray is an n-dimensional array. Both objects are interchangeable.

Example

import numpy.matlib 
import numpy as np  

i = np.matrix('1,2;3,4')  
print(i)

Output:

[[1  2] 
 [3  4]]

Example

import numpy.matlib 
import numpy as np  

j = np.asarray(i)  
print(j)

Output:

[[1  2] 
 [3  4]]

Example

import numpy.matlib 
import numpy as np  

k = np.asmatrix(j)  
print(k)

Output:

[[1  2] 
 [3  4]]