Difference between Numpy array and Numpy Matrix

Table of Differences between the Numpy Array and Numpy Matrix

Matrix is 2-dimensional while ndarray can be multi-dimensional

Example 1:

Here, we can print all the dimensions of an array in np.array.

Python3

import numpy as np
 
arr1 = np.array([1, 2, 3])
print("1D array\n", arr1)
print("\n")
 
arr2 = np.array([[1, 2], [3, 4]])
print("2D array\n", arr2)
print("\n")
 
C = np.array([[[1, 2], [3, 4]],
             [[5, 6], [7, 8]],
             [[9, 10], [11, 12]]])
print("3D array\n", C)

Output:

1D array
 [1 2 3]


2D array
 [[1 2]
 [3 4]]


3D array
 [[[ 1  2]
  [ 3  4]]

 [[ 5  6]
  [ 7  8]]

 [[ 9 10]
  [11 12]]]

Example 2:

Matrix works normally for a 2D matrix and if a 1D matrix will convert into 2D Matrix, but if we pass a 3D matrix it will through an error.

Python3

import numpy as np
 
arr1 = np.matrix([1, 2, 3])
print(arr1)
print("Dimensions:", arr1.ndim)
print("\n")
 
arr2 = np.matrix([[[1, 2], [3, 4]],
                  [[5, 6], [7, 8]],
                  [[9, 10], [11, 12]]])
print("2D array\n", arr2)

Output:

Different functionality of * operator in ndarray and Matrix

Example 1:

Array * operator does simple multiplication.

Python3

a = np.array([[1, 2], 
             [3, 4]])
b = np.array([[1, 2], 
             [3, 4]])
 
print("Array multiplication: \n", a*b)

Output:

Array multiplication: 
 [[ 1  4]
 [ 9 16]]

Example 2:

While it does matrix multiplication.

Python3

a = np.matrix([[1, 2], 
             [3, 4]])
b = np.matrix([[1, 2], 
             [3, 4]])
 
print("Matrix multiplication: \n", a*b)

Output:

Matrix multiplication: 
 [[ 7 10]
 [15 22]]

Matrix has an array.I for inverse, but ndarray has linalg.inv

Example 1:

The inverse can be done with array.I in ndarray.

Python3

arr1 = np.matrix([[1, 2], 
              [3, 4]])
 
print('Inverse \n', arr1.I)

Output:

Inverse 
 [[-2.   1. ]
 [ 1.5 -0.5]]

Example 2:

The inverse can be done with np.linalg.inv in matrix.

Python3

b = np.array([[1, 2], 
             [3, 4]])
 
print('Inverse \n', np.linalg.inv(b))

Output:

Inverse 
 [[-2.   1. ]
 [ 1.5 -0.5]]