Importance of matrix class in numpy library
- D array is called => Vector
- 2-D array is called => Matrix
- matrix class is specially designed class to create 2-D arrays
Example
Python
In [417]:
import numpy as np
help(np.matrix) Output
PowerShell
Help on class matrix in module numpy:
class matrix(ndarray)
| matrix(data, dtype=None, copy=True)
|
| matrix(data, dtype=None, copy=True)
|
| .. note:: It is no longer recommended to use this class, even for linear
| algebra. Instead use regular arrays. The class may be removed
| in the future. creating 2-D arrays
- By using matrix class
- By using ndarray class
class matrix(ndarray)
matrix(data, dtype=None, copy=True)
- data : array_like or string
- If data is a string, it is interpreted as a matrix with commas or spaces separating
columns, and semicolons separating rows.
Parameters
- . data : array_like or string
- If data is a string, it is interpreted as a matrix with commas
- or spaces separating columns, and semicolons separating rows.
- dtype : data-type
- Data-type of the output matrix.
- copy : bool
- If data is already an ndarray, then this flag determines
- whether the data is copied (the default), or whether a view is constructed.
Example
Python
In [418]:
# Creating matrix object from string
# a = np.matrix('col1 col2 col3;col1 col2 col3')
# a = np.matrix('col1,col2,col3;col1,col2,col3')
a = np.matrix('10,20;30,40')
b = np.matrix('10 20;30 40')
print(f"type of a : type(a)")
print(f"type of b : type(b)")
print(f"Matrix object creation from string with comma : \n{a}")
print(f"Matrix object creation from string with space : \n{b}") Output
PowerShell
type of a : type(a)
type of b : type(b)
Matrix object creation from string with comma :
[[10 20]
[30 40]]
Matrix object creation from string with space :
[[10 20]
[30 40]]Example
Python
In [419]:
# Creating matrix object from nested list
a = np.matrix([[10,20],[30,40]])
a Output
PowerShell
Out[419]:
matrix([[10, 20],
[30, 40]])Example
Python
In [420]:
# create a matrix from ndarray
a = np.arange(6).reshape(3,2)
b = np.matrix(a)
print(f"type of a : type(a)")
print(f"type of b : type(b)")
print(f'ndarray :\n {a}')
print(f'matrix :\n {b}') Output
PowerShell
type of a : type(a)
type of b : type(b)
ndarray :
[[0 1]
[2 3]
[4 5]]
matrix :
[[0 1]
[2 3]
[4 5]] operator in ndarray and matrix
- In case of both ndarray and matrix + operator behaves in the same way
Figer bnanan
Example
Python
In [421]:
# + operator in ndarray and matrix
a = np.array([[1,2],[3,4]])
m = np.matrix([[1,2],[3,4]])
addition_a = a+a
addition_m = m+m
print(f'ndarray addition :\n {addition_a}')
print(f'matrix addition :\n {addition_m}') Output
PowerShell
ndarray addition :
[[2 4]
[6 8]]
matrix addition :
[[2 4]
[6 8]]operator in ndarray and matrix
- In case of ndarray * operator performs element level multiplication
- In case of matrix * operator performs matrix multiplication
Example
Python
In [422]:
# * operator in ndarray and matrix
a = np.array([[1,2],[3,4]])
m = np.matrix([[1,2],[3,4]])
element_mul = a*a
matrix_mul = m*m
print(f'ndarray multiplication :\n {element_mul}')
print(f'matrix multiplication :\n {matrix_mul}') Output
PowerShell
ndarray multiplication :
[[ 1 4]
[ 9 16]]
matrix multiplication :
[[ 7 10]
[15 22]] ** operator in ndarray and
- In case of ndarray ** operator performs power operation at element level
- In case of matrix ** operator performs power operation at matrix level
m ** 2 ==> m *m
Figer banana
Example
Python
In [423]:
# ** operator in ndarray and matrix
a = np.array([[1,2],[3,4]])
m = np.matrix([[1,2],[3,4]])
element_power = a**2
matrix_power = m**2
print(f'ndarray power :\n {element_power}')
print(f'matrix power :\n {matrix_power}') Output
PowerShell
ndarray power :
[[ 1 4]
[ 9 16]]
matrix power :
[[ 7 10]
[15 22]]T in ndarray and matrix
- In case of both ndarray and matrix T behaves in the same way
Example
Python
In [424]:
# ** operator in ndarray and matrix
a = np.array([[1,2],[3,4]])
m = np.matrix([[1,2],[3,4]])
ndarray_T = a.T
matrix_T = m.T
print(f'ndarray transpose :\n {ndarray_T}')
print(f'matrix transpose :\n {matrix_T}') Output
PowerShell
ndarray transpose :
[[1 3]
[2 4]]
matrix transpose :
[[1 3]
[2 4]]Conclusions
- matrix class is the child class of ndarray class. Hence all methods and properties of
- ndarray class are bydefault available to the matrix class.
- We can use +, *, T, ** for matrix objects also.
- In the case of ndarray, operator performs element level multiplication. But in case of
matrix, operator preforms matrix multiplication. - In the case of ndarray, operator performs power operation at element level. But
in the case of matrix, operator performs ‘matrix’ power. - matrix class always meant for 2-D array only.
- It is no longer recommended to use.
Differences between ndarray and matrix
ndarray
- It can represent any n-dimension array.
- We can create from any array_like object but not from string.
- * operator meant for element mulitplication but not for dot product.
- ** operator meant for element level power operation
- It is the parent class
- It is the recommended to use
matrix
- It can represent only 2-dimension array.
- We can create from either array_like object or from string
- * operator meant for for dot product but not for element mulitplication.
- ** operator meant for for matrix power operation
- It is the child class
- It is not recommended to use and it is deprecated.