A matrix represents a collection of numbers arranged in the order of rows and columns. It is necessary to enclose the elements of a matrix in parentheses or brackets. A constant matrix is a type of matrix whose elements are the same i.e. the element does not change irrespective of any index value thus acting as a constant.
Examples:
M = [[ x, x, x ]
[ x ,x ,x]
[ x, x, x]]
Here M is the constant matrix and x is the constant element.
Below are some examples of Constant Matrix:
A = [[ 5 , 5]
[ 5, 5]]
B = [[ 12, 12, 12, 12, 12, 12]]
There are various methods in numpy module, which can be used to create a constant matrix such as numpy.full(), numpy.ones(), and numpy.zeroes().
Using numpy.full() method
Syntax:
numpy.full(shape, fill_value, dtype = None, order = ‘C’)
Parameters:
- shape: Number of rows
- order: C_contiguous or F_contiguous
- dtype: [optional, float(by Default)] Data type of returned array.
- fill_value: [bool, optional] Value to fill in the array.
Returns: ndarray of a given constant having given shape, order and datatype.
Example 1:
Here, we will create a constant matrix of size (2,2) (rows = 2, column = 2) with a constant value of 6.3
Python3
# import required module import numpy as np # use full() with a # constant value of 6.3 array = np.full((2, 2), 6.3) # display matrix print(array) |
Output:
[[6.3 6.3] [6.3 6.3]]
Example 2:
A similar example to the one showed above
Python3
# import required module import numpy as np # use full() with a # constant value of 60 array = np.full((4, 3), 60) # display matrix print(array) |
Output:
[[60 60 60] [60 60 60] [60 60 60] [60 60 60]]
Using numpy.ones() method
Syntax:
numpy.ones(shape, dtype = None, order = ‘C’)
Parameters:
- shape: integer or sequence of integers
- order: C_contiguous or F_contiguous
- dtype: Data type of returned array.
Returns: ndarray of ones having given shape, order and datatype.
Example 1:
Now, suppose we want to print a matrix consisting of only ones(1s).
Python3
# import required module import numpy as np # use ones() array = np.ones((2,2)) # display matrix print(array) |
Output:
[[1. 1.] [1. 1.]]
Here by-default, the data type is float, hence all the numbers are written as 1. An alteration, to the above code. Now, we want the data type to be of an integer.
Python3
# import required module import numpy as np # use ones() with integer constant array = np.ones((2, 2), dtype=np.uint8) # display matrix print(array) |
Output:
[[1 1] [1 1]]
Notice the change in the last two outputs, one of them shows, 1. And the other is showing 1 only, which means we converted the data type to integer in the second one. uint8 stands for an unsigned 8-bit integer which can represent values ranging from 0 to 255.
Example 2:
Here we create a one-dimensional matrix of only 1s.
Python3
# import required module import numpy as np # use ones() with integer constant array = np.ones((5), dtype=np.uint8) # display matrix print(array) |
Output:
[1 1 1 1 1]
Using numpy.zeroes() method
Syntax:
numpy.zeros(shape, dtype = None, order = ‘C’)
Parameters:
- shape: integer or sequence of integers
- order: C_contiguous or F_contiguous
- dtype: Data type of returned array.
Returns: ndarray of zeros having given shape, order and datatype.
Example 1:
Now that we made a matrix of ones, let’s make one for zeroes.
Python3
# import required module import numpy as np # use zeroes() array = np.zeros((2,2)) # display matrix print(array) |
Output:
[[0. 0.] [0. 0.]]
To change it to an integer type,
Python3
# import required module import numpy as np # use zeroes() with integer constant array = np.zeros((2,2), dtype=np.uint8) # display matrix print(array) |
Output:
[[0 0] [0 0]]
Example 2:
Here is another example to create a constant one-dimensional matrix of zeroes.
Python3
# import required module import numpy as np # use zeroes() with integer constant array = np.zeros((5), dtype=np.uint8) # display matrix print(array) |
Output:
[0 0 0 0 0]
