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
# 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)
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Output:
[[6.3 6.3] [6.3 6.3]]
Example 2:
A similar example to the one showed above
# 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)
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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).
# import required module import numpy as np
# use ones() array = np.ones((2,2))
# display matrix print(array)
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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.
# import required module import numpy as np
# use ones() with integer constant array = np.ones((2, 2), dtype=np.uint8)
# display matrix print(array)
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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.
# import required module import numpy as np
# use ones() with integer constant array = np.ones((5), dtype=np.uint8)
# display matrix print(array)
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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.
# import required module import numpy as np
# use zeroes() array = np.zeros((2,2))
# display matrix print(array)
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Output:
[[0. 0.] [0. 0.]]
To change it to an integer type,
# import required module import numpy as np
# use zeroes() with integer constant array = np.zeros((2,2), dtype=np.uint8)
# display matrix print(array)
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Output:
[[0 0] [0 0]]
Example 2:
Here is another example to create a constant one-dimensional matrix of zeroes.
# import required module import numpy as np
# use zeroes() with integer constant array = np.zeros((5), dtype=np.uint8)
# display matrix print(array)
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Output:
[0 0 0 0 0]