Operation on the matrix:

1. add (): — This function is used to element-wise addition of a matrix .

2. subtract (): — This function is used to subtract matrix elements .

3. Divide (): — This function is used to elementwise division of a matrix .

# Python code for matrix operations demonstrations # add (), subtract () and divide ()    # numpy imports for matrix operations import numpy   # matrix initialization x = numpy.array ([[ 1 , 2 ], [ 4 , 5 ]] ) y = numpy.array ( [[ 7 , 8 ], [ 9 , 10 ]])   # using add ( ) to add matrices print ( " The element wise addition of matrix is: " ) print (numpy.add (x, y))   # using subtract () to subtract matrices print ( " The element wise subtraction of matrix is : " ) print (numpy. subtract (x, y))   # using the split () function to separate matrices print ( "The element wise division of matrix is: " ) print (numpy.divide (x, y))

Output:

 The element wise addition of matrix is: [[8 10] [13 15]] The element wise subtraction of matrix is: [[-6 -6] [-5 -5]] The element wise division of matrix is: [[0.14285714 0.25] [0.44444444 0.5]] 

4. multiply (): — This function is used to multiply a matrix by a element .

5. dot (): — This function is used to calculate matrix multiplication, not elementwise multiplication .

# Python code to demonstrate matrix operations # multiply () and dot ()   # numpy imports for matrix operations import numpy   # matrix initialization x = numpy.array ([[ 1 , 2 ] , [ 4 , 5 ]]) y = numpy.array ([[ 7 , 8 ], [ 9 , 10 ]])    # using multiply () to multiply matrices by elements print ( "The element wise multiplication of matrix is:" ) print (numpy.multiply (x, y))   # using dot () to multiply matrices print ( "T he product of matrices is: " ) print (numpy.dot (x, y))

Output:

 The element wise multiplication of matrix is: [[7 16] [36 50]] The product of matrices is: [[25 28] [73 82]] 

6. sqrt (): — This function is used to calculate the square root of each element of the matrix.

7. sum (x, axis): — This function is used to add all elements to the matrix . The optional axis argument calculates the column sum if the axis is 0, and the row sum if the axis is 1 .

8. "T": — This argument is used to transpose the specified matrix.

# Python code for matrix operations demonstrations # sqrt (), sum () and & quot; T & quot;   # numpy imports for matrix operations import numpy   # matrix initialization x = numpy.array ([[ 1 , 2 ], [ 4 , 5 ]]) y = numpy.array ([[ 7 , 8 ], [ 9 , 10 ]])   # using sqrt () to print square root of the matrix print ( "The element wise square root is: " ) print (numpy.sqrt (x))    # using sum ( ) to display the sum of all matrix elements print ( " The summation of all matri x element is: " ) print (numpy. sum (y))   # using the sum (axis = 0) to display the sum of all matrix columns print ( "The column wise summation of all matrix is:" ) print (numpy. sum (y, axis = 0 ))    # use the sum (axis = 1) for displaying the sum of all columns of the matrix print ( "The row wise summation of all matrix is:" ) print (numpy. sum (y, axis = 1 ))   # using "T" to transpose the matrix print ( "The transpose of the given matrix is:" ) print (xT)

Output:

 The element wise square root is: [[1. 1.41421356] [2. 2.23606798]] The summation of all matrix element is: 34 The column wise summation of all matrix is: [16 18] The row wise summation of all matrix is: [15 19] The transpose of a given matrix is: [[1 4] [2 5]] 

This article is courtesy of Manjit Singh 100