  # Matrix manipulation in Python

File handling | NumPy | Python Methods and Functions

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 .

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 ` # 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 .

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` # 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.

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 ` # 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) `
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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]] `