** 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**

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