# Matrix manipulation in Python

In python matrix can be implemented as 2D list or 2D Array. Forming matrix from latter, gives the additional functionalities for performing various operations in matrix. These operations and array are defines in module “**numpy**“.

**Operation on Matrix :**

**1. add() :-** This function is used to perform** element wise matrix addition**.

**2. subtract() :-** This function is used to perform **element wise matrix subtraction**.

**3. divide() :-** This function is used to perform** element wise matrix division**.

`# Python code to demonstrate matrix operations` `# add(), subtract() and divide()` ` ` `# importing numpy for matrix operations` `import` `numpy` ` ` `# initializing matrices` `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 divide() to divide 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 perform **element wise matrix multiplication**.

**5. dot() :-** This function is used to compute the** matrix multiplication, rather than element wise multiplication**.

`# Python code to demonstrate matrix operations` `# multiply() and dot()` ` ` `# importing numpy for matrix operations` `import` `numpy` ` ` `# initializing matrices` `x ` `=` `numpy.array([[` `1` `, ` `2` `], [` `4` `, ` `5` `]])` `y ` `=` `numpy.array([[` `7` `, ` `8` `], [` `9` `, ` `10` `]])` ` ` `# using multiply() to multiply matrices element wise` `print` `(` `"The element wise multiplication of matrix is : "` `)` `print` `(numpy.multiply(x,y))` ` ` `# using dot() to multiply matrices` `print` `(` `"The 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 compute the **square root of each element** of matrix.

**7. sum(x,axis) :-** This function is used to **add all the elements in matrix**. Optional “axis” argument computes the **column sum if axis is 0** and **row sum if axis is 1**.

**8. “T” :-** This argument is used to **transpose** the specified matrix.

`# Python code to demonstrate matrix operations` `# sqrt(), sum() and "T"` ` ` `# importing numpy for matrix operations` `import` `numpy` ` ` `# initializing matrices` `x ` `=` `numpy.array([[` `1` `, ` `2` `], [` `4` `, ` `5` `]])` `y ` `=` `numpy.array([[` `7` `, ` `8` `], [` `9` `, ` `10` `]])` ` ` `# using sqrt() to print the square root of matrix` `print` `(` `"The element wise square root is : "` `)` `print` `(numpy.sqrt(x))` ` ` `# using sum() to print summation of all elements of matrix` `print` `(` `"The summation of all matrix element is : "` `)` `print` `(numpy.` `sum` `(y))` ` ` `# using sum(axis=0) to print summation of all columns of matrix` `print` `(` `"The column wise summation of all matrix is : "` `)` `print` `(numpy.` `sum` `(y,axis` `=` `0` `))` ` ` `# using sum(axis=1) to print summation of all columns of 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 given matrix is : "` `)` `print` `(x.T)` |

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 given matrix is : [[1 4] [2 5]]

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