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 .

# 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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