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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