In Python we can solve the different matrix manipulations and operations. Numpy Module provides different methods for matrix operations.
add() − add elements of two matrices.
subtract() − subtract elements of two matrices.
divide() − divide elements of two matrices.
multiply() − multiply elements of two matrices.
dot() − It performs matrix multiplication, does not element wise multiplication.
sqrt() − square root of each element of matrix.
sum(x,axis) − add to all the elements in matrix. Second argument is optional, it is used when we want to compute the column sum if axis is 0 and row sum if axis is 1.
“T” − It performs transpose of the specified matrix.
Example code
import numpy
# Two matrices are initialized by value
x = numpy.array([[1, 2], [4, 5]])
y = numpy.array([[7, 8], [9, 10]])
# add()is used to add matrices
print ("Addition of two matrices: ")
print (numpy.add(x,y))
# subtract()is used to subtract matrices
print ("Subtraction of two matrices : ")
print (numpy.subtract(x,y))
# divide()is used to divide matrices
print ("Matrix Division : ")
print (numpy.divide(x,y))
print ("Multiplication of two matrices: ")
print (numpy.multiply(x,y))
print ("The product of two matrices : ")
print (numpy.dot(x,y))
print ("square root is : ")
print (numpy.sqrt(x))
print ("The summation of elements : ")
print (numpy.sum(y))
print ("The column wise summation : ")
print (numpy.sum(y,axis=0))
print ("The row wise summation: ")
print (numpy.sum(y,axis=1))
# using "T" to transpose the matrix
print ("Matrix transposition : ")
print (x.T)
Output
Addition of two matrices: [[ 8 10] [13 15]] Subtraction of two matrices : [[-6 -6] [-5 -5]] Matrix Division : [[0.14285714 0.25 ] [0.44444444 0.5 ]] Multiplication of two matrices: [[ 7 16] [36 50]] The product of two matrices : [[25 28] [73 82]] square root is : [[1. 1.41421356] [2. 2.23606798]] The summation of elements : 34 The column wise summation : [16 18] The row wise summation: [15 19] Matrix transposition : [[1 4] [2 5]]