# Matrix manipulation in Python

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In Python we can solve the different matrix manipulations and operations. Numpy Module provides different methods for matrix operations.

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

Live Demo

import numpy
# Two matrices are initialized by value
x = numpy.array([[1, 2], [4, 5]])
y = numpy.array([[7, 8], [9, 10]])
print ("Addition of two matrices: ")
# 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]]