Python中的矩阵操作

在Python中，矩阵可以实现为 2D 列表或 2D 数组。从后者形成矩阵，为在矩阵中执行各种操作提供了额外的功能。这些操作和数组在模块“ numpy ”中定义。

矩阵运算：

1. add() :-此函数用于执行元素矩阵加法。

2.subtract() :-此函数用于执行逐元素矩阵减法。

3. divide() :-此函数用于执行元素矩阵除法。

# 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))

输出 ：

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() :-此函数用于执行元素矩阵乘法。

5. dot() :-此函数用于计算矩阵乘法，而不是元素乘法。

# 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))

输出 ：

The element wise multiplication of matrix is : 
[[ 7 16]
 [36 50]]
The product of matrices is : 
[[25 28]
 [73 82]]

6. sqrt() :-该函数用于计算矩阵每个元素的平方根。

7. sum(x,axis) :-此函数用于将矩阵中的所有元素相加。可选的“axis”参数如果轴为 0 则计算列总和，如果轴为 1 则计算行总和。

8. “T” :-该参数用于转置指定的矩阵。

# 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)

输出 ：

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]]