Python Matrix Tutorial
We can implement a Python Matrix in the form of a 2-d List or a 2-d Array. To perform operations on Python Matrix, we need to import Python NumPy Module.
Python Matrix is essential in the field of statistics, data processing, image processing, etc.
Creation of a Python Matrix
Python Matrix can be created using one of the following techniques:
- By using Lists
- By using arange() method
- By using matrix() method
1. Creation of matrix using Lists
The numpy.array() function can be used to create an array using lists as input to it.
Example:
import numpyinput_arr = numpy.array([[ 10, 20, 30],[ 40, 50, 60]])print(input_arr) |
Output:
[[10 20 30] [40 50 60]] |
As seen above, the output represents a 2-D matrix with the given set of inputs in the form of list.
2. Creation of matrix using ‘numpy.arange()’ function
The numpy.arange() function along with the list inputs can be used to create a matrix in Python.
Example:
import numpyprint(numpy.array([numpy.arange(10,15), numpy.arange(15,20)])) |
Output:
[[10 11 12 13 14] [15 16 17 18 19]] |
3. Creation of Matrix using ‘numpy.matrix() function’
The numpy.matrix() function enables us to create a matrix in Python.
Syntax:
numpy.matrix(input,dtype) |
- input: The elements input to form a matrix.
- dtype: The data type of the corresponding output.
Example:
import numpy as pmatA = p.matrix([[10, 20], [30, 40]]) print('MatrixA:\n', matA)matB = p.matrix('[10,20;30,40]', dtype=p.int32) # Setting the data-type to intprint('\nMatrixB:\n', matB) |
Output:
MatrixA: [[10 20] [30 40]]MatrixB: [[10 20] [30 40]] |
Addition of Matrix in Python
The addition operation on Matrices can be performed in the following ways:
- Traditional method
- By using ‘+’ operator
1. Traditional method
In this traditional method, we basically take the input from the user and then perform the addition operation using the for loops (to traverse through the elements of the matrix) and ‘+’ operator.
Example:
import numpy as par1 = p.matrix([[11, 22], [33, 44]]) ar2 = p.matrix([[55, 66], [77, 88]]) res = p.matrix(p.zeros((2,2))) print('Matrix ar1 :\n', ar1)print('\nMatrix ar2 :\n', ar2)# traditional codefor x in range(ar1.shape[1]): for y in range(ar2.shape[0]): res[x, y] = ar1[x, y] + ar2[x, y]print('\nResult :\n', res) |
Note:Matrix.shape returns the dimensions of a particular matrix.
Output:
Matrix ar1 : [[11 22] [33 44]]Matrix ar2 : [[55 66] [77 88]]Result : [[ 66. 88.] [ 110. 132.]] |
2. Using ‘+’ operator
This method provides better efficiency to the code as it reduces the LOC (lines of code) and thus, optimizes the code.
Example:
import numpy as par1 = p.matrix([[11, 22], [33, 44]]) ar2 = p.matrix([[55, 66], [77, 88]]) res = p.matrix(p.zeros((2,2))) print('Matrix ar1 :\n', ar1)print('\nMatrix ar2 :\n', ar2)res = ar1 + ar2 # using '+' operatorprint('\nResult :\n', res) |
Output:
Matrix ar1 : [[11 22] [33 44]]Matrix ar2 : [[55 66] [77 88]]Result : [[ 66 88] [110 132]] |
Matrix Multiplication in Python
Matrix Multiplication in Python can be provided using the following ways:
- Scalar Product
- Matrix Product
Scalar Product
In the scalar product, a scalar/constant value is multiplied by each element of the matrix.
The ‘*’ operator is used to multiply the scalar value with the input matrix elements.
Example:
import numpy as pmatA = p.matrix([[11, 22], [33, 44]]) print("Matrix A:\n", matA)print("Scalar Product of Matrix A:\n", matA * 10) |
Output:
Matrix A: [[11 22] [33 44]]Scalar Product of Matrix A: [[110 220] [330 440]] |
Matrix Product
As referenced above, we can utilize the ‘*’ administrator just for Scalar duplication. To proceed with Matrix duplication, we need to utilize the numpy.dot() work.
The numpy.dot() work takes NumPy clusters as boundary esteems and performs augmentation as indicated by the fundamental principles of Matrix Multiplication.
Example:
import numpy as pmatA = p.matrix([[11, 22], [33, 44]]) matB = p.matrix([[2,2], [2,2]])print("Matrix A:\n", matA)print("Matrix B:\n", matB)print("Dot Product of Matrix A and Matrix B:\n", p.dot(matA, matB)) |
Output:
Matrix A: [[11 22] [33 44]]Matrix B: [[2 2] [2 2]]Dot Product of Matrix A and Matrix B: [[ 66 66] [154 154]] |
Subtraction of Python Matrix
The ‘-‘ operator is used to perform Subtraction on Python Matrix.
Example:
import numpy as pmatA = p.matrix([[11, 22], [33, 44]]) matB = p.matrix([[2,2], [2,2]])print("Matrix A:\n", matA)print("Matrix B:\n", matB)print("Subtraction of Matrix A and Matrix B:\n",(matA - matB)) |
Output:
Matrix A: [[11 22] [33 44]]Matrix B: [[2 2] [2 2]]Subtraction of Matrix A and Matrix B: [[ 9 20] [31 42]] |
Division of Python Matrix
Scalar Division can be performed on the elements of the Matrix in Python using the ‘/’ operator.
The ‘/’ operator divides each element of the Matrix with a scalar/constant value.
Example:
import numpy as pmatB = p.matrix([[2,2], [2,2]])print("Matrix B:\n", matB)print("Matrix B after Scalar Division operation:\n",(matB/2)) |
Output:
Matrix B: [[2 2] [2 2]]Matrix B after Scalar Division operation: [[ 1. 1.] [ 1. 1.]] |
Transpose of a Python Matrix
Translate of a matrix fundamentally includes the flipping of matrix over the relating diagonals for example it trades the lines and the segments of the information matrix. The lines become the segments and the other way around.
For instance: Let’s consider a matrix A with measurements 3×2 i.e 3 lines and 2 sections. In the wake of performing render activity, the components of the matrix A future 2×3 i.e 2 lines and 3 segments.
Matrix.T fundamentally plays out the render of the info matrix and produces another matrix because of the translate activity.
Example:
import numpy matA = numpy.array([numpy.arange(10,15), numpy.arange(15,20)])print("Original Matrix A:\n")print(matA)print('\nDimensions of the original MatrixA: ',matA.shape)print("\nTranspose of Matrix A:\n ")res = matA.Tprint(res)print('\nDimensions of the Matrix A after performing the Transpose Operation: ',res.shape) |
Output:
Original Matrix A:[[10 11 12 13 14] [15 16 17 18 19]]Dimensions of the original MatrixA: (2, 5)Transpose of Matrix A: [[10 15] [11 16] [12 17] [13 18] [14 19]]Dimensions of the Matrix A after performing the Transpose Operation: (5, 2) |
In the above snippet of code, I have created a matrix of dimensions 2×5 i.e. 2 rows and 5 columns.
After performing the transpose operation, the dimensions of the resultant matrix are 5×2 i.e. 5 rows and 2 columns.
Exponent of a Python Matrix
The exponent on a Matrix is calculated element-wise i.e. exponent of every element is calculated by raising the element to the power of an input scalar/constant value.
Example:
import numpy matA = numpy.array([numpy.arange(0,2), numpy.arange(2,4)])print("Original Matrix A:\n")print(matA)print("Exponent of the input matrix:\n")print(matA ** 2) # finding the exponent of every element of the matrix |
Output:
Original Matrix A:[[0 1] [2 3]]Exponent of the input matrix:[[0 1] [4 9]] |
In the above code snippet, we have found out the exponent of every element of the input matrix by raising it to the power of 2.
Matrix Multiplication Operation using NumPy Methods
The following techniques can be used to perform NumPy Matrix multiplication:
- Using the multiply() method
- Using the matmul() method
- Using the dot() method – Already covered in this article
Method 1: Using the multiply() method
The numpy.multiply() method performs element-wise multiplication on an input matrix.
Example:
import numpy as pmatA = p.matrix([[10, 20], [30, 40]]) print('MatrixA:\n', matA)matB = p.matrix('[10,20;30,40]', dtype=p.int32) # Setting the data-type to intprint('\nMatrixB:\n', matB)print("Matrix multplication using numpy.matrix() method")res = p.multiply(matA,matB)print(res) |
Output:
MatrixA: [[10 20] [30 40]]MatrixB: [[10 20] [30 40]]Matrix multplication using numpy.matrix() method[[ 100 400] [ 900 1600]] |
Method 2: Using the matmul() method
The numpy.matmul() method performs the matrix product on the input matrices.
Example:
import numpy as pmatA = p.matrix([[10, 20], [30, 40]]) print('MatrixA:\n', matA)matB = p.matrix('[10,20;30,40]', dtype=p.int32) # Setting the data-type to intprint('\nMatrixB:\n', matB)print("Matrix multplication using numpy.matmul() method")res = p.matmul(matA,matB)print(res) |
Output:
MatrixA: [[10 20] [30 40]]MatrixB: [[10 20] [30 40]]Matrix multplication using numpy.matmul() method[[ 700 1000] [1500 2200]] |
I would strongly recommend all the readers to go through the below tutorial to have a thorough understanding of NumPy Matrix Multiplication: NumPy Matrix Multiplication
NumPy Matrix Transpose
The numpy.transpose() function performs the transpose on the input matrix and results in a new matrix.
Example:
import numpy matA = numpy.array([numpy.arange(10,15), numpy.arange(15,20)])print("Original Matrix A:\n")print(matA)print('\nDimensions of the original MatrixA: ',matA.shape)print("\nTranspose of Matrix A:\n ")res = matA.transpose()print(res)print('\nDimensions of the Matrix A after performing the Transpose Operation: ',res.shape) |
Output:
Original Matrix A:[[10 11 12 13 14] [15 16 17 18 19]]Dimensions of the original MatrixA: (2, 5)Transpose of Matrix A: [[10 15] [11 16] [12 17] [13 18] [14 19]]Dimensions of the Matrix A after performing the Transpose Operation: (5, 2) |
Recommended read: NumPy Matrix transpose() function
Conclusion
Thus, in this article, we have understood the operations performed on Python Matrix and also had a look at the NumPy Matrix operations.
