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This article will introduce you Matrix in Python with every operation that concerns the topic with a programmatic demonstration. Following pointers will be covered in this article,
Matrix In Python
Matrix is nothing but a rectangular array of numbers or any other form of data. The basic concept of a matrix should be clear before operating on matrices within the boundaries of python programming language. The horizontal arrangement of data are rows and the vertical arrangement are columns. The size of any matrices or in other words, the number of elements inside a matrix is (R) X (C) where R is rows and C, columns. Python does not have a built in type for matrices, so we consider two or more lists together as a matrix.
Now, let us have a look at viewing elements of a matrix and its functionality. Consider the below illustrated python code.
print("nWELCOME TO EDUREKA !n") print("Below is a Matrixn") A= [[1,4,5,12], [-5,8,9,0] [-6,7,11,19]] print("A=", A) print("nAttempting to print the 2nd rown") print("A =", A) print("nAttempting to print the 2nd row, 3rd elementn") print("A=", A) print("nPriting last element of the 1st rown") print("A =", A) column = ; for row in A: column.append(row) print("n Displaying the 3rd column onlyn") print("3rd column=", column) print("n Thank you ! Have a nice day!")
NumPy Package For Matrices In Python
Numpy is a python library, which allows for scientific computing. Numpy can help users work on multidimensional arrays.
/Addition of Matrices print("nWELCOME TO EDUREKA!n") import numpy as np A= np.array([[24,41],[35,-9]]) B= np.array([[19,-36],[37,68]]) C=A+B print("Summing a matrix using Numpy is simplen") print(C) print("nThank You!")
Multiplication Of Matrices
The product of two matrices is found using Numpy libraries as illustrated below.
// Import numpy as np A= np.array([[7,1,3],[6,-2,0]]) B= np.array([[2,3],[9,5],[4,-2]]) C=A.dot(B) print("nThe product of two matrices is n") print(C) print("nThank you !n")
Transpose Of A Matrix
Transpose refers to a new matrix formed whose rows are now the columns and whose columns are now the rows of the initial matrix.
// Import numpy as np A=np.array([[1,1],[2,1],[3,-3]]) print(“n This is your original matrixn”) print(A) print(“this is your transpose”) print(A.transpose()) print(“nThank You”)
This brings us to the end of this article.
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