The NumPy library is very helpful for solving linear systems in python. For how to install NumPy look here
Importing NumPy
All the code-snippets below has numpy importet as np. To learn how see the installation of NumPy
Matrixes in Python
Creating a matrix
a = np.array([[1,2],[3,4]])
In Python rows and columns start at 0. For example index the data at first row second column :
Indexing matrix
b = a[0,1]
Linear algebra using NumPy
NumPy has several useful functions for linear algebra built-in. For full list look check the NumPy documentation.
Solve()
np.linalg.solve(a,b) #where a and b is matrixs #returns a array with solutions to the system
Eigenvalues and eigenvectors
np.linalg.eig(a) # where a is a square matrix #returns two arrays [v,w] #v containing the eigenvalues of a #w containing the eigenvectors of a
Determinant
np.linalg.det(a) # where a is a square matrix #returns the determinant of the matrix a
Finding the inverse
np.linalg.inv(a) # where a is the matrix to be inverted #Returns the inverse matrix of a
Example
Solving set of equations
import numpy as np # Solving following system of linear equation # 5a + 2b = 35 # 1a + 4b = 49 a = np.array([[5, 2],[1,4]]) # Lefthand-side of the equation b = np.array([35, 94]) #Righthand-side print(np.linalg.solve(a,b)) #Printing the solution