sort eigenvalues and associated eigenvectors after using numpy.linalg.eig in python

Use numpy.argsort. It returns the indices one would use to sort the array.

import numpy as npimport numpy.linalg as linalgA = np.random.random((3,3))eigenValues, eigenVectors = linalg.eig(A)idx = eigenValues.argsort()[::-1]   eigenValues = eigenValues[idx]eigenVectors = eigenVectors[:,idx]

If the eigenvalues are complex, the sort order is lexicographic (that is, complex numbers are sorted according to their real part first, with ties broken by their imaginary part).

Above answer by unutbu is very crisp and concise. But, here is another way we can do it which more general and can be used for lists as well.

eval, evec =  sp.eig(A)ev_list = zip( eval, evec )ev_list.sort(key=lambda tup:tup[0], reverse=False)eval, evec = zip(*ev_list)

This tup[0] is the eigenvalue based on which the sort function will sort the list.

reverse = False is for increasing order.

The ubuntu's piece of code doesn't work on my Python 3.6.5. It leads run-time errors. So, I refactored his/her code to this one which works ok on my test cases:

import numpy as npfrom numpy import linalg as npla#def eigen(A):    eigenValues, eigenVectors = npla.eig(A)    idx = np.argsort(eigenValues)    eigenValues = eigenValues[idx]    eigenVectors = eigenVectors[:,idx]    return (eigenValues, eigenVectors)