How to sort the rows of a numpy matrix with respect to their norm?

Question

I have a numpy array

array([[ 4.14022471,  3.96360618],  
       [ 0.37601032,  1.25528411],  
       [ 3.49313049,  0.94909878]])

Now I wish to sort this array's rows with respect to the norm of the rows, i.e

norm([ 4.14022471,  3.96360618]) = 5.73163455
norm([ 0.37601032,  1.25528411]) = 1.31039
norm([ 3.49313049,  0.94909878]) = 3.61977197

Hence the first row remains in first position, while 2nd and 3rd rows need to be swapped.

The solution will be

array([[ 4.14022471,  3.96360618],    
       [ 3.49313049,  0.94909878],
       [ 0.37601032,  1.25528411]])

How can I do this?

Does this answer your question? Sort array's rows by another array in Python For the "another array" by which you will sort, see How to apply numpy.linalg.norm to each row of a matrix? — pho
– pho, Commented Feb 10, 2023 at 22:13

RomanPerekhrest · Accepted Answer · 2023-02-10 21:53:16Z

1

With numpy.argsort and numpy.take:

arr = np.array([[ 4.14022471, 3.96360618], [ 0.37601032, 1.25528411], [ 3.49313049, 0.94909878]])
norms = np.linalg.norm(arr, axis=1)
arr_sorted = np.take(arr, np.argsort(norms)[::-1], axis=0)

[[4.14022471 3.96360618]
 [3.49313049 0.94909878]
 [0.37601032 1.25528411]]

answered Feb 10, 2023 at 21:53

RomanPerekhrest

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AnrimO · Accepted Answer · 2023-02-10 21:34:31Z

-1

Python's built-in sorted() function has a key parameter that accepts a function, simply pass in the norm function and reverse=True to sort largest to smallest

>>> arr = array([[ 4.14022471,3.96360618],[ 0.37601032,1.25528411],[3.49313049, 0.94909878]])
>>> array(sorted(arr, key=norm, reverse = True))
array([[4.14022471, 3.96360618],
       [3.49313049, 0.94909878],
       [0.37601032, 1.25528411]])

answered Feb 10, 2023 at 21:34

AnrimO

1

Comments

Кирилл Чекрыгин · Accepted Answer · 2023-02-10 21:53:25Z

-1

For your task it is enough to use the only library - numpy. Solution:

a = np.array([[ 4.14022471,  3.96360618],   [ 0.37601032,  1.25528411],   [ 3.49313049,  0.94909878]])

def get_norm(array):
    return np.linalg.norm(array)

sorted(a, reverse=True, key = get_norm)

It will be appropriate to use the lambda function. It's more concise and easier to read the code.

a = np.array([[ 4.14022471,  3.96360618],   [ 0.37601032,  1.25528411],   [ 3.49313049,  0.94909878]])
sorted(a, reverse=True, key = lambda x:np.linalg.norm(x))

In both cases the program will output:

[array([4.14022471, 3.96360618]),
 array([3.49313049, 0.94909878]),
 array([0.37601032, 1.25528411])]

answered Feb 10, 2023 at 21:53

Кирилл Чекрыгин

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