How to sort a numpy array in a specific order given by a separate list depending on the relative sizes of the values in the array
In a piece of code, I already have a numpy array created and I want to sort this 1st array given a specific order given in the list. The result will be the third array (new_values).
In the first array, the values will be sorted.
values = numpy.array([10.0, 30.1, 50, 40, 20])
The list represents the order specified by the indices of the values in new_values, which must be in descending order, where 0 is the largest number.
order=[0, 3, 1, 4, 2].
So,
new_values[0] > new_values[3] > new_values[1] > new_values[4] > new_values[2]
I tried to find a specific sorting function to accomplish this like argsort or with a key, however I didn't figure out how to adapt them to this situation.
Is there a simple quick way to accomplish this as I will be doing this for many iterations. I am willing to change the second array, ordering, to specify the indices in a different way if it was beneficial to sort with a better method.
I am currently using the following for loop.
size=len(values)
new_values = np.zeros(size)
values = sorted(values,reverse=True)
for val in range(0, size):
new_values[int(order[val])] = values[val]
Thank you in advance!
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You can simply use indexing for this:
>>> import numpy as np
>>> values = np.array([10.0, 30.1, 50, 40, 20])
>>> order=[0, 3, 1, 4, 2]
>>> sorted_array=values[order]
>>> sorted_array
array([ 10. , 40. , 30.1, 20. , 50. ])
Also like @Divakar mentioned in the comment if you want the following condition:
new_values[0] > new_values[3] > new_values[1] > new_values[4] > new_values[2]
You can do:
>>> values[order]=np.sort(values)[::-1]
>>> values
array([ 50. , 30.1, 10. , 40. , 20. ])
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