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I have two NumPy arrays with the shape (74395, 1) storing float values where arr1[0] correlates to arr2[0] and so on. I would like to sort them together in ascending order by the values stored in the second array.

As an example:

arr1: [[1]
       [2]
       [3]]

arr2: [[6]
       [2]
       [4]]

wanted result:

arr1: [[2]
       [3]
       [1]]

arr2: [[2]
       [4]
       [6]]

How could I do that in python?

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2 Answers 2

Reset to default
5

zip the two together, sort, zip again to transpose the result, and destructure:

>>> arr1 = [1, 2, 3]
>>> arr2 = [6, 2, 4]
>>> arr2, arr1 = zip(*sorted(zip(arr2, arr1)))
>>> arr1
(2, 3, 1)
>>> arr2
(2, 4, 6)
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Comments

2

Use numpy.argsort with numpy.take_along_axis:

ind = arr2.argsort(axis=0)

np.take_along_axis(arr1, ind, axis=0)
np.take_along_axis(arr2, ind, axis=0)

Output:

(array([[2],
        [3],
        [1]]),
 array([[2],
        [4],
        [6]]))
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8 Comments

Why bring a huge dependency like numpy into this…?
@deceze Given OP uses 2d arrays, I automatically assumed its numpy :P.
@Chris Correct - I am indeed using numpy arrays and won't get around it ^^. Two things - In order for the changes to take effect I had to overwrite the variables. so arr1 = np.take_along... etc. Secondly, my arr1 values are all 0 after the transformation
@FLOROID You mean sorted arr1 starts with many zeros or they are all zeros ((arr1 == 0).all() == True)?
It must've been some weird bug, because after re-running the application the data is correctly sorted now as far as I can tell with this much data haha
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