how numpy partition work


I am trying to figure out how np.partition function works. For example, consider

arr = np.array([ 5, 4, 1, 0, -1, -3, -4, 0])

If I call np.partition(arr, kth=2), I got

np.array([-4, -3, -1, 0, 1, 4, 5, 0])

I expect that after partition array will splits into elements less one, one and elements greater one. But the second zero placed on the last array position, which isn't its right place after partition.

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| numpy   | sorting   | python   | arrays   2017-01-05 12:01 1 Answers

Answers to how numpy partition work ( 1 )

  1. 2017-01-05 12:01

    The documentation is quite clear:

    Creates a copy of the array with its elements rearranged in such a way that the value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined.

    In the example you give, you have selected 2th element of the sorted list (starting from zero), which is -1, and it seems to be in the right position if the array was sorted.

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