[Solved] 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.
The documentation says:
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.
The docs talk of ‘a sorted array’.
np.partition starts by sorting the elements in the array provided. In this case the original array is:
arr = [ 5, 4, 1, 0, -1, -3, -4, 0]
When sorted, we have:
arr_sorted = [-4 -3 -1 0 0 1 4 5]
Hence the call,
np.partition(arr, kth=2), will actually have the
kth as the the element in position
2 of the
arr. The element is correctly picked as