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numpy.partition() in Python

Last Updated : 28 Dec, 2018
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numpy.partition() function is used to create a partitioned copy of input array with its elements rearranged in such a way that the value of the element in k-th position is in the position it would be in a sorted array. All elements smaller than the k-th 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.
Syntax : numpy.partition(arr, kth, axis=-1, kind='introselect', order=None) Parameters : arr : [array_like] Input array. kth : [int or sequence of ints ] Element index to partition by. axis : [int or None] Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis. kind : Selection algorithm. Default is ‘introselect’. order : [str or list of str] When arr is an array with fields defined, this argument specifies which fields to compare first, second, etc. Return : [ndarray] Partitioned array of the same type and shape as arr.
Code #1 :
Output:
Input array :  [2 0 1 5 4 9]
Output partitioned array :  [0 1 2 4 5 9]
  Code #2 :
Output:
Input array :  [2 0 1 5 4 9 3]
Output partitioned array :  [0 1 2 3 4 9 5]

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