numpy.partition() in Python

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 : Python3

# Python program explaining
# partition() function
 
import numpy as geek

# input array
in_arr = geek.array([ 2, 0,  1, 5, 4, 9])
print ("Input array : ", in_arr) 

out_arr = geek.partition(in_arr, 3)
print ("Output partitioned array : ", out_arr)

  Code #2 : Python3

# Python program explaining
# partition() function
 
import numpy as geek

# input array
in_arr = geek.array([ 2, 0,  1, 5, 4, 9, 3])
print ("Input array : ", in_arr) 

out_arr = geek.partition(in_arr, (0, 3))
print ("Output partitioned array : ", out_arr)