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numpy.argsort() in Python

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  • Difficulty Level : Medium
  • Last Updated : 16 Jun, 2022

numpy.argsort() function is used to perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as arr that  would sort the array.

Syntax : numpy.argsort(arr, axis=-1, kind=’quicksort’, order=None) Parameters : arr : [array_like] Input array. 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 : [‘quicksort’, ‘mergesort’, ‘heapsort’]Selection algorithm. Default is ‘quicksort’. 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 : [index_array, ndarray] Array of indices that sort arr along the specified axis.If arr is one-dimensional then arr[index_array] returns a sorted arr.

Code #1 : 

Python3




# Python program explaining
# argpartition() function
  
import numpy as geek
 
# input array
in_arr = geek.array([ 2, 01, 5, 4, 1, 9])
print ("Input unsorted array : ", in_arr)
 
out_arr = geek.argsort(in_arr)
print ("Output sorted array indices : ", out_arr)
print("Output sorted array : ", in_arr[out_arr])

Output:

Input unsorted array :  [2 0 1 5 4 1 9]
Output sorted array indices :  [1 2 5 0 4 3 6]
Output sorted array :  [0 1 1 2 4 5 9]

  Code #2 : 

Python3




# Python program explaining
# argpartition() function
 
import numpy as geek
 
# input 2d array
in_arr = geek.array([[ 2, 0, 1], [ 5, 4, 3]])
print ("Input array : ", in_arr)
 
# output sorted array indices
out_arr1 = geek.argsort(in_arr, kind ='mergesort', axis = 0)
print ("Output sorteded array indices along axis 0: ", out_arr1)
out_arr2 = geek.argsort(in_arr, kind ='heapsort', axis = 1)
print ("Output sorteded array indices along axis 1: ", out_arr2)

Output:

Input array :  [[2 0 1]
 [5 4 3]]
Output sorteded array indices along axis 0:  [[0 0 0]
 [1 1 1]]
Output sorteded array indices along axis 1:  [[1 2 0]
 [2 1 0]]

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