The numpy.argmin() method returns indices of the min element of the array in a particular axis.
Syntax :
numpy.argmin(array, axis = None, out = None)
Parameters :
array : Input array to work on axis : [int, optional]Along a specified axis like 0 or 1 out : [array optional]Provides a feature to insert output to the out array and it should be of appropriate shape and dtype
Return :
Array of indices into the array with same shape as array.shape with the dimension along axis removed.
Code 1 :
Python
# Python Program illustrating # working of argmin() import numpy as geek
# Working on 1D array array = geek.arange( 8 )
print ( "INPUT ARRAY : \n" , array)
# returning Indices of the min element # as per the indices print ( "\nIndices of min element : " , geek.argmin(array, axis = 0 ))
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Output :
INPUT ARRAY : [0 1 2 3 4 5 6 7] Indices of min element : 0
Code 2 :
Python
# Python Program illustrating # working of argmin() import numpy as geek
# Working on 2D array array = geek.random.randint( 16 , size = ( 4 , 4 ))
print ( "INPUT ARRAY : \n" , array)
# returning Indices of the min element # as per the indices ''' [[ 8 13 5 0]
[ 0 2 5 3]
[10 7 15 15]
[ 3 11 4 12]]
^ ^ ^ ^
0 2 4 0 - element
1 1 3 0 - indices
''' print ( "\nIndices of min element : " , geek.argmin(array, axis = 0 ))
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Output :
INPUT ARRAY : [[ 8 13 5 0] [ 0 2 5 3] [10 7 15 15] [ 3 11 4 12]] Indices of min element : [1 1 3 0]
Code 3 :
Python
# Python Program illustrating # working of argmin() import numpy as geek
# Working on 2D array array = geek.arange( 10 ).reshape( 2 , 5 )
print ( "array : \n" , array)
array[ 0 ][ 0 ] = 10
array[ 1 ][ 1 ] = 1
array[ 0 ][ 1 ] = 1
print ( "\narray : \n" , array)
# Returns min element print ( "\narray : " , geek.argmin(array))
# First occurrence of an min element is given print ( "\nmin ELEMENT INDICES : " , geek.argmin(array, axis = 0 ))
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Output :
array : [[0 1 2 3 4] [5 6 7 8 9]] array : [[10 1 2 3 4] [ 5 1 7 8 9]] array : 1 min ELEMENT INDICES : [1 0 0 0 0]