numpy.argmin() in Python

numpy.argmin(array, axis = None, out = None) : Returns indices of the min element of the array in a particular axis.
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 :



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# 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 :

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# Python Program illustarting
# 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 :

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# Python Program illustarting
# 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]

References :
https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.argmin.html#numpy.argmin

Note :
These codes won’t run on online-ID. Please run them on your systems to explore the working
.
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