np.argmin: How to Find Index of Minimum Element in Array

If you are working on data science or machine learning projects, you might need to find the maximum or minimum value or indices in the numpy array. That’s where the numpy library comes in.

np.argmin

The np.argmin() function is used to get the indices of the minimum element from an array (single-dimensional array) or any row or column (multidimensional array) of any given array. The numpy argmin() function takes arr, axis, and out as parameters and returns the array.

To find the index of a minimum element from the array, use the np.argmin() function.

Syntax

numpy.argmin(arr,axis=None,out=None)

Parameters

The numpy argmin() function takes three arguments:

  1. arr: The array from which we want the indices of the min element.
  2. axis: By default, it is None. But for the multidimensional array, if we’re going to find an index of any maximum of element row-wise or column-wise, we have to give axis=1 or axis=0, respectively.
  3. out: If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.

Return Value

The np.argmin() function returns an array containing the indices of the minimum elements.

Finding the index of the minimum element from a 1D array

See the following code.

#Importing numpy
import numpy as np

#We will create an 1D array
arr = np.array([4, 24, 63, 121, 4, 64])
#Printing the array
print("The array is: ", arr)
#Shape of the array
print("Shape of the array is : ", np.shape(arr))

#Now we will print index of min value of this array
print("Index of minimum value of the given array is: ", np.argmin(arr))

Output

The array is:  [  4  24  63 121   4  64]
Shape of the array is :  (6,)
Index of minimum value of the given array is:  0

Explanation

In this program, we have first declared an array with some

random numbers given by the user. Then we have printed the shape (size) of the array. Then we have called argmin() to get the index of the minimum element from the array. We can see that the minimum element of this array is 4, which is at position 0, so the output is 0.

Finding indices of maximum elements from Multidimensional Array

See the following code.

#Importing numpy
import numpy as np

#We will create a 2D array
#Of shape 4x3
arr = np.array([(1, 9, 4), (6, 55, 4), (1, 3, 40), (5, 6, 4)])
#Printing the array
print("The array is: ")
print(arr)
print("Shape of the array is: ", np.shape(arr))

#Now we will find the indices of minimum value for some cases

#Indices of minimum value of each row
a = np.argmin(arr, axis=1)
print("Indices of minimum value of each row of the array is: ", a)

#Indices of minimum value of each column
b = np.argmin(arr, axis=0)
print("Indices of minimum value of each column of the array is: ", b)

Output

The array is:
[[ 1  9  4]
 [ 6 55  4]
 [ 1  3 40]
 [ 5  6  4]]
Shape of the array is:  (4, 3)
Indices of minimum value of each row of the array is:  [0 2 0 2]
Indices of minimum value of each column of the array is:  [0 2 0]

Explanation

In this program, we have first declared a matrix of size 4×3; you can see the shape of the matrix also, which is (4,3). Then we have called argmin() to get the output of different cases.

See also

Numpy diag()

Numpy asmatrix()

Numpy main()

Numpy nanargmax()

Numpy nanargmin()

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