The Numpy library in Python comes with a number of useful built-in functions to work with and manipulate Numpy arrays. In this tutorial, we will look at how to get the index of the min value in a Numpy array with the help of some examples.
How to get the index of the minimum value in a Numpy array?
You can use the Numpy
argmin() function to get the index of the min value in a Numpy array. The following is the syntax –
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# get index of min value in numpy array ar.argmin()
It returns the index corresponding to the minimum value in the array.
You can apply this function on higher dimension (greater than 1) Numpy arrays well and specify the axis along which you want to compute the minimum.
Steps to get the index of the minimum value in Numpy
Let’s now look at a step-by-step example of using the above syntax –
Step 1 – Create a Numpy array
First, we will create a Numpy array that we will be using throughout this tutorial. You can use the
numpy.array() function to create Numpy array in Python.
import numpy as np # create numpy array ar = np.array([3, 2, 5, 1, 4]) # display the array print(ar)
[3 2 5 1 4]
Here, we create a Numpy array with some integer values. You can see that the minimum value in the above array is 1 which occurs at index 3.
Step 2 – Find the index of the min value
Use the Numpy
argmin() function to compute the index of the minimum value in the above array.
# get index of min value in array print(ar.argmin())
We get 3 as the index of the minimum value in the array which is the correct answer.
If the min value occurs at multiple indices in the array, the
argmin() function will return the index of the first occurrence of the min value. Let’s look at an example.
# create numpy array ar = np.array([3, 2, 1, 5, 1, 4]) # get index of min value in array print(ar.argmin())
Here, the minimum value in the array (1) occurs at indices 2 and 4 in the above array. The
argmin() function returns the index of the minimum value as 2, which is the index of the first occurrence of the minimum value in the array.
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