Let us discuss.

**NumPy Argmin Function Syntax**

The function provides a minimalistic syntax as shown:

**Parameters**

The function parameters are as shown below:

- a – refers to the input array. This is a non-optional parameter.
- Axis – specifies along which axis to apply the argmin() function. If set to None, the function will flatten the array and use the function on all elements.
- Out – specifies an alternative output array. The output array must have the same shape as the output value.
- Keepdims – a Boolean value that allows you to preserve the axes reduced in the result as dimensions with a size of one.

**Function Result**

The function will return an array of indices with the same shape as a.shape and the dimensions along the specified axis removed.

**Example 1**

The following is an example that illustrates how to use the argmin() function with a 1D array in Python.

import numpy as np

arr = np.array([1,2,3,4,5,6,7,8])

print(f"index of min element -> {np.argmin(arr)}")

In the code above, we have a 1D array holding elements from 1 to 8. We then check the minimum element in the array using the argmin() function and return its index.

The output is as shown:

**Example 2**

Let us see what happens when applying the same operation on a 2D array.

print(f"index of min element -> {np.argmin(arr_2d)}")

In the code above, we apply the argmin() function to a 2D array without specifying the axis. This flattens the array and applies the function.

The resulting value is as shown:

**Example 3**

To act along a specific axis, we can set the axis parameter as shown:

print(f"indices of min elements -> {np.argmin(arr_2d, axis=0)}")

The code above should apply the argmin() function along axis 0 and return the indices of the min elements as shown in the output array:

[0 0 0 0]]

**Example 4**

To apply the function on the last axis, we can set the axis value as -1 as shown below:

print(f"indices of min elements -> {np.argmin(arr_2d, axis=-1)}")

The code above should return:

**Conclusion**

Throughout this article, we explored the NumPy argmin function, its syntax, parameters, and return values. We also provided various examples illustrating how the function works in multiple scenarios.

Happy coding!!