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numpy.nanargmin (array, axis = None): Returns the indices of the min element of the array on the specified axis, ignoring NaN.
Results cannot be trusted if the slice contains only NaN and Infs.
Parameters:
array: Input array to work on axis: [int, optional] Along a specified axis like 0 or 1
Return:
Array of indices into the array with same shape as array.shape. with the dimension along axis removed.
Code 1:

Output:
INPUT ARRAY 1: [nan, 4, 2, 3, 1] Indices of min in array1: 4 INPUT ARRAY 2: [[nan 4.] [1. 3.]] Indices of min in array2: 2 Indices at axis 1 of array2: [1 0]
Code 2: Comparison of argmin and nanargmin operation

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]
Links:
argmin.html> https://docs.scipy.org/doc/numpy dev / reference / generated / numpy.nanargmin.html
Notes:
These codes will not work for online IDs. Please run them on your systems to see how they work
This article is provided by Mohit Gupta_OMG
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Boston  20221128
Simply put and clear. Thank you for sharing. numpy.nanargmin () in Python and other issues with argmin was always my weak point 😁. I just hope that will not emerge anymore
Texas  20221128
Maybe there are another answers? What numpy.nanargmin () in Python exactly means?. I am just not quite sure it is the best method
Singapore  20221128
NumPy is always a bit confusing 😭 numpy.nanargmin () in Python is not the only problem I encountered. I just hope that will not emerge anymore