# numpy.nanargmin () in Python

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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:

 ` # Python program illustrating ` ` # nanargmin () work ` ` import ` ` numpy as geek ` ` # Working with 1D array ` ` array ` ` = ` ` [geek.nan, ` ` 4 ` `, ` ` 2 ` `, ` ` 3 ` `, ` ` 1 ` `] ` ` print ` ` (` ` "INPUT ARRAY 1:" ` `, array) ` ` array2 ` ` = ` ` geek.array ([[geek.nan, ` ` 4 ` `], [` ` 1 ` `, ` ` 3 ` `]]) ` ` # returns the indexes of the min element ` ` # for metrics that include NaN ` ` print ` ` (` ` "Indices of min in array1:" ` `, geek.nanargmin (array )) ` ` # Working with 2D array ` ` print ` ` (` ` "INPUT ARRAY 2:" ` `, array2) ` ` print ` ` (` ` "Indices of min in array2:" ` `, geek.nanargmin (array2)) ` ` print ` ` (` ` "Indices at axis 1 of array2:" ` `, geek.nanargmin (array2, axis ` ` = ` ` 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

 ` # Python program illustrating ` # nanargmin () work ` import ` ` numpy as geek ` ` # Working with 2D array ` ` array ` ` = ` ` ([[` ` 8 ` `, ` ` 13 ` `, ` ` 5 ` `, ` ` 0 ` `], ` ` [geek.nan, geek. nan, ` ` 5 ` `, ` ` 3 ` `], ` ` ` ` [` ` 10 ` `, ` ` 7 ` `, ` ` 15 ` `, ` ` 15 ` `], ` ` [` ` 3 ` `, ` ` 11 ` `, ` ` 4 ` `, ` ` 12 ` `]]) ` ` print ` ` (` ` "INPUT ARRAY:" ` `, array) ` ` # returns min element indices ` ` # by metrics ` ` "" "` ` ` ` [[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 - indicators ` ` "" "` ` ` ` print ` ` (` ` "Indices of min using argmin:" ` `, geek.argmin (array, axis ` ` = ` ` 0 ` ` )) ` ` print ` ` (` ` "Indices of min using nanargmin:: "` `, geek.nanargmin (a rray, axis ` ` = ` ` 0 ` `)) `

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] `

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

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