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# Numpy MaskedArray.argmin() function | Python

• Last Updated : 27 Sep, 2019

In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The `numpy.ma` module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arrays that may have missing or invalid entries.

`numpy.MaskedArray.argmin()` function returns array of indices of the minimum values along the given axis. Masked values are treated as if they had the value fill_value.

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Syntax : `numpy.MaskedArray.argmin(axis=None, fill_value=None, out=None)`

Parameters:
axis : [None, integer] If None, the index is into the flattened array, otherwise along the specified axis.
fill_value : [ var, optional] Value used to fill in the masked values.
out : [ndarray, optional] A location into which the result is stored.
-> If provided, it must have a shape that the inputs broadcast to.
-> If not provided or None, a freshly-allocated array is returned.

Return : [index_array ]A new integer_array is returned unless out is specified, in which case a reference to out is returned.

Code #1 :

 `# Python program explaining``# numpy.MaskedArray.argmin() method `` ` `# importing numpy as geek ``# and numpy.ma module as ma``import` `numpy as geek``import` `numpy.ma as ma`` ` `# creating input array ``in_arr ``=` `geek.array([``1``, ``2``, ``3``, ``-``1``, ``5``])``print` `(``"Input array : "``, in_arr)`` ` `# Now we are creating a masked array ``# by making third entry as invalid. ``mask_arr ``=` `ma.masked_array(in_arr, mask ``=``[``0``, ``0``, ``1``, ``0``, ``0``])``print` `(``"Masked array : "``, mask_arr)`` ` `# applying MaskedArray.argmin methods to mask array``out_arr ``=` `mask_arr.argmin()``print` `(``"Index of min element in masked array : "``, out_arr)`
Output:
```Input array :  [ 1  2  3 -1  5]
Masked array :  [1 2 -- -1 5]
Index of min element in masked array :  3
```

Code #2 :

 `# Python program explaining``# numpy.MaskedArray.argmin() method `` ` `# importing numpy as geek ``# and numpy.ma module as ma``import` `numpy as geek``import` `numpy.ma as ma`` ` `# creating input array ``in_arr ``=` `geek.array([``10``, ``20``, ``30``, ``-``10``, ``50``])``print` `(``"Input array : "``, in_arr)`` ` `# Now we are creating a masked array ``# by making first third entry as invalid. ``mask_arr ``=` `ma.masked_array(in_arr, mask ``=``[``1``, ``0``, ``1``, ``0``, ``0``])``print` `(``"Masked array : "``, mask_arr)`` ` `# applying MaskedArray.argminmethods to mask array``# and filling the masked location by -100``out_arr ``=` `mask_arr.argmin(fill_value ``=` `100``)``print` `(``"Index of min element in masked array : "``, out_arr)`
Output:
```Input array :  [ 10  20  30 -10  50]
Masked array :  [-- 20 -- -10 50]
Index of min element in masked array :  0
```

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