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.argmax() function returns array of indices of the maximum values along the given axis. Masked values are treated as if they had the value fill_value..
Syntax :
numpy.MaskedArray.argmax(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.argmax() 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.argmax methods to mask array out_arr = mask_arr.argmax() print ("Index of max element in masked array : ", out_arr) |
Input array : [ 1 2 3 -1 5] Masked array : [1 2 -- -1 5] Index of max element in masked array : 4
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Code #2 :
# Python program explaining # numpy.MaskedArray.argmax() 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.argmax methods to mask array # and filling the masked location by 100 out_arr = mask_arr.argmax(fill_value = 100) print ("Index of max element in masked array : ", out_arr) |
Input array : [ 10 20 30 -10 50] Masked array : [-- 20 -- -10 50] Index of max element in masked array : 0
