Numpy MaskedArray.allequal() function | Python (original) (raw)

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.allequal() function return True if all entries of a and b are equal, using fill_value as a truth value where either or both are masked.

Syntax : numpy.ma.allequal(arr1, arr2, fill_value=True)

Parameters:
arr1, arr2 : [array_like] Input arrays to compare.
fill_value : [ bool, optional] Whether masked values in arr1 or arr2 are considered equal (True) or not (False).

Return : [ bool]Returns True if the two arrays are equal within the given tolerance, False otherwise. If either array contains NaN, then False is returned.

Code #1 :

import numpy as geek

import numpy.ma as ma

in_arr1 = geek.array([ 1e8 , 1e - 5 , - 15.0 ])

print ( "1st Input array : " , in_arr1)

mask_arr1 = ma.masked_array(in_arr1, mask = [ 0 , 0 , 1 ])

print ( "1st Masked array : " , mask_arr1)

in_arr2 = geek.array([ 1e8 , 1e - 5 , 15.0 ])

print ( "2nd Input array : " , in_arr2)

mask_arr2 = ma.masked_array(in_arr2, mask = [ 0 , 0 , 1 ])

print ( "2nd Masked array : " , mask_arr2)

out_arr = ma.allequal(mask_arr1, mask_arr2, fill_value = False )

print ( "Output array : " , out_arr)

Output:

1st Input array : [ 1.0e+08 1.0e-05 -1.5e+01] 1st Masked array : [100000000.0 1e-05 --] 2nd Input array : [1.0e+08 1.0e-05 1.5e+01] 2nd Masked array : [100000000.0 1e-05 --] Output array : False

Code #2 :