Numpy MaskedArray.masked_not_equal() 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.masked_not_equal()
function is used to mask an array where not equal to a given value.This function is a shortcut to masked_where
, with condition = (arr != value).
Syntax : numpy.ma.masked_not_equal(arr, value, copy=True)
Parameters:
arr : [ndarray] Input array which we want to mask.
value : [int] It is used to mask the array element which are != value.
copy : [bool] If True (default) make a copy of arr in the result. If False modify arr in place and return a view.
Return : [ MaskedArray] The resultant array after masking.
Code #1 :
import numpy as geek
import numpy.ma as ma
in_arr = geek.array([ 1 , 2 , 3 , - 1 , 2 ])
print ( "Input array : " , in_arr)
mask_arr = ma.masked_not_equal(in_arr, 2 )
print ( "Masked array : " , mask_arr)
|
Output:
Input array : [ 1 2 3 -1 2]
Masked array : [-- 2 -- -- 2]
Code #2 :
import numpy as geek
import numpy.ma as ma
in_arr = geek.array([ 5e8 , 3e - 5 , - 45.0 , 4e4 , 5e2 ])
print ( "Input array : " , in_arr)
mask_arr = ma.masked_not_equal(in_arr, 5e2 )
print ( "Masked array : " , mask_arr)
|
Output:
Input array : [ 5.0e+08 3.0e-05 -4.5e+01 4.0e+04 5.0e+02]
Masked array : [-- -- -- -- 500.0]
Share your thoughts in the comments
Please Login to comment...