Numpy MaskedArray.masked_equal() 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.masked_equal() function is used to mask an array where equal to a given value.

Syntax : numpy.ma.masked_equal(arr, value, copy=True) Parameters: arr : [ndarray] Input array which we want to mask.value : [int] Element of masked array which we want to mask.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 result of masking.

Code #1 :

Python3 `

Python program explaining

numpy.MaskedArray.masked_equal() 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, 2]) print ("Input array : ", in_arr)

applying MaskedArray.masked_equal methods

to input array where value = 2

mask_arr = ma.masked_equal(in_arr, 2) print ("Masked array : ", mask_arr)

`

Output:

Input array : [ 1 2 3 -1 2] Masked array : [1 -- 3 -1 --]

Code #2 :

Python3 `

Python program explaining

numpy.MaskedArray.masked_equal() 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([2e8, 3e-5, -45.0, 2e5, 5e2]) print ("Input array : ", in_arr)

applying MaskedArray.masked_equal methods

to input array where value = 5e2

mask_arr = ma.masked_equal(in_arr, 5e2) print ("Masked array : ", mask_arr)

`

Output:

Input array : [ 2.0e+08 3.0e-05 -4.5e+01 2.0e+05 5.0e+02] Masked array : [200000000.0 3e-05 -45.0 200000.0 --]

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