Numpy MaskedArray.masked_equal() function | Python

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)

  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)