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 :
# 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) |
Input array : [ 1 2 3 -1 2] Masked array : [1 -- 3 -1 --]
Code #2 :
# 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) |
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 --]
