numpy.ma.masked_values() function return a MaskedArray, masked where the data in array arr are approximately equal to value, determined using isclose. The default tolerances for masked_values are the same as those for isclose.
Syntax : numpy.ma.masked_values(arr, value, rtol = 1e-05, atol = 1e-08, copy = True, shrink = True)
Parameter :
arr : [array_like] Array to mask.
value : [float] Masking value.
rtol, atol : [float, optional] Must be convertible to an array of booleans with the same shape as data. True indicates a masked data.
copy : [bool, optional] Whether to return a copy of arr.
shrink : [bool, optional] Whether to collapse a mask full of False to nomask.Return : [MaskedArray] The result of masking arr where approximately equal to value.
Code #1 :
# Python program explaining # numpy.ma.masked_values() function   # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma    arr = geek.array([1, 1.5, 2, 1.5, 3])   gfg = ma.masked_values(arr, 1.5)   print (gfg) |
Output :
[1.0 -- 2.0 -- 3.0]
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Code #2 :
# Python program explaining # numpy.ma.masked_values() function   # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma    arr = geek.array([1, 2, 3, 4, 5, 6])   gfg = ma.masked_values(arr, 4)   print (gfg) |
Output :
[1 2 3 -- 5 6]
