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numpy.ma.masked_values() function | Python

  • Last Updated : 05 May, 2020

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]

 
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]

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