numpy.ma.mask_rows() function | Python

numpy.ma.mask_or() function | Python

Last Updated : 22 Apr, 2020

numpy.ma.mask_or() function combine two masks with the logical_or operator. The result may be a view on m1 or m2 if the other is nomask (i.e. False).

Syntax : numpy.ma.mask_or(m1, m2, copy = False, shrink = True) Parameters : m1, m2 : [ array_like] Input masks. copy : [bool, optional] If copy is False and one of the inputs is nomask, return a view of the other input mask. Defaults to False shrink : [bool, optional] Whether to shrink the output to nomask if all its values are False. Defaults to True. Return : The result masks values that are masked in either m1 or m2.

Code #1 :

Python3

# Python program explaining
# numpy.ma.mask_or() function

# importing numpy as geek  
# and numpy.ma module as ma 
import numpy as geek 
import numpy.ma as ma 

m1 = geek.ma.make_mask([1, 1, 0, 1])
m2 = geek.ma.make_mask([1, 0, 0, 0])

gfg = geek.ma.mask_or(m1, m2)

print (gfg)

Output :

[ True  True False  True]

Code #2 :

Python3

# Python program explaining
# numpy.ma.mask_or() function

# importing numpy as geek  
# and numpy.ma module as ma 
import numpy as geek 
import numpy.ma as ma 

m1 = geek.ma.make_mask([1, 0, 0, 0])
m2 = geek.ma.make_mask([1, 1, 0, 1])

gfg = geek.ma.mask_or(m1, m2)

print (gfg)

Output :

[ True  True False  True]

numpy.ma.mask_rows() function | Python

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