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Python | Numpy MaskedArray.__mod__

  • Last Updated : 18 Apr, 2020

What is a mask?
A boolean array, used to select only certain elements for an operation




# A mask example
import numpy as np
x = np.arange(5)
print(x)
mask = (x > 2)
print(mask)
x[mask] = -1
print(x)

Output:

[0 1 2 3 4]
[False False False  True  True]
[ 0  1  2 -1 -1]

numpy.ma.MaskedArray class is a subclass of ndarray designed to manipulate numerical arrays with missing data. With the help of Numpy MaskedArray.__mod__ every element in masked array is operated on binary operator i.e mod(%). Remember we can use any type of values in an array and value for mod is applied as the parameter in MaskedArray.__mod__().

Syntax: numpy.MaskedArray.__mod__

Return: Return self%value.

Example #1 :
We can see that value that we have passed through MaskedArray.__mod__() method is used to perform the mod operation with every element of an array.




# import the important module in python 
import numpy as np 
      
# make an array with numpy 
gfg = np.ma.array([1, 2.5, 3, 4.8, 5]) 
      
# applying MaskedArray.__mod__() method 
print(gfg.__mod__(2)) 
Output:
[1.0 0.5 1.0 0.7999999999999998 1.0]

 
Example #2:




# import the important module in python 
import numpy as np 
      
# make an array with numpy 
gfg = np.ma.array([[1, 2, 3, 4.45, 5], 
                [6, 5.5, 4, 3, 2.62]]) 
      
# applying MaskedArray.__mod__() method 
print(gfg.__mod__(3)) 
Output:
[[1.0 2.0 0.0 1.4500000000000002 2.0]
 [0.0 2.5 1.0 0.0 2.62]]

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