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
import numpy as np
x = np.arange( 5 )
print (x)
mask = (x > 2 )
print (mask)
x[mask] = - 1
print (x)
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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 numpy as np
gfg = np.ma.array([ 1 , 2.5 , 3 , 4.8 , 5 ])
print (gfg.__mod__( 2 ))
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Output:
[1.0 0.5 1.0 0.7999999999999998 1.0]
Example #2:
import numpy as np
gfg = np.ma.array([[ 1 , 2 , 3 , 4.45 , 5 ],
[ 6 , 5.5 , 4 , 3 , 2.62 ]])
print (gfg.__mod__( 3 ))
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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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