Python | Numpy MaskedArray.__mod__
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]]