Python | Numpy MaskedArray.__mod__
What is a mask?
A boolean array, used to select only certain elements for an operation
# A mask exampleimport numpy as npx = np.arange(5)print(x)mask = (x > 2)print(mask)x[mask] = -1print(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)) |
[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)) |
[[1.0 2.0 0.0 1.4500000000000002 2.0] [0.0 2.5 1.0 0.0 2.62]]
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