Python | Numpy MaskedArray.__rdivmod__
Last Updated :
02 Apr, 2019
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numpy.ma.MaskedArray class is a subclass of ndarray designed to manipulate numerical arrays with missing data. With the help of Numpy MaskedArray.__rdivmod__ we will get two arrays one is having elements that is divided by value that is provided in numpy.ma.__rdivmod__() method and second is having elements that perform mod operation with same value as provided in numpy.ma.__rdivmod__() method.
Syntax: numpy.MaskedArray.__rdivmod__ Return: Return divmod( value, self).Example #1 : In this example we can see that by using MaskedArray.__rdivmod__() method we get two arrays. One is with divided with value that is passed as parameter and other with mod values.
# import the important module in python
import numpy as np
# make an array with numpy
gfg = np.ma.array([1, 2, 3, 4, 5])
# applying MaskedArray.__rdivmod__() method
print(gfg.__rdivmod__(3))
Output:
Example #2:
(masked_array(data = [3 1 1 0 0],
mask = [False False False False False],
fill_value = 999999), masked_array(data = [0 1 0 3 3],
mask = [False False False False False],
fill_value = 999999)
)
# import the important module in python
import numpy as np
# make an array with numpy
gfg = np.ma.array([[1, 2, 3, 4, 5],
[6, 5, 4, 3, 2]])
# applying MaskedArray.__rdivmod__() method
print(gfg.__rdivmod__(3))
Output:
(masked_array(data =
[[3 1 1 0 0]
[0 0 0 1 1]],
mask =
[[False False False False False]
[False False False False False]],
fill_value = 999999), masked_array(data =
[[0 1 0 3 3]
[3 3 3 0 1]],
mask =
[[False False False False False]
[False False False False False]],
fill_value = 999999)
)
