Python | Numpy MaskedArray.__rdivmod__

Last Updated : 02 Apr, 2019

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:

(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)
)

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, 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)
)

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