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Python | Numpy MaskedArray.__divmod__

  • 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.__divmod__ we will get two arrays one is having elements that is divided by value that is provided in numpy.ma.__divmod__() method and second is having elements that perform mod operation with same value as provided in numpy.ma.__divmod__() method.

Syntax: numpy.MaskedArray.__divmod__

Return: Return divmod(self, value).

Example #1 :
In this example we can see that by using MaskedArray.__divmod__() 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.__divmod__() method 
print(gfg.__divmod__(3)) 
Output:
(masked_array(data = [0 0 1 1 1],
             mask = [False False False False False],
       fill_value = 999999), masked_array(data = [1 2 0 1 2],
             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.__divmod__() method 
print(gfg.__divmod__(3)) 
Output:
(masked_array(data =
 [[0 0 1 1 1]
 [2 1 1 1 0]],
             mask =
 [[False False False False False]
 [False False False False False]],
       fill_value = 999999), masked_array(data =
 [[1 2 0 1 2]
 [0 2 1 0 2]],
             mask =
 [[False False False False False]
 [False False False False False]],
       fill_value = 999999)
)


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