Last Updated : 18 Apr, 2020

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__().

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]]
```