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# Return the dot product of two masked arrays in Numpy

To return the dot product of two masked arrays, use the **ma.dot()** method in Python Numpy. This function is the equivalent of numpy.dot that takes masked values into account. The strict and out are in different position than in the method version. In order to maintain compatibility with the corresponding method, it is recommended that the optional arguments be treated as keyword only. At some point that may be mandatory.

The strict parameter sets whether masked data are propagated (True) or set to 0 (False) for the computation. Default is False. Propagating the mask means that if a masked value appears in a row or column, the whole row or column is considered masked.

The output parameter suggests that it must have the exact kind that would be returned if it was not used. In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for dot(a,b). This is a performance feature. Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible.

## Steps

At first, import the required library −

import numpy as np import numpy.ma as ma

Create Array 1, a 3x3 array with int elements using the numpy.arange() method −

arr1 = np.arange(9).reshape((3,3)) print("Array1...

", arr1) print("

Array type...

", arr1.dtype)

Get the dimensions of the Array −

print("

Array Dimensions...

",arr1.ndim)

Get the shape of the Array −

print("

Our Array Shape...

",arr1.shape)

Get the number of elements of the Array −

print("

Elements in the Array...

",arr1.size)

Create masked array 1 −

arr1 = ma.array(arr1)

Mask Array1 −

arr1[0, 1] = ma.masked arr1[1, 1] = ma.masked

Display Masked Array 1 −

print("

Masked Array1...

",arr1)

Create Array 2, another 3x3 array with int elements using the numpy.arange() method −

arr2 = np.arange(9).reshape((3,3)) print("

Array2...

", arr2) print("

Array type...

", arr2.dtype)

Create masked array2 −

arr2 = ma.array(arr2)

Mask Array2

arr2[2, 1] = ma.masked arr2[2, 2] = ma.masked

Display Masked Array 2 −

print("

Masked Array2...

",arr2)

To return the dot product of two masked arrays, use the ma.dot() method in Python Numpy −

print("

Result of dot product...

",np.ma.dot(arr1, arr2))

## Example

import numpy as np import numpy.ma as ma # Array 1 # Creating a 3x3 array with int elements using the numpy.arange() method arr1 = np.arange(9).reshape((3,3)) print("Array1...

", arr1) print("

Array type...

", arr1.dtype) # Get the dimensions of the Array print("

Array Dimensions...

",arr1.ndim) # Get the shape of the Array print("

Our Array Shape...

",arr1.shape) # Get the number of elements of the Array print("

Elements in the Array...

",arr1.size) # Create a masked array arr1 = ma.array(arr1) # Mask Array1 arr1[0, 1] = ma.masked arr1[1, 1] = ma.masked # Display Masked Array 1 print("

Masked Array1...

",arr1) # Array 2 # Creating another 3x3 array with int elements using the numpy.arange() method arr2 = np.arange(9).reshape((3,3)) print("

Array2...

", arr2) print("

Array type...

", arr2.dtype) # Get the dimensions of the Array print("

Array Dimensions...

",arr2.ndim) # Get the shape of the Array print("

Our Array Shape...

",arr2.shape) # Get the number of elements of the Array print("

Elements in the Array...

",arr2.size) # Create a masked array arr2 = ma.array(arr2) # Mask Array2 arr2[2, 1] = ma.masked arr2[2, 2] = ma.masked # Display Masked Array 2 print("

Masked Array2...

",arr2) # To return the dot product of two masked arrays, use the ma.dot() method in Python Numpy print("

Result of dot product...

",np.ma.dot(arr1, arr2))

## Output

Array1... [[0 1 2] [3 4 5] [6 7 8]] Array type... int64 Array Dimensions... 2 Our Array Shape... (3, 3) Elements in the Array... 9 Masked Array1... [[0 -- 2] [3 -- 5] [6 7 8]] Array2... [[0 1 2] [3 4 5] [6 7 8]] Array type... int64 Array Dimensions... 2 Our Array Shape... (3, 3) Elements in the Array... 9 Masked Array2... [[0 1 2] [3 4 5] [6 -- --]] Result of dot product... [[12 0 0] [30 3 6] [69 34 47]]

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