# Return the dot product of two masked arrays in Numpy

NumpyServer Side ProgrammingProgramming

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...\n", arr1)
print("\nArray type...\n", arr1.dtype)

Get the dimensions of the Array −

print("\nArray Dimensions...\n",arr1.ndim)

Get the shape of the Array −

print("\nOur Array Shape...\n",arr1.shape)

Get the number of elements of the Array −

print("\nElements in the Array...\n",arr1.size)


arr1 = ma.array(arr1)

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

print("\nMasked Array1...\n",arr1)

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

arr2 = np.arange(9).reshape((3,3))
print("\nArray2...\n", arr2)
print("\nArray type...\n", arr2.dtype)

arr2 = ma.array(arr2)

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

print("\nMasked Array2...\n",arr2)

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

print("\nResult of dot product...\n",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...\n", arr1)
print("\nArray type...\n", arr1.dtype)

# Get the dimensions of the Array
print("\nArray Dimensions...\n",arr1.ndim)

# Get the shape of the Array
print("\nOur Array Shape...\n",arr1.shape)

# Get the number of elements of the Array
print("\nElements in the Array...\n",arr1.size)

arr1 = ma.array(arr1)

# Array 2
# Creating another 3x3 array with int elements using the numpy.arange() method
arr2 = np.arange(9).reshape((3,3))
print("\nArray2...\n", arr2)
print("\nArray type...\n", arr2.dtype)

# Get the dimensions of the Array
print("\nArray Dimensions...\n",arr2.ndim)

# Get the shape of the Array
print("\nOur Array Shape...\n",arr2.shape)

# Get the number of elements of the Array
print("\nElements in the Array...\n",arr2.size)

arr2 = ma.array(arr2)

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

[[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

[69 34 47]]