# Return the outer product of two masked arrays in Numpy

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To return the outer product of two masked arrays, use the ma.outer() method in Python Numpy. The first parameter is the input vector. Input is flattened if not already 1-dimensional. The second parameter is the second input vector. Input is flattened if not already 1-dimensional.

A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.

## Steps

At first, import the required library −

import numpy as np

Creating Array1, 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)

Create masked array 1 −

arr1 = ma.array(arr1)


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

Display Masked Array 1 −

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


Creating Array2, 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)

Create masked array2 −

arr2 = ma.array(arr2)


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

Display Masked Array 2 −

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


To return the outer product of two masked arrays, use the ma.outer() method in Python Numpy. The masked values are replaced by 0 −

print("\nResult...\n",np.ma.outer(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)

# Create a masked array
arr1 = ma.array(arr1)

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

# Display Masked Array 1

# 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)

# Create a masked array
arr2 = ma.array(arr2)

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

# Display Masked Array 2

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

# The masked values are replaced by 0
print("\nResult...\n",np.ma.outer(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

[[0 1 2]
[3 4 5]
[6 -- --]]

Result...
[[0 0 0 0 0 0 0 -- --]
[-- -- -- -- -- -- -- -- --]
[0 2 4 6 8 10 12 -- --]
[0 3 6 9 12 15 18 -- --]
[-- -- -- -- -- -- -- -- --]
[0 5 10 15 20 25 30 -- --]
[0 6 12 18 24 30 36 -- --]
[0 7 14 21 28 35 42 -- --]
[0 8 16 24 32 40 48 -- --]]