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# Return the outer product of two masked arrays with different shapes in Numpy

To return the outer product of two masked arrays with different shapes, 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 &mius;

import numpy as np

Creating a 3D array with int elements using the numpy.arange() method −

arr1 = np.arange(4).reshape((1, 2, 2)) print("Array1...\n", arr1) print("\nArray type...\n", arr1.dtype)

Create masked array1 −

arr1 = ma.array(arr1)

Mask Array1 −

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

Display Masked Array 1 −

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

Create Array 2, a 2D array with int elements using the numpy.arange() method −

arr2 = np.arange(6).reshape((3,2)) print("\nArray2...\n", arr2) print("\nArray type...\n", arr2.dtype)

Create a masked array1 −

arr2 = ma.array(arr2)

Mask Array2 −

arr2[0, 1] = ma.masked

Display Masked Array 2 −

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

To return the outer product of two masked arrays with different shapes, use the ma.outer() method −

print("\nResult of outer product...\n",np.ma.outer(arr1, arr2))

## Example

import numpy as np import numpy.ma as ma # Array 1 # Creating a 3D array with int elements using the numpy.arange() method arr1 = np.arange(4).reshape((1, 2, 2)) 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) # Mask Array1 arr1[0, 0, 1] = ma.masked # Display Masked Array 1 print("\nMasked Array1...\n",arr1) # Array 2 # Creating a 2D array with int elements using the numpy.arange() method arr2 = np.arange(6).reshape((3,2)) 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) # Mask Array2 arr2[0, 1] = ma.masked # Display Masked Array 2 print("\nMasked Array2...\n",arr2) # To return the outer product of two masked arrays with different shapes, use the ma.outer() method in Python Numpy print("\nResult of outer product...\n",np.ma.outer(arr1, arr2))

## Output

Array1... [[[0 1] [2 3]]] Array type... int64 Array Dimensions... 3 Our Array Shape... (1, 2, 2) Elements in the Array... 4 Masked Array1... [[[0 --] [2 3]]] Array2... [[0 1] [2 3] [4 5]] Array type... int64 Array Dimensions... 2 Our Array Shape... (3, 2) Elements in the Array... 6 Masked Array2... [[0 --] [2 3] [4 5]] Result of outer product... [[0 -- 0 0 0 0] [-- -- -- -- -- --] [0 -- 4 6 8 10] [0 -- 6 9 12 15]]

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