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# Return the inner product of two masked Three Dimensional arrays in Numpy

To return the inner product of two masked arrays, use the **ma.inner()** method in Python Numpy. The out parameter suggests, if both the arrays are scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. out.shape = (*a.shape[:-1], *b.shape[:-1]).

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 import numpy.ma as ma

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

arr1 = np.arange(24).reshape((2,3,4)) print("Array1...

", arr1) print("

Array type...

", arr1.dtype)

Mask Array1 −

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

Display Masked Array 1 −

print("

Masked Array1...

",arr1)

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

arr2 = np.arange(24).reshape((2,3,4)) print("

Array2...

", arr2) print("

Array type...

", arr2.dtype)

Create another masked array2 −

arr2 = ma.array(arr2)

Mask Array2 −

arr2[0, 1, 2] = ma.masked arr2[1, 2, 2] = ma.masked

Display Masked Array 2 −

print("

Masked Array2...

",arr2)

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

print("

Result of inner product (3D arrays)...

",np.ma.inner(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(24).reshape((2,3,4)) 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, 0, 1] = ma.masked # Display Masked Array 1 print("

Masked Array1...

",arr1) # Array 2 # Creating another 3D array with int elements using the numpy.arange() method arr2 = np.arange(24).reshape((2,3,4)) 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[0, 1, 2] = ma.masked arr2[1, 2, 2] = ma.masked # Display Masked Array 2 print("

Masked Array2...

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

Result of inner product (3D arrays)...

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

## Output

Array1... [[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] [[12 13 14 15] [16 17 18 19] [20 21 22 23]]] Array type... int64 Array Dimensions... 3 Our Array Shape... (2, 3, 4) Elements in the Array... 24 Masked Array1... [[[0 -- 2 3] [4 5 6 7] [8 9 10 11]] [[12 13 14 15] [16 17 18 19] [20 21 22 23]]] Array2... [[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] [[12 13 14 15] [16 17 18 19] [20 21 22 23]]] Array type... int64 Array Dimensions... 3 Our Array Shape... (2, 3, 4) Elements in the Array... 24 Masked Array2... [[[0 1 2 3] [4 5 -- 7] [8 9 10 11]] [[12 13 14 15] [16 17 18 19] [20 21 -- 23]]] Result of inner product (3D arrays)... [[[[ 13 21 53] [ 73 93 69]] [[ 38 90 214] [ 302 390 346]] [[ 62 154 366] [ 518 670 602]]] [[[ 86 218 518] [ 734 950 858]] [[ 110 282 670] [ 950 1230 1114]] [[ 134 346 822] [1166 1510 1370]]]]

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