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

To return the inner product of two masked arrays, use the **ma.inner()** method in Python Numpy. Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.

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.array() method −

arr1 = np.array([5, 10, 15, 20, 25]) print("Array1...\n", arr1) print("\nArray type...\n", arr1.dtype)

Create masked array1 −

arr1 = ma.array(arr1)

Mask Array1 −

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

Display Masked Array 1 −

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

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

arr2 = np.array([7, 14, 21, 28, 35]) print("\nArray2...\n", arr2) print("\nArray type...\n", arr2.dtype)

Create a masked array2 −

arr2 = ma.array(arr2)

Mask Array2 −

arr2[3] = ma.masked arr2[4] = ma.masked

Display Masked Array 2 −

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

To return the inner product of two masked arrays, use the ma.inner() method in Python Numpy. Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes −

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

## Example

# Python ma.MaskedArray - Return the inner product of two masked One- Dimensional arrays import numpy as np import numpy.ma as ma # Array 1 # Creating a 1D array with int elements using the numpy.array() method arr1 = np.array([5, 10, 15, 20, 25]) 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] = ma.masked arr1[1] = ma.masked # Display Masked Array 1 print("\nMasked Array1...\n",arr1) # Array 2 # Creating another 1D array with int elements using the numpy.array() method arr2 = np.array([7, 14, 21, 28, 35]) 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[3] = ma.masked arr2[4] = ma.masked # Display Masked Array 2 print("\nMasked Array2...\n",arr2) # To return the inner product of two masked arrays, use the ma.inner() method in Python Numpy # Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes. print("\nResult of inner product...\n",np.ma.inner(arr1, arr2))

## Output

Array1... [ 5 10 15 20 25] Array type... int64 Array Dimensions... 1 Our Array Shape... (5,) Elements in the Array... 5 Masked Array1... [-- -- 15 20 25] Array2... [ 7 14 21 28 35] Array type... int64 Array Dimensions... 1 Our Array Shape... (5,) Elements in the Array... 5 Masked Array2... [7 14 21 -- --] Result of inner product... 315

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