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

NumpyServer Side ProgrammingProgramming

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)

arr1 = ma.array(arr1)


arr1 = ma.masked
arr1 = ma.masked

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)

arr2 = ma.array(arr2)


arr2 = ma.masked
arr2 = ma.masked

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)

arr1 = ma.array(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)

arr2 = ma.array(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

[-- -- 15 20 25]

Array2...
[ 7 14 21 28 35]

Array type...
int64

Array Dimensions...
1

Our Array Shape...
(5,)

Elements in the Array...
5

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