Return the outer product of two masked One-Dimensional Numpy arrays

NumpyServer Side ProgrammingProgramming

To return the outer product of two masked 1D 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

Create Array 1, 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)

# Create a 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 Array2, 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)

Create 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 outer product of two masked 1D arrays, use the ma.outer() method in Python Numpy −

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 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 outer product of two masked arrays, use the ma.outer() method in Python Numpy
print("\nResult of outer product...\n",np.ma.outer(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 outer product...
[[-- -- -- -- --]
[-- -- -- -- --]
[105 210 315 -- --]
[140 280 420 -- --]
[175 350 525 -- --]]
raja
Updated on 22-Feb-2022 08:07:47

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