Get the Outer product of two arrays in Python

To get the Outer product of two arrays, use the numpy.outer() method in Python. The 1st parameter a is the first input vector. Input is flattened if not already 1-dimensional. The 2nd parameter b is the second input vector. Input is flattened if not already 1-dimensional. The 3rd parameter out is a location where the result is stored.

Given two vectors, a = [a0, a1, ..., aM] and b = [b0, b1, ..., bN], the outer product [1] is −

[[a0*b0 a0*b1 ... a0*bN ]
[a1*b0 .
[ ... .
[aM*b0 aM*bN ]]

Steps

At first, import the required libraries-

import numpy as np

Creating two numpy One-Dimensional array using the array() method −

arr1 = np.array([5, 10, 15])
arr2 = np.array([20, 25, 30])

Display the arrays −

print("Array1...\n",arr1)
print("\nArray2...\n",arr2)

Check the Dimensions of both the arrays −

print("\nDimensions of Array1...\n",arr1.ndim)
print("\nDimensions of Array2...\n",arr2.ndim)

Check the Shape of both the arrays −

print("\nShape of Array1...\n",arr1.shape)
print("\nShape of Array2...\n",arr2.shape)

To get the Outer product of two arrays, use the numpy.outer() method in Python −

print("\nResult (Outer Product)...\n",np.outer(arr1, arr2))

Example

import numpy as np

# Creating two numpy One-Dimensional array using the array() method
arr1 = np.array([5, 10, 15])
arr2 = np.array([20, 25, 30])

# Display the arrays
print("Array1...\n",arr1)
print("\nArray2...\n",arr2)

# Check the Dimensions of both the arrays
print("\nDimensions of Array1...\n",arr1.ndim)
print("\nDimensions of Array2...\n",arr2.ndim)

# Check the Shape of both the arrays
print("\nShape of Array1...\n",arr1.shape)
print("\nShape of Array2...\n",arr2.shape)

# To get the Outer product of two arrays, use the numpy.outer() method in Python
print("\nResult (Outer Product)...\n",np.outer(arr1, arr2))

Output

Array1...
[ 5 10 15]

Array2...
[20 25 30]

Dimensions of Array1...
1

Dimensions of Array2...
1

Shape of Array1...
(3,)

Shape of Array2...
(3,)

Result (Outer Product)...
[[100 125 150]
[200 250 300]
[300 375 450]]

Updated on: 25-Feb-2022

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