5 Best Ways to Get the Outer Product of Two Arrays in Python

Rate this post

πŸ’‘ Problem Formulation: The outer product of two arrays is a matrix where each element (i,j) is the result of multiplying element i of the first array with element j of the second array. For instance, given two arrays [1, 2] and [3, 4], the outer product would be a matrix [[3, 4], [6, 8]]. In this article, we explore 5 different methods to compute this in Python effectively.

Method 1: Using NumPy’s Outer Function

The NumPy library provides a straightforward way to compute outer products using the numpy.outer() function. This function takes two input arrays and returns their outer product as a new NumPy array. It is efficient and concise, making it a go-to solution for those working within the NumPy ecosystem.

Here’s an example:

import numpy as np

a = np.array([1, 2])
b = np.array([3, 4])
result = np.outer(a, b)

Output:

[[ 3  4]
 [ 6  8]]

The code snippet above uses NumPy, a powerful numerical computing library in Python, to compute the outer product of the arrays a and b. By calling the np.outer() function, we get our desired outer product matrix efficiently.

Method 2: Using itertools and List Comprehension

Python’s itertools.product() function can be utilized to compute the Cartesian product of input iterables. By combining this with a list comprehension, we can mimic the outer product operation without needing any external libraries. This approach is more verbose but works without additional dependencies.

Here’s an example:

from itertools import product

a = [1, 2]
b = [3, 4]
result = [[x*y for x, y in product(a, [b_element])] for b_element in b]

Output:

[[3, 6], [4, 8]]

The example uses itertools.product() to generate pairs of elements from both arrays. The list comprehension then multiplies these elements accordingly to form the outer product matrix, with each inner list representing a row in the matrix.

Method 3: Using a Double For-Loop

For those preferring pure Python without any libraries, the outer product can be calculated with a double for-loop. This method iterates over each element in the first array and then over each element in the second array, multiplying them directly. It is easy to understand but may not be as efficient as library-based methods.

Here’s an example:

a = [1, 2]
b = [3, 4]
result = []
for i in a:
    result_row = []
    for j in b:
        result_row.append(i * j)
    result.append(result_row)

Output:

[[3, 4], [6, 8]]

This code builds the outer product manually, iterating over each element of the first array and then the second, storing the products in a new list of lists structure that represents the resulting matrix.

Method 4: Using the Python Standard Library

The built-in function map() along with a lambda function can be employed to achieve the outer product. Similar to list comprehension, but more functional in approach, it applies a function to every item of an iterable and returns a list of the results.

Here’s an example:

a = [1, 2]
b = [3, 4]
result = list(map(lambda x: [x * y for y in b], a))

Output:

[[3, 4], [6, 8]]

The map() function applies the lambda to each element of ‘a’. Inside the lambda, there’s a list comprehension that multiplies the current ‘a’ element by each element in ‘b’. The result is a list of lists, which represents the outer product matrix.

Bonus One-Liner Method 5: Using List Comprehension

A one-liner using nested list comprehension can also be constructed to compute the outer product in Python. This concise method collapses the logic into a single line, offering brevity at the expense of immediate readability for those unfamiliar with list comprehensions.

Here’s an example:

a = [1, 2]
b = [3, 4]
result = [[i * j for j in b] for i in a]

Output:

[[3, 4], [6, 8]]

This one-liner performs an outer product calculation using nested list comprehensions. The first comprehension iterates over array ‘a’ while the nested one iterates over ‘b’, multiplying corresponding elements together to form the result matrix.

Summary/Discussion

  • Method 1: NumPy’s Outer Function. Highly efficient and easy to use. Best for those already using NumPy. Might be excessive for simple scripts that don’t require NumPy’s full capabilities.
  • Method 2: itertools and List Comprehension. This approach is neat and utilizes standard libraries without needing NumPy. However, it could be less efficient and readable than NumPy’s optimized functions.
  • Method 3: Double For-Loop. Most straightforward and understandable. It’s a good teaching tool but not the most performant option for large-scale operations.
  • Method 4: Python Standard Library. Mixes functional programming with the readability of list comprehensions. It’s an intermediate approach between pure Python and NumPy functionality.
  • Bonus Method 5: List Comprehension One-Liner. Offers a clean and concise way to do the task in a single line of code. It favors concise over verbose solutions but can be a bit dense for beginners.