Element-wise addition of 2 lists?

Optimize with map and operator.add

In cases where performance matters (no, not the sports car kind!), you might want to consider using Python's built-in map function in combination with operator.add. This approach offers both high readability and is efficient:

import operator
result = list(map(operator.add, list1, list2)) # operator.add saying "Make it so, number one!". 
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On a side note, map and operator.add pack a performance punch 🥊 that is even stronger than list comprehensions, especially for larger datasets.

You could also introduce lambda functions into the mix for more customizable operations, turning your simple addition into a function transformer in disguise! 🎭:

result = list(map(lambda x, y: x + y, list1, list2)) # Lambda: "Yeah, I've got this covered!"
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Use NumPy for large datasets

NumPy is the hot rod 🏎️ of the Python data world, especially when it comes to large datasets. When working with lists that contain n >= 8 elements, NumPy's numpy.add offers superior performance:

import numpy as np
result = np.add(list1, list2).tolist() # NumPy: "Step aside, let me handle this!"
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Importantly, you need to have the NumPy package installed and understand that element-wise addition is faster and more intuitive for larger datasets. Having said that, if you're already juggling a lot of dependencies, adding NumPy to the mix just for list addition might feel like "bringing a rocket launcher to a thumb war".

Anticipating potential pitfalls

Length mismatches and itertools

Ensure both lists are of equal length to avoid any unwanted party crashers (aka errors) or unexpected results. If your lists refuse to play nice and insist on being different lengths, consider using itertools.zip_longest, which graciously handles unequal lengths:

import itertools
result = [a + b for a, b in itertools.zip_longest(list1, list2, fillvalue=0)] # Because no one should feel left out!
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Timing is key

Running timing comparisons between various methods could help you to find the most efficient solution for element-wise list addition. While map and operator.add may win the sprint, NumPy is surely winning the marathon!

Python 2 alternative

For the nostalgic Python 2 users, Python provides itertools.izip as a memory-efficient alternative to zip:

from itertools import izip
result = [a + b for a, b in izip(list1, list2)] # Lazy izip: "I'll do it... eventually."
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Just a small reminder though - Python 2 is not officially supported anymore, so upgrading to Python 3 is generally a wise move.