Adding two Python lists elements

In Python, a list is a built-in data structure that stores an ordered collection of multiple items in a single variable. Lists are mutable, meaning we can add, remove, and change their elements. This article demonstrates how to add corresponding elements of two Python lists element-wise.

Given two equal-sized lists, we need to create a new list containing the sum of corresponding elements from both lists.

Example Scenario

# Input lists
list1 = [3, 6, 9, 45, 6]
list2 = [11, 14, 21, 0, 6]

# Expected output: [14, 20, 30, 45, 12]
# Explanation: 3+11=14, 6+14=20, 9+21=30, 45+0=45, 6+6=12

Using For Loop

Iterate through both lists and add corresponding elements using a for loop with append() ?

list1 = [7, 5.7, 21, 18, 8/3]
list2 = [9, 15, 6.2, 1/3, 11]

new_list = []
for i in range(len(list1)):
    new_list.append(list1[i] + list2[i])
    
print(new_list)
[16, 20.7, 27.2, 18.333333333333332, 13.666666666666666]

Using map() and add() Functions

The map() function applies the add() operation to corresponding elements of both lists ?

from operator import add

list1 = [7, 5.7, 21, 18, 8/3]
list2 = [9, 15, 6.2, 1/3, 11]

new_list = list(map(add, list1, list2))
print(new_list)
[16, 20.7, 27.2, 18.333333333333332, 13.666666666666666]

Using zip() Function

The zip() function pairs corresponding elements, then sum() adds each pair ?

list1 = [7, 5.7, 21, 18, 8/3]
list2 = [9, 15, 6.2, 1/3, 11]

result = [sum(pair) for pair in zip(list1, list2)]
print(result)
[16, 20.7, 27.2, 18.333333333333332, 13.666666666666666]

Using NumPy Library

NumPy provides efficient array operations for numerical computing. The np.add() function adds corresponding elements ?

import numpy as np

list1 = [1, 3, 4, 6, 8]
list2 = [4, 5, 6, 2, 10]

result = np.add(list1, list2)
print(result)
[ 5  8 10  8 18]

Comparison

Method Best For Performance
For Loop Small lists, learning Moderate
map() + add() Functional programming Good
zip() + sum() Readable, Pythonic Good
NumPy Large arrays, scientific computing Excellent

Conclusion

Use NumPy for large datasets and scientific computing due to its superior performance. For small lists, zip() provides the most Pythonic approach, while for loops offer the most readable solution for beginners.

Farhan Muhamed
Farhan Muhamed

No Code Developer, Vibe Coder

Updated on: 2026-03-25T07:05:04+05:30

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