Writing Faster Code: 7 Simple Ways to Improve Your Python Loops

If you’ve been working with Python for a bit of time, you know how essential loops are to your code. Whether if you’re processing data, managing collections, or iterating over files, writing efficient loops can save you both time and resources.

Here, I’m going to outline 7 different ways you can make your code more efficient.

1. Avoiding Unnecessary Loops

One common pitfall is using nested loops where they aren’t needed. For example, instead of looping through lists to check if an element exists, use Python’s built-in in operator:

# Less efficient
found = False
for item in my_list:
    if item == target:
        found = True

# More efficient
found = target in my_list

Eliminating redundant loops can significantly reduce runtime.

2. Leverage break and continue appropriately

When you’re certain you don’t need to complete every iteration, use break to exit the loop early or continue to skip unnecessary work, which improves performance:

for item in my_list:
    if condition(item):
        break
    if skip_condition(item):
        continue
    # Process item here

» Not sure which one to use? I cover this in another article: Break vs Continue: Which Should You Use?

3. Use List Comprehensions

List comprehensions are a Pythonic way to make your code cleaner and more efficient. They’re generally faster than traditional for loops:

# Traditional loop
squares = []
for num in range(10):
    squares.append(num ** 2)

# More efficient
squares = [num ** 2 for num in range(10)]

With list comprehensions, you can iterate and process elements in a single, concise line. But avoid over-complicating this!

4. Don’t be afraid to use the itertools library!

The itertools module offers tools for looping over data in a memory-efficient way. For example, islice() lets you slice an iterable without converting it to a list:

from itertools import islice

# Efficiently iterate through the first 10 items
for item in islice(large_iterable, 10):
    print(item)

» I wrote about the itertools library in this article. This is one you don’t want to skip over if you want to learn more about it.

5. Use enumerate() for Indexing

When you need the index in addition to the item during iteration, avoid manually managing a counter. Use enumerate() for a cleaner, more efficient approach:

# Less efficient
index = 0
for item in my_list:
    print(index, item)
    index += 1

# More efficient (and cleaner)
for index, item in enumerate(my_list):
    print(index, item)

6. Leverage Built-in Functions

Python’s built-in functions like map(), filter(), and reduce() can replace explicit loops and are optimized for performance:

# Using map() for efficiency
numbers = [1, 2, 3, 4]
squares = list(map(lambda x: x ** 2, numbers))

These functions can often handle the looping for you, improving both performance and readability.

» For more information about map(), see this article.
» For more information about filter(), see this article.
» For more information about reduce(), see this article.

7. Use zip() to Iterate Over Multiple Iterables

When you need to loop over two (or more) iterables simultaneously, zip() is your best friend. It pairs up elements efficiently, preventing the need for nested loops:

names = ['Alice', 'Bob', 'Charlie']
scores = [85, 90, 95]

# Efficient looping with zip
for name, score in zip(names, scores):
    print(f'{name}: {score}')

» Pro tip: I use this all the time in my code! This is one

That’s all for this week’s snack! Happy coding!