Python isn’t slow — but your code might be.

How to Avoid the 5 Most Common Python Performance Pitfalls

These subtle mistakes can silently kill performance — but they’re easy to fix once you know them.

Photo by Todd Quackenbush on Unsplash

Python is beloved for its readability and simplicity. It’s often the go-to language for everything from scripting to machine learning. But beneath its elegant syntax lurk some performance traps that can quietly cripple your code — especially as it scales.

Whether you’re building a data-heavy application or just want to keep things snappy, avoiding common performance pitfalls is key. Here are five of the most frequent mistakes developers make in Python — and how to sidestep them without compromising clarity or maintainability.

1. Using Inefficient Loops Over Built-In Functions

The Pitfall:

It’s tempting to use for loops for everything. But looping over large datasets manually—especially when performing simple operations—can kill performance.

Example:

# Inefficient
squares = []
for i in range(1000000):
    squares.append(i * i)

The Fix: