How to Make Your Python Code 5x Faster With Simple Refactoring

You don’t need C extensions — just smarter Python.

How to Make Your Python Code 5x Faster With Simple Refactoring

“Premature optimization is the root of all evil.”
 — Donald Knuth

True — but ignoring optimization altogether is a recipe for sluggish, unresponsive software. The trick is to know when and how to refactor.

Python is a beautiful, expressive language — but it’s not known for speed. That said, you don’t need to jump into C extensions or rewrite everything in Rust to see significant performance gains.

In this article, I’ll show you how simple refactoring — no special libraries, no black magic — can make your Python code up to 5x faster. These are optimizations every developer can (and should) make part of their toolkit.

1. Use Built-in Functions and Libraries

Python’s built-in functions are written in C and heavily optimized. If you’re manually iterating or reinventing the wheel, there’s a good chance you’re slowing yourself down.

Bad:

result = [] 
for i in range(1000000): 
    result.append(i * 2)

Better:

result = list(map(lambda x: x * 2, range(1000000)))

Even Better:

result = [x * 2 for x in range(1000000)]

List comprehensions are not just cleaner — they’re faster. In most benchmarks, they outperform both for loops and map() with lambda.

2. Avoid Unnecessary Loops

Nested loops and repeated computations can destroy performance. Ask yourself: Can I do this in one pass? Can I use a set or dictionary for O(1) lookups?

Bad:

duplicates = [] 
for i in items: 
    if items.count(i) > 1 and i not in duplicates: 
        duplicates.append(i)

Better:

from collections import Counter 
 
duplicates = [item for item, count in Counter(items).items() if count > 1]

This version not only reads cleaner — it’s 10x faster on large datasets.

3. Utilize Generators Instead of Lists

If you’re working with large data and don’t need everything in memory at once, generators can dramatically reduce both memory usage and runtime.

Bad:

squares = [x * x for x in range(10**6)] 
total = sum(squares)

Better:

total = sum(x * x for x in range(10**6))

Using a generator expression avoids building an entire list in memory. For operations like sum(), it's all you need — and it’s way faster for big data.

4. Use Local Variables Inside Loops

This one’s subtle but surprisingly effective. Accessing local variables is faster than globals or object properties. In tight loops, this matters.

Bad:

def compute(nums): 
    result = [] 
    for num in nums: 
        result.append(math.sqrt(num)) 
    return result

Better:

def compute(nums): 
    result = [] 
    sqrt = math.sqrt  # local reference 
    for num in nums: 
        result.append(sqrt(num)) 
    return result

For high-iteration loops, this micro-optimization can shave off seconds.

5. Profile Before You Optimize

You can’t improve what you don’t measure.

Use tools like cProfile, line_profiler, or timeit to find real bottlenecks before refactoring blindly.

Quick example:

python -m cProfile my_script.py

Or inside a script:

import timeit 
 
print(timeit.timeit('sum(range(1000))', number=10000))

This ensures you’re focusing your efforts where they actually matter.

Real-World Impact

A few years ago, I was optimizing a data-processing script that was taking over 45 minutes to run. After:

• Replacing nested loops with dictionaries,
• Switching to generator expressions,
• Using pandas built-ins instead of manual iteration,

…the script ran in under 6 minutes.

These weren’t deep algorithmic overhauls — just smarter use of what Python already offers.

Final Thoughts

Python is elegant, readable, and surprisingly powerful when used wisely. By embracing built-in features, minimizing overhead, and profiling effectively, you can unlock serious performance gains with relatively minor changes.

Refactoring for speed doesn’t mean writing ugly, unreadable code. On the contrary, most of these tips make your code cleaner and faster at the same time.

Next time your script is running slow, don’t blame Python — take a closer look at how you’re using it.

Enjoyed this article? Give it a clap 👏 and share with a fellow Pythonista!

Got a performance tip of your own? Drop it in the comments — I’d love to hear it.

Follow me on Medium for more Python tips, productivity hacks, and developer insights.

#python #performance #refactoring #codingtips #developerproductivity