It is a truth known that a well-made app or website has its foundation in a well-built code. Developers have a tendency to obsess over performance. Most of your codebase probably runs just fine and doesn’t impact the overall speed of your app. Especially for scripts that run on a schedule or behind the scenes (like ETL jobs), performance usually isn’t a big deal.
However, performance becomes absolutely crucial when it’s tied to user experience. If your app takes too long to load or respond, people will notice, and obviously, no one likes waiting. The bottleneck is usually restricted to one or two parts of the codebase. Fixing that improves the overall performance.
In this blog, we will tackle strategies to optimise Python code so you can address your code performance issues. Meanwhile, you can look into the Data Science course by Imarticus Learning to diversify your career and get practical training in Python, SQL, Tableau, Power BI, etc.
Why You Should Care About Python Code Performance
Let’s be honest — most of us don’t start worrying about performance until something breaks. But sloppy code can creep up on you.
- Maybe you’re working with large datasets
- Or you’re automating reports that suddenly take 10 minutes instead of 30 seconds
- Or your backend just can’t keep up with API requests
That’s when Python code optimisation becomes your fallback guy.
And don’t worry, you don’t need to be some 10x dev to make your code faster. Small changes can go a long way.
1. Use Built-in Functions Wherever Possible
Python has a massive standard library. And most of it is built in C under the hood, which means it’s much faster than your hand-written loops.
For example:
| # Slower way
squared = [] for i in range(1000): squared.append(i*i) # Faster way squared = list(map(lambda x: x*x, range(1000))) # Even better squared = [i*i for i in range(1000)] |
That last one’s not just faster, it’s cleaner too.
Read: Built-in Functions — Python 3.13.2 documentation
2. Profile First, Optimise Later
You can’t fix what you can’t measure.
Start with the cProfile module. Just run:
| python -m cProfile myscript.py |
You’ll get a full breakdown of which parts of your script are slowing things down. Focus your Python code optimisation efforts there.
You can also use tools like:
- line_profiler
- memory_profiler
- Py-Spy (very handy)
Watch this ERROR HANDLING in Python – Write Robust & Bug-Free Code Python by Imarticus learning
3. Avoid Using Global Variables
This one’s sneaky. Global variables slow things down because Python has to look them up in a different scope. It’s a small hit, but over many iterations, it adds up.
| # Bad
counter = 0 def increment(): global counter counter += 1 # Better def increment(counter): return counter + 1 |
Keep variables local whenever possible.
4. Use Generators Instead of Lists When You Can
Generators are lazy. That’s a good thing. They don’t compute anything until you actually need it.
Compare:
| # Uses memory upfront
nums = [i for i in range(1000000)] # Efficient nums = (i for i in range(1000000)) |
If you’re just looping through data once, use generators. It saves a ton of memory and can improve performance in tight loops.
5. Don’t Recalculate Stuff You Already Know
Caching is your friend. Especially with expensive operations.
Use functools.lru_cache:
| from functools import lru_cache
@lru_cache(maxsize=None) def fib(n): if n < 2: return n return fib(n-1) + fib(n-2) |
This will save previously calculated results and reuse them.
6. Use NumPy for Heavy Math
If your Python code does a lot of number crunching, NumPy is a game-changer.
Why? Because
- It uses C in the background
- It works with arrays faster than native Python lists
- It’s super optimised
| Task | Native Python | NumPy |
| Summing a million numbers | ~50ms | ~5ms |
| Matrix Multiplication | Sluggish | Super fast |
Here’s an example:
| import numpy as np
a = np.arange(1000000) b = a * 2 |
That’s it. Blazing fast.
Read: the absolute basics for beginners — NumPy v2.2 Manual
7. Use Pandas with Care
Pandas is great. But not always fast.
Some tips to optimise Python scripts with Pandas:
- Use .loc[] or .iloc[] instead of chained indexing
- Avoid row-wise operations; go vectorised
- Use categorical dtype when dealing with repeating strings
- Drop unnecessary columns before heavy operations
Check this Advanced Pandas Techniques for Data Processing and Performance
8. Avoid Repeated Function Calls in Loops
Even a simple function can add overhead when called repeatedly in a loop.
| # Slower
for i in range(len(my_list)): process(my_list[i]) # Faster n = len(my_list) for i in range(n): process(my_list[i]) |
That len() call isn’t free. Cache it if you can!
9. Leverage Multi-threading or Multi-processing
Python’s Global Interpreter Lock (GIL) limits multi-threading with CPU-bound tasks. But you can still use it for IO-heavy ones.
For CPU-bound stuff, go with multiprocessing.
| Task Type | Use |
| IO-bound (e.g., web scraping) | threading |
| CPU-bound (e.g., image processing) | multiprocessing |
Also check joblib if you’re doing ML model training or parallel loops.
10. Use PyPy If You Can
PyPy is a faster alternative to the standard Python interpreter. It uses JIT (Just-in-Time) compilation.
You might see a 4–10x speedup without changing any of your code.
More about it here: https://www.pypy.org/
11. Avoid Unpacking in Loops
This can be surprisingly expensive in tight loops.
| # Slower
for key, value in my_dict.items(): print(key, value) # Faster items = my_dict.items() for item in items: print(item[0], item[1]) |
Not always a massive gain, but helps in big loops.
12. Use join() Instead of + for Strings
String concatenation with + creates new strings every time. That kills performance in large loops.
| # Slower
result = “” for s in list_of_strings: result += s # Faster result = “”.join(list_of_strings) |
Cleaner and faster.
Table: Quick Comparison of Python Code Optimisation Techniques
Here’s a comprehensive overview of the various Python code optimisation techniques, their uses and the performance levels:
| Optimisation Trick | Performance Gain | Where to Use |
| List Comprehensions | Medium | Loops & filtering |
| Generators | High | Memory-saving loops |
| NumPy Arrays | Very High | Math-heavy scripts |
| Caching (lru_cache) | High | Recursive or repeated functions |
| Multiprocessing | High | CPU-bound parallel tasks |
Watch More:
PYTHON for Beginners: Learn Python Programming from Scratch (Step-by-Step)
PANDAS in Python | Python for Beginners
Final Thoughts
You don’t need to over-optimise every single function. That’s a waste of time. Focus on the areas that cause real-world pain — where the app slows down, where the user gets frustrated, or where batch jobs take hours.
Start by profiling your code. Use built-in tools. Then apply fixes like switching to generators, NumPy, or caching results.
If you want to seriously upgrade your skills and learn how real companies optimise Python scripts, work with data, and build intelligent solutions — check out the full Postgraduate Program in Data Science and Analytics by Imarticus Learning.
It’s got real-world projects, solid instructors, and a focus on practical coding.
FAQs
- What’s the first step in Python code optimisation?
Start by profiling your Python code using tools like cProfile. Don’t guess. Measure what’s slow and fix that first.
- Does Python run slow because it’s interpreted?
Yes and no. It’s slower than compiled languages like C. But you can speed it up massively with things like NumPy, PyPy, and multiprocessing.
- Is it worth rewriting Python code in C or Cython?
If performance is really critical, yes. But for most cases, built-in modules, vectorisation, or JIT interpreters are enough.
- Can using functions slow down Python code?
Not always. But calling a function repeatedly inside a loop can add overhead. If it’s something simple, inlining it might help.
- What are some good tools to optimise Python scripts?
Try cProfile, line_profiler, memory_profiler, Py-Spy, and NumPy for performance. Joblib and multiprocessing help for parallelism.
- When should I not worry about optimisation?
If the script runs once a day and takes 2 minutes, who cares? Focus only when performance affects users or dev time.
- Is Python bad for large-scale applications?
Nope. Big companies use Python at scale. You just need to know where the bottlenecks are and how to fix them.
