How to Optimise Python Code for Better Performance

Last updated on May 15th, 2026 at 03:04 pm

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.

Last updated on May 15th, 2026 at 03:04 pm

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

Start by profiling your code. Use built-in tools. Then apply fixes like switching to generators, NumPy, or caching results.

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.