10 Python Performance Tips I Wish I Knew Years Ago! 🚀

MAKE YOUR PYTHON CODE LIGHTNING FAST!

10 Python Performance Tips I Wish I Knew Years Ago! 🚀

Python is a fantastic language for its simplicity and readability,

but it can sometimes be slow if not optimized properly.

Over the years, I’ve learned some game-changing performance tricks that made my Python code significantly faster and more efficient.

Here are 10 Python performance tips that I wish I had known years ago!

1. Use List Comprehensions Instead of Loops

Loops are useful, but they can be slow when working with large datasets. List comprehensions are faster and more Pythonic than traditional loops.

Bad (Using Loops)

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

Good (Using List Comprehension)

numbers = [i * 2 for i in range(1000000)]

Even Better (Using Generator Expression for Memory Efficiency)

numbers = (i * 2 for i in range(1000000))  # Generates values on demand

List comprehensions are optimized in C Language, making them faster than standard loops.

2. Use Built-in Functions and Libraries

Python’s built-in functions are implemented in C, making them much faster than manually written alternatives.

Bad (Manually Implementing a Sum Function)

def custom_sum(numbers): 
    total = 0 
    for num in numbers: 
        total += num 
    return total 
 
custom_sum([1, 2, 3, 4, 5])

Good (Using sum())

sum([1, 2, 3, 4, 5])  # Faster and more efficient

Use NumPy for Numerical Operations

import numpy as np 
arr = np.array([1, 2, 3, 4, 5]) 
arr.sum()  # Much faster than Python's sum()

Built-in functions like sum(), max(), and min() are optimized in C, making them significantly faster than manual implementations.

3. Use join() Instead of String Concatenation

String concatenation using + inside a loop is slow because Python creates a new string for each operation.

Bad (Using + in a Loop)

words = ["Python", "is", "fast"] 
sentence = "" 
for word in words: 
    sentence += word + " "

Good (Using join())

sentence = " ".join(words)

join() is optimized in C and executes in O(n) time, whereas using + in a loop takes O(n²).

4. Use Generators for Large Data Processing

Generators yield values lazily, which saves memory compared to storing large lists in RAM.

Bad (Using Lists for Large Data)

def get_numbers(): 
    return [i for i in range(1000000)]  # Uses a lot of memory

Good (Using Generators)

def get_numbers(): 
    for i in range(1000000): 
        yield i  # Generates values on demand

Generators don’t store data in memory, making them ideal for processing large datasets.

5. Use set() for Fast Lookups

Checking if an item exists in a list takes O(n) time, but checking in a set takes O(1) time on average.

Bad (Using a List for Membership Check)

items = [1, 2, 3, 4, 5] 
if 3 in items:  # O(n) time complexity 
    print("Found")

Good (Using a Set for Fast Lookups)

items = {1, 2, 3, 4, 5} 
if 3 in items:  # O(1) time complexity 
    print("Found")

set uses a hash table internally, making lookups much faster than lists.

6. Use Multiprocessing for CPU-bound Tasks

Python’s Global Interpreter Lock (GIL) prevents multiple threads from executing Python bytecode simultaneously. Use multiprocessing instead of multithreading for CPU-heavy tasks.

Bad (Using Threads for CPU-bound Tasks)

from threading import Thread 
 
def compute(): 
    print(sum(i * i for i in range(10**7))) 
threads = [Thread(target=compute) for _ in range(4)] 
for thread in threads: 
    thread.start() 
for thread in threads: 
    thread.join()

Good (Using Multiprocessing for CPU-bound Tasks)

from multiprocessing import Pool 
 
def compute(_): 
    return sum(i * i for i in range(10**7)) 
with Pool(4) as p: 
    p.map(compute, range(4))

Multiprocessing spawns separate processes, each with its own Python interpreter, bypassing the GIL.

7. Use lru_cache() for Expensive Function Calls

functools.lru_cache() caches function results to avoid redundant computations.

Bad (Recomputing the Same Expensive Function)

def slow_function(n): 
    print(f"Computing {n}...") 
    return n * n 
 
print(slow_function(10)) 
print(slow_function(10))  # Computed again

Good (Using lru_cache())

from functools import lru_cache 
 
@lru_cache(maxsize=1000) 
def slow_function(n): 
    print(f"Computing {n}...") 
    return n * n 
print(slow_function(10)) 
print(slow_function(10))  # Cached result, no recomputation

lru_cache() stores results, avoiding repeated function calls.

8. Use itertools for Efficient Iterations

The itertools module provides high-performance iterator functions.

Example: Use itertools.combinations() Instead of Nested Loops

from itertools import combinations 
 
items = ["a", "b", "c"] 
for combo in combinations(items, 2): 
    print(combo)

itertools operations run in optimized C code, making them faster than manual implementations.

9. Use cython or numba for Speeding Up Code

For performance-critical tasks, compile Python code to C using Cython or Numba.

Example (Using Numba for Speed Boost)

from numba import jit 
 
@jit(nopython=True) 
def fast_function(n): 
    return sum(i * i for i in range(n)) 
print(fast_function(10**7))  # Much faster!

Numba compiles Python code to machine code, making execution 10-100x faster.

10. Profile Your Code with cProfile

Find bottlenecks using cProfile and optimize accordingly.

Example (Profiling Code Execution)

python -m cProfile -s time my_script.py

Profiling identifies slow functions, allowing you to optimize them efficiently.

Final Thoughts

These 10 performance tips can significantly improve my Python code’s speed and efficiency.

Whether you’re working with large datasets, CPU-intensive tasks, or API calls, these techniques will help you write faster and cleaner Python code!

Which tip did you find the most useful? Let me know in the comments!