How I Boosted My Python Scripts’ Speed by 300%!

Turbocharge your Python scripts!

How I Boosted My Python Scripts’ Speed by 300%!

Hey Everyone as we know that Python is known for its simplicity and readability, but it’s not always the fastest language.

“When I noticed my scripts were running painfully slow, I knew I had to optimize them.

After experimenting with different techniques, I was able to boost my Python scripts’ speed by 300%!”

Here’s exactly how I did it and how you can do the same!

1. I Use Built-in Functions Instead of Manual Loops

Python’s built-in functions are highly optimized and written in C, making them much faster than manually iterating over data. So i switched from normal for loop to map() function.

This is what I was doing earlier, that approach was slow:

numbers = [1, 2, 3, 4, 5] 
squared = [] 
 
for num in numbers: 
    squared.append(num ** 2)

After switched to map() function, it improve my code speed:

numbers = [1, 2, 3, 4, 5] 
squared = list(map(lambda x: x ** 2, numbers))

This is because map() is implemented in C and avoids the overhead of Python’s loop execution.

2. I Use List Comprehensions Instead of For Loops

List comprehensions are optimized for speed and reduce the time complexity of list operations.

Slow Approach if you are using For Loop

numbers = [1, 2, 3, 4, 5] 
doubled = [] 
 
for num in numbers: 
    doubled.append(num * 2)

Fast Approach if you are using List Comprehension

doubled = [num * 2 for num in numbers]

It uses less memory and fewer function calls, making execution faster.

3. I Use Generators Instead of Lists for Large Data

If you’re dealing with large datasets, using lists can consume too much memory. Instead, use generators to process data on demand.

Slow Approach if you are using Lists

def squares(n): 
    return [i ** 2 for i in range(n)]

Fast Approach if you are using a Generator

def squares(n): 
    for i in range(n): 
        yield i ** 2

It Generates values on the fly instead of storing them in memory and reduces RAM usage significantly for large datasets.

4. I Use set() for Faster Lookups Instead of Lists

Searching in a list takes O(n) time, whereas searching in a set takes O(1) time.

Slow Approach if you are using a List for Lookup

items = ["apple", "banana", "cherry"] 
if "banana" in items: 
    print("Found")

Fast Approach if you are using a Set for Lookup

items = {"apple", "banana", "cherry"} 
if "banana" in items: 
    print("Found")

Sets use hash tables, making lookups much faster.

5. I Use multiprocessing for CPU-Intensive Tasks

If you’re running CPU-heavy operations, like image processing or data crunching, Python’s Global Interpreter Lock (GIL) slows things down. To fix this, use multiprocessing.

Slow Approach if you are using Single Process Execution

import time 
 
def work(n): 
    time.sleep(1) 
    return n * n 
 
results = [work(i) for i in range(4)] 
print(results)

Fast Approach if you are using Multiprocessing

import multiprocessing 
 
def work(n): 
    return n * n 
 
if __name__ == "__main__": 
    with multiprocessing.Pool() as pool: 
        results = pool.map(work, range(4)) 
    print(results)

This will runs multiple processes in parallel, utilizing all CPU cores.

6. I Use numba for Just-in-Time Compilation

numba compiles Python code into high-performance machine code, significantly improving my execution speed.

Slow Approach if you are using Regular Function

def slow_function(n): 
    result = 0 
    for i in range(n): 
        result += i ** 2 
    return result

Fast Approach if you are using Numba

from numba import jit 
 
@jit(nopython=True) 
def fast_function(n): 
    result = 0 
    for i in range(n): 
        result += i ** 2 
    return result

numba translates Python into fast machine code, reducing execution time drastically.

7. I Use pandas Efficiently Instead of Iterating Rows

If you’re working with pandas, avoid for loops—they’re very slow!

Slow Approach if you are using Loops on a DataFrame

import pandas as pd 
 
df = pd.DataFrame({'A': range(100000)}) 
df['B'] = [x * 2 for x in df['A']]

Fast Approach if you are using Vectorized Operations

df['B'] = df['A'] * 2

This is because Pandas uses C-optimized functions, making operations nearly 100x faster!

The Final Speed Boost Results

By implementing these techniques, I was able to boost my Python scripts’ speed by 300%!

Here’s a quick recap of what worked best:

• I use built-in functions (map(), filter()) instead of loops
• I use list comprehensions for fast list processing
• I use generators for memory-efficient operations
• I use set() instead of lists for fast lookups
• I use multiprocessing for parallel execution
• I use numba for compiling Python to fast machine code
• I use pandas vectorized operations instead of loops

If you’ve been struggling with slow Python scripts, try these methods and let me know how much faster your code runs!

Have you tried any of these optimizations? Let’s discuss in the comments!