🚀 10 Simple Ways to Speed Up Your Python Code

Python is great, but sometimes it can be slow—especially with large datasets, loops, and computational tasks. Want to boost performance and make your code run faster?
Here are 3 quick hacks (out of 10!) to get you started:
✅ Use Generators Instead of Lists

def squares_generator(data):
    for elem in data:
        yield elem * 2

🔥 Why? Saves memory by generating values on demand instead of storing them all at once.
✅ Leverage map() and filter() Instead of Loops

numbers = [1, 2, 3, 4]
squares = map(lambda x: x**2, numbers)  
print(list(squares))  # [1, 4, 9, 16]

🔥 Why? Faster than for loops since these functions optimize performance under the hood.
✅ Cache Results with lru_cache

from functools import lru_cache  

@lru_cache(maxsize=None)  
def slow_function(n):  
    return sum(range(n))  

🔥 Why? Saves previous results to avoid redundant calculations.
But that’s just the beginning…
📖 Read the full article with all 10 performance hacks here → 🔗 https://levelup.gitconnected.com/10-simple-ways-to-speed-up-your-python-code-4e64b4573201
💬 Which optimization trick do you use the most? Drop your favorite one in the comments! 👇