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! 👇
