Concurrency & performance
asyncio, threading, multiprocessing, and profiling-friendly performance patterns.
How to Use ProcessPoolExecutor for CPU Parallel Map in Python
Run a function over a sequence of inputs in parallel across multiple CPU cores with ProcessPoolExecutor.map.
from concurrent.futures import ProcessPoolExecutor
import math
def compute_square(num):
return num * num
def is_prime(n):
if n < 2:
return False
for i in range(2, int(math.sqrt(n)) + 1):
if n % i == 0:
return False
return True
if __name__ == "__main__":
numbers = rang…Using a Python Generator Instead of a List to Save Memory
Compare a list approach with a generator to stream values lazily, avoiding memory-heavy storage of large sequences.
def fibonacci_generator(limit):
a, b = 0, 1
count = 0
while count < limit:
yield a
a, b = b, a + b
count += 1
def sum_first_n(generator, n):
total = 0
for i, value in enumerate(generator):
if i >= n:
break
total += value
return total
if __…Browse by section
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Concurrency & performance — Python code examples
What you will find here
This page collects concurrency & performance snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.