Concurrency & performance
asyncio, threading, multiprocessing, and profiling-friendly performance patterns.
How to Share Memory Between Processes in Python with multiprocessing.Value and Array
Share a numeric value and a list-like array across multiple Python processes using multiprocessing.Value and multiprocessing.Array, with each process modifying the same memory.
import multiprocessing
def worker(shared_value, shared_array, index):
shared_value.value += 10
shared_array[index] = shared_array[index] * 2
if __name__ == "__main__":
shared_value = multiprocessing.Value("i", 5)
shared_array = multiprocessing.Array("i", [1, 2, 3, 4, 5])
processes = []
for i…How to Use Array Typecodes for Compact Numeric Storage in Python
This code demonstrates how to use the `array` module with typecodes to store integers, floats, and bytes in a memory-efficient way compared to standard Python lists.
from array import array
def demonstrate_array_types():
# Compact integer arrays
small_ints = array('i', [1, 2, 3, 4, 5])
unsigned_ints = array('I', [10, 20, 30])
# Floating point arrays
floats = array('f', [1.5, 2.5, 3.5])
doubles = array('d', [1.123456789, 2.987654321])
# Charac…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.