Reference library

Concurrency & performance

asyncio, threading, multiprocessing, and profiling-friendly performance patterns.

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Build a Python Performance Profiler That Generates Readable Reports

Use cProfile and pstats to profile Python functions and print a sorted performance report showing the top time-consuming calls.

profiling cprofile pstats

Python

import cProfile
import pstats
import io
from pathlib import Path

def slow_function():
    total = 0
    for i in range(500_000):
        total += i ** 2
    return total

def fast_function():
    total = sum(i * i for i in range(500_000))
    return total

def profile_functions():
    profiler = cProfile.Profile()
  …

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Concurrency & performance — Python code examples

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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.

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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.

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Build a Python Performance Profiler That Generates Readable Reports

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Concurrency & performance — Python code examples

What you will find here

Samples vs tutorials and challenges

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