Reference library

Concurrency & performance

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

3 matches

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()
  …

44 0 Open

Concurrency & performance medium

How to Profile CPU Hot Path in Python with cProfile and sort_stats cumtime

Profile a Python function's CPU usage by running cProfile, sorting stats by cumulative time, and printing a readable report to stdout.

cprofile profiling performance

Python

import cProfile
import pstats
import io


def slow_function():
    total = 0
    for i in range(100_000):
        total += i * i
    return total


def fast_function():
    return sum(i for i in range(100))


def main():
    slow_function()
    fast_function()


if __name__ == "__main__":
    profiler = cProfile.Profi…

12 0 Open

Concurrency & performance easy

How to Time Code Performance with timeit in Python

Benchmark two implementations of the same logic using Python's timeit module and compare their execution speeds.

timeit performance benchmark

Python

import timeit

# Implementation 1: Using a list comprehension
def list_comprehension_squares(n):
    return [i ** 2 for i in range(n)]

# Implementation 2: Using a for loop with append
def loop_squares(n):
    result = []
    for i in range(n):
        result.append(i ** 2)
    return result

if __name__ == "__main__"…

12 0 Open

Browse by section

Each section groups closely related Python snippets.