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Functions & basics

Reusable building blocks — parameters, returns, scope, and clear function design.

3 matches

Benchmark list append vs comprehension in Python

This micro-benchmark compares the speed of building a list with a for loop and append versus a list comprehension, using the timeit module to get precise timings.

timeit benchmark performance

Python

import timeit

# Build a list of the first 1,000,000 integers using append in a loop
def append_loop(n=1_000_000):
    result = []
    for i in range(n):
        result.append(i)
    return result

# Build the same list using a list comprehension
def comprehension(n=1_000_000):
    return [i for i in range(n)]

if __n…

14 0 Open

Functions & basics easy

How to Build a Simple Decorator That Logs Function Calls in Python

This code shows how to create a reusable decorator that logs each function call, including arguments, return value, and execution time.

decorator logging functools

Python

import functools
import time

def log_calls(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__} with args={args}, kwargs={kwargs}")
        start = time.time()
        result = func(*args, **kwargs)
        end = time.time()
        print(f"{func.__name__} return…

11 0 Open

Functions & basics easy

How to Create a Timing Decorator in Python

A Python decorator that measures and prints the execution time of any function using time.perf_counter.

decorator timing perf_counter

Python

import time
from functools import wraps


def timing_decorator(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        end = time.perf_counter()
        elapsed = end - start
        print(f"{func.__name__} took {elapsed:.6f} seconds"…

12 0 Open

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