Reference library

Functions & basics

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

4 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…

13 0 Open

Functions & basics easy

Cache expensive function with lru_cache in Python

Use functools.lru_cache to memoize an expensive recursive function and show the dramatic speedup on repeated calls.

lru_cache caching decorators

Python

from functools import lru_cache
import time


@lru_cache(maxsize=128)
def expensive_operation(n):
    """Simulate an expensive Fibonacci-like calculation."""
    if n < 2:
        return n
    return expensive_operation(n - 1) + expensive_operation(n - 2)


if __name__ == "__main__":
    # First call (uncached) - take…

16 0 Open

Functions & basics easy

How to Compare Two Implementations with timeit in Python

Measure and compare the execution time of iterative vs recursive factorial functions using the timeit module.

timeit benchmark performance

Python

import timeit

def factorial_iterative(n):
    result = 1
    for i in range(2, n + 1):
        result *= i
    return result

def factorial_recursive(n):
    if n == 0:
        return 1
    return n * factorial_recursive(n - 1)

if __name__ == "__main__":
    n = 10
    iterations = 10000

    iterative_time = timeit…

13 0 Open

Functions & basics easy

Profile Python functions with cProfile

Profile a Python program with cProfile, capture the stats in memory, and print a sorted performance report.

cprofile performance profiling

Python

import cProfile
import pstats
import io


def slow_function():
    total = 0
    for i in range(100000):
        total += i ** 2
    return total


def medium_function():
    return sum(range(10000))


def fast_function():
    return sum(range(100))


def main():
    result1 = slow_function()
    result2 = medium_func…

11 0 Open

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