Code Profiling & Performance

import time
import cProfile

# Simple timing
def time_it(func, *args):
    start = time.perf_counter()
    result = func(*args)
    elapsed = time.perf_counter() - start
    print(f"  {func.__name__}: {elapsed:.6f}s")
    return result

# Compare approaches
def sum_loop(n):
    total = 0
    for i in range(n):
        total += i
    return total

def sum_builtin(n):
    return sum(range(n))

def sum_formula(n):
    return n * (n - 1) // 2

print("=== Performance Comparison ===")
n = 1_000_000
time_it(sum_loop, n)
time_it(sum_builtin, n)
time_it(sum_formula, n)

# List vs Generator
print("\n=== List vs Generator ===")
import sys
list_comp = [x**2 for x in range(10000)]
gen_exp = (x**2 for x in range(10000))
print(f"  List: {sys.getsizeof(list_comp):>8} bytes")
print(f"  Generator: {sys.getsizeof(gen_exp):>5} bytes")

# String concatenation comparison
def concat_plus(n):
    s = ""
    for i in range(n):
        s += str(i)
    return s

def concat_join(n):
    return "".join(str(i) for i in range(n))

print("\n=== String Concat ===")
time_it(concat_plus, 10000)
time_it(concat_join, 10000)

# cProfile
print("\n=== cProfile Output ===")
cProfile.run('sum(range(100000))', sort='cumulative')