[dragonwriter]

I tested staying in CPython but jitting the main function with numba (no code changes but adding the jit decorator and expected type signature, and adding the same jit warmup call before the benchmark that the Julia version uses), and its about an 11× speedup. Code:

    import random
    import time
    from numba import jit, int32, float64


    @jit(float64(int32), nopython=True)
    def monte_carlo_pi(n):
        inside = 0
        for i in range(n):
            x = random.random()
            y = random.random()
            if x**2 + y**2 <= 1.0:
                inside += 1
        return 4.0 * inside / n

    # Warm up (compile)
    monte_carlo_pi(100)

    # Benchmark
    start = time.time()
    result = monte_carlo_pi(100_000_000)
    elapsed = time.time() - start
    print(f"Time: {elapsed:.3f} seconds")
    print(f"Estimated pi: {result}")

Base version (using the unmodified Python code from the slide):

   $ python -m monte
   Time: 13.758 seconds
   Estimated pi: 3.14159524

Numba version:

   $ python -m monte-numba
   Time: 1.212 seconds
   Estimated pi: 3.14143924