Why is this Python script with Matplotlib so slow?

Question

I'm trying so simulate coin tosses and profits and plot the graph in matplotlib:

from random import choice
import matplotlib.pyplot as plt
import time

start_time = time.time()
num_of_graphs = 2000
tries = 2000
coins = [150, -100]
last_loss = 0


for a in range(num_of_graphs):
    profit = 0
    line = []
    for i in range(tries):
        profit = profit + choice(coins)
        if (profit < 0 and last_loss < i):
            last_loss = i
        line.append(profit)
    plt.plot(line)
plt.show()

print("--- %s seconds ---" % (time.time() - start_time))
print("No losses after " + str(last_loss) + " iterations")

The end result is

--- 9.30498194695 seconds ---
No losses after 310 iterations

Why is it taking so long to run this script? If I change num_of_graphs to 10000, the scripts never finishes.

How would you optimize this?

Answer 1

Your measure of execution time is too rough. The following allows you to measure the time needed for the simulation, separate from the time needed for plotting:

It is using numpy.

import matplotlib.pyplot as plt
import numpy as np
import time


def run_sims(num_sims, num_flips):
    start = time.time()
    sims = [np.random.choice(coins, num_flips).cumsum() for _ in range(num_sims)]
    end = time.time()
    print(f"sim time = {end-start}")
    return sims


def plot_sims(sims):
    start = time.time()
    for line in sims:
        plt.plot(line)
    end = time.time()
    print(f"plotting time = {end-start}")
    plt.show()


if __name__ == '__main__':

    start_time = time.time()
    num_sims = 2000
    num_flips = 2000
    coins = np.array([150, -100])

    plot_sims(run_sims(num_sims, num_flips))

result:

sim time = 0.13962197303771973
plotting time = 6.621474981307983

As you can see, the sim time is greatly reduced (it was on the order of 7 seconds on my 2011 laptop); The plotting time is matplotlib dependent.