Implement clumsiness

With this neatly written code of yours, changing the number of times the random walk should be simulated is super-easy. You simply update the range() function in the top-level for loop.

There's still something we forgot! You're a bit clumsy and you have a 0.5% chance of falling down. That calls for another random number generation. Basically, you can generate a random float between 0 and 1. If this value is less than or equal to 0.005, you should reset step to 0.

This exercise is part of the course

Intermediate Python

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Exercise instructions

Change the range() function so that the simulation is performed 20 times.
Finish the if condition so that step is set to 0 if a random float is less or equal to 0.005. Use np.random.rand().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# numpy and matplotlib imported, seed set

# clear the plot so it doesn't get cluttered if you run this many times
plt.clf()

# Simulate random walk 20 times
all_walks = []
for i in range(5) :
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)
        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)

        # Implement clumsiness
        if ___ :
            step = 0

        random_walk.append(step)
    all_walks.append(random_walk)

# Create and plot np_aw_t
np_aw_t = np.transpose(np.array(all_walks))
plt.plot(np_aw_t)
plt.show()

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