Simulate multiple walks
A single random walk is one thing, but that doesn't tell you if you have a good chance at winning the bet.
To get an idea about how big your chances are of reaching 60 steps, you can repeatedly simulate the random walk and collect the results. That's exactly what you'll do in this exercise.
The sample code already sets you off in the right direction. Another for
loop is wrapped around the code you already wrote. It's up to you to add some bits and pieces to make sure all of the results are recorded correctly.
Note: Don't change anything about the initialization of all_walks
that is given. Setting any number inside the list will cause the exercise to crash!
This exercise is part of the course
Intermediate Python
Exercise instructions
- Fill in the specification of the
for
loop so that the random walk is simulated five times. - After the
random_walk
array is entirely populated, append the array to theall_walks
list. - Finally, after the top-level
for
loop, print outall_walks
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# NumPy is imported; seed is set
# Initialize all_walks (don't change this line)
all_walks = []
# Simulate random walk five times
for i in ___ :
# Code from before
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)
random_walk.append(step)
# Append random_walk to all_walks
___
# Print all_walks
___