Intermediate Python

# numpy and matplotlib imported, seed set
import numpy as np
import matplotlib.pyplot as plt
# Simulate random walk 500 times
all_walks = []
for i in range(500) :
    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)
        if np.random.rand() <= 0.001 :
            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))

# Select last row from np_aw_t: ends
ends = np_aw_t[-1]

# Plot histogram of ends, display plot
plt.hist(ends)
plt.show()
print(ends)

# All these fancy visualizations have put us on a sidetrack. We still have to solve the million-dollar problem: What are the odds that you'll reach 60 steps high on the Empire State Building?

# Basically, you want to know about the end points of all the random walks you've simulated. These end points have a certain distribution that you can visualize with a histogram.

# Note that if your code is taking too long to run, you might be plotting a histogram of the wrong data!

# 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(20) :
    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 np.random.rand() <= 0.005 :
            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()

# 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.

# NumPy is imported, seed is set

# Initialization
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)

# Import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

# Plot random_walk
plt.plot(random_walk)

# Show the plot
plt.show()

# Esto muestra un line chart con los output de el random walk codificado

# NumPy is imported, seed is set
import numpy as np
np.random.seed(123)
# Starting step
step = 50

# Roll the dice
dice = np.random.randint(1,7)

# Finish the control construct
if dice <= 2 :
    step = step - 1
elif dice <= 5:
    step = step + 1
else :
    step = step + np.random.randint(1,7)

# Print out dice and step
print(dice)
print(step)

# If you throw a dice and is less or equal to 2 then you go down 1 step, if it is 3, 4, or 5 you go up one step, if it is 6 then you roll the dice again and go up the number of steps that you get on your next throw.