plot-the-distribution.py

'''
Plot the distribution
100xp
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.

Instructions
To make sure we've got enough simulations, go crazy. Simulate the random walk 500 times.
From np_aw_t, select the last row. This contains the endpoint of all 500 random walks you've simulated. Store this Numpy array as ends.
Use plt.hist() to build a histogram of ends. Don't forget plt.show() to display the plot.
'''
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(123)
all_walks = []

# Simulate random walk 500 times
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.array(np_aw_t[-1])

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