visualize-all-walks.py

'''
Visualize all walks
100xp
all_walks is a list of lists: every sub-list represents a single random walk.
If you convert this list of lists to a Numpy array, you can start making interesting
plots! matplotlib.pyplot is already imported as plt.

The nested for loop is already coded for you - don't worry about it. For now,
focus on the code that comes after this for loop.

Instructions
-Use np.array() to convert all_walks to a Numpy array, np_aw.
-Try to use plt.plot() on np_aw. Also include plt.show(). Does it work out of the box?
-Transpose np_aw by calling np.transpose() on np_aw. Call the result np_aw_t.
Now every row in np_all_walks represents the position after 1 throw for the 10 random walks.
-Use plt.plot() to plot np_aw_t; also include a plt.show(). Does it look better this time?
'''
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(123)
all_walks = []
for i in range(10) :
    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)
    all_walks.append(random_walk)

# Convert all_walks to Numpy array: np_aw
np_aw = np.array(all_walks)

# Plot np_aw and show
plt.plot(np_aw)
plt.show()

# Clear the figure
plt.clf()

# Transpose np_aw: np_aw_t
np_aw_t = np.transpose(np_aw)

# Plot np_aw_t and show
plt.plot(np_aw_t)
plt.show()