# Simulate multiple walks: dice and distribution

Fri Nov 26 2021 11:07:35 GMT+0000 (Coordinated Universal Time)

```# Numpy is imported; seed is set

# Initialize all_walks (don't change this line)
all_walks = []

# Simulate random walk 10 times
for i in range(10):

# 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
all_walks.append(random_walk)

# Print all_walks
print(all_walks)

#####################################################################
# numpy and matplotlib imported, seed set

# Simulate random walk 250 times
all_walks = []
for i in range(250) :
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() <= 0.001 :
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))
plt.plot(np_aw_t)
plt.show()

```
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