Created
July 12, 2018 20:20
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DataCamp: Intermediate Python for Data Science https://www.datacamp.com/courses/intermediate-python-for-data-science
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import matplotlib.pyplot as plt | |
import numpy as np | |
np.random.seed(123) | |
all_walks = [] | |
# Simulate random walk 250 times | |
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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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_aw_t[-1] | |
# Plot histogram of ends, display plot | |
plt.hist(ends) | |
plt.show() |
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