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Introduction to Statistics in Python

Run the hidden code cell below to import the data used in this course.

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# Create probability distribution
size_dist = restaurant_groups['group_size'].value_counts() / restaurant_groups.shape[0]
# Reset index and rename columns
size_dist = size_dist.reset_index()
size_dist.columns = ['group_size', 'prob']

# Expected value
expected_value = np.sum(size_dist['group_size'] * size_dist['prob'])

# Subset groups of size 4 or more
groups_4_or_more = groups_4_or_more = size_dist[size_dist['group_size'] >= 4]
# Sum the probabilities of groups_4_or_more
prob_4_or_more = np.sum(groups_4_or_more['prob'])
print(prob_4_or_more)

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# Set random seed to 334
np.random.seed(334)

# Import uniform
from scipy.stats import uniform

# Generate 1000 wait times between 0 and 30 mins
wait_times = uniform.rvs(0, 30, size=1000)

# Create a histogram of simulated times and show plot
plt.hist(wait_times)
plt.show()