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Introduction to Statistics in Python

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

1 hidden cell

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Add notes about the concepts you've learned and code cells with code you want to keep.

Calculate the variance and standard deviation of co2_emission for each food_category by grouping and aggregating. Import matplotlib.pyplot with alias plt. Create a histogram of co2_emission for the beef food_category and show the plot. Create a histogram of co2_emission for the eggs food_category and show the plot.

# Print variance and sd of co2_emission for each food_category
print(food_consumption.groupby('food_category')['co2_emission'].agg([np.var, np.std]))

# Import matplotlib.pyplot with alias plt
import matplotlib.pyplot as plt

# Create histogram of co2_emission for food_category 'beef'
food_consumption[food_consumption['food_category'] == 'beef']['co2_emission'].hist()
# Show plot
plt.show()

# Create histogram of co2_emission for food_category 'eggs'
food_consumption[food_consumption['food_category'] == 'eggs']['co2_emission'].hist()
# Show plot
plt.show()
Hidden output

How to find outliers

# Calculate total co2_emission per country: emissions_by_country
emissions_by_country = food_consumption.groupby('country')['co2_emission'].sum()

# Compute the first and third quantiles and IQR of emissions_by_country
q1 = np.quantile(emissions_by_country, 0.25)
q3 = np.quantile(emissions_by_country, 0.75)
iqr = q3 - q1

# Calculate the lower and upper cutoffs for outliers
lower = q1 - 1.5 * iqr
upper = q3 + 1.5 * iqr

# Subset emissions_by_country to find outliers
outliers = outliers = emissions_by_country[(emissions_by_country < lower) | (emissions_by_country > upper)]
print(outliers)

Creating a probability distribution

# 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 = 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)