notebook.py

# edited/added
import pandas as pd
food_consumption = pd.read_csv("food_consumption.csv")

# Import numpy with alias np
import numpy as np

# Filter for Belgium
be_consumption = food_consumption[food_consumption['country'] == 'Belgium']

# Filter for USA
usa_consumption = food_consumption[food_consumption['country'] == 'USA']

# Calculate mean and median consumption in Belgium
print(np.mean(be_consumption['consumption']))
print(np.median(be_consumption['consumption']))

# Calculate mean and median consumption in USA
print(np.mean(usa_consumption['consumption']))
print(np.median(usa_consumption['consumption']))

# Import numpy as np
import numpy as np

# Subset for Belgium and USA only
be_and_usa = food_consumption[(food_consumption['country'] == "Belgium") | (food_consumption['country'] == 'USA')]

# Group by country, select consumption column, and compute mean and median
print(be_and_usa.groupby('country')['consumption'].agg([np.mean, np.median]))


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

# Subset for food_category equals rice
rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']

# Histogram of co2_emission for rice and show plot
rice_consumption['co2_emission'].hist()
plt.show()

# Subset for food_category equals rice
rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']

# Calculate mean and median of co2_emission with .agg()
print(rice_consumption['co2_emission'].agg([np.mean, np.median]))

# Calculate the quartiles of co2_emission
print(np.quantile(food_consumption['co2_emission'], [0, 0.25, 0.5, 0.75, 1]))

# Calculate the quintiles of co2_emission
print(np.quantile(food_consumption['co2_emission'], [0, 0.2, 0.4, 0.6, 0.8, 1]))

# Calculate the deciles of co2_emission
print(np.quantile(food_consumption['co2_emission'], np.linspace(0, 1, 11)))

# 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()

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

print(emissions_by_country)

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

# edited/added
amir_deals = pd.read_csv("amir_deals.csv")

# Count the deals for each product
counts = amir_deals['product'].value_counts()
print(counts)

# Count the deals for each product
counts = amir_deals['product'].value_counts()

# Calculate probability of picking a deal with each product
probs = counts / amir_deals.shape[0]
print(probs)

# Set random seed
np.random.seed(24)

# Sample 5 deals without replacement
sample_without_replacement = amir_deals.sample(5)
print(sample_without_replacement)

# Set random seed
np.random.seed(24)

# Sample 5 deals with replacement
sample_with_replacement = amir_deals.sample(5, replace=True)
print(sample_with_replacement)

# edited/added
restaurant_groups = pd.read_csv("restaurant_groups.csv")

# Create a histogram of restaurant_groups and show plot
restaurant_groups['group_size'].hist(bins=np.linspace(2,6,5))
plt.show()

# 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']

print(size_dist)


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


# 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)


# Min and max wait times for back-up that happens every 30 min
min_time = 0
max_time = 30

# Import uniform from scipy.stats
from scipy.stats import uniform

# Calculate probability of waiting less than 5 mins
prob_less_than_5 = uniform.cdf(5, min_time, max_time)
print(prob_less_than_5)


# Calculate probability of waiting more than 5 mins
prob_greater_than_5 = 1 - uniform.cdf(5, min_time, max_time)
print(prob_greater_than_5)


# Calculate probability of waiting 10-20 mins
prob_between_10_and_20 = uniform.cdf(20, min_time, max_time) - uniform.cdf(10, min_time, max_time)
print(prob_between_10_and_20)

# 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)

print(wait_times)

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

# Import binom from scipy.stats
from scipy.stats import binom

# Set random seed to 10
np.random.seed(10)

# Simulate a single deal
print(binom.rvs(1, 0.3, size=1))


# Simulate 1 week of 3 deals
print(binom.rvs(3, 0.3, size=1))


# Simulate 52 weeks of 3 deals
deals = binom.rvs(3, 0.3, size=52)

# Print mean deals won per week
print(np.mean(deals))

# Probability of closing 3 out of 3 deals
prob_3 = binom.pmf(3, 3, 0.3)

print(prob_3)

# Probability of closing <= 1 deal out of 3 deals
prob_less_than_or_equal_1 = binom.cdf(1, 3, 0.3)

print(prob_less_than_or_equal_1)

# Probability of closing > 1 deal out of 3 deals
prob_greater_than_1 = 1 - binom.cdf(1, 3, 0.3)

print(prob_greater_than_1)

# Expected number won with 30% win rate
won_30pct = 3 * 0.3
print(won_30pct)

# Expected number won with 25% win rate
won_25pct = 3 * 0.25
print(won_25pct)

# Expected number won with 35% win rate
won_35pct = 3 * 0.35
print(won_35pct)

# Histogram of amount with 10 bins and show plot
amir_deals['amount'].hist(bins=10)
plt.show()

# edited/added
from scipy.stats import norm

# Probability of deal < 7500
prob_less_7500 = norm.cdf(7500, 5000, 2000)

print(prob_less_7500)


# Probability of deal > 1000
prob_over_1000 = 1 - norm.cdf(1000, 5000, 2000)

print(prob_over_1000)


# Probability of deal between 3000 and 7000
prob_3000_to_7000 = norm.cdf(7000, 5000, 2000) - norm.cdf(3000, 5000, 2000)

print(prob_3000_to_7000)


# Calculate amount that 25% of deals will be less than
pct_25 = norm.ppf(0.25, 5000, 2000)

print(pct_25)

# Calculate new average amount
new_mean = 5000 * 1.2

# Calculate new standard deviation
new_sd = 2000 * 1.3

# Simulate 36 new sales
new_sales = norm.rvs(new_mean, new_sd, size=36)

# Create histogram and show
plt.hist(new_sales)
plt.show()

# Create a histogram of num_users and show
amir_deals['num_users'].hist()
plt.show()

# Set seed to 104
np.random.seed(104)

# Sample 20 num_users with replacement from amir_deals
samp_20 = amir_deals['num_users'].sample(20, replace=True)

# Take mean of samp_20
print(np.mean(samp_20))

# Set seed to 104
np.random.seed(104)

# Sample 20 num_users with replacement from amir_deals and take mean
samp_20 = amir_deals['num_users'].sample(20, replace=True)
np.mean(samp_20)

sample_means = []
# Loop 100 times
for i in range(100):
  # Take sample of 20 num_users
  samp_20 = amir_deals['num_users'].sample(20, replace=True)
  # Calculate mean of samp_20
  samp_20_mean = np.mean(samp_20)
  # Append samp_20_mean to sample_means
  sample_means.append(samp_20_mean)
  
print(sample_means)

# Set seed to 104
np.random.seed(104)

sample_means = []
# Loop 100 times
for i in range(100):
  # Take sample of 20 num_users
  samp_20 = amir_deals['num_users'].sample(20, replace=True)
  # Calculate mean of samp_20
  samp_20_mean = np.mean(samp_20)
  # Append samp_20_mean to sample_means
  sample_means.append(samp_20_mean)
  
# Convert to Series and plot histogram
sample_means_series = pd.Series(sample_means)
sample_means_series.hist()
# Show plot
plt.show()

# edited/added
all_deals = pd.read_csv("all_deals.csv")

# Set seed to 321
np.random.seed(321)

sample_means = []
# Loop 30 times to take 30 means
for i in range(30):
  # Take sample of size 20 from num_users col of all_deals with replacement
  cur_sample = all_deals['num_users'].sample(20, replace=True)
  # Take mean of cur_sample
  cur_mean = np.mean(cur_sample)
  # Append cur_mean to sample_means
  sample_means.append(cur_mean)

# Print mean of sample_means
print(np.mean(sample_means))

# Print mean of num_users in amir_deals
print(np.mean(amir_deals['num_users']))

# Import poisson from scipy.stats
from scipy.stats import poisson

# Probability of 5 responses
prob_5 = poisson.pmf(5, 4)

print(prob_5)


# Import poisson from scipy.stats
from scipy.stats import poisson

# Probability of 5 responses
prob_coworker = poisson.pmf(5, 5.5)

print(prob_coworker)


# Import poisson from scipy.stats
from scipy.stats import poisson

# Probability of 2 or fewer responses
prob_2_or_less = poisson.cdf(2, 4)

print(prob_2_or_less)


# Import poisson from scipy.stats
from scipy.stats import poisson

# Probability of > 10 responses
prob_over_10 = 1 - poisson.cdf(10, 4)

print(prob_over_10)


# Import expon from scipy.stats
from scipy.stats import expon

# Print probability response takes < 1 hour
print(expon.cdf(1, scale=2.5))


# Import expon from scipy.stats
from scipy.stats import expon

# Print probability response takes > 4 hours
print(1 - expon.cdf(4, scale=2.5))


# Import expon from scipy.stats
from scipy.stats import expon

# Print probability response takes 3-4 hours
print(expon.cdf(4, scale=2.5) - expon.cdf(3, scale=2.5))



# edited/added
import seaborn as sns
world_happiness = pd.read_csv("world_happiness.csv", index_col = 0)

# Create a scatterplot of happiness_score vs. life_exp and show
sns.scatterplot(x='life_exp', y='happiness_score', data=world_happiness)

# Show plot
plt.show()

# Create scatterplot of happiness_score vs life_exp with trendline
sns.lmplot(x='life_exp', y='happiness_score', data=world_happiness, ci=None)

# Show plot
plt.show()

# Correlation between life_exp and happiness_score
cor = world_happiness['life_exp'].corr(world_happiness['happiness_score'])

print(cor)

# Scatterplot of gdp_per_cap and life_exp
sns.scatterplot(x='gdp_per_cap', y='life_exp', data=world_happiness)

# Show plot
plt.show()

# Correlation between gdp_per_cap and life_exp
cor = world_happiness['gdp_per_cap'].corr(world_happiness['life_exp'])

print(cor)

# Scatterplot of happiness_score vs. gdp_per_cap
sns.scatterplot(x='gdp_per_cap', y='happiness_score', data=world_happiness)
plt.show()

# Calculate correlation
cor = world_happiness['gdp_per_cap'].corr(world_happiness['happiness_score'])
print(cor)

# Create log_gdp_per_cap column
world_happiness['log_gdp_per_cap'] = np.log(world_happiness['gdp_per_cap'])

# Scatterplot of happiness_score vs. log_gdp_per_cap
sns.scatterplot(x='log_gdp_per_cap', y='happiness_score', data=world_happiness)
plt.show()

# Calculate correlation
cor = world_happiness['log_gdp_per_cap'].corr(world_happiness['happiness_score'])
print(cor)

# edited/added
world_happiness = pd.read_csv("world_happiness_full.csv")

# Scatterplot of grams_sugar_per_day and happiness_score
sns.scatterplot(x='grams_sugar_per_day', y='happiness_score', data=world_happiness)
plt.show()

# Correlation between grams_sugar_per_day and happiness_score
cor = world_happiness['grams_sugar_per_day'].corr(world_happiness['happiness_score'])
print(cor)