diff --git a/Statistics.py b/Statistics.py
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+++ b/Statistics.py
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+import math
+import statistics
+import numpy as np
+import scipy.stats
+import pandas as pd
+from matplotlib import pyplot
+
+
+food_consumption = pd.read_csv('food_consumption.csv', index_col=0)
+food_consumption.head()
+#print (food_consumption)
+
+#filter for Belgium
+be_consumption = food_consumption[food_consumption['country'] == 'Belgium']
+
+# Filter for USA
+usa_consumption = food_consumption[food_consumption['country'] == 'USA']
+
+usa_consumption.head()
+print(be_consumption)
+
+#Calculate mean and median consumption in Belgium
+
+be_mean = sum(be_consumption.consumption) / len (be_consumption.consumption)
+print(f'Belgium consumption mean = {be_mean}')
+
+usa_mean = sum(usa_consumption.consumption) / len (usa_consumption.consumption)
+print(f'USA consumption mean = {usa_mean}')
+
+be_median = np.median(be_consumption.consumption)
+print(f'Belgium consumption median = {be_median}')
+
+usa_median = np.median(usa_consumption.consumption)
+print(f'USA consumption median = {usa_median}')
+
+rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']
+
+#Calculate mean and median of co2_emission with .agg()
+print(rice_consumption)
+rice_median = np.median(rice_consumption.co2_emission)
+print(f'Rice CO2 median emission = {rice_median}')
+
+rice_mean = sum(rice_consumption.co2_emission) / len (rice_consumption.co2_emission)
+print(f'Rice CO2 mean emission = {rice_mean}')
+
+print(food_consumption.groupby(["food_category"])["co2_emission"].median())
+print(food_consumption.groupby(["food_category"])["co2_emission"].mean())
+
+#Plot the histogram of co2_emission for rice
+pyplot.plot (rice_consumption.co2_emission)
+pyplot.show ()
+
+#Calculate the quintiles of co2_emission
+print(np.quantile(food_consumption["co2_emission"], np.linspace(0, 1, 6)))
+
+#Calculate the variance and standard deviation of co2_emission for food_categories
+print(food_consumption.groupby(["food_category"])["co2_emission"].var())
+print(food_consumption.groupby(["food_category"])["co2_emission"].std())
+
+#Create histogram of co2_emission for food_category 'beef'
+beef_consumption = food_consumption[food_consumption['food_category'] == 'beef']
+#pyplot.plot (food_consumption.food_category.beef)
+pyplot.plot(beef_consumption.co2_emission)
+pyplot.show ()
+
+emissions_by_country = food_consumption.groupby('country')['co2_emission'].sum()
+print(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 > upper) | (emissions_by_country < lower)]
+print(outliers)
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