diff --git a/main.py b/main.py
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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+plt.rcParams['figure.figsize'] = (10, 8)
+
+food_consumption = pd.read_csv('/Users/rumeysayakar/Desktop/derslik/statistic/Class7-MathStats-Module-Week16/food_consumption.csv', index_col=0)
+food_consumption.head()
+
+#filter for Belgium
+be_consumption = food_consumption[food_consumption['country'] == 'Belgium']
+
+
+# Filter for USA
+usa_consumption = food_consumption[food_consumption['country'] == 'USA']
+
+
+# Q-1) Calculate mean and median consumption in Belgium
+mean_of_belgium = np.mean(be_consumption['consumption'])
+median_of_belgium = np.median(be_consumption['consumption'])
+print(mean_of_belgium)
+print(median_of_belgium)
+
+
+# Q-2) Calculate mean and median consumption of USA
+mean_of_usa = np.mean(usa_consumption['consumption'])
+median_of_usa = np.median(usa_consumption['consumption'])
+print(mean_of_usa)
+print(median_of_usa)
+
+
+# # Work with both countries together
+be_and_usa = food_consumption[(food_consumption['country'] == 'Belgium') | 
+                              (food_consumption['country'] == 'USA')]
+
+
+# # Q-3) Group by country, select consumption column, and compute mean and median
+group_be_and_usa = be_and_usa.groupby('country')['consumption'].agg([np.mean, np.median])
+print(group_be_and_usa)
+
+
+rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']
+# Q-4)Plot the histogram of co2_emission for rice
+rice_consumption['co2_emission'].hist()
+# Q-5) Calculate mean and median of co2_emission with .agg()
+co2_mean_and_median = rice_consumption['co2_emission'].agg([np.mean, np.median])
+print(co2_mean_and_median)
+
+
+# Q-6) Calculate the quintiles of co2_emission
+co2_quintiles = np.quantile(food_consumption['co2_emission'], np.linspace(0, 1, 6))
+print(co2_quintiles)
+
+# Q-7) Calculate the variance and standard deviation of co2_emission for food_categories
+var_and_std = food_consumption.groupby('food_category')['co2_emission'].agg([np.var, np.std])
+print(var_and_std)
+
+# Q-8) Create histogram of co2_emission for food_category 'beef'
+food_consumption[food_consumption['food_category'] == 'beef']['co2_emission'].hist()
+
+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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