HW_done

Homework.py

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+import statistics
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+food_consumption = pd.read_csv('food_consumption.csv', index_col=0)
+# print(food_consumption.head(), ' \n')
+
+#####################################################################################################################################
+
+# be_consumption = food_consumption[food_consumption['country'] == 'Belgium']
+
+# mn1 = statistics.mean(be_consumption['consumption'])
+# print('Mean For Belgium: ', mn1, )
+
+# md1 = statistics.median(be_consumption['consumption'])
+# print('Median For Belgium: ', md1, ' \n')
+
+
+# usa_consumption = food_consumption[food_consumption['country'] == 'USA']
+
+# mn2 = statistics.mean(usa_consumption['consumption'])
+# print('Mean For USA: ', mn2)
+
+# md2 = statistics.median(usa_consumption['consumption'])
+# print('Median For USA: ', md2, ' \n')
+
+
+
+#####################################################################################################################################
+# rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']
+
+# # Group the data by country and calculate the sum of CO2 emissions for each country
+# armut=rice_consumption[["country","co2_emission"]]
+# co2_emission_by_country = rice_consumption.groupby('country')['co2_emission'].sum().reset_index()
+
+# # Find the country with the highest CO2 emissions
+# most_co2_emission_country = co2_emission_by_country.loc[co2_emission_by_country['co2_emission'].idxmax()]['country']
+
+# # Create the histogram
+# # Create the histogram
+# plt.hist(rice_consumption['co2_emission'], bins=130)
+
+# # Add the title, xlabel, ylabel
+# plt.xlabel("CO2 Emission (kg CO2 eq/kg food)")
+# plt.ylabel("Frequency")
+# plt.title("CO2 Emission of Rice Consumption")
+
+# # Get the highest CO2 emission value
+# max_co2_emission = co2_emission_by_country.loc[co2_emission_by_country['co2_emission'].idxmax()]['co2_emission']
+
+
+# # Add the most CO2 Emission Country
+# plt.text(x = max_co2_emission, y = max(plt.yticks()[0])*0.9,
+#     s = f"Country with highest CO2 emissions: {most_co2_emission_country}",
+#     fontsize = 10, color = 'blue',
+#     ha = 'center', va = 'center')
+# plt.show()
+
+
+# rice_consumption[["country","co2_emission"]].sort_values("co2_emission", ascending=False).to_json("co2_emission_of_rice_consumption_sorted.json", orient='index')
+
+
+#####################################################################################################################################
+
+# rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']
+
+# rice_mean = statistics.mean(rice_consumption['co2_emission'])
+# rice_median = statistics.median(rice_consumption['co2_emission'])
+
+# df = pd.DataFrame(
+#     {
+#         "Mean" : [(rice_mean)], '| '
+#         "Median" : [(rice_median)]
+#     }
+# )
+
+# print(df)
+# print()
+
+# #####################################################################################################################################
+
+# co2_emission = food_consumption['co2_emission']
+# sorted_data = food_consumption.sort_values("co2_emission", ascending=False).to_json("Quantile_sample.json", orient='index') 
+
+# print(sorted_data)
+
+# x = np.quantile(co2_emission, np.linspace(0, 1, 6))
+# # Quantile: bize veri gruplarinin belli bir orana bolunmesine ve bu oran grruplarinin en yuksek degerini vermesine yarar.
+
+# print(x)
+# print()
+
+# #####################################################################################################################################
+
+food_category = food_consumption['food_category']
+
+# co2_var = food_consumption.groupby('food_category')['co2_emission'].var()
+# # print(co2_var)
+# co2_std = food_consumption.groupby('food_category')['co2_emission'].std()
+# # print(co2_std)
+
+# data = {'co2_var': co2_var,
+#         'co2_std': co2_std}
+
+# df = pd.DataFrame(data)
+# print(df)
+# print()
+
+# df = food_consumption.groupby('food_category')['co2_emission'].agg(['var', 'std', 'max', 'min', 'mean', 'median', 'mad'])
+# print(df)
+
+# #####################################################################################################################################
+
+co2_beef = food_consumption[food_consumption['food_category'] == 'beef']
+plt.hist(co2_beef['co2_emission'], bins=30)
+plt.show()
+print()
+
+
+# co2_beef = food_consumption[food_consumption['food_category'] == 'beef']
+# co2_emissions = co2_beef['co2_emission']
+
+# # Divide the emissions data into 4 quantiles
+# co2_emissions_q = pd.qcut(co2_emissions, 30)
+
+# # Use your custom labels
+# labels = ['Q{}'.format(i) for i in range(1,31)]
+# # plt.figure(figsize=(5,5))
+# # plt.title("CO2 Emissions for Beef Consumption")
+# # # Create a pie chart
+# # plt.pie(co2_emissions_q.value_counts(), labels=labels, autopct='%1.1f%%')
+# # plt.show()
+
+
+# plt.figure(figsize=(5,5))
+# plt.title("CO2 Emissions for Beef Consumption")
+
+# plt.plot(labels, co2_emissions_q.value_counts(), marker='o', linestyle='--', color='b')
+
+# # Create a line chart with emissions data on the y-axis and 
+# # quantile labels on the x-axis
+# plt.plot(labels, co2_emissions_q.value_counts())
+# plt.xlabel('Quantile')
+# plt.ylabel('Emissions')
+# plt.show()