diff --git a/summery_sat.py b/summery_sat.py
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+import pandas as pd
+import matplotlib.pyplot as pt
+import numpy as np
+#read the data
+#./dataset/ is a path. Copy and paste the path of the CSV file in your computer to read the data. 
+food_consumption = pd.read_csv('food_consumption.csv',index_col=0)
+
+food_consumption.head(6)
+# print(food_consumption.head(6))
+be_consumption = food_consumption[food_consumption['country'] == 'Belgium']
+# print(be_consumption)
+
+# Q-1) Calculate mean and median consumption in Belgium
+
+mean=be_consumption['consumption'].mean()
+print(mean)
+median=be_consumption['consumption'].median()
+print(median)
+
+# Q-2) Calculate mean and median consumption of USA
+
+usa_consumption=food_consumption[food_consumption['country'] == 'USA']
+# print(usa_consumption)
+mean_usa=usa_consumption['consumption'].mean()
+print(mean)
+median_usa=usa_consumption['consumption'].median()
+print(median_usa)
+
+# Q-3) Group by country, select consumption column, and compute mean and median
+
+usa_and_be=food_consumption[(food_consumption['country'] == 'Belgium')|(food_consumption['country'] == 'USA')]
+print(usa_and_be)
+t=usa_and_be.groupby('country')['consumption'].mean()
+y=usa_and_be.groupby('country')['consumption'].median()
+s=[t,y]
+
+d=pd.concat(s,axis=1,join='inner',keys=["mean", "median"])
+print(d)
+rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']
+print(rice_consumption)
+
+# Q-4)Plot the histogram of co2_emission for rice
+
+rice_consumption["co2_emission"].diff().hist()
+pt.show () 
+
+# Q-5) Calculate mean and median of co2_emission with .agg()
+ag1=rice_consumption.agg((['mean', 'median']))
+print(ag1)
+
+print(rice_consumption["co2_emission"].mean())
+print(rice_consumption["co2_emission"].median())
+
+
+
+# Q-6) Calculate the quintiles of co2_emission
+print(np.quantile(food_consumption["co2_emission"], np.linspace(0, 1, 6)))
+
+# Q-7) Calculate the variance and standard deviation of co2_emission 
+# for food_categories
+
+food_consumption = pd.read_csv('food_consumption.csv',index_col=0)
+print(food_consumption)
+beef_cat=food_consumption[food_consumption['food_category'] == 'beef']
+
+# beef_cat["co2_emission"].diff().hist()
+# pt.show () 
+
+print(beef_cat)
+print(beef_cat['co2_emission'].mean())
+print(food_consumption[food_consumption['food_category'] == 'beef']['co2_emission'].median())
+
+n=['beef','dairy', 'eggs' ,'fish','lamb_goat','nuts','pork','poultry','rice','soybeans','wheat'             
+  ]
+df1=[]
+df2=[]
+for i in n:
+        
+        # df1[i]=food_consumption[food_consumption['food_category'] == i]['co2_emission'].var()
+         df1.append(food_consumption[food_consumption['food_category'] == i]['co2_emission'].var())
+print(i,df1)
+r1=pd.DataFrame(df1)
+print(r1)
+for i in n:
+        
+        # df2[i]=food_consumption[food_consumption['food_category'] == i]['co2_emission'].std()
+        df2.append(food_consumption[food_consumption['food_category'] == i]['co2_emission'].std())
+print(df2)
+r2=pd.DataFrame(df2)
+print(r2)
+s={"food_category":n,"mean":df1,"std":df2}
+s=pd.DataFrame(s)
+print(s)
+
+# Q-8) Create histogram of co2_emission for food_category 'beef'
+
+beef_cat["co2_emission"].diff().hist()
+pt.show () 
+
+
+
+