From cfd8cc3838f43697f864413d1ca96fa02f2d8239 Mon Sep 17 00:00:00 2001
From: Juan Obregon <jdor1262@gmail.com>
Date: Wed, 11 Jan 2023 01:21:01 +0100
Subject: [PATCH] solution to Assignment for Statistics with Python W-16

---
 solution.py | 94 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 94 insertions(+)
 create mode 100644 solution.py

diff --git a/solution.py b/solution.py
new file mode 100644
index 0000000..c82e428
--- /dev/null
+++ b/solution.py
@@ -0,0 +1,94 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+
+plt.rcParams['figure.figsize'] = (8, 5)
+
+
+food_consumption = pd.read_csv('food_consumption.csv', index_col=0)
+
+print("\n",food_consumption.head(),"\n")
+
+
+#filter for Belgium
+be_consumption = food_consumption[food_consumption['country'] == 'Belgium']
+
+mean = be_consumption['consumption'].mean()
+median = be_consumption['consumption'].median()
+
+print(mean)
+print(median,"\n")
+
+
+# Filter for USA
+usa_consumption = food_consumption[food_consumption['country'] == 'USA']
+
+print(usa_consumption['consumption'].mean())
+print(usa_consumption['consumption'].median(),"\n")
+
+
+
+
+# Q-3) Group by country, select consumption column, and compute mean and median
+be_and_usa = food_consumption[(food_consumption['country'] == 'Belgium') | 
+                              (food_consumption['country'] == 'USA')]
+
+df = pd.DataFrame(be_and_usa)
+grouped = df.groupby('country')
+
+# Select the consumption column
+consumption = grouped['consumption']
+
+# Calculate the mean and median
+mean = consumption.mean()
+median = consumption.median()
+
+print(mean,"\n")
+print(median,"\n")
+
+
+rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']
+
+# Create histogram
+sns.histplot(rice_consumption["consumption"], bins=69)
+
+# Add labels
+plt.xlabel('Rice Consumption')
+plt.ylabel('Frequency')
+plt.title('Histogram of Rice Consumption')
+
+# Show plot
+plt.show()
+
+print(rice_consumption['co2_emission'].agg(['mean','median']),"\n")
+
+
+# Q-6) Calculate the quintiles of co2_emission
+co2_emission = food_consumption['co2_emission']
+
+print(np.quantile(co2_emission, np.linspace(0, 1, 6)),"\n")
+
+
+
+# Q-7) Calculate the variance and standard deviation of co2_emission for food_categories
+
+food_category = food_consumption['food_category']
+print(food_consumption.groupby('food_category')['co2_emission'].agg(['var','std']),"\n")
+
+
+# Q-8) Create histogram of co2_emission for food_category 'beef'
+
+beef_consumption = food_consumption[food_consumption['food_category'] == 'beef']
+
+# Create histogram
+sns.histplot(beef_consumption["consumption"], bins=32)
+
+# Add labels
+plt.xlabel('Beef Consumption')
+plt.ylabel('Frequency')
+plt.title('Histogram of Beef Consumption')
+
+# Show plot
+plt.show()
\ No newline at end of file