McDonald's Nutritional Analysis.py

#!/usr/bin/env python
# coding: utf-8

# # Importing Libraries

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


# # Read Dataset

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data = pd.read_csv('menu.csv')
data.head(5)


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data.shape


# # No. of Food Category

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plt.figure(figsize=(10, 4), dpi=100)
menu_category = data.Category.value_counts()
menu_category.plot.bar(color=['#FF5733', '#FFC300', '#DAF1A1', '#C70039', '#900C3F', '#581845', '#7D3C98', '#2E86C1', '#1ABC9C'])
plt.title("Number of Menu Items for each Food Category")
plt.ylabel("Count")
plt.xlabel("Menu Category")
plt.xticks(rotation=45)
plt.show()


# # Calories vs Calories from Fat

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plt.figure(figsize=(10, 4), dpi=100)

correlation = data['Calories'].corr(data['Calories from Fat'])
plt.scatter(data.Calories, data['Calories from Fat'], color='black')
plt.text(250, 1000, 'Correlation coefficient: {}'.format(round(correlation, 2)), fontsize=12)
plt.xlabel("Calories")
plt.ylabel("Calories from Fat")
plt.title("Relationship between Calories and Calories from Fat")
plt.show()


# # Mean of the 'Trans Fat'

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trans_fat_mean_by_category = data.groupby('Category')['Trans Fat'].mean()
print(trans_fat_mean_by_category)


# # % of Saturated Fat in Cholesterol

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data['saturated_cholesterol'] = (data['Saturated Fat'] / data['Cholesterol']) * 100


# # Top 5 Food Categories (Saturated Fat)

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saturated_cholesterol = data.groupby('Category')['saturated_cholesterol'].mean().dropna().nlargest(5)


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plt.figure(figsize=(10, 4), dpi=100)
saturated_cholesterol.sort_values(ascending=False).plot.bar(color = 'orange')
plt.title("Top 5 Food Categories: Saturated Fat Percentage in Cholesterol")
plt.ylabel("Percentage")
plt.xlabel("Menu Category")
plt.xticks(rotation=45)
plt.show();


# # Mean of all Categories

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coffee_tea = data[data.Category == 'Coffee & Tea']
coffee_tea.groupby('Item')['saturated_cholesterol'].mean().sort_values(ascending=False)


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shakes = data[data.Category == 'Smoothies & Shakes']
shakes.groupby('Item')['saturated_cholesterol'].mean().sort_values()


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beef_pork = data[data.Category == 'Beef & Pork']
beef_pork.groupby('Item')['saturated_cholesterol'].mean().sort_values()


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salads = data[data.Category == 'Salads']
salads.groupby('Item')['saturated_cholesterol'].mean().sort_values()


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chicken_fish = data[data.Category == 'Chicken & Fish']
chicken_fish.groupby('Item')['saturated_cholesterol'].mean().sort_values()


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data.groupby('Category')['Carbohydrates (% Daily Value)'].mean()


# # Total fat vs Total Carbohydrate

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plt.figure(figsize=(10, 4), dpi=100)
plt.scatter(data['Total Fat (% Daily Value)'], data['Carbohydrates (% Daily Value)'], color='k')
correlation = data['Total Fat (% Daily Value)'].corr(data['Carbohydrates (% Daily Value)'])
plt.text(125,20,'r = {}'.format(round(correlation,2)))
plt.xlabel("% Total Fat")
plt.ylabel("% Total Carbohydrate")
plt.title("Relationship between % Total Fat and % Total Carbohydrate")
plt.show()


# # Vitamins & Minerals

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data.groupby('Category')['Vitamin A (% Daily Value)'].mean()


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data.groupby('Category')['Vitamin C (% Daily Value)'].mean()


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beverage = data[data.Category == 'Beverages']
beverage.groupby('Item')['Vitamin C (% Daily Value)'].mean().sort_values()


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iron = data.groupby('Category')['Iron (% Daily Value)'].mean()
calcium = data.groupby('Category')['Calcium (% Daily Value)'].mean()


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plt.figure(figsize=(10, 4), dpi=100)

categories = iron.index
iron_values = iron.values
calcium_values = calcium.values

x_axis = np.arange(len(categories))

plt.bar(x_axis - 0.2, iron_values, 0.4, label='Iron', color='skyblue')
plt.bar(x_axis + 0.2, calcium_values, 0.4, label='Calcium', color='darkorange')

plt.xticks(x_axis, categories)
plt.xlabel("Menu Categories")
plt.title("Iron and Calcium Distribution in McDonald's Foods")
plt.xticks(rotation=45)
plt.legend()
plt.show()


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