CFP_calculation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Food Carbon Footprint Calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Global warming and climate change are big issues in the world. With all other major reasons, food is also one of them. Food produces about 8 tons of emissions per household, or about 17% of the total. Food production is responsible for one-quarter of the world’s greenhouse gas emissions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective:  Calculate the Carbon Footprint\n",
    "* **Input** : The food items (name) and their quantity (in Kilogram) to be eaten by a person in a meal\n",
    "* **Output** : Total carbon footprint for the meal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data\n",
    "Data on various food items and their corresponding carbon footprint values are collected from the internet.\n",
    "\n",
    "\" https://www.kaggle.com/selfvivek/environment-impact-of-food-production \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Importing dataset (Reading data file)\n",
    "CFP_Data = pd.read_csv(\"Food_Production.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food product</th>\n",
       "      <th>Land use change</th>\n",
       "      <th>Animal Feed</th>\n",
       "      <th>Farm</th>\n",
       "      <th>Processing</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Packging</th>\n",
       "      <th>Retail</th>\n",
       "      <th>Total_emissions</th>\n",
       "      <th>Eutrophying emissions per 1000kcal (gPO₄eq per 1000kcal)</th>\n",
       "      <th>...</th>\n",
       "      <th>Freshwater withdrawals per 100g protein (liters per 100g protein)</th>\n",
       "      <th>Freshwater withdrawals per kilogram (liters per kilogram)</th>\n",
       "      <th>Greenhouse gas emissions per 1000kcal (kgCO₂eq per 1000kcal)</th>\n",
       "      <th>Greenhouse gas emissions per 100g protein (kgCO₂eq per 100g protein)</th>\n",
       "      <th>Land use per 1000kcal (m² per 1000kcal)</th>\n",
       "      <th>Land use per kilogram (m² per kilogram)</th>\n",
       "      <th>Land use per 100g protein (m² per 100g protein)</th>\n",
       "      <th>Scarcity-weighted water use per kilogram (liters per kilogram)</th>\n",
       "      <th>Scarcity-weighted water use per 100g protein (liters per 100g protein)</th>\n",
       "      <th>Scarcity-weighted water use per 1000kcal (liters per 1000 kilocalories)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wheat &amp; Rye (Bread)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maize (Meal)</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barley (Beer)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oatmeal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.281357</td>\n",
       "      <td>...</td>\n",
       "      <td>371.076923</td>\n",
       "      <td>482.4</td>\n",
       "      <td>0.945482</td>\n",
       "      <td>1.907692</td>\n",
       "      <td>2.897446</td>\n",
       "      <td>7.6</td>\n",
       "      <td>5.846154</td>\n",
       "      <td>18786.2</td>\n",
       "      <td>14450.92308</td>\n",
       "      <td>7162.104461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rice</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.514379</td>\n",
       "      <td>...</td>\n",
       "      <td>3166.760563</td>\n",
       "      <td>2248.4</td>\n",
       "      <td>1.207271</td>\n",
       "      <td>6.267606</td>\n",
       "      <td>0.759631</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.943662</td>\n",
       "      <td>49576.3</td>\n",
       "      <td>69825.77465</td>\n",
       "      <td>13449.891480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Food product  Land use change  Animal Feed  Farm  Processing  \\\n",
       "0  Wheat & Rye (Bread)              0.1          0.0   0.8         0.2   \n",
       "1         Maize (Meal)              0.3          0.0   0.5         0.1   \n",
       "2        Barley (Beer)              0.0          0.0   0.2         0.1   \n",
       "3              Oatmeal              0.0          0.0   1.4         0.0   \n",
       "4                 Rice              0.0          0.0   3.6         0.1   \n",
       "\n",
       "   Transport  Packging  Retail  Total_emissions  \\\n",
       "0        0.1       0.1     0.1              1.4   \n",
       "1        0.1       0.1     0.0              1.1   \n",
       "2        0.0       0.5     0.3              1.1   \n",
       "3        0.1       0.1     0.0              1.6   \n",
       "4        0.1       0.1     0.1              4.0   \n",
       "\n",
       "   Eutrophying emissions per 1000kcal (gPO₄eq per 1000kcal)  ...  \\\n",
       "0                                                NaN         ...   \n",
       "1                                                NaN         ...   \n",
       "2                                                NaN         ...   \n",
       "3                                           4.281357         ...   \n",
       "4                                           9.514379         ...   \n",
       "\n",
       "   Freshwater withdrawals per 100g protein (liters per 100g protein)  \\\n",
       "0                                                NaN                   \n",
       "1                                                NaN                   \n",
       "2                                                NaN                   \n",
       "3                                         371.076923                   \n",
       "4                                        3166.760563                   \n",
       "\n",
       "   Freshwater withdrawals per kilogram (liters per kilogram)  \\\n",
       "0                                                NaN           \n",
       "1                                                NaN           \n",
       "2                                                NaN           \n",
       "3                                              482.4           \n",
       "4                                             2248.4           \n",
       "\n",
       "   Greenhouse gas emissions per 1000kcal (kgCO₂eq per 1000kcal)  \\\n",
       "0                                                NaN              \n",
       "1                                                NaN              \n",
       "2                                                NaN              \n",
       "3                                           0.945482              \n",
       "4                                           1.207271              \n",
       "\n",
       "   Greenhouse gas emissions per 100g protein (kgCO₂eq per 100g protein)  \\\n",
       "0                                                NaN                      \n",
       "1                                                NaN                      \n",
       "2                                                NaN                      \n",
       "3                                           1.907692                      \n",
       "4                                           6.267606                      \n",
       "\n",
       "   Land use per 1000kcal (m² per 1000kcal)  \\\n",
       "0                                      NaN   \n",
       "1                                      NaN   \n",
       "2                                      NaN   \n",
       "3                                 2.897446   \n",
       "4                                 0.759631   \n",
       "\n",
       "   Land use per kilogram (m² per kilogram)  \\\n",
       "0                                      NaN   \n",
       "1                                      NaN   \n",
       "2                                      NaN   \n",
       "3                                      7.6   \n",
       "4                                      2.8   \n",
       "\n",
       "   Land use per 100g protein (m² per 100g protein)  \\\n",
       "0                                              NaN   \n",
       "1                                              NaN   \n",
       "2                                              NaN   \n",
       "3                                         5.846154   \n",
       "4                                         3.943662   \n",
       "\n",
       "   Scarcity-weighted water use per kilogram (liters per kilogram)  \\\n",
       "0                                                NaN                \n",
       "1                                                NaN                \n",
       "2                                                NaN                \n",
       "3                                            18786.2                \n",
       "4                                            49576.3                \n",
       "\n",
       "   Scarcity-weighted water use per 100g protein (liters per 100g protein)  \\\n",
       "0                                                NaN                        \n",
       "1                                                NaN                        \n",
       "2                                                NaN                        \n",
       "3                                        14450.92308                        \n",
       "4                                        69825.77465                        \n",
       "\n",
       "   Scarcity-weighted water use per 1000kcal (liters per 1000 kilocalories)  \n",
       "0                                                NaN                        \n",
       "1                                                NaN                        \n",
       "2                                                NaN                        \n",
       "3                                        7162.104461                        \n",
       "4                                       13449.891480                        \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at top rows of the data\n",
    "CFP_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Food product', 'Land use change', 'Animal Feed', 'Farm', 'Processing',\n",
       "       'Transport', 'Packging', 'Retail', 'Total_emissions',\n",
       "       'Eutrophying emissions per 1000kcal (gPO₄eq per 1000kcal)',\n",
       "       'Eutrophying emissions per kilogram (gPO₄eq per kilogram)',\n",
       "       'Eutrophying emissions per 100g protein (gPO₄eq per 100 grams protein)',\n",
       "       'Freshwater withdrawals per 1000kcal (liters per 1000kcal)',\n",
       "       'Freshwater withdrawals per 100g protein (liters per 100g protein)',\n",
       "       'Freshwater withdrawals per kilogram (liters per kilogram)',\n",
       "       'Greenhouse gas emissions per 1000kcal (kgCO₂eq per 1000kcal)',\n",
       "       'Greenhouse gas emissions per 100g protein (kgCO₂eq per 100g protein)',\n",
       "       'Land use per 1000kcal (m² per 1000kcal)',\n",
       "       'Land use per kilogram (m² per kilogram)',\n",
       "       'Land use per 100g protein (m² per 100g protein)',\n",
       "       'Scarcity-weighted water use per kilogram (liters per kilogram)',\n",
       "       'Scarcity-weighted water use per 100g protein (liters per 100g protein)',\n",
       "       'Scarcity-weighted water use per 1000kcal (liters per 1000 kilocalories)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding columns names of the data\n",
    "CFP_Data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>food</th>\n",
       "      <th>cfp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wheat &amp; Rye (Bread)</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maize (Meal)</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barley (Beer)</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oatmeal</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rice</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                  food  cfp\n",
       "0  Wheat & Rye (Bread)  1.4\n",
       "1         Maize (Meal)  1.1\n",
       "2        Barley (Beer)  1.1\n",
       "3              Oatmeal  1.6\n",
       "4                 Rice  4.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking only 2 columns food product and total emmissions\n",
    "cfp_food = CFP_Data.loc[:,['Food product','Total_emissions']]\n",
    "# renaming the columns to short names\n",
    "cfp_food.columns = ['food','cfp']\n",
    "cfp_food.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Here, column 'food' represents the name of food items (there are 43 unique food items in this dataset) and the column 'cfp' is having the value of greenhouse gas emissions per kg of food product (Kg CO2 - equivalents per kg product) for each food item.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the food item names to lower case\n",
    "cfp_food['food'] = cfp_food['food'].apply(lambda x:x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sorting the values of dataframe 'cfp_food' according to the column 'cfp'\n",
    "cfp_food = cfp_food.sort_values(by='cfp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 43 artists>"
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     "execution_count": 8,
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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting on barplot to compare the cfp values for each food item\n",
    "f, ax = plt.subplots(figsize=(10,15))\n",
    "plt.barh(cfp_food['food'],cfp_food['cfp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Here we can see the maximum carbon footprint value is around 60 kg for the meal beef (beef herd) while lamb and mutton are following this with the second-highest value of carbon footprint. In this dataset, nuts have a minimum carbon footprint value that is even less than 1.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The available food items names are:  ['nuts', 'citrus fruit', 'onions & leeks', 'root vegetables', 'potatoes', 'apples', 'brassicas', 'other vegetables', 'other fruit', 'peas', 'bananas', 'cassava', 'soymilk', 'berries & grapes', 'barley (beer)', 'maize (meal)', 'beet sugar', 'wine', 'tomatoes', 'wheat & rye (bread)', 'other pulses', 'oatmeal', 'groundnuts', 'cane sugar', 'milk', 'tofu', 'sunflower oil', 'rapeseed oil', 'rice', 'eggs', 'fish (farmed)', 'soybean oil', 'olive oil', 'poultry meat', 'pig meat', 'palm oil', 'shrimps (farmed)', 'coffee', 'dark chocolate', 'beef (dairy herd)', 'cheese', 'lamb & mutton', 'beef (beef herd)']\n"
     ]
    }
   ],
   "source": [
    "# Looking at the list of food items\n",
    "food_list = list(cfp_food['food'])\n",
    "print(\"The available food items names are: \", food_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter the meal name ( e.g. Breakfast/Lunch/Dinner): Breakfast\n"
     ]
    }
   ],
   "source": [
    "# Requesting for input (name of meal)\n",
    "meal_name = input(\"Please enter the meal name ( e.g. Breakfast/Lunch/Dinner): \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter the no. of food items you'll eat (e.g. 1,2,5,10...) : 2\n"
     ]
    }
   ],
   "source": [
    "# Requesting for input (quantity of meal)\n",
    "item_count = int(input(\"Please enter the no. of food items you'll eat (e.g. 1,2,5,10...) : \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the Item name and Quantity e.g. rice,0.5: milk,0.5\n",
      "Enter the Item name and Quantity e.g. rice,0.5: bread,0.25\n",
      "Food item name not found, please enter again!\n",
      "Enter the Item name and Quantity e.g. milk,0.5: milk,0.5\n"
     ]
    }
   ],
   "source": [
    "# Enter the Name of Food Item and Quantity for each item to be eaten\n",
    "# Create empty list to store the item and quantity values\n",
    "items = []\n",
    "# For each item take the input\n",
    "i = 0\n",
    "while i<item_count:\n",
    "    # input is a String, Separating Item name & Quantity by comma\n",
    "    (item,quantity) = input(\"Enter the Item name and Quantity e.g. rice,0.5: \").split(\",\")\n",
    "    # check if item is in the food list or not, if not prompt again to enter\n",
    "    if item not in food_list:\n",
    "        print(\"Food item name not found, please enter again!\")\n",
    "        (item,quantity) = input(\"Enter the Item name and Quantity e.g. milk,0.5: \").split(\",\")\n",
    "    # Convert the Quantity to numeric type\n",
    "    num_quantity = float(quantity)\n",
    "    # Add this item, num_quantity tuple to the Items list\n",
    "    items = items+[(item,num_quantity)]\n",
    "    # increment the i\n",
    "    i = i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('milk', 0.5), ('milk', 0.5)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of requested food items with quantity\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>itemname</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>milk</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>milk</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  itemname  quantity\n",
       "0     milk       0.5\n",
       "1     milk       0.5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Items dataframe\n",
    "items_df = pd.DataFrame(items,columns=['itemname','quantity'])\n",
    "items_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definign function 'cfp_calculator'\n",
    "def cfp_calculator(items):\n",
    "    '''A function to calculate the total carbon footprint\n",
    "    Input: A list of (food item, quantity) tuples\n",
    "    Output: Total carbon footprint of the list items\n",
    "    '''\n",
    "    # Looking for food items' carbon footprint values in cfp_food data\n",
    "    # empty list to store carbon footprints from different items\n",
    "    items_cfps = []\n",
    "    # For each item get the cfp\n",
    "    for i in items:\n",
    "        # Unpack the tuple i as item and quantity\n",
    "        (item,quantity) = i\n",
    "        # Obtain the CFP value of food item from data\n",
    "        item_cfp = cfp_food.loc[cfp_food['food']==item,:]['cfp'].values[0]\n",
    "        # Multiply it with quantity\n",
    "        item_cfp_q = quantity*item_cfp\n",
    "        # append this to the cfp list\n",
    "        items_cfps = items_cfps+[item_cfp_q]\n",
    "    # Find the total Carbon foot print of the meal\n",
    "    total_cfp = sum(items_cfps)\n",
    "    return total_cfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning total carbon footprint value to a variable\n",
    "total_cfp = cfp_calculator(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Carbon foot print of the meal is : 2.8 Kgs\n"
     ]
    }
   ],
   "source": [
    "# Printing resulted value\n",
    "print(\"Total Carbon foot print of the meal is : \"+str(round(total_cfp,2))+\" Kgs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meal_name</th>\n",
       "      <th>item_count</th>\n",
       "      <th>item_list</th>\n",
       "      <th>total_cfp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>2</td>\n",
       "      <td>[('milk', 0.5), ('milk', 0.5)]</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   meal_name  item_count                       item_list  total_cfp\n",
       "0  Breakfast           2  [('milk', 0.5), ('milk', 0.5)]        2.8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the DataFrame of Meal with Items count and Total CFP value\n",
    "meal_dict = { 'meal_name': meal_name,\n",
    "'item_count': item_count,\n",
    "'item_list' : str(items),\n",
    "'total_cfp' :total_cfp }\n",
    "meal_df = pd.DataFrame(meal_dict,index=[0])\n",
    "meal_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Writing the meal dataframe file in excel format\n",
    "meal_df.to_excel('Meal_Carbon_FootPrint.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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