{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Abstract\n",
    "When we first look at the file we can see that it is the biggest one of all, most of the data will not be used, we will create samples. Lets set an a priory goal to see if there is a connection between the countries and some of the ingredients that don't have that much NAN values. We should be careful with the path we take, it could lead us to a dead end. This may require starting the whole thing again almost from the start."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Author\n",
    "## Stefan Panev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "For the first dataset the large open food facts file is used. Due to its large size this file will not be uploaded. The data can be seen in this link: https://www.kaggle.com/openfoodfacts/world-food-facts/home"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents:\n",
    "* [Introduction](#first-bullet)\n",
    "* [Reading the Data and Preprocessing](#second-bullet)\n",
    "* [Correlation Analysis](#third-bullet)\n",
    "* [EDA - grouping and plotting](#fourth-bullet)\n",
    "* [Hypothesis testing via t test](#fifth-bullet)\n",
    "* [Conclusion](#sixth-bullet)\n",
    "* [Further Development](#seventh-bullet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the Data and Preprocessing\n",
    "The file is stored in the variable food_data, the separator tab or '\\t' has been used. At the beginning we have 356027 rows and 163 columns, the names of the columns can be viewed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2698: DtypeWarning: Columns (0,3,5,19,20,24,25,26,27,28,36,37,38,39,48) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "food_data = pd.read_csv('data\\en.openfoodfacts.org.products.txt',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(356027, 163)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>url</th>\n",
       "      <th>creator</th>\n",
       "      <th>created_t</th>\n",
       "      <th>created_datetime</th>\n",
       "      <th>last_modified_t</th>\n",
       "      <th>last_modified_datetime</th>\n",
       "      <th>product_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>...</th>\n",
       "      <th>fruits-vegetables-nuts_100g</th>\n",
       "      <th>fruits-vegetables-nuts-estimate_100g</th>\n",
       "      <th>collagen-meat-protein-ratio_100g</th>\n",
       "      <th>cocoa_100g</th>\n",
       "      <th>chlorophyl_100g</th>\n",
       "      <th>carbon-footprint_100g</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "      <th>nutrition-score-uk_100g</th>\n",
       "      <th>glycemic-index_100g</th>\n",
       "      <th>water-hardness_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3087</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>openfoodfacts-contributors</td>\n",
       "      <td>1474103866</td>\n",
       "      <td>2016-09-17T09:17:46Z</td>\n",
       "      <td>1474103893</td>\n",
       "      <td>2016-09-17T09:18:13Z</td>\n",
       "      <td>Farine de blé noir</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1kg</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>4530</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>1489069957</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>1489069957</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>Banana Chips Sweetened (Whole)</td>\n",
       "      <td>NaN</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>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4559</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>1489069957</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>1489069957</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>Peanuts</td>\n",
       "      <td>NaN</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16087</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>1489055731</td>\n",
       "      <td>2017-03-09T10:35:31Z</td>\n",
       "      <td>1489055731</td>\n",
       "      <td>2017-03-09T10:35:31Z</td>\n",
       "      <td>Organic Salted Nut Mix</td>\n",
       "      <td>NaN</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>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16094</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>1489055653</td>\n",
       "      <td>2017-03-09T10:34:13Z</td>\n",
       "      <td>1489055653</td>\n",
       "      <td>2017-03-09T10:34:13Z</td>\n",
       "      <td>Organic Polenta</td>\n",
       "      <td>NaN</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 163 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code                                                url  \\\n",
       "0   3087  http://world-en.openfoodfacts.org/product/0000...   \n",
       "1   4530  http://world-en.openfoodfacts.org/product/0000...   \n",
       "2   4559  http://world-en.openfoodfacts.org/product/0000...   \n",
       "3  16087  http://world-en.openfoodfacts.org/product/0000...   \n",
       "4  16094  http://world-en.openfoodfacts.org/product/0000...   \n",
       "\n",
       "                      creator   created_t      created_datetime  \\\n",
       "0  openfoodfacts-contributors  1474103866  2016-09-17T09:17:46Z   \n",
       "1             usda-ndb-import  1489069957  2017-03-09T14:32:37Z   \n",
       "2             usda-ndb-import  1489069957  2017-03-09T14:32:37Z   \n",
       "3             usda-ndb-import  1489055731  2017-03-09T10:35:31Z   \n",
       "4             usda-ndb-import  1489055653  2017-03-09T10:34:13Z   \n",
       "\n",
       "  last_modified_t last_modified_datetime                    product_name  \\\n",
       "0      1474103893   2016-09-17T09:18:13Z              Farine de blé noir   \n",
       "1      1489069957   2017-03-09T14:32:37Z  Banana Chips Sweetened (Whole)   \n",
       "2      1489069957   2017-03-09T14:32:37Z                         Peanuts   \n",
       "3      1489055731   2017-03-09T10:35:31Z          Organic Salted Nut Mix   \n",
       "4      1489055653   2017-03-09T10:34:13Z                 Organic Polenta   \n",
       "\n",
       "  generic_name quantity         ...         fruits-vegetables-nuts_100g  \\\n",
       "0          NaN      1kg         ...                                 NaN   \n",
       "1          NaN      NaN         ...                                 NaN   \n",
       "2          NaN      NaN         ...                                 NaN   \n",
       "3          NaN      NaN         ...                                 NaN   \n",
       "4          NaN      NaN         ...                                 NaN   \n",
       "\n",
       "  fruits-vegetables-nuts-estimate_100g collagen-meat-protein-ratio_100g  \\\n",
       "0                                  NaN                              NaN   \n",
       "1                                  NaN                              NaN   \n",
       "2                                  NaN                              NaN   \n",
       "3                                  NaN                              NaN   \n",
       "4                                  NaN                              NaN   \n",
       "\n",
       "  cocoa_100g chlorophyl_100g carbon-footprint_100g nutrition-score-fr_100g  \\\n",
       "0        NaN             NaN                   NaN                     NaN   \n",
       "1        NaN             NaN                   NaN                    14.0   \n",
       "2        NaN             NaN                   NaN                     0.0   \n",
       "3        NaN             NaN                   NaN                    12.0   \n",
       "4        NaN             NaN                   NaN                     NaN   \n",
       "\n",
       "  nutrition-score-uk_100g glycemic-index_100g water-hardness_100g  \n",
       "0                     NaN                 NaN                 NaN  \n",
       "1                    14.0                 NaN                 NaN  \n",
       "2                     0.0                 NaN                 NaN  \n",
       "3                    12.0                 NaN                 NaN  \n",
       "4                     NaN                 NaN                 NaN  \n",
       "\n",
       "[5 rows x 163 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['code', 'url', 'creator', 'created_t', 'created_datetime',\n",
       "       'last_modified_t', 'last_modified_datetime', 'product_name',\n",
       "       'generic_name', 'quantity', 'packaging', 'packaging_tags', 'brands',\n",
       "       'brands_tags', 'categories', 'categories_tags', 'categories_en',\n",
       "       'origins', 'origins_tags', 'manufacturing_places',\n",
       "       'manufacturing_places_tags', 'labels', 'labels_tags', 'labels_en',\n",
       "       'emb_codes', 'emb_codes_tags', 'first_packaging_code_geo', 'cities',\n",
       "       'cities_tags', 'purchase_places', 'stores', 'countries',\n",
       "       'countries_tags', 'countries_en', 'ingredients_text', 'allergens',\n",
       "       'allergens_en', 'traces', 'traces_tags', 'traces_en',\n",
       "       'serving_size', 'no_nutriments', 'additives_n', 'additives',\n",
       "       'additives_tags', 'additives_en', 'ingredients_from_palm_oil_n',\n",
       "       'ingredients_from_palm_oil', 'ingredients_from_palm_oil_tags',\n",
       "       'ingredients_that_may_be_from_palm_oil_n',\n",
       "       'ingredients_that_may_be_from_palm_oil',\n",
       "       'ingredients_that_may_be_from_palm_oil_tags', 'nutrition_grade_uk',\n",
       "       'nutrition_grade_fr', 'pnns_groups_1', 'pnns_groups_2', 'states',\n",
       "       'states_tags', 'states_en', 'main_category', 'main_category_en',\n",
       "       'image_url', 'image_small_url', 'energy_100g',\n",
       "       'energy-from-fat_100g', 'fat_100g', 'saturated-fat_100g',\n",
       "       '-butyric-acid_100g', '-caproic-acid_100g', '-caprylic-acid_100g',\n",
       "       '-capric-acid_100g', '-lauric-acid_100g', '-myristic-acid_100g',\n",
       "       '-palmitic-acid_100g', '-stearic-acid_100g', '-arachidic-acid_100g',\n",
       "       '-behenic-acid_100g', '-lignoceric-acid_100g', '-cerotic-acid_100g',\n",
       "       '-montanic-acid_100g', '-melissic-acid_100g',\n",
       "       'monounsaturated-fat_100g', 'polyunsaturated-fat_100g',\n",
       "       'omega-3-fat_100g', '-alpha-linolenic-acid_100g',\n",
       "       '-eicosapentaenoic-acid_100g', '-docosahexaenoic-acid_100g',\n",
       "       'omega-6-fat_100g', '-linoleic-acid_100g', '-arachidonic-acid_100g',\n",
       "       '-gamma-linolenic-acid_100g', '-dihomo-gamma-linolenic-acid_100g',\n",
       "       'omega-9-fat_100g', '-oleic-acid_100g', '-elaidic-acid_100g',\n",
       "       '-gondoic-acid_100g', '-mead-acid_100g', '-erucic-acid_100g',\n",
       "       '-nervonic-acid_100g', 'trans-fat_100g', 'cholesterol_100g',\n",
       "       'carbohydrates_100g', 'sugars_100g', '-sucrose_100g',\n",
       "       '-glucose_100g', '-fructose_100g', '-lactose_100g', '-maltose_100g',\n",
       "       '-maltodextrins_100g', 'starch_100g', 'polyols_100g', 'fiber_100g',\n",
       "       'proteins_100g', 'casein_100g', 'serum-proteins_100g',\n",
       "       'nucleotides_100g', 'salt_100g', 'sodium_100g', 'alcohol_100g',\n",
       "       'vitamin-a_100g', 'beta-carotene_100g', 'vitamin-d_100g',\n",
       "       'vitamin-e_100g', 'vitamin-k_100g', 'vitamin-c_100g',\n",
       "       'vitamin-b1_100g', 'vitamin-b2_100g', 'vitamin-pp_100g',\n",
       "       'vitamin-b6_100g', 'vitamin-b9_100g', 'folates_100g',\n",
       "       'vitamin-b12_100g', 'biotin_100g', 'pantothenic-acid_100g',\n",
       "       'silica_100g', 'bicarbonate_100g', 'potassium_100g',\n",
       "       'chloride_100g', 'calcium_100g', 'phosphorus_100g', 'iron_100g',\n",
       "       'magnesium_100g', 'zinc_100g', 'copper_100g', 'manganese_100g',\n",
       "       'fluoride_100g', 'selenium_100g', 'chromium_100g',\n",
       "       'molybdenum_100g', 'iodine_100g', 'caffeine_100g', 'taurine_100g',\n",
       "       'ph_100g', 'fruits-vegetables-nuts_100g',\n",
       "       'fruits-vegetables-nuts-estimate_100g',\n",
       "       'collagen-meat-protein-ratio_100g', 'cocoa_100g', 'chlorophyl_100g',\n",
       "       'carbon-footprint_100g', 'nutrition-score-fr_100g',\n",
       "       'nutrition-score-uk_100g', 'glycemic-index_100g',\n",
       "       'water-hardness_100g'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names = ['nutrition-score-fr_100g', 'calcium_100g', 'potassium_100g','vitamin-pp_100g','vitamin-b2_100g','vitamin-b1_100g','vitamin-c_100g','vitamin-a_100g',\n",
    "'sodium_100g', 'salt_100g','iron_100g', 'calcium_100g', 'proteins_100g', 'fiber_100g', 'sugars_100g', 'carbohydrates_100g', 'cholesterol_100g','trans-fat_100g', \n",
    "'polyunsaturated-fat_100g', 'monounsaturated-fat_100g', 'saturated-fat_100g', 'fat_100g', 'energy_100g', 'image_small_url', 'pnns_groups_2', 'pnns_groups_1',\n",
    "'nutrition_grade_fr', 'ingredients_that_may_be_from_palm_oil_tags', 'ingredients_that_may_be_from_palm_oil_n', 'ingredients_from_palm_oil_n', 'additives', 'serving_size', 'traces', \n",
    "'allergens', 'ingredients_text', 'countries', 'stores', 'purchase_places', 'labels',\n",
    "'manufacturing_places', 'origins_tags', 'categories', 'brands', 'packaging', 'quantity', 'generic_name', 'product_name', 'last_modified_datetime', 'created_datetime',\n",
    "'creator', 'code', 'url']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "food_data = food_data[names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Ferme t'y R'nao</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1kg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Farine de blé noir</td>\n",
       "      <td>2016-09-17T09:18:13Z</td>\n",
       "      <td>2016-09-17T09:17:46Z</td>\n",
       "      <td>openfoodfacts-contributors</td>\n",
       "      <td>3087</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Banana Chips Sweetened (Whole)</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>4530</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.63500</td>\n",
       "      <td>...</td>\n",
       "      <td>Torn &amp; Glasser</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peanuts</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>4559</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.143</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>0.482</td>\n",
       "      <td>1.22428</td>\n",
       "      <td>...</td>\n",
       "      <td>Grizzlies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Organic Salted Nut Mix</td>\n",
       "      <td>2017-03-09T10:35:31Z</td>\n",
       "      <td>2017-03-09T10:35:31Z</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>16087</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>...</td>\n",
       "      <td>Bob's Red Mill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Organic Polenta</td>\n",
       "      <td>2017-03-09T10:34:13Z</td>\n",
       "      <td>2017-03-09T10:34:13Z</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>16094</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   nutrition-score-fr_100g  calcium_100g  potassium_100g  vitamin-pp_100g  \\\n",
       "0                      NaN           NaN             NaN              NaN   \n",
       "1                     14.0         0.000             NaN              NaN   \n",
       "2                      0.0         0.071             NaN              NaN   \n",
       "3                     12.0         0.143             NaN              NaN   \n",
       "4                      NaN           NaN             NaN              NaN   \n",
       "\n",
       "   vitamin-b2_100g  vitamin-b1_100g  vitamin-c_100g  vitamin-a_100g  \\\n",
       "0              NaN              NaN             NaN             NaN   \n",
       "1              NaN              NaN          0.0214             0.0   \n",
       "2              NaN              NaN          0.0000             0.0   \n",
       "3              NaN              NaN             NaN             NaN   \n",
       "4              NaN              NaN             NaN             NaN   \n",
       "\n",
       "   sodium_100g  salt_100g                        ...                          \\\n",
       "0          NaN        NaN                        ...                           \n",
       "1        0.000    0.00000                        ...                           \n",
       "2        0.250    0.63500                        ...                           \n",
       "3        0.482    1.22428                        ...                           \n",
       "4          NaN        NaN                        ...                           \n",
       "\n",
       "            brands  packaging  quantity  generic_name  \\\n",
       "0  Ferme t'y R'nao        NaN       1kg           NaN   \n",
       "1              NaN        NaN       NaN           NaN   \n",
       "2   Torn & Glasser        NaN       NaN           NaN   \n",
       "3        Grizzlies        NaN       NaN           NaN   \n",
       "4   Bob's Red Mill        NaN       NaN           NaN   \n",
       "\n",
       "                     product_name  last_modified_datetime  \\\n",
       "0              Farine de blé noir    2016-09-17T09:18:13Z   \n",
       "1  Banana Chips Sweetened (Whole)    2017-03-09T14:32:37Z   \n",
       "2                         Peanuts    2017-03-09T14:32:37Z   \n",
       "3          Organic Salted Nut Mix    2017-03-09T10:35:31Z   \n",
       "4                 Organic Polenta    2017-03-09T10:34:13Z   \n",
       "\n",
       "       created_datetime                     creator   code  \\\n",
       "0  2016-09-17T09:17:46Z  openfoodfacts-contributors   3087   \n",
       "1  2017-03-09T14:32:37Z             usda-ndb-import   4530   \n",
       "2  2017-03-09T14:32:37Z             usda-ndb-import   4559   \n",
       "3  2017-03-09T10:35:31Z             usda-ndb-import  16087   \n",
       "4  2017-03-09T10:34:13Z             usda-ndb-import  16094   \n",
       "\n",
       "                                                 url  \n",
       "0  http://world-en.openfoodfacts.org/product/0000...  \n",
       "1  http://world-en.openfoodfacts.org/product/0000...  \n",
       "2  http://world-en.openfoodfacts.org/product/0000...  \n",
       "3  http://world-en.openfoodfacts.org/product/0000...  \n",
       "4  http://world-en.openfoodfacts.org/product/0000...  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(356027, 52)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first reduction brings as to 52 columns, but a lot of the columns contain missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gaps = food_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gaps = gaps.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "last_modified_datetime                             0\n",
       "creator                                            3\n",
       "created_datetime                                  10\n",
       "url                                               26\n",
       "code                                              26\n",
       "countries                                        275\n",
       "product_name                                   17512\n",
       "brands                                         29050\n",
       "energy_100g                                    60660\n",
       "proteins_100g                                  61866\n",
       "salt_100g                                      66288\n",
       "sodium_100g                                    66333\n",
       "ingredients_text                               72134\n",
       "ingredients_from_palm_oil_n                    72160\n",
       "ingredients_that_may_be_from_palm_oil_n        72160\n",
       "additives                                      72202\n",
       "fat_100g                                       76530\n",
       "carbohydrates_100g                             76807\n",
       "sugars_100g                                    76841\n",
       "saturated-fat_100g                             92204\n",
       "nutrition_grade_fr                            101171\n",
       "nutrition-score-fr_100g                       101171\n",
       "fiber_100g                                    135344\n",
       "serving_size                                  139406\n",
       "cholesterol_100g                              211857\n",
       "trans-fat_100g                                212584\n",
       "calcium_100g                                  214510\n",
       "calcium_100g                                  214510\n",
       "vitamin-c_100g                                214967\n",
       "iron_100g                                     215367\n",
       "vitamin-a_100g                                218321\n",
       "pnns_groups_2                                 223338\n",
       "pnns_groups_1                                 227021\n",
       "quantity                                      236742\n",
       "categories                                    252728\n",
       "packaging                                     266067\n",
       "image_small_url                               280302\n",
       "purchase_places                               289462\n",
       "labels                                        296929\n",
       "generic_name                                  298313\n",
       "stores                                        298326\n",
       "manufacturing_places                          314019\n",
       "allergens                                     318851\n",
       "traces                                        327606\n",
       "origins_tags                                  331015\n",
       "potassium_100g                                331179\n",
       "polyunsaturated-fat_100g                      332952\n",
       "monounsaturated-fat_100g                      332987\n",
       "ingredients_that_may_be_from_palm_oil_tags    341699\n",
       "vitamin-pp_100g                               344196\n",
       "vitamin-b1_100g                               344750\n",
       "vitamin-b2_100g                               345102\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a new variable named gaps, that sums the NAN values, after that the values are being sorted in an ascending order. Some of the foods don't contain the present ingredients, so this could be the reason for their absence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gaps = gaps[gaps < 101172]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names2 = gaps.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "food_data = food_data[names2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(356027, 22)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We choose to remove the columns that have more then 101172 NAN values and place them in the new variable names2. Due to the fact that the file remains large with 22 columns and 356027 rows, lets make a sample containing only the first 10000 rows to speed the computational process. The data is exported to a food.csv file it is commented out on purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "food_data = food_data.head(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#food_data.to_csv('food.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_modified_datetime</th>\n",
       "      <th>creator</th>\n",
       "      <th>created_datetime</th>\n",
       "      <th>url</th>\n",
       "      <th>code</th>\n",
       "      <th>countries</th>\n",
       "      <th>product_name</th>\n",
       "      <th>brands</th>\n",
       "      <th>energy_100g</th>\n",
       "      <th>proteins_100g</th>\n",
       "      <th>...</th>\n",
       "      <th>ingredients_text</th>\n",
       "      <th>ingredients_from_palm_oil_n</th>\n",
       "      <th>ingredients_that_may_be_from_palm_oil_n</th>\n",
       "      <th>additives</th>\n",
       "      <th>fat_100g</th>\n",
       "      <th>carbohydrates_100g</th>\n",
       "      <th>sugars_100g</th>\n",
       "      <th>saturated-fat_100g</th>\n",
       "      <th>nutrition_grade_fr</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-09-17T09:18:13Z</td>\n",
       "      <td>openfoodfacts-contributors</td>\n",
       "      <td>2016-09-17T09:17:46Z</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>3087</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>Farine de blé noir</td>\n",
       "      <td>Ferme t'y R'nao</td>\n",
       "      <td>NaN</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>2017-03-09T14:32:37Z</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>4530</td>\n",
       "      <td>US</td>\n",
       "      <td>Banana Chips Sweetened (Whole)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2243.0</td>\n",
       "      <td>3.57</td>\n",
       "      <td>...</td>\n",
       "      <td>Bananas, vegetable oil (coconut oil, corn oil ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[ bananas -&gt; en:bananas  ]  [ vegetable-oil -...</td>\n",
       "      <td>28.57</td>\n",
       "      <td>64.29</td>\n",
       "      <td>14.29</td>\n",
       "      <td>28.57</td>\n",
       "      <td>d</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>4559</td>\n",
       "      <td>US</td>\n",
       "      <td>Peanuts</td>\n",
       "      <td>Torn &amp; Glasser</td>\n",
       "      <td>1941.0</td>\n",
       "      <td>17.86</td>\n",
       "      <td>...</td>\n",
       "      <td>Peanuts, wheat flour, sugar, rice flour, tapio...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[ peanuts -&gt; en:peanuts  ]  [ wheat-flour -&gt; ...</td>\n",
       "      <td>17.86</td>\n",
       "      <td>60.71</td>\n",
       "      <td>17.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>b</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-09T10:35:31Z</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>2017-03-09T10:35:31Z</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>16087</td>\n",
       "      <td>US</td>\n",
       "      <td>Organic Salted Nut Mix</td>\n",
       "      <td>Grizzlies</td>\n",
       "      <td>2540.0</td>\n",
       "      <td>17.86</td>\n",
       "      <td>...</td>\n",
       "      <td>Organic hazelnuts, organic cashews, organic wa...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[ organic-hazelnuts -&gt; en:organic-hazelnuts  ...</td>\n",
       "      <td>57.14</td>\n",
       "      <td>17.86</td>\n",
       "      <td>3.57</td>\n",
       "      <td>5.36</td>\n",
       "      <td>d</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-09T10:34:13Z</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>2017-03-09T10:34:13Z</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>16094</td>\n",
       "      <td>US</td>\n",
       "      <td>Organic Polenta</td>\n",
       "      <td>Bob's Red Mill</td>\n",
       "      <td>1552.0</td>\n",
       "      <td>8.57</td>\n",
       "      <td>...</td>\n",
       "      <td>Organic polenta</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[ organic-polenta -&gt; en:organic-polenta  ]  [...</td>\n",
       "      <td>1.43</td>\n",
       "      <td>77.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  last_modified_datetime                     creator      created_datetime  \\\n",
       "0   2016-09-17T09:18:13Z  openfoodfacts-contributors  2016-09-17T09:17:46Z   \n",
       "1   2017-03-09T14:32:37Z             usda-ndb-import  2017-03-09T14:32:37Z   \n",
       "2   2017-03-09T14:32:37Z             usda-ndb-import  2017-03-09T14:32:37Z   \n",
       "3   2017-03-09T10:35:31Z             usda-ndb-import  2017-03-09T10:35:31Z   \n",
       "4   2017-03-09T10:34:13Z             usda-ndb-import  2017-03-09T10:34:13Z   \n",
       "\n",
       "                                                 url   code countries  \\\n",
       "0  http://world-en.openfoodfacts.org/product/0000...   3087     en:FR   \n",
       "1  http://world-en.openfoodfacts.org/product/0000...   4530        US   \n",
       "2  http://world-en.openfoodfacts.org/product/0000...   4559        US   \n",
       "3  http://world-en.openfoodfacts.org/product/0000...  16087        US   \n",
       "4  http://world-en.openfoodfacts.org/product/0000...  16094        US   \n",
       "\n",
       "                     product_name           brands  energy_100g  \\\n",
       "0              Farine de blé noir  Ferme t'y R'nao          NaN   \n",
       "1  Banana Chips Sweetened (Whole)              NaN       2243.0   \n",
       "2                         Peanuts   Torn & Glasser       1941.0   \n",
       "3          Organic Salted Nut Mix        Grizzlies       2540.0   \n",
       "4                 Organic Polenta   Bob's Red Mill       1552.0   \n",
       "\n",
       "   proteins_100g           ...             \\\n",
       "0            NaN           ...              \n",
       "1           3.57           ...              \n",
       "2          17.86           ...              \n",
       "3          17.86           ...              \n",
       "4           8.57           ...              \n",
       "\n",
       "                                    ingredients_text  \\\n",
       "0                                                NaN   \n",
       "1  Bananas, vegetable oil (coconut oil, corn oil ...   \n",
       "2  Peanuts, wheat flour, sugar, rice flour, tapio...   \n",
       "3  Organic hazelnuts, organic cashews, organic wa...   \n",
       "4                                    Organic polenta   \n",
       "\n",
       "   ingredients_from_palm_oil_n ingredients_that_may_be_from_palm_oil_n  \\\n",
       "0                          NaN                                     NaN   \n",
       "1                          0.0                                     0.0   \n",
       "2                          0.0                                     0.0   \n",
       "3                          0.0                                     0.0   \n",
       "4                          0.0                                     0.0   \n",
       "\n",
       "                                           additives  fat_100g  \\\n",
       "0                                                NaN       NaN   \n",
       "1   [ bananas -> en:bananas  ]  [ vegetable-oil -...     28.57   \n",
       "2   [ peanuts -> en:peanuts  ]  [ wheat-flour -> ...     17.86   \n",
       "3   [ organic-hazelnuts -> en:organic-hazelnuts  ...     57.14   \n",
       "4   [ organic-polenta -> en:organic-polenta  ]  [...      1.43   \n",
       "\n",
       "  carbohydrates_100g  sugars_100g  saturated-fat_100g  nutrition_grade_fr  \\\n",
       "0                NaN          NaN                 NaN                 NaN   \n",
       "1              64.29        14.29               28.57                   d   \n",
       "2              60.71        17.86                0.00                   b   \n",
       "3              17.86         3.57                5.36                   d   \n",
       "4              77.14          NaN                 NaN                 NaN   \n",
       "\n",
       "   nutrition-score-fr_100g  \n",
       "0                      NaN  \n",
       "1                     14.0  \n",
       "2                      0.0  \n",
       "3                     12.0  \n",
       "4                      NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x170247642e8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WvMzff/Qvjm/+aPwou9Id35xat4uVrUawOnwMVR69HYdPkZyKNO+6yOdHaknr/+D04/dx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wy8AXz36Y+0bfgCbRu+wLL5K2n+tGsQqdxDyb+nCQ89ycULFyn3kOstZPOnm7JiwSoA8vq5Pp83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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f9f95ccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.heatmap(food_data.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have created a heatmap with the correlation matrix, some of the variables are interchangeable, which have a correlation coefficient of 1 like salt and sodium. One of it is derived by the other. Now we will use scatter plot to view the data points with high correlation coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'nutrition-score-fr_100g')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IrHaffyGFc5XgN61x3/rLue4bT0Q2UirkcyNz2MuHsX39AyPHzrfBhqsX0rOo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\nlLTvkJgd1e8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f96a82940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(food_data['energy_100g'], food_data['nutrition-score-fr_100g'])\n",
    "plt.xlabel('energy_100g')\n",
    "plt.ylabel('nutrition-score-fr_100g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is definitely a link here between the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'fat_100g')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9CSQdqrWYrtAqehGJ1/ID1zNveUIBIqWqtZiu0Cp6EYmXuiBhZmeb2QYze8nM\nemp5rrl3PMnGbe/W8hRSpmoupiu0il5E4qWqu8nM2oDvAjOBLcBTZvawuz9fzfNM7FlSzbeTGhjY\nO8h3H98Y2R0U9fvblGedxdGdHZHpVJwgo6+yy6afxpSGq+e/R9paEp8AXnL3V9x9D3APcH41T6AA\n0Tg2bnuXmbc8Maws7veX7/c68Yj4bqu7Vm3mut71ZdVP6iMzptQfTijJjCm1apqcev97pC1IdAGv\nZz3eEpZJi6pGd+CqV3bmff7u1a/nfV6SpTGl4er975G2IBG1P+WI2ahmdpWZ9ZlZ3/bt2+tQLWlk\nhfbG1t7Z6aYxpeHq/e+RtiCxBcjeo/IYYGvuQe5+u7t3u3v32LFj61Y5aUyF9sbW3tnpli8zcyuq\n979H2oLEU8BkM5tkZqOBS4CHE66TJGjyuIMqfo9Ce2Nr7+x0y5eZuRXV+98jVUHC3d8HvgQsBV4A\n7nX356p5jnyzYCRdJo87iGXXnDGsLO73l+/3euOcKVw2fcKIFkObGZdNn6DZTSk3Z1oXCy6YolTy\noXr/e5g3eH9sd3e39/X1JV0NEZGGYmZr3L270HGpakmIiEi6KEiIiEgsBQkREYmlICEiIrEUJERE\nJFbDz24ys+3Aa2W+/Ejg91WsTlo043U14zVBc15XM14TNN91HefuBVcjN3yQqISZ9RUzBazRNON1\nNeM1QXNeVzNeEzTvdRWi7iYREYmlICEiIrFaPUjcnnQFaqQZr6sZrwma87qa8Zqgea8rr5YekxAR\nkfxavSUhIiJ5tGyQMLOzzWyDmb1kZj1J1ycfM/uxmW0zs2ezysaY2TIz2xj+PDwsNzP7Tnhdz5jZ\nx7Nec3l4/EYzuzyJa8mqy7Fm9riZvWBmz5nZ34fljX5dB5jZr83s6fC6bgjLJ5nZ6rCOi8NU+JjZ\n/uHjl8LnJ2a91/ywfIOZzUrmij5gZm1mttbMHgkfN8M1bTKz9Wa2zsz6wrKG/husOndvuRvQBrwM\nHA+MBp4GTkq6Xnnq+5fAx4Fns8r+B9AT3u8BvhXePxf4vwS7/E0HVoflY4BXwp+Hh/cPT/CajgI+\nHt4/BPgtcFITXJcBB4f324HVYX3vBS4Jy38AfDG8/1+AH4T3LwEWh/dPCv8u9wcmhX+vbQn/HV4D\n/Bx4JHzcDNe0CTgyp6yh/warfWvVlsQngJfc/RV33wPcA5yfcJ1iufsvgR05xecDd4b37wTmZJX/\nzAOrgE4zOwqYBSxz9x3uvhNYBpxd+9pHc/c33P034f13CPYP6aLxr8vd/Y/hw/bw5sCZwP1hee51\nZa73fuAsM7Ow/B53/5O7vwoHIf+FAAAFWElEQVS8RPB3mwgzOwaYDfwwfGw0+DXl0dB/g9XWqkGi\nC3g96/GWsKyRjHf3NyD4wAXGheVx15baaw67I6YRfOtu+OsKu2XWAdsIPjBeBnZ5sKkWDK/jUP3D\n598CjiB913Ub8FVgX/j4CBr/miAI4L8wszVmdlVY1vB/g9W0X9IVSEjUpsbNMs0r7tpSec1mdjDw\nAHC1u79t8ftNN8x1ufsgMNXMOoF/Az4adVj4M/XXZWafBra5+xozOyNTHHFow1xTlhnuvtXMxgHL\nzOzFPMc20nVVTau2JLYA2RsbHwNsTagu5XozbOoS/twWlsddW+qu2czaCQLEInd/MCxu+OvKcPdd\nwBME/dedZpb5UpZdx6H6h88fRtC1mKbrmgGcZ2abCLpmzyRoWTTyNQHg7lvDn9sIAvonaKK/wWpo\n1SDxFDA5nJ0xmmBw7eGE61Sqh4HMLIrLgYeyyv8mnIkxHXgrbDIvBf7KzA4PZ2v8VViWiLCP+kfA\nC+5+S9ZTjX5dY8MWBGbWAXyKYLzlceDC8LDc68pc74XACg9GQx8GLglnCk0CJgO/rs9VDOfu8939\nGHefSPB/ZYW7z6WBrwnAzA4ys0My9wn+dp6lwf8Gqy7pkfOkbgQzFX5L0F98bdL1KVDXu4E3gL0E\n31quIOjjXQ5sDH+OCY814Lvhda0HurPe528JBgtfAj6f8DX9BUGT/BlgXXg7twmu62RgbXhdzwL/\nLSw/nuAD8SXgPmD/sPyA8PFL4fPHZ73XteH1bgDOSfrvMKzTGXwwu6mhryms/9Ph7bnM50Cj/w1W\n+6YV1yIiEqtVu5tERKQIChIiIhJLQUJERGIpSIiISCwFCRERiaUgISIisRQkRBJiESngw/KSU1WL\n1IqChEgJstJQVMNPic4W2gMsd/fJBIu5MvudnEOwSnkycBXw/SrWRSSSgoQ0NTO7zIJNgNaZ2b+E\nGVr/aGY3WbAx0CozGx8eO9bMHjCzp8LbjLD8ejO73cx+AfzMzA40s3vDb/OLLdhYp9vMrjCzW7PO\nfaWZ3RJTNTw6BTyUmKrazEaZ2fcs2OToETN71MwujHhfkZIpSEjTMrOPAhcTZPqcCgwCc4GDgFXu\nfgrwS+DK8CXfBm51938PfIZw74TQqcD57v45gk11drr7ycA3w+cgSH53Xpi4EODzwE/KqHqpqaov\nACYCU4AvAKeXcU6RSK2aKlxaw1kEH+BPhSnIOwgyeu4BHgmPWQPMDO9/CjgpK135oZkEcMDD7j4Q\n3v8LgoCCuz9rZs+E9981sxXAp83sBaDd3ddX8XriUlL/BXCfu+8Dfmdmj1fxnNLiFCSkmRlwp7vP\nH1Zo9o/+QdKyQT74fzAKOD0rGGSOB3g3533j/BD4OvAi5bUiIExV7e5vFJmqOl99RCqi7iZpZsuB\nC8MNZTKzho7Lc/wvgC9lHpjZ1JjjfgVcFB5zEkE3DwDuvprgg/xzBNl7y1FqqupfAZ8JxybGE2Rq\nFakKBQlpWu7+PHAdwfaUzxBsJXpUnpd8GegOB6SfB/5zzHHfA8aG7/k1grTgb2U9fy+w0oP9jmOZ\n2d3Ak8CJZrbFzK4In1oIzDSzjQRdYQvD8keBVwjSUd9BMDYCwcZNWwhSk/8LwTaw2fURKZtShYuU\nyMzaCMYb3jOzEwhaLP/O3feEzz9CMAC+vI51Otjd/2hmRxDs4TDD3X9Xr/NL89KYhEjpDgQeD2cx\nGfBFd98T7kj3a+DpegaI0CPh+UcD31SAkGpRS0KkhsJv9lEB4yx3/0O96yNSKgUJERGJpYFrERGJ\npSAhIiKxFCRERCSWgoSIiMRSkBARkVj/H/hnynYLRylJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17024764630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(food_data['energy_100g'], food_data['fat_100g'])\n",
    "plt.xlabel('energy_100g')\n",
    "plt.ylabel('fat_100g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we have some outliers in the bottom right and the upper left, but still there is a link in the majority of the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'fat_100g')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dizo4/6SOkTTR7JrRV33o7aEXtVo4ec40rlv+rpocO2kDBwaZe9k99D+7j2vP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f98ab12e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(food_data['saturated-fat_100g'], food_data['fat_100g'])\n",
    "plt.xlabel('saturated-fat_100g')\n",
    "plt.ylabel('fat_100g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is some connection in the beginning near the start of the coordinate system, but later there seems to be a dispersal of the points. This is a sing of Heteroscedasticity. For more information you can read here http://www.statsmakemecry.com/smmctheblog/confusing-stats-terms-explained-heteroscedasticity-heteroske.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'carbohydrates_100g')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A6N9HVEMbA0H6K83ilUupz2MLziqpGCu5UR9EWkmIwyLgdJwy34/jdJIz6oSl\n7589NjAUJn9VQlAGslE+lSiI3rmdJWf7UfI3yplASNHPsOsXr1z88ily2cxYKfBbli4IbElq1J6S\nKwkR+TDw34DnAK94+2nAu0TkP6rqT1KUz4iIlwvRc7qT1JYE1cx7CMOL8692XkK5CDA12xKrp0UU\nrujpZuuLB0Kfy+J3zgg9R6kqxUFowc+w1dCou5IppFQzIGsWVN+UzJMQkV8Cf6iqu4u2nwE8qKq/\nk554pWnkPIlyyIUMTrlspqoO7SSppD9zo5AR4Z0z20N9OzPas7S3tQYOtkl93oOSJ4tb8xr1S5J5\nEq3AKz7bB4HkQziMigibvXpZt5ORpBREipVCUmdEledLOP8PHM6Pc0ivcDsHFjZsSkoWMxE1B1GU\nxLeBn4vI50XkT9x/nwe2MQk7x01mMiL0zu0sq15ONWsN1SsZkVRCU6tJXAOgt//g0DAr730yMTm8\nqKmwKCqjMSjpk1DVdSLyQ+BjwHk4JslXgKtU1UJfq8hXrzyXvoVdoWUQWgSmtE40K42osvr+AebN\nmtZUoaiFjKhOqlIaSZMf1UQi1rwVQ6koKqMxiBQC6yqDX4hIp/NSw+PwjFQoLH/QIv75CaNKoN9h\nOD/C4WOj9M7tjJyYZ9QflQz0ChVPFI7kR0w5NBElzU0i0i0id4vI6zgmpv8jIq+72+akLWCjUY6p\nyMNr/L7hoWfLzibeOzTMxmvOS6y+UKVlyI145LIZfm9uZ0VlPx6+/nxuXbqgbP+MAu9a3VgBH0Yw\nUXwSm4AfAKeo6jxVnQecAvQDd6cpXCNSqfN479BwRZmoxTV8KiEjwmgta180IeuWzGf3b4bLXkl4\n3foqmWgAoc2njMYiipJ4u6puUtUxG4aqjqjq3ThF/4wY9C3sqmhwPrUjV3Ymql8Nn3JlyWUzfPXK\ncy0ju4p0deToW9hV9iQhmxFu/KiTlW8lL4yoRPFJ7BCR/wbcAbzsbpsNXA3sTEuwRsSLMPIqbm7c\ntid24/o5J+W4oqd7QuOhMIobt3iNYrxY+nJs1MP5kUmT3NYorLzoTNb0D5StmPMjyg33PMm92/eU\nbI5lGB5RlMQngc8Aa3H6SAiOsvgRFgIbi8JsWC8zOm5y25YX9k9wOnd15PjNwSO+7UULu4jBxM52\naSWonTy9jdcOHiu9oxGZz9/3FEePV5bFPaKaSNBCq7mimoYoIbDHgG+4/4wK2PLCfhZ+6Seokmiv\nh30Hj/D3l5/LF/sHxvXGLlYQkF5xNy8D18sKfnHf4cSv0eyEKYiTp7fRmslUJSu9VeD5ddYxrlmI\nXAXWDxH5oqp+KSlhmoE04vSUuXRVAAAfM0lEQVSPjTg5EFGSmdIaRF5YdwlQvZ7axniqsWpbHtBH\n3WhsKlISwJ8BpiTqAC88tpSSCKq5UylnrNrMqR05c4g2IH691o3mIUoV2DeD/gRYwfc6IsoAHVVB\neM7uvUPDtLZAqYKmXq2gpAkrWJgW1kfjLbIZYcPl51ryXBMTZSUxBLxPVV8r/oOIvOyzf8OQjTA4\n1hNRQmOjVlO9qsi00L9zkLU/erqqZS2WL+6m5/TOqkdRmYJ4i/yIRlqhGo1LlDyJ7+I0G/LjewnK\nUnfMOqE+FkoC3Lp0AbcuXcCUVv9HFrUCp18DGD+KTQt9C7u48aNnk82UF9YyrS1+pvmdW/ew+v6n\nyrpeKazgYXTMhNjcRIluWhPyt897v4vI2ar6dFKC1QP18OUQnFm9l9/Q2iIc9dnn4++dWGxtTf8A\nd217eSzqqNCuHDY797Jyi9nw0LPkfcJso5DNtJDNjMY+Pg1TU+/cTs6Y+TZzsEfE2og2N5U6rgv5\n/4D3RN1ZRDqAbwK/i7PC/1PgWZwyIHOA3cCVtSwmWGuzQ0cuO9a3esHanwSGzSqw+alXefSZfWMJ\ncnNOyo2Lhx9RHRsUt734m9DrHjic56rbH2fjNeeN216JzyHJkN8k6Dm905SED8WNqaxHhJGkkoi7\nfv868GNVvVxE2oB24K+Bn6rqehFZBawCPh92kkbm6PFRtr+0P1LS3YHD+TF/weDQcOCAHnVg3PLC\nftb0D4wzO6UVGVVt/BISm5WMyFgJemBCNr61ETWSVBKRRw8ROQH4APApGEvYOyYilwHnu7vdATxG\nEyuJ4fzImLmoFty5dQ89p3eODRKNoCCM8RQqCMB6RBgTiOK4ToN3AvuA74jIThH5pohMA05W1VcB\n3J+z/A4WkWtFZLuIbN+3b1/1pK4BtR6YV98/MNb2ssts0w1DtsUJhjCFYJQiSSURJ+WzFcd/8Q1V\nXQgcwjEtRUJVb1PVHlXtmTlzZkwxJxdRonBy2fR0vZekB05kVNb6R5RNLttSUT+RSunqyHHr0gXs\nXn8pz/3tpaYgjEhEHl1EpNed7SMiy0XkFhEZC41V1cUxrvsK8IqqbnNffx9HabwmIqe41zgFeD3G\nORuObIswNUQBdOSyLF/czZRW/4EnqfF8cGh4bDVRUbebJmc4P4rUKBxi3qxpbFl1gSkGIzZxpqDf\nAA6LyLnA54CXcHIoYqOqvwZeFhEvbOJDwC+AB3BKkOP+/GE5528ExP3v0LHxDutsC3iLizeP5Pne\n1j2BkUOjmlw+wHWbdnHdpl2+IayttrqIzOEaZGfOmzWNh68/v+rXNRqDOI7r46qqrnP566r6LRG5\nuuRRwfwlsNGNbHoR+DSO0rpHRD4D7AGuqOD8kxoF3wG5cIyJ0llsRHVCWGM2I4yMKEkNV8etO13d\nkctmIhV8NIxSxFlJHBSR1cAngM0ikgH8s64ioKq7XL/COarap6oHVPU3qvoht03qh1TV4hQrpKsj\nx7ol8+nqyCHu66Xvm02mzMxpo34pfMamIIykiLOSWAr8CfCnqvprEekGNqQjlpEEXiJUcVhj7/pH\nys6cNuqXLasuqLUIRgMSeSXh+hHuA6a4m/4N+EEaQhmVE1SqA9LrKWEYRuMRJ7rpGpwopP/ubuoC\n+tMQyphIV0eO3rmdkfdX4NFnJuaQ9O8ctAClBmTerGm1FsFoUOL4JP4C6AXeBFDV5whIdjOSRXBM\nCRuvOY/li7sjHzcudNVlw0PP1rwmlZEsUzNi0UtGasTxSRxV1WPihlSKSCu1r4HXFFRShXOFG7ra\n5dbhScLU1Cg1nBqB3rmdEwoxGkaSxFES/yQifw3kRORC4D8CP0pHLKOQg8NOMns5/aO9oXxwaJjV\n9w/QIv6hsxkR3jmznedeP1TynKYgao8pB6NaxFESq4DPAAPAvwceVNXbU5HKGMebR50ch7u2BTcC\nzGakZMRSWCXZEVUevv583rlqc2L5E0ZydHXkLHrJqAlxfBJ/qaq3q+oVqnq5qt4uIp9NTTJjAmEz\n+A2Xn1tRAT7v2FuWLqhpfSFjItbTwaglcZSEX3b1pxKSw4hAUImNjAh9C7sitSbtyGUn7FM4CPUt\n7BpLvjNqT0bEEuOMmlLS3CQiy3CS6M4QkQcK/jQdCG9xZiTCydPbAFi2aLavT2LZotmAM8Bvf2l/\nYA+KXDYz1unOr7GM13DG8ijqg2xG2HD5uaYgjJoSxSfxM+BV4O3AVwu2HwTS6VJvjHHy9Da2feFC\ngLEucUF9q/t3DnLfjsFxCkJwnNddRV3Gigee/p2DrL5/oGQHPKM6TGlt4e8+fo4pCKPmlFQSqvoS\nTsVXC6VImcKZY2Ebyd71j4wN8Df3zR/XUrSQlffuorjIqKcuPnjWzNABZ8NDz5qCqBMscsmoJyJH\nN4nIYuAfgN8B2oAMcEhVT0hJtubDHdGLZ/Ve+CpMXAF4XHX74xMURCGemSpIwew1E1NdYN3ijHoj\nTgjsPwJ/DNwL9ACfBN6VhlDNSn5Ux7rAFc/qvQ5xQQPIlhdKF8y9a9vLY0piTf9ATftnG+NpFXh+\n3aW1FsMwJhBHSaCqz4tIRlVHcPpT/ywluZqWsBl9pbN9TyGUk5RnJE+2RdhwhTmmjfomjpI47DYI\n2iUif4/jzLaqYglzYi7LtCmtvhFGlZTngLdCaMOS8ozqkG3BFIQxKYijJD6Bk1fxn4AVwGzg42kI\n1cwcOnacj5x7CvftGBxncvJLqCp0bk9pbeHo8fBc6anZFs5YtdkKbtWY5Yu7A31DhlFvRFISbhe6\nL6vqcuAIsDZVqZqY/Ijy6DP7WLdkvm8ug0exc/vo8dHAukwexf2yjfIo9T4HccKUDE+tvTh5gQwj\nRSIpCVUdEZGZItKmqsfSFqrZGRwantBNrhi/kNVRHV/jp5LcBwG+tnQB123aFfvYRibbQmgUWRC7\n15tT2picxDE37Qa2uFnXY6VCVfWWpIVqdgRngA9TEkFO7MLtleQ+KE6ZcWM8piCMZiOOktjr/mvB\nKclhpIRCaLgrOE5sP+e2wljyXaXRUOa7KA+r2Go0EpGVhKqaH6KKDBZlWhez8qIzWfn9J33Lg3vJ\ndyfmsgwN56shruFiFVuNRiNKgb8fETKpVNWPJSqRMUbJTOuQqf5wfoSp2ThFfo04+PXv6Mhluelj\nZ1tYq9FQRFlJfMX9uQR4B3Cn+3oZjp/CSJHh/Ag33PMkMF5R3PTA0+RLhNgMHc7T0QSriXmzprHv\n4LGq3ue0tlamTWkNjD4zjEYhSoG/fwIQkb9R1Q8U/OlHIvLPqUlmjDGiOm5F0b9zMNKA6A1eK+99\nsqRCmazMmzWNVw4cqXpxwjeG8+y68cNVvaZh1II49oiZIvJO74WInAHMTF4kww+vdhMw9jMMzzbe\nt7CLDVecS0cum7aI45jWVp3udrv/7VBNqtdWmv1uGJOFOEpiBfCYiDwmIo8BjwLWvrSKeNFKpaKW\nZrRnmdLawopNu+hd/wgAu278cFVDMauVuFdOSGqltIA5p42mIU50049FZB5wlrvpGVU9mo5Yhh/e\n7DUo/BWckg+FJT0Knd9Qfraw4eDUXLJy3kbzEKefxHbg28BdqnogPZEMPwpDK1dedCYrNu3yDW7y\nK/89nB9h7Y+e5kh+1BREBVivB6MZiWNu+mOgC/i5iNwtIheJuGVFjdQQnOSsdUvmj2s9GjTWB/WH\nOHA472u7z9gjDKWrI8etSxewe/2lpiCMpiSOuel54Asi8v8AH8FZVYyKyLeBr6tq6a43RbiFA7cD\ng6r6EdcZfjfQCTwBfKLZa0X9KsCP0BVgcsqIxGokNKo61gfbGM+8WdN4+Przay2GYdSUWNlWInIO\n8FVgA3AfcDnwJvBImdf/LPDLgtd/B3xNVecBB4DPlHneyPTO7Uz7EmVTOMvv3zlI7/pHOGPVZnrX\nP8Kck3L4rQGmtArZzPi/5LKZwOgmxRSEH6YgDMMhjk9iBzAEfAtYVeC03iYivXEvLCKnAZcCXwau\nd01XFwB/4u5yB3AT8I24545DlLaftWLZotmAf8/rIMf14fwo2RZhRnuWocP5sVwJIFZF2Fw2U5PQ\n0lpjysEwxhOnwN8Vqvqi3x9UdUkZ174V+BxvFQs8CRhS1ePu61dwfCBNR0aEZYtmjzWmiVvNNT+q\ntLe1svOLE5O9opT+zog0pYI4YUrGFIRhFBFHSQyKyJ8AcwqPU9Uvxb2oiHwEeF1Vd4jI+d5mn119\nLSEici1wLUB3d3fcy9c1Aryw7pJx28qp5lp8jNfFLgpxfBqNgq0gDMOfOD6JHwKXAcdx+kl4/8qh\nF/iYiOzGcVRfgLOy6BARTwGdhlOafAKqepuq9qhqz8yZjZX07ZfJW052b+ExnrkqyERVawQnATAJ\nWsoI1lq+uNsUhGEEEGclcZqqJtJ7UVVXA6sB3JXEf1bVq0TkXhxn+N3A1TiKqWkozIUo7F99Yi7r\nW3U0CGF8RnAlzYeqQUd7lt8eOV56xwjEyQOxvAfDKE2clcTPRCTt7u2fx3FiP4/jo/hWytdLnK4K\navp4uRCFM38Fp5ifRpttC3DV4u5xg18Uc1Ut8yUOHM5XvQCh5T0YRjSi9JMYwPENtAKfFpEXgaM4\n45Gq6jmVCKCqjwGPub+/CLy/kvPVEq8j2Zr+Ae7cuif2sd6g5Tfzz48qqoTmNHQFlKzuaM9y4PDE\nqrGFHdTOWLU5lrxRuHXpAm6458m68nFYK1HDiEcUc9NHUpeiASgs+nbXtpdD9y0e6Iu7mQXN/MPK\ngwtMaJnZv3OQmx542ve4bEbGrnnhLY+lkivxhR8MmIIwjElOlH4SL3m/i8i5wL9zX/5vVX0yLcEm\nG6PA9pf207ewK3Rg3L3+0nH+Br+GNeW0HS12bq/pH2Dj1j2Bg/+0tlb6FnZx4S2P8dzr5cYfhFON\nSrC5bIYprS2+75f1mjaMyonskxCRzwIbgVnuvztF5C/TEmwy8r1te8ZKc/tRyu7fv3OQhV/6SVkd\n1g4Ov1W9pH/nYKiCAKdpDpCagqgWw/kRRBxlUYj1mjaMZIjjuP4MsEhVv6iqXwQWA9ekI9bkZFQJ\nDTNdtmj2BKf04NAwKzbtYs6qzazYtMvXdxCFN4+OcM6NPwYcn0YpI08jNc0ZOpxn3ZL5dHXkfAsi\nGoZRPnFCYAUotB+M4J8AZ/iQcQP4/Ry5WvSzXN48OkL/zsFI0UwfPGvy5JeUKkB4quv0N6VgGMkT\nR0l8B6dO0w9wvreXMQlDVGvFyKiWNAElQZSyG+A41zfGjMCqFUp4dVszKxlGesQpFX6L27b0991N\nn1bVnalI1aDUT5xPfZXemDdrGq8cOBKa8Bckb+/cTltBGEaKxCoV7uKt/s3UZCTCc68f4vhI6Uio\n5Yu7x5z/GRGWL+5m4zXnpS2eYTQ1cUqFfxG4AqePhADfEZF7VfXmtIQzmof8aOl9bu6bP1YZ1zCM\n6hBnJbEMeJ+q3qSqN+JEN12VjljNQXHYZhzasy0sX9xdVkE7wzCMqMRREruBqQWvpwAvJCpNEzGj\nPcu6JeXPihXY9POXIxW0K1Qk7dmWsmyMhmE0J1FqN/0Dzph0FHhaRB52X18I/Eu64jUuR1z7Stye\n1B7DUewzLoWK5HCM4+oJK6lhGLUhik9iu/tzB/CDgu2PJS5NEzGcH2HDQ8+ybNHs2MUAmwEr420Y\n9UGU2k13iEgGuENVl1dBpqZhcGiYO7fuKZks1mxYWKth1A+RzNOqOgLMFJG2lOVpSjwFMTXT3F5o\nC2s1jPojTsb1bmCLiDxAQdtSVb0laaGalSMRO881GrlsxmotGUadEkdJ7HX/tQDT0xGn+mRbosXo\nG8mRbRHeNrWVocN531LphmHUD3HKcqxNU5BacbxBJu8zArrP1SMbrjjXlIJhTBLiZFzPBD4HnE1B\nvoSqTuquLnVUwogWYEo2E1rDKIj2ttZJoyRMQRjG5CFOXtVG4BngDGAtjo/i5ynI1LSMAh9/b3kD\naFgfC8MwjHKJoyROUtVvAXlV/SdV/VOc0hxGgjz6zL6yopxKdb2rF06YUn4pEsMwqk8cJeHZMl4V\nkUtFZCFwWgoyNTV7h4Y5afrU0jsWkMtm6qr0dxAnTMnw1NqLay2GYRgxiBPddLOInAjcAPwDcAJw\nXSpSNTFeS9OotGdb+Nsl89nw0LN1aXKyzGnDmNzEWUlcAYiq/quqfhCndtMfpSOWEZXD+VG2v7Sf\nOSfVX89qy5w2jMlPnJXEOao65L1Q1f2uycmoMfVY+6l3bqdlThtGAxBHSbSIyAxVPQAgIp0xjzca\nHMucNozGI84g/1XgZyLyfRzT+ZXAl1ORyph0CJiCMIwGJE7G9XdFZDtwAc6YsERVf5GaZMak4mvm\noDaMhiSWuchVCqYYjDFaW4SvWJkNw2hYzKdglMXyxd3c3Fd++1XDMCYHpiSM2Fjug2E0DzVREiIy\nG/gu8A6ckkW3qerX3YipTcAcnNpQV3rRVEZ9YL2mDaO5iJNMlyTHgRtU9Xdw6j/9hYi8G1gF/FRV\n5wE/dV8bNSYjzurBFIRhNB81WUmo6qvAq+7vB0Xkl0AXcBlwvrvbHcBjwOdrIKLhYklxhtHc1Nwn\nISJzgIXANuBkV4Ggqq+KyKyAY64FrgXo7u6ujqBNiPkeDMOoqZIQkbcB9wHXqeqbErHctareBtwG\n0NPTU//lTycZAvzKTEuGYVBDJSEiWRwFsVFV73c3vyYip7iriFOA12slXzMyb9Y0Hr7+/FqLYRhG\nHVGr6CYBvgX8UlVvKfjTA8DVwHr35w9rIF7NOGFKhjePxm9dmgTmlDYMw49arSR6gU8AAyKyy932\n1zjK4R4R+QywB6c8edNQbQVhBfkMwyhFraKb/gXH9O3Hh6opS7PS1ZFj5UVnmoIwDCOUmkc3GdXH\nSmoYhhGVWiXTGTXihCkZUxCGYUTGlEQTMW/WNJ5ae3GtxTAMYxJh5qYGx3IeDMOoBFtJNDimIAzD\nqARTEg3KydPbLPfBMIyKMXNTgzE1Izzz5UtqLYZhGA2CrSQaiHmzppmCMAwjUWwl0SCYackwjDSw\nlcQkx3wPhmGkiSmJSU5rJlNrEQzDaGBMSUxy9g4N11oEwzAaGFMSk5xTO3K1FsEwjAbGlMQkYfni\nbnLZ8aalXDbDyovOrJFEhmE0AxbdNAnwHNM9p3ey4aFn2Ts0zKlW6tswjCpgSqLOKYxc6lvYZUrB\nMIyqYkqiTrGwVsMw6gFTEnWGKQfDMOoJc1zXCa1iCsIwjPrDlEQdMDUjPL/OFIRhGPWHmZtqyAlT\nMtYpzjCMusZWEjXi5OltpiAMw6h7bCVRZebNmsbD159fazEMwzAiYUqiity6dIHlORiGMaloSiXR\nv3NwLHO5WvTO7TQFYRjGpKPpfBL9OwdZff8Ag0PDaBWvu+WF/Vx1++NVvKJhGEblNJ2S2PDQswzn\nR2py7S0v7K/JdQ3DMMql6ZSE9V8wDMOITtMpibT7L0iqZzcMw6guTackVl505oS+DElyakeO3rmd\nvn8L2m4YhlGv1J2SEJGLReRZEXleRFYlff6+hV2sWzKfro5c4rN+rwnQxmvOm6AQeud2svGa8xK+\nomEYRrqIajVjfMIRkQzwf4ELgVeAnwPLVPUXQcf09PTo9u3by77mnFWbyz52+eJuHn1mH3uHhjkx\nl0UEhg7nyWVbGD4+iipkRFi2aDY3980v+zpJs6Z/gLu2vcyIal3KZxhG+ojIDlXtKbVfva0k3g88\nr6ovquox4G7gshrL5Esum6Hn9E62rLqAry1dwNHjoxw4nEeBw3lHQQCMqHLn1j2s6R+oqbwea/oH\nuHPrHkZcAetNPsMw6ot6UxJdwMsFr19xt9Udw/kRNjz0LBAtrPaubS+H/r1aBMlRL/IZhlFf1JuS\n8HMTTLCHici1IrJdRLbv27evCmL544XTRgmrHakTs16QHPUin2EY9UW9KYlXgNkFr08D9hbvpKq3\nqWqPqvbMnDmzasIV44XTRgmrzUh9BMcGyVEv8hmGUV/Um5L4OTBPRM4QkTbgj4EHaiyTL14kE0QL\nq122aHbo36tFkBz1Ip9hGPVFXSkJVT0O/CfgIeCXwD2q+nSa1wxqGbp8cffY7DojQu/czrGw2a6O\nHOuWzB8r2FccVtuebcGbmGdEWL64u26ih27umz/h3upJPsMw6ou6CoEth0pDYA3DMJqRyRoCaxiG\nYdQRpiQMwzCMQExJGIZhGIGYkjAMwzACMSVhGIZhBDLpo5tEZB/wUkKnezvwbwmdazJg99vY2P02\nPpXc8+mqWjIbedIriSQRke1RQsIaBbvfxsbut/Gpxj2buckwDMMIxJSEYRiGEYgpifHcVmsBqozd\nb2Nj99v4pH7P5pMwDMMwArGVhGEYhhGIKQlARC4WkWdF5HkRWVVreZJGRGaLyKMi8ksReVpEPutu\n7xSRh0XkOffnjFrLmiQikhGRnSLyP93XZ4jINvd+N7nl6BsGEekQke+LyDPusz6vkZ+xiKxwP8//\nKiJ3icjURnrGIvJtEXldRP61YJvv8xSH/+KOYU+JyHuSkqPplYSIZID/Cvwh8G5gmYi8u7ZSJc5x\n4AZV/R1gMfAX7j2uAn6qqvOAn7qvG4nP4pSc9/g74Gvu/R4APlMTqdLj68CPVfUs4Fyce2/IZywi\nXcBfAT2q+rtABqf/TCM94/8BXFy0Leh5/iEwz/13LfCNpIRoeiUBvB94XlVfVNVjwN3AZTWWKVFU\n9VVVfcL9/SDO4NGFc593uLvdAfTVRsLkEZHTgEuBb7qvBbgA+L67S6Pd7wnAB4BvAajqMVUdooGf\nMdAK5ESkFWgHXqWBnrGq/jOwv2hz0PO8DPiuOmwFOkTklCTkMCXhDJYvF7x+xd3WkIjIHGAhsA04\nWVVfBUeRALNqJ1ni3Ap8Dhh1X58EDLmNraDxnvM7gX3Ad1wT2zdFZBoN+oxVdRD4CrAHRzm8Aeyg\nsZ8xBD/P1MYxUxLg19y5IUO+RORtwH3Adar6Zq3lSQsR+QjwuqruKNzss2sjPedW4D3AN1R1IXCI\nBjEt+eHa4i8DzgBOBabhmFyKaaRnHEZqn29TEo7GLWzwfBqwt0aypIaIZHEUxEZVvd/d/Jq3JHV/\nvl4r+RKmF/iYiOzGMR9egLOy6HBNE9B4z/kV4BVV3ea+/j6O0mjUZ/wHwK9UdZ+q5oH7gd+jsZ8x\nBD/P1MYxUxLwc2CeGxXRhuP8eqDGMiWKa4//FvBLVb2l4E8PAFe7v18N/LDasqWBqq5W1dNUdQ7O\n83xEVa8CHgUud3drmPsFUNVfAy+LyJnupg8Bv6BBnzGOmWmxiLS7n2/vfhv2GbsEPc8HgE+6UU6L\ngTc8s1SlWDIdICKX4Mw0M8C3VfXLNRYpUUTk94H/DQzwlo3+r3H8EvcA3ThfuitUtdhRNqkRkfOB\n/6yqHxGRd+KsLDqBncByVT1aS/mSREQW4Djq24AXgU/jTAQb8hmLyFpgKU703k7gz3Ds8A3xjEXk\nLuB8nEqvrwE3Av34PE9XUf4jTjTUYeDTqro9ETlMSRiGYRhBmLnJMAzDCMSUhGEYhhGIKQnDMAwj\nEFMShmEYRiCmJAzDMIxATEkYhmEYgZiSMIwaICIfEJEnROS4iFxe9Ler3VLQz4nI1QXb3ysiA245\n6P/ixsYbRqqYkjCMhHDLzkdlD/Ap4HtF5+jESZpahFOh+MaCHhDfwCkD7ZWELi4jbRiJY0rCaGhE\nZJqIbBaRJ93mNEtFZLeIvN39e4+IPOb+PtNt5PKEiPx3EXmpYL9+EdnhNrm5tuD8vxWRL4nINuA8\nEVkvIr9wG798JUguVd2tqk/xVga8x0XAw6q6X1UPAA8DF7t1ek5Q1cfVyYD9Lm6ZaBF5n3u9x0Vk\nQ2GTGsOoFFMSRqNzMbBXVc91m9P8OGTfG3HqPL0H+AFO6QOPP1XV9wI9wF+JyEnu9mnAv6rqIpza\nQX8EnK2q5wA3lyFvUMnnLvf34u0A3wH+XFXPA0bKuKZhBGJKwmh0BoA/EJG/E5F/p6pvhOz7+zh1\nf1DVH+N0NvP4KxF5EtiKU21znrt9BKe6LsCbwBHgmyKyBKeGTlyCSj77bheRDmC6qv7M3fY9n/0M\no2xMSRgNjar+X+C9OMpinYh8EacgnPfZn1qwu68j2C0S+AfAeap6Lk7hOO+4I6o64l7rOI4f4T4c\nU1DYqiWIoJLPr7i/F28357WRKqYkjIZGRE4FDqvqnTidzN4D7MZRHAAfL9j9X4Ar3eM+DHgO4xOB\nA6p6WETOwukT7nettwEnquqDwHXAgjJEfgj4sIjMcB3WHwYecss+HxSRxW5U0yeBH7p+i4NueWhw\nSqMbRmK0lt7FMCY184ENIjIK5IH/AOSAb4mIVy7dYy1wl4gsBf4Jpy3mQZwVwZ+LyFPAszgmJz+m\nAz8Ukak4M/wVQUKJyPtw/B4zgI+KyFpVPdst+/w3OH1OAL5UUNr7PwD/w5X/f7n/AD4D3C4ih4DH\ncFp5GkYiWKlww3ARkSnAiKoeF5HzcFqBlrMaqCoi8jZV/a37+yrgFFX9bI3FMhoEW0kYxlt0A/eI\nSAtwDLimxvJE5VIRWY3zfX4JJ//CMBLBVhKGkSIi8gXgiqLN9zZa90OjcTElYRiGYQRi0U2GYRhG\nIKYkDMMwjEBMSRiGYRiBmJIwDMMwAjElYRiGYQTy/wM/KfTIBa5ujAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f98aede10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(food_data['sugars_100g'], food_data['carbohydrates_100g'])\n",
    "plt.xlabel('sugars_100g')\n",
    "plt.ylabel('carbohydrates_100g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last one is interesting with the exception of an outlier near 20 part of the x-axis all the data is from the left of the scatter plot, there seems also like the case above to be a declining link between the 2 when the values increase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA - grouping and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Sugars per 100g histogram')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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teg2wH0BbviuwfrR8jNeMruPMqlpSVUvmz58/leZJkqZgMlcBzU+yW5t+EvBy4AbgUuBV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\n6v8DOr4XY8Atyy0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f98b51278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(food_data['sugars_100g'].dropna())\n",
    "plt.title('Sugars per 100g histogram')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution is definitely not normal, it seems more like exponential distribution for more information check here: http://www.statisticshowto.com/exponential-distribution/ or log normal distribution see here: http://www.statisticshowto.com/lognormal-distribution/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5919986030245741"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data['sugars_100g'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First plotting the sugars_100g data we see it is skewed to the right, the skewness coefficient confirms the observation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Sugars per 100g histogram 20 bins')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mZoVyAJiZFcoBYGZWKAeAmVmhHABmZoVyAJiZFcoBYGZWKAeAmVmhHABmZoVyAJiZFcoB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\n7G+yJsc6OQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f98b6b198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(food_data['sugars_100g'].dropna(), bins = 20)\n",
    "plt.title('Sugars per 100g histogram 20 bins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'carbohydrates_100g histogram 20 bins')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i4CbgIEldks7Mg2ax9QXh1wArJP0CuBI4KyJ6Lyq/G/gGsBq4F98ZZGbWdIM2B0XEqf3E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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f98c28978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(food_data['carbohydrates_100g'].dropna(), bins = 20)\n",
    "plt.title('carbohydrates_100g histogram 20 bins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49806600679389967"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data['carbohydrates_100g'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The carbohydrates per 100g are less skewed compared to the sugars per 100g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'saturated-fat_100g histogram 20 bins')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f98cac550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(food_data['saturated-fat_100g'].dropna(), bins = 20)\n",
    "plt.title('saturated-fat_100g histogram 20 bins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2270638695002658"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data['saturated-fat_100g'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The saturated fat per 100g is very highly skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'energy_100g histogram 20 bins')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3ACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVNTBkCSc5NsTHL9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\np/4/LLAmzE50DdUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f98ccc1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(food_data['energy_100g'].dropna(), bins = 20)\n",
    "plt.title('energy_100g histogram 20 bins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42197135739831415"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data['energy_100g'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have similar situation with the carbohydrates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counties_food = food_data.groupby(food_data['countries'], axis=0).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>energy_100g</th>\n",
       "      <th>proteins_100g</th>\n",
       "      <th>salt_100g</th>\n",
       "      <th>sodium_100g</th>\n",
       "      <th>ingredients_from_palm_oil_n</th>\n",
       "      <th>ingredients_that_may_be_from_palm_oil_n</th>\n",
       "      <th>fat_100g</th>\n",
       "      <th>carbohydrates_100g</th>\n",
       "      <th>sugars_100g</th>\n",
       "      <th>saturated-fat_100g</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>countries</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australie,Belgique,Suisse</th>\n",
       "      <td>419.000000</td>\n",
       "      <td>1.50</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>2.362205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40</td>\n",
       "      <td>22.60</td>\n",
       "      <td>20.8000</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgique,France</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>7.4000</td>\n",
       "      <td>2.913386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgique,France,Pays-Bas,Royaume-Uni</th>\n",
       "      <td>1309.500000</td>\n",
       "      <td>9.95</td>\n",
       "      <td>0.8050</td>\n",
       "      <td>0.316929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.25</td>\n",
       "      <td>33.05</td>\n",
       "      <td>16.2500</td>\n",
       "      <td>8.45000</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1034.833333</td>\n",
       "      <td>13.23</td>\n",
       "      <td>1.0805</td>\n",
       "      <td>0.425500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9.20</td>\n",
       "      <td>32.26</td>\n",
       "      <td>10.9248</td>\n",
       "      <td>3.04825</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      energy_100g  proteins_100g  salt_100g  \\\n",
       "countries                                                                     \n",
       "Australia                                     NaN            NaN        NaN   \n",
       "Australie,Belgique,Suisse              419.000000           1.50     6.0000   \n",
       "Belgique,France                         46.000000           0.60     7.4000   \n",
       "Belgique,France,Pays-Bas,Royaume-Uni  1309.500000           9.95     0.8050   \n",
       "Canada                                1034.833333          13.23     1.0805   \n",
       "\n",
       "                                      sodium_100g  \\\n",
       "countries                                           \n",
       "Australia                                     NaN   \n",
       "Australie,Belgique,Suisse                2.362205   \n",
       "Belgique,France                          2.913386   \n",
       "Belgique,France,Pays-Bas,Royaume-Uni     0.316929   \n",
       "Canada                                   0.425500   \n",
       "\n",
       "                                      ingredients_from_palm_oil_n  \\\n",
       "countries                                                           \n",
       "Australia                                                     NaN   \n",
       "Australie,Belgique,Suisse                                     NaN   \n",
       "Belgique,France                                               0.0   \n",
       "Belgique,France,Pays-Bas,Royaume-Uni                          0.0   \n",
       "Canada                                                        0.0   \n",
       "\n",
       "                                      ingredients_that_may_be_from_palm_oil_n  \\\n",
       "countries                                                                       \n",
       "Australia                                                                 NaN   \n",
       "Australie,Belgique,Suisse                                                 NaN   \n",
       "Belgique,France                                                           0.0   \n",
       "Belgique,France,Pays-Bas,Royaume-Uni                                      0.0   \n",
       "Canada                                                                    0.2   \n",
       "\n",
       "                                      fat_100g  carbohydrates_100g  \\\n",
       "countries                                                            \n",
       "Australia                                  NaN                 NaN   \n",
       "Australie,Belgique,Suisse                 0.40               22.60   \n",
       "Belgique,France                           0.00                1.80   \n",
       "Belgique,France,Pays-Bas,Royaume-Uni     15.25               33.05   \n",
       "Canada                                    9.20               32.26   \n",
       "\n",
       "                                      sugars_100g  saturated-fat_100g  \\\n",
       "countries                                                               \n",
       "Australia                                     NaN                 NaN   \n",
       "Australie,Belgique,Suisse                 20.8000             0.10000   \n",
       "Belgique,France                            0.0000             0.00000   \n",
       "Belgique,France,Pays-Bas,Royaume-Uni      16.2500             8.45000   \n",
       "Canada                                    10.9248             3.04825   \n",
       "\n",
       "                                      nutrition-score-fr_100g  \n",
       "countries                                                      \n",
       "Australia                                                 NaN  \n",
       "Australie,Belgique,Suisse                                12.0  \n",
       "Belgique,France                                           9.0  \n",
       "Belgique,France,Pays-Bas,Royaume-Uni                     11.5  \n",
       "Canada                                                    4.5  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counties_food.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Australia', 'Australie,Belgique,Suisse', 'Belgique,France',\n",
       "       'Belgique,France,Pays-Bas,Royaume-Uni', 'Canada', 'Deutschland',\n",
       "       'España', 'France', 'France, US', 'France,Allemagne',\n",
       "       'France,Belgique,Pays-Bas,Royaume-Uni', 'France,Royaume-Uni',\n",
       "       'France,Suisse,États-Unis,Canada', 'France,UK', 'France,United Kingdom',\n",
       "       'France,United States', 'France,États-Unis', 'Germany', 'Ireland',\n",
       "       'Nederland', 'Royaume-Uni,France', 'Suisse', 'Switzerland', 'UK,France',\n",
       "       'US', 'United Kingdom', 'United Kingdom, US', 'United States', 'Usa',\n",
       "       'en:AU', 'en:AU, en:france', 'en:BD', 'en:BR', 'en:CA', 'en:CA, US',\n",
       "       'en:CH', 'en:DE', 'en:DO', 'en:ES', 'en:FR', 'en:FR, US',\n",
       "       'en:FR,France', 'en:GB', 'en:GP', 'en:IT', 'en:PT', 'en:RE, en:france',\n",
       "       'en:RO', 'en:SI', 'en:TW', 'en:US', 'en:US,United Kingdom',\n",
       "       'États-Unis'],\n",
       "      dtype='object', name='countries')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counties_food.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We tried to group by country and the mean value of the ingredients of the food, there are still a lot of missing values, but our biggest problem is that many of the names are the same, this can be seen with the index function. Some of the counties have more the one country. Some of the names are of different languages. Lets assume that the leading country is the first one (this may not be true at all, but we need to start somewhere). We will use the reset index country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countries_food = counties_food.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>countries</th>\n",
       "      <th>energy_100g</th>\n",
       "      <th>proteins_100g</th>\n",
       "      <th>salt_100g</th>\n",
       "      <th>sodium_100g</th>\n",
       "      <th>ingredients_from_palm_oil_n</th>\n",
       "      <th>ingredients_that_may_be_from_palm_oil_n</th>\n",
       "      <th>fat_100g</th>\n",
       "      <th>carbohydrates_100g</th>\n",
       "      <th>sugars_100g</th>\n",
       "      <th>saturated-fat_100g</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australie,Belgique,Suisse</td>\n",
       "      <td>419.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.362205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>20.800000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgique,France</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>2.913386</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belgique,France,Pays-Bas,Royaume-Uni</td>\n",
       "      <td>1309.500000</td>\n",
       "      <td>9.950000</td>\n",
       "      <td>0.805000</td>\n",
       "      <td>0.316929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.250000</td>\n",
       "      <td>33.050000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>8.450000</td>\n",
       "      <td>11.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1034.833333</td>\n",
       "      <td>13.230000</td>\n",
       "      <td>1.080500</td>\n",
       "      <td>0.425500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>32.260000</td>\n",
       "      <td>10.924800</td>\n",
       "      <td>3.048250</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Deutschland</td>\n",
       "      <td>1650.100000</td>\n",
       "      <td>75.785000</td>\n",
       "      <td>0.931185</td>\n",
       "      <td>0.366608</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.210000</td>\n",
       "      <td>9.240000</td>\n",
       "      <td>17.375000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>España</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>France</td>\n",
       "      <td>1135.487500</td>\n",
       "      <td>6.803797</td>\n",
       "      <td>0.776477</td>\n",
       "      <td>0.305699</td>\n",
       "      <td>0.013699</td>\n",
       "      <td>0.109589</td>\n",
       "      <td>12.865926</td>\n",
       "      <td>32.283433</td>\n",
       "      <td>16.273418</td>\n",
       "      <td>4.646837</td>\n",
       "      <td>10.303797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>France, US</td>\n",
       "      <td>641.000000</td>\n",
       "      <td>6.837500</td>\n",
       "      <td>9.560000</td>\n",
       "      <td>3.763780</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.917500</td>\n",
       "      <td>56.145000</td>\n",
       "      <td>24.233333</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>11.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France,Allemagne</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>France,Belgique,Pays-Bas,Royaume-Uni</td>\n",
       "      <td>1809.000000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.326772</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>70.200000</td>\n",
       "      <td>35.700000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>France,Royaume-Uni</td>\n",
       "      <td>1483.571429</td>\n",
       "      <td>5.421429</td>\n",
       "      <td>0.935714</td>\n",
       "      <td>0.368391</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.387500</td>\n",
       "      <td>56.275000</td>\n",
       "      <td>25.321429</td>\n",
       "      <td>11.935714</td>\n",
       "      <td>13.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>France,Suisse,États-Unis,Canada</td>\n",
       "      <td>2260.000000</td>\n",
       "      <td>5.410000</td>\n",
       "      <td>0.103000</td>\n",
       "      <td>0.040500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.400000</td>\n",
       "      <td>56.800000</td>\n",
       "      <td>56.800000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>France,UK</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>10.150000</td>\n",
       "      <td>0.915000</td>\n",
       "      <td>0.360236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>8.450000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>France,United Kingdom</td>\n",
       "      <td>1046.200000</td>\n",
       "      <td>6.620000</td>\n",
       "      <td>0.484000</td>\n",
       "      <td>0.190551</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.933333</td>\n",
       "      <td>29.133333</td>\n",
       "      <td>13.140000</td>\n",
       "      <td>6.810000</td>\n",
       "      <td>8.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>France,United States</td>\n",
       "      <td>2260.000000</td>\n",
       "      <td>5.410000</td>\n",
       "      <td>0.103000</td>\n",
       "      <td>0.040500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.400000</td>\n",
       "      <td>56.800000</td>\n",
       "      <td>56.800000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>France,États-Unis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>17</th>\n",
       "      <td>Germany</td>\n",
       "      <td>973.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.101600</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>40.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>19</th>\n",
       "      <td>Nederland</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Royaume-Uni,France</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.787402</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Suisse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>22</th>\n",
       "      <td>Switzerland</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UK,France</td>\n",
       "      <td>1251.666667</td>\n",
       "      <td>10.566667</td>\n",
       "      <td>1.125867</td>\n",
       "      <td>0.443255</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>15.220000</td>\n",
       "      <td>36.080000</td>\n",
       "      <td>16.650000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>US</td>\n",
       "      <td>1133.316143</td>\n",
       "      <td>6.210389</td>\n",
       "      <td>2.017641</td>\n",
       "      <td>0.794347</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.057985</td>\n",
       "      <td>12.482433</td>\n",
       "      <td>33.740209</td>\n",
       "      <td>17.241450</td>\n",
       "      <td>5.036940</td>\n",
       "      <td>9.626326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1108.000000</td>\n",
       "      <td>5.488889</td>\n",
       "      <td>0.245386</td>\n",
       "      <td>0.096609</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>12.550000</td>\n",
       "      <td>34.983333</td>\n",
       "      <td>19.877778</td>\n",
       "      <td>4.482353</td>\n",
       "      <td>5.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>United Kingdom, US</td>\n",
       "      <td>1494.000000</td>\n",
       "      <td>7.140000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.570000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>United States</td>\n",
       "      <td>1172.083673</td>\n",
       "      <td>6.844469</td>\n",
       "      <td>0.795820</td>\n",
       "      <td>0.313414</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069767</td>\n",
       "      <td>12.091531</td>\n",
       "      <td>36.891837</td>\n",
       "      <td>19.709318</td>\n",
       "      <td>4.691538</td>\n",
       "      <td>7.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Usa</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>en:AU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>30</th>\n",
       "      <td>en:AU, en:france</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>en:BD</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>en:BR</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>en:CA</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>en:CA, US</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020320</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.330000</td>\n",
       "      <td>88.330000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>en:CH</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>en:DE</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>en:DO</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>en:ES</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>en:FR</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>en:FR, US</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.086360</td>\n",
       "      <td>0.034000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.050000</td>\n",
       "      <td>5.340000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>en:FR,France</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.188976</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>en:GB</td>\n",
       "      <td>1141.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</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>43</th>\n",
       "      <td>en:GP</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>en:IT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>45</th>\n",
       "      <td>en:PT</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>en:RE, en:france</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>en:RO</td>\n",
       "      <td>2278.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>48</th>\n",
       "      <td>en:SI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>49</th>\n",
       "      <td>en:TW</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>en:US</td>\n",
       "      <td>1412.785714</td>\n",
       "      <td>8.777857</td>\n",
       "      <td>0.799556</td>\n",
       "      <td>0.314786</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>15.622143</td>\n",
       "      <td>41.694286</td>\n",
       "      <td>24.082143</td>\n",
       "      <td>7.436667</td>\n",
       "      <td>12.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>en:US,United Kingdom</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>États-Unis</td>\n",
       "      <td>2286.500000</td>\n",
       "      <td>5.462500</td>\n",
       "      <td>0.351925</td>\n",
       "      <td>0.138700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.600000</td>\n",
       "      <td>34.775000</td>\n",
       "      <td>39.086667</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>18.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               countries  energy_100g  proteins_100g  \\\n",
       "0                              Australia          NaN            NaN   \n",
       "1              Australie,Belgique,Suisse   419.000000       1.500000   \n",
       "2                        Belgique,France    46.000000       0.600000   \n",
       "3   Belgique,France,Pays-Bas,Royaume-Uni  1309.500000       9.950000   \n",
       "4                                 Canada  1034.833333      13.230000   \n",
       "5                            Deutschland  1650.100000      75.785000   \n",
       "6                                 España   320.000000       3.600000   \n",
       "7                                 France  1135.487500       6.803797   \n",
       "8                             France, US   641.000000       6.837500   \n",
       "9                       France,Allemagne          NaN            NaN   \n",
       "10  France,Belgique,Pays-Bas,Royaume-Uni  1809.000000       6.100000   \n",
       "11                    France,Royaume-Uni  1483.571429       5.421429   \n",
       "12       France,Suisse,États-Unis,Canada  2260.000000       5.410000   \n",
       "13                             France,UK  1452.000000      10.150000   \n",
       "14                 France,United Kingdom  1046.200000       6.620000   \n",
       "15                  France,United States  2260.000000       5.410000   \n",
       "16                     France,États-Unis          NaN            NaN   \n",
       "17                               Germany   973.000000       2.500000   \n",
       "18                               Ireland          NaN            NaN   \n",
       "19                             Nederland          NaN            NaN   \n",
       "20                    Royaume-Uni,France   564.000000      24.500000   \n",
       "21                                Suisse          NaN            NaN   \n",
       "22                           Switzerland          NaN            NaN   \n",
       "23                             UK,France  1251.666667      10.566667   \n",
       "24                                    US  1133.316143       6.210389   \n",
       "25                        United Kingdom  1108.000000       5.488889   \n",
       "26                    United Kingdom, US  1494.000000       7.140000   \n",
       "27                         United States  1172.083673       6.844469   \n",
       "28                                   Usa          NaN            NaN   \n",
       "29                                 en:AU          NaN            NaN   \n",
       "30                      en:AU, en:france          NaN            NaN   \n",
       "31                                 en:BD          NaN            NaN   \n",
       "32                                 en:BR          NaN            NaN   \n",
       "33                                 en:CA          NaN            NaN   \n",
       "34                             en:CA, US  1393.000000       0.000000   \n",
       "35                                 en:CH          NaN            NaN   \n",
       "36                                 en:DE          NaN            NaN   \n",
       "37                                 en:DO          NaN            NaN   \n",
       "38                                 en:ES          NaN            NaN   \n",
       "39                                 en:FR          NaN            NaN   \n",
       "40                             en:FR, US   117.000000       0.000000   \n",
       "41                          en:FR,France   335.000000       2.100000   \n",
       "42                                 en:GB  1141.000000            NaN   \n",
       "43                                 en:GP          NaN            NaN   \n",
       "44                                 en:IT          NaN            NaN   \n",
       "45                                 en:PT          NaN            NaN   \n",
       "46                      en:RE, en:france          NaN            NaN   \n",
       "47                                 en:RO  2278.000000            NaN   \n",
       "48                                 en:SI          NaN            NaN   \n",
       "49                                 en:TW          NaN            NaN   \n",
       "50                                 en:US  1412.785714       8.777857   \n",
       "51                  en:US,United Kingdom          NaN            NaN   \n",
       "52                            États-Unis  2286.500000       5.462500   \n",
       "\n",
       "    salt_100g  sodium_100g  ingredients_from_palm_oil_n  \\\n",
       "0         NaN          NaN                          NaN   \n",
       "1    6.000000     2.362205                          NaN   \n",
       "2    7.400000     2.913386                     0.000000   \n",
       "3    0.805000     0.316929                     0.000000   \n",
       "4    1.080500     0.425500                     0.000000   \n",
       "5    0.931185     0.366608                     0.000000   \n",
       "6         NaN          NaN                     0.000000   \n",
       "7    0.776477     0.305699                     0.013699   \n",
       "8    9.560000     3.763780                          NaN   \n",
       "9         NaN          NaN                          NaN   \n",
       "10   0.830000     0.326772                     0.000000   \n",
       "11   0.935714     0.368391                     0.000000   \n",
       "12   0.103000     0.040500                     0.000000   \n",
       "13   0.915000     0.360236                          NaN   \n",
       "14   0.484000     0.190551                     0.000000   \n",
       "15   0.103000     0.040500                     0.000000   \n",
       "16        NaN          NaN                     0.000000   \n",
       "17   0.101600     0.040000                     0.000000   \n",
       "18        NaN          NaN                     0.000000   \n",
       "19        NaN          NaN                          NaN   \n",
       "20   2.000000     0.787402                     0.000000   \n",
       "21        NaN          NaN                     0.000000   \n",
       "22        NaN          NaN                          NaN   \n",
       "23   1.125867     0.443255                     0.200000   \n",
       "24   2.017641     0.794347                     0.000000   \n",
       "25   0.245386     0.096609                     0.000000   \n",
       "26   0.000000     0.000000                     0.000000   \n",
       "27   0.795820     0.313414                     0.000000   \n",
       "28        NaN          NaN                          NaN   \n",
       "29        NaN          NaN                     0.000000   \n",
       "30        NaN          NaN                          NaN   \n",
       "31        NaN          NaN                          NaN   \n",
       "32        NaN          NaN                          NaN   \n",
       "33        NaN          NaN                          NaN   \n",
       "34   0.020320     0.008000                          NaN   \n",
       "35        NaN          NaN                          NaN   \n",
       "36        NaN          NaN                          NaN   \n",
       "37        NaN          NaN                          NaN   \n",
       "38        NaN          NaN                          NaN   \n",
       "39        NaN          NaN                          NaN   \n",
       "40   0.086360     0.034000                          NaN   \n",
       "41   0.480000     0.188976                     0.000000   \n",
       "42        NaN          NaN                     0.000000   \n",
       "43        NaN          NaN                          NaN   \n",
       "44        NaN          NaN                     0.000000   \n",
       "45        NaN          NaN                          NaN   \n",
       "46        NaN          NaN                          NaN   \n",
       "47        NaN          NaN                     0.000000   \n",
       "48        NaN          NaN                     0.000000   \n",
       "49        NaN          NaN                          NaN   \n",
       "50   0.799556     0.314786                     0.000000   \n",
       "51        NaN          NaN                          NaN   \n",
       "52   0.351925     0.138700                     0.000000   \n",
       "\n",
       "    ingredients_that_may_be_from_palm_oil_n   fat_100g  carbohydrates_100g  \\\n",
       "0                                       NaN        NaN                 NaN   \n",
       "1                                       NaN   0.400000           22.600000   \n",
       "2                                  0.000000   0.000000            1.800000   \n",
       "3                                  0.000000  15.250000           33.050000   \n",
       "4                                  0.200000   9.200000           32.260000   \n",
       "5                                  0.000000   5.210000            9.240000   \n",
       "6                                  0.000000   3.500000            7.500000   \n",
       "7                                  0.109589  12.865926           32.283433   \n",
       "8                                       NaN   2.917500           56.145000   \n",
       "9                                       NaN        NaN                 NaN   \n",
       "10                                 0.000000  13.300000           70.200000   \n",
       "11                                 0.000000  15.387500           56.275000   \n",
       "12                                 0.000000  32.400000           56.800000   \n",
       "13                                      NaN        NaN                 NaN   \n",
       "14                                 0.000000   8.933333           29.133333   \n",
       "15                                 0.000000  32.400000           56.800000   \n",
       "16                                 0.000000        NaN                 NaN   \n",
       "17                                 0.000000   0.000000           54.500000   \n",
       "18                                 0.000000        NaN                 NaN   \n",
       "19                                      NaN        NaN                 NaN   \n",
       "20                                 0.000000   2.000000            4.300000   \n",
       "21                                 0.000000        NaN                 NaN   \n",
       "22                                      NaN        NaN                 NaN   \n",
       "23                                 0.200000  15.220000           36.080000   \n",
       "24                                 0.057985  12.482433           33.740209   \n",
       "25                                 0.240000  12.550000           34.983333   \n",
       "26                                 0.000000   0.000000           78.570000   \n",
       "27                                 0.069767  12.091531           36.891837   \n",
       "28                                      NaN        NaN                 NaN   \n",
       "29                                 0.000000        NaN                 NaN   \n",
       "30                                      NaN        NaN                 NaN   \n",
       "31                                      NaN        NaN                 NaN   \n",
       "32                                      NaN        NaN                 NaN   \n",
       "33                                      NaN        NaN                 NaN   \n",
       "34                                      NaN   0.000000           88.330000   \n",
       "35                                      NaN        NaN                 NaN   \n",
       "36                                      NaN        NaN                 NaN   \n",
       "37                                      NaN        NaN                 NaN   \n",
       "38                                      NaN        NaN                 NaN   \n",
       "39                                      NaN        NaN                 NaN   \n",
       "40                                      NaN   0.000000            6.050000   \n",
       "41                                 0.000000   1.600000           14.000000   \n",
       "42                                 0.333333        NaN                 NaN   \n",
       "43                                      NaN        NaN                 NaN   \n",
       "44                                 0.000000        NaN                 NaN   \n",
       "45                                      NaN        NaN                 NaN   \n",
       "46                                      NaN        NaN                 NaN   \n",
       "47                                 0.000000        NaN                 NaN   \n",
       "48                                 0.000000        NaN                 NaN   \n",
       "49                                      NaN        NaN                 NaN   \n",
       "50                                 0.142857  15.622143           41.694286   \n",
       "51                                      NaN        NaN                 NaN   \n",
       "52                                 0.000000  43.600000           34.775000   \n",
       "\n",
       "    sugars_100g  saturated-fat_100g  nutrition-score-fr_100g  \n",
       "0           NaN                 NaN                      NaN  \n",
       "1     20.800000            0.100000                12.000000  \n",
       "2      0.000000            0.000000                 9.000000  \n",
       "3     16.250000            8.450000                11.500000  \n",
       "4     10.924800            3.048250                 4.500000  \n",
       "5     17.375000           21.100000                26.000000  \n",
       "6           NaN                 NaN                      NaN  \n",
       "7     16.273418            4.646837                10.303797  \n",
       "8     24.233333            0.600000                11.666667  \n",
       "9           NaN                 NaN                      NaN  \n",
       "10    35.700000            7.000000                18.000000  \n",
       "11    25.321429           11.935714                13.142857  \n",
       "12    56.800000           10.800000                23.000000  \n",
       "13     6.400000            8.450000                 6.500000  \n",
       "14    13.140000            6.810000                 8.400000  \n",
       "15    56.800000           10.800000                23.000000  \n",
       "16          NaN                 NaN                      NaN  \n",
       "17    40.750000            0.000000                10.000000  \n",
       "18          NaN                 NaN                      NaN  \n",
       "19          NaN                 NaN                      NaN  \n",
       "20     2.900000            0.700000                 4.000000  \n",
       "21          NaN                 NaN                      NaN  \n",
       "22          NaN                 NaN                      NaN  \n",
       "23    16.650000            6.800000                11.000000  \n",
       "24    17.241450            5.036940                 9.626326  \n",
       "25    19.877778            4.482353                 5.647059  \n",
       "26          NaN                 NaN                      NaN  \n",
       "27    19.709318            4.691538                 7.923077  \n",
       "28          NaN                 NaN                      NaN  \n",
       "29          NaN                 NaN                      NaN  \n",
       "30          NaN                 NaN                      NaN  \n",
       "31          NaN                 NaN                      NaN  \n",
       "32          NaN                 NaN                      NaN  \n",
       "33          NaN                 NaN                      NaN  \n",
       "34    88.330000                 NaN                      NaN  \n",
       "35          NaN                 NaN                      NaN  \n",
       "36          NaN                 NaN                      NaN  \n",
       "37          NaN                 NaN                      NaN  \n",
       "38          NaN                 NaN                      NaN  \n",
       "39          NaN                 NaN                      NaN  \n",
       "40     5.340000            0.000000                 1.000000  \n",
       "41     2.200000            0.800000                 1.000000  \n",
       "42          NaN                 NaN                      NaN  \n",
       "43          NaN                 NaN                      NaN  \n",
       "44          NaN                 NaN                      NaN  \n",
       "45          NaN                 NaN                      NaN  \n",
       "46          NaN                 NaN                      NaN  \n",
       "47          NaN                 NaN                      NaN  \n",
       "48          NaN                 NaN                      NaN  \n",
       "49          NaN                 NaN                      NaN  \n",
       "50    24.082143            7.436667                12.250000  \n",
       "51          NaN                 NaN                      NaN  \n",
       "52    39.086667            9.900000                18.333333  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 12)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_food.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "After analysing the results if we continue to remove the rows that hove only NaN values we will be left with too few, and if we continue to merge the rows even fewer, so this is a dead end for the grouping process we should try another way. It is said that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"data_image.png\" style=\"max-height: 230px\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['last_modified_datetime', 'creator', 'created_datetime', 'url', 'code',\n",
       "       'countries', 'product_name', 'brands', 'energy_100g', 'proteins_100g',\n",
       "       'salt_100g', 'sodium_100g', 'ingredients_text',\n",
       "       'ingredients_from_palm_oil_n',\n",
       "       'ingredients_that_may_be_from_palm_oil_n', 'additives', 'fat_100g',\n",
       "       'carbohydrates_100g', 'sugars_100g', 'saturated-fat_100g',\n",
       "       'nutrition_grade_fr', 'nutrition-score-fr_100g'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['openfoodfacts-contributors', 'usda-ndb-import', 'chris13',\n",
       "       'date-limite-app', 'canieatthis-app', 'tacinte', 'teolemon', 'b7',\n",
       "       'bcatelin', 'sigoise', 'tacite', 'kiliweb', 'sebleouf',\n",
       "       'agamitsudo', 'twoflower', 'andre', 'syefulislam', 'javichu',\n",
       "       'jeanbono', 'allfitnessfactory-de', 'beniben', 'kyzh', 'malikele',\n",
       "       'gildubs', 'lbhlbh', 'wengong27514', 'brent', 'ysculo', 'chandon',\n",
       "       'stephane', 'jm0804', 'woshilapin', 'max4food', 'foodorigins',\n",
       "       'aleene', 'rjhunter', 'bdwyer', 'lilimarti', 'kp757',\n",
       "       'trevorpetersen', 'recipekeepapp', 'aeon', 'php93', 'toni',\n",
       "       'jiehong', 'pitajay', 'arig', 'bitnapper', 'openfood-ch-import',\n",
       "       'jcnascimento', 'manuc66', 'xmunoz', 'roblaw', 'amaurea',\n",
       "       'veganeamos'], dtype=object)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_data['creator'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "food_creator = food_data.groupby(food_data['creator'], axis=0).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>energy_100g</th>\n",
       "      <th>proteins_100g</th>\n",
       "      <th>salt_100g</th>\n",
       "      <th>sodium_100g</th>\n",
       "      <th>ingredients_from_palm_oil_n</th>\n",
       "      <th>ingredients_that_may_be_from_palm_oil_n</th>\n",
       "      <th>fat_100g</th>\n",
       "      <th>carbohydrates_100g</th>\n",
       "      <th>sugars_100g</th>\n",
       "      <th>saturated-fat_100g</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creator</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aeon</th>\n",
       "      <td>1197.000</td>\n",
       "      <td>28.57000</td>\n",
       "      <td>1.54178</td>\n",
       "      <td>0.607000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.860</td>\n",
       "      <td>3.5700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.71</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agamitsudo</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>aleene</th>\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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>allfitnessfactory-de</th>\n",
       "      <td>1588.875</td>\n",
       "      <td>83.45625</td>\n",
       "      <td>1.21158</td>\n",
       "      <td>0.477000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.225</td>\n",
       "      <td>4.5125</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amaurea</th>\n",
       "      <td>1928.000</td>\n",
       "      <td>5.10000</td>\n",
       "      <td>0.81000</td>\n",
       "      <td>0.318898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.700</td>\n",
       "      <td>68.5000</td>\n",
       "      <td>44.4</td>\n",
       "      <td>10.10</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      energy_100g  proteins_100g  salt_100g  sodium_100g  \\\n",
       "creator                                                                    \n",
       "aeon                     1197.000       28.57000    1.54178     0.607000   \n",
       "agamitsudo                    NaN            NaN        NaN          NaN   \n",
       "aleene                        NaN            NaN        NaN          NaN   \n",
       "allfitnessfactory-de     1588.875       83.45625    1.21158     0.477000   \n",
       "amaurea                  1928.000        5.10000    0.81000     0.318898   \n",
       "\n",
       "                      ingredients_from_palm_oil_n  \\\n",
       "creator                                             \n",
       "aeon                                          0.0   \n",
       "agamitsudo                                    0.0   \n",
       "aleene                                        NaN   \n",
       "allfitnessfactory-de                          0.0   \n",
       "amaurea                                       0.0   \n",
       "\n",
       "                      ingredients_that_may_be_from_palm_oil_n  fat_100g  \\\n",
       "creator                                                                   \n",
       "aeon                                                      0.0    17.860   \n",
       "agamitsudo                                                0.0       NaN   \n",
       "aleene                                                    NaN       NaN   \n",
       "allfitnessfactory-de                                      0.0     2.225   \n",
       "amaurea                                                   0.0    17.700   \n",
       "\n",
       "                      carbohydrates_100g  sugars_100g  saturated-fat_100g  \\\n",
       "creator                                                                     \n",
       "aeon                              3.5700          0.0               10.71   \n",
       "agamitsudo                           NaN          NaN                 NaN   \n",
       "aleene                               NaN          NaN                 NaN   \n",
       "allfitnessfactory-de              4.5125          6.0                 NaN   \n",
       "amaurea                          68.5000         44.4               10.10   \n",
       "\n",
       "                      nutrition-score-fr_100g  \n",
       "creator                                        \n",
       "aeon                                     19.0  \n",
       "agamitsudo                                NaN  \n",
       "aleene                                    NaN  \n",
       "allfitnessfactory-de                      NaN  \n",
       "amaurea                                  24.0  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_creator.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55, 11)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_creator.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After careful thinking a new grouping has been chosen this time by the creator column. It will be devided by 2 populations some of them seem legitimate (like the data is from an organization or an app), other seem just like the names of common users, we will brand them as nicknames. I agree that this is not very scientific but it will be used for testing hypothesis based on a smaller then 30 samples, we will use the t test, so view the code more for demonstration purposes then from extraction useful information. There is a lot of subjective bias into the process, but don't make such a fuss from it there all all kind of idiotic scientific articles published even published by journal like this one : https://www.sciencealert.com/predatory-journal-fell-for-hoax-paper-about-uk-politician-s-bum-wiping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "legitemate = food_creator.loc[['openfoodfacts-contributors', 'usda-ndb-import','date-limite-app', 'canieatthis-app',\n",
    "                              'allfitnessfactory-de','syefulislam', 'recipekeepapp', 'openfood-ch-import',\n",
    "                              'foodorigins']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nicknames = food_creator.loc[['chris13', 'tacinte', 'teolemon', 'b7', 'bcatelin', 'sigoise', 'tacite', 'kiliweb',\n",
    "                              'sebleouf','agamitsudo', 'twoflower', 'andre','javichu', 'jeanbono',\n",
    "                              'beniben', 'kyzh', 'malikele',\n",
    "                               'gildubs', 'lbhlbh', 'wengong27514', 'brent', 'ysculo', 'chandon',\n",
    "                               'stephane', 'jm0804', 'woshilapin', 'max4food','aleene', 'rjhunter', 'bdwyer',\n",
    "                              'lilimarti', 'kp757','trevorpetersen','aeon', 'php93', 'toni',\n",
    "                               'jiehong', 'pitajay', 'arig', 'bitnapper','jcnascimento', 'manuc66', 'xmunoz', \n",
    "                              'roblaw', 'amaurea', 'veganeamos']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 11)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legitemate.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46, 11)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nicknames.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis Testing\n",
    "The first sample contains just 9 rows, while the second 46. Lets take the salt per 100g to test a hypothesis. We have dropped the NAN values so that they don't get in the way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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zgEO62x6wHvAAsGOavnFqP4cCPwL+Cfg9MCHXZs9P7WVb4P4Cbe5l4B1pmUXA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\nCCd8M7OK+P+ab17rswgGrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f99ecf9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([legitemate['salt_100g'].dropna(), nicknames['salt_100g'].dropna()], labels = [\"legitemate\", \"nicknames\"])\n",
    "plt.ylabel(\"Salt per 100 grams\")\n",
    "plt.title('Boxplot for legitemate names and nicknames')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the nicknames are more symmetrically distributed then the legitemate. Both have 2 large outliers, considering we don't have that many observations we decide that the outliers will stay, now lets move to the t test, for more information about the t test read here: https://towardsdatascience.com/inferential-statistics-series-t-test-using-numpy-2718f8f9bf2f.\n",
    "<br>\n",
    "We formulate the zeroth hypothesis as \n",
    "<br>\n",
    "H0: that there is no statistically significant difference between the mean of the the 2 populations and \n",
    "<br>\n",
    "H1: that there is a statistically significant difference between the mean of the the 2 categories.\n",
    "<br>\n",
    "We take a 0.05 significance value, if the p-value is larger then 0.05 we cannot reject the null hypothesis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t, p = stats.ttest_ind(legitemate['salt_100g'].dropna(), nicknames['salt_100g'].dropna() , equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.2665620087526213, 0.25247116987644719)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t, p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the end we will conclude that the H0 hypothesis is the correct one. This is not 100% true we say that there exist a 5% chance that we are wrong (known as type alpha error also knows as type 1 error), there is also the issue for type beta error (also known as type 2 error). For more information you can check here: https://www.students4bestevidence.net/type-i-and-type-ii-errors/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "To conclude this dataset is very large and unstructured, it requires a lot of formatting to get something more useful out of it, taking some paths can lead us to nowhere, sometimes we have to start from the beginning. Here it is probably an error we choose to remove so many column for having more then the stated threshold of NAN values. They could have been used for grouping. Finally a t test was used due to the small sample sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further development\n",
    "This dataset need a lot more attention in the future, considering its size several possible paths could be taken. The process is likely going to resemble trial and error, this should be taken under consideration when stating depending on the goal that might shift and the required resources and time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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