Assignment_python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import statements\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import html5lib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (0,3,5,27,36) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "foodFacts = pd.read_csv('FoodFacts.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "code                        object\n",
       "url                         object\n",
       "creator                     object\n",
       "created_t                   object\n",
       "created_datetime            object\n",
       "                            ...   \n",
       "cocoa_100g                 float64\n",
       "chlorophyl_100g            float64\n",
       "carbon_footprint_100g      float64\n",
       "nutrition_score_fr_100g    float64\n",
       "nutrition_score_uk_100g    float64\n",
       "Length: 159, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>caffeine_100g</th>\n",
       "      <th>taurine_100g</th>\n",
       "      <th>ph_100g</th>\n",
       "      <th>fruits_vegetables_nuts_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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>000000000000012866</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>date-limite-app</td>\n",
       "      <td>1447004364</td>\n",
       "      <td>2015-11-08T17:39:24Z</td>\n",
       "      <td>1447004364</td>\n",
       "      <td>2015-11-08T17:39:24Z</td>\n",
       "      <td>Poêlée à la sarladaise</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",
       "      <td>1</td>\n",
       "      <td>0000000024600</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>date-limite-app</td>\n",
       "      <td>1434530704</td>\n",
       "      <td>2015-06-17T08:45:04Z</td>\n",
       "      <td>1434535914</td>\n",
       "      <td>2015-06-17T10:11:54Z</td>\n",
       "      <td>Filet de bœuf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.46 kg</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",
       "      <td>2</td>\n",
       "      <td>0000000036252</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>tacinte</td>\n",
       "      <td>1422221701</td>\n",
       "      <td>2015-01-25T21:35:01Z</td>\n",
       "      <td>1422221855</td>\n",
       "      <td>2015-01-25T21:37:35Z</td>\n",
       "      <td>Lion Peanut x2</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",
       "      <td>3</td>\n",
       "      <td>0000000039259</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>tacinte</td>\n",
       "      <td>1422221773</td>\n",
       "      <td>2015-01-25T21:36:13Z</td>\n",
       "      <td>1422221926</td>\n",
       "      <td>2015-01-25T21:38:46Z</td>\n",
       "      <td>Twix x2</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",
       "      <td>4</td>\n",
       "      <td>0000000039529</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>teolemon</td>\n",
       "      <td>1420147051</td>\n",
       "      <td>2015-01-01T21:17:31Z</td>\n",
       "      <td>1439141740</td>\n",
       "      <td>2015-08-09T17:35:40Z</td>\n",
       "      <td>Pack de 2 Twix</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",
       "      <td>5</td>\n",
       "      <td>0000001071894</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>bcatelin</td>\n",
       "      <td>1409411252</td>\n",
       "      <td>2014-08-30T15:07:32Z</td>\n",
       "      <td>1439141739</td>\n",
       "      <td>2015-08-09T17:35:39Z</td>\n",
       "      <td>Flute</td>\n",
       "      <td>Flute</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",
       "      <td>6</td>\n",
       "      <td>0000005200016</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>sigoise</td>\n",
       "      <td>1441186657</td>\n",
       "      <td>2015-09-02T09:37:37Z</td>\n",
       "      <td>1442570752</td>\n",
       "      <td>2015-09-18T10:05:52Z</td>\n",
       "      <td>lentilles vertes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 kg</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",
       "      <td>7</td>\n",
       "      <td>0000007020254</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>teolemon</td>\n",
       "      <td>1420150193</td>\n",
       "      <td>2015-01-01T22:09:53Z</td>\n",
       "      <td>1420210373</td>\n",
       "      <td>2015-01-02T14:52:53Z</td>\n",
       "      <td>NaN</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",
       "      <td>8</td>\n",
       "      <td>0000010090206</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>sebleouf</td>\n",
       "      <td>1370977431</td>\n",
       "      <td>2013-06-11T19:03:51Z</td>\n",
       "      <td>1445083431</td>\n",
       "      <td>2015-10-17T12:03:51Z</td>\n",
       "      <td>Thé de Noël aromatisé orange-cannelle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75 g</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",
       "      <td>9</td>\n",
       "      <td>0000020364373</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>openfoodfacts-contributors</td>\n",
       "      <td>1393970573</td>\n",
       "      <td>2014-03-04T22:02:53Z</td>\n",
       "      <td>1393970733</td>\n",
       "      <td>2014-03-04T22:05:33Z</td>\n",
       "      <td>Zumo de Piña</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>10 rows × 159 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 code                                                url  \\\n",
       "0  000000000000012866  http://world-en.openfoodfacts.org/product/0000...   \n",
       "1       0000000024600  http://world-en.openfoodfacts.org/product/0000...   \n",
       "2       0000000036252  http://world-en.openfoodfacts.org/product/0000...   \n",
       "3       0000000039259  http://world-en.openfoodfacts.org/product/0000...   \n",
       "4       0000000039529  http://world-en.openfoodfacts.org/product/0000...   \n",
       "5       0000001071894  http://world-en.openfoodfacts.org/product/0000...   \n",
       "6       0000005200016  http://world-en.openfoodfacts.org/product/0000...   \n",
       "7       0000007020254  http://world-en.openfoodfacts.org/product/0000...   \n",
       "8       0000010090206  http://world-en.openfoodfacts.org/product/0000...   \n",
       "9       0000020364373  http://world-en.openfoodfacts.org/product/0000...   \n",
       "\n",
       "                      creator   created_t      created_datetime  \\\n",
       "0             date-limite-app  1447004364  2015-11-08T17:39:24Z   \n",
       "1             date-limite-app  1434530704  2015-06-17T08:45:04Z   \n",
       "2                     tacinte  1422221701  2015-01-25T21:35:01Z   \n",
       "3                     tacinte  1422221773  2015-01-25T21:36:13Z   \n",
       "4                    teolemon  1420147051  2015-01-01T21:17:31Z   \n",
       "5                    bcatelin  1409411252  2014-08-30T15:07:32Z   \n",
       "6                     sigoise  1441186657  2015-09-02T09:37:37Z   \n",
       "7                    teolemon  1420150193  2015-01-01T22:09:53Z   \n",
       "8                    sebleouf  1370977431  2013-06-11T19:03:51Z   \n",
       "9  openfoodfacts-contributors  1393970573  2014-03-04T22:02:53Z   \n",
       "\n",
       "  last_modified_t last_modified_datetime  \\\n",
       "0      1447004364   2015-11-08T17:39:24Z   \n",
       "1      1434535914   2015-06-17T10:11:54Z   \n",
       "2      1422221855   2015-01-25T21:37:35Z   \n",
       "3      1422221926   2015-01-25T21:38:46Z   \n",
       "4      1439141740   2015-08-09T17:35:40Z   \n",
       "5      1439141739   2015-08-09T17:35:39Z   \n",
       "6      1442570752   2015-09-18T10:05:52Z   \n",
       "7      1420210373   2015-01-02T14:52:53Z   \n",
       "8      1445083431   2015-10-17T12:03:51Z   \n",
       "9      1393970733   2014-03-04T22:05:33Z   \n",
       "\n",
       "                            product_name generic_name quantity  ...  \\\n",
       "0                 Poêlée à la sarladaise          NaN      NaN  ...   \n",
       "1                          Filet de bœuf          NaN  2.46 kg  ...   \n",
       "2                         Lion Peanut x2          NaN      NaN  ...   \n",
       "3                                Twix x2          NaN      NaN  ...   \n",
       "4                         Pack de 2 Twix          NaN      NaN  ...   \n",
       "5                                  Flute        Flute      NaN  ...   \n",
       "6                      lentilles vertes           NaN    1 kg   ...   \n",
       "7                                    NaN          NaN      NaN  ...   \n",
       "8  Thé de Noël aromatisé orange-cannelle          NaN     75 g  ...   \n",
       "9                           Zumo de Piña          NaN      NaN  ...   \n",
       "\n",
       "  caffeine_100g taurine_100g ph_100g fruits_vegetables_nuts_100g  \\\n",
       "0           NaN          NaN     NaN                         NaN   \n",
       "1           NaN          NaN     NaN                         NaN   \n",
       "2           NaN          NaN     NaN                         NaN   \n",
       "3           NaN          NaN     NaN                         NaN   \n",
       "4           NaN          NaN     NaN                         NaN   \n",
       "5           NaN          NaN     NaN                         NaN   \n",
       "6           NaN          NaN     NaN                         NaN   \n",
       "7           NaN          NaN     NaN                         NaN   \n",
       "8           NaN          NaN     NaN                         NaN   \n",
       "9           NaN          NaN     NaN                         NaN   \n",
       "\n",
       "  collagen_meat_protein_ratio_100g cocoa_100g chlorophyl_100g  \\\n",
       "0                              NaN        NaN             NaN   \n",
       "1                              NaN        NaN             NaN   \n",
       "2                              NaN        NaN             NaN   \n",
       "3                              NaN        NaN             NaN   \n",
       "4                              NaN        NaN             NaN   \n",
       "5                              NaN        NaN             NaN   \n",
       "6                              NaN        NaN             NaN   \n",
       "7                              NaN        NaN             NaN   \n",
       "8                              NaN        NaN             NaN   \n",
       "9                              NaN        NaN             NaN   \n",
       "\n",
       "  carbon_footprint_100g nutrition_score_fr_100g nutrition_score_uk_100g  \n",
       "0                   NaN                     NaN                     NaN  \n",
       "1                   NaN                     NaN                     NaN  \n",
       "2                   NaN                     NaN                     NaN  \n",
       "3                   NaN                     NaN                     NaN  \n",
       "4                   NaN                     NaN                     NaN  \n",
       "5                   NaN                     NaN                     NaN  \n",
       "6                   NaN                     NaN                     NaN  \n",
       "7                   NaN                     NaN                     NaN  \n",
       "8                   NaN                     NaN                     NaN  \n",
       "9                   NaN                     NaN                     NaN  \n",
       "\n",
       "[10 rows x 159 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['code', 'url', 'creator', 'created_t', 'created_datetime',\n",
       "       'last_modified_t', 'last_modified_datetime', 'product_name',\n",
       "       'generic_name', 'quantity',\n",
       "       ...\n",
       "       'caffeine_100g', 'taurine_100g', 'ph_100g',\n",
       "       'fruits_vegetables_nuts_100g', 'collagen_meat_protein_ratio_100g',\n",
       "       'cocoa_100g', 'chlorophyl_100g', 'carbon_footprint_100g',\n",
       "       'nutrition_score_fr_100g', 'nutrition_score_uk_100g'],\n",
       "      dtype='object', length=159)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['code', 'created_t', 'last_modified_t', 'cities', 'allergens_en'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts.columns[[0,3,5,27,36]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>created_t</th>\n",
       "      <th>last_modified_t</th>\n",
       "      <th>cities</th>\n",
       "      <th>allergens_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>000000000000012866</td>\n",
       "      <td>1447004364</td>\n",
       "      <td>1447004364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0000000024600</td>\n",
       "      <td>1434530704</td>\n",
       "      <td>1434535914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0000000036252</td>\n",
       "      <td>1422221701</td>\n",
       "      <td>1422221855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0000000039259</td>\n",
       "      <td>1422221773</td>\n",
       "      <td>1422221926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0000000039529</td>\n",
       "      <td>1420147051</td>\n",
       "      <td>1439141740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0000001071894</td>\n",
       "      <td>1409411252</td>\n",
       "      <td>1439141739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0000005200016</td>\n",
       "      <td>1441186657</td>\n",
       "      <td>1442570752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0000007020254</td>\n",
       "      <td>1420150193</td>\n",
       "      <td>1420210373</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0000010090206</td>\n",
       "      <td>1370977431</td>\n",
       "      <td>1445083431</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0000020364373</td>\n",
       "      <td>1393970573</td>\n",
       "      <td>1393970733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0000027533024</td>\n",
       "      <td>1418732959</td>\n",
       "      <td>1442914346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0000027533048</td>\n",
       "      <td>1418732915</td>\n",
       "      <td>1439141741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0000030053014</td>\n",
       "      <td>1430153513</td>\n",
       "      <td>1430153514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0000040608754</td>\n",
       "      <td>1345024108</td>\n",
       "      <td>1439141731</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>00000758</td>\n",
       "      <td>1409582884</td>\n",
       "      <td>1424687936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0000084154071</td>\n",
       "      <td>1351359717</td>\n",
       "      <td>1440779523</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0000087177756</td>\n",
       "      <td>1433586486</td>\n",
       "      <td>1433589206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>00001373</td>\n",
       "      <td>1412788478</td>\n",
       "      <td>1441556289</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>00002929</td>\n",
       "      <td>1424460617</td>\n",
       "      <td>1424829673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>00003100</td>\n",
       "      <td>1415119256</td>\n",
       "      <td>1428327438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  code   created_t last_modified_t cities allergens_en\n",
       "0   000000000000012866  1447004364      1447004364    NaN          NaN\n",
       "1        0000000024600  1434530704      1434535914    NaN          NaN\n",
       "2        0000000036252  1422221701      1422221855    NaN          NaN\n",
       "3        0000000039259  1422221773      1422221926    NaN          NaN\n",
       "4        0000000039529  1420147051      1439141740    NaN          NaN\n",
       "5        0000001071894  1409411252      1439141739    NaN          NaN\n",
       "6        0000005200016  1441186657      1442570752    NaN          NaN\n",
       "7        0000007020254  1420150193      1420210373    NaN          NaN\n",
       "8        0000010090206  1370977431      1445083431    NaN          NaN\n",
       "9        0000020364373  1393970573      1393970733    NaN          NaN\n",
       "10       0000027533024  1418732959      1442914346    NaN          NaN\n",
       "11       0000027533048  1418732915      1439141741    NaN          NaN\n",
       "12       0000030053014  1430153513      1430153514    NaN          NaN\n",
       "13       0000040608754  1345024108      1439141731    NaN          NaN\n",
       "14            00000758  1409582884      1424687936    NaN          NaN\n",
       "15       0000084154071  1351359717      1440779523    NaN          NaN\n",
       "16       0000087177756  1433586486      1433589206    NaN          NaN\n",
       "17            00001373  1412788478      1441556289    NaN          NaN\n",
       "18            00002929  1424460617      1424829673    NaN          NaN\n",
       "19            00003100  1415119256      1428327438    NaN          NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts[:20][['code', 'created_t', 'last_modified_t', 'cities', 'allergens_en']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "foodFacts_3and5Removed = foodFacts.drop(foodFacts.columns[[3,5]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['code', 'created_datetime', 'product_name', 'purchase_places',\n",
       "       'traces_tags'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts_3and5Removed.columns[[0,3,5,27,36]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['code', 'url', 'creator', 'created_datetime', 'last_modified_datetime',\n",
       "       'product_name', 'generic_name', 'quantity', 'packaging',\n",
       "       'packaging_tags',\n",
       "       ...\n",
       "       'caffeine_100g', 'taurine_100g', 'ph_100g',\n",
       "       'fruits_vegetables_nuts_100g', 'collagen_meat_protein_ratio_100g',\n",
       "       'cocoa_100g', 'chlorophyl_100g', 'carbon_footprint_100g',\n",
       "       'nutrition_score_fr_100g', 'nutrition_score_uk_100g'],\n",
       "      dtype='object', length=157)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts_3and5Removed.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "percentage_UK = (foodFacts_3and5Removed[foodFacts_3and5Removed.nutrition_score_uk_100g.notnull()].size/ \\\n",
    "      foodFacts_3and5Removed.size)*100\n",
    "a = percentage_UK.round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'48.0%'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(a) + '%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts_3and5Removed.caffeine_100g.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'8861    Red Bull energy drink\\nName: product_name, dtype: object'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(foodFacts_3and5Removed[foodFacts_3and5Removed.caffeine_100g == 32].product_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12756    0.090\n",
       "32423    0.082\n",
       "55289    0.064\n",
       "54897    0.044\n",
       "12835    0.007\n",
       "Name: arachidonic_acid_100g, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_facts_arch_acid = foodFacts_3and5Removed.arachidonic_acid_100g.sort_values(ascending = False).dropna()\n",
    "food_facts_arch_acid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_name</th>\n",
       "      <th>arachidonic_acid_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>12756</td>\n",
       "      <td>Lait Gallia Calisma</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32423</td>\n",
       "      <td>Dès la naissance</td>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55289</td>\n",
       "      <td>Dès la naissance</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54897</td>\n",
       "      <td>Nidal Natéa 2</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12835</td>\n",
       "      <td>Gallia croissance</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              product_name  arachidonic_acid_100g\n",
       "12756  Lait Gallia Calisma                  0.090\n",
       "32423     Dès la naissance                  0.082\n",
       "55289     Dès la naissance                  0.064\n",
       "54897        Nidal Natéa 2                  0.044\n",
       "12835    Gallia croissance                  0.007"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts_3and5Removed.nlargest(5,'arachidonic_acid_100g')[['product_name','arachidonic_acid_100g']].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>url</th>\n",
       "      <th>creator</th>\n",
       "      <th>created_datetime</th>\n",
       "      <th>last_modified_datetime</th>\n",
       "      <th>product_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>packaging</th>\n",
       "      <th>packaging_tags</th>\n",
       "      <th>...</th>\n",
       "      <th>caffeine_100g</th>\n",
       "      <th>taurine_100g</th>\n",
       "      <th>ph_100g</th>\n",
       "      <th>fruits_vegetables_nuts_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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>12756</td>\n",
       "      <td>3041090001864</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/3041...</td>\n",
       "      <td>minouche</td>\n",
       "      <td>2012-06-30T11:29:50Z</td>\n",
       "      <td>2014-02-02T22:59:40Z</td>\n",
       "      <td>Lait Gallia Calisma</td>\n",
       "      <td>Lait en poudre premier âge</td>\n",
       "      <td>900 g</td>\n",
       "      <td>Carton,Plastique</td>\n",
       "      <td>carton,plastique</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035</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>26.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32423</td>\n",
       "      <td>3379365001449</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/3379...</td>\n",
       "      <td>dnicolas80</td>\n",
       "      <td>2014-05-03T09:16:30Z</td>\n",
       "      <td>2015-05-14T10:13:14Z</td>\n",
       "      <td>Dès la naissance</td>\n",
       "      <td>Lait pour nourissons</td>\n",
       "      <td>900 g</td>\n",
       "      <td>boite</td>\n",
       "      <td>boite</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.039</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>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55289</td>\n",
       "      <td>7613034726087</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/7613...</td>\n",
       "      <td>openfoodfacts-contributors</td>\n",
       "      <td>2014-11-23T09:05:39Z</td>\n",
       "      <td>2015-10-31T16:50:32Z</td>\n",
       "      <td>Dès la naissance</td>\n",
       "      <td>Lait pour nourrissons en poudre de la naissanc...</td>\n",
       "      <td>820 g</td>\n",
       "      <td>Boîte,Métal</td>\n",
       "      <td>boite,metal</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035</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>27.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54897</td>\n",
       "      <td>7613032506636</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/7613...</td>\n",
       "      <td>dalisson</td>\n",
       "      <td>2012-09-22T19:36:34Z</td>\n",
       "      <td>2013-12-20T21:13:58Z</td>\n",
       "      <td>Nidal Natéa 2</td>\n",
       "      <td>Lait infantile reconstitué</td>\n",
       "      <td>800 g</td>\n",
       "      <td>Boîte</td>\n",
       "      <td>boite</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>25.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12835</td>\n",
       "      <td>3041090086342</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/3041...</td>\n",
       "      <td>openfoodfacts-contributors</td>\n",
       "      <td>2014-11-04T00:19:24Z</td>\n",
       "      <td>2014-11-04T05:29:48Z</td>\n",
       "      <td>Gallia croissance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900 g</td>\n",
       "      <td>Bocal,Fer</td>\n",
       "      <td>bocal,fer</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.037</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>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 157 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                code                                                url  \\\n",
       "12756  3041090001864  http://world-en.openfoodfacts.org/product/3041...   \n",
       "32423  3379365001449  http://world-en.openfoodfacts.org/product/3379...   \n",
       "55289  7613034726087  http://world-en.openfoodfacts.org/product/7613...   \n",
       "54897  7613032506636  http://world-en.openfoodfacts.org/product/7613...   \n",
       "12835  3041090086342  http://world-en.openfoodfacts.org/product/3041...   \n",
       "\n",
       "                          creator      created_datetime  \\\n",
       "12756                    minouche  2012-06-30T11:29:50Z   \n",
       "32423                  dnicolas80  2014-05-03T09:16:30Z   \n",
       "55289  openfoodfacts-contributors  2014-11-23T09:05:39Z   \n",
       "54897                    dalisson  2012-09-22T19:36:34Z   \n",
       "12835  openfoodfacts-contributors  2014-11-04T00:19:24Z   \n",
       "\n",
       "      last_modified_datetime         product_name  \\\n",
       "12756   2014-02-02T22:59:40Z  Lait Gallia Calisma   \n",
       "32423   2015-05-14T10:13:14Z     Dès la naissance   \n",
       "55289   2015-10-31T16:50:32Z     Dès la naissance   \n",
       "54897   2013-12-20T21:13:58Z        Nidal Natéa 2   \n",
       "12835   2014-11-04T05:29:48Z    Gallia croissance   \n",
       "\n",
       "                                            generic_name quantity  \\\n",
       "12756                         Lait en poudre premier âge    900 g   \n",
       "32423                               Lait pour nourissons    900 g   \n",
       "55289  Lait pour nourrissons en poudre de la naissanc...    820 g   \n",
       "54897                         Lait infantile reconstitué    800 g   \n",
       "12835                                                NaN    900 g   \n",
       "\n",
       "              packaging    packaging_tags  ... caffeine_100g taurine_100g  \\\n",
       "12756  Carton,Plastique  carton,plastique  ...           NaN        0.035   \n",
       "32423             boite             boite  ...           NaN        0.039   \n",
       "55289       Boîte,Métal       boite,metal  ...           NaN        0.035   \n",
       "54897             Boîte             boite  ...           NaN          NaN   \n",
       "12835         Bocal,Fer         bocal,fer  ...           NaN        0.037   \n",
       "\n",
       "      ph_100g fruits_vegetables_nuts_100g collagen_meat_protein_ratio_100g  \\\n",
       "12756     NaN                         NaN                              NaN   \n",
       "32423     NaN                         NaN                              NaN   \n",
       "55289     NaN                         NaN                              NaN   \n",
       "54897     NaN                         NaN                              NaN   \n",
       "12835     NaN                         NaN                              NaN   \n",
       "\n",
       "      cocoa_100g chlorophyl_100g carbon_footprint_100g  \\\n",
       "12756        NaN             NaN                   NaN   \n",
       "32423        NaN             NaN                   NaN   \n",
       "55289        NaN             NaN                   NaN   \n",
       "54897        NaN             NaN                   NaN   \n",
       "12835        NaN             NaN                   NaN   \n",
       "\n",
       "      nutrition_score_fr_100g nutrition_score_uk_100g  \n",
       "12756                    26.0                    26.0  \n",
       "32423                    22.0                    22.0  \n",
       "55289                    27.0                    27.0  \n",
       "54897                    25.0                    25.0  \n",
       "12835                    16.0                    16.0  \n",
       "\n",
       "[5 rows x 157 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts_3and5Removed.nlargest(5,'arachidonic_acid_100g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this data, it seems like this data is depicting different products sold in the UK and France, their manufacturers, typical quantities they are sold in, their packaging information and most importantly, their nutritional value disected by multiple different compounds. All this data is used to aggregate a total nutrition score in France and UK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "code\n",
      "url\n",
      "creator\n",
      "created_datetime\n",
      "last_modified_datetime\n",
      "product_name\n",
      "generic_name\n",
      "quantity\n",
      "packaging\n",
      "packaging_tags\n",
      "brands\n",
      "brands_tags\n",
      "categories\n",
      "categories_tags\n",
      "categories_en\n",
      "origins\n",
      "origins_tags\n",
      "manufacturing_places\n",
      "manufacturing_places_tags\n",
      "labels\n",
      "labels_tags\n",
      "labels_en\n",
      "emb_codes\n",
      "emb_codes_tags\n",
      "first_packaging_code_geo\n",
      "cities\n",
      "cities_tags\n",
      "purchase_places\n",
      "stores\n",
      "countries\n",
      "countries_tags\n",
      "countries_en\n",
      "ingredients_text\n",
      "allergens\n",
      "allergens_en\n",
      "traces\n",
      "traces_tags\n",
      "traces_en\n",
      "serving_size\n",
      "no_nutriments\n",
      "additives_n\n",
      "additives\n",
      "additives_tags\n",
      "additives_en\n",
      "ingredients_from_palm_oil_n\n",
      "ingredients_from_palm_oil\n",
      "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\n",
      "nutrition_grade_uk\n",
      "nutrition_grade_fr\n",
      "pnns_groups_1\n",
      "pnns_groups_2\n",
      "states\n",
      "states_tags\n",
      "states_en\n",
      "main_category\n",
      "main_category_en\n",
      "image_url\n",
      "image_small_url\n",
      "energy_100g\n",
      "energy_from_fat_100g\n",
      "fat_100g\n",
      "saturated_fat_100g\n",
      "butyric_acid_100g\n",
      "caproic_acid_100g\n",
      "caprylic_acid_100g\n",
      "capric_acid_100g\n",
      "lauric_acid_100g\n",
      "myristic_acid_100g\n",
      "palmitic_acid_100g\n",
      "stearic_acid_100g\n",
      "arachidic_acid_100g\n",
      "behenic_acid_100g\n",
      "lignoceric_acid_100g\n",
      "cerotic_acid_100g\n",
      "montanic_acid_100g\n",
      "melissic_acid_100g\n",
      "monounsaturated_fat_100g\n",
      "polyunsaturated_fat_100g\n",
      "omega_3_fat_100g\n",
      "alpha_linolenic_acid_100g\n",
      "eicosapentaenoic_acid_100g\n",
      "docosahexaenoic_acid_100g\n",
      "omega_6_fat_100g\n",
      "linoleic_acid_100g\n",
      "arachidonic_acid_100g\n",
      "gamma_linolenic_acid_100g\n",
      "dihomo_gamma_linolenic_acid_100g\n",
      "omega_9_fat_100g\n",
      "oleic_acid_100g\n",
      "elaidic_acid_100g\n",
      "gondoic_acid_100g\n",
      "mead_acid_100g\n",
      "erucic_acid_100g\n",
      "nervonic_acid_100g\n",
      "trans_fat_100g\n",
      "cholesterol_100g\n",
      "carbohydrates_100g\n",
      "sugars_100g\n",
      "sucrose_100g\n",
      "glucose_100g\n",
      "fructose_100g\n",
      "lactose_100g\n",
      "maltose_100g\n",
      "maltodextrins_100g\n",
      "starch_100g\n",
      "polyols_100g\n",
      "fiber_100g\n",
      "proteins_100g\n",
      "casein_100g\n",
      "serum_proteins_100g\n",
      "nucleotides_100g\n",
      "salt_100g\n",
      "sodium_100g\n",
      "alcohol_100g\n",
      "vitamin_a_100g\n",
      "beta_carotene_100g\n",
      "vitamin_d_100g\n",
      "vitamin_e_100g\n",
      "vitamin_k_100g\n",
      "vitamin_c_100g\n",
      "vitamin_b1_100g\n",
      "vitamin_b2_100g\n",
      "vitamin_pp_100g\n",
      "vitamin_b6_100g\n",
      "vitamin_b9_100g\n",
      "vitamin_b12_100g\n",
      "biotin_100g\n",
      "pantothenic_acid_100g\n",
      "silica_100g\n",
      "bicarbonate_100g\n",
      "potassium_100g\n",
      "chloride_100g\n",
      "calcium_100g\n",
      "phosphorus_100g\n",
      "iron_100g\n",
      "magnesium_100g\n",
      "zinc_100g\n",
      "copper_100g\n",
      "manganese_100g\n",
      "fluoride_100g\n",
      "selenium_100g\n",
      "chromium_100g\n",
      "molybdenum_100g\n",
      "iodine_100g\n",
      "caffeine_100g\n",
      "taurine_100g\n",
      "ph_100g\n",
      "fruits_vegetables_nuts_100g\n",
      "collagen_meat_protein_ratio_100g\n",
      "cocoa_100g\n",
      "chlorophyl_100g\n",
      "carbon_footprint_100g\n",
      "nutrition_score_fr_100g\n",
      "nutrition_score_uk_100g\n"
     ]
    }
   ],
   "source": [
    "for col in foodFacts_3and5Removed.columns: \n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1195cc650>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "foodFacts_3and5Removed.groupby('countries_en').arachidonic_acid_100g.sum().plot(figsize=(30,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>categories</th>\n",
       "      <th>arachidonic_acid_100g</th>\n",
       "      <th>countries</th>\n",
       "      <th>countries_tags</th>\n",
       "      <th>main_category_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Filet de bœuf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>fr:Filet-de-boeuf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Plant-based foods and beverages,Plant-based fo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>en:united-kingdom</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Aliments et boissons à base de végétaux,Alimen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Root bier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>fr:Root-bier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Aliments et boissons à base de végétaux,Boisso...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:ES</td>\n",
       "      <td>en:spain</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Snacks sucrés,Desserts,Biscuits et gâteaux,Gât...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Royaume-Uni</td>\n",
       "      <td>en:united-kingdom</td>\n",
       "      <td>Desserts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Desserts,Sugary snacks,Biscuits and cakes,Cake...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>en:united-kingdom</td>\n",
       "      <td>Sugary snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Sodas au cola</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Plant-based foods and beverages,Plant-based fo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>en:united-kingdom</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Comestibles,Condimentos,Salsas,Salsas de mostaza</td>\n",
       "      <td>NaN</td>\n",
       "      <td>España</td>\n",
       "      <td>en:spain</td>\n",
       "      <td>Groceries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Sodas au citron,Boissons sucrées</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Plant-based foods and beverages,Plant-based fo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>en:united-kingdom</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Flat Parsley,Parsley,Herbs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>en:united-kingdom</td>\n",
       "      <td>Groceries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Viandes de porc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Meats</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Chips,Chips and fries</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>en:united-states</td>\n",
       "      <td>Chips and fries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:GB</td>\n",
       "      <td>en:united-kingdom</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Sugary snacks,Confectioneries,Candies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Germany</td>\n",
       "      <td>en:germany</td>\n",
       "      <td>Sugary snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Sugary snacks,Confectioneries,Candies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Germany</td>\n",
       "      <td>en:germany</td>\n",
       "      <td>Sugary snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Bonbons</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Usa</td>\n",
       "      <td>en:united-states</td>\n",
       "      <td>Sugary snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:GB</td>\n",
       "      <td>en:united-kingdom</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>onions</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>en:united-kingdom</td>\n",
       "      <td>Groceries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Soda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>Gâteaux,Goûters individuels</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Sugary snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>Foie gras de canard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Fish and meat and eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>Foie gras de canard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Fish and meat and eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>fr:Pâté au foie de canard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>fr:Pate-au-foie-de-canard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>Bloc de foie gras,Foie gras de canard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Fish and meat and eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>foie gras de canard aux cèpes,tartinable au fo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Fish and meat and eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Foies gras de d'oies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Fish and meat and eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>Foie gras de d'oie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Fish and meat and eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>fr:Bonbons à la menthe,en:mints</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France,United Kingdom</td>\n",
       "      <td>en:france,en:united-kingdom</td>\n",
       "      <td>Groceries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>Cookies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Sugary snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           categories  arachidonic_acid_100g  \\\n",
       "0                                                 NaN                    NaN   \n",
       "1                                       Filet de bœuf                    NaN   \n",
       "2                                                 NaN                    NaN   \n",
       "3                                                 NaN                    NaN   \n",
       "4                                                 NaN                    NaN   \n",
       "5   Plant-based foods and beverages,Plant-based fo...                    NaN   \n",
       "6   Aliments et boissons à base de végétaux,Alimen...                    NaN   \n",
       "7                                           Root bier                    NaN   \n",
       "8   Aliments et boissons à base de végétaux,Boisso...                    NaN   \n",
       "9                                                 NaN                    NaN   \n",
       "10  Snacks sucrés,Desserts,Biscuits et gâteaux,Gât...                    NaN   \n",
       "11  Desserts,Sugary snacks,Biscuits and cakes,Cake...                    NaN   \n",
       "12                                                NaN                    NaN   \n",
       "13                                      Sodas au cola                    NaN   \n",
       "14  Plant-based foods and beverages,Plant-based fo...                    NaN   \n",
       "15   Comestibles,Condimentos,Salsas,Salsas de mostaza                    NaN   \n",
       "16                   Sodas au citron,Boissons sucrées                    NaN   \n",
       "17  Plant-based foods and beverages,Plant-based fo...                    NaN   \n",
       "18                         Flat Parsley,Parsley,Herbs                    NaN   \n",
       "19                                    Viandes de porc                    NaN   \n",
       "20                              Chips,Chips and fries                    NaN   \n",
       "21                                                NaN                    NaN   \n",
       "22              Sugary snacks,Confectioneries,Candies                    NaN   \n",
       "23              Sugary snacks,Confectioneries,Candies                    NaN   \n",
       "24                                            Bonbons                    NaN   \n",
       "25                                                NaN                    NaN   \n",
       "26                                             onions                    NaN   \n",
       "27                                               Soda                    NaN   \n",
       "28                                                NaN                    NaN   \n",
       "29                        Gâteaux,Goûters individuels                    NaN   \n",
       "30                                Foie gras de canard                    NaN   \n",
       "31                                                NaN                    NaN   \n",
       "32                                                NaN                    NaN   \n",
       "33                                                NaN                    NaN   \n",
       "34                                                NaN                    NaN   \n",
       "35                                                NaN                    NaN   \n",
       "36                                                NaN                    NaN   \n",
       "37                                Foie gras de canard                    NaN   \n",
       "38                          fr:Pâté au foie de canard                    NaN   \n",
       "39                                                NaN                    NaN   \n",
       "40              Bloc de foie gras,Foie gras de canard                    NaN   \n",
       "41                                                NaN                    NaN   \n",
       "42  foie gras de canard aux cèpes,tartinable au fo...                    NaN   \n",
       "43                               Foies gras de d'oies                    NaN   \n",
       "44                                 Foie gras de d'oie                    NaN   \n",
       "45                                                NaN                    NaN   \n",
       "46                    fr:Bonbons à la menthe,en:mints                    NaN   \n",
       "47                                                NaN                    NaN   \n",
       "48                                            Cookies                    NaN   \n",
       "49                                                NaN                    NaN   \n",
       "\n",
       "                countries               countries_tags  \\\n",
       "0                   en:FR                    en:france   \n",
       "1                  France                    en:france   \n",
       "2                  France                    en:france   \n",
       "3                  France                    en:france   \n",
       "4                  France                    en:france   \n",
       "5          United Kingdom            en:united-kingdom   \n",
       "6                  France                    en:france   \n",
       "7                  France                    en:france   \n",
       "8                  France                    en:france   \n",
       "9                   en:ES                     en:spain   \n",
       "10            Royaume-Uni            en:united-kingdom   \n",
       "11         United Kingdom            en:united-kingdom   \n",
       "12                  en:FR                    en:france   \n",
       "13                 France                    en:france   \n",
       "14         United Kingdom            en:united-kingdom   \n",
       "15                 España                     en:spain   \n",
       "16                 France                    en:france   \n",
       "17         United Kingdom            en:united-kingdom   \n",
       "18         United Kingdom            en:united-kingdom   \n",
       "19                 France                    en:france   \n",
       "20          United States             en:united-states   \n",
       "21                  en:GB            en:united-kingdom   \n",
       "22                Germany                   en:germany   \n",
       "23                Germany                   en:germany   \n",
       "24                    Usa             en:united-states   \n",
       "25                  en:GB            en:united-kingdom   \n",
       "26         United Kingdom            en:united-kingdom   \n",
       "27                 France                    en:france   \n",
       "28                  en:FR                    en:france   \n",
       "29                 France                    en:france   \n",
       "30                 France                    en:france   \n",
       "31                  en:FR                    en:france   \n",
       "32                  en:FR                    en:france   \n",
       "33                  en:FR                    en:france   \n",
       "34                  en:FR                    en:france   \n",
       "35                  en:FR                    en:france   \n",
       "36                  en:FR                    en:france   \n",
       "37                 France                    en:france   \n",
       "38                 France                    en:france   \n",
       "39                  en:FR                    en:france   \n",
       "40                 France                    en:france   \n",
       "41                  en:FR                    en:france   \n",
       "42                 France                    en:france   \n",
       "43                 France                    en:france   \n",
       "44                 France                    en:france   \n",
       "45                  en:FR                    en:france   \n",
       "46  France,United Kingdom  en:france,en:united-kingdom   \n",
       "47                  en:FR                    en:france   \n",
       "48                 France                    en:france   \n",
       "49                 France                    en:france   \n",
       "\n",
       "                   main_category_en  \n",
       "0                               NaN  \n",
       "1                 fr:Filet-de-boeuf  \n",
       "2                               NaN  \n",
       "3                               NaN  \n",
       "4                               NaN  \n",
       "5   Plant-based foods and beverages  \n",
       "6   Plant-based foods and beverages  \n",
       "7                      fr:Root-bier  \n",
       "8                         Beverages  \n",
       "9                               NaN  \n",
       "10                         Desserts  \n",
       "11                    Sugary snacks  \n",
       "12                              NaN  \n",
       "13                        Beverages  \n",
       "14  Plant-based foods and beverages  \n",
       "15                        Groceries  \n",
       "16                        Beverages  \n",
       "17  Plant-based foods and beverages  \n",
       "18                        Groceries  \n",
       "19                            Meats  \n",
       "20                  Chips and fries  \n",
       "21                              NaN  \n",
       "22                    Sugary snacks  \n",
       "23                    Sugary snacks  \n",
       "24                    Sugary snacks  \n",
       "25                              NaN  \n",
       "26                        Groceries  \n",
       "27                        Beverages  \n",
       "28                              NaN  \n",
       "29                    Sugary snacks  \n",
       "30           Fish and meat and eggs  \n",
       "31                              NaN  \n",
       "32                              NaN  \n",
       "33                              NaN  \n",
       "34                              NaN  \n",
       "35                              NaN  \n",
       "36                              NaN  \n",
       "37           Fish and meat and eggs  \n",
       "38        fr:Pate-au-foie-de-canard  \n",
       "39                              NaN  \n",
       "40           Fish and meat and eggs  \n",
       "41                              NaN  \n",
       "42           Fish and meat and eggs  \n",
       "43           Fish and meat and eggs  \n",
       "44           Fish and meat and eggs  \n",
       "45                              NaN  \n",
       "46                        Groceries  \n",
       "47                              NaN  \n",
       "48                    Sugary snacks  \n",
       "49                              NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts_3and5Removed[:50][['categories', 'arachidonic_acid_100g','countries','countries_tags','main_category_en']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_name</th>\n",
       "      <th>arachidonic_acid_100g</th>\n",
       "      <th>countries</th>\n",
       "      <th>countries_tags</th>\n",
       "      <th>main_category_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>12756</td>\n",
       "      <td>Lait Gallia Calisma</td>\n",
       "      <td>0.09</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Baby foods</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              product_name  arachidonic_acid_100g countries countries_tags  \\\n",
       "12756  Lait Gallia Calisma                   0.09    France      en:france   \n",
       "\n",
       "      main_category_en  \n",
       "12756       Baby foods  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts_3and5Removed[foodFacts_3and5Removed.arachidonic_acid_100g ==\\\n",
    "foodFacts_3and5Removed.arachidonic_acid_100g.max()]\\\n",
    "[['product_name','arachidonic_acid_100g','countries','countries_tags','main_category_en']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What this tells us is that Baby foods in France have the highest amount of arachidonic acid per 100 grams of product. Since arachidonic acid is promotes the growth and repair of skeletal muscle and bone growth, its presence in high quantities in baby food makes sense. Now looking at the top 5 product main categories that have the highest arachidonic acid per 100 grams of product, below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_name</th>\n",
       "      <th>arachidonic_acid_100g</th>\n",
       "      <th>countries</th>\n",
       "      <th>countries_tags</th>\n",
       "      <th>main_category_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>12756</td>\n",
       "      <td>Lait Gallia Calisma</td>\n",
       "      <td>0.090</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Baby foods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32423</td>\n",
       "      <td>Dès la naissance</td>\n",
       "      <td>0.082</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Baby foods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55289</td>\n",
       "      <td>Dès la naissance</td>\n",
       "      <td>0.064</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Baby foods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54897</td>\n",
       "      <td>Nidal Natéa 2</td>\n",
       "      <td>0.044</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Baby foods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12835</td>\n",
       "      <td>Gallia croissance</td>\n",
       "      <td>0.007</td>\n",
       "      <td>France</td>\n",
       "      <td>en:france</td>\n",
       "      <td>Baby foods</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              product_name  arachidonic_acid_100g countries countries_tags  \\\n",
       "12756  Lait Gallia Calisma                  0.090    France      en:france   \n",
       "32423     Dès la naissance                  0.082    France      en:france   \n",
       "55289     Dès la naissance                  0.064    France      en:france   \n",
       "54897        Nidal Natéa 2                  0.044    France      en:france   \n",
       "12835    Gallia croissance                  0.007    France      en:france   \n",
       "\n",
       "      main_category_en  \n",
       "12756       Baby foods  \n",
       "32423       Baby foods  \n",
       "55289       Baby foods  \n",
       "54897       Baby foods  \n",
       "12835       Baby foods  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts_3and5Removed.nlargest(5,'arachidonic_acid_100g')\\\n",
    "[['product_name','arachidonic_acid_100g','countries','countries_tags','main_category_en']].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data frame instance sliced above confirms that high presence of arachidonic acid per 100 grams of product is found in Baby foods. Additionally, we can also infer the top 5 products in that category along with their country of origin: France. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a9291d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12473 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12490 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12483 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12463 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 33747 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 23376 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 29006 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 33590 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 37284 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 27833 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12402 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12394 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12354 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12425 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12428 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12459 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12501 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12521 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12540 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12513 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12531 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12461 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12515 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12486 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12451 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12488 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12522 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12520 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12523 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12358 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12414 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 12356 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26834 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12473 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12490 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12483 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12463 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 33747 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 23376 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 29006 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 33590 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 37284 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 27833 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12402 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12394 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12354 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12425 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12428 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12459 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12501 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12521 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12540 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12513 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12531 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12461 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12515 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12486 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12451 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12488 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12522 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12520 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12523 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12358 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12414 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 12356 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/a1394620/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26834 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "foodFacts_3and5Removed.groupby('main_category_en').nutrition_score_uk_100g.sum().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13631dad0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "foodFacts_3and5Removed[:100].nutrition_score_uk_100g.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f700590>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "foodFacts_3and5Removed['nutrition_score_uk_100g'].dropna().value_counts().plot(kind='bar',figsize=(50,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_fr_nutritional_count = foodFacts_3and5Removed.nutrition_score_fr_100g.dropna().count()\n",
    "total_fr_nutritional_sum = foodFacts_3and5Removed.nutrition_score_fr_100g.dropna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_uk_nutritional_count = foodFacts_3and5Removed.nutrition_score_uk_100g.dropna().count()\n",
    "total_uk_nutritional_sum = foodFacts_3and5Removed.nutrition_score_fr_100g.dropna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.948168978078865"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_mean = (total_fr_nutritional_sum + total_uk_nutritional_sum)/ (total_fr_nutritional_count + total_uk_nutritional_count)\n",
    "total_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.948168978078865\n",
      "7.68719243305426\n"
     ]
    }
   ],
   "source": [
    "mean_fr_nutrition = foodFacts_3and5Removed.nutrition_score_fr_100g.dropna().mean()\n",
    "mean_uk_nutrition = foodFacts_3and5Removed.nutrition_score_uk_100g.dropna().mean()\n",
    "print(mean_fr_nutrition)\n",
    "print(mean_uk_nutrition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "fr_min = (foodFacts_3and5Removed.nutrition_score_fr_100g.dropna().min())\n",
    "fr_max = (foodFacts_3and5Removed.nutrition_score_fr_100g.dropna().max())\n",
    "uk_min = (foodFacts_3and5Removed.nutrition_score_uk_100g.dropna().min())\n",
    "uk_max = (foodFacts_3and5Removed.nutrition_score_uk_100g.dropna().max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-14.0\n",
      "35.0\n",
      "-14.0\n",
      "33.0\n"
     ]
    }
   ],
   "source": [
    "print(fr_min)\n",
    "print(fr_max)\n",
    "print(uk_min)\n",
    "print(uk_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.84842370513745"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_mean = mean_fr_nutrition/(fr_min + fr_max) * 100\n",
    "fr_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.45890754239084"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uk_mean = mean_uk_nutrition/(uk_min + uk_max) * 100\n",
    "uk_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.15366562376414"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_mean_percentage = (fr_mean + uk_mean)/2\n",
    "total_mean_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a total average nutritional score percentage of 39.15%, majority of food types in this dataset are unhealthy. They need to be an average of 50% or above to be certified as a majorly healthy dataset of food types and categories. \n",
    "That being said, the average nutritional score percentage of the same food types is higher in the UK (37.84%) versus France. (40.45%).\n",
    "\n",
    "This data set is skewed since there is no explanation of why a nutritional score would max out at 35/33 and go into negative values. The formula of calculating the nutritional score is unknown which makes this data set flawed. \n",
    "\n",
    "The other reason of why this data set is not a 100 percent accurate is that the contributors of this data are not certified government bodies who have proofs of the reliability of this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                date-limite-app\n",
       "1                date-limite-app\n",
       "2                        tacinte\n",
       "3                        tacinte\n",
       "4                       teolemon\n",
       "5                       bcatelin\n",
       "6                        sigoise\n",
       "7                       teolemon\n",
       "8                       sebleouf\n",
       "9     openfoodfacts-contributors\n",
       "10                       tacinte\n",
       "11                       tacinte\n",
       "12               date-limite-app\n",
       "13                         andre\n",
       "14                       tacinte\n",
       "15                       javichu\n",
       "16                        tacite\n",
       "17                       tacinte\n",
       "18                       tacinte\n",
       "19    openfoodfacts-contributors\n",
       "20    openfoodfacts-contributors\n",
       "21                          kyzh\n",
       "22                      malikele\n",
       "23                      malikele\n",
       "24                      malikele\n",
       "25                          kyzh\n",
       "26                          kyzh\n",
       "27    openfoodfacts-contributors\n",
       "28               date-limite-app\n",
       "29                       beniben\n",
       "30               date-limite-app\n",
       "31               date-limite-app\n",
       "32               date-limite-app\n",
       "33               date-limite-app\n",
       "34               date-limite-app\n",
       "35               date-limite-app\n",
       "36               date-limite-app\n",
       "37               date-limite-app\n",
       "38               date-limite-app\n",
       "39               date-limite-app\n",
       "40               date-limite-app\n",
       "41               date-limite-app\n",
       "42               date-limite-app\n",
       "43               date-limite-app\n",
       "44               date-limite-app\n",
       "45    openfoodfacts-contributors\n",
       "46    openfoodfacts-contributors\n",
       "47                      teolemon\n",
       "48    openfoodfacts-contributors\n",
       "49                      teolemon\n",
       "Name: creator, dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodFacts_3and5Removed.creator.dropna()[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65435"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_unique_creators = foodFacts_3and5Removed.creator.dropna().count()\n",
    "total_unique_creators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15814"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_openfoodfacts_contributors = foodFacts_3and5Removed.creator[foodFacts_3and5Removed.creator == \"openfoodfacts-contributors\"].index.size\n",
    "total_openfoodfacts_contributors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_openfoodfacts_contributor_percentage = ((total_openfoodfacts_contributors/total_unique_creators)*100).round()\n",
    "total_openfoodfacts_contributor_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "openfoodfacts-contributors    15814\n",
       "tacite                         4452\n",
       "date-limite-app                3980\n",
       "sebleouf                       3260\n",
       "tacinte                        3189\n",
       "javichu                        2544\n",
       "stephane                       2367\n",
       "jeanbono                       1721\n",
       "miles67off                     1406\n",
       "teolemon                       1277\n",
       "Name: creator, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10Creators = foodFacts_3and5Removed.creator.dropna().value_counts().head(10)\n",
    "top10Creators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_graph = top10Creators.plot(kind='bar',figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fat_100g</th>\n",
       "      <th>nutrition_score_uk_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fat_100g  nutrition_score_uk_100g\n",
       "10       7.0                     10.0\n",
       "11       7.0                     10.0\n",
       "13       0.0                      2.0\n",
       "16       0.0                      2.0\n",
       "22       0.0                     14.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelling_df = foodFacts_3and5Removed[['fat_100g','nutrition_score_uk_100g']].dropna()\n",
    "modelling_df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f0d33d0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xorEYVNLeUKv82rPeB48fh78aXKcr508fEaHaqDvEWPP5V/t9aWMu9G3Mx6mfq2t/+1rK1F6/SmldtrMbavDOgaNJ6/hVwu3L0sc+2OkmTvWUcsl1ZcaxSE1Ep1s09wF4l5kLniXOrUjd0d2Pc+9aZ7ls1ZXzMs5oSjdLwy7eQSgdVAIeuWIeZk6sxZ+37sWNv9uSss6qK+dhxQMvp+Q4spufn65ORLpZTDt7jmJG/Sj4fSpqAir29A3iiw+1JX2/jHP8O7r70d55aMRmMVnFLAR9Cn6+shUnTxmdVV2NHy6fiy89ujml3acQIrH0f1errpyHo+EoDg9GMLrah5OnjMmq5orTWiGlnuvKqziI/wRwOoDNiFeJjP9eT0T/xMxP52zpCJLOL7tu+4GMA0S6OeV28Q5C6eBTFfh9Kuprg/DZ+MHXbT+QMlc/3fz89s5Dtjds8/fJ+Nn8XTwSiiKgqhiOHHsuM87xb2qoG9HprVYxCwFVwZhqf9q/kXRxDk+8vtey3Ymkt7PnKJa1Tsu8YhpbjLVCzO3mOBO77e3iUUoJNxrETgCnMXMrM38AwGkAtgA4F8BtebAtr6Tzy2bSITLROK4aURbfUinDfMxnbfddWTRrgqUeZff98UoLKLbcStnYk0l7WXrqJJslmUcIt+fZzv6WaWMdHVexXQ8vcTNAvI+Zt+ofmHkbtAHjHe/Nyj9NDXVYOX96SvvCpnpXAXNAsjiluwa+ct5sqDKBqWQ57/3HofdIKOGuubg5+YZ11onjAQCfPWsG/Aowyq8goBKuXHgCtuzpwyLTlMpFs+qxp28IPQPDCfG6bUcPNnUeQkd3P9a9vR/r3t6Hju7+FKHTLH7qukWVX0Fd0Icqv4JvLJ2Drt5B9AwMo21HD+58+i207ehxLZxmI7Tq9gR9SuLntkvnAkDavq5a3ISgj1L+TlbOn46FJx2HlsYxSe0Lm+rxg8u0/ejbmN8odM3FzXFYnc/bLp2LpoY6y3bzW4Hd9qX+9gC40yAeAXAQwG/jTR8HMAHApwCsZ+YP5sVCh2QTKDfve3/GXoNIPX6UD6/edL6rPozi1FAkCmaGSoThKCPoUxCOxuJJxhQMhqOwcp/6CAmRTjH8LuQPXfD1kuSYZmfrWG0TVAmkEG67dG5SQSiz+Kk/jGzZ3YdvP7nNtmhUXdDnSDjNRWg1i/ILm+rxyru9GYtjhaIx1AZU9Bw9lrp78ugAegcj8CsKhiMxfGj2RHxx4Uy0zqzH6vbduPa37Sn7r/IpYAC3L0t/ztJhpys6jQovpVxXRjwpGERE1QD+GcDZ0L7X66HpEkMARjHzgDfmZofbAeL3r3ZaimA/XD4XHzvdmf8y20I/guCEoI8AUNJUT7P46eY7mCmRX7ZCa7oJH+a+ALj+mzFuO+97zyDdpk7OmZCMJ5HUzDwI4L8BfI6ZP8bMP2Dmo8wcK/TgkA12IphduxW5BDkJQiZUUlICLc1BZ26+g+kC1nIJcHMSiKf3lc3fjHHbTBqEk3MmOCfjLCYimg5NhF4C4FC8bQyAtQBuZOad+TQwXyw9dRKeeWNfSvvJk2qxqfNQUoGSmoA2Q0T/X3+FzCXISRAyMRiOwme6l4ai0aRAvNd2HUyazZSOdMJpLkKrE1F4OBLBa7sO4pQpY1z/zSTbkd7jEeUYwMkDRCgaQ99gGD0Dw3l9izAXAiuHN5aMLiYi2gDghwBWMXM03qYCuAzAl5g5c3a7ESAbDeL9//4HDFrEKqgEKAqBmeFTFQyFY/ApQCSW7B++uGUqblr9elKglCDkmx9d3oK2nQeTfP568rpcNAinwX2W25r+DsxJA42Y9Ylx1b6kgFWjBmG2e037blxjoUEYbW49fnwi8FDXBY1Be/kIYLMrBFYKwXI5aRBEtJ2ZZ7ldNtJ4GSjnBD2Q5sIf/1UC4oSi4N8ueB+++4c3U9rv+cRpGQsWeRHspc/4mlE/KiWA0MyqK+fB71MTb+PPbtuLp7d147w5DVgyZ1LGQNSnt+7F1j2H8cgru5I0CaNesXVPX0pAYT70iEyFwIr9TSLXQLmNRPSfAB4EoKeOnAbg0wBe88bEkSfXBGZ6II0ExAnFQsc+aylwKBzNeJPyIthLD9DbZBFgZsYczLZkziQsmXNsKnG6QNT62iA+cebx2NR5CKvb9yBseGvSbW6eNhZjqgMpAYX5CGDLVAis2AeIdDgZIFYC+DyAbwGYCu1ttgvAGgD357JzIpoG4CEAkwDEANzLzHcT0XgAjwCYAS1Abzkz9+ayLzO5Bi2FolHMqB+FcEwGB6E4mGJTQMfJd91Og6gJqAlNzslsJv0NIpPO8Iv1f0PLtLFZpwRJlxhQt/mXL7yT4nIz6ypeTE3VknKm3geOhqL451++gps/dio2dfUlipJlu0+98Njz2w/gZ3/dkWjf+O/n5m16bUELBhHRZACTmflVIqoDsBHAxwB8BsBBZr6FiG4EMI6Zb0jXVzYaRPN//Al9Q+lv8GqauAR/PFpH6j0II4lPAU6YWIO3bXz8RtzoCGvad1smDXQST2DWL9JpELnaaRV7pGsMyz/QiEc3dtm6t4z78SLBnt6H02m7sxtq8O7BQdf7zFR4zKnOZEWuGoQP2hvEx6C9QTCAPQBWA7ifmcNpNndr6GoA98R/FjPze/FB5C/MPDvdtm4HiGe37cXnH9qYk72CUKyoCvDIFzMnnTSjP93WBFRHieqA3PU8nXTJDI322SUGnDKmKsVmM+niMdzqE17EQTnZp6ZxrHWU+jwbjSXXOIhfAmiB5mK6AMCF8d+bAfzKsRWZjZwBLb/TSwAamPk9AIj/f5zNNlcQURsRte3fv9/V/p7e1p2TvYJQzARVFX6fmnlFE/W1QTRPG4sjoajjuAivChI56ccqjkJPDGhls5l08Rhu4yW8iINysk83hce8jvlwokGcbvH03gXgRSJ62wsjiKgWwOPQps0eJnJ2Mpj5XgD3AtobhJt9njenAY+0dbk1VRBKglA0is6DR1Nid5xipUkMR7RYIN0XDhBOnjLasySE3YeH0NHdnzYlet9gOKVGxHAkhsfaOnF8/aiUZWaODEcSKdPdFDayQitolFulAyf7bBxXnVKYKpf+3ODExfQigDsAPM6spSglIgVaHMSXmfnMnAwg8gN4AsBTzHxnvO0t5NnFBAAn3PhkmnkWGvqc7GiMU/x/qkLwq4ShcMxRHh5ByBU9Duf48dWOffx6MSG3/uk17bvxFZPfW43HB+n3K58C3Lm8Bfc8t92RJuIEKz3CqBdYxXq4pS7oyzlGwqy7OMFPgOpTXGsQVtfCSL40CCdvEJcDuBXAfxKRPpNoLIDn4suyhrRXhfsBvKEPDnHWQJtGe0v8/9W57MeK37/amXFwAID3Dodw/8oPwO9T8YUH25KeUHwKcO+nPoBRfhX/eP/LCEn5OMElCoDJY6uw+9BQxnV1X/sov2JZkMgO3Ud+/eObsaBpguM3iQVNE0Cmxx7zk2wkBnzl0fa0+ZHc8tCGXVg5b0biTaJnYDghBKebOusGfZAJ+hT85JOn2xY2sqOju9/14AAAYQZu//tTMHNinau3uotbpmJB0wSsuPcFvLHvaMryGeOD+MXn53s+iymjA42ZdzLzx5l5IoD5AM5i5uPibTsybZ+BBdCywZ5DRO3xnwugDQwfJqLtAD4c/+wpbnIuberqw5hqP4I+s+9TxZjqAPw+FUGPiqsLlUWVX8Wlpzc6Wlf3te/sSb1BOCEbH7tPcaJjeJ/X3qhH5DPnWabCRnbkortseOcgmqeNdb3P+togth+wvvZvdB/JS7yFq5rUzNxj/ExEH2bmP2e7c2ZeD/tv15Js+3WCXS4mK46rDeCpre/haCj51fZoKIKntr6HgKqkLBMEJ4SjUUQc5ibS5/jrLiO36BoCALTt6MG67QcSc/OtcF74ynvnqlHXyGfOs2x99rnoLufNach62zOOH4sXdqQOTmcc740OZCanOAgi2sXMqVV3CkC+4iAEodAoBAR8SlJcgpUfPqhqdUjM6HnE9IFl+vjqJL1gYVM9fvkF65Rqa9p348uGHEPmOhoE4O7LW3DDqk2Wec0yUeVXUqaJTh4dwIZ//XCKHdc/rqXnzzSt1Gmtj1zzJZnzT/lVgqpQWvtmN9TgqesWZ7U/nRk3PpnStvOWC7PuLycNgojW2C0C4G6SdRHRMzCMIcmhJJQAMQZu+ftTcOPvtqT1w1sNDgCgP3zrNy6zmPzXjh607eixfJPQfd9b9xzG4cEwvvJYe1LOoYBPweGjoawGBwD45787AXc+05HU9t7hUIo9F7dMxZzJo3HBj/6asc8Ya/mnrv7NsUxAY6tUHDI9DCqk6SzZcvMlp2LlvBmJ6HG/T8WB/iHL+KpzZk/AJ888PimdSDb8/tVO23andWzc4MTFtBDACgDmRC8E4AzPLRohunoH8+A5FYT8sOGdgxnzG+XCuu0HbF1N9bVBLDppIjZ1HkrJbRRQFaze/F7W+928+7Bje46Eogj6VISimd25Q+Fo0lP1ps5DWHHfS0lvXgFVzTlXkp5/SufOp9+yXO+UqWNzHhyA9HVs8jFAOHFmvgjgKDP/n+nnLwCsz0YJ0DiuWqalCiXDeXMaMvrh7R54nDwILZqV+UnaLl/TJXMnO9iDNUtPtb5pWtnjRoswawS51Ltwg915dHJ+nWB3vuzac8XJLKaPMvNzNssWeW/SyFBfG8Tty+ba/vEsb52KgJqP+RlCORL0KXAY7GoLQZvPbub06WOwZM4k3HbpXARUWM6Ym1lfjX84bUpK+5gqFXdd3oIqv4KagAq/Spg6NvmJeWFTfeJpvWdgGJs6D6FnYDilr/raIG67dC6q/Arqgj5U+RXcdulcrDhrJiaPDrg+3o/MacA7B46ieepoW3sy7d9qv2edON6x7V7P/Jk5sRYtjWOS2uyOJxs+dvq0lGOePDqQl7cHwOUspnQQ0QZmnu9VfyPBl37bbvsW8VjbbnnDEBzjJE9OJhQiLJw1AX/YkuxGeHVXH25a/TrAQCgKwMLNtKNnEDt6Uqew9g1FcXHLVPQPRfDNNVsRiTF2H0q++c+cWAPAWfI6XZMwZw+98YI5uDZNIR8jKgHTxlfjT9u68ad4yptTp9bhQ7Mb0s6qstv/s9v2YtWrXdixfwBvdh/BC387iHPvWpcScGdnu1fo588cK6KfX68wFley+uwlnmVzJaLXmPk0TzrLArezmL77v1vw8+ffzaNFglAcjK9WcCScfhBbdeW8lCI/ThO/2RXMccuqK90nF9SxSxjoJAGgF2RK3OeVHZ+5bwP+0nEwpX1x03j84gvZPZ/nmqzPKSX1wP3EFueBcoJQyhwcjGVM9rZu+4Gsk9cdK5iTG+u2H8h6W7vANa8SCWYiUzCfV3a8sCN1cEjXnisVG/679JT8iDqCUGzU+FNTZJhZNGuCrYibTpcA3ATUpWfRrAnoGRjGurf3Y93b+2z3Z4Vd4JpXiQQzkUlA98qOs2am6ivp2nPFywGipPTcf7volEKbIAiOWTl/OlbOzy4mNZN7aeX86WidWY/lrckpP5a3NmJ9xwEsuHUtVtz3EhbcuhZr2nenbK9N+GiGz8HdRCHNdTW7Idkvv7CpHrv7hnDm957BygdexsoHXsG87z9ruT8rmhrqUs7PyvnTR8S9BGjn4IPHj7Nc5id4ZoedGylb91ImHIvURPRRZv6jqe2fmPmn8Y+f8tSyPHPuD9YW2gRBcERABa5dchLqa4O4eO4UXP7zF5GrJh5QFfzrBe/D2U0T0NRQh56BYTxqSn//yCtdeOSVTgxHOBF/YZfwTw9k++jd65IS9wVU4OoPNeFv+4/gQ7MnJiWpM6b7mDmxFmfd8mzScYWjjK+tcp5g0Bi4lk0Z01zo6O7HXzt6LJeFGfjVCzuw4qyZOe+nGAPldL5BRMPMvBYAiOgGAIsB/BQAmHmL59blkY4D3hXVEIR8EvT5EgFdfp+Kar8v55TXQZ+C06aPS9xEdR+6MRBPVQhgAnAsAlnXJaxu2EdCUVT5fQgbbAv6fPi72Q245txUF0vrzGPTPzd1HorrGMnRzqpCroLZzIFrI0UmjWH15vc8GSCKMVBO52IA3yOihUT0XWhR1Ayq+nQAACAASURBVBd7btEI0TTB2wAZQcgXxoCumoCK4Uju+cPMQWJWPvRojFO0hXTBZVa2pVtf1zY6uvvRNxi2TFoYjbHnwWxeoh/DjPpRadfLJZjQyEgHyjl+g2DmA0R0MYBnAGwEsIy9miNbAJ756jmWSa8EoVgwFoGprw0m5tkrCgFRThQPWt7aiF+/tCuj28mvEqp8alKfOnog2fWmOAgAKW1WT/N2tmVaH9ByRAVVQow5JdFeNBbD8x0Hsk6ol0/McSMLm+ot3UxjqlRP3h4ALVDuy49uToqEUeLt+cBJsr5+aFNY9aJpAQAnAFhGRMzMo9NtX6zI4CAUM+ZCNsaiOTpMhCevPhvjagJ45JWutGnDAyrwh2sWpi0/ahdIlim4LJ1tVu4eq/X1RIMBFWBGQseIxNwXOhoJrIoYvfJuL1ZdOQ87e46is2cAL+zoxSVzJ3s2OADAs9v2poRJxuLtXuR6MpNxgGDmkXfoCUKFYy5kY6URBFUFR0JRHAkNIqAqaWcq+RStNnVzhumW9bXBlBuxVZuRdLY5Xd9oJwgIh5zpHoXC6hj8igK/T8WyVu1p/ro87PfpeOS5VXtBBggdIrLMu8TMqeGLgiDkRCgaQ99gGD0Dw6ivDaJxXHWKf384EkXfYAhTxmROYhdl54npegaGHaWj0NcLR6IYjqbXKox9posZiHIsLozb91UMjFTyPzPnzWnAI6bZZnp7PnAzi+lrht+roInUGwGc46lFI8TOWy4UN5Ngi6q50vOG7q8141e1m2M0FsNVD7+a8Pu37TyIkMmgUJTx+QfboMZ1CL2Y0FAkimiME758nwLcvqzZ0RO4k3xMxvUATUOIm50oSmTUHqz61PUOfXujZgE40z0KiZ1mk287l8yZhNkNNXjLUNNjdkNNXt4egBxyMRHRNAC3MfMnvDUpO9zmYpLBQfArQIbiZCPOl89twk/+8rekvEZBX3r3EaDdmJ+4+uyExgAAW/f0AaCEjpEJq3xCVvmY0uUdCqiEP1yzMKE9pOsT0Fw1NQE1RRtx+hZTaApl57Pb9uLpbd04b05DzoNDThXl0tAFQMKRhZJFUZRj5daKhM27D6cU5XGCX1FSNIZFJx3nqg87v7rZ/59OQwj61CTtIV2fzdPG2t5UM+kexUKh7FwyZ1Le3hqMuNEgfoxjb8UKgBYAm/JhlCBUKotnTcD6juSkdZnyKAGpmoUVdk+7Hd39ibKZZr96KKrpHD0Dw+g9EkqsF4paC9BO4iuKUVPwmlJ5A8qEYxcTEX3a8DECYCczP58Xq7LArYsJEDdTpbOwqR4zJ9YkFZ4vND4FmH+C9Xz6dBCAWkPchFk3sNMWbvr963joxWPHv7CpHq+82wu/omAwHAGRFjtxNBxNGqjM8QpGDcG87zXtu1N89cUY1+AVTnWcYiGdi8nLehCPM/OlnnSWBaJBCNlw/8oPWBaZd8NVi09AQFXw4+f+hoiDp/1c+Np5J2Hq2Cp8ddVm28A4s25gpwP86nNnYNnPXkzZftWV83A0HMMXH2pzVAgp6FPw85WtabWOcnmizoRTHaeYGKl6ECd42JcgjAh288rdMHNCLRa/rwHVftUDi9LTMLoKMyfWodpv7x0213GwqlXgVxTb+gs7e45iTLUfAYvSplaYYzasqK8NptUcygW7c+2krkYxUrEFgwQB8Gb+eMu0sRnrAXiFk3051QEWzZqQ9T7S7a+SKTfNpWILBu285cJCmyCMIHVBX0pVtZXzp2PJnElp6yyoCqEmqMKnEFRCYr6/zjmzJyamdF61uAl+lRz9UdmtoxJwcfMk+JXUAisXN09Ce+ch9B4J4bZL5yLoUzAqoMKvEnwKUBNU4VeAC0+ZhN4jx+oU63P2q/wK6oI+VPkV3HbpXLTOrLetoWDexnzuVIWS+hrpN4NMRYwKhd259vr83PXUG1h8+3O466k3PO3XTMXWpAZEhyhl7ALNrJYtb23EJ888Hg+/uBOPbjxWgEYvar+6fTe+9lg7ojEtOC7oUxBjxtfOm42aoA/f+t+t8KsKQtEYYjFOCaCbPDqA3sGIbT1iABjlAxae1IBn3txnOSuJoAm/PoUSeYkA7fPy1kYMDEewZtN7ifaFTfV4eedBqKQgyjFc0jIVj7V1JR23fnw6mWYxWdVQMG6jz2JqmTYW42oCBdMUSkEEzqfmMuvrTyJsuNB+ArZ/P/sHXs9EaiKqBjCdmd+yWHYeMz+dtZU54naAmP31JzEsTrGKwU6MXnXlPKx44GXLm3vQpwDgpKC1XPAplJWIHYgPTtnwzHWLClIfIV+UogjsJXc99Qbufu6dlPZrP3QCrjv//Vn16YlITUQXAWgH8Kf45xYiWqMvL+TgkA0yOFQWdmL0uu0HbIvNqwrFi9h4Qyzrt/Xsv6yZCtmUGuUmArtl9WbrgkF27bni5tv/H9DyLx0CAGZuBzDDe5NGhmBJVdAWcmX+CdZF3U+YMMq2AE80xojEci/Oo6NQtl+67L+sLRmyt7ql0L7/chOB3XLJXOvoabv2XHEzQESYuS8vVhSAt3Lw2Qmlx1dXbcbk0YGU9i89utnW7ROKxHKu/axT7SPcubwZist7vaoQfnDZXCxsqrdcHlQJVX4FK+dPT+lbF5y9YnX7biy4dS1W3PcSFty6Fmvad2feyGNGSgQuVq47//3wm66zn5C1eykTbnIxbSGifwSgEtEsANcAeCEvVo0AIlBXFpEY8N7hkOUyO1mAoRWvcUpAJXzl3Fm49am3LYu6TBlTZbsvO3wKMGfyaLy886Dl8hiAP8YL81y75CRs+NsBHBgI4eymCZ4ODlYFcgpVyMeusFGlsP37F+Kup97A6s17ccncSXkbHAB3A8S/APg3AMMAfg3gKQDfyYdRglCKBH0q6uuqUBVQcdRULEcl+8C0dARUFe2dh+JaSKq7S0/SB2hP10ub8zObx2kiv5GiVJL55Yvrzn9/XgcGHUcDBBGpAL7FzF+DNkh4BhE9AGApgH3MfEq8bTyAR6BpHDsBLGfmXi/3KwheE4pGUeVXLKexhqPRtCVB7QjHYmiZNlYrpGOzvCaQvwhufbpmTUC1TORn5fs3bpOuxGklpd8o1eN0k6xvLTN7XhwoXqluAMBDhgHiNgAHmfkWIroRwDhmviFdPxIHIeRKlV9BKBJz5AYiAKqCJI1CIaAm4MNgOIIY27uunOBTAJ+qJOb4r2nfjS8/2p6iiaRLkpcr5niD5a2N+PVLuxI2+FXCHZc1J+3XXEjIzr5SiGXwglI4Tk/iIIjoDgCzADwGIFHOiJn/xwMDZwB4wjBAvAVgMTO/R0STAfyFmWen60OS9QlOCKjAtUtOwt3Pbk+q0Bb0KbjjsmZ85bFNjhPUxZgRzlPZOXPhHUB7Et265zD29B7FN1ZvSSp25HUsgFW8QdBHACjp/Bj3m66QUKb1yjGWoVSO06tkfeMB9EArMXpR/Gdp7uZZ0sDM7wFA/H/LyidEdAURtRFR2/79+/NkilBOBH0+NIyuQtCX7JYJqAqGwlHHCepUhWzjJ7zAXHgH0Pzui06aiPdPGYMqU7I+r2MBrOINVFJSUm4Y92u1jdP1yjGWoRyO07FIzcyfzach2cDM9wK4F9DeIApsjlAETB0dwG6b2UrAMZ++1Vx6q3Y7NJ0hf1+5dHP78xkLoL2l9OHwYDilKFCUYzBLIcZCRY3jqm0LCQ2GIwhHougZGEbfYDglMrwcYxnKIWbDTSR1IxH9joj2EVE3ET1ORI15sqs77lpC/P99Xu9AkvWVJzdcMCft8uWtjWhqqLOcS29u96uUEltQ5VdQ5Vdw+7K5uH1Zs20yOyeoipZkry7og0/RfPpO5vbnKxZgdftuzPv+s1j5wCu4+jftCEc5YV+VX8HHW6elxIwMR2K46uFXseDWtbj7mbdTdBf9rERiwLKfvYjW7zyDqx5+FdFYzPHxlirlELPhRoP4M7Tprb+MN60A8Elm/nDORqRqELcD6DGI1OOZ+fp0fbjVIE7/1h9xcLC46hELuZMp35HZF241u8TYDgBb9/QBIEwZU5UyK0d/4taK6zh/o7jl70/Bh0/Wol+N+3Iz28XL2TE9A8M465a1KfpLQCXc9+kPYsqYKlz44/WO9BnjtqE0Gk3QR/FCQ2NK6qbplmKfxZROg3ATBzGRmf/b8PkXRPSl3EwDiOg3ABYDmEBEXQC+CeAWAI8S0ecB7AJwWa77MSODQ3mSqX6zce6+3Vx6c/uikywlsMS6Y6oDCKgqhiMRx3b6VCWxD+O+3NxAvIwF6OodtHwL8ilaMaAjoajrhB9E6XLuajEeY6oDRXnT9JJSjtlwM0AcIKIVAH4T//wJaKJ1TjDzJ2wWLcm173SMr1ZkkChD1AxvEE58wE6e+IzrZFMsKFOOJCsbrOILgGNvOOlKfpr7Nm4DAH2DYdv4jZqAinE1Ac8Vl1Lzx1ciblxM0wHcA2A+tMeCFwBcy8zv5s8850gchAAAP7q8BY+1deKvHcnPLlV+TW7LNA/dybx1q3UALfUExzipnoMVk0cHsOFf7T2zVv0zkBJfEGUGgxI3dp8C3Lm8JePxffWxTYnpuQppP9V+H/qHU9+A/CpBjccxAMB1j26yfUtb2FSPV97tTbK77d2DeGjDrsQ6BKA26CvamIBKxLN6EMWM2wHiil+8hKffdJ/6QCg+jqvxY8X84/HJeTMST9BtO3rwp63daJpYg9YZ49NG9Oo4mbeebp3eIyFc8OP1CDnw06+6ch5aZ6Ym4HMaf2BH0Ed44cYltpHLVjqDE/RjBIANf+vBuz0D8ViS5HWeuPrslHNtLEhUyEJDgjWeaBBE9CC0N4ZD8c/jANzBzJ/zxsyRZV1Hzt4xoUhQfQquOTc5jrJ1Zr3lDTgdTvINpVsHAIKq4miAWLf9gKV9Vv2rZFF/1AaV7PMj2ekMTtCPsXnaWCxtnoJNnYfw0//bgVA0krTOkVAUzSb3WVNDXVLAnwwMpYObSJ+5+uAAAPHcSAUrMZori2zSJwulx9JTvMmF72Teerp13GgRi2ZNcGxDlGMZxXfjuuniJ5z2Y8bNeRDKBzcDhBJ/awCQSKjnRuQuKu79zJmFNkHwiNmTx6Cju992udMiN07mrevrBH0KRgVUBH3H1qmvDeJT847P+LC/sOnY203bjh7c+fRbaNtx7I32Y81ToBDgVwCVgA+/vwFf+fBJiRgMQMvB5FOQ9EbgU4DblzWnjZ+4fdlc+NVj22h1sLVYB6uXC32fduehlOf4C5lxc4O/A8ALRLQq/vkyAN/13iRhpFGAlPoFOgEFCNksvPCUSZgythoPPL8DxEAEQPPU0di8+3DSjJdFTfW46/LT0L6rF49t7MKB/mG8uusQqvwKBsOxlNkxfkUTXs27VUnztJzdNAFv7evHuGoftu09gq+u0sTblfOn4+ZLTk3axm2yNCe1Blj/l5OncZ5311/wdveRlPV15k4Zg5suen9icFhx34tYH3d1/mhtB05qqEnaXn/Yf+L1vXji9b1Y3joVnzxzRk6zmPTjM89i0o+390gI6zv2Y0JtFd43qS6tdlPpdRkqAVciNRHNgZaLiQA8y8zb8mWYW9yK1Et/+Bds2Wv/xyw4I6ACIQdVOZ+5bhGaGurSJnRzSpVfwa8+dwaW/exF2/0A+UmWZtfnLX9/Cr706OaM2+v2te3osbTf6faC4BWeJOsjohMB/I2Z7wHwOoBzicjbgrcjyFYZHDyBHH6F2js1+SpdQjen+BX74jv6fuz2lWuyNLs+n3jdWdF43b5sigcZtxeEkcDNX+rjAKJE1ATgPgAzoaXeKElOnlRTaBPKglRHkDVVfhUd3f3oGwzZJnRzSjgWsxV5Z9SPSugN2QqpumbRtqMHq9o6E/qGlmgu1f5wLIalpzoTymfUjwIANDeOcbS+GScBdk70FidY9eVl/0Lx4yZQ7lVmPp2IrgcwyMw/JqLXmLkoZjJJoNzIs7CpHpe1TsP1pqIyv9ywK0lXIGh5eYajnKi4xsyo9vswMBxJWbc2aN9u1BFuWv16UhCWVaAWgCT7nAbKRWPJtR6MfQ9Fogn7jX2ef9df8FYaDUI/jk/Nm45HN3Zl5Wab3VCDp65bnNZ2L4rTpAvWK+biN4J7vCoY9BKAH0IrOXoRM+8goi16gr1C43aAWHTLM9h1SJ6CcsEYPGUUKnsGhuPBVEdw97NvW2oUQZ8ST9Q2Gr1HQkmBVFv3HI4nv4tZrm/UD/QgrBn1o7DigZct9QazfXa40Ufs7Hl22148trELf97WjTzVEsL9Kz+AJXOS31i81Fusg/UUAJyUkLAYi98I7vGqYNBnoaXZ+G58cJgJ4FdeGFgIZHDIHWMQWfO0sUnJ55Y2T8HZsyYi6LOeKBdQtSRw9bVBNDXUYVnrNDQ11MWT3/lTCvcY1zeib+v3qbZ6g9k+O9zoI3b2LJkzCf9vcRNGBfI3A/zpbd0pbV7qLZbFghTSAvY86F8oHdwUDNoG4BrD5x3Qsq4CAIjocWa+1Fvz8sf0sUEZJHIkFI2ibzCEju5+7OkbBEAY5Vews+coWqaNTVtAxqwFZEp+Z6cdGBPY5Rq45SbQLZ09VgVxvOS8OQ0pbVa2h6JR23Nsl4qjq3cQ4UgUwxFTsSCLAkkSGFf+ePmYc4KHfeWddTeeKxpEjoSjjC8+2GabnG5hU31KARlj0jz9JmXl717e2pikLyxvbUy5qZm3W97aiEfbupL6cZs++7ZL5+L6DBqEXd9J9tgMEATgU/On49G2LoQisZTzY8SvUkrN69kNNSnuJaPtX360HbpnLsbA8x0HcHHL1Iz6hL4c0JIB+uIvC0GVQIZkfWY9R9xL5Y1nyfp0EduTzrLArQZxxrefwr4jzvP3C7mjFZ9JLhDjNDmd06R5Vsni3GJ8ktbfhvQYDrsn8Ez6haoA37nkFJx38qSkYkUH+oewqasPi2ZNQN9gGP+7eQ+e2PQejLWHVIXwdydNwCfPmG45OBhtMCfj08/J0nvW2+oT6WwP+BT84V/OTootkcC48sKrgkFlhQwOI0/Ql1ogxmlyOqdJ86ySxbnFWODFmFAvXeEXK3uMjPL7MMcwMBr7Mt70J9RV4dk39iel3h7lV3HtkpMyHldX7yACqpI0QPgVBe2dh9ImIUxne1DVzqmTcyCUH14OENmliSwQx9X4ZJAYYYwF7vWbjJXvPBKLwvx1KnSyOKtiPcYbZSb9wmn8hV2chZPjsjsnLdPGpj1X6Wx3c07l7aL8yC2kNZkbPOwr77z8jfMLbUJZ4EvzDVrYVJ9I5uZXCdHYsQL3a9p3A0hN+uZTAAYhZnJ9mjWIkUwWt7p9NxbcuhbLf/oCzr1rHT5+74akY7Cyxx9PpufUNn0fVz38GmIMV9va2aBv29RQl/ZcGbczJgN0s2/d/hX3vZRyboTSxU0cxAIA/wHgeGhvHgSAmbkoxGm3GsS87zyNvQPhPFpUGQRUwq+/cCaOhrWnfuMsJt1vv3VPXzyuwX4OvbZeavyD3fo6+X5qTeeft7LJaA+QffyFXZyFU5ut9ut0FpPdW5Ib+yVGonTwSoO4H8B1ADYCyC1XQhEgg4M3BH0q/D4Viwy+erPffkx1AAFVxXAkubiMUVMwxj9YDRDm9Y395/MmlM4/b2WT2R4ntlntwy7Owgl25yTTucr2XDoptCSUJm4GiD5m/mPeLBlhJtX6ZZDwgOGIVtTeiPlJ1ale4NQXPpK+bq/88273UUoxBqVuv2CPGw3iOSK6nYjmE9Hp+k/eLMszL/77eYU2oWhxU5VSUQhL71mf8Dlb+aLra4NY/oHGpO2s4hqc+MJH2tet22QssgNoMQpeaR6lXnyn1O0X7HGjQTxn0czMfI63JmWHWw1i1o1PQt4frAmohNsuPRVb9vSjaWINqvwK/vL2Afxv+x5b32K6+faZ5uGbsfOFF8rXrcUXPJukoQR9hBduXOLpfkt9FlCp21+peKJBMPOHvDOp8MjgYE/Qp2LmxDp87PRpibaZE+vw7Bv7kubnG0k33z7TPHwzdr7wQvm6tfiCZA0loKqe77fUYwxK3X4hFTcFg8YQ0Z1E1Bb/uYOIsktqXwT4C21AEeNWH9C3sZtvn2kevlMK5esWH7tQqbjRIB4A0A9gefznMID/zodRI8H2Wy4stAlFAUGr5Wz0H3/jwjno6h1MFIV5dtte/NvvNuO0xrFQCajyKVBJ88NXqdqX6FNnHm87335cTQBXLW5C0GfYx1JtHx3d/Y4L0Oi+7qBPwaiAiqBvZHzd2frYjcV1pNCOUIq4mcV0oilb67eIqN1rg4SRgaAJwExA6/Hjce2Sk9DVO4gtu/vw7Se3JRKyja32Ye/hUNK20UgMfpUwbpQP+/o1Z93P1+/Auu378NR1i5MK2a/vOIAFt66Np49mXLHoBIyvCeDbT2wDxzhRRAiAowI0rP/LBHN20XxyccvUpOPKNDgYk+PZFRgShGLHjUi9AcDXmHl9/PMCAD9g5vl5tM8xbkVqyeR6DGNhHacFc+wwFrNxmojPbIfdzbdUArIyJe4rRpuFysWrgkH/D8BPiGgnEb0L4B4A/+SFgUJh0YVeNwVz7DAWs7EsPEMKVJt5tJkK0HhZFCefZDqPxWizIFjhZhZTO4BmIhod/3w4b1YJI8pwJIrOg0cxaXQQwzkWupl/wnhs6jyExnHVqAmoqYVnOBZ3D6USisbw2q5e1ATURHppI6UiFnuRuE8QioGMLiYiWsHMvyKiL1stZ+Y782KZS9y6mABxM1nhUwAL748jxlSpGI4y/IqCwXAERARVIQyFY5aFZ8KRmG3d5pXzp+PmS05NaV/TvjulaE0x+vONdooGIRQzucZB1MT/T32kG0mV0GNkcLAm28EBAPqGtLeFY3EKx6qyMRGevPpY4Zk5k0fjgh+vR9Rmhw9t2IWV82akvEm4FYsLhdlOwFniPkEoJjIOEMz8s/ivzzDz88ZlcaFaEDJiLjxzJBRFUFUQSjMitXcesnQ1lUpAVjaJ+wShmHCjSP7YYZsnENFHiOgtIuogohvztR9hZDAn2+sbDCOUQe9oyaEynF3cgd7e0d2PdW/vx7q39+UtNsFtHERHdz9WtXWio7vf0bEIQr7J+AZBRPMBnAVgokmHGA1Atd4qN4hIBfATAB8G0AXgFSJaw8zbvNrHzlsuFDeTAwiATz2mJbjBrxKqfGpSgXtjfEA0FoNCQMzCUbly/nTLtwcnGPdh9Pnr7QCSjsWvEu64rNlTXcBog67HGM+FeV83/f51PPTirsRnXYOxOxZBGAmcaBABALXxdY1/sYcBLMuHUQDOANDBzO8AABH9FsAlADwbIGRwcIY/XhDoH+97KaldLxTU3nkI3/nDm5bb/uYLZ8LvU5OS7d3w+GYMhWO2tZv9KuE3XzgzqaaEG6z2cf3jmzFn8uhEu5lwlPG1VZuxoGmCJ24g6+NkhKORhD3GfXV09ycNDoCmwVw8d4rlsXhlpyBkwokG8X8A/o+IfsHM746ATQAwFUCn4XMXgDPNKxHRFQCuAIDp06ePjGUVhk/RKsQFfSpC0WPJ6vRCQYeH7Ot67+w5imWtxxL+pSu+o1MV7zdb7BL6WSUMNKIq5FnyvUzHaU4w2N55yHK9ddsPSCEeoaBk1CCI6IfxX+8hojXmnzzZZTVRPsURwcz3MnMrM7dOnDgxT6ZUNuFoFDPqR9nGHyyaNcF2W7OGUBNQM8ZZDEdjiQJE2fje7WIlrBIGGonG2LPYBLdxEHZay6JZE0oi7kMoX5yI1L+M//8DAHdY/OSDLgDTDJ8bAezxcgc7KyxZ3+yGmkTOIzeEY8An7nsJy1sbLZPV7e4bsiwwZNYQVrfvxtJ71oNMcTeKnvQvbhsxY+k963HT6tezKgxkl1jPmEjQ6jycMWOcZ0/lZht8inaMdon+mhrqsHJ+8hvwyvnT0TqzXgrxCAXFUS6muGj8IDOvyL9JABH5ALwNYAmA3QBeAfCPzLzVbhvJxWSNAuDn8fxIbTt6sOxnL6as41e0gSAdQZ+CJ//l7IwFfHwK4bdfTNYQMuUmCqgAgxIxE1a4zV9kV7ymZ2AYG/7Wg6t/81rKNs9ctyhrYTyTDUDmOIiO7n60dx5Cy7SxSXZIIR4hn+RcMIiZo0Q0kYgCzBzKvEVuMHOEiK4G8BS0mVIPpBscBHuq/Com1FUB0DQBKxRSgDS6AKD56I+Eomg2uEOsfO3V/lQNIZNP3qeoAGnuLDvc+t7tYiXqa4MYClvvxy7uIlvcxkE0NdSVdNyHUH64Sfe9E8Dzcd3hiN6Yr1QbzPwHAH/IR9+VRJSP+aztfN2cYXAArH30TnMjZfLJp8vPlK7fbLE7D7nEXQhCOeLGKb0HwBPxberiP7X5MGokqAQNQgGw7PRp6D2ivfRZ+bovaZmMfznnJAR9hKCq3aT9avLN2q8Sbl+W6vs2+tprgioCPgVfPvekpGJDunvkG0vnpPjk9aI/ty9rxu3LkgsBmYsYmX3vdkFlOukEbjufv93bgwSqCZWKmzeIbcz8mLGBiC7z2B4hC/wERBhYdFI9/uVDs3A0HMWDL+zEs2/ux8Mv78LDL+9KBF7dfMmpaBw7Crc99SYIwOr291Dl7wZAuPqcJnz0lEk4EoqiJqBiT98QAMbJU8bYujgubpmK/qEIvvW/WwFmfO+PbyZE4OWtjXi0rSsR5PWNC+fglKljsGV3H25+Ymt8XlqiBBCMhYCMRYzMvne7oDIdJ8FlN19yKlbOm2Hp8zcigWpCJeOmYNCrzHx6prZCISL1MSG390gI5961LmX5M9ctwriagK1gnE0hm0wCtLn/J64+G0vvWZ+xiJCdLR3d/bbH1tRQ52lRoVIpUCQIuZCTSE1EHwVwAYCpRPQjw6LRAOyjpIQRRxdyt9u4Xdo7D2FWQ52tYJxNEJaT4Ddj/1YBayopKZEvdrbYBZXpArNdoFw2wWVejEeUzQAAD3NJREFU9iUIpYgTF9MeAG0ALgaw0dDeD+C6fBglZEcoGkXfYAgz6kdZLm+ZNhbjagK2gnE2QrBVUSA77ALWrERqO1syCcxeFhUqlQJFgpAvMorUzLyJmR8EcCIzP2j4+R9m7h0BG/NCuYnUBC3p3VUPv4ZPmPImAVqgXFNDXZKwrIvSevCY2yAsPfhNiUfK6dq23p+V0GwMWNPbdZHaSUBYJoHZLlAumyd+L/sShFLEjQaxA9bpLk7w2qhsEA0iM8ZAMH12UU1ATQp+c4qVfz7gU/Drz5+RkqDPLmDN3O4mIMwuqCxd/9kigWpCOZNzoFwcYwdVAC4DMD4Xw4SRxRgIlmvwlZV/Pqgq8PvUpGC6dAFrVtNmndpkF1SWTV+ZkEA1oVJxPEAwc4+p6YdEtB7ATd6aJOSLGfWjsKnzUFLqh2zfIMQ/XxrI24+QC44HCCIyTmdVoL1ReJeXYIQph4JBxoI8y1sb8euXdtnWlF7YVI8VD7ycVMBGATAc5UTcgps5/rp//npTjIDchIoHieEQcsWNi+kOHNMgItBSb5RsoFyxDg4BlRCNMdLkrTsGM37yydNw8pQxAIBHXulCxPBU71cJX1rShDNnaoODuYCNjq4juC1Gc3HLVCxomiBPqEWIXeEkKTYkuMFNqo2PArgfwLMAnoeWZfXyfBhVyfhUBUGHBXMURcGY6gDqa4Po6h1EQE2+nFU+FWfPOg5+nwq/kvlS63P83VBfG0TztLFy0ykydI3ISDbXV6hs3LxB/B7AIQCvAhjKjzlCJBqDxWQxS5iPJdDLpAmkS5Zntf5IUek+8lxnk9khGpHgBW4GiEZm/kjeLBlhilWDCEUZqqLlI8rEmTPHJ24mmTQB4zJdg4hEOWkvHzzeu6I5Tqh0H7l+/IDm5guqBFLIk/MgGpHgBW7iIO4F8GNmfj2/JmVHpcZBmIvcpHsiNy7bsX/AsniQ10Vz7Kj0PEfpclh5eR4q/Q1NyIxXcRBnA/hMPGBuGFrwLjPzXA9sFLLEXOQm3Zx947Ln3tznqL98Uel5jtLlsPLyPEgMh5ALbgaIj+bNCiFrzHmXnD4xFrpoTiX7yHsGhtE3GEYo6l1OLEHIB45nMTHzu1Y/+TQunxRzLia/eqx4TzpUAlY88DLWtO8GoPm0F9y6FivuewkLbl2baLfCbdEcr6nUPEf6Nbrq4VcRjcW0WJZ4HEow/nslnAehNHCsQRQ75aJBBH2Ehz9/Jv7xvpdTnjADKuG/Pnk6rvzVxqSAOLs6C0582ZlyGuWbSvKRW+kOQR/h5ytbMWVMtaezmATBKV5pEMIIEFBV7Ow5Cp9KCJmyaPsUBb1Hw6j2+9A/fKwUh12dBSe+7Ew5jfJNJfnIrXSHgKpiTHWgoNdAEOxwEygnjAB6zYRoLPXNLsrW9RTs6iyIL7u4qGTdRShNKnaAKIQGoRBS/M12NRNuXzYXfoMO4VOA25c1W9ZTsKuzIL7s4qJSdRehdKlYDQIYGR1CATD/xHpcd+4szJxYaxk1m65mwtY9fQAIJ08ZnbGeQrp2oXiQayQUE6JBWLD0h38Zkf3EAGzc1YuZE2td1UbQ2xeddJxlv277EooHuUZCqVCxLqate4+M2L4kSZogCKVIxQ4QJ0+qGbF95SJE9gwMY1PnIfQMDHtslSAIQnoq1sX0xJcW512DMBbiycalUOnJ7ARBKCwVO0DkW4PwK4R7P/UBnDxlTFaDgxR8EQSh0FSsiynfGoSiUKKYTzZIwRdBEApNxQ4QI6FB5BIAJUFVgiAUmoodIJ740uK89a0qhNuX5RYAJUFVgiAUmorVIK74xUt56devEv54zUJPcutc3DIVC5omSFCVIAgFoWIHiHUdPXnpt8qnRUl7hQRVCYJQKArmYiKiy4hoKxHFiKjVtOzrRNRBRG8R0fn52P+ipvp8dCs6gSAIZUMhNYgtAP4BwDpjIxHNAXA5gJMBfATAfxKR6vXO7/3MmZ70o5DmVhKdQBCEcqNgLiZmfgMAiFIqp10C4LfMPAxgBxF1ADgDwAYv9//7VzsdrzuuSkXvULLbSCXg7stPw/wTtTcR0QkEQSg3inEW01QAxrt3V7wtBSK6gojaiKht//79rnbyxOt7Ha/7qbNmoi6YPJaOCvgwbfyohEbQPG2sDA6CIJQVeR0giOgZItpi8XNJus0s2ixzkjPzvczcysytEydOdGXb0lMnOV63uXGMxCQIglBx5HWAYOZzmfkUi5/VaTbrAjDN8LkRwB6vbfvY6dMyrxTn8w9txPLWRolJEAShoijGaa5rAPyaiO4EMAXALAAve72Tf/+fTa7W/81LnfjjtQulsLwgCBVDwQYIIvp7AD8GMBHAk0TUzsznM/NWInoUwDYAEQBXMbN3gQVx/ri129X6DMaRUBTN08Z6bYogCEJRUjCRmpl/x8yNzBxk5gZmPt+w7LvMfCIzz2bmP+Zj/x89ucHV+gQSzUEQhIqiGGcxjQjf+YdmV+vfsbw5UT96U+chdHT3SyEfQRDKmmLUIEaMs5vqsd5Byo3vfuwUXNwyNVHABwCGwjEEVQIpJIV8BEEoSyr2DaJtR4+jwQEA/mPNFnR09x8r4BPWprwORxlD4Riuf3yzvEkIglB2VOwAsW77ARdrE9o7D6UU8NGRQj6CIJQjFTtALJo1wcXajJZpY1OC5XQkaE4QhHKkYgeI1pn1WOgwo+sdy1vQ1FCXKOBT5ddOW1AlCZoTBKFsqWiR+psXnYxz71pnu/yKhTNx5d+dmLj5Gwv41ARUCZoTBKGsqegBor3zUNrlVX415eYvBXwEQagUKtbFBAAtGaKiJ4+pcj07SY+TkFlNgiCUOhU9QDQ11GHl/Om2y7/3hzex4Na1WNO+21F/q9t3Y8Gta7HivpdcbScIglCMVPQAAQA3X3IqVl05z3JZ/3DEcZxDz8BwIk7CzXaCIAjFSsUPEADg96kpBYGSljuIc+jqHUyJk5D4CEEQShkZIAA0jqu2jXEAnMU5WPUh8RGCIJQyMkBAm5mkxzjUBX3wqwSfAlfFgcx9SHyEIAilDjFbVvMsOVpbW7mtrS2nPnoGhtHVO5h46td/d3OTN/Yhg4MgCMUOEW1k5larZRUdB2HGHOOQzQ1e4iQEQSgXxMUkCIIgWCIDhCAIgmCJDBCCIAiCJTJACIIgCJbIACEIgiBYUjbTXIloP4B3s9x8AgA3JebKgUo7Zjne8qbSjhfw7piPZ+aJVgvKZoDIBSJqs5sHXK5U2jHL8ZY3lXa8wMgcs7iYBEEQBEtkgBAEQRAskQFC495CG1AAKu2Y5XjLm0o7XmAEjlk0CEEQBMESeYMQBEEQLJEBQhAEQbCk4gcIIvoIEb1FRB1EdGOh7fEaIppGRM8R0RtEtJWIro23jyeiPxPR9vj/4wptq5cQkUpErxHRE/HPM4nopfjxPkJEgULb6CVENJaIVhHRm/FrPb+crzERXRf/Pm8hot8QUVU5XWMieoCI9hHRFkOb5fUkjR/F72Gbieh0r+yo6AGCiFQAPwHwUQBzAHyCiOYU1irPiQD4CjO/H8A8AFfFj/FGAM8y8ywAz8Y/lxPXAnjD8PlWAHfFj7cXwOcLYlX+uBvAn5j5fQCaoR17WV5jIpoK4BoArcx8CgAVwOUor2v8CwAfMbXZXc+PApgV/7kCwH95ZURFDxAAzgDQwczvMHMIwG8BXFJgmzyFmd9j5lfjv/dDu3FMhXacD8ZXexDAxwpjofcQUSOACwHcF/9MAM4BsCq+Srkd72gAiwDcDwDMHGLmQyjjawytlk01EfkAjALwHsroGjPzOgAHTc121/MSAA+xxosAxhLRZC/sqPQBYiqATsPnrnhbWUJEMwCcBuAlAA3M/B6gDSIAjiucZZ7zQwDXA9CLhNcDOMTMkfjncrvOJwDYD+C/4261+4ioBmV6jZl5N4AfANgFbWDoA7AR5X2NAfvrmbf7WKUPEGTRVpbzfomoFsDjAL7EzIcLbU++IKKlAPYx80Zjs8Wq5XSdfQBOB/BfzHwagCMoE3eSFXHf+yUAZgKYAqAGmpvFTDld43Tk7ftd6QNEF4Bphs+NAPYUyJa8QUR+aIPDw8z8P/Hmbv01NP7/vkLZ5zELAFxMRDuhuQzPgfZGMTbujgDK7zp3Aehi5pfin1dBGzDK9RqfC2AHM+9n5jCA/wFwFsr7GgP21zNv97FKHyBeATArPvshAE3oWlNgmzwl7n+/H8AbzHynYdEaAJ+O//5pAKtH2rZ8wMxfZ+ZGZp4B7XquZeZPAngOwLL4amVzvADAzHsBdBLR7HjTEgDbUKbXGJpraR4RjYp/v/XjLdtrHMfueq4BsDI+m2kegD7dFZUrFR9JTUQXQHvCVAE8wMzfLbBJnkJEZwP4K4DXccwn/6/QdIhHAUyH9gd3GTObRbGShogWA/gqMy8lohOgvVGMB/AagBXMPFxI+7yEiFqgifIBAO8A+Cy0B8CyvMZE9C0AH4c2S+81AF+A5ncvi2tMRL8BsBhaSu9uAN8E8HtYXM/4IHkPtFlPRwF8lpnbPLGj0gcIQRAEwZpKdzEJgiAINsgAIQiCIFgiA4QgCIJgiQwQgiAIgiUyQAiCIAiWyAAhCIIgWCIDhCCYIKJr4imzH7ZZ3hKPn0nXx/uIaAMRDRPRV03LLFPMl1O6aqE8kAFCEFL5ZwAXxCOwrWgBkHaAgJaJ8xpoSeUSZEgxX07pqoUyQAYIQTBARD+Flh11DRHdQEQvxDOkvkBEs+NP9TcD+DgRtRPRx636YeZ9zPwKgLBpkWWK+XQpyYnoRCJ6kYheIaKbiWjA8wMXBAtkgBAEA8z8T9ASnX0IWuGVRfEMqTcB+F78pn4TgEeYuYWZH3G5C7vUzOlSkt8N4G5m/iDKLwGdUMT4Mq8iCBXLGAAPEtEsaOmT/R70aZeaOV3K5vk4Vhzm1zC5rQQhX8gbhCDY820Az8XLWl4EoMqDPu1SMx9A+aerFkoMGSAEwZ4xAHbHf/+Mob0fQF2WfVqmmGcta6ZduuoXAVwa//3yLPcrCK6RbK6CYCJebKgVWhH4B6GV81wL4FPMPIOIxgN4CprL6ftWOgQRTQLQBmA0tDTrAwDmMPNhuxTzdinJ4y6uX0FzQz0J4ApmLrdymkIRIgOEIBQ5RDQKwCAzMxFdDuATzHxJoe0Syh8RqQWh+PkAgHviU2EPAfhcge0RKgR5gxCEHCCizwK41tT8PDNfVQh7BMFLZIAQBEEQLJFZTIIgCIIlMkAIgiAIlsgAIQiCIFgiA4QgCIJgyf8H7U0fdBjmbGUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelling_df.plot.scatter(x='fat_100g', y='nutrition_score_uk_100g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = np.random.rand(len(modelling_df)) < 0.7\n",
    "train = modelling_df[mask]\n",
    "test = modelling_df[~mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "x_train = train[['fat_100g']]\n",
    "y_train = train['nutrition_score_uk_100g']\n",
    "model = LinearRegression().fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4412427566489327"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = test[['fat_100g']]\n",
    "y_test = test['nutrition_score_uk_100g']\n",
    "predicted = model.predict(x_test)\n",
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12f83a350>]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.scatter(test[['fat_100g']], test['nutrition_score_uk_100g'])\n",
    "plt.plot(test[['fat_100g']], predicted, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These results do not surprise me since it is clear that items that are high in nutrition majorly have low to medium fat per 100 grams of item. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "interesting_df_1 = foodFacts_3and5Removed[['main_category_en','nutrition_score_uk_100g','fat_100g']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No axis named nutrition_score_uk_100g for object type <class 'pandas.core.frame.DataFrame'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-122-c649f322cbae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0minteresting_df_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minteresting_df_1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minteresting_df_1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nutrition_score_uk_100g'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfinal_interesting_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minteresting_df_2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fat_100g'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'nutrition_score_uk_100g'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36msort_values\u001b[0;34m(self, by, axis, ascending, inplace, kind, na_position)\u001b[0m\n\u001b[1;32m   4972\u001b[0m     ):\n\u001b[1;32m   4973\u001b[0m         \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_bool_kwarg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"inplace\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4974\u001b[0;31m         \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4976\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_axis_number\u001b[0;34m(cls, axis)\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No axis named {0} for object type {1}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: No axis named nutrition_score_uk_100g for object type <class 'pandas.core.frame.DataFrame'>"
     ]
    }
   ],
   "source": [
    "interesting_df_2 = interesting_df_1[interesting_df_1['nutrition_score_uk_100g'].isin([15, 28])]\n",
    "final_interesting_df = interesting_df_2.sort_values('fat_100g','nutrition_score_uk_100g',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>main_category_en</th>\n",
       "      <th>nutrition_score_uk_100g</th>\n",
       "      <th>fat_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>19775</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29594</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4247</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50271</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33909</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48353</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22298</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28355</td>\n",
       "      <td>Fresh foods</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46935</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26339</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29028</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28356</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41862</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22294</td>\n",
       "      <td>Fresh foods</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34282</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22297</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20443</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15481</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35340</td>\n",
       "      <td>Fresh foods</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35337</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14855</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62301</td>\n",
       "      <td>Spreads</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63308</td>\n",
       "      <td>fr:Cuisson-des-aliments</td>\n",
       "      <td>28.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54893</td>\n",
       "      <td>Groceries</td>\n",
       "      <td>15.0</td>\n",
       "      <td>76.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3143</td>\n",
       "      <td>Groceries</td>\n",
       "      <td>15.0</td>\n",
       "      <td>74.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63246</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2952</td>\n",
       "      <td>Groceries</td>\n",
       "      <td>15.0</td>\n",
       "      <td>60.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7849</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>60.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53328</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>59.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50580</td>\n",
       "      <td>Groceries</td>\n",
       "      <td>15.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64991</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23195</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>15.0</td>\n",
       "      <td>53.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41745</td>\n",
       "      <td>Sugary snacks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>53.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13280</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>52.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55107</td>\n",
       "      <td>Sugary snacks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>51.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34757</td>\n",
       "      <td>Sugary snacks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>51.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25947</td>\n",
       "      <td>Sugary snacks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49608</td>\n",
       "      <td>Sugary snacks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28018</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16141</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18203</td>\n",
       "      <td>Sugary snacks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38558</td>\n",
       "      <td>Groceries</td>\n",
       "      <td>15.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40065</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23374</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30250</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16079</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62580</td>\n",
       "      <td>Sugary snacks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56881</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26346</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32009</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1619</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1632</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3068</td>\n",
       "      <td>Sugary snacks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18834</td>\n",
       "      <td>Sugary snacks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60241</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>49.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48350</td>\n",
       "      <td>Sugary snacks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17791</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1620</td>\n",
       "      <td>Plant-based foods and beverages</td>\n",
       "      <td>15.0</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      main_category_en  nutrition_score_uk_100g  fat_100g\n",
       "19775  Plant-based foods and beverages                     15.0     100.0\n",
       "29594  Plant-based foods and beverages                     15.0     100.0\n",
       "4247   Plant-based foods and beverages                     15.0     100.0\n",
       "50271  Plant-based foods and beverages                     15.0     100.0\n",
       "33909  Plant-based foods and beverages                     15.0      90.0\n",
       "48353                          Spreads                     28.0      80.5\n",
       "22298                          Spreads                     28.0      80.0\n",
       "28355                      Fresh foods                     28.0      80.0\n",
       "46935                          Spreads                     28.0      80.0\n",
       "26339                          Spreads                     28.0      80.0\n",
       "29028                          Spreads                     28.0      80.0\n",
       "28356                          Spreads                     28.0      80.0\n",
       "41862                          Spreads                     28.0      80.0\n",
       "22294                      Fresh foods                     28.0      80.0\n",
       "34282                          Spreads                     28.0      80.0\n",
       "22297                          Spreads                     28.0      80.0\n",
       "20443                          Spreads                     28.0      80.0\n",
       "15481                          Spreads                     28.0      80.0\n",
       "35340                      Fresh foods                     28.0      80.0\n",
       "35337                          Spreads                     28.0      80.0\n",
       "14855                          Spreads                     28.0      80.0\n",
       "62301                          Spreads                     28.0      80.0\n",
       "63308          fr:Cuisson-des-aliments                     28.0      79.0\n",
       "54893                        Groceries                     15.0      76.1\n",
       "3143                         Groceries                     15.0      74.5\n",
       "51086                              NaN                     15.0      74.0\n",
       "63246  Plant-based foods and beverages                     15.0      70.0\n",
       "2952                         Groceries                     15.0      60.7\n",
       "7849   Plant-based foods and beverages                     15.0      60.3\n",
       "53328  Plant-based foods and beverages                     15.0      59.4\n",
       "50580                        Groceries                     15.0      57.0\n",
       "64991  Plant-based foods and beverages                     15.0      54.0\n",
       "23195                          Seafood                     15.0      53.2\n",
       "41745                    Sugary snacks                     15.0      53.2\n",
       "13280  Plant-based foods and beverages                     15.0      52.7\n",
       "55107                    Sugary snacks                     15.0      51.9\n",
       "34757                    Sugary snacks                     15.0      51.2\n",
       "25947                    Sugary snacks                     15.0      51.0\n",
       "49608                    Sugary snacks                     15.0      51.0\n",
       "28018  Plant-based foods and beverages                     15.0      51.0\n",
       "16141  Plant-based foods and beverages                     15.0      51.0\n",
       "18203                    Sugary snacks                     15.0      51.0\n",
       "38558                        Groceries                     15.0      51.0\n",
       "40065  Plant-based foods and beverages                     15.0      50.8\n",
       "23374  Plant-based foods and beverages                     15.0      50.4\n",
       "30250  Plant-based foods and beverages                     15.0      50.4\n",
       "16079  Plant-based foods and beverages                     15.0      50.0\n",
       "62580                    Sugary snacks                     15.0      50.0\n",
       "56881  Plant-based foods and beverages                     15.0      50.0\n",
       "26346  Plant-based foods and beverages                     15.0      50.0\n",
       "32009  Plant-based foods and beverages                     15.0      50.0\n",
       "1619   Plant-based foods and beverages                     15.0      50.0\n",
       "1632   Plant-based foods and beverages                     15.0      50.0\n",
       "55753                              NaN                     15.0      50.0\n",
       "3068                     Sugary snacks                     15.0      50.0\n",
       "18834                    Sugary snacks                     15.0      50.0\n",
       "60241  Plant-based foods and beverages                     15.0      49.7\n",
       "48350                    Sugary snacks                     15.0      49.0\n",
       "17791  Plant-based foods and beverages                     15.0      49.0\n",
       "1620   Plant-based foods and beverages                     15.0      48.5"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_interesting_df.head(60)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above mentioned dataframe, it seems like plant based foods & beverages, and spreads are a few product categories that have high nutrition as well as high fat. \n",
    "\n",
    "Majority of the items with that have a high overall nutrition score from the dataset, also have fat content between 0 to 50 grams of fat per 100 grams of item(s), with maximum spread at 0 and least spread at 50. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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