Nutriscore calculation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nutrient info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nutrients_df = pd.read_json('dNutrients.json')\n",
    "nutrients_df = nutrients_df.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21580"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_nutri = set(nutrients_df.index.values)\n",
    "len(columns_nutri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>nutrients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0011210006508</th>\n",
       "      <td>10117</td>\n",
       "      <td>{'it': 'Mc. Ilhenny Co. Avery Island L.A. Taba...</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0011210115224</th>\n",
       "      <td>4432</td>\n",
       "      <td>{'it': 'Tabasco Pepper Sauce', 'fr': 'Tabasco ...</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0012086314018</th>\n",
       "      <td>9625</td>\n",
       "      <td>{'it': 'Joseph Droukin Montagny', 'fr': 'Josep...</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0014100096986</th>\n",
       "      <td>2060</td>\n",
       "      <td>{'it': 'Chocolate Chunk : Dark Double Chocolat...</td>\n",
       "      <td>{'energy': {'per_portion': 597.0, 'per_hundred...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0014100096993</th>\n",
       "      <td>3542</td>\n",
       "      <td>{'it': 'Pepperidge farm Cookies aux morceaux d...</td>\n",
       "      <td>{'energy': {'per_portion': 556.0, 'per_hundred...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id                                               name  \\\n",
       "0011210006508  10117  {'it': 'Mc. Ilhenny Co. Avery Island L.A. Taba...   \n",
       "0011210115224   4432  {'it': 'Tabasco Pepper Sauce', 'fr': 'Tabasco ...   \n",
       "0012086314018   9625  {'it': 'Joseph Droukin Montagny', 'fr': 'Josep...   \n",
       "0014100096986   2060  {'it': 'Chocolate Chunk : Dark Double Chocolat...   \n",
       "0014100096993   3542  {'it': 'Pepperidge farm Cookies aux morceaux d...   \n",
       "\n",
       "                                                       nutrients  \n",
       "0011210006508                                                 {}  \n",
       "0011210115224                                                 {}  \n",
       "0012086314018                                                 {}  \n",
       "0014100096986  {'energy': {'per_portion': 597.0, 'per_hundred...  \n",
       "0014100096993  {'energy': {'per_portion': 556.0, 'per_hundred...  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jai ho 7616500648623\n"
     ]
    }
   ],
   "source": [
    "# Test if particular nutrients exist in the nutrient list\n",
    "for column in columns:\n",
    "    nutris = set(nutrients_df['nutrients'][column].keys())\n",
    "    if('saturated_fat' in nutris and info_df['unit'][column]):\n",
    "        print('jai ho', column)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'carbohydrates': {'per_day': 31.0,\n",
       "  'per_hundred': None,\n",
       "  'per_portion': 38.0,\n",
       "  'unit': 'g'},\n",
       " 'energy': {'per_day': 8.0,\n",
       "  'per_hundred': None,\n",
       "  'per_portion': 280.0,\n",
       "  'unit': 'kJ'},\n",
       " 'energy_kcal': {'per_day': 0.0,\n",
       "  'per_hundred': None,\n",
       "  'per_portion': 65.0,\n",
       "  'unit': 'kCal'},\n",
       " 'fat': {'per_day': 0.0, 'per_hundred': None, 'per_portion': 0.0, 'unit': 'g'},\n",
       " 'fiber': {'per_day': None,\n",
       "  'per_hundred': None,\n",
       "  'per_portion': 4.0,\n",
       "  'unit': 'g'},\n",
       " 'protein': {'per_day': 3.0,\n",
       "  'per_hundred': None,\n",
       "  'per_portion': 1.5,\n",
       "  'unit': 'g'},\n",
       " 'salt': {'per_day': 0.0,\n",
       "  'per_hundred': None,\n",
       "  'per_portion': 0.0,\n",
       "  'unit': 'g'},\n",
       " 'saturated_fat': {'per_day': 15.0,\n",
       "  'per_hundred': None,\n",
       "  'per_portion': 0.0,\n",
       "  'unit': 'g'},\n",
       " 'sugars': {'per_day': None,\n",
       "  'per_hundred': None,\n",
       "  'per_portion': 28.0,\n",
       "  'unit': 'g'}}"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients_df['nutrients']['7610029176308']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ingredient info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ingredients_df = pd.read_json('dIngredients.json') \n",
    "ingredients_df = ingredients_df.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>ingredients</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0011210006508</th>\n",
       "      <td>10117</td>\n",
       "      <td>{'de': 'Weissweinessig, Habanero Chili, Rohrzu...</td>\n",
       "      <td>{'it': 'Mc. Ilhenny Co. Avery Island L.A. Taba...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0011210115224</th>\n",
       "      <td>4432</td>\n",
       "      <td>{'it': 'aceto di acqua-vite, peperoncini rossi...</td>\n",
       "      <td>{'it': 'Tabasco Pepper Sauce', 'fr': 'Tabasco ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0012086314018</th>\n",
       "      <td>9625</td>\n",
       "      <td>{'de': 'Sulfite', 'en': 'sulphites'}</td>\n",
       "      <td>{'it': 'Joseph Droukin Montagny', 'fr': 'Josep...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0014100096986</th>\n",
       "      <td>2060</td>\n",
       "      <td>{}</td>\n",
       "      <td>{'it': 'Chocolate Chunk : Dark Double Chocolat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0014100096993</th>\n",
       "      <td>3542</td>\n",
       "      <td>{'it': 'FARINA DI FRUMENTO FORTIFICATA (FARINA...</td>\n",
       "      <td>{'it': 'Pepperidge farm Cookies aux morceaux d...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id                                        ingredients  \\\n",
       "0011210006508  10117  {'de': 'Weissweinessig, Habanero Chili, Rohrzu...   \n",
       "0011210115224   4432  {'it': 'aceto di acqua-vite, peperoncini rossi...   \n",
       "0012086314018   9625               {'de': 'Sulfite', 'en': 'sulphites'}   \n",
       "0014100096986   2060                                                 {}   \n",
       "0014100096993   3542  {'it': 'FARINA DI FRUMENTO FORTIFICATA (FARINA...   \n",
       "\n",
       "                                                            name  \n",
       "0011210006508  {'it': 'Mc. Ilhenny Co. Avery Island L.A. Taba...  \n",
       "0011210115224  {'it': 'Tabasco Pepper Sauce', 'fr': 'Tabasco ...  \n",
       "0012086314018  {'it': 'Joseph Droukin Montagny', 'fr': 'Josep...  \n",
       "0014100096986  {'it': 'Chocolate Chunk : Dark Double Chocolat...  \n",
       "0014100096993  {'it': 'Pepperidge farm Cookies aux morceaux d...  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ingredients_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21580"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_ingri = set(ingredients_df.index.values)\n",
    "len(columns_ingri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "if(columns_ingri == columns_nutri):\n",
    "    print('1')\n",
    "    columns = list(columns_ingri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'de': 'Schweizer Hartkäse, vollfett, mit Rohmilch hergestellt',\n",
       " 'fr': 'Fromage suisse à pâte dure, gras, au lait cru',\n",
       " 'it': 'Formaggio svizzero a pasta dura, grasso fabbricato con latte crudo\\r\\n'}"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ingredients_df['ingredients'][list(columns_ingri)[275]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "madarchod 7610307532635 Zucker, Kakaobutter, VOLLMILCHPULVER, Kakaomasse, eingesottene BUTTER, MILCHZUCKER, Emulgator (SOJALECITHIN), natürliche Aroma (Vanille)\n"
     ]
    }
   ],
   "source": [
    "# Test for languages for ingredients\n",
    "for column in columns:\n",
    "    if('fr' in set(ingredients_df['ingredients'][column].keys())):\n",
    "        ing = ingredients_df['ingredients'][column]['fr']\n",
    "    elif(len(list(ingredients_df['ingredients'][column].keys())) != 0):\n",
    "        print('madarchod', column, ingredients_df['ingredients'][column]['de'])\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "info_df = pd.read_json('dInfo.json') \n",
    "info_df = info_df.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['g', 'mL'], dtype=object)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_df['unit'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>portion</th>\n",
       "      <th>portion unit</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0011210006508</th>\n",
       "      <td>0</td>\n",
       "      <td>10117</td>\n",
       "      <td>{'it': 'Mc. Ilhenny Co. Avery Island L.A. Taba...</td>\n",
       "      <td>0</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0011210115224</th>\n",
       "      <td>0</td>\n",
       "      <td>4432</td>\n",
       "      <td>{'it': 'Tabasco Pepper Sauce', 'fr': 'Tabasco ...</td>\n",
       "      <td>0</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0012086314018</th>\n",
       "      <td>0</td>\n",
       "      <td>9625</td>\n",
       "      <td>{'it': 'Joseph Droukin Montagny', 'fr': 'Josep...</td>\n",
       "      <td>0</td>\n",
       "      <td>g</td>\n",
       "      <td>750</td>\n",
       "      <td>mL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0014100096986</th>\n",
       "      <td>0</td>\n",
       "      <td>2060</td>\n",
       "      <td>{'it': 'Chocolate Chunk : Dark Double Chocolat...</td>\n",
       "      <td>28</td>\n",
       "      <td>g</td>\n",
       "      <td>220</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0014100096993</th>\n",
       "      <td>0</td>\n",
       "      <td>3542</td>\n",
       "      <td>{'it': 'Pepperidge farm Cookies aux morceaux d...</td>\n",
       "      <td>26</td>\n",
       "      <td>g</td>\n",
       "      <td>204</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              alcohol     id  \\\n",
       "0011210006508       0  10117   \n",
       "0011210115224       0   4432   \n",
       "0012086314018       0   9625   \n",
       "0014100096986       0   2060   \n",
       "0014100096993       0   3542   \n",
       "\n",
       "                                                            name portion  \\\n",
       "0011210006508  {'it': 'Mc. Ilhenny Co. Avery Island L.A. Taba...       0   \n",
       "0011210115224  {'it': 'Tabasco Pepper Sauce', 'fr': 'Tabasco ...       0   \n",
       "0012086314018  {'it': 'Joseph Droukin Montagny', 'fr': 'Josep...       0   \n",
       "0014100096986  {'it': 'Chocolate Chunk : Dark Double Chocolat...      28   \n",
       "0014100096993  {'it': 'Pepperidge farm Cookies aux morceaux d...      26   \n",
       "\n",
       "              portion unit quantity unit  \n",
       "0011210006508            g        0    g  \n",
       "0011210115224            g        0    g  \n",
       "0012086314018            g      750   mL  \n",
       "0014100096986            g      220    g  \n",
       "0014100096993            g      204    g  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = nutrients_df[['name', 'nutrients']]\n",
    "df2 = ingredients_df[['ingredients']]\n",
    "df3 = info_df[['alcohol', 'unit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_combined = df1.join(df2, how='inner')\n",
    "df_combined = df_combined.join(df3, how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>nutrients</th>\n",
       "      <th>ingredients</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0011210006508</th>\n",
       "      <td>{'it': 'Mc. Ilhenny Co. Avery Island L.A. Taba...</td>\n",
       "      <td>{}</td>\n",
       "      <td>{'de': 'Weissweinessig, Habanero Chili, Rohrzu...</td>\n",
       "      <td>0</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0011210115224</th>\n",
       "      <td>{'it': 'Tabasco Pepper Sauce', 'fr': 'Tabasco ...</td>\n",
       "      <td>{}</td>\n",
       "      <td>{'it': 'aceto di acqua-vite, peperoncini rossi...</td>\n",
       "      <td>0</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0012086314018</th>\n",
       "      <td>{'it': 'Joseph Droukin Montagny', 'fr': 'Josep...</td>\n",
       "      <td>{}</td>\n",
       "      <td>{'de': 'Sulfite', 'en': 'sulphites'}</td>\n",
       "      <td>0</td>\n",
       "      <td>mL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0014100096986</th>\n",
       "      <td>{'it': 'Chocolate Chunk : Dark Double Chocolat...</td>\n",
       "      <td>{'energy': {'per_portion': 597.0, 'per_hundred...</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0014100096993</th>\n",
       "      <td>{'it': 'Pepperidge farm Cookies aux morceaux d...</td>\n",
       "      <td>{'energy': {'per_portion': 556.0, 'per_hundred...</td>\n",
       "      <td>{'it': 'FARINA DI FRUMENTO FORTIFICATA (FARINA...</td>\n",
       "      <td>0</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            name  \\\n",
       "0011210006508  {'it': 'Mc. Ilhenny Co. Avery Island L.A. Taba...   \n",
       "0011210115224  {'it': 'Tabasco Pepper Sauce', 'fr': 'Tabasco ...   \n",
       "0012086314018  {'it': 'Joseph Droukin Montagny', 'fr': 'Josep...   \n",
       "0014100096986  {'it': 'Chocolate Chunk : Dark Double Chocolat...   \n",
       "0014100096993  {'it': 'Pepperidge farm Cookies aux morceaux d...   \n",
       "\n",
       "                                                       nutrients  \\\n",
       "0011210006508                                                 {}   \n",
       "0011210115224                                                 {}   \n",
       "0012086314018                                                 {}   \n",
       "0014100096986  {'energy': {'per_portion': 597.0, 'per_hundred...   \n",
       "0014100096993  {'energy': {'per_portion': 556.0, 'per_hundred...   \n",
       "\n",
       "                                                     ingredients alcohol unit  \n",
       "0011210006508  {'de': 'Weissweinessig, Habanero Chili, Rohrzu...       0    g  \n",
       "0011210115224  {'it': 'aceto di acqua-vite, peperoncini rossi...       0    g  \n",
       "0012086314018               {'de': 'Sulfite', 'en': 'sulphites'}       0   mL  \n",
       "0014100096986                                                 {}       0    g  \n",
       "0014100096993  {'it': 'FARINA DI FRUMENTO FORTIFICATA (FARINA...       0    g  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nutriscore Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "from food_db import build_database\n",
    "f_and_v = build_database()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Points A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "def points_A_not_beverages(barcode):\n",
    "    a = b = c = d = 0\n",
    "    nutrients = df_combined['nutrients'][barcode]\n",
    "    nutris_present = set(nutrients.keys())\n",
    "    per = 'per_hundred'\n",
    "    if('energy' in nutris_present):\n",
    "        if(nutrients['energy']['per_hundred'] is None):\n",
    "            per = 'per_portion'\n",
    "            if(nutrients['energy']['per_portion'] <= 335):\n",
    "                a = 0\n",
    "            elif(nutrients['energy']['per_portion'] <= 670):\n",
    "                a = 1\n",
    "            elif(nutrients['energy']['per_portion'] <= 1005):\n",
    "                a = 2\n",
    "            elif(nutrients['energy']['per_portion'] <= 1340):\n",
    "                a = 3\n",
    "            elif(nutrients['energy']['per_portion'] <= 1675):\n",
    "                a = 4\n",
    "            elif(nutrients['energy']['per_portion'] <= 2010):\n",
    "                a = 5\n",
    "            elif(nutrients['energy']['per_portion'] <= 2345):\n",
    "                a = 6\n",
    "            elif(nutrients['energy']['per_portion'] <= 2680):\n",
    "                a = 7\n",
    "            elif(nutrients['energy']['per_portion'] <= 3015):\n",
    "                a = 8\n",
    "            elif(nutrients['energy']['per_portion'] <= 3350):\n",
    "                a = 9\n",
    "            else:\n",
    "                a = 10\n",
    "        else:\n",
    "            if(nutrients['energy']['per_hundred'] <= 335):\n",
    "                a = 0\n",
    "            elif(nutrients['energy']['per_hundred'] <= 670):\n",
    "                a = 1\n",
    "            elif(nutrients['energy']['per_hundred'] <= 1005):\n",
    "                a = 2\n",
    "            elif(nutrients['energy']['per_hundred'] <= 1340):\n",
    "                a = 3\n",
    "            elif(nutrients['energy']['per_hundred'] <= 1675):\n",
    "                a = 4\n",
    "            elif(nutrients['energy']['per_hundred'] <= 2010):\n",
    "                a = 5\n",
    "            elif(nutrients['energy']['per_hundred'] <= 2345):\n",
    "                a = 6\n",
    "            elif(nutrients['energy']['per_hundred'] <= 2680):\n",
    "                a = 7\n",
    "            elif(nutrients['energy']['per_hundred'] <= 3015):\n",
    "                a = 8\n",
    "            elif(nutrients['energy']['per_hundred'] <= 3350):\n",
    "                a = 9\n",
    "            else:\n",
    "                a = 10\n",
    "    else: a = 0\n",
    "    \n",
    "    if('sugars' in nutris_present):\n",
    "        if(nutrients['sugars'][per] <= 4.5):\n",
    "            b = 0\n",
    "        elif(nutrients['sugars'][per] <= 9):\n",
    "            b = 1\n",
    "        elif(nutrients['sugars'][per] <= 13.5):\n",
    "            b = 2\n",
    "        elif(nutrients['sugars'][per] <= 18):\n",
    "            b = 3\n",
    "        elif(nutrients['sugars'][per] <= 22.5):\n",
    "            b = 4\n",
    "        elif(nutrients['sugars'][per] <= 27):\n",
    "            b = 5\n",
    "        elif(nutrients['sugars'][per] <= 31):\n",
    "            b = 6\n",
    "        elif(nutrients['sugars'][per] <= 36):\n",
    "            b = 7\n",
    "        elif(nutrients['sugars'][per] <= 40):\n",
    "            b = 8\n",
    "        elif(nutrients['sugars'][per] <= 45):\n",
    "            b = 9\n",
    "        else:\n",
    "            b = 10\n",
    "    else: b = 0\n",
    "    \n",
    "    if('saturated_fat' in nutris_present):\n",
    "        if(nutrients['saturated_fat'][per] <= 1):\n",
    "            c = 0\n",
    "        elif(nutrients['saturated_fat'][per] <= 2):\n",
    "            c = 1\n",
    "        elif(nutrients['saturated_fat'][per] <= 3):\n",
    "            c = 2\n",
    "        elif(nutrients['saturated_fat'][per] <= 4):\n",
    "            c = 3\n",
    "        elif(nutrients['saturated_fat'][per] <= 5):\n",
    "            c = 4\n",
    "        elif(nutrients['saturated_fat'][per] <= 6):\n",
    "            c = 5\n",
    "        elif(nutrients['saturated_fat'][per] <= 7):\n",
    "            c = 6\n",
    "        elif(nutrients['saturated_fat'][per] <= 8):\n",
    "            c = 7\n",
    "        elif(nutrients['saturated_fat'][per] <= 9):\n",
    "            c = 8\n",
    "        elif(nutrients['saturated_fat'][per] <= 10):\n",
    "            c = 9\n",
    "        else:\n",
    "            c = 10\n",
    "    else: c = 0\n",
    "    \n",
    "    if('sodium' in nutris_present):\n",
    "        if(nutrients['sodium'][per] <= 90):\n",
    "            d = 0\n",
    "        elif(nutrients['sodium'][per] <= 180):\n",
    "            d = 1\n",
    "        elif(nutrients['sodium'][per] <= 270):\n",
    "            d = 2\n",
    "        elif(nutrients['sodium'][per] <= 360):\n",
    "            d = 3\n",
    "        elif(nutrients['sodium'][per] <= 450):\n",
    "            d = 4\n",
    "        elif(nutrients['sodium'][per] <= 540):\n",
    "            d = 5\n",
    "        elif(nutrients['sodium'][per] <= 630):\n",
    "            d = 6\n",
    "        elif(nutrients['sodium'][per] <= 720):\n",
    "            d = 7\n",
    "        elif(nutrients['sodium'][per] <= 810):\n",
    "            d = 8\n",
    "        elif(nutrients['sodium'][per] <= 800):\n",
    "            d = 9\n",
    "        else:\n",
    "            d = 10\n",
    "    else: d = 0\n",
    "    return a, b, c, d\n",
    "\n",
    "\n",
    "def points_A_beverages(barcode):\n",
    "    a = b = c = d = 0\n",
    "    nutrients = df_combined['nutrients'][barcode]\n",
    "    nutris_present = set(nutrients.keys())\n",
    "    per = 'per_hundred'\n",
    "    if('energy' in nutris_present):\n",
    "        if(nutrients['energy']['per_hundred'] is None):\n",
    "            per = 'per_portion'\n",
    "            if(nutrients['energy']['per_portion'] <= 0):\n",
    "                a = 0\n",
    "            elif(nutrients['energy']['per_portion'] <= 30):\n",
    "                a = 1\n",
    "            elif(nutrients['energy']['per_portion'] <= 60):\n",
    "                a = 2\n",
    "            elif(nutrients['energy']['per_portion'] <= 90):\n",
    "                a = 3\n",
    "            elif(nutrients['energy']['per_portion'] <= 120):\n",
    "                a = 4\n",
    "            elif(nutrients['energy']['per_portion'] <= 150):\n",
    "                a = 5\n",
    "            elif(nutrients['energy']['per_portion'] <= 180):\n",
    "                a = 6\n",
    "            elif(nutrients['energy']['per_portion'] <= 210):\n",
    "                a = 7\n",
    "            elif(nutrients['energy']['per_portion'] <= 240):\n",
    "                a = 8\n",
    "            elif(nutrients['energy']['per_portion'] <= 270):\n",
    "                a = 9\n",
    "            else:\n",
    "                a = 10\n",
    "        else:\n",
    "            if(nutrients['energy']['per_hundred'] <= 0):\n",
    "                a = 0\n",
    "            elif(nutrients['energy']['per_hundred'] <= 30):\n",
    "                a = 1\n",
    "            elif(nutrients['energy']['per_hundred'] <= 60):\n",
    "                a = 2\n",
    "            elif(nutrients['energy']['per_hundred'] <= 90):\n",
    "                a = 3\n",
    "            elif(nutrients['energy']['per_hundred'] <= 120):\n",
    "                a = 4\n",
    "            elif(nutrients['energy']['per_hundred'] <= 150):\n",
    "                a = 5\n",
    "            elif(nutrients['energy']['per_hundred'] <= 180):\n",
    "                a = 6\n",
    "            elif(nutrients['energy']['per_hundred'] <= 210):\n",
    "                a = 7\n",
    "            elif(nutrients['energy']['per_hundred'] <= 240):\n",
    "                a = 8\n",
    "            elif(nutrients['energy']['per_hundred'] <= 270):\n",
    "                a = 9\n",
    "            else:\n",
    "                a = 10\n",
    "    else: a = 0\n",
    "    \n",
    "    if('sugars' in nutris_present):\n",
    "        if(nutrients['sugars']['per_hundred'] <= 0):\n",
    "            b = 0\n",
    "        elif(nutrients['sugars']['per_hundred'] <= 1.5):\n",
    "            b = 1\n",
    "        elif(nutrients['sugars']['per_hundred'] <= 3):\n",
    "            b = 2\n",
    "        elif(nutrients['sugars']['per_hundred'] <= 4.5):\n",
    "            b = 3\n",
    "        elif(nutrients['sugars']['per_hundred'] <= 6):\n",
    "            b = 4\n",
    "        elif(nutrients['sugars']['per_hundred'] <= 7.5):\n",
    "            b = 5\n",
    "        elif(nutrients['sugars']['per_hundred'] <= 9):\n",
    "            b = 6\n",
    "        elif(nutrients['sugars']['per_hundred'] <= 10.5):\n",
    "            b = 7\n",
    "        elif(nutrients['sugars']['per_hundred'] <= 12):\n",
    "            b = 8\n",
    "        elif(nutrients['sugars']['per_hundred'] <= 13.5):\n",
    "            b = 9\n",
    "        else:\n",
    "            b = 10\n",
    "    else: b = 0\n",
    "    \n",
    "    if('saturated_fat' in nutris_present):\n",
    "        if(nutrients['saturated_fat']['per_hundred'] <= 10):\n",
    "            c = 0\n",
    "        elif(nutrients['saturated_fat']['per_hundred'] <= 16):\n",
    "            c = 1\n",
    "        elif(nutrients['saturated_fat']['per_hundred'] <= 22):\n",
    "            c = 2\n",
    "        elif(nutrients['saturated_fat']['per_hundred'] <= 28):\n",
    "            c = 3\n",
    "        elif(nutrients['saturated_fat']['per_hundred'] <= 34):\n",
    "            c = 4\n",
    "        elif(nutrients['saturated_fat']['per_hundred'] <= 40):\n",
    "            c = 5\n",
    "        elif(nutrients['saturated_fat']['per_hundred'] <= 46):\n",
    "            c = 6\n",
    "        elif(nutrients['saturated_fat']['per_hundred'] <= 52):\n",
    "            c = 7\n",
    "        elif(nutrients['saturated_fat']['per_hundred'] <= 58):\n",
    "            c = 8\n",
    "        elif(nutrients['saturated_fat']['per_hundred'] <= 64):\n",
    "            c = 9\n",
    "        else:\n",
    "            c = 10\n",
    "    else: c = 0\n",
    "    \n",
    "    if('sodium' in nutris_present):\n",
    "        if(nutrients['sodium']['per_hundred'] <= 90):\n",
    "            d = 0\n",
    "        elif(nutrients['sodium']['per_hundred'] <= 180):\n",
    "            d = 1\n",
    "        elif(nutrients['sodium']['per_hundred'] <= 270):\n",
    "            d = 2\n",
    "        elif(nutrients['sodium']['per_hundred'] <= 360):\n",
    "            d = 3\n",
    "        elif(nutrients['sodium']['per_hundred'] <= 450):\n",
    "            d = 4\n",
    "        elif(nutrients['sodium']['per_hundred'] <= 540):\n",
    "            d = 5\n",
    "        elif(nutrients['sodium']['per_hundred'] <= 630):\n",
    "            d = 6\n",
    "        elif(nutrients['sodium']['per_hundred'] <= 720):\n",
    "            d = 7\n",
    "        elif(nutrients['sodium']['per_hundred'] <= 810):\n",
    "            d = 8\n",
    "        elif(nutrients['sodium']['per_hundred'] <= 800):\n",
    "            d = 9\n",
    "        else:\n",
    "            d = 10\n",
    "    else: d = 0\n",
    "    return a, b, c, d\n",
    "    \n",
    "\n",
    "def calculate_A(barcode):\n",
    "    a = b = c = d = 0\n",
    "    if(df_combined['unit'][barcode] == 'g'):\n",
    "        a, b, c, d = points_A_not_beverages(barcode)\n",
    "    else:\n",
    "        a, b, c, d = points_A_beverages(barcode)\n",
    "    return a + b + c + d\n",
    "        \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2, 0)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points_C('0014100096986')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Points C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_fruit_or_vegetable(ingredient):    \n",
    "    for items in f_and_v:\n",
    "        if(ingredient in items):\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "def get_fruit_veggie_proportion(barcode):\n",
    "    languages = set(ingredients_df['ingredients'][column].keys())\n",
    "    \n",
    "    if('fr' in languages):\n",
    "        ingris = ingredients_df['ingredients'][column]['fr']        \n",
    "    elif('de' in languages):\n",
    "        ingris = ingredients_df['ingredients'][column]['de']        \n",
    "    elif('en' in languages):\n",
    "        ingris = ingredients_df['ingredients'][column]['en']\n",
    "    else:\n",
    "        return -1\n",
    "    \n",
    "    lItems = ingris.split(',')\n",
    "    \n",
    "    percent_total = 0\n",
    "    estimate = 50\n",
    "    for l in lItems:\n",
    "        if(is_fruit_or_vegetable(l)):\n",
    "            percentage = re.findall(r'\\d+%', l)\n",
    "            if(len(percentage) == 0):\n",
    "                percent_total = percent_total + estimate\n",
    "            else:\n",
    "                percent_total = percent_total + percentage[0].rstrip('%')  \n",
    "        estimate = estimate/2\n",
    "    return percent_total\n",
    "                \n",
    "def C_non_beverage(barcode):\n",
    "    fruit = get_fruit_veggie_proportion(barcode)\n",
    "    if(fruit == -1): return -1\n",
    "    elif(fruit <= 40): return 0\n",
    "    elif(fruit <= 60): return 1\n",
    "    elif(fruit <= 80): return 2\n",
    "    else: return 5\n",
    "    \n",
    "def C_beverage(barcode):\n",
    "    fruit = get_fruit_veggie_proportion(barcode)\n",
    "    if(fruit == -1): return -1\n",
    "    elif(fruit <= 40): return 0\n",
    "    elif(fruit <= 60): return 2\n",
    "    elif(fruit <= 80): return 4\n",
    "    else: return 10\n",
    "\n",
    "def points_C(barcode):\n",
    "    a = b = c = 0\n",
    "    \n",
    "    nutrients = df_combined['nutrients'][barcode]\n",
    "    nutris_present = set(nutrients.keys())\n",
    "    \n",
    "    ingredients = df_combined['ingredients'][barcode]\n",
    "    languages_present = set(ingredients.keys())\n",
    "    \n",
    "    if('fiber' in nutris_present):\n",
    "        if(nutrients['fiber']['per_hundred'] <= 0.7):\n",
    "            a = 0\n",
    "        elif(nutrients['fiber']['per_hundred'] <= 1.4):\n",
    "            a = 1\n",
    "        elif(nutrients['fiber']['per_hundred'] <= 2.1):\n",
    "            a = 2\n",
    "        elif(nutrients['fiber']['per_hundred'] <= 2.8):\n",
    "            a = 3\n",
    "        elif(nutrients['fiber']['per_hundred'] <= 3.5):\n",
    "            a = 4\n",
    "        else:\n",
    "            a = 5\n",
    "    else: a = 0\n",
    "    \n",
    "    if('protein' in nutris_present):\n",
    "        if(nutrients['protein']['per_hundred'] <= 1.6):\n",
    "            b = 0\n",
    "        elif(nutrients['protein']['per_hundred'] <= 3.2):\n",
    "            b = 1\n",
    "        elif(nutrients['protein']['per_hundred'] <= 4.8):\n",
    "            b = 2\n",
    "        elif(nutrients['protein']['per_hundred'] <= 6.4):\n",
    "            b = 3\n",
    "        elif(nutrients['protein']['per_hundred'] <= 8):\n",
    "            b = 4\n",
    "        else:\n",
    "            b = 5\n",
    "    else: b = 0\n",
    "    \n",
    "    if(df_combined['unit'][barcode] == 'g' and C_non_beverage(barcode) != -1):\n",
    "        a = C_non_beverage(barcode)\n",
    "    elif(C_beverage(barcode) != -1):\n",
    "        a = C_beverage(barcode)\n",
    "        \n",
    "    return a, b, c\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def break_name(lang, barcode):\n",
    "    return [x.lower() for x in df_combined['name'][barcode][lang].split(' ')]\n",
    "\n",
    "def is_cheese(barcode):    \n",
    "    languages = set(df_combined['name'][barcode].keys())\n",
    "    if('en' in languages):\n",
    "        lang = 'en'\n",
    "        if('cheese' in break_name(lang, barcode) and 'with' not in break_name(lang, barcode)):\n",
    "            return True\n",
    "        else: return False\n",
    "    elif('fr' in languages):\n",
    "        lang = 'fr'\n",
    "        if('fromage' in break_name(lang, barcode) and 'avec' not in break_name(lang, barcode)):\n",
    "            return True\n",
    "        else: return False\n",
    "    elif('de' in languages):\n",
    "        lang = 'de'\n",
    "        if('kase' in break_name(lang, barcode) and 'mit' not in break_name(lang, barcode)):\n",
    "            return True\n",
    "        else: return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "nutriscore = {}\n",
    "for barcode in columns:\n",
    "    try:\n",
    "        int(barcode)\n",
    "    except:\n",
    "        continue\n",
    "    \n",
    "    \n",
    "    try:\n",
    "        A = calculate_A(barcode)\n",
    "        fruit, fiber, protein = points_C(barcode)\n",
    "\n",
    "        if(A < 11 or is_cheese(barcode)):\n",
    "            score = A - (fruit + fiber + protein)\n",
    "        elif(fruit >= 5):\n",
    "            score = A - (fruit + fiber + protein)\n",
    "        else:\n",
    "            score = A - (fiber + fruit)\n",
    "        print(barcode, '')\n",
    "    except:\n",
    "        score = -100\n",
    "    \n",
    "    if(score > -99 and score < -1):\n",
    "        nutriscore[barcode] = \"A\"\n",
    "    elif(score >= 0 and score <=2):\n",
    "        nutriscore[barcode] = \"B\"\n",
    "    elif(score >= 3 and score <= 10):\n",
    "        nutriscore[barcode] = \"C\"\n",
    "    elif(score >= 11 and score <= 18):\n",
    "        nutriscore[barcode] = \"D\"\n",
    "    elif(score > 18):\n",
    "        nutriscore[barcode] = \"E\"\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2108355006253': 'B',\n",
       " '7613269303138': 'B',\n",
       " '4003220093018': 'B',\n",
       " '7616500648623': 'E',\n",
       " '20828387': 'A',\n",
       " '7610100026713': 'D',\n",
       " '7617400045406': 'C',\n",
       " '7613331038715': 'B',\n",
       " '20811594': 'B',\n",
       " '8016861151081': 'B',\n",
       " '20422103': 'C',\n",
       " '7610029004762': 'B',\n",
       " '7617700358305': 'B',\n",
       " '4009700079251': 'B',\n",
       " '7613312056677': 'B',\n",
       " '7617027633482': 'A',\n",
       " '7616500050259': 'E',\n",
       " '7610491168306': 'D',\n",
       " '8600102938083': 'B',\n",
       " '7610307532635': 'B',\n",
       " '20294571': 'C',\n",
       " '7617400041385': 'D',\n",
       " '76880001': 'D',\n",
       " '3497761001112': 'B',\n",
       " '7610032043307': 'D',\n",
       " '4008400510125': 'E',\n",
       " '8031993104088': 'B',\n",
       " '8014737727507': 'B',\n",
       " '20708191': 'D',\n",
       " '7610900183654': 'B',\n",
       " '3302740447020': 'A',\n",
       " '3080216045979': 'B',\n",
       " '7616800812359': 'C',\n",
       " '7610900085309': 'B',\n",
       " '5035766044673': 'B',\n",
       " '7613269116523': 'B',\n",
       " '7613269139041': 'B',\n",
       " '8001100063737': 'D',\n",
       " '7613356248083': 'C',\n",
       " '7610256500013': 'D',\n",
       " '20295912': 'B',\n",
       " '3336660021848': 'C',\n",
       " '7610403072509': 'E',\n",
       " '7610107118961': 'C',\n",
       " '7613312035047': 'B',\n",
       " '4104420057500': 'D',\n",
       " '7616700037937': 'A',\n",
       " '7613269633440': 'D',\n",
       " '7610900080717': 'B',\n",
       " '5601213181651': 'B',\n",
       " '7611654106951': 'B',\n",
       " '7616700193152': 'C',\n",
       " '7613312096925': 'D',\n",
       " '7610095035004': 'B',\n",
       " '7610434012550': 'D',\n",
       " '7610849006250': 'D',\n",
       " '7640101094831': 'C',\n",
       " '76102103': 'C',\n",
       " '7610849539062': 'A',\n",
       " '7617700607106': 'D',\n",
       " '7617700462156': 'B',\n",
       " '7617400037845': 'E',\n",
       " '4001686372586': 'B',\n",
       " '7613034663375': 'C',\n",
       " '7612478001002': 'B',\n",
       " '7613269295631': 'B',\n",
       " '7616600710459': 'C',\n",
       " '4104420142121': 'C',\n",
       " '7613269227595': 'B',\n",
       " '7610029027754': 'B',\n",
       " '7616800400655': 'B',\n",
       " '7610845783377': 'B',\n",
       " '7610848685159': 'A',\n",
       " '7610848360568': 'B',\n",
       " '7613312133965': 'B',\n",
       " '7640115251114': 'B',\n",
       " '7610029142846': 'C',\n",
       " '2148538005659': 'C',\n",
       " '78586895': 'C',\n",
       " '7610849946235': 'E',\n",
       " '8712566451357': 'C',\n",
       " '7610827503849': 'D',\n",
       " '7613035107113': 'D',\n",
       " '3105560042907': 'D',\n",
       " '8076809569583': 'D',\n",
       " '4009176240308': 'B',\n",
       " '7613030013648': 'B',\n",
       " '7610818224197': 'C',\n",
       " '9002100022624': 'C',\n",
       " '20480257': 'B',\n",
       " '5021047104433': 'C',\n",
       " '7613032247836': 'D',\n",
       " '7613312131688': 'D',\n",
       " '7610973190412': 'B',\n",
       " '7610029011623': 'C',\n",
       " '5411223030074': 'B',\n",
       " '4104420057326': 'C',\n",
       " '7613312047729': 'B',\n",
       " '4104420031449': 'B',\n",
       " '7611654536925': 'A',\n",
       " '7613269535843': 'B',\n",
       " '3017760686693': 'D',\n",
       " '7617400049848': 'D',\n",
       " '7613269741398': 'C',\n",
       " '7640146947727': 'B',\n",
       " '2159780005052': 'C',\n",
       " '7610818199372': 'B',\n",
       " '3178530410907': 'D',\n",
       " '3023290231753': 'C',\n",
       " '7613269296751': 'C',\n",
       " '7610491921666': 'B',\n",
       " '5060108450355': 'D',\n",
       " '5051882115288': 'B',\n",
       " '8711000501153': 'C',\n",
       " '20234744': 'B',\n",
       " '7617027948814': 'C',\n",
       " '3257982267441': 'B',\n",
       " '20463236': 'E',\n",
       " '4104420055742': 'E',\n",
       " '4062300281974': 'B',\n",
       " '3252755350009': 'C',\n",
       " '7610400030335': 'E',\n",
       " '28502548': 'B',\n",
       " '7610807022339': 'C',\n",
       " '20379018': 'A',\n",
       " '7610848588153': 'D',\n",
       " '4043696650261': 'C',\n",
       " '7610900128877': 'C',\n",
       " '4000521145903': 'D',\n",
       " '7617400048957': 'D',\n",
       " '8000320010026': 'B',\n",
       " '5000159455893': 'E',\n",
       " '7613033068959': 'D',\n",
       " '7616600708623': 'C',\n",
       " '7613031275915': 'C',\n",
       " '7613331631909': 'B',\n",
       " '7613030013341': 'B',\n",
       " '7610200010889': 'B',\n",
       " '7617100217790': 'C',\n",
       " '7613312058947': 'D',\n",
       " '7610807149555': 'E',\n",
       " '8032919465030': 'D',\n",
       " '7610058213272': 'C',\n",
       " '7613312036877': 'B',\n",
       " '7640119920092': 'B',\n",
       " '7616700213744': 'B',\n",
       " '7798113551115': 'B',\n",
       " '7610809098523': 'D',\n",
       " '7610095014009': 'C',\n",
       " '3291960001542': 'B',\n",
       " '6001495062577': 'B',\n",
       " '5202425000053': 'B',\n",
       " '7612188002269': 'D',\n",
       " '7617027005456': 'B',\n",
       " '3021761203155': 'B',\n",
       " '7610849519002': 'C',\n",
       " '4023300811202': 'B',\n",
       " '24002790': 'B',\n",
       " '8015118075064': 'B',\n",
       " '20523411': 'A',\n",
       " '7616500648395': 'E',\n",
       " '4000522060519': 'C',\n",
       " '7610848714149': 'C',\n",
       " '7610849654802': 'B',\n",
       " '7617027762021': 'A',\n",
       " '7610400086462': 'E',\n",
       " '7613312004890': 'B',\n",
       " '7613034958051': 'B',\n",
       " '7613331344281': 'E',\n",
       " '7613269302513': 'A',\n",
       " '7610089061750': 'B',\n",
       " '0070177118525': 'B',\n",
       " '7610846879062': 'D',\n",
       " '7610036012507': 'E',\n",
       " '7610404500001': 'C',\n",
       " '52017087': 'C',\n",
       " '20245627': 'A',\n",
       " '7617400030648': 'B',\n",
       " '7613312053195': 'D',\n",
       " '7610848730781': 'B',\n",
       " '7613312098400': 'B',\n",
       " '4043696650063': 'C',\n",
       " '20289904': 'B',\n",
       " '5601151170755': 'A',\n",
       " '7613356227972': 'B',\n",
       " '0014113912341': 'C',\n",
       " '7613312019054': 'B',\n",
       " '7617027633567': 'B',\n",
       " '7613312082805': 'D',\n",
       " '4066600601920': 'B',\n",
       " '7610849876815': 'E',\n",
       " '2185093001900': 'B',\n",
       " '4104420015289': 'D',\n",
       " '7616500051065': 'E',\n",
       " '7613033653957': 'C',\n",
       " '7616700013535': 'C',\n",
       " '20287528': 'B',\n",
       " '4260409960015': 'B',\n",
       " '7610029155556': 'D',\n",
       " '0796030214943': 'B',\n",
       " '7612464723123': 'B',\n",
       " '7617400050189': 'C',\n",
       " '9003600482819': 'E',\n",
       " '7610809021767': 'D',\n",
       " '7610374749677': 'B',\n",
       " '7617400031720': 'D',\n",
       " '7613312090664': 'B',\n",
       " '20163402': 'B',\n",
       " '7613269523093': 'A',\n",
       " '3258691489735': 'B',\n",
       " '2131115012652': 'C',\n",
       " '3514124232225': 'B',\n",
       " '8712100682926': 'E',\n",
       " '7613312112564': 'B',\n",
       " '8723400777410': 'D',\n",
       " '7613269292746': 'C',\n",
       " '7610403072561': 'D',\n",
       " '7613269226383': 'B',\n",
       " '7612900337983': 'B',\n",
       " '4002566003569': 'B',\n",
       " '7610338964054': 'B',\n",
       " '7610029003161': 'B',\n",
       " '7610027025455': 'C',\n",
       " '80723103': 'E',\n",
       " '40004525': 'D',\n",
       " '7613356364301': 'A',\n",
       " '7610849233557': 'D',\n",
       " '8032825760137': 'B',\n",
       " '7612188002252': 'D',\n",
       " '7614800321000': 'D',\n",
       " '80837503': 'C',\n",
       " '7617067012841': 'C',\n",
       " '20005047': 'B',\n",
       " '0087692000051': 'B',\n",
       " '7610057019608': 'B',\n",
       " '7611300035703': 'B',\n",
       " '7610029158939': 'B',\n",
       " '7630017624475': 'D',\n",
       " '7613269009252': 'C',\n",
       " '7617027938877': 'B',\n",
       " '80525387': 'B',\n",
       " '7611100141123': 'B',\n",
       " '7611654934004': 'A',\n",
       " '7610200390998': 'D',\n",
       " '5038862134415': 'D',\n",
       " '7610200385192': 'D',\n",
       " '7610097152075': 'B',\n",
       " '7610807053746': 'B',\n",
       " '7610807240153': 'B',\n",
       " '7610029011661': 'D',\n",
       " '24038751': 'B',\n",
       " '7640119220208': 'B',\n",
       " '7611100088084': 'C',\n",
       " '7613312013052': 'A',\n",
       " '7613269300649': 'B',\n",
       " '4260402480435': 'D',\n",
       " '7613312021156': 'C',\n",
       " '7640126171159': 'B',\n",
       " '7613312016282': 'D',\n",
       " '7613035327931': 'C',\n",
       " '7610167617008': 'B',\n",
       " '8715035400306': 'C',\n",
       " '8076809540629': 'D',\n",
       " '20695989': 'A',\n",
       " '5000112609653': 'B',\n",
       " '20573126': 'C',\n",
       " '7613269510987': 'B',\n",
       " '7616500657298': 'E',\n",
       " '7610848666899': 'B',\n",
       " '7616800214061': 'B',\n",
       " '20632083': 'B',\n",
       " '7610700901137': 'B',\n",
       " '7611654898597': 'C',\n",
       " '7613312044193': 'B',\n",
       " '7611466580024': 'B',\n",
       " '20084134': 'C',\n",
       " '4014400913361': 'D',\n",
       " '7613331953933': 'C',\n",
       " '22037282': 'B',\n",
       " '7610848057468': 'A',\n",
       " '7610100725265': 'B',\n",
       " '7613312099384': 'B',\n",
       " '7616700129236': 'A',\n",
       " '7613331580573': 'E',\n",
       " '20758806': 'B',\n",
       " '7610848383512': 'A',\n",
       " '7640119220901': 'B',\n",
       " '7616500640498': 'E',\n",
       " '7617014133957': 'B',\n",
       " '7613269644408': 'B',\n",
       " '7610026426536': 'B',\n",
       " '7613312067529': 'D',\n",
       " '7611100101547': 'A',\n",
       " '20595753': 'A',\n",
       " '7610200232830': 'C',\n",
       " '7610121601302': 'B',\n",
       " '7616700034943': 'E',\n",
       " '7610846150772': 'B',\n",
       " '7610491033628': 'A',\n",
       " '7640151710309': 'C',\n",
       " '7613331744036': 'B',\n",
       " '7610029151008': 'C',\n",
       " '7610829573369': 'A',\n",
       " '5425002402310': 'B',\n",
       " '7610097112185': 'B',\n",
       " '7616600702539': 'D',\n",
       " '20708412': 'C',\n",
       " '20428082': 'B',\n",
       " '5000159481540': 'E',\n",
       " '7610200337160': 'B',\n",
       " '7617400030488': 'C',\n",
       " '8014737767503': 'B',\n",
       " '7640151710040': 'C',\n",
       " '7613269260301': 'B',\n",
       " '3276170017804': 'A',\n",
       " '7616500653504': 'E',\n",
       " '7616800700335': 'B',\n",
       " '28140955': 'B',\n",
       " '3179071002842': 'B',\n",
       " '22112385': 'C',\n",
       " '8712100708756': 'B',\n",
       " '7613269475675': 'D',\n",
       " '7611100133227': 'B',\n",
       " '7610046250708': 'E',\n",
       " '8001060001985': 'C',\n",
       " '7613269501688': 'A',\n",
       " '20207557': 'B',\n",
       " '5060194790090': 'C',\n",
       " '7611687092016': 'D',\n",
       " '4011800562212': 'D',\n",
       " '7610114031611': 'B',\n",
       " '7610071221452': 'B',\n",
       " '859216004018': 'B',\n",
       " '7617500197708': 'E',\n",
       " '7610097154260': 'D',\n",
       " '8005369000779': 'B',\n",
       " '4260402480947': 'B',\n",
       " '7610800014249': 'D',\n",
       " '3576280104280': 'C',\n",
       " '7613196011304': 'B',\n",
       " '7613196005150': 'C',\n",
       " '2122691004551': 'C',\n",
       " '20514648': 'B',\n",
       " '7610027000575': 'B',\n",
       " '3760132081366': 'D',\n",
       " '7613356654563': 'C',\n",
       " '7614200112482': 'B',\n",
       " '5060194790465': 'C',\n",
       " '4000521142810': 'B',\n",
       " '7611654125488': 'C',\n",
       " '4036554703414': 'C',\n",
       " '7613331704009': 'D',\n",
       " '7618500947379': 'D',\n",
       " '8000500166819': 'E',\n",
       " '7610807121124': 'B',\n",
       " '3228023980070': 'C',\n",
       " '7640119920054': 'B',\n",
       " '7613269508755': 'B',\n",
       " '7610029170580': 'A',\n",
       " '7616700013061': 'C',\n",
       " '0039047010277': 'E',\n",
       " '7610244100706': 'B',\n",
       " '20455002': 'A',\n",
       " '7610200052742': 'A',\n",
       " '3023470001015': 'D',\n",
       " '4067700014610': 'B',\n",
       " '7613033351839': 'B',\n",
       " '7610800977445': 'C',\n",
       " '7610029172195': 'B',\n",
       " '7613269462231': 'C',\n",
       " '7610238272310': 'D',\n",
       " '7610335003244': 'C',\n",
       " '7610095201003': 'C',\n",
       " '7610200309938': 'C',\n",
       " '7613269549031': 'A',\n",
       " '2155430004055': 'C',\n",
       " '7613269274070': 'B',\n",
       " '7610200046291': 'B',\n",
       " '7613269339359': 'E',\n",
       " '7610400083508': 'E',\n",
       " '7610167690827': 'B',\n",
       " '4008467055096': 'C',\n",
       " '7613312044056': 'E',\n",
       " '4009900406918': 'B',\n",
       " '20000684': 'D',\n",
       " '033984013209': 'B',\n",
       " '7610807160000': 'C',\n",
       " '22140807': 'C',\n",
       " '3045320097511': 'D',\n",
       " '7640107060984': 'E',\n",
       " '7613269165415': 'B',\n",
       " '3760177742772': 'B',\n",
       " '7610200323712': 'E',\n",
       " '8000305005092': 'B',\n",
       " '7610807144956': 'B',\n",
       " '7613269267263': 'B',\n",
       " '3191010046083': 'B',\n",
       " '7616500644427': 'E',\n",
       " '7610849660254': 'B',\n",
       " '7610849473670': 'A',\n",
       " '20805555': 'A',\n",
       " '20812331': 'B',\n",
       " '7610278000782': 'C',\n",
       " '76129155': 'B',\n",
       " '4250442210178': 'B',\n",
       " '7610148210273': 'E',\n",
       " '8008698017807': 'B',\n",
       " '7610011005890': 'A',\n",
       " '7610800004967': 'B',\n",
       " '4104420133341': 'B',\n",
       " '7616500050051': 'D',\n",
       " '8002270026546': 'D',\n",
       " '3033710065066': 'B',\n",
       " '7610121601418': 'B',\n",
       " '7613269373971': 'B',\n",
       " '7616700037715': 'B',\n",
       " '7613269297215': 'C',\n",
       " '7610216121081': 'D',\n",
       " '3036811363147': 'C',\n",
       " '20665432': 'C',\n",
       " '7610200010971': 'B',\n",
       " '7613269762249': 'B',\n",
       " '4104420031883': 'D',\n",
       " '5413588602019': 'B',\n",
       " '2110212004852': 'C',\n",
       " '7610097111294': 'D',\n",
       " '7613331360236': 'D',\n",
       " '5018357009077': 'C',\n",
       " '24023269': 'B',\n",
       " '7613269373797': 'C',\n",
       " '7616700242980': 'A',\n",
       " '7613356060845': 'B',\n",
       " '76129582': 'B',\n",
       " '7610029125986': 'C',\n",
       " '5021047101319': 'C',\n",
       " '5760466920490': 'B',\n",
       " '22016072': 'C',\n",
       " '7613312022580': 'D',\n",
       " '7613030009191': 'D',\n",
       " '22140616': 'B',\n",
       " '7610058003408': 'C',\n",
       " '20060039': 'C',\n",
       " '7613269207924': 'C',\n",
       " '7610807051056': 'A',\n",
       " '7610632982426': 'B',\n",
       " '7610200010841': 'B',\n",
       " '7610800047674': 'A',\n",
       " '8021228310056': 'A',\n",
       " '7610849955626': 'C',\n",
       " '9002540816685': 'B',\n",
       " '8711000513286': 'B',\n",
       " '8001505000399': 'B',\n",
       " '7613379127242': 'E',\n",
       " '7610277004316': 'A',\n",
       " '8016118101005': 'B',\n",
       " '7610235006604': 'D',\n",
       " '7616500656192': 'E',\n",
       " '7610141504003': 'B',\n",
       " '84100733': 'E',\n",
       " '7613269301165': 'B',\n",
       " '7610846044606': 'C',\n",
       " '7640120460105': 'D',\n",
       " '4304493261532': 'B',\n",
       " '7613269684268': 'A',\n",
       " '7613035184084': 'D',\n",
       " '8028178000242': 'E',\n",
       " '1602256950000': 'C',\n",
       " '7610829175808': 'C',\n",
       " '7616500639805': 'E',\n",
       " '3514120106377': 'B',\n",
       " '2153471007806': 'B',\n",
       " '7613269317371': 'A',\n",
       " '7611613001198': 'B',\n",
       " '7613269197607': 'A',\n",
       " '3263289990203': 'B',\n",
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       " '7610374113072': 'B',\n",
       " '7613312029091': 'E',\n",
       " '7610188129443': 'D',\n",
       " '7613269487142': 'B',\n",
       " '4104420181113': 'C',\n",
       " '7616500639898': 'D',\n",
       " '7613032445294': 'E',\n",
       " '7610849933228': 'E',\n",
       " '7613269197966': 'B',\n",
       " '7613331285812': 'C',\n",
       " '7617400037623': 'C',\n",
       " '7610200266569': 'A',\n",
       " '5021047107328': 'C',\n",
       " '8421401201209': 'B',\n",
       " '5060237280922': 'C',\n",
       " '7610807062137': 'D',\n",
       " '7610253406462': 'B',\n",
       " '7610029156171': 'B',\n",
       " '3256540011526': 'D',\n",
       " '7617027579902': 'A',\n",
       " '7616700034578': 'B',\n",
       " '7613269430988': 'B',\n",
       " '3018300000245': 'B',\n",
       " '7613269176169': 'D',\n",
       " ...}"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutriscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('nutriscore.json', 'w') as fp:\n",
    "    json.dump(nutriscore, fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'90169571'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-232-8a758ea5f022>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnutriscore\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'90169571'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: '90169571'"
     ]
    }
   ],
   "source": [
    "nutriscore['90169571']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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