P4C1.ipynb

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   "source": [
    "<a id=\"sommaire\"></a>\n",
    "### Table of Contents\n",
    "\n",
    "* [Requete 1 La proportion de personnes en état de sous-nutrition pour l'année 2017.](#requete1)\n",
    "    * [Filtre du dataframe ```Population``` 1.1](#requete_1_1)\n",
    "        * [Création dataframe ```Population ``` 1.1.2](#requete_1_1_2)\n",
    "        * [Filtre de la zone et la valeur pour l'année 2017 1.1.3](#requete_1_1_3)\n",
    "        * [Modifier la colone <span style=\"color:crimson\">Valeur</span> pour que le resultat soit exprimé en unité 1.1.4](#requete_1_1_4)\n",
    "    * [Filtre du dataframe ```Sous-nutrition``` 1.2](#requete_1_2)\n",
    "        * [Création dataframe ```Sous-nutrition``` 1.2.1](#requete_1_2_1)\n",
    "        * [Filtre de la zone et la valeur pour l'année 2017 1.2.2](#requete_1_2_2)\n",
    "        * [Convertir Valeur de Object à Float 1.2.3](#requete_1_2_3)\n",
    "        * [Modifier la colone <span style=\"color:crimson\">Valeur</span> pour que le resultat soit exprimé en unité 1.2.4](#requete_1_2_4)\n",
    "        * [Convertir Valeur de Object à Float 1.2.5](#requete_1_2_5)\n",
    "    * [Le pourcentage de la population en état de sous nutrition dans le monde en 2017 1.3](#requete_1_3)\n",
    "* [Requete 2: Le nombre théorique de personnes qui pourraient être nourries.](#requete2)\n",
    "    * [Création du data frame ```data_dispo_alimentaire(aide alimentaire)```  2.1](#section_2_1)\n",
    "    * [Merge des df ```zone_data_alimentaire``` et ```data_pop_zone_val_2017(population en 2017)``` 2.2](#section_2_2)\n",
    "        * [Shape des tables 2.2.1](#section_2_2_1)\n",
    "        * [Conaitre les valeurs non attribuées dans la merge 2.2.2](#section_2_2_2)\n",
    "        * [Savoir comment est écrit Royaume-Uni et Tchéquie dans la table population en 2017 2.2.3](#section_2_2_3)\n",
    "        * [Reset l'index dans le df dispo alimentaire et remplacer les deux pays par la bonne orthographe 2.2.4](#section_2_2_4)\n",
    "        * [Création de la colone <span style=\"color:crimson\">Disponibilité alimentaire (Kcal/jour)</span> 2.2.5](#section_2_2_5)\n",
    "        * [Création de la colone <span style=\"color:crimson\">Population totale par pays pouvant etre nourri</span> 2.2.6](#section_2_2_6)\n",
    "    * [Résultat du nombre théorique de personnes pouvant etre nourri 2.3](#section_2_3)\n",
    "    * [Resulat en pourcentage 2.4](#section_2_4)\n",
    "* [Requete 3: Disponibilité alimentaire des produits végétaux.](#requete3)\n",
    "    * [Filtre par Origine vegetale du df ``` data_dispo_alimentaire```  3.1](#section_3_1)\n",
    "        * [Shape des tables 2.2.1](#section_3_1_1)\n",
    "    * [Merge des df ```data_dispo_alimentaire_vegetale_zone``` et ```data_pop_zone_val_2017``` 3.2](#section_3_2)\n",
    "        * [Conaitre les valeurs non attribuées dans la merge 3.2.1](#section_3_2_1)\n",
    "        * [Reset l'index du df ```dispo alimentaire``` et remplacer les deux pays par la bonne orthographe 3.2.2](#section_3_2_2)\n",
    "        * [Création de la colone <span style=\"color:crimson\">Disponibilité alimentaire (Kcal/jour)</span> 3.2.3](#section_3_2_3)\n",
    "        * [Création de la colone <span style=\"color:crimson\">Population totale par pays pouvant etre nourri</span> 3.2.4](#section_3_2_4)\n",
    "    * [Résultat du nombre théorique de personnes pouvant etre nourri (origine vegetale) 3.3](#section_3_3)\n",
    "* [Requete 4: L’utilisation de la disponibilité intérieure, en particulier la part qui est attribuée à l’alimentation animale, celle qui est perdue et celle qui est concrètement utilisée pour l'alimentation humaine.](#requete4)\n",
    "    * [Creation de la variable disponibilite intérieur totale: ```disponibilite_int_totale``` 4.1](#section_4_1)\n",
    "    * [Création de la variable disponibilité alimentation animale: ```disponibilite_animale``` 4.2](#section_4_2)\n",
    "    * [Calcule de la part attribuée à l'alimentation animale 4.3](#section_4_3)\n",
    "    * [Calcule de la perte 4.4](#section_4_4)\n",
    "        * [Création de la variable ```perte``` 4.4.1](#section_4_4_1)\n",
    "        * [La part perdue 4.4.2](#section_4_4_2)\n",
    "    * [Calcule de la Disponibilité alimentaire pour l'alimentation humaine 4.5](#section_4_5)\n",
    "        * [Création de la variable disponibilité alimentation pour les humains: ```dispo_alimentaire_humain``` 4.5.1](#section_4_5_1)\n",
    "        * [La part attribuée au humains 4.5.2](#section_4_5_2)\n",
    "    * [Calcule pour savoir si le resultat obtenue est le bon (calcule de la valeur totale) 4.6](#section_4_6)\n",
    "        * [Création de la variable disponibilité pour la semence: ```dispo_semences``` 4.6.1](#section_4_6_1)\n",
    "        * [La part attribuée pour la semence 4.6.2](#section_4_6_2)\n",
    "        * [Calcule de la part attribuée aux traitements 4.6.3](#section_4_6_3)\n",
    "        * [Création de la variable disponibilité pour le traitement: ```dispo_traitement``` 4.6.4](#section_4_6_4)\n",
    "        * [La part attribuée pour la traitement 4.6.5](#section_4_6_5)\n",
    "        * [Calcule de la disponibilité des autres utilisations 4.6.6](#section_4_6_6)\n",
    "        * [Création de la variable disponibilité pour les autres utilisations: ```dispo_autre_utilisation``` 4.6.7](#section_4_6_7)\n",
    "        * [La part attribuée pour les autres utilisations 4.6.8](#section_4_6_8)\n",
    "        * [Calcule de la disponibilité totale 4.6.9](#section_4_6_9)\n",
    "* [Les pays pour lesquels la proportion de personnes sous-alimentées est la plus forte en 2017.](#section5)\n",
    "    * [Nettoyage des données](#section_5_1)\n",
    "    * [Création de la colone <span style=\"color:crimson\">Proportion (%)</span> qui corresponf à la proportion de personnes sous-alimentées dans leur pays respestif ](#section_5_2)\n",
    "    * [Resultat des pays où la sous-alimentation est la plus foorte en 2017](#section_5_3)\n",
    "* [Les 10 pays qui ont le plus bénéficiés d’aide depuis 2013.](#section6)\n",
    "* [Les pays ayant le plus/moins de disponibilité alimentaire.](#section7)\n",
    "* [Bonus.](#section8)\n",
    "* [Question de Julien.](#section9)\n",
    "    * [Logique entre l’ensemble des 10 variables](#section_9_1)\n",
    "        * [Vérification de l'équation 9.1.1](#section_9_1_1)\n",
    "    * [Chiffres intéressants sur l’utilisation des céréales, notamment la répartition entre l’alimentation humaine (colonne Nourriture) et l’alimentation pour animaux (pour l'année 2017). 9.2](#section_9_2)\n",
    "        * [Créatin du dataframe bilans alimentaires pour les céréales: ```data_cereales_2017```. 9.2.1](#section_9_2_1)\n",
    "        * [Répartition des céréales entre l'alimentation pour les humains et celle pour les animaux. 9.2.2](#section_9_2_2)\n",
    "        * [Conclusion. 9.2.3](#section_9_2_3)\n",
    "* [Conclusion finale.](#section10)"
   ]
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   "source": [
    "### Importing the required libraries"
   ]
  },
  {
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   "id": "fe79e19e-40d0-46f8-8e59-7888c266a357",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9d20072-0a17-4622-b1be-16b5c64567d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fonction utile\n",
    "# Fonction qui permet de rechercher dans une table un pays en particulier\n",
    "# appel de la fonction => find_substring_on_table(nom_de_la_table, 'substring_du_pays')\n",
    "def find_substring_on_table(table,substring):\n",
    "    return table[table.apply(lambda row: row.astype(str).str.contains(substring, case=False).any(), axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4851715-be45-437a-91e9-43ec00611cb8",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Requete 1: La proportion de personnes en état de sous-nutrition pour l'année 2017.<a class=\"anchor\" id=\"requete1\"></a>\n",
    "[Retour au sommaire](#sommaire)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "494fdbb6-a994-45fd-9b1a-6d23592aa0ff",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Filtre du dataframe ```Population``` 1.1 <a class=\"anchor\" id=\"requete_1_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6bd9a25-73b0-4942-b350-99613c1a5b6e",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Création dataframe ```Population ``` 1.1.2 <a class=\"anchor\" id=\"requete_1_1_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3cf6872-1ffe-4a15-a4ef-3bc8d90c6c23",
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    {
     "data": {
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       "    }\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>Zone</th>\n",
       "      <th>Année</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>32269.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>33370.794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>34413.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2016</td>\n",
       "      <td>35383.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2017</td>\n",
       "      <td>36296.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2018</td>\n",
       "      <td>37171.921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2013</td>\n",
       "      <td>53687.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2014</td>\n",
       "      <td>54544.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2015</td>\n",
       "      <td>55386.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2016</td>\n",
       "      <td>56207.646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone  Année     Valeur\n",
       "0     Afghanistan   2013  32269.589\n",
       "1     Afghanistan   2014  33370.794\n",
       "2     Afghanistan   2015  34413.603\n",
       "3     Afghanistan   2016  35383.032\n",
       "4     Afghanistan   2017  36296.113\n",
       "5     Afghanistan   2018  37171.921\n",
       "6  Afrique du Sud   2013  53687.121\n",
       "7  Afrique du Sud   2014  54544.186\n",
       "8  Afrique du Sud   2015  55386.367\n",
       "9  Afrique du Sud   2016  56207.646"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_population = pd.read_csv('population.csv')\n",
    "data_population.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9552351a-d2cd-4738-803b-bfdad7e3a0d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Zone       object\n",
       "Année       int64\n",
       "Valeur    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_population.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a20a34-0533-4916-98db-7a3c2006823c",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Filtre de la zone et la valeur pour l'année 2017 1.1.3 <a class=\"anchor\" id=\"requete_1_1_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bde3bd8b-39ad-418f-b850-09334f11f72f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36296.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>57009.756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2884.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>41389.189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>82658.409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone     Valeur\n",
       "4      Afghanistan  36296.113\n",
       "10  Afrique du Sud  57009.756\n",
       "16         Albanie   2884.169\n",
       "22         Algérie  41389.189\n",
       "28       Allemagne  82658.409"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2017 = data_population.loc[data_population['Année'] == 2017, ['Zone', 'Valeur']]\n",
    "data_pop_zone_val_2017.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74be2a53-c356-41e5-a21f-664872b4b44d",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Modifier la colone <span style=\"color:crimson\">Valeur</span> pour que le resultat soit exprimé en unité 1.1.4 <a class=\"anchor\" id=\"requete_1_1_4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f76eac33-8a18-4e31-94d9-d058563d3529",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36296113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>57009756.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2884169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>41389189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>82658409.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone      Valeur\n",
       "4      Afghanistan  36296113.0\n",
       "10  Afrique du Sud  57009756.0\n",
       "16         Albanie   2884169.0\n",
       "22         Algérie  41389189.0\n",
       "28       Allemagne  82658409.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2017['Valeur'] = data_population.iloc[: , 2].mul(1000)\n",
    "data_pop_zone_val_2017.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57614cff-2e89-4162-bc07-14f39b4d2ec9",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Filtre du dataframe Sous Nutrition 1.2 <a class=\"anchor\" id=\"requete_1_2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c6da4c5-ac0c-46f2-a843-442cfe415b66",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Création dataframe ```data_sn(data sous_nutrition)``` 1.2.1 <a class=\"anchor\" id=\"requete_1_2_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7631405-aecc-48e3-9f01-96e474355afe",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Année</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012-2014</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013-2015</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014-2016</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015-2017</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2016-2018</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2017-2019</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2012-2014</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2013-2015</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2014-2016</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2015-2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone      Année Valeur\n",
       "0     Afghanistan  2012-2014    8.6\n",
       "1     Afghanistan  2013-2015    8.8\n",
       "2     Afghanistan  2014-2016    8.9\n",
       "3     Afghanistan  2015-2017    9.7\n",
       "4     Afghanistan  2016-2018   10.5\n",
       "5     Afghanistan  2017-2019   11.1\n",
       "6  Afrique du Sud  2012-2014    2.2\n",
       "7  Afrique du Sud  2013-2015    2.5\n",
       "8  Afrique du Sud  2014-2016    2.8\n",
       "9  Afrique du Sud  2015-2017      3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sn = pd.read_csv('sous_nutrition.csv')\n",
    "data_sn.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe9804e0-d322-41d4-b61a-ffa564461853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Zone      object\n",
       "Année     object\n",
       "Valeur    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sn.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2312af1-2a7d-4659-8a02-91d975b316bc",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Filtre de la zone et la valeur pour l'année 2017 1.2.2 <a class=\"anchor\" id=\"requete_1_2_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3c504d0d-c2bb-4e79-979b-3cef583ab33a",
   "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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Andorre</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Angola</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Antigua-et-Barbuda</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Arabie saoudite</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Argentine</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Zone Valeur\n",
       "4          Afghanistan   10.5\n",
       "10      Afrique du Sud    3.1\n",
       "16             Albanie    0.1\n",
       "22             Algérie    1.3\n",
       "28           Allemagne    NaN\n",
       "34             Andorre    NaN\n",
       "40              Angola    5.8\n",
       "46  Antigua-et-Barbuda    NaN\n",
       "52     Arabie saoudite    1.6\n",
       "58           Argentine    1.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sn_zone_val_2017 = data_sn.loc[data_sn['Année'] == '2016-2018', ['Zone', 'Valeur']]\n",
    "data_sn_zone_val_2017.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f52494-8eed-44fc-842d-96f2ad439bef",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Convertir Valeur de Object à Float 1.2.3 <a class=\"anchor\" id=\"requete_1_2_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48df8994-0b46-40d6-81a1-e00f7a81b4a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 203 entries, 4 to 1216\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Zone    203 non-null    object \n",
      " 1   Valeur  84 non-null     float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 4.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data_sn_zone_val_2017['Valeur'] = pd.to_numeric(data_sn_zone_val_2017['Valeur'], errors='coerce')\n",
    "data_sn_zone_val_2017.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d782399-136f-4205-8188-c8f0f2bc8cf0",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Modifier la colone <span style=\"color:crimson\">Valeur</span> pour que le resultat soit exprimé en unité 1.2.4 <a class=\"anchor\" id=\"requete_1_2_4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "80e2f1f8-0b81-4d64-95bc-8c53a2c5fad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>10500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>3100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>1300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Andorre</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Angola</td>\n",
       "      <td>5800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Antigua-et-Barbuda</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Arabie saoudite</td>\n",
       "      <td>1600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Argentine</td>\n",
       "      <td>1500000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Zone      Valeur\n",
       "4          Afghanistan  10500000.0\n",
       "10      Afrique du Sud   3100000.0\n",
       "16             Albanie    100000.0\n",
       "22             Algérie   1300000.0\n",
       "28           Allemagne         NaN\n",
       "34             Andorre         NaN\n",
       "40              Angola   5800000.0\n",
       "46  Antigua-et-Barbuda         NaN\n",
       "52     Arabie saoudite   1600000.0\n",
       "58           Argentine   1500000.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sn_zone_val_2017['Valeur'] = data_sn_zone_val_2017.iloc[: , 1].mul(1000000)\n",
    "data_sn_zone_val_2017.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f5556c-d14a-4be4-8d2b-a2e44a268f24",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Pays où la valeur n'est pas renseignée 1.2.5 <a class=\"anchor\" id=\"requete_1_2_5\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "faaee1d4-ecca-402f-9271-eb7e8dffbe1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Il y a 119 pays où les données sont manquantes notament : \n",
      "          Zone  Valeur\n",
      "1204     Yémen     NaN\n",
      "1210    Zambie     NaN\n",
      "1216  Zimbabwe     NaN\n"
     ]
    }
   ],
   "source": [
    "zone_nan = data_sn_zone_val_2017.loc[data_sn_zone_val_2017['Valeur'].isna()]\n",
    "print(f'Il y a {zone_nan.shape[0]} pays où les données sont manquantes notament : \\n{zone_nan.tail(3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5e4f0eb-edc7-428b-a88e-ea2608bb38ef",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Le pourcentage de la population en état de sous nutrition dans le monde en 2017 1.3 <a class=\"anchor\" id=\"requete_1_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "15e1775c-57db-4074-ae72-eb3bf0f1836e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environ 7.097 % de la population mondiale est concidéré comme etant en sous nutrition\n"
     ]
    }
   ],
   "source": [
    "proportion_pop_2017 = ( data_sn_zone_val_2017['Valeur'].sum() * 100 ) / data_pop_zone_val_2017['Valeur'].sum() \n",
    "print('Environ ' + str(round(proportion_pop_2017,3)) +  ' % de la population mondiale est concidéré comme etant en sous nutrition')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1de4af87-2e08-4908-875c-884a444dc82e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92.90288179671693"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_nourri_2017 = 100 - proportion_pop_2017\n",
    "pop_nourri_2017"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a213a60c-5496-422d-b81b-236c216aec61",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Requete 2: Le nombre théorique de personnes qui pourraient être nourries. <a class=\"anchor\" id=\"requete2\"></a>\n",
    "[Retour au sommaire](#sommaire)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14fd1a1-3bc9-4e3a-aedb-f1f6ef2a620a",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Création du data frame ```data_dispo_alimentaire(aide alimentaire)``` 2.1 <a class=\"anchor\" id=\"section_2_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4489abb-0903-4ef3-8979-0840136996f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>Zone</th>\n",
       "      <th>Produit</th>\n",
       "      <th>Origine</th>\n",
       "      <th>Aliments pour animaux</th>\n",
       "      <th>Autres Utilisations</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
       "      <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité de protéines en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité intérieure</th>\n",
       "      <th>Exportations - Quantité</th>\n",
       "      <th>Importations - Quantité</th>\n",
       "      <th>Nourriture</th>\n",
       "      <th>Pertes</th>\n",
       "      <th>Production</th>\n",
       "      <th>Semences</th>\n",
       "      <th>Traitement</th>\n",
       "      <th>Variation de stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Abats Comestible</td>\n",
       "      <td>animale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.77</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Agrumes, Autres</td>\n",
       "      <td>vegetale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Aliments pour enfants</td>\n",
       "      <td>vegetale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Ananas</td>\n",
       "      <td>vegetale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Bananes</td>\n",
       "      <td>vegetale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>82.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Zone                Produit   Origine  Aliments pour animaux  \\\n",
       "0  Afghanistan       Abats Comestible   animale                    NaN   \n",
       "1  Afghanistan        Agrumes, Autres  vegetale                    NaN   \n",
       "2  Afghanistan  Aliments pour enfants  vegetale                    NaN   \n",
       "3  Afghanistan                 Ananas  vegetale                    NaN   \n",
       "4  Afghanistan                Bananes  vegetale                    NaN   \n",
       "\n",
       "   Autres Utilisations  Disponibilité alimentaire (Kcal/personne/jour)  \\\n",
       "0                  NaN                                             5.0   \n",
       "1                  NaN                                             1.0   \n",
       "2                  NaN                                             1.0   \n",
       "3                  NaN                                             0.0   \n",
       "4                  NaN                                             4.0   \n",
       "\n",
       "   Disponibilité alimentaire en quantité (kg/personne/an)  \\\n",
       "0                                               1.72        \n",
       "1                                               1.29        \n",
       "2                                               0.06        \n",
       "3                                               0.00        \n",
       "4                                               2.70        \n",
       "\n",
       "   Disponibilité de matière grasse en quantité (g/personne/jour)  \\\n",
       "0                                               0.20               \n",
       "1                                               0.01               \n",
       "2                                               0.01               \n",
       "3                                                NaN               \n",
       "4                                               0.02               \n",
       "\n",
       "   Disponibilité de protéines en quantité (g/personne/jour)  \\\n",
       "0                                               0.77          \n",
       "1                                               0.02          \n",
       "2                                               0.03          \n",
       "3                                                NaN          \n",
       "4                                               0.05          \n",
       "\n",
       "   Disponibilité intérieure  Exportations - Quantité  Importations - Quantité  \\\n",
       "0                      53.0                      NaN                      NaN   \n",
       "1                      41.0                      2.0                     40.0   \n",
       "2                       2.0                      NaN                      2.0   \n",
       "3                       0.0                      NaN                      0.0   \n",
       "4                      82.0                      NaN                     82.0   \n",
       "\n",
       "   Nourriture  Pertes  Production  Semences  Traitement  Variation de stock  \n",
       "0        53.0     NaN        53.0       NaN         NaN                 NaN  \n",
       "1        39.0     2.0         3.0       NaN         NaN                 NaN  \n",
       "2         2.0     NaN         NaN       NaN         NaN                 NaN  \n",
       "3         0.0     NaN         NaN       NaN         NaN                 NaN  \n",
       "4        82.0     NaN         NaN       NaN         NaN                 NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dispo_alimentaire = pd.read_csv('dispo_alimentaire.csv')\n",
    "data_dispo_alimentaire.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "949ae649-f06c-4c95-9b85-b9f2e1cf0d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Zone                                                              object\n",
       "Produit                                                           object\n",
       "Origine                                                           object\n",
       "Aliments pour animaux                                            float64\n",
       "Autres Utilisations                                              float64\n",
       "Disponibilité alimentaire (Kcal/personne/jour)                   float64\n",
       "Disponibilité alimentaire en quantité (kg/personne/an)           float64\n",
       "Disponibilité de matière grasse en quantité (g/personne/jour)    float64\n",
       "Disponibilité de protéines en quantité (g/personne/jour)         float64\n",
       "Disponibilité intérieure                                         float64\n",
       "Exportations - Quantité                                          float64\n",
       "Importations - Quantité                                          float64\n",
       "Nourriture                                                       float64\n",
       "Pertes                                                           float64\n",
       "Production                                                       float64\n",
       "Semences                                                         float64\n",
       "Traitement                                                       float64\n",
       "Variation de stock                                               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dispo_alimentaire.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d64a864-225d-4945-a840-741697ce2e04",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Filtre par zone "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "64349c12-ed4f-42c2-b987-346ca534de31",
   "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>Aliments pour animaux</th>\n",
       "      <th>Autres Utilisations</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
       "      <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité de protéines en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité intérieure</th>\n",
       "      <th>Exportations - Quantité</th>\n",
       "      <th>Importations - Quantité</th>\n",
       "      <th>Nourriture</th>\n",
       "      <th>Pertes</th>\n",
       "      <th>Production</th>\n",
       "      <th>Semences</th>\n",
       "      <th>Traitement</th>\n",
       "      <th>Variation de stock</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>768.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>351.41</td>\n",
       "      <td>33.50</td>\n",
       "      <td>58.26</td>\n",
       "      <td>13515.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>2988.0</td>\n",
       "      <td>10735.0</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>11171.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>-368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afrique du Sud</th>\n",
       "      <td>5309.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>564.80</td>\n",
       "      <td>83.58</td>\n",
       "      <td>85.33</td>\n",
       "      <td>61256.0</td>\n",
       "      <td>11674.0</td>\n",
       "      <td>7517.0</td>\n",
       "      <td>29812.0</td>\n",
       "      <td>2193.0</td>\n",
       "      <td>63263.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>22819.0</td>\n",
       "      <td>2153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albanie</th>\n",
       "      <td>660.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>3188.0</td>\n",
       "      <td>1095.54</td>\n",
       "      <td>106.48</td>\n",
       "      <td>111.37</td>\n",
       "      <td>4758.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>933.0</td>\n",
       "      <td>3476.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>3964.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>-45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algérie</th>\n",
       "      <td>4352.0</td>\n",
       "      <td>1707.0</td>\n",
       "      <td>3293.0</td>\n",
       "      <td>809.13</td>\n",
       "      <td>76.48</td>\n",
       "      <td>91.92</td>\n",
       "      <td>42630.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>17403.0</td>\n",
       "      <td>31729.0</td>\n",
       "      <td>3753.0</td>\n",
       "      <td>26359.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>637.0</td>\n",
       "      <td>-479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allemagne</th>\n",
       "      <td>30209.0</td>\n",
       "      <td>7711.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>957.52</td>\n",
       "      <td>141.62</td>\n",
       "      <td>101.39</td>\n",
       "      <td>162275.0</td>\n",
       "      <td>57431.0</td>\n",
       "      <td>65353.0</td>\n",
       "      <td>79238.0</td>\n",
       "      <td>3781.0</td>\n",
       "      <td>154547.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>39842.0</td>\n",
       "      <td>-192.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Aliments pour animaux  Autres Utilisations  \\\n",
       "Zone                                                         \n",
       "Afghanistan                     768.0                415.0   \n",
       "Afrique du Sud                 5309.0                876.0   \n",
       "Albanie                         660.0                174.0   \n",
       "Algérie                        4352.0               1707.0   \n",
       "Allemagne                     30209.0               7711.0   \n",
       "\n",
       "                Disponibilité alimentaire (Kcal/personne/jour)  \\\n",
       "Zone                                                             \n",
       "Afghanistan                                             2087.0   \n",
       "Afrique du Sud                                          3020.0   \n",
       "Albanie                                                 3188.0   \n",
       "Algérie                                                 3293.0   \n",
       "Allemagne                                               3503.0   \n",
       "\n",
       "                Disponibilité alimentaire en quantité (kg/personne/an)  \\\n",
       "Zone                                                                     \n",
       "Afghanistan                                                351.41        \n",
       "Afrique du Sud                                             564.80        \n",
       "Albanie                                                   1095.54        \n",
       "Algérie                                                    809.13        \n",
       "Allemagne                                                  957.52        \n",
       "\n",
       "                Disponibilité de matière grasse en quantité (g/personne/jour)  \\\n",
       "Zone                                                                            \n",
       "Afghanistan                                                 33.50               \n",
       "Afrique du Sud                                              83.58               \n",
       "Albanie                                                    106.48               \n",
       "Algérie                                                     76.48               \n",
       "Allemagne                                                  141.62               \n",
       "\n",
       "                Disponibilité de protéines en quantité (g/personne/jour)  \\\n",
       "Zone                                                                       \n",
       "Afghanistan                                                 58.26          \n",
       "Afrique du Sud                                              85.33          \n",
       "Albanie                                                    111.37          \n",
       "Algérie                                                     91.92          \n",
       "Allemagne                                                  101.39          \n",
       "\n",
       "                Disponibilité intérieure  Exportations - Quantité  \\\n",
       "Zone                                                                \n",
       "Afghanistan                      13515.0                    278.0   \n",
       "Afrique du Sud                   61256.0                  11674.0   \n",
       "Albanie                           4758.0                     94.0   \n",
       "Algérie                          42630.0                    655.0   \n",
       "Allemagne                       162275.0                  57431.0   \n",
       "\n",
       "                Importations - Quantité  Nourriture  Pertes  Production  \\\n",
       "Zone                                                                      \n",
       "Afghanistan                      2988.0     10735.0  1135.0     11171.0   \n",
       "Afrique du Sud                   7517.0     29812.0  2193.0     63263.0   \n",
       "Albanie                           933.0      3476.0   276.0      3964.0   \n",
       "Algérie                         17403.0     31729.0  3753.0     26359.0   \n",
       "Allemagne                       65353.0     79238.0  3781.0    154547.0   \n",
       "\n",
       "                Semences  Traitement  Variation de stock  \n",
       "Zone                                                      \n",
       "Afghanistan        395.0        63.0              -368.0  \n",
       "Afrique du Sud     253.0     22819.0              2153.0  \n",
       "Albanie             55.0       113.0               -45.0  \n",
       "Algérie            449.0       637.0              -479.0  \n",
       "Allemagne         1551.0     39842.0              -192.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_data_alimentaire = data_dispo_alimentaire.groupby('Zone').sum()\n",
    "zone_data_alimentaire.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca8f1795-25a5-44f3-8fe1-94055af82043",
   "metadata": {
    "tags": []
   },
   "source": [
    "####  Merge des df ```zone_data_alimentaire``` et ```data_pop_zone_val_2017(population en 2017)``` 2.2 <a class=\"anchor\" id=\"section_2_2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d773af-4baf-4cb6-a84a-3a05173b540e",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Shape des tables 2.2.1 <a class=\"anchor\" id=\"section_2_2_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3da50e50-d439-453e-b2a8-d37a53e59177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(174, 15)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_data_alimentaire.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f9474e03-e825-4208-898d-b72bbb863cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(236, 2)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2017.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "853a6dd5-ea97-4010-8f21-e1dc5cca4a77",
   "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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>36296113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>57009756.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>3188.0</td>\n",
       "      <td>2884169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>3293.0</td>\n",
       "      <td>41389189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>82658409.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone  Disponibilité alimentaire (Kcal/personne/jour)      Valeur\n",
       "0     Afghanistan                                          2087.0  36296113.0\n",
       "1  Afrique du Sud                                          3020.0  57009756.0\n",
       "2         Albanie                                          3188.0   2884169.0\n",
       "3         Algérie                                          3293.0  41389189.0\n",
       "4       Allemagne                                          3503.0  82658409.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire = zone_data_alimentaire[['Disponibilité alimentaire (Kcal/personne/jour)']].merge(data_pop_zone_val_2017, how ='left', on= \"Zone\")\n",
    "df_merge_population_dispo_alimentaire.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "835addfe-6128-4544-9026-79ba5f5842e4",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Conaitre les valeurs non attribuées dans la merge 2.2.2 <a class=\"anchor\" id=\"section_2_2_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1a557b8c-2214-414c-bae1-79fd01547d01",
   "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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Royaume-Uni</td>\n",
       "      <td>3425.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Tchéquie (la)</td>\n",
       "      <td>3255.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone  Disponibilité alimentaire (Kcal/personne/jour)  Valeur\n",
       "126    Royaume-Uni                                          3425.0     NaN\n",
       "152  Tchéquie (la)                                          3255.0     NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire.loc[df_merge_population_dispo_alimentaire.isnull().any(axis=1)] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "334837e9-befa-4704-acd1-0532be64cd6c",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Savoir comment est écrit Royaume-Uni et Tchéquie dans la table population en 2017 2.2.3 <a class=\"anchor\" id=\"section_2_2_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b61e82c-ebb0-4eca-a0dd-1a319aba6ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>Royaume-Uni de Grande-Bretagne et d'Irlande du...</td>\n",
       "      <td>66727461.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Zone      Valeur\n",
       "1114  Royaume-Uni de Grande-Bretagne et d'Irlande du...  66727461.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2017.loc[data_pop_zone_val_2017[\"Zone\"].str.contains('Royaume')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a08a727a-7cac-49c2-ad4e-04a7611da85e",
   "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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Tchad</td>\n",
       "      <td>15016753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>Tchéquie</td>\n",
       "      <td>10641034.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Zone      Valeur\n",
       "1300     Tchad  15016753.0\n",
       "1306  Tchéquie  10641034.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2017.loc[data_pop_zone_val_2017[\"Zone\"].str.contains('Tch')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ddfecc-17dd-497c-b32e-700869722d17",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Reset l'index dans le df dispo alimentaire et remplacer les deux pays par la bonne orthographe 2.2.4 <a class=\"anchor\" id=\"section_2_2_4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ee19eb59-a41e-4c67-b753-3100deded83b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Zone</th>\n",
       "      <th>Aliments pour animaux</th>\n",
       "      <th>Autres Utilisations</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
       "      <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité de protéines en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité intérieure</th>\n",
       "      <th>Exportations - Quantité</th>\n",
       "      <th>Importations - Quantité</th>\n",
       "      <th>Nourriture</th>\n",
       "      <th>Pertes</th>\n",
       "      <th>Production</th>\n",
       "      <th>Semences</th>\n",
       "      <th>Traitement</th>\n",
       "      <th>Variation de stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>768.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>351.41</td>\n",
       "      <td>33.50</td>\n",
       "      <td>58.26</td>\n",
       "      <td>13515.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>2988.0</td>\n",
       "      <td>10735.0</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>11171.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>-368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>5309.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>564.80</td>\n",
       "      <td>83.58</td>\n",
       "      <td>85.33</td>\n",
       "      <td>61256.0</td>\n",
       "      <td>11674.0</td>\n",
       "      <td>7517.0</td>\n",
       "      <td>29812.0</td>\n",
       "      <td>2193.0</td>\n",
       "      <td>63263.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>22819.0</td>\n",
       "      <td>2153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>660.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>3188.0</td>\n",
       "      <td>1095.54</td>\n",
       "      <td>106.48</td>\n",
       "      <td>111.37</td>\n",
       "      <td>4758.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>933.0</td>\n",
       "      <td>3476.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>3964.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>-45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>4352.0</td>\n",
       "      <td>1707.0</td>\n",
       "      <td>3293.0</td>\n",
       "      <td>809.13</td>\n",
       "      <td>76.48</td>\n",
       "      <td>91.92</td>\n",
       "      <td>42630.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>17403.0</td>\n",
       "      <td>31729.0</td>\n",
       "      <td>3753.0</td>\n",
       "      <td>26359.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>637.0</td>\n",
       "      <td>-479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>30209.0</td>\n",
       "      <td>7711.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>957.52</td>\n",
       "      <td>141.62</td>\n",
       "      <td>101.39</td>\n",
       "      <td>162275.0</td>\n",
       "      <td>57431.0</td>\n",
       "      <td>65353.0</td>\n",
       "      <td>79238.0</td>\n",
       "      <td>3781.0</td>\n",
       "      <td>154547.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>39842.0</td>\n",
       "      <td>-192.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone  Aliments pour animaux  Autres Utilisations  \\\n",
       "0     Afghanistan                  768.0                415.0   \n",
       "1  Afrique du Sud                 5309.0                876.0   \n",
       "2         Albanie                  660.0                174.0   \n",
       "3         Algérie                 4352.0               1707.0   \n",
       "4       Allemagne                30209.0               7711.0   \n",
       "\n",
       "   Disponibilité alimentaire (Kcal/personne/jour)  \\\n",
       "0                                          2087.0   \n",
       "1                                          3020.0   \n",
       "2                                          3188.0   \n",
       "3                                          3293.0   \n",
       "4                                          3503.0   \n",
       "\n",
       "   Disponibilité alimentaire en quantité (kg/personne/an)  \\\n",
       "0                                             351.41        \n",
       "1                                             564.80        \n",
       "2                                            1095.54        \n",
       "3                                             809.13        \n",
       "4                                             957.52        \n",
       "\n",
       "   Disponibilité de matière grasse en quantité (g/personne/jour)  \\\n",
       "0                                              33.50               \n",
       "1                                              83.58               \n",
       "2                                             106.48               \n",
       "3                                              76.48               \n",
       "4                                             141.62               \n",
       "\n",
       "   Disponibilité de protéines en quantité (g/personne/jour)  \\\n",
       "0                                              58.26          \n",
       "1                                              85.33          \n",
       "2                                             111.37          \n",
       "3                                              91.92          \n",
       "4                                             101.39          \n",
       "\n",
       "   Disponibilité intérieure  Exportations - Quantité  Importations - Quantité  \\\n",
       "0                   13515.0                    278.0                   2988.0   \n",
       "1                   61256.0                  11674.0                   7517.0   \n",
       "2                    4758.0                     94.0                    933.0   \n",
       "3                   42630.0                    655.0                  17403.0   \n",
       "4                  162275.0                  57431.0                  65353.0   \n",
       "\n",
       "   Nourriture  Pertes  Production  Semences  Traitement  Variation de stock  \n",
       "0     10735.0  1135.0     11171.0     395.0        63.0              -368.0  \n",
       "1     29812.0  2193.0     63263.0     253.0     22819.0              2153.0  \n",
       "2      3476.0   276.0      3964.0      55.0       113.0               -45.0  \n",
       "3     31729.0  3753.0     26359.0     449.0       637.0              -479.0  \n",
       "4     79238.0  3781.0    154547.0    1551.0     39842.0              -192.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_data_alimentaire.reset_index(inplace = True)\n",
    "zone_data_alimentaire['Zone'].replace({\"Royaume-Uni\": \"Royaume-Uni de Grande-Bretagne et d'Irlande du Nord\", \"Tchéquie (la)\": \"Tchéquie\"}, inplace=True) \n",
    "zone_data_alimentaire.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cacabbbe-7bc9-4d2e-8955-a304bf6a6f22",
   "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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>36296113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>57009756.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>3188.0</td>\n",
       "      <td>2884169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>3293.0</td>\n",
       "      <td>41389189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>82658409.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone  Disponibilité alimentaire (Kcal/personne/jour)      Valeur\n",
       "0     Afghanistan                                          2087.0  36296113.0\n",
       "1  Afrique du Sud                                          3020.0  57009756.0\n",
       "2         Albanie                                          3188.0   2884169.0\n",
       "3         Algérie                                          3293.0  41389189.0\n",
       "4       Allemagne                                          3503.0  82658409.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire = zone_data_alimentaire[['Zone', 'Disponibilité alimentaire (Kcal/personne/jour)']].merge(data_pop_zone_val_2017, how ='left', on= \"Zone\")\n",
    "df_merge_population_dispo_alimentaire.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a0bc975-d2e7-4822-81b9-9a01aa1ebefc",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Création de la colone <span style=\"color:crimson\">Disponibilité alimentaire (Kcal/jour)</span> 2.2.5 <a class=\"anchor\" id=\"section_2_2_5\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2961e8b1-807c-4d05-8cfc-709072ebfec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/jour)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>36296113.0</td>\n",
       "      <td>7.574999e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>57009756.0</td>\n",
       "      <td>1.721695e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>3188.0</td>\n",
       "      <td>2884169.0</td>\n",
       "      <td>9.194731e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>3293.0</td>\n",
       "      <td>41389189.0</td>\n",
       "      <td>1.362946e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>82658409.0</td>\n",
       "      <td>2.895524e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone  Disponibilité alimentaire (Kcal/personne/jour)      Valeur  \\\n",
       "0     Afghanistan                                          2087.0  36296113.0   \n",
       "1  Afrique du Sud                                          3020.0  57009756.0   \n",
       "2         Albanie                                          3188.0   2884169.0   \n",
       "3         Algérie                                          3293.0  41389189.0   \n",
       "4       Allemagne                                          3503.0  82658409.0   \n",
       "\n",
       "   Disponibilité alimentaire (Kcal/jour)  \n",
       "0                           7.574999e+10  \n",
       "1                           1.721695e+11  \n",
       "2                           9.194731e+09  \n",
       "3                           1.362946e+11  \n",
       "4                           2.895524e+11  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire['Disponibilité alimentaire (Kcal/jour)'] = df_merge_population_dispo_alimentaire['Disponibilité alimentaire (Kcal/personne/jour)'] * df_merge_population_dispo_alimentaire['Valeur'] \n",
    "df_merge_population_dispo_alimentaire.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0843e51d-f9e9-47d9-b5f3-031feba22f74",
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/jour)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Zone, Disponibilité alimentaire (Kcal/personne/jour), Valeur, Disponibilité alimentaire (Kcal/jour)]\n",
       "Index: []"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire.loc[df_merge_population_dispo_alimentaire.isnull().any(axis=1)] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca20192-a64e-44c7-b232-261afa00db81",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Création de la colone <span style=\"color:crimson\">Population totale par pays pouvant etre nourri</span> 2.2.6 <a class=\"anchor\" id=\"section_2_2_6\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a8e1c6d1-8478-45a1-895d-2ab2449960c8",
   "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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/jour)</th>\n",
       "      <th>population totale par pays pouvant etre nourri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>36296113.0</td>\n",
       "      <td>7.574999e+10</td>\n",
       "      <td>3.156249e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>57009756.0</td>\n",
       "      <td>1.721695e+11</td>\n",
       "      <td>7.173728e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>3188.0</td>\n",
       "      <td>2884169.0</td>\n",
       "      <td>9.194731e+09</td>\n",
       "      <td>3.831138e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>3293.0</td>\n",
       "      <td>41389189.0</td>\n",
       "      <td>1.362946e+11</td>\n",
       "      <td>5.678942e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>82658409.0</td>\n",
       "      <td>2.895524e+11</td>\n",
       "      <td>1.206468e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone  Disponibilité alimentaire (Kcal/personne/jour)      Valeur  \\\n",
       "0     Afghanistan                                          2087.0  36296113.0   \n",
       "1  Afrique du Sud                                          3020.0  57009756.0   \n",
       "2         Albanie                                          3188.0   2884169.0   \n",
       "3         Algérie                                          3293.0  41389189.0   \n",
       "4       Allemagne                                          3503.0  82658409.0   \n",
       "\n",
       "   Disponibilité alimentaire (Kcal/jour)  \\\n",
       "0                           7.574999e+10   \n",
       "1                           1.721695e+11   \n",
       "2                           9.194731e+09   \n",
       "3                           1.362946e+11   \n",
       "4                           2.895524e+11   \n",
       "\n",
       "   population totale par pays pouvant etre nourri  \n",
       "0                                    3.156249e+07  \n",
       "1                                    7.173728e+07  \n",
       "2                                    3.831138e+06  \n",
       "3                                    5.678942e+07  \n",
       "4                                    1.206468e+08  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire['population totale par pays pouvant etre nourri'] = df_merge_population_dispo_alimentaire['Disponibilité alimentaire (Kcal/jour)'] / 2400 \n",
    "df_merge_population_dispo_alimentaire.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fe91926-8018-442c-a4cd-f9dc7efc2ed5",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Résultat du nombre théorique de personnes pouvant etre nourri 2.3 <a class=\"anchor\" id=\"section_2_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1d1d87c4-eda3-4d14-9a75-a2c3c21aa702",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le nombre théorique de personnes qui pourraient être nourries est: 8825901144.55\n"
     ]
    }
   ],
   "source": [
    "nbr_theorique_pers_pouvant_etre_nourri = df_merge_population_dispo_alimentaire.iloc[: , 4].sum()\n",
    "print(f'Le nombre théorique de personnes qui pourraient être nourries est: {round(nbr_theorique_pers_pouvant_etre_nourri,2)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4df57cc1-1c22-433f-9e7e-184b4179b141",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Resulat en pourcentage 2.4 <a class=\"anchor\" id=\"section_2_4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "caf8e706-9557-4425-8f4f-dba78c697c2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le nombre théorique de personnes qui pourraient être nourries est: 119.77 %\n"
     ]
    }
   ],
   "source": [
    "nombre_total_pers_mondial = df_merge_population_dispo_alimentaire.iloc[: , 2].sum()\n",
    "pourcentage_pers_pouvant_etre_nourri = round(nbr_theorique_pers_pouvant_etre_nourri * 100 / nombre_total_pers_mondial,2)\n",
    "print(f'Le nombre théorique de personnes qui pourraient être nourries est: {pourcentage_pers_pouvant_etre_nourri} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a44988d8-e03c-4c88-bc3a-ae0dea8300e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "pop = ['Population pouvant\\n se nourrir', 'Population nourrit', 'Population en \\nsous-nutrition']\n",
    "counts = [pourcentage_pers_pouvant_etre_nourri, pop_nourri_2017, proportion_pop_2017]\n",
    "bar_labels = ['pop', 'blue', 'orange']\n",
    "bar_colors = ['tab:blue', 'tab:blue', 'tab:blue']\n",
    "\n",
    "ax.bar(pop, counts, label=bar_labels, color=bar_colors)\n",
    "plt.grid(axis='y')\n",
    "plt.ylim(0, 110)\n",
    "plt.yticks([0, 10, 50, 70, 90, 110, 120, 130])\n",
    "for i in range(3):\n",
    "    plt.text(i - 0.06, counts[i] , round(counts[i],1))\n",
    "\n",
    "ax.set_ylabel('Pourcent de la population')\n",
    "ax.set_title('Variation entre la population pouvant etre nourrit\\n et celle en état de sous nutrition.', fontsize=10, color= '#5f89c4', fontweight='bold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9b821c-734b-4116-9b99-ed05f3f9fac8",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Requete 3: disponibilité alimentaire des produits végétaux. <a class=\"anchor\" id=\"requete3\"></a>\n",
    "[Retour au sommaire](#sommaire)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363dcebf-fa4a-4060-9c09-58cf7ceb819d",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Filtre par Origine vegetale du df ``` data_dispo_alimentaire``` 3.1 <a class=\"anchor\" id=\"section_3_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "18afc338-6450-4482-a9bf-c3c120db71a3",
   "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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1369.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Zone  Disponibilité alimentaire (Kcal/personne/jour)\n",
       "1   Afghanistan                                             1.0\n",
       "2   Afghanistan                                             1.0\n",
       "3   Afghanistan                                             0.0\n",
       "4   Afghanistan                                             4.0\n",
       "6   Afghanistan                                             0.0\n",
       "7   Afghanistan                                          1369.0\n",
       "8   Afghanistan                                             0.0\n",
       "9   Afghanistan                                             0.0\n",
       "10  Afghanistan                                             0.0\n",
       "12  Afghanistan                                             0.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dispo_alimentaire_vegetale = data_dispo_alimentaire.loc[data_dispo_alimentaire['Origine'] == 'vegetale', ['Zone', 'Disponibilité alimentaire (Kcal/personne/jour)']]\n",
    "data_dispo_alimentaire_vegetale.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71022de-96c0-4665-bc60-e949659dbc35",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Agrégation par Zone du df ```dispo_alimentaire_vegetale``` 3.1.1 <a class=\"anchor\" id=\"section_3_1_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d36a1bfd-510a-4352-be94-542eef69f11f",
   "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>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Émirats arabes unis</th>\n",
       "      <td>2718.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Équateur</th>\n",
       "      <td>1732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>États-Unis d'Amérique</th>\n",
       "      <td>2698.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Éthiopie</th>\n",
       "      <td>2005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Îles Salomon</th>\n",
       "      <td>2187.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Disponibilité alimentaire (Kcal/personne/jour)\n",
       "Zone                                                                 \n",
       "Émirats arabes unis                                            2718.0\n",
       "Équateur                                                       1732.0\n",
       "États-Unis d'Amérique                                          2698.0\n",
       "Éthiopie                                                       2005.0\n",
       "Îles Salomon                                                   2187.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dispo_alimentaire_vegetale_zone = data_dispo_alimentaire_vegetale.groupby('Zone').sum()\n",
    "data_dispo_alimentaire_vegetale_zone.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bba29d7-3020-4b0e-a15b-192fe0dbc901",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Merge des df ```data_dispo_alimentaire_vegetale_zone``` et ```data_pop_zone_val_2017``` 3.2 <a class=\"anchor\" id=\"section_3_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7fea1a25-9bc0-4aef-9033-82a7a9734a9b",
   "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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1871.0</td>\n",
       "      <td>36296113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2533.0</td>\n",
       "      <td>57009756.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>2884169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>41389189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>2461.0</td>\n",
       "      <td>82658409.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone  Disponibilité alimentaire (Kcal/personne/jour)      Valeur\n",
       "0     Afghanistan                                          1871.0  36296113.0\n",
       "1  Afrique du Sud                                          2533.0  57009756.0\n",
       "2         Albanie                                          2203.0   2884169.0\n",
       "3         Algérie                                          2915.0  41389189.0\n",
       "4       Allemagne                                          2461.0  82658409.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire_vegetale = data_dispo_alimentaire_vegetale_zone[['Disponibilité alimentaire (Kcal/personne/jour)']].merge(data_pop_zone_val_2017, how ='left', on= \"Zone\")\n",
    "df_merge_population_dispo_alimentaire_vegetale.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e874bcbb-daba-4cc0-9ce7-cb454ca605ab",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Conaitre les valeurs non attribuées dans la merge 3.2.1 <a class=\"anchor\" id=\"section_3_2_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "25ba3f38-bb77-435b-95f5-e86e2920dfb1",
   "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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Royaume-Uni</td>\n",
       "      <td>2443.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Tchéquie (la)</td>\n",
       "      <td>2418.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone  Disponibilité alimentaire (Kcal/personne/jour)  Valeur\n",
       "126    Royaume-Uni                                          2443.0     NaN\n",
       "152  Tchéquie (la)                                          2418.0     NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire_vegetale.loc[df_merge_population_dispo_alimentaire_vegetale.isnull().any(axis=1)] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfea5be9-8b85-43d4-a8e7-7b3a5a1adad0",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Reset l'index du df ```dispo alimentaire``` et remplacer les deux pays par la bonne orthographe 3.2.2 <a class=\"anchor\" id=\"section_3_2_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c7aa2b95-3fbb-4445-833a-c6e1df760848",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dispo_alimentaire_vegetale_zone.reset_index(inplace = True)\n",
    "data_dispo_alimentaire_vegetale_zone['Zone'].replace({\"Royaume-Uni\": \"Royaume-Uni de Grande-Bretagne et d'Irlande du Nord\", \"Tchéquie (la)\": \"Tchéquie\"}, inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "39848229-b22b-46ae-8663-1b61253a4b8c",
   "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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1871.0</td>\n",
       "      <td>36296113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2533.0</td>\n",
       "      <td>57009756.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>2884169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>41389189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>2461.0</td>\n",
       "      <td>82658409.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone  Disponibilité alimentaire (Kcal/personne/jour)      Valeur\n",
       "0     Afghanistan                                          1871.0  36296113.0\n",
       "1  Afrique du Sud                                          2533.0  57009756.0\n",
       "2         Albanie                                          2203.0   2884169.0\n",
       "3         Algérie                                          2915.0  41389189.0\n",
       "4       Allemagne                                          2461.0  82658409.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire_vegetale = data_dispo_alimentaire_vegetale_zone[['Zone', 'Disponibilité alimentaire (Kcal/personne/jour)']].merge(data_pop_zone_val_2017, how ='left', on= \"Zone\")\n",
    "df_merge_population_dispo_alimentaire_vegetale.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "910e889d-96c5-4326-a115-61fefaba8865",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Création de la colone <span style=\"color:crimson\">Disponibilité alimentaire (Kcal/jour)</span> 3.2.3 <a class=\"anchor\" id=\"section_3_2_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dd5f2948-dd19-424f-aac2-ee6f6540aeb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/jour)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1871.0</td>\n",
       "      <td>36296113.0</td>\n",
       "      <td>6.791003e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2533.0</td>\n",
       "      <td>57009756.0</td>\n",
       "      <td>1.444057e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>2884169.0</td>\n",
       "      <td>6.353824e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>41389189.0</td>\n",
       "      <td>1.206495e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>2461.0</td>\n",
       "      <td>82658409.0</td>\n",
       "      <td>2.034223e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone  Disponibilité alimentaire (Kcal/personne/jour)      Valeur  \\\n",
       "0     Afghanistan                                          1871.0  36296113.0   \n",
       "1  Afrique du Sud                                          2533.0  57009756.0   \n",
       "2         Albanie                                          2203.0   2884169.0   \n",
       "3         Algérie                                          2915.0  41389189.0   \n",
       "4       Allemagne                                          2461.0  82658409.0   \n",
       "\n",
       "   Disponibilité alimentaire (Kcal/jour)  \n",
       "0                           6.791003e+10  \n",
       "1                           1.444057e+11  \n",
       "2                           6.353824e+09  \n",
       "3                           1.206495e+11  \n",
       "4                           2.034223e+11  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire_vegetale['Disponibilité alimentaire (Kcal/jour)'] = df_merge_population_dispo_alimentaire_vegetale['Disponibilité alimentaire (Kcal/personne/jour)'] * df_merge_population_dispo_alimentaire_vegetale['Valeur'] \n",
    "df_merge_population_dispo_alimentaire_vegetale.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0cc7e164-9e68-4c6e-96cb-b52eb0adb338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/jour)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Zone, Disponibilité alimentaire (Kcal/personne/jour), Valeur, Disponibilité alimentaire (Kcal/jour)]\n",
       "Index: []"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire_vegetale.loc[df_merge_population_dispo_alimentaire_vegetale.isnull().any(axis=1)] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0087258f-3820-4aef-a4df-03027d6bc473",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Création de la colone <span style=\"color:crimson\">Population totale par pays pouvant etre nourri</span> 3.2.4 <a class=\"anchor\" id=\"section_3_2_4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d8f383a2-dd0d-451e-8cb3-fba2d5814230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/jour)</th>\n",
       "      <th>population totale par pays pouvant etre nourri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1871.0</td>\n",
       "      <td>36296113.0</td>\n",
       "      <td>6.791003e+10</td>\n",
       "      <td>2.829584e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2533.0</td>\n",
       "      <td>57009756.0</td>\n",
       "      <td>1.444057e+11</td>\n",
       "      <td>6.016905e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>2884169.0</td>\n",
       "      <td>6.353824e+09</td>\n",
       "      <td>2.647427e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>41389189.0</td>\n",
       "      <td>1.206495e+11</td>\n",
       "      <td>5.027062e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>2461.0</td>\n",
       "      <td>82658409.0</td>\n",
       "      <td>2.034223e+11</td>\n",
       "      <td>8.475931e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone  Disponibilité alimentaire (Kcal/personne/jour)      Valeur  \\\n",
       "0     Afghanistan                                          1871.0  36296113.0   \n",
       "1  Afrique du Sud                                          2533.0  57009756.0   \n",
       "2         Albanie                                          2203.0   2884169.0   \n",
       "3         Algérie                                          2915.0  41389189.0   \n",
       "4       Allemagne                                          2461.0  82658409.0   \n",
       "\n",
       "   Disponibilité alimentaire (Kcal/jour)  \\\n",
       "0                           6.791003e+10   \n",
       "1                           1.444057e+11   \n",
       "2                           6.353824e+09   \n",
       "3                           1.206495e+11   \n",
       "4                           2.034223e+11   \n",
       "\n",
       "   population totale par pays pouvant etre nourri  \n",
       "0                                    2.829584e+07  \n",
       "1                                    6.016905e+07  \n",
       "2                                    2.647427e+06  \n",
       "3                                    5.027062e+07  \n",
       "4                                    8.475931e+07  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire_vegetale['population totale par pays pouvant etre nourri'] = df_merge_population_dispo_alimentaire_vegetale['Disponibilité alimentaire (Kcal/jour)'] / 2400 \n",
    "df_merge_population_dispo_alimentaire_vegetale.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07147a19-a58d-4919-8485-09a336e43e3e",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Résultat du nombre théorique de personnes pouvant etre nourri (origine vegetale) 3.3 <a class=\"anchor\" id=\"section_3_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4d72b982-56cd-44d3-a030-101c98d52914",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le nombre théorique de personnes qui pourraient être nourries (origine vegetale) est: 7270628924.56\n"
     ]
    }
   ],
   "source": [
    "nbr_theorique_pers_pouvant_etre_nourri = df_merge_population_dispo_alimentaire_vegetale.iloc[: , 4].sum()\n",
    "print(f'Le nombre théorique de personnes qui pourraient être nourries (origine vegetale) est: {round(nbr_theorique_pers_pouvant_etre_nourri,2)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd0b065-b5d0-4069-ab06-aca692a274ad",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Conaitre les valeurs non attribuées dans la merge "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "385fcfa0-ff08-4d12-b786-83e5551b9e82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/jour)</th>\n",
       "      <th>population totale par pays pouvant etre nourri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Zone, Disponibilité alimentaire (Kcal/personne/jour), Valeur, Disponibilité alimentaire (Kcal/jour), population totale par pays pouvant etre nourri]\n",
       "Index: []"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire_vegetale.loc[df_merge_population_dispo_alimentaire_vegetale.isnull().any(axis=1)] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b66befb-86d5-4b56-8dcf-58c4f1c546c9",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Reset l'index du df ```dispo alimentaire``` et remplacer les deux pays par la bonne orthographe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f3395fb1-5e71-4789-8cdf-7a6ddcd61230",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dispo_alimentaire_vegetale_zone.reset_index(inplace = True)\n",
    "data_dispo_alimentaire_vegetale_zone['Zone'].replace({\"Royaume-Uni\": \"Royaume-Uni de Grande-Bretagne et d'Irlande du Nord\", \"Tchéquie (la)\": \"Tchéquie\"}, inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a405c77e-e3fd-4714-a00a-012539084f6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1871.0</td>\n",
       "      <td>36296113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2533.0</td>\n",
       "      <td>57009756.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>2884169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>41389189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>2461.0</td>\n",
       "      <td>82658409.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone  Disponibilité alimentaire (Kcal/personne/jour)      Valeur\n",
       "0     Afghanistan                                          1871.0  36296113.0\n",
       "1  Afrique du Sud                                          2533.0  57009756.0\n",
       "2         Albanie                                          2203.0   2884169.0\n",
       "3         Algérie                                          2915.0  41389189.0\n",
       "4       Allemagne                                          2461.0  82658409.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire_vegetale = data_dispo_alimentaire_vegetale_zone[['Zone', 'Disponibilité alimentaire (Kcal/personne/jour)']].merge(data_pop_zone_val_2017, how ='left', on= \"Zone\")\n",
    "df_merge_population_dispo_alimentaire_vegetale.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed0ed8e7-5b49-426b-9241-862b626a869b",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Création de la colone <span style=\"color:crimson\">Disponibilité alimentaire (Kcal/jour)</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "32547053-7065-4b59-8289-f14d9f219df4",
   "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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/jour)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1871.0</td>\n",
       "      <td>36296113.0</td>\n",
       "      <td>6.791003e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2533.0</td>\n",
       "      <td>57009756.0</td>\n",
       "      <td>1.444057e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>2884169.0</td>\n",
       "      <td>6.353824e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>41389189.0</td>\n",
       "      <td>1.206495e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>2461.0</td>\n",
       "      <td>82658409.0</td>\n",
       "      <td>2.034223e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone  Disponibilité alimentaire (Kcal/personne/jour)      Valeur  \\\n",
       "0     Afghanistan                                          1871.0  36296113.0   \n",
       "1  Afrique du Sud                                          2533.0  57009756.0   \n",
       "2         Albanie                                          2203.0   2884169.0   \n",
       "3         Algérie                                          2915.0  41389189.0   \n",
       "4       Allemagne                                          2461.0  82658409.0   \n",
       "\n",
       "   Disponibilité alimentaire (Kcal/jour)  \n",
       "0                           6.791003e+10  \n",
       "1                           1.444057e+11  \n",
       "2                           6.353824e+09  \n",
       "3                           1.206495e+11  \n",
       "4                           2.034223e+11  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire_vegetale['Disponibilité alimentaire (Kcal/jour)'] = df_merge_population_dispo_alimentaire_vegetale['Disponibilité alimentaire (Kcal/personne/jour)'] * df_merge_population_dispo_alimentaire_vegetale['Valeur'] \n",
    "df_merge_population_dispo_alimentaire_vegetale.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "29b68053-cf77-42fe-afae-3e9ed460ea24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/jour)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Zone, Disponibilité alimentaire (Kcal/personne/jour), Valeur, Disponibilité alimentaire (Kcal/jour)]\n",
       "Index: []"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire_vegetale.loc[df_merge_population_dispo_alimentaire_vegetale.isnull().any(axis=1)] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e716ccd-7c0e-4019-8c3e-64ff2d1b14b5",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Création de la colone <span style=\"color:crimson\">Population totale par pays pouvant etre nourri</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f313ad63-40c8-4a51-a739-1f85073b9c0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Valeur</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/jour)</th>\n",
       "      <th>population totale par pays pouvant etre nourri</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1871.0</td>\n",
       "      <td>36296113.0</td>\n",
       "      <td>6.791003e+10</td>\n",
       "      <td>2.829584e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2533.0</td>\n",
       "      <td>57009756.0</td>\n",
       "      <td>1.444057e+11</td>\n",
       "      <td>6.016905e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>2884169.0</td>\n",
       "      <td>6.353824e+09</td>\n",
       "      <td>2.647427e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>41389189.0</td>\n",
       "      <td>1.206495e+11</td>\n",
       "      <td>5.027062e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>2461.0</td>\n",
       "      <td>82658409.0</td>\n",
       "      <td>2.034223e+11</td>\n",
       "      <td>8.475931e+07</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "             Zone  Disponibilité alimentaire (Kcal/personne/jour)      Valeur  \\\n",
       "0     Afghanistan                                          1871.0  36296113.0   \n",
       "1  Afrique du Sud                                          2533.0  57009756.0   \n",
       "2         Albanie                                          2203.0   2884169.0   \n",
       "3         Algérie                                          2915.0  41389189.0   \n",
       "4       Allemagne                                          2461.0  82658409.0   \n",
       "\n",
       "   Disponibilité alimentaire (Kcal/jour)  \\\n",
       "0                           6.791003e+10   \n",
       "1                           1.444057e+11   \n",
       "2                           6.353824e+09   \n",
       "3                           1.206495e+11   \n",
       "4                           2.034223e+11   \n",
       "\n",
       "   population totale par pays pouvant etre nourri  \n",
       "0                                    2.829584e+07  \n",
       "1                                    6.016905e+07  \n",
       "2                                    2.647427e+06  \n",
       "3                                    5.027062e+07  \n",
       "4                                    8.475931e+07  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge_population_dispo_alimentaire_vegetale['population totale par pays pouvant etre nourri'] = df_merge_population_dispo_alimentaire_vegetale['Disponibilité alimentaire (Kcal/jour)'] / 2400 \n",
    "df_merge_population_dispo_alimentaire_vegetale.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1024376e-3d90-4aa0-a05d-58276d888f3f",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Résultat du nombre théorique de personnes pouvant etre nourri (origine vegetale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "23e9d265-ecde-4d3b-af1a-5a2d79e5791a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le nombre théorique de personnes qui pourraient être nourries (origine vegetale) est: 7270628924.56\n"
     ]
    }
   ],
   "source": [
    "nbr_theorique_pers_pouvant_etre_nourri = df_merge_population_dispo_alimentaire_vegetale.iloc[: , 4].sum()\n",
    "print(f'Le nombre théorique de personnes qui pourraient être nourries (origine vegetale) est: {round(nbr_theorique_pers_pouvant_etre_nourri,2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ed6a15c3-9848-466d-8d46-38ac8145db9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le nombre théorique de personnes qui pourraient être nourries est: 98.66 %\n"
     ]
    }
   ],
   "source": [
    "nombre_total_pers_mondial_produit_vegetale = df_merge_population_dispo_alimentaire_vegetale.iloc[: , 2].sum()\n",
    "pourcentage_pers_pouvant_etre_nourri_vegetale = round(nbr_theorique_pers_pouvant_etre_nourri * 100 / nombre_total_pers_mondial_produit_vegetale,2)\n",
    "print(f'Le nombre théorique de personnes qui pourraient être nourries est: {pourcentage_pers_pouvant_etre_nourri_vegetale} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "282d1907-3fbe-4df8-acf6-49c0fc4aac18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "\n",
    "pop = ['Population\\n pouvant\\n se nourrir', 'Population\\n pouvant se\\n nourrir en\\n produits végétaux', 'Population nourrit', 'Population en \\nsous-nutrition']\n",
    "counts = [pourcentage_pers_pouvant_etre_nourri, pourcentage_pers_pouvant_etre_nourri_vegetale, pop_nourri_2017, proportion_pop_2017]\n",
    "# bar_labels = ['pop', 'blue', 'orange']\n",
    "bar_colors = ['tab:blue', 'tab:blue', 'tab:blue', 'tab:blue']\n",
    "\n",
    "ax.bar(pop, counts, label=bar_labels, color=bar_colors)\n",
    "plt.grid(axis='y')\n",
    "# plt.ylim(0, 110)\n",
    "plt.yticks([0, 10, 50, 70, 90, 110, 120, 130])\n",
    "for i in range(4):\n",
    "    plt.text(i - 0.06, counts[i] , round(counts[i],1))\n",
    "\n",
    "ax.set_ylabel('Pourcent de la population')\n",
    "ax.set_title('Variation entre la population pouvant etre nourrit\\n et celle en état de sous nutrition.', fontsize=10, color= '#5f89c4', fontweight='bold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0e4f061-05f5-4e1a-844d-5c7dd509247c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Requete 4: l’utilisation de la disponibilité intérieure, en particulier la part qui est attribuée à l’alimentation animale, celle qui est perdue et celle qui est concrètement utilisée pour l'alimentation humaine. <a class=\"anchor\" id=\"requete4\"></a>\n",
    "[Retour au sommaire](#sommaire)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e891967-dd17-4e0c-8975-e278313aaca6",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Creation de la variable disponibilite intérieur totale: ```disponibilite_int_totale``` 4.1 <a class=\"anchor\" id=\"section_4_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7a82c574-d080-4099-a953-b3717409423d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la disponibilité intérieur totale est de: 9848994.0\n"
     ]
    }
   ],
   "source": [
    "disponibilite_int_totale = data_dispo_alimentaire['Disponibilité intérieure'].sum()\n",
    "print(f\"la disponibilité intérieur totale est de: {disponibilite_int_totale}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5453d6f5-ba14-4cc3-afeb-bd204a35d8fe",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Création de la variable disponibilité alimentation animale: ```disponibilite_animale``` 4.2 <a class=\"anchor\" id=\"section_4_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "838ddfc7-7d08-4251-bc49-a10adec008be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la disponibilité pour l'alimentation animale est de: 1304245.0\n"
     ]
    }
   ],
   "source": [
    "disponibilite_animale = data_dispo_alimentaire['Aliments pour animaux'].sum()\n",
    "print(f\"la disponibilité pour l'alimentation animale est de: {disponibilite_animale}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59f8d168-d2e1-4369-9971-671610389311",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Calcule de la part attribuée à l'alimentation animale 4.3 <a class=\"anchor\" id=\"section_4_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fc9302d9-1314-424a-9fd6-3c53eaa0c3b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la part attribuée pour l'alimentation animale est de: 13.24%\n"
     ]
    }
   ],
   "source": [
    "calc_part_animale = round(disponibilite_animale * 100 / disponibilite_int_totale,2)\n",
    "print(f\"la part attribuée pour l'alimentation animale est de: {calc_part_animale}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3526c8-1433-4cff-b3a8-a2292f0ec2a6",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Calcule de la perte 4.4 <a class=\"anchor\" id=\"section_4_4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd573f60-e112-4773-8afb-82f6a6bd1cbc",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Création de la variable ```perte``` 4.4.1 <a class=\"anchor\" id=\"section_4_4_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6689be5c-7ad1-4e9b-9ab9-ff81ae0207fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "453698.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perte = data_dispo_alimentaire['Pertes'].sum()\n",
    "perte"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cadbb51-1733-4d7d-bd2d-a6e9c55e2e87",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### La part perdue 4.4.2 <a class=\"anchor\" id=\"section_4_4_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cfab1a05-08d1-427e-83b2-eaf4798247fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la part perdue est de: 4.61%\n"
     ]
    }
   ],
   "source": [
    "calc_perte = round(perte * 100 / disponibilite_int_totale,2)\n",
    "print(f\"la part perdue est de: {calc_perte}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "170e2c21-be5b-411e-8a36-92e73264fdd7",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Calcule de la Disponibilité alimentaire pour l'alimentation humaine 4.5 <a class=\"anchor\" id=\"section_4_5\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c1dcb6d-671e-47f4-8898-9f6598f51085",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Création de la variable disponibilité alimentation pour les humains: ```dispo_alimentaire_humain``` 4.5.1 <a class=\"anchor\" id=\"section_4_5_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7a36a025-8bf3-4281-a673-86b086bd6bb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4876258.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_alimentaire_humain = data_dispo_alimentaire['Nourriture'].sum()\n",
    "dispo_alimentaire_humain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbfdd335-4b6b-44b5-a165-66c2b0b20b75",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### La part attribuée au humains 4.5.2 <a class=\"anchor\" id=\"section_4_5_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "37554998-2a45-4321-a214-bfa3211f43a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour l'alimentation humaine est de 49.51 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_humain = round(dispo_alimentaire_humain * 100 / disponibilite_int_totale,2)\n",
    "print(f\"La part attibuée pour l'alimentation humaine est de {calc_part_humain} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dfe60bc-f846-4bf1-963a-535c8c2f4c51",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Calcule pour savoir si le resultat obtenue est le bon (calcule de la valeur totale) 4.6 <a class=\"anchor\" id=\"section_4_6\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2adc37fc-46e8-4b48-ad5d-b2ab138e190e",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Création de la variable disponibilité pour la semence: ```dispo_semences``` 4.6.1 <a class=\"anchor\" id=\"section_4_6_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "54b41a8d-cfa0-47c2-aff8-58bc0ad5a399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154681.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_semences = data_dispo_alimentaire['Semences'].sum() \n",
    "dispo_semences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788c161a-b3b9-447c-8583-e949327884b0",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### La part attribuée pour la semence 4.6.2 <a class=\"anchor\" id=\"section_4_6_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6f5f114f-1c52-4c68-9f61-7c553b9fd74e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour la semence est de 1.57 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_semence = round(dispo_semences * 100 / disponibilite_int_totale,2)\n",
    "print(f\"La part attibuée pour la semence est de {calc_part_semence} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "288af7c1-d965-48d3-8550-8ad7dc82c27f",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Calcule de la part attribuée aux traitements 4.6.3 <a class=\"anchor\" id=\"section_4_6_3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c5a5e9e-75a2-401a-b809-166e9bdde65a",
   "metadata": {
    "tags": []
   },
   "source": [
    "###### Création de la variable disponibilité pour le traitement: ```dispo_traitement``` 4.6.4 <a class=\"anchor\" id=\"section_4_6_4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d53193aa-6434-4dae-9107-1d2f28512498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2204687.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_traitement = data_dispo_alimentaire['Traitement'].sum() \n",
    "dispo_traitement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5505588f-82a6-454e-b954-951cf37784c4",
   "metadata": {
    "tags": []
   },
   "source": [
    "###### La part attribuée pour la traitement 4.6.5 <a class=\"anchor\" id=\"section_4_6_5\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e66d5763-387e-4b3b-81f4-8852bac37d69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour le traitement est de 22.38 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_traitement = round(dispo_traitement * 100 / disponibilite_int_totale,2)\n",
    "print(f\"La part attibuée pour le traitement est de {calc_part_traitement} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5637df54-8073-4ec9-9b37-d5cb90b34f38",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Calcule de la disponibilité des autres utilisations 4.6.6 <a class=\"anchor\" id=\"section_4_6_6\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d17a6c9-5879-4cf4-929b-0f191c1e7f72",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Création de la variable disponibilité pour les autres utilisations: ```dispo_autre_utilisation``` 4.6.7 <a class=\"anchor\" id=\"section_4_6_7\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7ed1d2d6-8809-40a3-90e1-7a7b4fd79c31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "865023.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_autre_utilisation = data_dispo_alimentaire['Autres Utilisations'].sum() \n",
    "dispo_autre_utilisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b86601-a7c9-43c7-85b2-11abcfb5b8ff",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### La part attribuée pour les autres utilisations 4.6.8 <a class=\"anchor\" id=\"section_4_6_8\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "23d2b717-148c-4f3a-b661-4f09bab3804a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour lees autres utilisation est de 8.78 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_autre_utilisation = round(dispo_autre_utilisation * 100 / disponibilite_int_totale,2)\n",
    "print(f\"La part attibuée pour lees autres utilisation est de {calc_part_autre_utilisation} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5967c345-ce7b-429b-8c62-1df339261ba1",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Calcule de la disponibilité totale 4.6.9 <a class=\"anchor\" id=\"section_4_6_9\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0ae8433d-d967-4c97-af04-43fe2c6f1b65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La disponibilité totale est de: 100.1 % \n",
      "Nous avons bien le bon resultat car nous trouvons 100% de la disponibilité totale.\n"
     ]
    }
   ],
   "source": [
    "calc_utilisation_total = round(calc_part_animale + calc_perte + calc_part_humain + calc_part_semence + calc_part_traitement + calc_part_autre_utilisation,1)\n",
    "print(f\"La disponibilité totale est de: {calc_utilisation_total} % \\nNous avons bien le bon resultat car nous trouvons 100% de la disponibilité totale.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5d8ce0ce-f2c2-4ba3-83aa-70476be584a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define data\n",
    "data = [calc_part_animale, calc_perte, calc_part_humain, calc_part_semence, calc_part_traitement, calc_part_autre_utilisation]\n",
    "labels = ['Animales', 'Pertes', 'Humains', 'Semences', 'Traitements', 'Autres utilisations']\n",
    "\n",
    "#define Seaborn color palette to use\n",
    "colors = sns.color_palette('pastel')[0:5]\n",
    "\n",
    "#create pie chart\n",
    "plt.pie(data, labels = labels, colors = colors, autopct='%.0f%%')\n",
    "plt.title(\"Utilisation de la disponibilité alimentaire mondiale\",color='b')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80dca327-f250-4645-a171-f71cdd78d77b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Les pays pour lesquels la proportion de personnes sous-alimentées est la plus forte en 2017 <a class=\"anchor\" id=\"section5\"></a>\n",
    "[Retour au sommaire](#sommaire)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e569bc-7260-4f79-9325-e5b74bfd377b",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Nettoyage des données. 5.1<a class=\"anchor\" id=\"section_5_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5bf88191-33d0-4bef-9307-a542d41ab3b5",
   "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>Zone</th>\n",
       "      <th>Valeur_x</th>\n",
       "      <th>Valeur_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36296113.0</td>\n",
       "      <td>10500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>57009756.0</td>\n",
       "      <td>3100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2884169.0</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>41389189.0</td>\n",
       "      <td>1300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>82658409.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone    Valeur_x    Valeur_y\n",
       "0     Afghanistan  36296113.0  10500000.0\n",
       "1  Afrique du Sud  57009756.0   3100000.0\n",
       "2         Albanie   2884169.0    100000.0\n",
       "3         Algérie  41389189.0   1300000.0\n",
       "4       Allemagne  82658409.0         NaN"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sn_2017 = pd.merge(data_pop_zone_val_2017, data_sn_zone_val_2017, on='Zone',how='right')\n",
    "sn_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8a9725af-3f4b-49d6-a294-8c551dcb5b1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nous avons 119 données manquantes \n"
     ]
    }
   ],
   "source": [
    "donnee_manquante_sn_2017 = sn_2017.loc[sn_2017.isnull().any(axis=1)].shape[0]\n",
    "print(f'Nous avons {donnee_manquante_sn_2017} données manquantes ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8063303-9cde-40e8-8ae1-63f4f7e009c9",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Création de la colone <span style=\"color:crimson\">Proportion (%)</span> qui corresponf à la proportion de personnes sous-alimentées dans leur pays respestif. 5.2 <a class=\"anchor\" id=\"section_5_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7fd95463-8ff2-49ec-9aaf-2e4c4ba12a1d",
   "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>Zone</th>\n",
       "      <th>Valeur_x</th>\n",
       "      <th>Valeur_y</th>\n",
       "      <th>Proportion (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36296113.0</td>\n",
       "      <td>10500000.0</td>\n",
       "      <td>28.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>57009756.0</td>\n",
       "      <td>3100000.0</td>\n",
       "      <td>5.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2884169.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>3.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone    Valeur_x    Valeur_y  Proportion (%)\n",
       "0     Afghanistan  36296113.0  10500000.0           28.93\n",
       "1  Afrique du Sud  57009756.0   3100000.0            5.44\n",
       "2         Albanie   2884169.0    100000.0            3.47"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sn_2017['Proportion (%)'] = round(sn_2017['Valeur_y']*100 / sn_2017['Valeur_x'], 2)\n",
    "sn_2017.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb50df18-2d82-4dd1-97f0-8da72ae70917",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Resultat des pays où la sous-alimentation est la plus forte en 2017. 5.3 <a class=\"anchor\" id=\"section_5_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "65d3c2e4-0e70-48b4-83b7-134d889a58b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sn_2017_ascending = sn_2017.sort_values(by='Proportion (%)', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "76fea175-0387-4b00-ba64-3833bd81c6d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#find_substring_on_table(sn_2017, 'thi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "eeefc946-f96f-4b06-8572-8f00f3bb278f",
   "metadata": {},
   "outputs": [
    {
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       "#T_cc01b_row0_col3, #T_cc01b_row1_col2, #T_cc01b_row9_col1 {\n",
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       "#T_cc01b_row1_col3 {\n",
       "  background-color: #0d0dfe;\n",
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       "}\n",
       "#T_cc01b_row2_col2, #T_cc01b_row9_col2 {\n",
       "  background-color: #1f1ffd;\n",
       "  color: #f1f1f1;\n",
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       "  background-color: #3434fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
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       "  background-color: #3636fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cc01b_row7_col3 {\n",
       "  background-color: #c0c0f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cc01b_row8_col1, #T_cc01b_row8_col2, #T_cc01b_row9_col3 {\n",
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       "  color: #000000;\n",
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       "</style>\n",
       "<table id=\"T_cc01b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cc01b_level0_col0\" class=\"col_heading level0 col0\" >Zone</th>\n",
       "      <th id=\"T_cc01b_level0_col1\" class=\"col_heading level0 col1\" >Valeur_x</th>\n",
       "      <th id=\"T_cc01b_level0_col2\" class=\"col_heading level0 col2\" >Valeur_y</th>\n",
       "      <th id=\"T_cc01b_level0_col3\" class=\"col_heading level0 col3\" >Proportion (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cc01b_level0_row0\" class=\"row_heading level0 row0\" >78</th>\n",
       "      <td id=\"T_cc01b_row0_col0\" class=\"data row0 col0\" >Haïti</td>\n",
       "      <td id=\"T_cc01b_row0_col1\" class=\"data row0 col1\" >10982366.000000</td>\n",
       "      <td id=\"T_cc01b_row0_col2\" class=\"data row0 col2\" >5300000.000000</td>\n",
       "      <td id=\"T_cc01b_row0_col3\" class=\"data row0 col3\" >48.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc01b_level0_row1\" class=\"row_heading level0 row1\" >157</th>\n",
       "      <td id=\"T_cc01b_row1_col0\" class=\"data row1 col0\" >République populaire démocratique de Corée</td>\n",
       "      <td id=\"T_cc01b_row1_col1\" class=\"data row1 col1\" >25429825.000000</td>\n",
       "      <td id=\"T_cc01b_row1_col2\" class=\"data row1 col2\" >12000000.000000</td>\n",
       "      <td id=\"T_cc01b_row1_col3\" class=\"data row1 col3\" >47.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc01b_level0_row2\" class=\"row_heading level0 row2\" >108</th>\n",
       "      <td id=\"T_cc01b_row2_col0\" class=\"data row2 col0\" >Madagascar</td>\n",
       "      <td id=\"T_cc01b_row2_col1\" class=\"data row2 col1\" >25570512.000000</td>\n",
       "      <td id=\"T_cc01b_row2_col2\" class=\"data row2 col2\" >10500000.000000</td>\n",
       "      <td id=\"T_cc01b_row2_col3\" class=\"data row2 col3\" >41.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc01b_level0_row3\" class=\"row_heading level0 row3\" >103</th>\n",
       "      <td id=\"T_cc01b_row3_col0\" class=\"data row3 col0\" >Libéria</td>\n",
       "      <td id=\"T_cc01b_row3_col1\" class=\"data row3 col1\" >4702226.000000</td>\n",
       "      <td id=\"T_cc01b_row3_col2\" class=\"data row3 col2\" >1800000.000000</td>\n",
       "      <td id=\"T_cc01b_row3_col3\" class=\"data row3 col3\" >38.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc01b_level0_row4\" class=\"row_heading level0 row4\" >100</th>\n",
       "      <td id=\"T_cc01b_row4_col0\" class=\"data row4 col0\" >Lesotho</td>\n",
       "      <td id=\"T_cc01b_row4_col1\" class=\"data row4 col1\" >2091534.000000</td>\n",
       "      <td id=\"T_cc01b_row4_col2\" class=\"data row4 col2\" >800000.000000</td>\n",
       "      <td id=\"T_cc01b_row4_col3\" class=\"data row4 col3\" >38.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc01b_level0_row5\" class=\"row_heading level0 row5\" >183</th>\n",
       "      <td id=\"T_cc01b_row5_col0\" class=\"data row5 col0\" >Tchad</td>\n",
       "      <td id=\"T_cc01b_row5_col1\" class=\"data row5 col1\" >15016753.000000</td>\n",
       "      <td id=\"T_cc01b_row5_col2\" class=\"data row5 col2\" >5700000.000000</td>\n",
       "      <td id=\"T_cc01b_row5_col3\" class=\"data row5 col3\" >37.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc01b_level0_row6\" class=\"row_heading level0 row6\" >161</th>\n",
       "      <td id=\"T_cc01b_row6_col0\" class=\"data row6 col0\" >Rwanda</td>\n",
       "      <td id=\"T_cc01b_row6_col1\" class=\"data row6 col1\" >11980961.000000</td>\n",
       "      <td id=\"T_cc01b_row6_col2\" class=\"data row6 col2\" >4200000.000000</td>\n",
       "      <td id=\"T_cc01b_row6_col3\" class=\"data row6 col3\" >35.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc01b_level0_row7\" class=\"row_heading level0 row7\" >121</th>\n",
       "      <td id=\"T_cc01b_row7_col0\" class=\"data row7 col0\" >Mozambique</td>\n",
       "      <td id=\"T_cc01b_row7_col1\" class=\"data row7 col1\" >28649018.000000</td>\n",
       "      <td id=\"T_cc01b_row7_col2\" class=\"data row7 col2\" >9400000.000000</td>\n",
       "      <td id=\"T_cc01b_row7_col3\" class=\"data row7 col3\" >32.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc01b_level0_row8\" class=\"row_heading level0 row8\" >186</th>\n",
       "      <td id=\"T_cc01b_row8_col0\" class=\"data row8 col0\" >Timor-Leste</td>\n",
       "      <td id=\"T_cc01b_row8_col1\" class=\"data row8 col1\" >1243258.000000</td>\n",
       "      <td id=\"T_cc01b_row8_col2\" class=\"data row8 col2\" >400000.000000</td>\n",
       "      <td id=\"T_cc01b_row8_col3\" class=\"data row8 col3\" >32.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc01b_level0_row9\" class=\"row_heading level0 row9\" >0</th>\n",
       "      <td id=\"T_cc01b_row9_col0\" class=\"data row9 col0\" >Afghanistan</td>\n",
       "      <td id=\"T_cc01b_row9_col1\" class=\"data row9 col1\" >36296113.000000</td>\n",
       "      <td id=\"T_cc01b_row9_col2\" class=\"data row9 col2\" >10500000.000000</td>\n",
       "      <td id=\"T_cc01b_row9_col3\" class=\"data row9 col3\" >28.930000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fac2e8d6640>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = sns.light_palette(\"blue\", as_cmap=True)\n",
    "\n",
    "s = sn_2017_ascending.style.background_gradient(cmap=cm)\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e733519a-eef5-4763-858e-c20b3bcabc1c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Les 10 pays qui ont le plus bénéficiés d’aide depuis 2013. <a class=\"anchor\" id=\"section6\"></a>\n",
    "[Retour au sommaire](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a1a88b5e-7c36-4206-8052-a8e7a64cc442",
   "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>Pays bénéficiaire</th>\n",
       "      <th>Année</th>\n",
       "      <th>Produit</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Autres non-céréales</td>\n",
       "      <td>682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Autres non-céréales</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Blé et Farin</td>\n",
       "      <td>39224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Blé et Farin</td>\n",
       "      <td>15160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>40504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Pays bénéficiaire  Année              Produit  Valeur\n",
       "0       Afghanistan   2013  Autres non-céréales     682\n",
       "1       Afghanistan   2014  Autres non-céréales     335\n",
       "2       Afghanistan   2013         Blé et Farin   39224\n",
       "3       Afghanistan   2014         Blé et Farin   15160\n",
       "4       Afghanistan   2013             Céréales   40504"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_aide_alimentaire = pd.read_csv('aide_alimentaire.csv')\n",
    "data_aide_alimentaire.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "04ed670e-26a7-43bb-bc74-82ab8a80e167",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nous avons auccune données maquantes:  \n",
      "0\n"
     ]
    }
   ],
   "source": [
    "donnee_manquante = data_aide_alimentaire.loc[data_aide_alimentaire.isnull().any(axis=1)].shape[0]\n",
    "print(f'Nous avons auccune données maquantes:  \\n{donnee_manquante}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "bc7fbeee-b896-49ff-a9f1-fefbaf0bfc38",
   "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>Année</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pays bénéficiaire</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>32216</td>\n",
       "      <td>185452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algérie</th>\n",
       "      <td>78561</td>\n",
       "      <td>81114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>8054</td>\n",
       "      <td>5014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>50347</td>\n",
       "      <td>348188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhoutan</th>\n",
       "      <td>24170</td>\n",
       "      <td>2666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Année  Valeur\n",
       "Pays bénéficiaire               \n",
       "Afghanistan        32216  185452\n",
       "Algérie            78561   81114\n",
       "Angola              8054    5014\n",
       "Bangladesh         50347  348188\n",
       "Bhoutan            24170    2666"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_aide_alimentaire_since_2013 = data_aide_alimentaire[data_aide_alimentaire['Année']>=2013].groupby('Pays bénéficiaire').sum()\n",
    "data_aide_alimentaire_since_2013.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6f8ce603-d461-46a4-ae65-e6a1563d54da",
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_aide_alimentaire = data_aide_alimentaire_since_2013['Valeur'].sort_values(ascending=False)\n",
    "df_rank_aide_alimentaire = rank_aide_alimentaire.to_frame().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4ada2744-d6da-4cd2-8f89-1aa353697572",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "#T_eb45f_row2_col0 {\n",
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       "#T_eb45f_row3_col0 {\n",
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       "  color: #000000;\n",
       "}\n",
       "#T_eb45f_row4_col0 {\n",
       "  background-color: #b5b5f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_eb45f_row5_col0 {\n",
       "  background-color: #c7c7f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_eb45f_row6_col0 {\n",
       "  background-color: #e6e6f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_eb45f_row7_col0, #T_eb45f_row8_col0 {\n",
       "  background-color: #efeff3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_eb45f_row9_col0 {\n",
       "  background-color: #f0f0f3;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_eb45f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_eb45f_level0_col0\" class=\"col_heading level0 col0\" >Valeur</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Pays bénéficiaire</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_eb45f_level0_row0\" class=\"row_heading level0 row0\" >République arabe syrienne</th>\n",
       "      <td id=\"T_eb45f_row0_col0\" class=\"data row0 col0\" >1858943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb45f_level0_row1\" class=\"row_heading level0 row1\" >Éthiopie</th>\n",
       "      <td id=\"T_eb45f_row1_col0\" class=\"data row1 col0\" >1381294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb45f_level0_row2\" class=\"row_heading level0 row2\" >Yémen</th>\n",
       "      <td id=\"T_eb45f_row2_col0\" class=\"data row2 col0\" >1206484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb45f_level0_row3\" class=\"row_heading level0 row3\" >Soudan du Sud</th>\n",
       "      <td id=\"T_eb45f_row3_col0\" class=\"data row3 col0\" >695248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb45f_level0_row4\" class=\"row_heading level0 row4\" >Soudan</th>\n",
       "      <td id=\"T_eb45f_row4_col0\" class=\"data row4 col0\" >669784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb45f_level0_row5\" class=\"row_heading level0 row5\" >Kenya</th>\n",
       "      <td id=\"T_eb45f_row5_col0\" class=\"data row5 col0\" >552836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb45f_level0_row6\" class=\"row_heading level0 row6\" >Bangladesh</th>\n",
       "      <td id=\"T_eb45f_row6_col0\" class=\"data row6 col0\" >348188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb45f_level0_row7\" class=\"row_heading level0 row7\" >Somalie</th>\n",
       "      <td id=\"T_eb45f_row7_col0\" class=\"data row7 col0\" >292678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb45f_level0_row8\" class=\"row_heading level0 row8\" >République démocratique du Congo</th>\n",
       "      <td id=\"T_eb45f_row8_col0\" class=\"data row8 col0\" >288502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb45f_level0_row9\" class=\"row_heading level0 row9\" >Niger</th>\n",
       "      <td id=\"T_eb45f_row9_col0\" class=\"data row9 col0\" >276344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fac2e760bb0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cms = sns.light_palette(\"blue\", as_cmap=True)\n",
    "\n",
    "so = df_rank_aide_alimentaire.style.background_gradient(cmap=cms)\n",
    "so"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba0adc5-dee9-46fd-b015-5e4aac09233b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Les pays ayant le plus/moins de disponibilité alimentaire. <a class=\"anchor\" id=\"section7\"></a>\n",
    "[Retour au sommaire](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1cda9410-bfce-4697-8238-56d40e22d497",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Aliments pour animaux</th>\n",
       "      <th>Autres Utilisations</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
       "      <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité de protéines en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité intérieure</th>\n",
       "      <th>Exportations - Quantité</th>\n",
       "      <th>Importations - Quantité</th>\n",
       "      <th>Nourriture</th>\n",
       "      <th>Pertes</th>\n",
       "      <th>Production</th>\n",
       "      <th>Semences</th>\n",
       "      <th>Traitement</th>\n",
       "      <th>Variation de stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Autriche</td>\n",
       "      <td>3412.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>3770.0</td>\n",
       "      <td>1069.20</td>\n",
       "      <td>170.44</td>\n",
       "      <td>106.20</td>\n",
       "      <td>19308.0</td>\n",
       "      <td>6733.0</td>\n",
       "      <td>8463.0</td>\n",
       "      <td>9085.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>18129.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>5178.0</td>\n",
       "      <td>-556.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Belgique</td>\n",
       "      <td>5533.0</td>\n",
       "      <td>3226.0</td>\n",
       "      <td>3737.0</td>\n",
       "      <td>1002.09</td>\n",
       "      <td>161.95</td>\n",
       "      <td>99.37</td>\n",
       "      <td>29358.0</td>\n",
       "      <td>30345.0</td>\n",
       "      <td>35276.0</td>\n",
       "      <td>11134.0</td>\n",
       "      <td>564.0</td>\n",
       "      <td>24496.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>8960.0</td>\n",
       "      <td>-64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Turquie</td>\n",
       "      <td>17750.0</td>\n",
       "      <td>3006.0</td>\n",
       "      <td>3708.0</td>\n",
       "      <td>968.15</td>\n",
       "      <td>119.64</td>\n",
       "      <td>108.12</td>\n",
       "      <td>131377.0</td>\n",
       "      <td>14593.0</td>\n",
       "      <td>12136.0</td>\n",
       "      <td>72551.0</td>\n",
       "      <td>12036.0</td>\n",
       "      <td>137432.0</td>\n",
       "      <td>2553.0</td>\n",
       "      <td>23560.0</td>\n",
       "      <td>-3599.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>États-Unis d'Amérique</td>\n",
       "      <td>148432.0</td>\n",
       "      <td>154699.0</td>\n",
       "      <td>3682.0</td>\n",
       "      <td>1002.39</td>\n",
       "      <td>161.37</td>\n",
       "      <td>109.42</td>\n",
       "      <td>777992.0</td>\n",
       "      <td>163524.0</td>\n",
       "      <td>81887.0</td>\n",
       "      <td>320889.0</td>\n",
       "      <td>7162.0</td>\n",
       "      <td>894668.0</td>\n",
       "      <td>10403.0</td>\n",
       "      <td>141010.0</td>\n",
       "      <td>-35032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Israël</td>\n",
       "      <td>2525.0</td>\n",
       "      <td>801.0</td>\n",
       "      <td>3610.0</td>\n",
       "      <td>922.14</td>\n",
       "      <td>151.39</td>\n",
       "      <td>128.00</td>\n",
       "      <td>11267.0</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>5883.0</td>\n",
       "      <td>7132.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>6906.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>564.0</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Irlande</td>\n",
       "      <td>3758.0</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>3602.0</td>\n",
       "      <td>1150.15</td>\n",
       "      <td>125.69</td>\n",
       "      <td>109.91</td>\n",
       "      <td>10738.0</td>\n",
       "      <td>6828.0</td>\n",
       "      <td>6254.0</td>\n",
       "      <td>5320.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>11501.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>-192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Italie</td>\n",
       "      <td>16144.0</td>\n",
       "      <td>3228.0</td>\n",
       "      <td>3578.0</td>\n",
       "      <td>993.20</td>\n",
       "      <td>154.69</td>\n",
       "      <td>108.49</td>\n",
       "      <td>97405.0</td>\n",
       "      <td>23643.0</td>\n",
       "      <td>38692.0</td>\n",
       "      <td>60584.0</td>\n",
       "      <td>1861.0</td>\n",
       "      <td>80855.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>14870.0</td>\n",
       "      <td>1496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>147.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3540.0</td>\n",
       "      <td>1150.66</td>\n",
       "      <td>141.49</td>\n",
       "      <td>113.64</td>\n",
       "      <td>802.0</td>\n",
       "      <td>821.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Égypte</td>\n",
       "      <td>15084.0</td>\n",
       "      <td>2262.0</td>\n",
       "      <td>3518.0</td>\n",
       "      <td>803.79</td>\n",
       "      <td>63.09</td>\n",
       "      <td>103.22</td>\n",
       "      <td>115133.0</td>\n",
       "      <td>5014.0</td>\n",
       "      <td>24100.0</td>\n",
       "      <td>65957.0</td>\n",
       "      <td>7608.0</td>\n",
       "      <td>95848.0</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>22924.0</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>30209.0</td>\n",
       "      <td>7711.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>957.52</td>\n",
       "      <td>141.62</td>\n",
       "      <td>101.39</td>\n",
       "      <td>162275.0</td>\n",
       "      <td>57431.0</td>\n",
       "      <td>65353.0</td>\n",
       "      <td>79238.0</td>\n",
       "      <td>3781.0</td>\n",
       "      <td>154547.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>39842.0</td>\n",
       "      <td>-192.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Zone  Aliments pour animaux  Autres Utilisations  \\\n",
       "11                Autriche                 3412.0               1001.0   \n",
       "16                Belgique                 5533.0               3226.0   \n",
       "159                Turquie                17750.0               3006.0   \n",
       "171  États-Unis d'Amérique               148432.0             154699.0   \n",
       "74                  Israël                 2525.0                801.0   \n",
       "72                 Irlande                 3758.0               1120.0   \n",
       "75                  Italie                16144.0               3228.0   \n",
       "89              Luxembourg                  147.0                 11.0   \n",
       "168                 Égypte                15084.0               2262.0   \n",
       "4                Allemagne                30209.0               7711.0   \n",
       "\n",
       "     Disponibilité alimentaire (Kcal/personne/jour)  \\\n",
       "11                                           3770.0   \n",
       "16                                           3737.0   \n",
       "159                                          3708.0   \n",
       "171                                          3682.0   \n",
       "74                                           3610.0   \n",
       "72                                           3602.0   \n",
       "75                                           3578.0   \n",
       "89                                           3540.0   \n",
       "168                                          3518.0   \n",
       "4                                            3503.0   \n",
       "\n",
       "     Disponibilité alimentaire en quantité (kg/personne/an)  \\\n",
       "11                                             1069.20        \n",
       "16                                             1002.09        \n",
       "159                                             968.15        \n",
       "171                                            1002.39        \n",
       "74                                              922.14        \n",
       "72                                             1150.15        \n",
       "75                                              993.20        \n",
       "89                                             1150.66        \n",
       "168                                             803.79        \n",
       "4                                               957.52        \n",
       "\n",
       "     Disponibilité de matière grasse en quantité (g/personne/jour)  \\\n",
       "11                                              170.44               \n",
       "16                                              161.95               \n",
       "159                                             119.64               \n",
       "171                                             161.37               \n",
       "74                                              151.39               \n",
       "72                                              125.69               \n",
       "75                                              154.69               \n",
       "89                                              141.49               \n",
       "168                                              63.09               \n",
       "4                                               141.62               \n",
       "\n",
       "     Disponibilité de protéines en quantité (g/personne/jour)  \\\n",
       "11                                              106.20          \n",
       "16                                               99.37          \n",
       "159                                             108.12          \n",
       "171                                             109.42          \n",
       "74                                              128.00          \n",
       "72                                              109.91          \n",
       "75                                              108.49          \n",
       "89                                              113.64          \n",
       "168                                             103.22          \n",
       "4                                               101.39          \n",
       "\n",
       "     Disponibilité intérieure  Exportations - Quantité  \\\n",
       "11                    19308.0                   6733.0   \n",
       "16                    29358.0                  30345.0   \n",
       "159                  131377.0                  14593.0   \n",
       "171                  777992.0                 163524.0   \n",
       "74                    11267.0                   1648.0   \n",
       "72                    10738.0                   6828.0   \n",
       "75                    97405.0                  23643.0   \n",
       "89                      802.0                    821.0   \n",
       "168                  115133.0                   5014.0   \n",
       "4                    162275.0                  57431.0   \n",
       "\n",
       "     Importations - Quantité  Nourriture   Pertes  Production  Semences  \\\n",
       "11                    8463.0      9085.0    459.0     18129.0     173.0   \n",
       "16                   35276.0     11134.0    564.0     24496.0     153.0   \n",
       "159                  12136.0     72551.0  12036.0    137432.0    2553.0   \n",
       "171                  81887.0    320889.0   7162.0    894668.0   10403.0   \n",
       "74                    5883.0      7132.0    220.0      6906.0      71.0   \n",
       "72                    6254.0      5320.0    236.0     11501.0      89.0   \n",
       "75                   38692.0     60584.0   1861.0     80855.0     736.0   \n",
       "89                    1076.0       607.0     11.0       593.0       9.0   \n",
       "168                  24100.0     65957.0   7608.0     95848.0    1365.0   \n",
       "4                    65353.0     79238.0   3781.0    154547.0    1551.0   \n",
       "\n",
       "     Traitement  Variation de stock  \n",
       "11       5178.0              -556.0  \n",
       "16       8960.0               -64.0  \n",
       "159     23560.0             -3599.0  \n",
       "171    141010.0            -35032.0  \n",
       "74        564.0               128.0  \n",
       "72        399.0              -192.0  \n",
       "75      14870.0              1496.0  \n",
       "89         24.0               -41.0  \n",
       "168     22924.0               199.0  \n",
       "4       39842.0              -192.0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_data_alimentaire.sort_values(by='Disponibilité alimentaire (Kcal/personne/jour)', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8efdadbf-36f6-4243-86d0-9295847fe6a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pays_plus_dispo_alimentaire = zone_data_alimentaire.loc[zone_data_alimentaire.index[11],'Zone']\n",
    "#print(f\"Le pays ayant le plus de Diponibilité alimentaire est l'{pays_plus_dispo_alimentaire}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e33779c0-e19a-44de-bec6-df55b7d5161b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>Zone</th>\n",
       "      <th>Aliments pour animaux</th>\n",
       "      <th>Autres Utilisations</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
       "      <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité de protéines en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité intérieure</th>\n",
       "      <th>Exportations - Quantité</th>\n",
       "      <th>Importations - Quantité</th>\n",
       "      <th>Nourriture</th>\n",
       "      <th>Pertes</th>\n",
       "      <th>Production</th>\n",
       "      <th>Semences</th>\n",
       "      <th>Traitement</th>\n",
       "      <th>Variation de stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>République centrafricaine</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1879.0</td>\n",
       "      <td>453.73</td>\n",
       "      <td>59.41</td>\n",
       "      <td>46.04</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2092.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>2503.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Zambie</td>\n",
       "      <td>408.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>350.12</td>\n",
       "      <td>42.05</td>\n",
       "      <td>55.22</td>\n",
       "      <td>10041.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>5088.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>10526.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4178.0</td>\n",
       "      <td>-182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>842.0</td>\n",
       "      <td>2522.0</td>\n",
       "      <td>2056.0</td>\n",
       "      <td>425.07</td>\n",
       "      <td>24.49</td>\n",
       "      <td>46.69</td>\n",
       "      <td>15216.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>9741.0</td>\n",
       "      <td>966.0</td>\n",
       "      <td>14160.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>844.0</td>\n",
       "      <td>375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>768.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>351.41</td>\n",
       "      <td>33.50</td>\n",
       "      <td>58.26</td>\n",
       "      <td>13515.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>2988.0</td>\n",
       "      <td>10735.0</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>11171.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>-368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Haïti</td>\n",
       "      <td>481.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2089.0</td>\n",
       "      <td>397.78</td>\n",
       "      <td>48.92</td>\n",
       "      <td>47.70</td>\n",
       "      <td>6175.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1204.0</td>\n",
       "      <td>4101.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>5072.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>-77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>République populaire démocratique de Corée</td>\n",
       "      <td>816.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>2093.0</td>\n",
       "      <td>458.93</td>\n",
       "      <td>36.41</td>\n",
       "      <td>54.99</td>\n",
       "      <td>14876.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>993.0</td>\n",
       "      <td>11425.0</td>\n",
       "      <td>1066.0</td>\n",
       "      <td>13883.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Tchad</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2109.0</td>\n",
       "      <td>319.45</td>\n",
       "      <td>47.10</td>\n",
       "      <td>62.75</td>\n",
       "      <td>5304.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>4097.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>5153.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>-141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>131.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2113.0</td>\n",
       "      <td>336.59</td>\n",
       "      <td>57.18</td>\n",
       "      <td>48.32</td>\n",
       "      <td>9507.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>1342.0</td>\n",
       "      <td>4762.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>7914.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4330.0</td>\n",
       "      <td>531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Ouganda</td>\n",
       "      <td>348.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2126.0</td>\n",
       "      <td>535.58</td>\n",
       "      <td>46.57</td>\n",
       "      <td>52.64</td>\n",
       "      <td>26624.0</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>20121.0</td>\n",
       "      <td>991.0</td>\n",
       "      <td>26321.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>4982.0</td>\n",
       "      <td>269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2129.0</td>\n",
       "      <td>369.10</td>\n",
       "      <td>51.08</td>\n",
       "      <td>57.51</td>\n",
       "      <td>447.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Zone  Aliments pour animaux  \\\n",
       "128                   République centrafricaine                    1.0   \n",
       "166                                      Zambie                  408.0   \n",
       "91                                   Madagascar                  842.0   \n",
       "0                                   Afghanistan                  768.0   \n",
       "65                                        Haïti                  481.0   \n",
       "133  République populaire démocratique de Corée                  816.0   \n",
       "151                                       Tchad                  104.0   \n",
       "167                                    Zimbabwe                  131.0   \n",
       "114                                     Ouganda                  348.0   \n",
       "154                                 Timor-Leste                    9.0   \n",
       "\n",
       "     Autres Utilisations  Disponibilité alimentaire (Kcal/personne/jour)  \\\n",
       "128                 27.0                                          1879.0   \n",
       "166                 69.0                                          1924.0   \n",
       "91                2522.0                                          2056.0   \n",
       "0                  415.0                                          2087.0   \n",
       "65                 115.0                                          2089.0   \n",
       "133                695.0                                          2093.0   \n",
       "151                  2.0                                          2109.0   \n",
       "167                 23.0                                          2113.0   \n",
       "114                 68.0                                          2126.0   \n",
       "154                  2.0                                          2129.0   \n",
       "\n",
       "     Disponibilité alimentaire en quantité (kg/personne/an)  \\\n",
       "128                                             453.73        \n",
       "166                                             350.12        \n",
       "91                                              425.07        \n",
       "0                                               351.41        \n",
       "65                                              397.78        \n",
       "133                                             458.93        \n",
       "151                                             319.45        \n",
       "167                                             336.59        \n",
       "114                                             535.58        \n",
       "154                                             369.10        \n",
       "\n",
       "     Disponibilité de matière grasse en quantité (g/personne/jour)  \\\n",
       "128                                              59.41               \n",
       "166                                              42.05               \n",
       "91                                               24.49               \n",
       "0                                                33.50               \n",
       "65                                               48.92               \n",
       "133                                              36.41               \n",
       "151                                              47.10               \n",
       "167                                              57.18               \n",
       "114                                              46.57               \n",
       "154                                              51.08               \n",
       "\n",
       "     Disponibilité de protéines en quantité (g/personne/jour)  \\\n",
       "128                                              46.04          \n",
       "166                                              55.22          \n",
       "91                                               46.69          \n",
       "0                                                58.26          \n",
       "65                                               47.70          \n",
       "133                                              54.99          \n",
       "151                                              62.75          \n",
       "167                                              48.32          \n",
       "114                                              52.64          \n",
       "154                                              57.51          \n",
       "\n",
       "     Disponibilité intérieure  Exportations - Quantité  \\\n",
       "128                    2582.0                      0.0   \n",
       "166                   10041.0                    675.0   \n",
       "91                    15216.0                    236.0   \n",
       "0                     13515.0                    278.0   \n",
       "65                     6175.0                     15.0   \n",
       "133                   14876.0                     83.0   \n",
       "151                    5304.0                      0.0   \n",
       "167                    9507.0                    278.0   \n",
       "114                   26624.0                   1218.0   \n",
       "154                     447.0                     23.0   \n",
       "\n",
       "     Importations - Quantité  Nourriture  Pertes  Production  Semences  \\\n",
       "128                     76.0      2092.0   122.0      2503.0      63.0   \n",
       "166                    380.0      5088.0   242.0     10526.0      76.0   \n",
       "91                     924.0      9741.0   966.0     14160.0     375.0   \n",
       "0                     2988.0     10735.0  1135.0     11171.0     395.0   \n",
       "65                    1204.0      4101.0   679.0      5072.0      36.0   \n",
       "133                    993.0     11425.0  1066.0     13883.0     194.0   \n",
       "151                    290.0      4097.0   459.0      5153.0     101.0   \n",
       "167                   1342.0      4762.0   188.0      7914.0      76.0   \n",
       "114                   1257.0     20121.0   991.0     26321.0     154.0   \n",
       "154                    100.0       419.0    14.0       371.0       2.0   \n",
       "\n",
       "     Traitement  Variation de stock  \n",
       "128       278.0                 3.0  \n",
       "166      4178.0              -182.0  \n",
       "91        844.0               375.0  \n",
       "0          63.0              -368.0  \n",
       "65        762.0               -77.0  \n",
       "133       678.0                84.0  \n",
       "151       542.0              -141.0  \n",
       "167      4330.0               531.0  \n",
       "114      4982.0               269.0  \n",
       "154         3.0                 2.0  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_data_alimentaire.sort_values(by='Disponibilité alimentaire (Kcal/personne/jour)', ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "263f0f01-9e9e-453d-8143-59cbbc3ea4e1",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Question de Julien. <a class=\"anchor\" id=\"section9\"></a>\n",
    "[Retour au sommaire](#sommaire)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46593616-fd4f-4dda-9580-7fda58bf811b",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Logique entre l’ensemble des 10 variables. 9.1 <a class=\"anchor\" id=\"section_9_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82fd01bf-0e99-429b-9ed9-34cea19923a1",
   "metadata": {},
   "source": [
    "Ces variables  sont toutes exprimées en milliers de tonnes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d44b5b6-6c47-460c-bd7e-50c837807aed",
   "metadata": {},
   "source": [
    "```(Production + Importations - Exportations) - Variation de stock = Disponsibilité intérierieur = Aliments pour animaux + Autres Utilisations + Nourriture + Pertes + Semences + Traitement```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59f1784c-f427-4842-a505-3feab2971309",
   "metadata": {},
   "source": [
    "##### Vérification de l'équation 9.1.1 <a class=\"anchor\" id=\"section_9_1_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "85f8a85d-dfee-4b21-9921-2b4d20d23cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#zone_data_alimentaire['Zone'].str.contains('Éth').any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3587fe0f-f797-4a27-9512-9f56432cc024",
   "metadata": {},
   "outputs": [],
   "source": [
    "#table[col].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "a1a6635f-2de2-4dba-90e6-d169faa59e4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\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>Zone</th>\n",
       "      <th>Aliments pour animaux</th>\n",
       "      <th>Autres Utilisations</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
       "      <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité de protéines en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité intérieure</th>\n",
       "      <th>Exportations - Quantité</th>\n",
       "      <th>Importations - Quantité</th>\n",
       "      <th>Nourriture</th>\n",
       "      <th>Pertes</th>\n",
       "      <th>Production</th>\n",
       "      <th>Semences</th>\n",
       "      <th>Traitement</th>\n",
       "      <th>Variation de stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>5309.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>564.80</td>\n",
       "      <td>83.58</td>\n",
       "      <td>85.33</td>\n",
       "      <td>61256.0</td>\n",
       "      <td>11674.0</td>\n",
       "      <td>7517.0</td>\n",
       "      <td>29812.0</td>\n",
       "      <td>2193.0</td>\n",
       "      <td>63263.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>22819.0</td>\n",
       "      <td>2153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>France</td>\n",
       "      <td>22769.0</td>\n",
       "      <td>14405.0</td>\n",
       "      <td>3482.0</td>\n",
       "      <td>937.43</td>\n",
       "      <td>159.15</td>\n",
       "      <td>110.50</td>\n",
       "      <td>146905.0</td>\n",
       "      <td>65945.0</td>\n",
       "      <td>33769.0</td>\n",
       "      <td>60272.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>178136.0</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>44966.0</td>\n",
       "      <td>944.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Polynésie française</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>735.66</td>\n",
       "      <td>126.09</td>\n",
       "      <td>97.27</td>\n",
       "      <td>335.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>République centrafricaine</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1879.0</td>\n",
       "      <td>453.73</td>\n",
       "      <td>59.41</td>\n",
       "      <td>46.04</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2092.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>2503.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Zone  Aliments pour animaux  Autres Utilisations  \\\n",
       "1               Afrique du Sud                 5309.0                876.0   \n",
       "53                      France                22769.0              14405.0   \n",
       "122        Polynésie française                    4.0                 44.0   \n",
       "128  République centrafricaine                    1.0                 27.0   \n",
       "\n",
       "     Disponibilité alimentaire (Kcal/personne/jour)  \\\n",
       "1                                            3020.0   \n",
       "53                                           3482.0   \n",
       "122                                          2923.0   \n",
       "128                                          1879.0   \n",
       "\n",
       "     Disponibilité alimentaire en quantité (kg/personne/an)  \\\n",
       "1                                               564.80        \n",
       "53                                              937.43        \n",
       "122                                             735.66        \n",
       "128                                             453.73        \n",
       "\n",
       "     Disponibilité de matière grasse en quantité (g/personne/jour)  \\\n",
       "1                                                83.58               \n",
       "53                                              159.15               \n",
       "122                                             126.09               \n",
       "128                                              59.41               \n",
       "\n",
       "     Disponibilité de protéines en quantité (g/personne/jour)  \\\n",
       "1                                                85.33          \n",
       "53                                              110.50          \n",
       "122                                              97.27          \n",
       "128                                              46.04          \n",
       "\n",
       "     Disponibilité intérieure  Exportations - Quantité  \\\n",
       "1                     61256.0                  11674.0   \n",
       "53                   146905.0                  65945.0   \n",
       "122                     335.0                     10.0   \n",
       "128                    2582.0                      0.0   \n",
       "\n",
       "     Importations - Quantité  Nourriture  Pertes  Production  Semences  \\\n",
       "1                     7517.0     29812.0  2193.0     63263.0     253.0   \n",
       "53                   33769.0     60272.0  3261.0    178136.0    1648.0   \n",
       "122                    176.0       202.0     3.0       159.0       0.0   \n",
       "128                     76.0      2092.0   122.0      2503.0      63.0   \n",
       "\n",
       "     Traitement  Variation de stock  \n",
       "1       22819.0              2153.0  \n",
       "53      44966.0               944.0  \n",
       "122        76.0                 9.0  \n",
       "128       278.0                 3.0  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_substring_on_table(zone_data_alimentaire, 'fr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1add86fc-73b2-4cb7-ab90-7479fad44ec2",
   "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>Zone</th>\n",
       "      <th>Aliments pour animaux</th>\n",
       "      <th>Autres Utilisations</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
       "      <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité de protéines en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité intérieure</th>\n",
       "      <th>Exportations - Quantité</th>\n",
       "      <th>Importations - Quantité</th>\n",
       "      <th>Nourriture</th>\n",
       "      <th>Pertes</th>\n",
       "      <th>Production</th>\n",
       "      <th>Semences</th>\n",
       "      <th>Traitement</th>\n",
       "      <th>Variation de stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>4352.0</td>\n",
       "      <td>1707.0</td>\n",
       "      <td>3293.0</td>\n",
       "      <td>809.13</td>\n",
       "      <td>76.48</td>\n",
       "      <td>91.92</td>\n",
       "      <td>42630.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>17403.0</td>\n",
       "      <td>31729.0</td>\n",
       "      <td>3753.0</td>\n",
       "      <td>26359.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>637.0</td>\n",
       "      <td>-479.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Zone  Aliments pour animaux  Autres Utilisations  \\\n",
       "3  Algérie                 4352.0               1707.0   \n",
       "\n",
       "   Disponibilité alimentaire (Kcal/personne/jour)  \\\n",
       "3                                          3293.0   \n",
       "\n",
       "   Disponibilité alimentaire en quantité (kg/personne/an)  \\\n",
       "3                                             809.13        \n",
       "\n",
       "   Disponibilité de matière grasse en quantité (g/personne/jour)  \\\n",
       "3                                              76.48               \n",
       "\n",
       "   Disponibilité de protéines en quantité (g/personne/jour)  \\\n",
       "3                                              91.92          \n",
       "\n",
       "   Disponibilité intérieure  Exportations - Quantité  Importations - Quantité  \\\n",
       "3                   42630.0                    655.0                  17403.0   \n",
       "\n",
       "   Nourriture  Pertes  Production  Semences  Traitement  Variation de stock  \n",
       "3     31729.0  3753.0     26359.0     449.0       637.0              -479.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_data_alimentaire_pays = zone_data_alimentaire.iloc[[3]]\n",
    "zone_data_alimentaire_pays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f3ea3938-aca6-48b5-8b47-40facb467b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    42628.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relation_1_dispo_interieur = (zone_data_alimentaire_pays['Production'] + zone_data_alimentaire_pays['Importations - Quantité'] - zone_data_alimentaire_pays['Exportations - Quantité']) +\\\n",
    "zone_data_alimentaire_pays['Variation de stock']\n",
    "relation_1_dispo_interieur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e30dc173-b508-40ae-8873-86c3c229114c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    42627.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relation_2_dispo_interieur = zone_data_alimentaire_pays['Aliments pour animaux'] + zone_data_alimentaire_pays['Autres Utilisations'] + zone_data_alimentaire_pays['Nourriture'] +\\\n",
    "zone_data_alimentaire_pays['Pertes'] + zone_data_alimentaire_pays['Semences'] + zone_data_alimentaire_pays['Traitement']\n",
    "relation_2_dispo_interieur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "cef524bd-0cf4-419b-99b3-390958465c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    42630.0\n",
       "Name: Disponibilité intérieure, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_data_alimentaire_pays['Disponibilité intérieure']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bfc6824-4732-4ef2-bd53-b5fd6bddc1bb",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Chiffres intéressants sur l’utilisation des céréales, notamment la répartition entre l’alimentation humaine (colonne Nourriture) et l’alimentation pour animaux(pour l'année 2017). 9.2 <a class=\"anchor\" id=\"section_9_2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02629585-444e-4456-ac0b-b5b4ac9b729a",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Créatin du dataframe bilans alimentaires pour les céréales: ```data_cereales_2017```. 9.2.1 <a class=\"anchor\" id=\"section_9_2_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2151dea8-1a49-45b1-b5a2-83c78bcda91b",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code Domaine</th>\n",
       "      <th>Domaine</th>\n",
       "      <th>Code zone (M49)</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Code Élément</th>\n",
       "      <th>Élément</th>\n",
       "      <th>Code Produit (CPC)</th>\n",
       "      <th>Produit</th>\n",
       "      <th>Code année</th>\n",
       "      <th>Année</th>\n",
       "      <th>Unité</th>\n",
       "      <th>Valeur</th>\n",
       "      <th>Symbole</th>\n",
       "      <th>Description du Symbole</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SCL</td>\n",
       "      <td>Compte Disponibilités et Utilisations (CDU)</td>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>5520</td>\n",
       "      <td>Aliments pour animaux</td>\n",
       "      <td>F1716</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>tonnes</td>\n",
       "      <td>913955</td>\n",
       "      <td>I</td>\n",
       "      <td>Valeur imputée</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SCL</td>\n",
       "      <td>Compte Disponibilités et Utilisations (CDU)</td>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>5141</td>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>F1716</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>tonnes</td>\n",
       "      <td>5811099</td>\n",
       "      <td>I</td>\n",
       "      <td>Valeur imputée</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SCL</td>\n",
       "      <td>Compte Disponibilités et Utilisations (CDU)</td>\n",
       "      <td>710</td>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>5520</td>\n",
       "      <td>Aliments pour animaux</td>\n",
       "      <td>F1716</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>tonnes</td>\n",
       "      <td>6631754</td>\n",
       "      <td>I</td>\n",
       "      <td>Valeur imputée</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SCL</td>\n",
       "      <td>Compte Disponibilités et Utilisations (CDU)</td>\n",
       "      <td>710</td>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>5141</td>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>F1716</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>tonnes</td>\n",
       "      <td>8154940</td>\n",
       "      <td>I</td>\n",
       "      <td>Valeur imputée</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SCL</td>\n",
       "      <td>Compte Disponibilités et Utilisations (CDU)</td>\n",
       "      <td>8</td>\n",
       "      <td>Albanie</td>\n",
       "      <td>5520</td>\n",
       "      <td>Aliments pour animaux</td>\n",
       "      <td>F1716</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>tonnes</td>\n",
       "      <td>535826</td>\n",
       "      <td>I</td>\n",
       "      <td>Valeur imputée</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SCL</td>\n",
       "      <td>Compte Disponibilités et Utilisations (CDU)</td>\n",
       "      <td>8</td>\n",
       "      <td>Albanie</td>\n",
       "      <td>5141</td>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>F1716</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>tonnes</td>\n",
       "      <td>322322</td>\n",
       "      <td>I</td>\n",
       "      <td>Valeur imputée</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SCL</td>\n",
       "      <td>Compte Disponibilités et Utilisations (CDU)</td>\n",
       "      <td>12</td>\n",
       "      <td>Algérie</td>\n",
       "      <td>5520</td>\n",
       "      <td>Aliments pour animaux</td>\n",
       "      <td>F1716</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>tonnes</td>\n",
       "      <td>6476029</td>\n",
       "      <td>I</td>\n",
       "      <td>Valeur imputée</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SCL</td>\n",
       "      <td>Compte Disponibilités et Utilisations (CDU)</td>\n",
       "      <td>12</td>\n",
       "      <td>Algérie</td>\n",
       "      <td>5141</td>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>F1716</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>tonnes</td>\n",
       "      <td>6436481</td>\n",
       "      <td>I</td>\n",
       "      <td>Valeur imputée</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SCL</td>\n",
       "      <td>Compte Disponibilités et Utilisations (CDU)</td>\n",
       "      <td>276</td>\n",
       "      <td>Allemagne</td>\n",
       "      <td>5520</td>\n",
       "      <td>Aliments pour animaux</td>\n",
       "      <td>F1716</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>tonnes</td>\n",
       "      <td>27415610</td>\n",
       "      <td>I</td>\n",
       "      <td>Valeur imputée</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SCL</td>\n",
       "      <td>Compte Disponibilités et Utilisations (CDU)</td>\n",
       "      <td>276</td>\n",
       "      <td>Allemagne</td>\n",
       "      <td>5141</td>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>F1716</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>tonnes</td>\n",
       "      <td>7544615</td>\n",
       "      <td>I</td>\n",
       "      <td>Valeur imputée</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Code Domaine                                      Domaine  Code zone (M49)  \\\n",
       "0          SCL  Compte Disponibilités et Utilisations (CDU)                4   \n",
       "1          SCL  Compte Disponibilités et Utilisations (CDU)                4   \n",
       "2          SCL  Compte Disponibilités et Utilisations (CDU)              710   \n",
       "3          SCL  Compte Disponibilités et Utilisations (CDU)              710   \n",
       "4          SCL  Compte Disponibilités et Utilisations (CDU)                8   \n",
       "5          SCL  Compte Disponibilités et Utilisations (CDU)                8   \n",
       "6          SCL  Compte Disponibilités et Utilisations (CDU)               12   \n",
       "7          SCL  Compte Disponibilités et Utilisations (CDU)               12   \n",
       "8          SCL  Compte Disponibilités et Utilisations (CDU)              276   \n",
       "9          SCL  Compte Disponibilités et Utilisations (CDU)              276   \n",
       "\n",
       "             Zone  Code Élément  \\\n",
       "0     Afghanistan          5520   \n",
       "1     Afghanistan          5141   \n",
       "2  Afrique du Sud          5520   \n",
       "3  Afrique du Sud          5141   \n",
       "4         Albanie          5520   \n",
       "5         Albanie          5141   \n",
       "6         Algérie          5520   \n",
       "7         Algérie          5141   \n",
       "8       Allemagne          5520   \n",
       "9       Allemagne          5141   \n",
       "\n",
       "                                          Élément Code Produit (CPC)  \\\n",
       "0                           Aliments pour animaux              F1716   \n",
       "1  Disponibilité alimentaire en quantité (tonnes)              F1716   \n",
       "2                           Aliments pour animaux              F1716   \n",
       "3  Disponibilité alimentaire en quantité (tonnes)              F1716   \n",
       "4                           Aliments pour animaux              F1716   \n",
       "5  Disponibilité alimentaire en quantité (tonnes)              F1716   \n",
       "6                           Aliments pour animaux              F1716   \n",
       "7  Disponibilité alimentaire en quantité (tonnes)              F1716   \n",
       "8                           Aliments pour animaux              F1716   \n",
       "9  Disponibilité alimentaire en quantité (tonnes)              F1716   \n",
       "\n",
       "    Produit  Code année  Année   Unité    Valeur Symbole  \\\n",
       "0  Céréales        2017   2017  tonnes    913955       I   \n",
       "1  Céréales        2017   2017  tonnes   5811099       I   \n",
       "2  Céréales        2017   2017  tonnes   6631754       I   \n",
       "3  Céréales        2017   2017  tonnes   8154940       I   \n",
       "4  Céréales        2017   2017  tonnes    535826       I   \n",
       "5  Céréales        2017   2017  tonnes    322322       I   \n",
       "6  Céréales        2017   2017  tonnes   6476029       I   \n",
       "7  Céréales        2017   2017  tonnes   6436481       I   \n",
       "8  Céréales        2017   2017  tonnes  27415610       I   \n",
       "9  Céréales        2017   2017  tonnes   7544615       I   \n",
       "\n",
       "  Description du Symbole  \n",
       "0         Valeur imputée  \n",
       "1         Valeur imputée  \n",
       "2         Valeur imputée  \n",
       "3         Valeur imputée  \n",
       "4         Valeur imputée  \n",
       "5         Valeur imputée  \n",
       "6         Valeur imputée  \n",
       "7         Valeur imputée  \n",
       "8         Valeur imputée  \n",
       "9         Valeur imputée  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data_cereales_2017 = pd.read_csv('FAOSTAT_data_fr_10-18-2022.csv')\n",
    "df_data_cereales_2017.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f92181a9-8890-467c-8273-af0d439e03b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Élément</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Aliments pour animaux</td>\n",
       "      <td>913955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>5811099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>Aliments pour animaux</td>\n",
       "      <td>6631754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>8154940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>Aliments pour animaux</td>\n",
       "      <td>535826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>322322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>Aliments pour animaux</td>\n",
       "      <td>6476029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>6436481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>Aliments pour animaux</td>\n",
       "      <td>27415610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>7544615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Zone                                         Élément    Valeur\n",
       "0     Afghanistan                           Aliments pour animaux    913955\n",
       "1     Afghanistan  Disponibilité alimentaire en quantité (tonnes)   5811099\n",
       "2  Afrique du Sud                           Aliments pour animaux   6631754\n",
       "3  Afrique du Sud  Disponibilité alimentaire en quantité (tonnes)   8154940\n",
       "4         Albanie                           Aliments pour animaux    535826\n",
       "5         Albanie  Disponibilité alimentaire en quantité (tonnes)    322322\n",
       "6         Algérie                           Aliments pour animaux   6476029\n",
       "7         Algérie  Disponibilité alimentaire en quantité (tonnes)   6436481\n",
       "8       Allemagne                           Aliments pour animaux  27415610\n",
       "9       Allemagne  Disponibilité alimentaire en quantité (tonnes)   7544615"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data_cereales_2017 = df_data_cereales_2017[['Zone', 'Élément', 'Valeur']]\n",
    "df_data_cereales_2017.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f25aaf78-06f1-4177-84c9-67380075539e",
   "metadata": {},
   "source": [
    "__Nous allons prendre comme exemple 2 pays où la sous-nutrition est très faible (Allemagne, France) et à l'inverse 2 pays où la sous-nutrition est très élévé (Haiti, Libéria)et on va les comparer__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e79225e3-dcb6-4ba8-9601-79fa502e1185",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7544615</td>\n",
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       "                                          Élément    Valeur\n",
       "8                           Aliments pour animaux  27415610\n",
       "9  Disponibilité alimentaire en quantité (tonnes)   7544615"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data_2017_cereales_allemagne = df_data_cereales_2017.loc[df_data_cereales_2017['Zone'] == 'Allemagne', ['Élément', 'Valeur']]\n",
    "df_data_2017_cereales_allemagne.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9f6499d8-d324-45c5-a298-b7004f3485bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>119</th>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>7400378</td>\n",
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       "                                            Élément    Valeur\n",
       "118                           Aliments pour animaux  20061535\n",
       "119  Disponibilité alimentaire en quantité (tonnes)   7400378"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_data_2017_cereales_france = df_data_cereales_2017.loc[df_data_cereales_2017['Zone'] == 'France', ['Élément', 'Valeur']]\n",
    "df_data_2017_cereales_france.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "5b9fc659-2f2b-4313-a316-11f415ddda44",
   "metadata": {},
   "outputs": [
    {
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       "      <td>254507</td>\n",
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       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>1115182</td>\n",
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       "                                            Élément   Valeur\n",
       "140                           Aliments pour animaux   254507\n",
       "141  Disponibilité alimentaire en quantité (tonnes)  1115182"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data_2017_cereales_haiti = df_data_cereales_2017.loc[df_data_cereales_2017['Zone'] == 'Haïti' , ['Élément', 'Valeur']]\n",
    "df_data_2017_cereales_haiti.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8a34e263-8d0a-4681-9ce1-d2bbe112ac2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>186</th>\n",
       "      <td>Aliments pour animaux</td>\n",
       "      <td>27319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Disponibilité alimentaire en quantité (tonnes)</td>\n",
       "      <td>544571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Élément  Valeur\n",
       "186                           Aliments pour animaux   27319\n",
       "187  Disponibilité alimentaire en quantité (tonnes)  544571"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data_2017_cereales_liberia = df_data_cereales_2017.loc[df_data_cereales_2017['Zone'] == 'Libéria' , ['Élément', 'Valeur']]\n",
    "df_data_2017_cereales_liberia.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c360993-c02b-4163-972f-09183673d25f",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Répartition des céréales entre l'alimentation pour les humains et celle pour les animaux. 9.2.2 <a class=\"anchor\" id=\"section_9_2_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a7672007-2cc2-4704-bf45-f8d668b9fc34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ipFMn8UP/889iOD7tBZH2h+a333K2pdUnPV38GGT9IdPeFdRO271b3AnbuVN3nteu6Z9nUdx1yg9nZ3FXTZJ0lx0RIdYvtx/fgixHu22yyj5Nu7ylS3MfRUd7geziItqZf/+9uPDes0e0y46IEMMh6uPkJC4k8+r86+Ii7mLOmaP/fe2Fmpa+/ebiIu4+59aWX7uu+T1Gss9DoRD9UPT1c8k6rXFj8cwRSRKjsvj7i7beVlZiuxmK0Zj9UZL0be+NG4GuXUVtQ1bZO8oaS3vn39Cx4uQkEp0hQ0THfn0M9fXRMnRuaL9LHB1Fv43snj4V/2r3lfYZINkHMsgteSnq75yNG0Wfni1bdOedPZ7i4OIi9sfp0/oTYe220W6rFStyv0lkzHffgQOiT0SXLqLjur5lNm4s+vZkp53WsGHeyzFk/XrxXc0O0ESUCyYNhbVggegkOX26+NI3MRHNRszMRKfU3Jo/ZLdpk6iW1tq8Wfyr7USn/dHMevEmSWIYvvww5m5+1rKAceW7dBF3BnfvzuxwCWTeMcveybWgOnUS2/z6dd0mStrtpdW2rRgZ5vZt/Z2kc1O1qih/7FjO0XKysrISTc1+/VUkBLldGPj6igsCT0/9z/Ewhq+v6Mzu7Gz4wVKFOUZ8fUXH0SdPRHMWYygUYh98951IHK5cyb1sQfeHHBSKnInT1auiiVNBHuxVv76o9VmzRnSq1XdBaGcnmvZcvSq2aW41Nnk5ckTUbGgTFbVanJd162bWOHTpIgYiCA/XTdTWrxe1bC1aiL+1I6vduKH7/JY9ewoWW34pFKI5V9aE4enTknlAn6+vGIwgNFR8r+emfXsxYMGdO2LErYI4eFAsw9tbJPy5NWF7803xG3H5MvDKK2Jaerr47WjbtvBJ99q1YgQ5PpuBiHLBpKGwHB3FqC5ffCEuXAcOFD+2s2eLETnu38/s+xAeLpqK2NiIkVm0LCyAb78V7YmbN88cPalXr8wH9nTrJsq9955YlkolRuiIjs5fvNq7xNu2iTuXSqWYlltZQDR18fMTFzJ16+r2RdAaNEjcmfXzE6PCNG4s7tJ9841ok921a/7izM3YseLHzcdHbCPt6ElBQbrlbG3FXW0/P1F936+fqHKPjBQJR2Sk2H6xsSIR6d9fjBhiZycuDg8dMnyxAIgRbtq1E0/5njRJtPcPDxcXVatXi3nNni0u5Nq0ET/4deuKfRccLJKJVavybuI0dmzm6D3jxomaC41G1Ir88YcYtrNly8IdI23biiEyhwwBLl0Sy7KxERdNp0+L/Tl6tLhgW7FCNMeoWVMkJTt3ilFu9I3skt/9URr4+orRf2bPFvs3KEj8v6BP0FYoxLnxxhtiONIxY8TF2aNH4tjQDnX6ww/iIrRDB1HbUL26qN34919xkZ+9X5A+Tk6iL9O0aWLoz6VLxeezPtl35kxxoertLZrjVaggYjh8WHwPac/vVq3EMT1unLi7X6GCOA7PnSvYdsgvX19xTH7yibhgfvRI7Ad3d/3NxIpSx45iRKRBg0QNaocOYnuGhorauJdfFrHZ24v9NnSoqIHp21ckbBER4riOjhYjVOXm5EnxPePhIb5Dsg/J3bBh5v4YPlycI337iuNT+3C3e/dy3hgIDhbnMSC2lSRlHgM1amQmHVpnzojjfPr0wg3XSkRlm9w9sV8Y2tGTso9gI0mSlJwsRj2pXVuMmqG1e7ckdeokSfb2YsSNatXEyCVHj2aW8fMTo3bcuCFJ3t5ilBQnJ0kaPVp3JBBJEiNbNG0qRj7x8JCkzz+XpIMHdUcNkiQxykvDhvrXIzhYjJJiZyc+px1BR9/oSZIkSZMnS5K7uxi5I+tyso+eJEmS9Py5GOmlcmUxelS1auLzKpVuOUD/KCDVqontkZfbt8VoSUql2FbDhknS77/n3A6SJEZz8fER5czNxXbz8ckc2UalEjE3aSL2k5WVJNWtK0bwSUw0Lpa33pIkZ2dJsrAQx8HgwbrrHBkpSZ9+Kkk1aogYnJwk6ZVXxEgu2n2s3f65jVqSkCBJ06aJ2CwsxIgtjRtL0rhxYuQpLWOPkeyjJ2mtXStJLVuKY9LKSozmNGiQGDVJkiQpKEiS3ntPTLeyEnG0aCFJ/v55bytJynt/SFLm6EmRkbqf1Z6D+kbzykp7ThmiHQFo166c76lUkjR+vDjulUqxr/bskaQBA8R6a2lHT/ruO93Pa0fz+eor3elnzkhSz57iOFMqJalWLbF/srp3Txw/Hh5i+7i6ipGJ8hpVTLvMMWMkaelSSapZU3y+fn1J2rIlZ/nr1yXJ1zfzu8nLS4yylV1QkCR17SrKubqK+e/Zo3/0pKZNDceYlTGjJ2k0YhS56tVFjA0aSNKaNTk/m3Xds/PwEN8PWZcLSFJ0tG45fSNFaTRieosWkmRtLY73WrXE8XXliu7njx+XpF69JMnRUZyfVaqI7btjR97bQd9IX9pX1m0sSZIUGipJ778vzh+lUpJat9Y/Sp52ffS9sm4PrSFDxKh0wcGG4yWick0hSZIkd+JSrg0eLO4AJSTIHQkRvajS00VN4Jgxom8OERFREWM9JBERERERGcSkgYiIiIiIDGLzJCIiIiIiMog1DUREREREZBCTBiIiIiIiMohJAxERERERGcSkgYiIiIiIDGLSQEREREREBjFpICIiIiIig5g0EBERERGRQUwaiIiIiIjIICYNRERERERkEJMGIiIiIiIyiEkDEREREREZxKSBiIiIiIgMYtJAREREREQGMWkgIiIiIiKDmDQQEREREZFBTBqIiIiIiMggJg1ERERERGQQkwYiIiIiIjKISQMRERERERnEpIGIiIiIiAxi0kBERERERAYxaSAiIiIiIoOYNBARERERkUFMGoiIiIiIyCAmDUREREREZBCTBiIiIiIiMohJAxERERERGcSkgYiIiIiIDGLSQEREREREBjFpICIiIiIig5g0EBERERGRQUwaiIiIiIjIIDO5AyCi8iUpNR0xSWmITkpFbFIaopPSEJOcipikNMQkpSIpVQ21RhIvScIc662wUscDJmaAibn419QMsLQH7CoD9pUBO3fxr5Wj3KtHRERUJikkSZLkDoKIypbYpDQ8eJ6I4GeJePAsEcFZ/h+nSs/XvO5WnAizuMfGFTa3/i+RcM/8V/t/13qAS21AoSjAGhEREZVvrGkgokJ5lpCCq49icPVRNK49jsGd0DhEJ6XJE0xaEhB1T7z0UToA7s2AKq8CHq+Kf21cSjZGIiKiFxCTBiIymloj4dbTWFx5GI0rj2Jw9XE0Hkclyx2W8VSxwP3j4qVVoZpuEuHWBDBXyhcjERFRKcSkgYgMiklKxcl/InHsTgT+/DcSMXLVIhSXmIfidXOH+NvEHKjcBKjbG2j4JuDsKW98REREpQD7NBBRDkFhcQgIikDAnQhcfRwDtUa+r4l89WkoDhUbAg3eABr+D3CtK18cREREMmLSQEQAgOBnifjtcgh2X3uCkOjS0+RI9qQhK5e6mQlEpYZyR0NERFRimDQQlWPxqjTsvxGK3y6H4NLDaLnD0atUJQ1ZOdcGGrwONPifaM5ERERUhjFpICpnNBoJZ+49w2+XQ3D4VhhUaRq5QzKo1CYNWVVuCrQYBTTuB5hZyh0NERFRkWPSQFROqNLU+O1yCNacfoAHzxLlDsdoL0TSoGXtArziBzQfLp4PQUREVEYwaSAq42KSUrE+8CHWBwbjWUKq3OHk2wuVNGiZmIm+D+3GAW6N5Y6GiIio0Jg0EJVRj6OS8POp+9h+KQTJaWq5wymwFzJpyKpWN6D9eKBaG7kjISIiKjA+p4GojHn0PAnfHvkb+26EyjpUKv3n7hHxeqmVSB7q9JA7IiIionxjTQNRGRGTlIofjt3FxnMPkaou3Z2b8+OFr2nIrnp7oNd8DtlKREQvFCYNRC84VZoa/meDseL4XcSp0uUOp8iVuaQBABSmwKtDgU5TAGsnuaMhIiLKE5MGoheUJEnYeeUJFh/5B09iSs/D2IpamUwatKycgM5TgVeGAiYmckdDRESUKyYNRC+g649jMHX3X7j5JE7uUIpdmU4atCo1Fk2WqreVOxIiIiK9mDQQvUASUtKx6PDfWB8YjPLSx7lcJA1aDfsA3b8GHDzkjoSIiEgHR08iekEEBIVj6q6bCI1VyR0KFZdbO4F/DgHtxgNtP+XTpYmIqNRgTQNRKReblIZZe29h59Uncocii3JV05BVpcbAW+sAl9pyR0JERAT2vCMqxY4HRaDbdyfLbcJQroX/BazuCFzbLHckRERETBqISqN0tQbfHLiDob9cRER8itzhkFzSEoHdo4Gdo4CUBLmjISKicox9GohKmdDYZHy8+SouP4yWOxQqLW5sBZ5cAvqtAyo3kTsaIiIqh1jTQFSKHP87Ar2XnGLCQDk9vwv83BU4v1ruSKgE3bhxA9bW1li6dKncoRCVWjxPSgaTBqJSIF2twbyDQRjqfxHRSWlyh0OllToFOPgFsHUAkFy8iaW/vz8UCkXGS6lUws3NDZ06dcLcuXMRERGR4zMzZ86EQqEo1riKmre3N7y9vTP+Dg4OhkKhwKJFi/L8rHYbBQcHZ0wbPHgwqlevrlOuevXqGDx4cMbfT58+xcyZM3Ht2jWD84+Pj0e/fv3wySef4JNPPjFmdYyWfb0BQKFQYObMmUW6nKJ2+/ZtzJw5U2eb55e+/UYl48CBA7keYyV9nty7dw+WlpYIDAzMmLZ582Z8//33Rs/jRZOWlgZPT88CryObJxHJ7FlCCj7ceAUXgqPkDoVeFEH7gNDrQN+fgaqtinVR69atQ7169ZCWloaIiAicPn0a8+fPx6JFi7Bt2zZ07do1o+zw4cPRs2fPYo2nqK1YsaLAn/Xx8UFgYCAqV65ssNyuXbtgb2+f8ffTp08xa9YsVK9eHV5eXrl+btiwYWjRogXmzZtX4BjzIzAwEFWqVCmRZRXU7du3MWvWLHh7e+dIzoxl7H6jonfgwAEsX75cb+JQ0ufJhAkT0K1bN7Ru3Tpj2ubNm3Hz5k2MHTs2X/N6UZibm2P69OkYN24c3n//fTg7O+fr80waiGR0LzIBg9ddwOOoZLlDoRdN7GPA3xd4cxXQuF+xLaZRo0Z49dVXM/7u27cvxo0bh3bt2qFPnz74999/UalSJQBAlSpVSv1FZ3YNGjQo8GddXV3h6uqaZ7mXX365QPPfvn17gT5XUK1aFW8CWloYu9+SkpJgbW1dAhERULLnyZ07d7B7924cOnSoQMt8kb333nsYP348Vq9ejSlTpuTrs2yeRCSTCw+i0HflWSYMVHCaNGDH8BLv51C1alV8++23iI+Px+rVmcvW1zwpICAA3t7ecHZ2hpWVFapWrYq+ffsiKSkJQGZzoAULFmDOnDmoWrUqlEolXn31VRw7dizHsk+fPo0uXbrAzs4O1tbWaNOmDfbv369TRtv85Pjx4xg9ejRcXFzg7OyMPn364OnTpzpl9TXTAQCNRpNnPMY2c8na7OLEiRNo3rw5AGDIkCEZzb+y3nm9dOkSXn/9dTg5OUGpVOLll182+sJo1qxZaNmyJZycnGBvb49mzZphzZo1MOaRTNnj0K5fQEAARowYAWdnZ9jb22PQoEFITExEWFgY3n77bVSoUAGVK1fGhAkTkJam27wyNTUVX3/9NerVqwdLS0u4urpiyJAhiIyMzLGNfH19cejQITRr1gxWVlaoV68e1q5dqxPPW2+9BQDo1KlTxrbz9/cHABw5cgRvvPEGqlSpAqVSiVq1amHUqFF49uyZzrL07Tdvb280atQIf/75J9q0aQNra2sMHTo04/1t27ahdevWsLGxga2tLXr06IGrV6/muU0BICwsDKNGjUKVKlVgYWGBGjVqYNasWUhPT88ok7VZ3OLFi1GjRg3Y2tqidevWOHfunFHLOXfuHNq2bQulUgl3d3dMnjwZP/30U451za0ZWvbmQZGRkfjwww/RoEED2NraomLFiujcuTNOnTql8zljYx88eDCWL1+eEYP2pY2tJM+TlStXws3NDd26dcuY5u3tjf379+Phw4c68WlFRUXhww8/hIeHBywsLFCzZk1MnToVKSm6IxwqFAp8/PHH2MaLj8YAACAASURBVLBhA+rXrw9ra2s0bdoU+/bt0ymn/b68desW3nvvPTg4OKBSpUoYOnQoYmNjdcpKkoQVK1bAy8sLVlZWcHR0RL9+/XD//n2dclevXoWvry8qVqwIS0tLuLu7w8fHByEhIRllLCws8M477+DHH3806nshKyYNRDLYe/0pBq45jxj2X6BCk0Q/h4CvS3SpvXv3hqmpKf78889cywQHB8PHxwcWFhZYu3YtDh06hHnz5sHGxgapqak6ZZctW4ZDhw7h+++/x8aNG2FiYoJevXrptDc+efIkOnfujNjYWKxZswZbtmyBnZ0dXnvtNWzbti3H8ocPHw5zc3Ns3rwZCxYswIkTJzBw4ECj1s+YeAqiWbNmWLduHQBg2rRpCAwMRGBgIIYPHw4AOH78ONq2bYuYmBisWrUKv//+O7y8vPDOO+9kXBwbEhwcjFGjRmH79u3YuXMn+vTpg08++QRfffVVgWMePnw4HBwcsHXrVkybNg2bN2/GiBEj4OPjg6ZNm+K3336Dn58fvv32W52OqBqNBm+88QbmzZuH/v37Y//+/Zg3bx6OHDkCb29vJCfr3jC5fv06PvvsM4wbNw6///47mjRpgmHDhmUcYz4+Pvjmm28AAMuXL8/Ydj4+PgBEG/XWrVtj5cqV+OOPPzB9+nScP38e7dq1y5HM6BMaGoqBAweif//+OHDgAD788EMAwDfffIP33nsPDRo0wPbt27FhwwbEx8ejffv2uH37tsF5hoWFoUWLFjh8+DCmT5+OgwcPYtiwYZg7dy5GjBiRo/zy5ctx5MgRfP/999i0aRMSExPRu3fvHBeR2d2+fRtdunRBTEwM/P39sWrVKly9ehVff13w74WoKNFkdsaMGdi/fz/WrVuHmjVrwtvbGydOnMh37F9++SX69RO1otp9l1szseI+T/bv348OHTrAxCTzMnjFihVo27Yt3NzcdOIDAJVKhU6dOmH9+vUYP3489u/fj4EDB2LBggXo06eP3vkvW7YMs2fPxo4dO+Dk5IQ333wzx0U+IGpv69Spgx07dmDSpEnYvHkzxo0bp1Nm1KhRGDt2LLp27Yrdu3djxYoVuHXrFtq0aYPw8HAAQGJiIrp164bw8HCdfVG1alXEx8frzM/b2xsPHz7EzZs389xWWbF5ElEJW3XyHuYfCgKfxU5F6s+FQOIzwGcxYFL894NsbGzg4uKS4859VpcvX4ZKpcLChQvRtGnTjOn9+/fPUVatVuPIkSNQKpUAgB49eqB69eqYPn06jhw5AgCYNGkSHB0dceLECdja2gIAfH194eXlhQkTJuDtt9/WuTPYs2dP/PDDDxl/R0VF4YsvvkBYWBjc3NwMrp8x8RSEvb09GjVqBADw9PTM0SToww8/RMOGDREQEAAzM7OMZT979gxTpkzBoEGDdC50stNeaAHiot3b2xuSJGHJkiX48ssvC9RR3dfXN6NjeLdu3RAYGIgtW7Zg8eLFGRc3Xbt2xeHDh7Fp0yaMHz8egGg2cujQIezYsUPnwqpp06Zo3rw5/P39MXr06Izpz549w5kzZ1C1alUAQIcOHXDs2DFs3rwZHTp0gKurK2rXFk9Ib9CgQY5t98EHH2T8X5IktGnTBt7e3qhWrRoOHjyI119/3eB6RkVF4ddff0Xnzp0zpj1+/BgzZszAxx9/rHMsdevWDbVr18asWbP0JqxaM2fORHR0NG7dupWxXl26dIGVlRUmTJiAzz//XKeJnJ2dHfbt2wdTU1MAgLu7O1q0aIGDBw/i3XffzXU5s2fPhiRJCAgIyGgu6OPjk3GsFUTdunV1+vyo1Wr06NEDwcHB+OGHH3LU0OUVu6enZ0ZseTWFK87zJCIiAvfv38fIkSN1pjdo0AAVKlSApaVljuX98ssvuHHjBrZv355R29WtWzfY2tpi4sSJOHLkiE6tRXJyMo4ePQo7OzsAIglyd3fH9u3bMWnSJJ15Dxs2DJ9//jkAcR7dvXsXa9euxZo1a6BQKHDu3Dn89NNP+PbbbzPOLQBo37496tSpg8WLF2P+/PkICgrC8+fPsWbNGrzxxhsZ5d5+++0c26BZs2YAgDNnzqBx48Z6t5M+rGkgKiEajYRpu//CvINMGKiYXF4H/OoHpJfMAwHzqtr28vKChYUFRo4ciV9++UXvXTatPn36ZFygA8ioQfjzzz+hVquRmJiI8+fPo1+/fhkJAwCYmpri/fffR0hICP7++2+deWa/SGzSRDzj4uHDh3muW17xFIe7d+8iKCgIAwYMAACkp6dnvHr37o3Q0NAc65hdQEAAunbtCgcHB5iammZ0fHz+/LneEa+M4evrq/N3/fr1ASDjDn/W6Vm37b59+1ChQgW89tprOuvi5eUFNze3HHervby8Mi6sAUCpVKJOnTpG7S9AXAx+8MEHeOmll2BmZgZzc3NUq1YNgGjDnhdHR0edhAEADh8+jPT0dAwaNEhnHZRKJTp27Kj3jntW+/btQ6dOneDu7q7z+V69egEQtWdZ+fj4ZFx0A8Yfs8ePH0eXLl0yLsoBcW688847ea63IatWrUKzZs2gVCoztumxY8f0bs+Cxp5fhT1PtDc6KlasaPQyAwICYGNjk1FToqVtTpW96WKnTp0yEgYAqFSpEipWrKh3W+j7nlKpVBnn6759+6BQKDBw4ECddXVzc0PTpk0zjsFatWrB0dEREydOxKpVqwzWgmnX/cmTJ8ZtgP8waSAqARqNhM9+vY6N5x7JHQqVdXf2AJv6ASnxeZcthMTERDx//hzu7u65lvH09MTRo0dRsWJFfPTRR/D09ISnpyeWLFmSo6y+O/9ubm5ITU1FQkICoqOjIUmS3qYM2hieP3+uMz37yCCWlpYAkKNZjD55xVMctM0MJkyYAHNzc52XtqlM9vb5WV24cAHdu3cHAPz00084c+YMLl68iKlTpwIwbr31cXJy0vnbwsIi1+kqlUpnfWJiYmBhYZFjfcLCwnKsi76RXCwtLY2KW6PRoHv37ti5cye++OILHDt2DBcuXMhoU2/MPPQdW9p90rx58xzrsG3bNoP7Q/v5vXv35vhsw4YNAeTcnwU9Zp8/f57rMVtQixcvxujRo9GyZUvs2LED586dw8WLF9GzZ0+98RTmfMuPwp4n2niy3hTIi3b7Zq+pq1ixIszMzPL87gFyP5bz2m7h4eGQJAmVKlXKsb7nzp3LWFcHBwecPHkSXl5emDJlCho2bAh3d3fMmDEjR/M87brnd9+weRJRMdNoJEz47Tp2Xc1fRk9UYA/+BPx9gAE7ANu8R4kpiP3790OtVuvtRJxV+/bt0b59e6jValy6dAlLly7F2LFjUalSJZ3mFmFhYTk+GxYWBgsLC9ja2sLMzAwmJiYIDQ3NUU5759DFxaVwK5Vt2YbiKQ7a+CdPnqy3nTQgmozkZuvWrTA3N8e+fft0Loh2795dtIEaSdsBPbcRarLeiS2smzdv4vr16/D394efn1/G9Lt37xo9D31Nt7T75LfffsuotcgPFxcXNGnSBHPmzNH7vqGkOz+cnZ1zPWazs7S0zNF5F8iZdG/cuBHe3t5YuXKlzvTs7eNLWmHPE+3ntX02jOHs7Izz589DkiSd4yQiIgLp6elF+t2TnYuLCxQKBU6dOpWRUGSVdVrjxo2xdetWSJKEGzduwN/fH7Nnz4aVlZVOsyjtuuc3biYNRMVIo5Hw+W83sPMKEwYqYaHXgbU9gPd3AY75v9gx5NGjR5gwYQIcHBwwatQooz5jamqKli1bol69eti0aROuXLmikzTs3LkTCxcuzLjYjY+Px969e9G+fXuYmprCxsYGLVu2xM6dO7Fo0SJYWVkBEHeYN27ciCpVqqBOnTpFto55xVMYud2BrVu3LmrXro3r169ndPjND4VCATMzM534kpOTsWHDhkLFW1C+vr7YunUr1Go1WrZsWSTzzG3baS/ksl9UZR3dqyB69OgBMzMz3Lt3D3379s335319fXHgwAF4enrC0dGxULEY0qlTJ+zZswfh4eEZTZTUarXe/hbVq1fHjRs3dKYFBATkqEFTKBQ5tueNGzcQGBiIl156qUBxZt1/2nPYmLJZFfY8qVatGqysrHDv3j29y9R3971Lly7Yvn07du/ejTfffDNj+vr16zPeLy6+vr6YN28enjx5ord/gj4KhQJNmzbFd999B39/f1y5ckXnfW1T0fwOOc2kgaiYaDQSJu64gR1XQvIuTFQcou6JZzkMOwzYF+yO5s2bNzPa0EZERODUqVNYt24dTE1NsWvXLoPj3a9atQoBAQHw8fFB1apVoVKpMobQzPpQOEAkFd26dcP48eOh0Wgwf/58xMXFYdasWRll5s6di27duqFTp06YMGECLCwssGLFCty8eRNbtmwp0qdRGxNPQXl6esLKygqbNm1C/fr1YWtrC3d3d7i7u2P16tXo1asXevTogcGDB8PDwwNRUVG4c+cOrly5gl9//TXX+fr4+GDx4sXo378/Ro4ciefPn2PRokV6706WhHfffRebNm1C7969MWbMGLRo0QLm5uYICQnB8ePH8cYbb+hcgBlD2zn2xx9/hJ2dHZRKJWrUqIF69erB09MTkyZNgiRJcHJywt69ewvVaR0QF9izZ8/G1KlTcf/+ffTs2ROOjo4IDw/HhQsXYGNjY/CYmD17No4cOYI2bdrg008/Rd26daFSqRAcHIwDBw5g1apVRfJsk2nTpmHPnj3o3Lkzpk+fDmtrayxfvhyJiYk5yr7//vv48ssvMX36dHTs2BG3b9/GsmXL4ODgoFPO19cXX331FWbMmIGOHTvi77//xuzZs1GjRg2d4WLzQ9vpdv78+ejVqxdMTU3RpEmTjCZvWRXXeWJhYZHrULaNGzfGzp07sXLlSrzyyiswMTHBq6++ikGDBmH58uXw8/NDcHAwGjdujNOnT+Obb75B7969c3yfFaW2bdti5MiRGDJkCC5duoQOHTrAxsYGoaGhOH36NBo3bozRo0dj3759WLFiBf73v/+hZs2akCQJO3fuRExMjE4nbUAMz2tqaooOHTrkKxYmDUTFQJIkTNp5A79eZsJAMot9BGzoAww5AFg75V0+myFDhgAQP7QVKlRA/fr1MXHiRAwfPjzPB2R5eXnhjz/+wIwZMxAWFgZbW1s0atQIe/bsyWh7r/Xxxx9DpVLh008/RUREBBo2bIj9+/ejbdu2GWU6duyIgIAAzJgxA4MHD4ZGo0HTpk2xZ8+eHJ11C8uYeArK2toaa9euxaxZs9C9e3ekpaVhxowZmDlzJjp16oQLFy5gzpw5GDt2LKKjo+Hs7IwGDRrkeZexc+fOWLt2LebPn4/XXnsNHh4eGDFiBCpWrIhhw4YVOu78MjU1xZ49e7BkyRJs2LABc+fOhZmZGapUqYKOHTvma9QWrRo1auD777/HkiVL4O3tDbVajXXr1mHw4MHYu3cvxowZg1GjRsHMzAxdu3bF0aNHdTpXF8TkyZPRoEEDLFmyBFu2bEFKSgrc3NzQvHlznRGb9KlcuTIuXbqEr776CgsXLkRISAjs7OxQo0aNjASkKDRq1AhHjx7FZ599Bj8/Pzg6OuL9999H3759c4wS9PnnnyMuLg7+/v5YtGgRWrRoge3bt+uMuAMAU6dORVJSEtasWYMFCxagQYMGWLVqFXbt2pVnB/Dc9O/fH2fOnMGKFSsyRnx68OCB3qd7F9d5AgADBgzAyJEjERoaqtOXZcyYMbh16xamTJmC2NhYSJIESZKgVCpx/PhxTJ06FQsXLkRkZCQ8PDwwYcIEzJgxo0DbIj9Wr16NVq1aYfXq1VixYgU0Gg3c3d3Rtm1btGjRAgBQu3ZtVKhQAQsWLMDTp09hYWGBunXr5miyB4gmi71790aFChXyFYdCyu+THYgoT5N3/oUtF9jpuSjcrTgRZnGP5Q7jxVelBTDod8CidD3hNjg4GDVq1MDChQsxYcIEucMhKlP8/f0xZMiQXC/MyyuVSoWqVavis88+w8SJE+UOp0Tdu3cPtWvXxuHDh3PUQOSFoycRFbFlAf8yYaDSJ+QCsH0QoOYDBYmofFMqlZg1axYWL16stwlXWfb111+jS5cu+U4YACYNREVqz/Wn+PbIP3KHQaTf3SPA7x/JHQURkexGjhyJsWPHGnx+TFmTnp4OT09PLF++vECfZ/MkoiJyKTgK/X8+j9R0jdyhlClsnlQMvCcD3pPyLkdERPQf1jQQFYHgZ4kYueEyEwZ6MZyYC9zYLncURET0AmHSQFRIMUmpGOp/EVGJqXKHQmS83z8GHgbKHQUREb0gmDQQFUKaWoORGy7j/rPy1ZGKygB1CrBtABBVftrzEhFRwTFpICqEBYeCcOGB8Y+iJypVkp6LEZXSWUtGRESGMWkgKqCAoHD8fPqB3GEQFU7YX8Cxwj/lmIiIyjYmDUQFEBarwmfbr4Njj1GZELgcuH9C7iiIiKgUY9JAlE9qjYRPt1xFdBIfkkVlhQTs+gBIYlM7IiLSj0kDUT59d+QfXAjmxRWVMfGhwN5P5Y6CiIhKKSYNRPlw+t9nWHHirtxhEBWPO3uBy7/IHQUREZVCTBqIjBSnSsP47degYT8GKssOTQae35M7CiIiKmWYNBAZae6BIETEp8gdBlHxSksEdgwD1OyzQ0REmZg0EBnh/P3n2HrxkdxhEJWMp1eB49/IHQUREZUiTBqI8pCSrsbkXX9xeFUqX858Dzw8K3cURERUSjBpIMrD8oC7uB+ZKHcYRCVL0gAHvgA0GrkjISKiUoBJA5EBf4fFY+VJdgqlcir8L+DaRrmjICKiUoBJA1EuNBoJk3beQJqa7ZKoHAuYA6QkyB0FERHJjEkDUS52Xn2Cq49i5A6DSF4JYaJ/AxERlWtMGoj0SElX47sj/8gdBlHpcHYZEPtE7iiIiEhGTBqI9Fh/9iGexCTLHQZR6ZCeDBybJXcUREQkIyYNRNnEJqdh+Ym7codBVLrc2A48uSJ3FEREJBMmDUTZrDxxDzFJfBoukS4JODxF7iCIiEgmZnIHQFSahMYmw//sA7nDIBkEPEjHxhtpOPtYjcdxGlRQKvCquymmd7DEK+6mOmXT1BKWXkjFumtpuBulgaUp0MDVFIu6W6LNS4a/VlPVEr7+MwUbbqThSZyEynYK9G9kjukdLWFlrsgoF50s4cMDyTh0Nx2OSgUmtbPEyFcsdOZ1PiQd3r8k4cpIG9R3Nc2+qKL3KBC4tRto+L/iXxYREZUqTBqIsvjuyD9QpfFhVuXRykupeJ4kYUxLCzRwNUFkkoRvA1PRak0iDg+0Ruca4utSrZHw5rZknH6Uji/aWqLNS6ZITJVwOVSDxNS8l/PejmQc+Dcd0ztYormHKQIfq/H1qRTcitRgz3vWGeU++0OFq6EabHzTCv8812D0fhXqu5igfTURR7pGwsh9KnzRxqJkEgatozOAur0BM4u8yxIRUZnBpIHoP4+eJ2HHFY4QU14t761ERRvdFps9a5mh1g8J+OZUSkbSsPRCKg7eTceZodZoVSXzK9SnTt7LOBeSjp130vFtd0uMb20JAOha0wxmJsCUgBQcuZeObp5invv/Tcf3PZTwqWMOHwAH76Zj/7/pGUnDorOpSEkHprS3LIK1z4foYOD6FuAVv5JdLhERyYp9Goj+8+Ope1Br+CC38ip7wgAAthYKNHA1weO4zONiyflUdKhmqpMwGOvMIzUAoHdt3c/61hF/77iT2ZdGlS7BJsvNfFsLBVTp4v/3ozX46s8UrPZVwtJMgRJ3fnXJL5OIiGTFpIEIwLOEFPx6KUTuMKiUiVVJuBKqRkNX8VX5OFaD4BgJjSuaYsoxFSotiofZ7Dg0XJGAX67l3TYpVeQMsDTVvdC3/C+HuBGe2TSuzUumWHYhFRGJGpx5lI7D99LR5iXRDGn0/mS829AcHavLVFkccQt48Kc8yyYiIlmweRIRAP8zwUhJZ18G0vXRARUS04Cp/zUBehIvjpFfrqeiir0JlvVSwkGpwE9XUjH4dxVS1cCIV3Jv69/gv+TjzON01HDMLHf6vxqI58mZNRrf91DitS3JqLQoAQAw1MscbzUww8YbqbgWpsGWvtaQ1fnVQI0O8sZAREQlhkkDlXuqNDU2nX8odxhUynwZoMKmv9KwtJcyY/Qkbes1VTpwoL81qlUQSUC3mqZ49adEzP4zxWDS0Ku2GWo5mWDi0RRUsjFBcw9TnAtRY8qxFJgqAJMsFRB1XUwR9LEN7kdLqKAEXKxNEJUsYfzhFHzfUwknKwVWXEzFt4EpiFUBPWqZYlkvKzhalVBzpb8PANEPAcdqJbM8IiKSFZsnUbm36+oTRPO5DJTFrBMp+PpUKuZ0tsTHLTKTAOf/LsjruZhkJAwAoFAo0MPTDCFxEiISc6+xsjBV4OAAa1R1MEH3jUlwnB+PftuTMKW9BRytFPCw073gN1EoUMvJBC7WYlkT/lDh5com6N/YHMfup2PiURW29bPG3U9tEZkoYexhVVFuBsMkDXDhx5JbHhERyYo1DVTurTvD5zJQplknUjDzZApmdrTMMTKRp5MJrM31f076rxbCJI8b/bWcTBA4zAZP4jSISpbg6WSCWJWEMYdS0KFa7l/JJ4LTse1WGv4abQtAjKbU3dMMr/5XC/JxCwsM21OCSQMAXN0AdJoCWNiU7HKJiKjEsaaByrWzd5/hn/AEucOgUuKrkyJhmNbeAjO8cw5lamaiwBt1zXAnUoPgmMwaBUmScOheOjwdFRm1AnnxsDdB40qmsDZXYOHZVNiYA8Ne1p+RpKRLGLVPhRkdLVHT0eS/ZQKJqZl9IBJSMxOXEqOKBa5vLeGFEhGRHFjTQOXar5c5YhIJ355NwfQTKehZyxQ+dcxwLiRd533tEKtfdVLi4N0E9NyYhJnelrC3BH6+kobrYRpsf8tK5zNms+PQsbopjg3KvBO/4EwK3GwVqOpggvAECdtvp2F3UDo2vGkFD3v9CcecUylQmgHjW2c2lepRywxLzqfih/MpqOVkgtknU9Czlgxf6edXA82HlfxyiYioRDFpoHIrMSUdh2+FyR0GlRJ7/xFJwqG7ahy6m5TjfWmGPQDRROnUEBtMOpqCkXuTkaYBvNxMsec9K/jW0a0pUEuAOlsXB1U6MPtkCkLiJFiZA62qmOKEn3XGQ9uyuxOpxsKzqTjhZw2zLG2funuaYWE3S3wbmIoYlYTunmb4vmcJP+gNAJ79DdwLADw7l/yyiYioxCgkqcQrtIlKhd8uh2DCr9flDoPycLfiRJjFPZY7DDKkTk+g/za5oyAiomLEPg1Ubu26yqZJREXi3z+AhAi5oyAiomLEpIHKpdDYZATeey53GERlg6QB7uyROwoiIipGTBqoXNp19UnGg7qIqAjc2i13BEREVIyYNFC5tOvKE7lDICpbHp4FEp/JHQURERUTJg1U7vwTHo9/I/hsBqIiJanZRImIqAxj0kDlzvEgdtgkKha3mTQQEZVVTBqo3Dn+N5MGomIRfBpIiZc7CiIiKgZMGqhcSUhJx+WH0XKHQVQ2adKAe8fljoKIiIoBkwYqV07/G4k0NYdNIio2//4hdwRERFQMmDRQuXLi70i5QyAq2+4elTsCIiIqBkwaqFxh0kBUzOJDgdDrckdBRERFjEkDlRtBYXEIi1PJHQZR2XcvQO4IiIioiDFpoHLj/P0ouUMgKh+eXJE7AiIiKmJMGqjcuP44Ru4QiMoHNk8iIipzmDRQuXEthEkDUYmIeQgksWaPiKgsYdJA5UKcKg0PniXKHQZR+cHaBiKiMoVJA5ULNx7HQuLjGYhKDpMGIqIyhUkDlQvX2TSJqGSFXpM7AiIiKkJMGqhcuMZO0EQlizUNRERlCpMGMkpaWhosLS3x119/yR1KgfwVEit3CETlS9QDQMXzjoiorGDSQEYxNzeHh4cHNBqN3KHkW7wqjQ91IypxEmsbiIjKECYNZLQpU6Zg2rRpiI19se4ePnyeJHcIROUTkwYiojLDTO4A6MWxevVqBAUFoXLlyqhRowZsbGx03r9w4YJMkRn2KIpJA5EsnrIzNBFRWcGkgYzWs2dP9OzZU+4w8o01DUQyefaP3BEQEVERYdJARvvqq6/kDqFAHkXxoW5EskiMlDsCIiIqIuzTQGVe8DPWNBDJIvEZ+FRFIqKygTUNZDRzc3MoFIpc309NTS3BaIzHPg1EMtGkAcnRgLWT3JEQEVEhMWkgo23dulXn77S0NFy9ehUbN27EjBkzZIrKsNR0DUJjk+UOg6j8Soxk0kBEVAYwaSCj9e3bN8e0d999F40bN8aOHTswcuRIGaIyLDopFRq2jiCST0IE4FpX7iiIiKiQ2KeBCq1169Y4cuSI3GHoFZecJncIROVbYoTcERARURFg0kCFkpqaihUrVsDd3V3uUPSKZdJAJK8EjqBERFQWsHkSGc3V1VWnI7QkSYiJiYFSqcT69etljCx3cSomDUSyYk0DEVGZwKSBjDZv3jydv01MTODq6opWrVrBxcVFpqgMY00Dkcz4rAYiojKBSQMZbdiwYXKHkG+xSUwaiGTF5klERGUC+zRQvgQGBmLw4MHo0KEDnj59CgDYtGkTzp49K3Nk+sWp0uUOgah8Y/MkIqIygUkDGW3Xrl3o3LkzFAoFLly4AJVKBQCIjo7GnDlzZI5OP46eRCSzNJXcERARURFg0kBG++qrr7By5UqsW7cO5ubmGdPbtm2Ly5cvyxhZ7tL5kAYieZmYyh0BEREVASYNZLSgoCB06tQpx3QHBwfExMTIEFHesgz2RERyMGHXOSKisoBJAxnNzc0N9+7dyzH9zJkzqFmzpgwR5U0BZg1EsmLSQERUJvDbnIw2YsQIjBkzBv7+/lAoFAgPD8fFixfx+eefY/LkyXKHp5cJc4YX3tsp0/CycwzqWEajmulzuEkRcEwLh03yE5gmhEKhYWf3Us3UIUsxLAAAIABJREFUPO8yRERU6jFpIKNNmjQJMTExaNeuHVJSUtC2bVtYWFhg3LhxGDNmjNzh6WXCrOGFdyXWDldi7QC8lOM9cxMJjWwT0Ng2HnWVUahm+hyVpUg4pYfDNvkpzBKeQKFOLfmgKRP7NBARlQkKSZLYU5TyJSEhATdv3oRGo0GjRo1gb28vd0i5mnvwDlafvC93GCQThUJCfZskNLGLQz1lDKqbPYc7IuGcHg675KcwT3gCRXqy3GGWbZ6dgfd3yR0FEREVEmsaKN9sbW3RqlUrucMwCvs0lG+SpMDtBBvcTrABUFlvmVrWyWhqF4d6VtGoaR4ND0TAWR0BO9VTWCY8gSI1oWSDLmvYp4GIqEzgtzkZ9Pbbbxtddvv27cUYScGYsqs/5eFukhXuJlkBqKT3/ZeUKnjZx6O+VQw8zaPgoYiEqzoC9imhsEx8AhNV6Rw5rNRg0kBEVCbw25wMsrS0lDuEQrG24CFOhfNYpcRjlRJ74ar3fTfLVHjZx6OBVQw8LaJQxeQZKqoj4JASBmXiE5gkPyvhiEsZJg1ERGUCv83JoA0bNsgdQqE42VjIHQKVcWEpFjgU6YxDcAbgmeN9R/N0eNnFoaFNHGpZRKGqqUgqKqSGwSrpCUwSI6BAGe5axqSBiKhM4Lc5lWmO1kwaSF7RaWY4HuWE41FOAKrneN/GTA0vu3g0solDbYsoVP1vWNkKqWGwTn4K04QwKCR1icddZJg0EBGVCfw2p3zZvXs3tm/fjkePHiE1VXcoywsXLsgUVe5Y00ClXWK6Kc5EV8CZ6AoAquZ439JEgyZ2CWhsG4s6ljEZz6pwSguDTfLT/55VkVbygRvLylHuCIiIqAgwaSCjLVu2DJMmTcLAgQNx8eJFDBo0CHfv3sXVq1fxwQcfyB2eXk42fLAUvdhSNCa4GGuPi7H20PesClOFBo3sktDENhZ1LKNR3SwKlREJ5/+SCvOEp1CoU0o+cC17/aNWERHRi4VJAxlt2bJlWL16NQYMGIBNmzZh8uTJqFmzJqZOnYr4+Hi5w9OLzZOorFNLJrgeZ4vrcbYAPHK8r1BIqGvz37CyltGobh4Fd0TCJT0ctqpQWCQ8gSItsfgCtHMvvnkTEVGJYdJARnv06BHatWsHALCysspIFAYPHozWrVvjhx9+kDM8vSpYW8BEAWjKcD9TIkMkSYGgBGsEJVgDcNNbpqa1Ck3t4lDfKgY1zJ7DQ/EMrupw2KlCYZkYAkVKIW4KsKaBiKhMYNJARqtUqRKioqJQrVo1VKtWDRcuXEDTpk3x8OFDaDQaucPTy9REAQcrc0QnleI230Qyu5+kxP0kJXahot73PZQp8LKLR33rGHiaP0cVxTO4qiPgkBoKy4QnMFFF5z5z1jQQEZUJTBrIaJ07d8bevXvx8ssvY8iQIRg7dix27tyJ8+fP4/XXX5c7vFy5OVgxaSAqhCcqSzxRWWI/XADUyvG+q0UaXraPQwPrWHiaR+Elk2eoqImAQ2oYbFjTQERUJigkSWLDDTLo2rVr8PLyQnp6OtRqdcYD3zZv3ozTp0+jVq1a+Oijj0rtg+BGb7yMgzfD5A6DqNxxtDbH1end5Q6DiIiKAJMGypOJiQlefvllDB8+HP3794eDg4PcIeXL3IN3sPrkfbnDICp3mr5UAb9/1FbuMIiIqAiYyB0AlX5nzpxBs2bNMGnSJFSuXBkDBw7E8ePH5Q7LaDWcbeQOgahcqupkLXcIRERURJg0UJ5at26Nn376CWFhYVi5ciVCQkLQtWtXeHp6Ys6cOQgJCZE7RINquDBpIJJDNSYNRERlBpMGMpqVlRX8/Pxw4sQJ/PPPP3jvvfewevVq1KhRA71795Y7vFzVqWQndwhE5VIdN557RERlBZMGKhBPT09MmjQJU6dOhb29PQ4fPix3SLlytLGAsw0f8kZU0hq528sdAhERFREmDZRvJ0+ehJ+fH9zc3PDFF1+gT58+OHPmjNxhGVSroq3cIRCVK3aWZmwaSERUhvA5DWSUx48fw9/fH/7+/njw4AHatGmDpUuX4u2334aNTem/MGjk4YDzD6LkDoOo3Kjvbg+FQiF3GEREVESYNFCeunXrhuPHj8PV1RWDBg3C0KFDUbduXbnDypdmVR2xBg/kDoOo3Gjs8WINzUxERIYxaaA8WVlZYceOHfD19YWpqanc4RTIK9Uc5Q6BqFxp5MH+DEREZQmTBsrTnj175A6h0NwclPCoYIUnMclyh0JULrCmgYiobGFHaCo3mrG2gahEWFuYoqYLBx8gIipLmDRQufFK1Qpyh0BULrxa3QkmJuwETURUljBpoHLjlWpOcodAVC50qO0idwhERFTEmDRQuVG/sh2sLV7MjtxEL5L2tV3lDoGIiIoYkwYqN8xMTdC6prPcYRCVaZXsLVHXzU7uMIiIqIgxaaBypWuDSnKHQFSmta3FpklERGURkwYqV7rUqwg+pJao+HRg0yQiojKJSQOVKxXtlWjC8eOJioVCAbRjJ2giojKJSQOVO13rs4kSUXFoUNkeLraWcodBRETFgEkDlTtdmDQQFQufJpXlDoGIiIoJkwYqdxq428OjgpXcYRCVKQoF8HpTd7nDICKiYsKkgcqlbhxFiahIvVLVEVUcreUOg4iIigmTBiqX3nzZQ+4QiMqUN7xYy0BEVJaZyR0AkRyavlQB9dzsEBQWX+LLfrb/OyTePJbr+24DF8HSox7iLu1B4u2TSI8JhSY1CabWjrD0qAeHNu/CwrVanstJunsBSUGnkBp+H2lRIYBGjWoT9+Uop1YlIOqPFVDdvwwTpS3sW70FO6+eOmVSnv6N8C2TUdlvCcxdXsr/SlOZZmaigE+T/7d352FR13vfwN8zw+wjw44sIpsiiEgqiriA0TFZ1DQT5aZE1NL0dPlcmUvHRL3pKNjpMVMfPbdoeG5J0mPuS5Rii0uSHRfKLcMVNEAQ2Zd5/iCmRnAEBH4s79d1zaUz853f9/Mbwn7vme/C0EBE1JExNFCnNaG/I+IO/Nzq/WoDJqGLb0idx+//ezlEEilkdj0AANUlhVC69ofMxgVihQaV+dkoOL0T2f96G3ZTVkNq6Wi0n+IrJ1F29zJktm4QmUhRnn2t3nYPjm5C+b3rsBw9D5V5d5D3xXpILR2h6OYNANBVVyH38McwHfgyAwPVa2gPK1ioZUKXQURELYihgTqt8f0cEX/4EiqqdK3ar9TcDjA3XGWm9OYFVJc8hHZwBERiCQDAbNh/Gb7QqQ/k9r1wN3EWin5Kg9mwKKP9WIb8FSJRzQjEvNT/98TQUPJLOiyCZ0Dl5ge4+aHk+g8o+SVdHxoefr8LuqpKaAdPbMrpUifAoUlERB0f5zRQp2WhliG4V9uYEP3o/BcARFD7/MVoO7HK9Pe/SJ56zNrA8DS6ynKIpIo/XidTQFdZDgCoyM9GwYntsHxxNkQm0gYdjzqXLgoTjPTqKnQZRETUwhgaqFOb6Gd8iE9rqC4rQvHlE1B07wupWd2LL111FXSVFajIvYXcwx9DrDKDps8Lzda/3METhWf3o6ooH6W3f0Lpr2chd/AEAOQdWQdVr+FQOPVptv6oY4kY0A1qOb+0JiLq6PgvPXVqgT1tYGsqx72HZYLVUPTTcegqy6B5wrcMNz+cAFRVAABMLBzQdfIKmJhaN1v/FsEzcP/fy3F7bc1wJ3Wfv0DVaygeZRxD+f1fYT9mfrP1RR2LWARMCXAWugwiImoFDA3UqUnEIkQO7I7/++UVwWp4dD4VYqUpVD0D6n2+a9QqoKoSFflZKEzfg+zti2Ab8X6DVlBqCKmlI+xnbEBlfjbEcjUkKi2qSgrx4OgmWATPgETZBYVnD+Dhmc9RXVYMhctzsPjLLEgUmmbpn9qvFzxt0c2CezMQEXUGHJ5End6UgO5QyZ4+R6AllN//FeXZV6H2CnrinAF5V3fIHXpB03sEbCf9HdAB+V9vbdY6RCIxpOb2kKi0AIAHxxIhs3GF2isIJZn/wYPjn8BqzAI4vP5PVBc/xIOv/tms/VP7NHWIi9AlEBFRK2FooE7PTCVDhJ8wS4nWTIAGNH1HNqi9WK6C1NIRFXl3Wqym0pvnUXzpG1i8OLvm/vUfoHR+DnK7HhArNOjSLxwlv6S3WP/UPnjamWKwm6XQZRARUSthaCACMGOYK6QSUav2qausQFFGGmR2PSGzdm7Qa6qKC1DxW2bNsq0tVFPukXXQDpmsn5Stgw7VFaV/tKkoaZG+qX2ZyrkMRESdCuc0EAGwN1NiTF8H/Pvs7Vbrs/jqSVSXFkLjM6XOc9VlRbiXshhqzyCYWNhDZCJDZd4dPEzfC11VBbRDIg3a30gYA4WTd83wpd9VFtxHWVbNXI2KB9kAgKJL3wIATLS2kP++idyfFZxMgUgihanfOP1jSpd+KEzfi4fpeyE1t0P+d9uhdOn37G8AtVtWGhnGcG8GIqJOhaGB6Hezglyx68fb0LXSXm+PzqdCJFVA7Tm8znMiiQwyaxc8OncYlYU50FWWQ6I2h8KpD0zHvQuZlZPhC3TV0FVXGzxUevM8cg+uNngsZ89KAIDaOxjysP9j8FxFzi08/H4XbCev0G8wB9SEBvMRMTUToUuLoHR5DubBM57l1KmdmxXkDoVUmHlAREQkDJFO11qXSERt34yt6Uj96Z7QZRC1WXZaBdLeCYLchKGBiKgz4ZwGoj+ZPcJd6BKI2rS/Pt+DgYGIqBNiaCD6E99uZgjzaZlJxkTtnbOlChMHCL+LOhERtT6GBqLHLBzVCzIT/moQPW7uCz1hIuHvBhFRZ8R//Yke081ChWguJ0lkwMO2C8b05YpJRESdFUMDUT1mj3CHuar+HZqJOqO3R/aEWNy6e5kQEVHbwdBAVA+tUoq3guvuY0DUGQ3vaY2RvbsKXQYREQmIoYHoCaL8u8PFSi10GUSCkpuI8d9jewtdBhERCYyhgegJpBIx/hbqKXQZRIKaPcId3S0ZnomIOjuGBiIjXvCy5RKs1Gm5WqsxM9BN6DKIiKgNYGggeorlY3rDQi0TugyiVhc31pvLDxMREQCGBqKnstTIETvaS+gyiFrVS772CHC3EroMIiJqIxgaiBpgrK8DXvC0FboMolZhppLib2EMykRE9AeGBqIGen+cN0wVJkKXQdTiVo7vA+sucqHLICKiNoShgaiBbE0VWMxPX6mDmzywG0Z5c/I/EREZYmggaoSJft04TIk6LHcbDZaEc08GIiKqi6GBqJH+MbEvulkohS6DqFnJTMT4aJIvlDKJ0KUQEVEbxNBA1EhapRTrI/tzKUrqUOa/6IHe9lqhyyAiojaKVz1ETdDHUYv3wjm/gTqGwJ7WmDbURegyiIioDWNoIGqiV/27Y6yvvdBlED0Te60C/5jYFyKRSOhSiIioDWNoIHoGK8b3gbuNRugyiJpEIRXjn68NgJWGy6sSEZFxDA1Ez0AlM8GGqH5Qc/IotUMJE/rC24HzGIiI6OkYGoiekbtNF6z9r36QiDm8g9qP2SPcMKYvh9cREVHDMDQQNYMRHjZYOobr21P7EOZjh3kjPYQug4iI2hGGBqJm8qp/d7w+3FXoMoiM6udkhn+8wonPRETUOE0ODWvWrIFIJIK3t/cT24hEIixdulR/Py0tDSKRCGlpaU3ttlUcPHjQoG56dkFBQQgKChK6jBa3KKQXV1SiNsvFSo3/eW0AFFLOwSEiosZpcmjYvHkzACAjIwOnT59u0Gv69euHkydPol+/fk3ttlUcPHgQy5YtE7qMDmX9+vVYv3690GW0OJFIhA9e6YthPayELoXIQDcLJZJnDIIlV0oiIqImaFJoSE9Px7lz5xAWFgYASExMbNDrTE1N4e/vD1NT06Z0S82ouLi4Vfvz8vKCl1fn2AxNKhFjQ1R/9O1mJnQpRABq9mJInu4PO61S6FKIiKidalJoqA0JK1euREBAALZv396gi9D6hidFR0dDo9Hg0qVLePHFF6FWq2FnZ4eVK1cCAE6dOoWhQ4dCrVajZ8+eSEpKqnPc7OxsvPHGG3B0dIRMJoOLiwuWLVuGyspKfZvMzMyaT4E/+AAffvghXFxcoNFoMHjwYJw6dcqgnnXr1gGo+dS49paZmQkA2LFjBwYNGgStVguVSgVXV1fExMQ89dxFIhHmzJmDjRs3omfPnpDL5fDy8sL27dvrtL148SLGjh0Lc3NzKBQK+Pr61jnvTz75xKAuY+9xUFAQvL298fXXXyMgIAAqlcpozenp6Zg0aRKcnZ2hVCrh7OyMyZMn48aNG/XWcOzYMcyaNQtWVlawtLTE+PHjcffuXYO2jw9Pqv15rFq1CvHx8fq+goKCcOXKFVRUVGDhwoWwt7eHVqvFuHHjcP/+fYNjpqSkYOTIkbCzs4NSqYSnpycWLlyIoqIio33Xio6OhrOzs/7+ypUrIRaLsW/fvjrtVCoVLly48MT37HFquQm2xgxkcCDB2XSRY9sMf3SzUAldChERtWONDg0lJSX49NNP4efnB29vb8TExKCwsBA7duxochEVFRUYP348wsLCsGfPHoSEhGDRokV49913MWXKFMTExODzzz+Hh4cHoqOj8cMPP+hfm52djYEDB+LIkSNYsmQJDh06hGnTpmHFihWYMWNGnb7WrVuH1NRUrF69Gtu2bUNRURFCQ0NRUFAAAHjvvfcwYcIEAMDJkyf1Nzs7O5w8eRIRERFwdXXF9u3bceDAASxZssQgnBizd+9erFmzBsuXL8fOnTvRvXt3TJ48GTt37tS3uXz5MgICApCRkYE1a9Zg165d8PLyQnR0NBISEpr8HmdlZSEqKgqRkZE4ePAg3nzzzSe2zczMhIeHB1avXo0jR44gPj4eWVlZ8PPzQ05OTp3206dPh1QqRXJyMhISEpCWloaoqKgG1bVu3Tp89913WLduHTZt2oRLly5h9OjRmDZtGn777Tds3rwZCQkJ+PLLLzF9+nSD1169ehWhoaFITEzE4cOHMXfuXHz22WcYPXp0496c3y1YsAAhISGYMmWKPiBt2bIFSUlJ+Pjjj9GnT59GHU+rlOJ/pw1E/+7mTaqH6FlZqmXYNn0QXKzUQpdCRETtnEljX7Bz504UFBRg2rRpAICIiAjMnTsXiYmJmDJlSpOKKC8vR1xcHMaPHw+g5pPh/fv3Y8WKFTh79iyee+45AMCAAQNgY2OD5ORk9O/fHwCwdOlSPHjwABkZGXBycgIABAcHQ6lUYt68eXjnnXcMhsV06dIF+/fvh0RSMxHQ3t4eAwcOxKFDhzBp0iS4ubnB1tYWAODv729Q54kTJ6DT6bBhwwZotX9siBQdHd2g88zJycGZM2f0xw8NDYW3tzcWLVqkDypLly5FeXk5jh07hm7duunb5efnY9myZXjjjTcM+m6ovLw87NixA88///xT206YMEFfDwBUVVUhPDwctra2SE5OxltvvWXQftSoUVizZo1BX/Pnz0d2dja6du1qtC8zMzPs3r0bYnFNfs3JycHcuXPRq1cv7NmzR9/u0qVLWL16NR4+fKgf3rZ48WL98zqdDkOGDIGnpycCAwNx/vx5+Pj4PPVc/0wkEmHr1q3w9fXFxIkTsWHDBsyZMwdRUVH6/94bq4tCiq0xAzH1kzP4/te8Jh2DqCm0Sin+NW0Qeth2EboUIiLqABr9TUNiYiKUSiUmTZoEANBoNHjllVfwzTff4OrVq00qQiQSITQ0VH/fxMQE7u7usLOz0wcGALCwsICNjY3BMJn9+/djxIgRsLe3R2Vlpf4WEhICADh+/LhBX2FhYfrAAEB/Yfn40Jv6+Pn5AQAmTpyIzz77DHfu3GnUeQYHB+sDAwBIJBJERETg2rVruH37NgDg6NGjCA4O1geGWtHR0SguLsbJkycb1Wctc3PzBgUGAHj06BEWLFgAd3d3mJiYwMTEBBqNBkVFRfj555/rtB8zZozB/ca8p6GhofrAAACenp4AoJ8v8/jjN2/e1D92/fp1REZGomvXrpBIJJBKpQgMDASAeutsCEtLS6SkpODs2bMICAiAk5MTNmzY0KRj1VLLTZA0dSAC3Cyf6ThEDWWlqfmGwcue88eIiKh5NCo0XLt2DV9//TXCwsKg0+mQn5+P/Px8/afStSsqNZZKpYJCoTB4TCaTwcLCok5bmUyG0tJS/f179+5h3759kEqlBrfevWs22np8OI2lpeGFm1xes5JISUnJU+scPnw4du/ejcrKSrz22mtwdHSEt7c3Pv300wadZ32futc+lpubq//Tzs6uTjt7e3uDdo1V3zGfJDIyEmvXrsX06dNx5MgRfP/99zhz5gysra3rfZ+e5T19/Gcsk8mMPl77s3/06BGGDRuG06dPIy4uDmlpaThz5gx27drV4L6fZNCgQejduzdKS0sxa9YsqNXPPrRDKZNgc7QfV1WiFtfdUoWdMwPg7dD4bySJiIiepFHDkzZv3gydToedO3cajMOvlZSUhLi4OINP8lualZUVfHx88P7779f7fO3FdnMZO3Ysxo4di7KyMpw6dQorVqxAZGQknJ2dMXjwYKOvzc7OfuJjtRfelpaWyMrKqtOudmKxlVXNRWdtyCorKzNoV9+cAwAN3sipoKAA+/fvR2xsLBYuXKh/vKysDHl5bWd4zdGjR3H37l2kpaXpv10AgPz8/DptFQqFfs7Knz3pvYqNjcWFCxfQv39/LFmyBOHh4XB1ffZN2xRSCTZNGYC/Jv+IL36698zHI3pcHwcttkz1gxWXVSUiombW4G8aqqqqkJSUBDc3Nxw7dqzO7e2330ZWVhYOHTrUkvXWER4ejosXL8LNzQ0DBgyoc2tKaGjIJ+VyuRyBgYGIj48HAPz4449PPe5XX32Fe/f+uFisqqpCSkoK3Nzc4OjoCKBmCFPtBfGfbd26FSqVSj/PonbVn/Pnzxu027t371PrMEYkEkGn0+nfg1qbNm1CVVXVMx27OdWGoMfr3LhxY522zs7OuHLlikHAys3NxYkTJ+q0TU1NxYoVK7B48WKkpqZCq9UiIiIC5eXlzVK33ESCDVH9uXM0NbthPayw/XV/BgYiImoRDf6m4dChQ7h79y7i4+PrXb7S29sba9euRWJiIsLDw5uzRqOWL1+O1NRUBAQE4K233oKHhwdKS0uRmZmJgwcPYsOGDfoL8oaqXSUnPj4eISEhkEgk8PHxQVxcHG7fvo3g4GA4OjoiPz8fH330kcFYemOsrKzw/PPP47333oNarcb69etx6dIlg2VXY2Nj9fM0lixZAgsLC2zbtg0HDhxAQkKCfhK0n58fPDw8MG/ePFRWVsLc3Byff/45vv3220ad6+NMTU0xfPhwrFq1ClZWVnB2dsbx48eRmJgIM7O2s3xoQEAAzM3NMXPmTMTGxkIqlWLbtm04d+5cnbavvvoqNm7ciKioKMyYMQO5ublISEios19I7QpTgYGBiI2NhVgsRkpKCoYPH4758+dj9erVzVK7WCzCu6GecLVS4709F1FRpWuW41LnNe45ByRM8IFU0uT9OomIiIxq8P9hEhMTIZPJMHXq1Hqft7Kywrhx47B//36DT9Nbmp2dHdLT0zFy5EisWrUKo0aNwquvvorNmzfD19cX5uaNX+4yMjIS06dPx/r16zF48GD4+fnh7t27GDRoELKzs7FgwQKMHDkSr7/+OpRKJY4ePaqfQ2HMmDFjMGfOHCxevBgvv/wyMjMzsW3bNkREROjbeHh44MSJE/Dw8MDs2bPx0ksv4eLFi9iyZQveeecdfTuJRIJ9+/ahV69emDlzJl577TXI5XKsXbu20ef7uOTkZIwYMQLz58/H+PHjkZ6erv/Uva2wtLTEgQMHoFKpEBUVhZiYGGg0GqSkpNRpO2TIECQlJSEjIwNjx45FXFwcFi1aZBB+q6qqMHnyZIhEIiQnJ+snZ/v7++Pvf/87PvroI+zevbtZz2HSQCckTR0IU0WjFzEj0psZ6IYPJ/ZlYCAiohYl0ul0/JizFYhEIsyePbtZLuqpY/nlt0eI+eQMbuS27i7d1L6pZRIkTOiLMJ+GL3JARETUVPxoikhgbtYa7H5zCPxd664WRlQfdxsN9swZysBARESthqGBqA0wV8uwbbo/5r7QAxJxw1a6os5pdF977Jk9BO42GqFLISKiToTDk4jamNPXczE35T/IKih9emPqNKSSmgn0U4e4CF0KERF1QgwNRG1QfnE53tl5Hqncz4EAOJgpsWayL/p35xA2IiISBkMDURu29WQm4g78jPLKaqFLIYFMHuiEv4V5QiPnKltERCQchgaiNu7nrIeYt+McMu4+FLoUakUOZkrEv+yDoT2shC6FiIiIoYGoPaiq1mHLd7/iw9QrKC5vOztzU8vgtwtERNTWMDQQtSN38ksQu+civvz5vtClUAvgtwtERNRWMTQQtUOHLmRh6b4M3HtYJnQp1AzkJmJMH+aCN4Pcoea3C0RE1AYxNBC1U4WlFfjgyGVsO30TldX8NW6vwn3ssDCkFxzNVUKXQkRE9EQMDUTt3K85Rfjgi8s4eCEL/G1uP/p2M8OScE8uo0pERO0CQwNRB3HxTgHiD1/CN1dzhC6FjLDTKjB/lAde8nWASMTdv4mIqH1gaCDqYE78koOEw5fxn1v5QpdCf+JgpsSMYS6YNNAJCqlE6HKIiIgahaGBqIM6kpGN9ceu4dztAqFL6dRcrdWYGeiGcc85QCoRC10OERFRkzA0EHVw6Zl5SPz2V3zx0z1UccJ0q+ltb4o3g9wR4t0VYjGHIRERUfvG0EDUSdzKK8aW7zLxWfotPCqrFLqcDkksAoa4WyFmqAtGeNgIXQ4REVGzYWgg6mQKSyuQcuYW/nXqBm7kFgtdTodgp1XglQHdMHGAI5dOJSKiDomhgagT++HGA+z+8Q4OXMhCXlG50OW0KyZiEYI9bTDJzwmBPa05BImIiDo0hgYiQkVVNY5f/g37Vo2OAAAC00lEQVSf/+cOvvr5HkorqoUuqc3q66hFaB87jO/nCOsucqHLISIiahUMDURk4FFZJQ5dyMKRjHs48UsOisurhC5JUBKxCP2dzPGid1eM8u4KBzOl0CURERG1OoYGInqissoqnLqeh2OX7uPrq7/h+m9FQpfUKrRKKYb3tEZwLxsEeVjDTCUTuiQiIiJBMTQQUYPdzS/Bt9dycOJaDn68ld9hJlI7misxoLs5BjhbwM/ZAj1tNdytmYiI6E8YGoioyQqKK3DhTgHO3c7HhdsFuHCnAHfyS4Quy6guchO42mjg66jVh4SuWoXQZREREbVpDA1E1KxyH5Xhwp0C3Mgtxq28YtzMK8atByW4nVeMwlbcH8LWVA53Gw3crDUGf9qaMiAQERE1FkMDEbWa/OJy3Morwe0HxXhQXIHC0go8KqtEYWklHpZWoLC0EoWlFSgqq4IOOkhEIkjEf9xMxGKIxSJIxSKYKqWwVMtgqZHDUiODlUYGS3Xt3+VQSCVCny4REVGHwdBARERERERGiYUugIiIiIiI2jaGBiIiIiIiMoqhgYiIiIiIjGJoICIiIiIioxgaiIiIiIjIKIYGIiIiIiIyiqGBiIiIiIiMYmggIiIiIiKjGBqIiIiIiMgohgYiIiIiIjKKoYGIiIiIiIxiaCAiIiIiIqMYGoiIiIiIyCiGBiIiIiIiMoqhgYiIiIiIjGJoICIiIiIioxgaiIiIiIjIKIYGIiIiIiIyiqGBiIiIiIiMYmggIiIiIiKjGBqIiIiIiMgohgYiIiIiIjKKoYGIiIiIiIxiaCAiIiIiIqMYGoiIiIiIyCiGBiIiIiIiMoqhgYiIiIiIjGJoICIiIiIioxgaiIiIiIjIKIYGIiIiIiIyiqGBiIiIiIiMYmggIiIiIiKjGBqIiIiIiMgohgYiIiIiIjKKoYGIiIiIiIxiaCAiIiIiIqMYGoiIiIiIyKj/D9a3k1H57k+UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "# plot chart\n",
    "ax1 = plt.subplot(121, aspect='equal')\n",
    "df_data_2017_cereales_allemagne.plot(kind='pie', y = 'Valeur', ax=ax1, autopct='%1.1f%%', \n",
    " startangle=90, shadow=False, labels=df_data_2017_cereales_allemagne['Élément'], legend = False, fontsize=12)\n",
    "# Title\n",
    "plt.title(\"Répartition des céréales en Allemagne pour l'année 2017\",color='r')\n",
    "# plotting function here, e.g. plt.hist()\n",
    "plt.savefig('allemagne')\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "ax1 = plt.subplot(121, aspect='equal')\n",
    "df_data_2017_cereales_france.plot(kind='pie', y = 'Valeur', ax=ax1, autopct='%1.1f%%', \n",
    " startangle=90, shadow=False, labels=df_data_2017_cereales_france['Élément'], legend = False, fontsize=12)\n",
    "plt.title(\"Répartition des céréales en France pour l'année 2017\",color='r')\n",
    "plt.savefig('france')\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "ax1 = plt.subplot(121, aspect='equal')\n",
    "df_data_2017_cereales_haiti.plot(kind='pie', y = 'Valeur', ax=ax1, autopct='%1.1f%%', \n",
    " startangle=90, shadow=False, labels=df_data_2017_cereales_haiti['Élément'], legend = False, fontsize=12)\n",
    "plt.title(\"Répartition des céréales à Haïti pour l'année 2017\",color='r')\n",
    "plt.savefig('haiti')\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "ax1 = plt.subplot(121, aspect='equal')\n",
    "df_data_2017_cereales_liberia.plot(kind='pie', y = 'Valeur', ax=ax1, autopct='%1.1f%%', \n",
    " startangle=90, shadow=False, labels=df_data_2017_cereales_liberia['Élément'], legend = False, fontsize=12)\n",
    "plt.title(\"Répartition des céréales au Libéria pour l'année 2017\",color='r')\n",
    "plt.savefig('Libéria')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "442b4b6a-4c91-4a0f-a4db-61da1564abfc",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### __Conclusion__ 9.2.3<a class=\"anchor\" id=\"section_9_2_3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a070a2a-2c39-4995-bc28-8d30cd4f0f8f",
   "metadata": {},
   "source": [
    "Nous pouvons voir que pour les deux pays où la sous-nutrition est très faible, les céréales sont surtout utilisés pour nourrir les animaux alors que pour les deux pays qui sont en état de sous-nutritions très élevé, l'utilisation des céréales sont essentiellement pour l'alimentation humaine.\n",
    "D'après une étude, le blé constitue une céréale très peu chère. En Côte d’Ivoire par exemple, on estime qu’il est 25% moins cher que la semoule de manioc. D’autre part, la farine de blé étant facile à travailler, celle-ci facilite la fabrication de pain qui est très apprécié par la population locale, notamment les jeunes et les citadins. Au Sénégal, on estime ainsi à plus de 8 millions les baguettes de pain consommées par jour, soit une évolution de la consommation moyenne de pain par tête de 22 kg en 2000 à 32 kg en 2016 (source : Jacques Berthelot, SOL).\n",
    "\n",
    "De l'autre coté : La France, 3e producteur européen d’aliments pour animaux\n",
    "\n",
    "Pour la campagne 2020/2021, plus de 9 millions de tonnes\n",
    "de céréales ont été utilisées par l’industrie de l’alimentation\n",
    "animale, ce qui en fait la première utilisatrice de céréales en grains.\n",
    "Ce secteur consomme également la plupart des coproduits\n",
    "de transformation des céréales. Une partie de la production de céréales grains est également consommée directement à la ferme. D’autre part,\n",
    "1,4 millions d’hectares de maïs fourrage et ensilage (récolté vert, plante entière) ont été cultivés en 2021,\n",
    "pour alimenter les élevages de ruminants. Les pailles de céréales sont également utilisées pour compléter\n",
    "le fourrage et ce, particulièrement en période de sècheresse où les prairies produisent moins. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dcf748b-978f-45ad-ab59-b82e3f40eafa",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Bonus <a class=\"anchor\" id=\"section8\"></a>\n",
    "[Retour au sommaire](#sommaire)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef449439-b905-4ad1-b937-28b86a08383e",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Aide alimentaire en 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e5162b1e-db0a-403c-92d3-de92625c7dc6",
   "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>Année</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pays bénéficiaire</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algérie</th>\n",
       "      <td>16128</td>\n",
       "      <td>9476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhoutan</th>\n",
       "      <td>4032</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>4032</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bénin</th>\n",
       "      <td>4032</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cameroun</th>\n",
       "      <td>4032</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Année  Valeur\n",
       "Pays bénéficiaire               \n",
       "Algérie            16128    9476\n",
       "Bhoutan             4032     218\n",
       "Burkina Faso        4032      72\n",
       "Bénin               4032     144\n",
       "Cameroun            4032     578"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_aide_alimentaire_since_2016 = data_aide_alimentaire[data_aide_alimentaire['Année']>=2016].groupby('Pays bénéficiaire').sum()\n",
    "data_aide_alimentaire_since_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "77fc3911-d82b-40a0-801a-cd54bf50a5a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Les pays ayant le plus bénéficié d’aide depuis 2016 sont: \n",
      "                            Valeur\n",
      "Pays bénéficiaire                \n",
      "Yémen                      465574\n",
      "République arabe syrienne  118558\n",
      "Niger                       92742\n",
      "Haïti                       12462\n",
      "Algérie                      9476\n",
      "Côte d'Ivoire                8786\n",
      "Colombie                     5466\n",
      "Honduras                     4492\n",
      "Libye                        4414\n",
      "Népal                        3484\n"
     ]
    }
   ],
   "source": [
    "rank_aide_alimentaire_2016 = data_aide_alimentaire_since_2016['Valeur'].sort_values(ascending=False)\n",
    "df_rank_aide_alimentaire_2016 = rank_aide_alimentaire_2016.to_frame().head(10)\n",
    "print(f'Les pays ayant le plus bénéficié d’aide depuis 2016 sont: \\n { df_rank_aide_alimentaire_2016}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeefe839-1d39-4927-810a-afff2ed0b737",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### La population mondiale concidérée comme etant en sous nutrition en 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "128b2243-f9a4-4126-abc4-3d5e36ee3c54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>34413.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>55386.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2890.513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>39728.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>81787.411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone     Valeur\n",
       "2      Afghanistan  34413.603\n",
       "8   Afrique du Sud  55386.367\n",
       "14         Albanie   2890.513\n",
       "20         Algérie  39728.025\n",
       "26       Allemagne  81787.411"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2015 = data_population.loc[data_population['Année'] == 2015, ['Zone', 'Valeur']]\n",
    "data_pop_zone_val_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c665ef0e-55fc-4b30-a708-7b383e4c727c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>34413603.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>55386367.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2890513.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>39728025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>81787411.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone      Valeur\n",
       "2      Afghanistan  34413603.0\n",
       "8   Afrique du Sud  55386367.0\n",
       "14         Albanie   2890513.0\n",
       "20         Algérie  39728025.0\n",
       "26       Allemagne  81787411.0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2015['Valeur'] = data_population.iloc[: , 2].mul(1000)\n",
    "data_pop_zone_val_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "770507ce-0bb6-4760-961a-aa49757af3f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Andorre</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Angola</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Antigua-et-Barbuda</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Arabie saoudite</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Argentine</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Zone Valeur\n",
       "2          Afghanistan    8.9\n",
       "8       Afrique du Sud    2.8\n",
       "14             Albanie    0.1\n",
       "20             Algérie    1.3\n",
       "26           Allemagne    NaN\n",
       "32             Andorre    NaN\n",
       "38              Angola    5.3\n",
       "44  Antigua-et-Barbuda    NaN\n",
       "50     Arabie saoudite    1.5\n",
       "56           Argentine    1.1"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sn_zone_val_2015 = data_sn.loc[data_sn['Année'] == '2014-2016', ['Zone', 'Valeur']]\n",
    "data_sn_zone_val_2015.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "d6756b80-246f-4889-917c-bf7082cd3be6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 203 entries, 2 to 1214\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Zone    203 non-null    object \n",
      " 1   Valeur  84 non-null     float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 4.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data_sn_zone_val_2015['Valeur'] = pd.to_numeric(data_sn_zone_val_2015['Valeur'], errors='coerce')\n",
    "data_sn_zone_val_2015.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d5fad855-9181-4590-b574-9c67a7a87fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>8900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>2800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>1300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Andorre</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Angola</td>\n",
       "      <td>5300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Antigua-et-Barbuda</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Arabie saoudite</td>\n",
       "      <td>1500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Argentine</td>\n",
       "      <td>1100000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Zone     Valeur\n",
       "2          Afghanistan  8900000.0\n",
       "8       Afrique du Sud  2800000.0\n",
       "14             Albanie   100000.0\n",
       "20             Algérie  1300000.0\n",
       "26           Allemagne        NaN\n",
       "32             Andorre        NaN\n",
       "38              Angola  5300000.0\n",
       "44  Antigua-et-Barbuda        NaN\n",
       "50     Arabie saoudite  1500000.0\n",
       "56           Argentine  1100000.0"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sn_zone_val_2015['Valeur'] = data_sn_zone_val_2015.iloc[: , 1].mul(1000000)\n",
    "data_sn_zone_val_2015.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c832db4a-15e3-4157-969e-b349c00a55cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environ 7.11 % de la population mondiale est concidéré comme etant en sous nutrition en 2015\n"
     ]
    }
   ],
   "source": [
    "proportion_pop_2015 = ( data_sn_zone_val_2015['Valeur'].sum() * 100 ) / data_pop_zone_val_2015['Valeur'].sum() \n",
    "print('Environ ' + str(round(proportion_pop_2015,3)) +  ' % de la population mondiale est concidéré comme etant en sous nutrition en 2015')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c49bfc-2e7f-4c13-be2c-d38915a6c6b0",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### La population mondiale concidérée comme etant en sous nutrition en 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "95dd00ad-f459-428e-84c2-c819d816308b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>35383.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>56207.646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2886.438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>40551.392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>82193.768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone     Valeur\n",
       "3      Afghanistan  35383.032\n",
       "9   Afrique du Sud  56207.646\n",
       "15         Albanie   2886.438\n",
       "21         Algérie  40551.392\n",
       "27       Allemagne  82193.768"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2016 = data_population.loc[data_population['Année'] == 2016, ['Zone', 'Valeur']]\n",
    "data_pop_zone_val_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "23433102-fcdb-42d1-9f07-419093684b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>35383032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>56207646.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2886438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>40551392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>82193768.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone      Valeur\n",
       "3      Afghanistan  35383032.0\n",
       "9   Afrique du Sud  56207646.0\n",
       "15         Albanie   2886438.0\n",
       "21         Algérie  40551392.0\n",
       "27       Allemagne  82193768.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2016['Valeur'] = data_population.iloc[: , 2].mul(1000)\n",
    "data_pop_zone_val_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c3cf0264-17bd-4492-94d0-008b806f13fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Andorre</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Angola</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Antigua-et-Barbuda</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Arabie saoudite</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Argentine</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Zone Valeur\n",
       "3          Afghanistan    9.7\n",
       "9       Afrique du Sud      3\n",
       "15             Albanie    0.1\n",
       "21             Algérie    1.3\n",
       "27           Allemagne    NaN\n",
       "33             Andorre    NaN\n",
       "39              Angola    5.6\n",
       "45  Antigua-et-Barbuda    NaN\n",
       "51     Arabie saoudite    1.5\n",
       "57           Argentine    1.3"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sn_zone_val_2016 = data_sn.loc[data_sn['Année'] == '2015-2017', ['Zone', 'Valeur']]\n",
    "data_sn_zone_val_2016.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c75e61df-fced-4e5e-b4ca-c2cf8ac4f70c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 203 entries, 3 to 1215\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Zone    203 non-null    object \n",
      " 1   Valeur  83 non-null     float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 4.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data_sn_zone_val_2016['Valeur'] = pd.to_numeric(data_sn_zone_val_2016['Valeur'], errors='coerce')\n",
    "data_sn_zone_val_2016.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "a1c74d01-cad0-4a9f-9393-2e2d80103e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>9700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>3000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>1300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Andorre</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Angola</td>\n",
       "      <td>5600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Antigua-et-Barbuda</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Arabie saoudite</td>\n",
       "      <td>1500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Argentine</td>\n",
       "      <td>1300000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Zone     Valeur\n",
       "3          Afghanistan  9700000.0\n",
       "9       Afrique du Sud  3000000.0\n",
       "15             Albanie   100000.0\n",
       "21             Algérie  1300000.0\n",
       "27           Allemagne        NaN\n",
       "33             Andorre        NaN\n",
       "39              Angola  5600000.0\n",
       "45  Antigua-et-Barbuda        NaN\n",
       "51     Arabie saoudite  1500000.0\n",
       "57           Argentine  1300000.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sn_zone_val_2016['Valeur'] = data_sn_zone_val_2016.iloc[: , 1].mul(1000000)\n",
    "data_sn_zone_val_2016.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cdb0929a-3891-4d3d-948d-19ee6f68ad21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environ 7.082 % de la population mondiale est concidéré comme etant en sous nutrition en 2016\n"
     ]
    }
   ],
   "source": [
    "proportion_pop_2016 = ( data_sn_zone_val_2016['Valeur'].sum() * 100 ) / data_pop_zone_val_2016['Valeur'].sum() \n",
    "print('Environ ' + str(round(proportion_pop_2016,3)) +  ' % de la population mondiale est concidéré comme etant en sous nutrition en 2016')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bff77a79-0bcd-47ab-89ba-bb7e13f08728",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "####  La population mondiale concidérée comme etant en sous nutrition en 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "449cf7fc-a051-45cc-9f2e-b266ffe35d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36296.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>57009.756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2884.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>41389.189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>82658.409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone     Valeur\n",
       "4      Afghanistan  36296.113\n",
       "10  Afrique du Sud  57009.756\n",
       "16         Albanie   2884.169\n",
       "22         Algérie  41389.189\n",
       "28       Allemagne  82658.409"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2017 = data_population.loc[data_population['Année'] == 2017, ['Zone', 'Valeur']]\n",
    "data_pop_zone_val_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "5f00dabe-b367-4132-8031-6643b40d2ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36296113.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>57009756.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2884169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>41389189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>82658409.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone      Valeur\n",
       "4      Afghanistan  36296113.0\n",
       "10  Afrique du Sud  57009756.0\n",
       "16         Albanie   2884169.0\n",
       "22         Algérie  41389189.0\n",
       "28       Allemagne  82658409.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2017['Valeur'] = data_population.iloc[: , 2].mul(1000)\n",
    "data_pop_zone_val_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "e31008d5-bf94-4e99-8074-07f3c2ecf8d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Andorre</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Angola</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Antigua-et-Barbuda</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Arabie saoudite</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Argentine</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Zone Valeur\n",
       "4          Afghanistan   10.5\n",
       "10      Afrique du Sud    3.1\n",
       "16             Albanie    0.1\n",
       "22             Algérie    1.3\n",
       "28           Allemagne    NaN\n",
       "34             Andorre    NaN\n",
       "40              Angola    5.8\n",
       "46  Antigua-et-Barbuda    NaN\n",
       "52     Arabie saoudite    1.6\n",
       "58           Argentine    1.5"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sn_zone_val_2017 = data_sn.loc[data_sn['Année'] == '2016-2018', ['Zone', 'Valeur']]\n",
    "data_sn_zone_val_2017.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "59bf91d2-c038-4183-bd51-8a337f02d37d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 203 entries, 4 to 1216\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Zone    203 non-null    object \n",
      " 1   Valeur  84 non-null     float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 4.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data_sn_zone_val_2017['Valeur'] = pd.to_numeric(data_sn_zone_val_2017['Valeur'], errors='coerce')\n",
    "data_sn_zone_val_2017.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "4fb08616-9c6f-4b05-ba00-1d6f60e9184d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>10500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>3100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>1300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Andorre</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Angola</td>\n",
       "      <td>5800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Antigua-et-Barbuda</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Arabie saoudite</td>\n",
       "      <td>1600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Argentine</td>\n",
       "      <td>1500000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Zone      Valeur\n",
       "4          Afghanistan  10500000.0\n",
       "10      Afrique du Sud   3100000.0\n",
       "16             Albanie    100000.0\n",
       "22             Algérie   1300000.0\n",
       "28           Allemagne         NaN\n",
       "34             Andorre         NaN\n",
       "40              Angola   5800000.0\n",
       "46  Antigua-et-Barbuda         NaN\n",
       "52     Arabie saoudite   1600000.0\n",
       "58           Argentine   1500000.0"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sn_zone_val_2017['Valeur'] = data_sn_zone_val_2017.iloc[: , 1].mul(1000000)\n",
    "data_sn_zone_val_2017.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "8b60fb5d-21fc-462e-944e-998a0c5bb1a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environ 7.097 % de la population mondiale est concidéré comme etant en sous nutrition en 2017\n"
     ]
    }
   ],
   "source": [
    "proportion_pop_2017 = ( data_sn_zone_val_2017['Valeur'].sum() * 100 ) / data_pop_zone_val_2017['Valeur'].sum() \n",
    "print('Environ ' + str(round(proportion_pop_2017,3)) +  ' % de la population mondiale est concidéré comme etant en sous nutrition en 2017')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "925c958b-451d-4a51-938a-294767076619",
   "metadata": {
    "tags": []
   },
   "source": [
    "####  La population mondiale concidérée comme etant en sous nutrition en 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "2a144a93-c25c-49fd-bf2c-b4226028c6dc",
   "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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37171.921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>57792.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2882.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>42228.408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>83124.418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone     Valeur\n",
       "5      Afghanistan  37171.921\n",
       "11  Afrique du Sud  57792.518\n",
       "17         Albanie   2882.740\n",
       "23         Algérie  42228.408\n",
       "29       Allemagne  83124.418"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2018 = data_population.loc[data_population['Année'] == 2018, ['Zone', 'Valeur']]\n",
    "data_pop_zone_val_2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "7ed7fa60-1700-49eb-8396-7cb92a29e567",
   "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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37171921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>57792518.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>2882740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>42228408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>83124418.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone      Valeur\n",
       "5      Afghanistan  37171921.0\n",
       "11  Afrique du Sud  57792518.0\n",
       "17         Albanie   2882740.0\n",
       "23         Algérie  42228408.0\n",
       "29       Allemagne  83124418.0"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pop_zone_val_2018['Valeur'] = data_population.iloc[: , 2].mul(1000)\n",
    "data_pop_zone_val_2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "86eb4d36-e732-44ff-8dce-3feffe33b4ef",
   "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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Andorre</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Angola</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Antigua-et-Barbuda</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Arabie saoudite</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Argentine</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Zone Valeur\n",
       "5          Afghanistan   11.1\n",
       "11      Afrique du Sud    3.3\n",
       "17             Albanie    0.1\n",
       "23             Algérie    1.2\n",
       "29           Allemagne    NaN\n",
       "35             Andorre    NaN\n",
       "41              Angola    5.7\n",
       "47  Antigua-et-Barbuda    NaN\n",
       "53     Arabie saoudite    1.6\n",
       "59           Argentine    1.7"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sn_zone_val_2018 = data_sn.loc[data_sn['Année'] == '2017-2019', ['Zone', 'Valeur']]\n",
    "data_sn_zone_val_2018.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "36841f07-0b76-4520-89cf-bd050e7dd03c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 203 entries, 5 to 1217\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Zone    203 non-null    object \n",
      " 1   Valeur  85 non-null     float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 4.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data_sn_zone_val_2018['Valeur'] = pd.to_numeric(data_sn_zone_val_2018['Valeur'], errors='coerce')\n",
    "data_sn_zone_val_2018.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "e3341ec6-7e14-4380-a676-bbf2e007e19d",
   "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>Zone</th>\n",
       "      <th>Valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>11100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afrique du Sud</td>\n",
       "      <td>3300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Albanie</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Algérie</td>\n",
       "      <td>1200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Zone      Valeur\n",
       "5      Afghanistan  11100000.0\n",
       "11  Afrique du Sud   3300000.0\n",
       "17         Albanie    100000.0\n",
       "23         Algérie   1200000.0\n",
       "29       Allemagne         NaN"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sn_zone_val_2018['Valeur'] = data_sn_zone_val_2018.iloc[: , 1].mul(1000000)\n",
    "data_sn_zone_val_2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "a4401778-af9a-455a-8af2-768964479d8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environ 7.131 % de la population mondiale est concidéré comme etant en sous nutrition en 2018\n"
     ]
    }
   ],
   "source": [
    "proportion_pop_2018 = ( data_sn_zone_val_2018['Valeur'].sum() * 100 ) / data_pop_zone_val_2018['Valeur'].sum() \n",
    "print('Environ ' + str(round(proportion_pop_2018,3)) +  ' % de la population mondiale est concidéré comme etant en sous nutrition en 2018')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb086d9-ba28-46b3-b859-e9e5200a4a4b",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Comparaison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "14159a81-fa21-49ec-b40d-d38f1dbd4d8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La population mondiale concidéré comme etant en sous nutrition à augmenté de -0.028% entre 2015 et 2016\n"
     ]
    }
   ],
   "source": [
    "difference_sn_2015_2016 = round(proportion_pop_2016 - proportion_pop_2015, 3)\n",
    "print(f'La population mondiale concidéré comme etant en sous nutrition à augmenté de {difference_sn_2015_2016}% entre 2015 et 2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "920217a6-3942-4d11-ac2c-93b2e748dc2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La population mondiale concidéré comme etant en sous nutrition à augmenté de 0.015% entre 2016 et 2017\n"
     ]
    }
   ],
   "source": [
    "difference_sn_2016_2017 = round(proportion_pop_2017 - proportion_pop_2016, 3)\n",
    "print(f'La population mondiale concidéré comme etant en sous nutrition à augmenté de {difference_sn_2016_2017}% entre 2016 et 2017')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "d84cf573-5416-44e0-bf77-cab1c194e686",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La population mondiale concidéré comme etant en sous nutrition à augmenté de 0.034% entre 2017 et 2018\n"
     ]
    }
   ],
   "source": [
    "difference_sn_2017_2018 = round(proportion_pop_2018 - proportion_pop_2017, 3)\n",
    "print(f'La population mondiale concidéré comme etant en sous nutrition à augmenté de {difference_sn_2017_2018}% entre 2017 et 2018')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "aac183a5-31db-4279-9866-17dde6a54d99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.1096914793284585 7.081713923027183 7.097118203283074 7.131093176274328\n"
     ]
    }
   ],
   "source": [
    "print(proportion_pop_2015, proportion_pop_2016, proportion_pop_2017, proportion_pop_2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e7a158a5-d024-4d5e-9de4-bf150c1c3a50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define data values\n",
    "x = 2015, 2016, 2017, 2018   # X-axis points\n",
    "y = proportion_pop_2015, proportion_pop_2016, proportion_pop_2017, proportion_pop_2018  # Y-axis points\n",
    "\n",
    "#plt.xlabel(\"Année\")  # add X-axis label\n",
    "plt.ylabel(\"% de la population en état de sous-nutrition\")  # add Y-axis label\n",
    "plt.title(\"la population en état de sous nutrition dans le monde entre 2015 et 2018\")  # add title\n",
    "plt.savefig('evolution pop')\n",
    "    \n",
    "plt.plot(x, y)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "2239dd1a-024d-4fe8-b465-20a7d4f970a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://lafaimexpliquee.org/La_faim_expliquee/Faits_et_chiffres_files/Figure2_22_1.jpg\" width=\"750\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url='https://lafaimexpliquee.org/La_faim_expliquee/Faits_et_chiffres_files/Figure2_22_1.jpg', width=750)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c4201b5-733a-4164-a7fb-ce57393cae64",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Part de la disoponibilité en ethiopie "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "c3c2327d-3599-46b1-b2b1-528b2e290acf",
   "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>Zone</th>\n",
       "      <th>Aliments pour animaux</th>\n",
       "      <th>Autres Utilisations</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
       "      <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité de protéines en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité intérieure</th>\n",
       "      <th>Exportations - Quantité</th>\n",
       "      <th>Importations - Quantité</th>\n",
       "      <th>Nourriture</th>\n",
       "      <th>Pertes</th>\n",
       "      <th>Production</th>\n",
       "      <th>Semences</th>\n",
       "      <th>Traitement</th>\n",
       "      <th>Variation de stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>France</td>\n",
       "      <td>22769.0</td>\n",
       "      <td>14405.0</td>\n",
       "      <td>3482.0</td>\n",
       "      <td>937.43</td>\n",
       "      <td>159.15</td>\n",
       "      <td>110.5</td>\n",
       "      <td>146905.0</td>\n",
       "      <td>65945.0</td>\n",
       "      <td>33769.0</td>\n",
       "      <td>60272.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>178136.0</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>44966.0</td>\n",
       "      <td>944.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Zone  Aliments pour animaux  Autres Utilisations  \\\n",
       "53  France                22769.0              14405.0   \n",
       "\n",
       "    Disponibilité alimentaire (Kcal/personne/jour)  \\\n",
       "53                                          3482.0   \n",
       "\n",
       "    Disponibilité alimentaire en quantité (kg/personne/an)  \\\n",
       "53                                             937.43        \n",
       "\n",
       "    Disponibilité de matière grasse en quantité (g/personne/jour)  \\\n",
       "53                                             159.15               \n",
       "\n",
       "    Disponibilité de protéines en quantité (g/personne/jour)  \\\n",
       "53                                              110.5          \n",
       "\n",
       "    Disponibilité intérieure  Exportations - Quantité  \\\n",
       "53                  146905.0                  65945.0   \n",
       "\n",
       "    Importations - Quantité  Nourriture  Pertes  Production  Semences  \\\n",
       "53                  33769.0     60272.0  3261.0    178136.0    1648.0   \n",
       "\n",
       "    Traitement  Variation de stock  \n",
       "53     44966.0               944.0  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_substring_on_table(zone_data_alimentaire, 'france')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "43752e7a-1098-4ba7-83aa-371b4aac56b6",
   "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>Zone</th>\n",
       "      <th>Aliments pour animaux</th>\n",
       "      <th>Autres Utilisations</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
       "      <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité de protéines en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité intérieure</th>\n",
       "      <th>Exportations - Quantité</th>\n",
       "      <th>Importations - Quantité</th>\n",
       "      <th>Nourriture</th>\n",
       "      <th>Pertes</th>\n",
       "      <th>Production</th>\n",
       "      <th>Semences</th>\n",
       "      <th>Traitement</th>\n",
       "      <th>Variation de stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Éthiopie</td>\n",
       "      <td>685.0</td>\n",
       "      <td>3641.0</td>\n",
       "      <td>2129.0</td>\n",
       "      <td>354.55</td>\n",
       "      <td>25.89</td>\n",
       "      <td>60.39</td>\n",
       "      <td>44014.0</td>\n",
       "      <td>941.0</td>\n",
       "      <td>1711.0</td>\n",
       "      <td>33361.0</td>\n",
       "      <td>2256.0</td>\n",
       "      <td>46102.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>3443.0</td>\n",
       "      <td>-2857.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Zone  Aliments pour animaux  Autres Utilisations  \\\n",
       "172  Éthiopie                  685.0               3641.0   \n",
       "\n",
       "     Disponibilité alimentaire (Kcal/personne/jour)  \\\n",
       "172                                          2129.0   \n",
       "\n",
       "     Disponibilité alimentaire en quantité (kg/personne/an)  \\\n",
       "172                                             354.55        \n",
       "\n",
       "     Disponibilité de matière grasse en quantité (g/personne/jour)  \\\n",
       "172                                              25.89               \n",
       "\n",
       "     Disponibilité de protéines en quantité (g/personne/jour)  \\\n",
       "172                                              60.39          \n",
       "\n",
       "     Disponibilité intérieure  Exportations - Quantité  \\\n",
       "172                   44014.0                    941.0   \n",
       "\n",
       "     Importations - Quantité  Nourriture  Pertes  Production  Semences  \\\n",
       "172                   1711.0     33361.0  2256.0     46102.0     640.0   \n",
       "\n",
       "     Traitement  Variation de stock  \n",
       "172      3443.0             -2857.0  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_data_alimentaire_ethiopie = zone_data_alimentaire.iloc[[172]]\n",
    "zone_data_alimentaire_ethiopie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "a32c8ee3-3bc3-4ace-8eaf-ac8f409643e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la disponibilité intérieur totale est de: 44014.0\n"
     ]
    }
   ],
   "source": [
    "disponibilite_int_totale_ethiopie = zone_data_alimentaire_ethiopie['Disponibilité intérieure'].sum()\n",
    "print(f\"la disponibilité intérieur totale est de: {disponibilite_int_totale_ethiopie}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "7cffc0ac-b091-4b70-adbb-a6ef990076ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la disponibilité pour l'alimentation animale est de: 685.0\n"
     ]
    }
   ],
   "source": [
    "disponibilite_animale_ethiopie = zone_data_alimentaire_ethiopie['Aliments pour animaux'].sum()\n",
    "print(f\"la disponibilité pour l'alimentation animale est de: {disponibilite_animale_ethiopie}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "9f4fd066-19c6-40bd-85e4-5191f3face49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la part attribuée pour l'alimentation animale est de: 1.56%\n"
     ]
    }
   ],
   "source": [
    "calc_part_animale_ethiopie = round(disponibilite_animale_ethiopie * 100 / disponibilite_int_totale_ethiopie,2)\n",
    "print(f\"la part attribuée pour l'alimentation animale est de: {calc_part_animale_ethiopie}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "4d6c2071-7198-4cfd-8e62-50a4a64395dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2256.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perte_ethiopie = zone_data_alimentaire_ethiopie['Pertes'].sum()\n",
    "perte_ethiopie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e1dfd9b4-2eea-48f1-8aa0-791eec5cfa8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la part perdue est de: 5.13%\n"
     ]
    }
   ],
   "source": [
    "calc_perte_ethiopie = round(perte_ethiopie * 100 / disponibilite_int_totale_ethiopie,2)\n",
    "print(f\"la part perdue est de: {calc_perte_ethiopie}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "55bc8f17-7fc1-423b-8272-906f56ee273a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33361.0"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_alimentaire_humain_ethiopie = zone_data_alimentaire_ethiopie['Nourriture'].sum()\n",
    "dispo_alimentaire_humain_ethiopie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "80e2ac0b-0509-4c0b-ad4e-40a115383944",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour l'alimentation humaine est de 75.8 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_humain_ethiopie = round(dispo_alimentaire_humain_ethiopie * 100 / disponibilite_int_totale_ethiopie,2)\n",
    "print(f\"La part attibuée pour l'alimentation humaine est de {calc_part_humain_ethiopie} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "53d317d8-0215-4e63-8acd-ae07725654d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "640.0"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_semences_ethiopie = zone_data_alimentaire_ethiopie['Semences'].sum() \n",
    "dispo_semences_ethiopie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "4454bdd9-6f00-44af-a986-8ae7a44bc31d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour la semence est de 1.45 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_semence_ethiopie = round(dispo_semences_ethiopie * 100 / disponibilite_int_totale_ethiopie,2)\n",
    "print(f\"La part attibuée pour la semence est de {calc_part_semence_ethiopie} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "f11b99f1-1536-4591-80ef-0515b79a69ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3443.0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_traitement_ethiopie = zone_data_alimentaire_ethiopie['Traitement'].sum() \n",
    "dispo_traitement_ethiopie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "89cdae1e-c360-4424-9b26-613f63e035d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour le traitement est de 7.82 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_traitement_ethiopie = round(dispo_traitement_ethiopie * 100 / disponibilite_int_totale_ethiopie,2)\n",
    "print(f\"La part attibuée pour le traitement est de {calc_part_traitement_ethiopie} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "4b46c91f-91ff-4e64-9e44-fe802be95267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3641.0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_autre_utilisation_ethiopie = zone_data_alimentaire_ethiopie['Autres Utilisations'].sum() \n",
    "dispo_autre_utilisation_ethiopie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "6418363e-6263-4d1b-9bf9-f836de2cd99b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour lees autres utilisation est de 8.27 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_autre_utilisation_ethiopie = round(dispo_autre_utilisation_ethiopie * 100 / disponibilite_int_totale_ethiopie,2)\n",
    "print(f\"La part attibuée pour lees autres utilisation est de {calc_part_autre_utilisation_ethiopie} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "1c909f22-f0bd-4f32-ac71-e8969a785955",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La disponibilité totale est de: 100.0 % \n",
      "Nous avons bien le bon resultat car nous trouvons 100% de la disponibilité totale.\n"
     ]
    }
   ],
   "source": [
    "calc_utilisation_total_ethiopie = round(calc_part_animale_ethiopie + calc_perte_ethiopie + calc_part_humain_ethiopie + calc_part_semence_ethiopie + calc_part_traitement_ethiopie + calc_part_autre_utilisation_ethiopie,1)\n",
    "print(f\"La disponibilité totale est de: {calc_utilisation_total_ethiopie} % \\nNous avons bien le bon resultat car nous trouvons 100% de la disponibilité totale.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3872dd80-4005-485f-b7d4-637bcfcefd31",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Part de disponibilité au Gabon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f750a74f-8be3-4ba2-8f9a-00d23f805eee",
   "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>Zone</th>\n",
       "      <th>Aliments pour animaux</th>\n",
       "      <th>Autres Utilisations</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
       "      <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité de protéines en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité intérieure</th>\n",
       "      <th>Exportations - Quantité</th>\n",
       "      <th>Importations - Quantité</th>\n",
       "      <th>Nourriture</th>\n",
       "      <th>Pertes</th>\n",
       "      <th>Production</th>\n",
       "      <th>Semences</th>\n",
       "      <th>Traitement</th>\n",
       "      <th>Variation de stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>251.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2826.0</td>\n",
       "      <td>760.93</td>\n",
       "      <td>59.1</td>\n",
       "      <td>86.28</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>-32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Zone  Aliments pour animaux  Autres Utilisations  \\\n",
       "55  Gabon                  251.0                 47.0   \n",
       "\n",
       "    Disponibilité alimentaire (Kcal/personne/jour)  \\\n",
       "55                                          2826.0   \n",
       "\n",
       "    Disponibilité alimentaire en quantité (kg/personne/an)  \\\n",
       "55                                             760.93        \n",
       "\n",
       "    Disponibilité de matière grasse en quantité (g/personne/jour)  \\\n",
       "55                                               59.1               \n",
       "\n",
       "    Disponibilité de protéines en quantité (g/personne/jour)  \\\n",
       "55                                              86.28          \n",
       "\n",
       "    Disponibilité intérieure  Exportations - Quantité  \\\n",
       "55                    2047.0                      1.0   \n",
       "\n",
       "    Importations - Quantité  Nourriture  Pertes  Production  Semences  \\\n",
       "55                    561.0      1266.0   223.0      1521.0      26.0   \n",
       "\n",
       "    Traitement  Variation de stock  \n",
       "55       233.0               -32.0  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_data_alimentaire_gabon = zone_data_alimentaire.iloc[[55]]\n",
    "zone_data_alimentaire_gabon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "eb020475-6828-479c-8ef9-89b97d6ec1f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la disponibilité intérieur totale est de: 2047.0\n"
     ]
    }
   ],
   "source": [
    "disponibilite_int_totale_gabon = zone_data_alimentaire_gabon['Disponibilité intérieure'].sum()\n",
    "print(f\"la disponibilité intérieur totale est de: {disponibilite_int_totale_gabon}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "07e79c1d-272b-4341-b410-57e0df1837d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la disponibilité pour l'alimentation animale est de: 251.0\n"
     ]
    }
   ],
   "source": [
    "disponibilite_animale_gabon = zone_data_alimentaire_gabon['Aliments pour animaux'].sum()\n",
    "print(f\"la disponibilité pour l'alimentation animale est de: {disponibilite_animale_gabon}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "a62541ac-33a2-422b-b6c9-c26c74749938",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la part attribuée pour l'alimentation animale est de: 12.26%\n"
     ]
    }
   ],
   "source": [
    "calc_part_animale_gabon = round(disponibilite_animale_gabon * 100 / disponibilite_int_totale_gabon,2)\n",
    "print(f\"la part attribuée pour l'alimentation animale est de: {calc_part_animale_gabon}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "077355c2-d38f-45f7-aaa4-56d4ed7f2361",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223.0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perte_gabon = zone_data_alimentaire_gabon['Pertes'].sum()\n",
    "perte_gabon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "013a352a-a668-4e45-989e-11be0974c147",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la part prdue est de: 10.89%\n"
     ]
    }
   ],
   "source": [
    "calc_perte_gabon = round(perte_gabon * 100 / disponibilite_int_totale_gabon,2)\n",
    "print(f\"la part prdue est de: {calc_perte_gabon}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "1121051f-726b-4ce4-bcc1-5e03e17b39ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1266.0"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_alimentaire_humain_gabon = zone_data_alimentaire_gabon['Nourriture'].sum()\n",
    "dispo_alimentaire_humain_gabon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "7d94097c-8c33-45f1-87b4-52ec0d73438d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour l'alimentation humaine est de 61.85 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_humain_gabon = round(dispo_alimentaire_humain_gabon * 100 / disponibilite_int_totale_gabon,2)\n",
    "print(f\"La part attibuée pour l'alimentation humaine est de {calc_part_humain_gabon} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "3074eab0-ed58-4877-abfd-0f0f291d2992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.0"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_semences_gabon = zone_data_alimentaire_gabon['Semences'].sum() \n",
    "dispo_semences_gabon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "2f8a2504-37a5-455a-8c06-3d16227258d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour la semence est de 1.27 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_semence_gabon = round(dispo_semences_gabon * 100 / disponibilite_int_totale_gabon,2)\n",
    "print(f\"La part attibuée pour la semence est de {calc_part_semence_gabon} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "1faae11e-486b-4e6e-a61d-da44caebf2bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "233.0"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_traitement_gabon = zone_data_alimentaire_gabon['Traitement'].sum() \n",
    "dispo_traitement_gabon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "b2781e4e-9f5f-4d8d-9bee-fbe9691d79a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour le traitement est de 11.38 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_traitement_gabon = round(dispo_traitement_gabon * 100 / disponibilite_int_totale_gabon,2)\n",
    "print(f\"La part attibuée pour le traitement est de {calc_part_traitement_gabon} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "32b39576-0f50-4075-9b07-2f32c00be961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.0"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_autre_utilisation_gabon = zone_data_alimentaire_gabon['Autres Utilisations'].sum() \n",
    "dispo_autre_utilisation_gabon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "64612b0e-23e7-47c4-aaee-5c6afd867f19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour lees autres utilisation est de 2.3 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_autre_utilisation_gabon = round(dispo_autre_utilisation_gabon * 100 / disponibilite_int_totale_gabon,2)\n",
    "print(f\"La part attibuée pour lees autres utilisation est de {calc_part_autre_utilisation_gabon} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "58efcda8-f06f-4993-98c0-c81d71b9553e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La disponibilité totale est de: 100.0 % \n",
      "Nous avons bien le bon resultat car nous trouvons 100% de la disponibilité totale.\n"
     ]
    }
   ],
   "source": [
    "calc_utilisation_total_gabon = round(calc_part_animale_gabon + calc_perte_gabon + calc_part_humain_gabon + calc_part_semence_gabon + calc_part_traitement_gabon + calc_part_autre_utilisation_gabon,0)\n",
    "print(f\"La disponibilité totale est de: {calc_utilisation_total_gabon} % \\nNous avons bien le bon resultat car nous trouvons 100% de la disponibilité totale.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a62aa335-f884-4838-b947-938da1ddd363",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Part de disponibilité en France"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "330a6341-c0dd-4935-b902-3b3571340c3d",
   "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>Zone</th>\n",
       "      <th>Aliments pour animaux</th>\n",
       "      <th>Autres Utilisations</th>\n",
       "      <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
       "      <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
       "      <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité de protéines en quantité (g/personne/jour)</th>\n",
       "      <th>Disponibilité intérieure</th>\n",
       "      <th>Exportations - Quantité</th>\n",
       "      <th>Importations - Quantité</th>\n",
       "      <th>Nourriture</th>\n",
       "      <th>Pertes</th>\n",
       "      <th>Production</th>\n",
       "      <th>Semences</th>\n",
       "      <th>Traitement</th>\n",
       "      <th>Variation de stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>France</td>\n",
       "      <td>22769.0</td>\n",
       "      <td>14405.0</td>\n",
       "      <td>3482.0</td>\n",
       "      <td>937.43</td>\n",
       "      <td>159.15</td>\n",
       "      <td>110.5</td>\n",
       "      <td>146905.0</td>\n",
       "      <td>65945.0</td>\n",
       "      <td>33769.0</td>\n",
       "      <td>60272.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>178136.0</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>44966.0</td>\n",
       "      <td>944.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Zone  Aliments pour animaux  Autres Utilisations  \\\n",
       "53  France                22769.0              14405.0   \n",
       "\n",
       "    Disponibilité alimentaire (Kcal/personne/jour)  \\\n",
       "53                                          3482.0   \n",
       "\n",
       "    Disponibilité alimentaire en quantité (kg/personne/an)  \\\n",
       "53                                             937.43        \n",
       "\n",
       "    Disponibilité de matière grasse en quantité (g/personne/jour)  \\\n",
       "53                                             159.15               \n",
       "\n",
       "    Disponibilité de protéines en quantité (g/personne/jour)  \\\n",
       "53                                              110.5          \n",
       "\n",
       "    Disponibilité intérieure  Exportations - Quantité  \\\n",
       "53                  146905.0                  65945.0   \n",
       "\n",
       "    Importations - Quantité  Nourriture  Pertes  Production  Semences  \\\n",
       "53                  33769.0     60272.0  3261.0    178136.0    1648.0   \n",
       "\n",
       "    Traitement  Variation de stock  \n",
       "53     44966.0               944.0  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_data_alimentaire_france = zone_data_alimentaire.iloc[[53]]\n",
    "zone_data_alimentaire_france"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "bcc74342-cda3-4a4d-8cb8-52d3c9376385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146905.0"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disponibilite_int_totale_france = zone_data_alimentaire_france['Disponibilité intérieure'].sum()\n",
    "disponibilite_int_totale_france"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "770ff066-606b-4a8b-92f9-2c451ef5fe09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22769.0"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disponibilite_animale_france = zone_data_alimentaire_france['Aliments pour animaux'].sum()\n",
    "disponibilite_animale_france"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "179905a3-0ac0-4cab-856f-cfa9de5a15f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la part attribuée pour l'alimentation animale est de: 15.5%\n"
     ]
    }
   ],
   "source": [
    "calc_part_animale_france = round(disponibilite_animale_france * 100 / disponibilite_int_totale_france,2)\n",
    "print(f\"la part attribuée pour l'alimentation animale est de: {calc_part_animale_france}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "25e488bc-3ccf-4da5-be13-aae6ddfe95a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3261.0"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perte_france = zone_data_alimentaire_france ['Pertes'].sum()\n",
    "perte_france "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "50950bf0-6a41-4f69-9e38-de93a23085be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la part prdue est de: 2.22%\n"
     ]
    }
   ],
   "source": [
    "calc_perte_france = round(perte_france * 100 / disponibilite_int_totale_france,2)\n",
    "print(f\"la part prdue est de: {calc_perte_france}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "b0da660e-1414-46e0-ae6b-8d5d41b2cab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60272.0"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_alimentaire_humain_france = zone_data_alimentaire_france['Nourriture'].sum()\n",
    "dispo_alimentaire_humain_france"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "0fddd4c5-47cb-4220-8ae7-7e702dd34dcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour l'alimentation humaine est de 41.03 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_humain_france = round(dispo_alimentaire_humain_france * 100 / disponibilite_int_totale_france,2)\n",
    "print(f\"La part attibuée pour l'alimentation humaine est de {calc_part_humain_france} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "2615729e-8b97-4598-a64c-1d04645ba5dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1648.0"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_semences_france = zone_data_alimentaire_france['Semences'].sum() \n",
    "dispo_semences_france"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "69858175-e7e0-46e2-94b2-29521e42dfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour la semence est de 1.12 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_semence_france = round(dispo_semences_france * 100 / disponibilite_int_totale_france,2)\n",
    "print(f\"La part attibuée pour la semence est de {calc_part_semence_france} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "aea099a1-1198-4be6-abbf-f490c78c48df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44966.0"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_traitement_france = zone_data_alimentaire_france['Traitement'].sum() \n",
    "dispo_traitement_france"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "432db232-9c61-4ab3-be03-9ef94df51d59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour le traitement est de 30.61 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_traitement_france = round(dispo_traitement_france * 100 / disponibilite_int_totale_france,2)\n",
    "print(f\"La part attibuée pour le traitement est de {calc_part_traitement_france} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "005192c1-ed58-494c-9d70-238c8add1b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14405.0"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispo_autre_utilisation_france = zone_data_alimentaire_france['Autres Utilisations'].sum() \n",
    "dispo_autre_utilisation_france"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "7425869c-cb54-40cf-96a0-b3149e20f1d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La part attibuée pour lees autres utilisation est de 9.81 %\n"
     ]
    }
   ],
   "source": [
    "calc_part_autre_utilisation_france = round(dispo_autre_utilisation_france * 100 / disponibilite_int_totale_france,2)\n",
    "print(f\"La part attibuée pour lees autres utilisation est de {calc_part_autre_utilisation_france} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "409a71c7-685c-4191-b43f-aa2911040c24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La disponibilité totale est de: 100.0 % \n",
      "Nous avons bien le bon resultat car nous trouvons 100% de la disponibilité totale.\n"
     ]
    }
   ],
   "source": [
    "calc_utilisation_total_france = round(calc_part_animale_france + calc_perte_france + calc_part_humain_france + calc_part_semence_france + calc_part_traitement_france + calc_part_autre_utilisation_france,0)\n",
    "print(f\"La disponibilité totale est de: {calc_utilisation_total_france} % \\nNous avons bien le bon resultat car nous trouvons 100% de la disponibilité totale.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "ebd3e6fe-51d4-47bf-ae3d-d14393c6140d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define data for Éthiopie\n",
    "data = [calc_part_animale_ethiopie, calc_perte_ethiopie, calc_part_humain_ethiopie, calc_part_semence_ethiopie, calc_part_traitement_ethiopie, calc_part_autre_utilisation_ethiopie]\n",
    "labels = ['Animales', 'Pertes', 'Humains', 'Semences', 'Traitements', 'Autres utilisations']\n",
    "\n",
    "#define Seaborn color palette to use\n",
    "colors = sns.color_palette('pastel')[0:5]\n",
    "\n",
    "#create pie chart\n",
    "plt.pie(data, labels = labels, colors = colors, autopct='%.0f%%')\n",
    "plt.title(\"Part de disponibilité alimentaire pour l'Éthiopie\",color='r')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#define data fore Gabon\n",
    "data = [calc_part_animale_gabon, calc_perte_gabon, calc_part_humain_gabon, calc_part_semence_gabon, calc_part_traitement_gabon, calc_part_autre_utilisation_gabon]\n",
    "labels = ['Animales', 'Pertes', 'Humains', 'Semences', 'Traitements', 'Autres utilisations']\n",
    "\n",
    "#define Seaborn color palette to use\n",
    "colors = sns.color_palette('pastel')[0:5]\n",
    "\n",
    "#create pie chart\n",
    "plt.pie(data, labels = labels, colors = colors, autopct='%.0f%%')\n",
    "plt.title(\"Part de disponibilité alimentaire pour le Gabon\",color='b')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#define data for France\n",
    "data = [calc_part_animale_france, calc_perte_france, calc_part_humain_france, calc_part_semence_france, calc_part_traitement_france, calc_part_autre_utilisation_france]\n",
    "labels = ['Animales', 'Pertes', 'Humains', 'Semences', 'Traitements', 'Autres utilisations']\n",
    "\n",
    "#define Seaborn color palette to use\n",
    "colors = sns.color_palette('pastel')[0:5]\n",
    "\n",
    "#create pie chart\n",
    "plt.pie(data, labels = labels, colors = colors, autopct='%.0f%%')\n",
    "plt.title(\"Part de disponibilité alimentaire pour la France\",color='b')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "491e3f36-17c3-40c2-91ec-e47de7c4bc55",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Conclusion <a class=\"anchor\" id=\"section10\"></a>\n",
    "[Retour au sommaire](#sommaire)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12ee638a-b1ff-4063-95b6-c27db8d12a92",
   "metadata": {},
   "source": [
    "Le changement climatique, la COVID-19, les guerres et le manque de coopération sont autant de facteurs qui contribuent à la crise alimentaire mondiale.<br/> Il est important de rappeler que la plupart des personnes sous-alimentées dans le monde vivent dans les pays en développement, où les catastrophes naturelles ont un impact plus important.\n",
    "\n",
    "Au vu des chiffres, il est clair que malgré les améliorations de la sécurité alimentaire dans de nombreuses régions, d'énormes problèmes subsistent.<br/>\n",
    "Par exemple les aides alimentaire ne sont pas bien distribuées: \n",
    "\n",
    "<img src=\"Food_and_Agriculture_Organization_of_the_United_Nations.png\" alt=\"drawing\" width=\"700\"/>\n",
    "\n",
    "Les principales victimes de la faim et de la malnutrition sont les femmes et les enfants.\n",
    "Les chiffres concernant les enfants et les adolescents sous-alimentés sont encore plus alarmants. L'Unicef révèle que chaque jour, près de 6 millions de jeunes de moins de 5 ans meurent de maladies causées par le manque de nourriture.\n"
   ]
  }
 ],
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