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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Journal de bord du Mooc / Mooc's logbook\n",
+    "\n",
+    "## Mission 1\n",
+    "### Ex 01-1 \n",
+    "**Recherche gitlab**\n",
+    "\n",
+    "Quels sont les deux fichiers contenant la chaîne de caractères \"LE MOOC RECHERCHE REPRODUCTIBLE C'EST GENIAL\" ?\n",
+    "- module1/exo1/aebef6b0a5.txt\n",
+    "- module1/exo1/f683bbad4b.txt\n",
+    "\n",
+    "**Historique Gitlab**\n",
+    "\n",
+    "\n",
+    "Retrouvez le fichier module1/exo2/readme.md et, en utilisant la fonction blame ou history de GitLab, retrouvez le commit responsable de l'ajout du titre Helloworld Python.\n",
+    "*Pas de fichier readme dans module1/exo2. Fonction Blame et History en haut a droite du fichier ouvert, permet de retrouver les modifications faites par les utilisateurs du fichier.*\n",
+    "\n",
+    "### Ex 01-2  s'initier à Markdown\n",
+    "Fichier markdown.md\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mission 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mission 2\n",
+    "\n",
+    "Recherche reproductible difficile :\n",
+    "    - manque info (source de données, choix pas explicite), cachier de labo essentiel\n",
+    "    - Ordinateur source d'erreur \n",
+    "\n",
+    "Article = version simplifier de la procédure \n",
+    "\n",
+    "## DOC computationnel \n",
+    "Science moderne derière un PC, jupiter moyen de retrouver les données apres publication et faciliter la recherche reproductible.\n",
+    "\n",
+    "## Ex1-Utilisation du notebook\n",
+    "\n",
+    "## Ex 2-Savoir faire un calcul simple soi-même\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.113000000000001"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "l=[14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
+    "np.mean(l)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.8"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.min(l)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23.4"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.max(l)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.5"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.median(l)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.312369534258399"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.std(l)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Ex 3-Réaliser un affichage graphique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(*args, **kw)>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.hist(l)\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(*args, **kw)>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.plot(l)\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mission 4\n",
+    "## Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle\n",
+    "### Données de Playfai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'Wages')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "url=\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\"\n",
+    "c=pd.read_csv(url)\n",
+    "c\n",
+    "\n",
+    "c1 = pd.DataFrame(c,\n",
+    "                   columns=['rownames','Year','Wheat','Wages'])\n",
+    "c1\n",
+    "\n",
+    "x=c1['Year']\n",
+    "y1=c1['Wheat']\n",
+    "y2=c1['Wages']\n",
+    "\n",
+    "fig, ax1 = plt.subplots(figsize=(9, 6))\n",
+    "plt.plot(x,y2, color='r')\n",
+    "plt.fill_between(x, y2) \n",
+    "plt.bar(x,y1,width = 5, color='black')\n",
+    "ax2 = ax1.twinx()  \n",
+    "plt.xlabel('year')\n",
+    "ax1.set_ylabel('Wheat')\n",
+    "ax2.set_ylabel('Wages')\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Changement des unités\n",
+    "\n",
+    "### Wheat price "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Wheat2=c1['Wheat']/6.8\n",
+    "c1.insert(3, 'Wheat2', Wheat2 )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Shilling per month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'Wages/Month')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Wages2=4*c1['Wages']\n",
+    "c1.insert(5, 'Wages/month', Wages2 )\n",
+    "\n",
+    "c1\n",
+    "\n",
+    "y3=c1['Wheat2']\n",
+    "y4=c1['Wages/month']\n",
+    "fig, ax1 = plt.subplots(figsize=(9, 6))\n",
+    "plt.bar(x,y3*10,width = 5, color='r')\n",
+    "plt.plot(x,y4)\n",
+    "ax2 = ax1.twinx()  \n",
+    "ax1.set_xlabel('year')\n",
+    "ax1.set_ylabel('shillings/kg')\n",
+    "ax2.set_ylabel('Wages/Month')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Le salaire par mois a augementé avec le prix du blé pouvoir d'achat devrait augementer fin du 18e siècle.\n",
+    "\n",
+    "### Pouvoir d'achat "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'Wages/Wheat')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p=c1['Wages']/c1['Wheat2']\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(x,p)\n",
+    "plt.xlabel('Years')\n",
+    "plt.ylabel('Wages/Wheat')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pouvoir d'achat au plus haut vers la fin du 18e siècle."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mission 5 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interet de la recherche reproductible "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La recherche reproductible permet à chacun de retrouver toutes les étapes des recherches et toutes les données et calculs utiliser pour la rélaiser. \n",
+    "Ceci permet de verifier la justesse des recherches réaliser par un paire, utiliser et approfondir ses recherches..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gitlab\n",
+    "Gitlab permet de tenir un cahier de laboratoire accessible par tous et donc les modifications sont enregister et garder dans un historique des modifications. La première mission consistait à comprendre le fonctionnement de cet espace et le markdown qui peremet de mettre en forme ses note de manière simple et lisible par de nombeux logiciel."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Jupyter\n",
+    "Dans la deuximème mission nous avons explorer les fonctionnalité de Jupiter qui en lien avec GitLab permet de generer des presentation combinant markdown et python et ainsi nous permettre de réaliser les clalules et explication de nous recherhce dans le même espace. Git commit and push permet de publier le document Jupiter dans Gitlab et donc utiliser les fonctionnalité de GitLab pour ce document. La mission 3 nous a permit de comprendre comment réaliser des calculs statisitques à partir de données importé dans jupyter\n",
+    "\n",
+    "## Recherche reproductible \n",
+    "La mission 4 nous a permit de réaliser un cahier de laboratoire sur sur jupyter et de l'importer dans GitLab. Le but était de reproduire une étude réaliser il y a près de 250 ans de les vérifier, améliorer et les rendre reproductible en rendant accessible les étapes de calculs statisitque "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explication de la mission 4\n",
+    "Pour réaliser la mission 4 nous avons utiliser et importer des données obtenues par numérisation du graphe sont aujourd'hui téléchargeables en version .csv dans jupyter à l'aide d'un code python.\n",
+    "Pour cela nous utlisrerons plusieurs bibliothèque permettant d'importer, d'organiser, et d'afficher les données."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pour importer les données "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "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>rownames</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Wheat</th>\n",
+       "      <th>Wages</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1565</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1570</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>5.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1575</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>5.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1580</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>5.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1585</td>\n",
+       "      <td>41.5</td>\n",
+       "      <td>5.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1590</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>5.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1595</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>5.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>5.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1605</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>5.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1610</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>5.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>1615</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>5.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>1620</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>6.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>1625</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>6.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>1630</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>6.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>1635</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>6.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>1640</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>6.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>1645</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>6.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1650</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>6.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>1655</td>\n",
+       "      <td>40.5</td>\n",
+       "      <td>6.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>1660</td>\n",
+       "      <td>46.5</td>\n",
+       "      <td>6.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>21</td>\n",
+       "      <td>1665</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>6.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>22</td>\n",
+       "      <td>1670</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>6.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>1675</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>7.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>24</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>7.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>25</td>\n",
+       "      <td>1685</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>7.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>26</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>8.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>27</td>\n",
+       "      <td>1695</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>28</td>\n",
+       "      <td>1700</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>1705</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>10.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>30</td>\n",
+       "      <td>1710</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>31</td>\n",
+       "      <td>1715</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>11.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>32</td>\n",
+       "      <td>1720</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>12.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>33</td>\n",
+       "      <td>1725</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>13.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>34</td>\n",
+       "      <td>1730</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>13.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>35</td>\n",
+       "      <td>1735</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>13.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>36</td>\n",
+       "      <td>1740</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>14.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>37</td>\n",
+       "      <td>1745</td>\n",
+       "      <td>27.5</td>\n",
+       "      <td>14.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>38</td>\n",
+       "      <td>1750</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>15.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>39</td>\n",
+       "      <td>1755</td>\n",
+       "      <td>35.5</td>\n",
+       "      <td>15.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>40</td>\n",
+       "      <td>1760</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>16.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>41</td>\n",
+       "      <td>1765</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>17.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>42</td>\n",
+       "      <td>1770</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>18.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>43</td>\n",
+       "      <td>1775</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>19.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>44</td>\n",
+       "      <td>1780</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>21.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>45</td>\n",
+       "      <td>1785</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>23.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>46</td>\n",
+       "      <td>1790</td>\n",
+       "      <td>47.5</td>\n",
+       "      <td>25.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>47</td>\n",
+       "      <td>1795</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>27.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>48</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>28.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>49</td>\n",
+       "      <td>1805</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>29.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>50</td>\n",
+       "      <td>1810</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>30.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>51</td>\n",
+       "      <td>1815</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>52</td>\n",
+       "      <td>1820</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>53</td>\n",
+       "      <td>1821</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    rownames  Year  Wheat  Wages\n",
+       "0          1  1565   41.0   5.00\n",
+       "1          2  1570   45.0   5.05\n",
+       "2          3  1575   42.0   5.08\n",
+       "3          4  1580   49.0   5.12\n",
+       "4          5  1585   41.5   5.15\n",
+       "5          6  1590   47.0   5.25\n",
+       "6          7  1595   64.0   5.54\n",
+       "7          8  1600   27.0   5.61\n",
+       "8          9  1605   33.0   5.69\n",
+       "9         10  1610   32.0   5.78\n",
+       "10        11  1615   33.0   5.94\n",
+       "11        12  1620   35.0   6.01\n",
+       "12        13  1625   33.0   6.12\n",
+       "13        14  1630   45.0   6.22\n",
+       "14        15  1635   33.0   6.30\n",
+       "15        16  1640   39.0   6.37\n",
+       "16        17  1645   53.0   6.45\n",
+       "17        18  1650   42.0   6.50\n",
+       "18        19  1655   40.5   6.60\n",
+       "19        20  1660   46.5   6.75\n",
+       "20        21  1665   32.0   6.80\n",
+       "21        22  1670   37.0   6.90\n",
+       "22        23  1675   43.0   7.00\n",
+       "23        24  1680   35.0   7.30\n",
+       "24        25  1685   27.0   7.60\n",
+       "25        26  1690   40.0   8.00\n",
+       "26        27  1695   50.0   8.50\n",
+       "27        28  1700   30.0   9.00\n",
+       "28        29  1705   32.0  10.00\n",
+       "29        30  1710   44.0  11.00\n",
+       "30        31  1715   33.0  11.75\n",
+       "31        32  1720   29.0  12.50\n",
+       "32        33  1725   39.0  13.00\n",
+       "33        34  1730   26.0  13.30\n",
+       "34        35  1735   32.0  13.60\n",
+       "35        36  1740   27.0  14.00\n",
+       "36        37  1745   27.5  14.50\n",
+       "37        38  1750   31.0  15.00\n",
+       "38        39  1755   35.5  15.70\n",
+       "39        40  1760   31.0  16.50\n",
+       "40        41  1765   43.0  17.60\n",
+       "41        42  1770   47.0  18.50\n",
+       "42        43  1775   44.0  19.50\n",
+       "43        44  1780   46.0  21.00\n",
+       "44        45  1785   42.0  23.00\n",
+       "45        46  1790   47.5  25.50\n",
+       "46        47  1795   76.0  27.50\n",
+       "47        48  1800   79.0  28.50\n",
+       "48        49  1805   81.0  29.50\n",
+       "49        50  1810   99.0  30.00\n",
+       "50        51  1815   78.0    NaN\n",
+       "51        52  1820   54.0    NaN\n",
+       "52        53  1821   54.0    NaN"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "url=\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\"\n",
+    "c=pd.read_csv(url)\n",
+    "c\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pour lire et afficher les données en fonction de la presentation des données dans le fichier .csv, on tutilise pandas et on defini le nons de chaque colonne puis on affiche les données tel que PlayFair l'avais imaginer pour les comparer avec ses résultats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'Wages')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "c1 = pd.DataFrame(c,\n",
+    "                   columns=['rownames','Year','Wheat','Wages'])\n",
+    "c1\n",
+    "\n",
+    "x=c1['Year']\n",
+    "y1=c1['Wheat']\n",
+    "y2=c1['Wages']\n",
+    "\n",
+    "fig, ax1 = plt.subplots(figsize=(9, 6))\n",
+    "plt.plot(x,y2, color='r')\n",
+    "plt.fill_between(x, y2) \n",
+    "plt.bar(x,y1,width = 5, color='black')\n",
+    "ax2 = ax1.twinx()  \n",
+    "plt.xlabel('year')\n",
+    "ax1.set_ylabel('Wheat')\n",
+    "ax2.set_ylabel('Wages')\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Le graphique n'etant pas très lisible, nous allons maintenant modifier la présentation et les unités utiliser pour rendre les données plus exploitables. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'Wages/Month')"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Wheat2=c1['Wheat']/6.8\n",
+    "c1.insert(3, 'Wheat2', Wheat2 )\n",
+    "Wages2=4*c1['Wages']\n",
+    "c1.insert(5, 'Wages/month', Wages2 )\n",
+    "\n",
+    "c1\n",
+    "\n",
+    "y3=c1['Wheat2']\n",
+    "y4=c1['Wages/month']\n",
+    "fig, ax1 = plt.subplots(figsize=(9, 6))\n",
+    "plt.bar(x,y3*10,width = 5, color='r')\n",
+    "plt.plot(x,y4)\n",
+    "ax2 = ax1.twinx()  \n",
+    "ax1.set_xlabel('year')\n",
+    "ax1.set_ylabel('shillings/kg')\n",
+    "ax2.set_ylabel('Wages/Month')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'Wages/Wheat')"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p=c1['Wages']/c1['Wheat2']\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(x,p)\n",
+    "plt.xlabel('Years')\n",
+    "plt.ylabel('Wages/Wheat')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}