{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
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    "hidePrompt": false
   },
   "source": [
    "# Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle\n",
    "\n",
    "Dans le cadre de cette exercice évalué par les pairs (Mooc RR, mod3) j'ai choisi le sujet n°2 intitulé :\n",
    "**Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle**\n",
    "\n",
    "## Contexte de l'étude\n",
    "\n",
    "William Playfair un des pionnier de la représentation graphique des données, a réalisé un graphique montrant l'évolution du prix du blé et du salaire moyen entre 1565 et 1821. Ce graphique a été publié en 1822 dans son livre *A Letter on our Agricultural Distresses, Their Causes and Remedies*. Ci-dessous une reproduction hébergée sur [Wikipédia][graph original].\n",
    "\n",
    "![Chart Showing at One View the Price of the Quarter of Wheat, and Wages of Labour by the Week, from 1565 to 1821](playfair_ori_prixble_salaire.png)\n",
    "\n",
    "[graph original]:https://fr.wikipedia.org/wiki/William_Playfair#/media/File:Chart_Showing_at_One_View_the_Price_of_the_Quarter_of_Wheat,_and_Wages_of_Labour_by_the_Week,_from_1565_to_1821.png\n",
    "\n",
    "Le premier objectif de l'étude est de reproduire ce graphe, puis dans un second temps de corriger le graphique.Een effet W. Playfair a utilisé la même unité pour représenter deux quantités différentes sur l'axe des ordonnées. Enfin dans un troisième temps le but sera d'améliorer la représentation du pouvoir d'achat des agriculteurs anglais sur cette période.\n",
    "\n",
    "## Les données\n",
    "\n",
    "### Sources\n",
    "\n",
    "W. Playfair n'a pas publié les données numériques brutes de son étude. Néanmoins une version numérisée est diponible [ici][data_url], réalisé par [Vincent Arel-Bundock] et publié sur son site [R datasets][vab r datasets].\n",
    "\n",
    "[data_url]: https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\n",
    "[Vincent Arel-Bundock]: https://github.com/vincentarelbundock\n",
    "[vab r datasets]: https://vincentarelbundock.github.io/Rdatasets/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "# import des bibliothèques\n",
    "import urllib\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from os import listdir\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fichier déjà téléchargé\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['exercice_python_en.org',\n",
       " 'exercice_fr.ipynb',\n",
       " 'Wheat.csv',\n",
       " 'exercice.ipynb',\n",
       " 'exercice_fr.Rmd',\n",
       " 'playfair_ori_prixble_salaire.png',\n",
       " 'exercice_python_fr.org',\n",
       " 'exercice_R_en.org',\n",
       " 'exercice_R_fr.org',\n",
       " 'exercice_en.Rmd',\n",
       " 'exercice_en.ipynb',\n",
       " '.ipynb_checkpoints']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# téléchargement du fichier\n",
    "data_url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv'\n",
    "filename = 'Wheat.csv'\n",
    "\n",
    "curFiles = set(listdir())\n",
    "\n",
    "# téléchargement automatique du fichier\n",
    "# si non présent dans le répertoire\n",
    "if not(filename in curFiles):\n",
    "    print('Téléchargement du fichier')\n",
    "    urllib.request.urlretrieve(data_url, filename)\n",
    "else:\n",
    "    print('Fichier déjà téléchargé')\n",
    "\n",
    "listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "# lecture du fichier\n",
    "rawdata = pd.read_csv(filename, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "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>Year</th>\n",
       "      <th>Wheat</th>\n",
       "      <th>Wages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1565</td>\n",
       "      <td>41.0</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1570</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1575</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1580</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1585</td>\n",
       "      <td>41.5</td>\n",
       "      <td>5.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Wheat  Wages\n",
       "1  1565   41.0   5.00\n",
       "2  1570   45.0   5.05\n",
       "3  1575   42.0   5.08\n",
       "4  1580   49.0   5.12\n",
       "5  1585   41.5   5.15"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawdata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### Vérification des données\n",
    "\n",
    "Les données sont vérifiées sur les critères suivants :\n",
    "\n",
    "* présence de lignes vides\n",
    "* rupture de date, ou division non régulière\n",
    "\n",
    "Il est à noter les données sont enregistrées tous les 5 ans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "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>Year</th>\n",
       "      <th>Wheat</th>\n",
       "      <th>Wages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1815</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1820</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1821</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Wheat  Wages\n",
       "51  1815   78.0    NaN\n",
       "52  1820   54.0    NaN\n",
       "53  1821   54.0    NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# présence de lignes vides\n",
    "rawdata[rawdata.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Les 3 dernières lignes sont vides pour la colonne *Wages*, ce qui explique l'arrêt de la ligne rouge et de la surface bleue dans le document originale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Années de rupture :\n",
      "1820\n"
     ]
    }
   ],
   "source": [
    "# rupture de date, ou division non régulière\n",
    "print(\"Années de rupture :\")\n",
    "expect_delta = 5\n",
    "for y1, y2 in zip(rawdata['Year'][:-1], rawdata['Year'][1:]):\n",
    "    delta_y = y2 - y1\n",
    "    if delta_y != expect_delta :\n",
    "        print(y1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Seule l'année 1820 présente une rupture. En effet l'enregistrement suivant est 1821. Ce qui explique la barre moins large sur le graphique original, pour les années 1820 à 1821.\n",
    "\n",
    "### Commentaire sur les unités\n",
    "\n",
    "Pour rappel les untités originales des données sont les suivantes :\n",
    "* Blé (Wheat) : Shillings pour un quart de boisseau (8,6 kg)\n",
    "* Salaire (Wages) : Shillings par semaine\n",
    "\n",
    "Avant 1971, la monnaie anglaise était découpée de la manière suivante :\n",
    "* 1 Livre valait 20 shillings\n",
    "* 1 Shilling valait 12 penses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Reproduction du graphique original\n",
    "\n",
    "Ci-dessous le graphique original est reproduit à l'aide de `Matplotlib`.\n",
    "(Pour représenter des dégradés voir ce lien : https://matplotlib.org/3.2.1/gallery/lines_bars_and_markers/gradient_bar.html?highlight=gradient)\n",
    "\n",
    "Les années 1820 et 1821 sont représentées séparémment du reste des données afin d'avoir une barre de largeur différente (voir le code ci-dessous)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Chart Showing at One View\\nthe Price of the Quarter of Wheat, and Wages of Labour\\nby the Week, from 1565 to 1821')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1097.28x548.64 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MultipleLocator\n",
    "from matplotlib.dates import DateFormatter\n",
    "\n",
    "fig1, ax1 = plt.subplots(1,1)\n",
    "\n",
    "fig1.set_size_inches(2.54*6, 2.54*3)\n",
    "\n",
    "# === représentation du prix du blé === #\n",
    "ax1.bar(rawdata['Year'][:-2], rawdata['Wheat'][:-2], align='edge', width=5, color='dimgrey')\n",
    "# bar de largeur 1 année pour les deux années 1820 et 1821\n",
    "ax1.bar(rawdata['Year'][-2:], rawdata['Wheat'][-2:], align='edge', width=1, color='dimgrey')\n",
    "\n",
    "# graduation proche de l'original, avec les siècles comme majeure\n",
    "# et une graduation mineure tous les 5 ans\n",
    "# aide sur les ticks locator :\n",
    "# https://matplotlib.org/gallery/ticks_and_spines/tick-locators.html#sphx-glr-gallery-ticks-and-spines-tick-locators-py\n",
    "ax1.xaxis.set_major_locator(MultipleLocator(100))\n",
    "ax1.xaxis.set_minor_locator(MultipleLocator(5))\n",
    "\n",
    "ax1.grid(True, which='both')\n",
    "\n",
    "# les marges de l'axe x sont diminuées\n",
    "ax1.set_xlim([int(rawdata['Year'][0:1]), int(rawdata['Year'][-1:]+5)])\n",
    "\n",
    "ax1.set_xlabel('5 Years each division')\n",
    "\n",
    "# === représentation du salaire === #\n",
    "# l'axe 2 partage l'axe x de l'axe 1\n",
    "ax2 = ax1.twinx()\n",
    "ax2.fill_between(rawdata['Year'], rawdata['Wages'])\n",
    "ax2.plot(rawdata['Year'], rawdata['Wages'], 'r', linewidth=2)\n",
    "\n",
    "# les deux axes ont les mêmes limites, pour reproduire l'original\n",
    "myylim = ax1.get_ylim()\n",
    "ax2.set_ylim(myylim)\n",
    "\n",
    "ax2.set_ylabel('Price of the Quarter of Wheat in Shillings')\n",
    "\n",
    "ax1.set_title(\"\"\"Chart Showing at One View\n",
    "the Price of the Quarter of Wheat, and Wages of Labour\n",
    "by the Week, from 1565 to 1821\"\"\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Graphique avec deux axes des ordonnées\n",
    "Le graphique précédent est repris et amélioré avec deux axes distincts cette fois-ci.\n",
    "Le jeu de couleur est changé pour un affichage plus lisible.\n",
    "La conversion vers l'unité moderne est ajouté en légende de l'axe y, pour information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Chart Showing at One View\\nthe Price of the Quarter of Wheat, and Wages of Labour\\nby the Week, from 1565 to 1821')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1097.28x548.64 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax11 = plt.subplots(1,1)\n",
    "\n",
    "fig2.set_size_inches(2.54*6, 2.54*3)\n",
    "\n",
    "# === représentation du prix du blé === #\n",
    "ax11.bar(rawdata['Year'][:-2], rawdata['Wheat'][:-2], align='edge', width=5, color='lightgrey')\n",
    "ax11.bar(rawdata['Year'][-2:], rawdata['Wheat'][-2:], align='edge', width=1, color='lightgrey')\n",
    "\n",
    "# graduation proche de l'original, avec les siècles comme majeure\n",
    "# et une graduation mineure tous les 5 ans\n",
    "ax11.xaxis.set_major_locator(MultipleLocator(100))\n",
    "ax11.xaxis.set_minor_locator(MultipleLocator(5))\n",
    "# grille\n",
    "ax11.grid(True, which='both')\n",
    "\n",
    "ax11.set_xlabel('5 Years each division')\n",
    "ax11.set_ylabel('Price of the Quarter (8.6 kg) of Wheat in Shillings')\n",
    "\n",
    "# les marges de l'axe x sont diminuées\n",
    "ax11.set_xlim([int(rawdata['Year'][0:1]), int(rawdata['Year'][-1:]+5)])\n",
    "\n",
    "# === représentation du salaire === #\n",
    "# l'axe 2 partage l'axe x de l'axe 1\n",
    "ax12 = ax11.twinx()\n",
    "ax12.plot(rawdata['Year'], rawdata['Wages'], 'r', linewidth=2, marker='x')\n",
    "\n",
    "# les deux axes ont des limites différentes pour montrer que les axes sont différents\n",
    "myylim = ax11.get_ylim()\n",
    "ax12.set_ylim([0,myylim[1]/2])\n",
    "\n",
    "ax12.set_ylabel('Wages in Shillings/Week')\n",
    "ax12.yaxis.label.set_color('r')\n",
    "ax12.tick_params(axis='y', colors='r')\n",
    "\n",
    "ax11.set_title(\"\"\"Chart Showing at One View\n",
    "the Price of the Quarter of Wheat, and Wages of Labour\n",
    "by the Week, from 1565 to 1821\"\"\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Représentation du pouvoir d'achat\n",
    "\n",
    "### Le pouvoir d'achat en équivalent blé acheté\n",
    "Le pouvoir d'achat est définie ici comme la quantité de blé qu'un ouvrier peut acheter avec son salaire hebdomadaire. \n",
    "\n",
    "Le prix du blé par boisseau est converti en prix du blé par kg pour une représentation plus moderne.\n",
    "Le pouvoir d'achat sera donc exprimé en kg de blé par semaine.\n",
    "\n",
    "Pour rappel, les années 1815, 1820, 1821 n'ont pas de salaire hebdomadaire renseigné. Ces lignes sont retirées pour le calcul du pouvoir d'achat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lignes supprimées :\n",
      "    Year  Wheat  Wages\n",
      "51  1815   78.0    NaN\n",
      "52  1820   54.0    NaN\n",
      "53  1821   54.0    NaN\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    1.048780\n",
       "2    0.965111\n",
       "3    1.040190\n",
       "4    0.898612\n",
       "5    1.067229\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# === on supprime les années sans salaires === #\n",
    "print(\"lignes supprimées :\")\n",
    "print(rawdata[rawdata.isnull().any(axis=1)])\n",
    "data = rawdata.dropna().copy()\n",
    "\n",
    "# === facteurs de conversion === #\n",
    "quarter_to_mass = 8.6  # in kg\n",
    "shilling_to_pence = 12\n",
    "\n",
    "purchasing_power = data['Wages']/(data['Wheat']/quarter_to_mass)\n",
    "purchasing_power.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,\"Pouvoir d'achat d'un ouvrier\\n en équivalent masse de blé hebdomadaire\\n de 1565 à 1810\")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1097.28x548.64 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3, ax31 = plt.subplots(1,1)\n",
    "fig3.set_size_inches(2.54*6, 2.54*3)\n",
    "\n",
    "ax31.bar(data['Year'], purchasing_power, align='edge', width=5)\n",
    "\n",
    "# les mêmes limites que précédemment sont conservées pour comparaison\n",
    "ax31.set_xlim([int(rawdata['Year'][0:1]), int(rawdata['Year'][-1:]+5)])\n",
    "\n",
    "# graduation proche de l'original, avec les siècles comme majeure\n",
    "# et une graduation mineure tous les 5 ans\n",
    "ax31.xaxis.set_major_locator(MultipleLocator(100))\n",
    "ax31.xaxis.set_minor_locator(MultipleLocator(5))\n",
    "\n",
    "# ax31.xaxis.set_major_formatter(DateFormatter('%Y'))\n",
    "# ax31.xaxis.set_minor_formatter(DateFormatter('%y'))\n",
    "\n",
    "\n",
    "ax31.grid(True, which='both')\n",
    "\n",
    "ax31.set_xlabel(\"Année\")\n",
    "ax31.set_ylabel(\"Pouvoir d'achat en masse de blé hebdomadaire [kg/sem]\")\n",
    "\n",
    "ax31.set_title(\"Pouvoir d'achat d'un ouvrier\\n en équivalent masse de blé hebdomadaire\\n de 1565 à 1810\")\n",
    "\n",
    "# fig3.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Le pouvoir d'achat représenté différemment\n",
    "Le pouvoir d'achat est ici représenté différemment, pour faire apparaitre les variations des deux grandeurs qui ont servi à sa construction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb73ff9a0b8>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1097.28x548.64 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "converted_wheat = data['Wheat']/quarter_to_mass\n",
    "purchasing_power = data['Wages']/(data['Wheat']/quarter_to_mass)\n",
    "\n",
    "fig4, ax4 = plt.subplots(1,2)\n",
    "ax41 = ax4[0]\n",
    "fig4.set_size_inches(2.54*6, 2.54*3)\n",
    "\n",
    "# ajout des marqueurs colorés en fonction de l'année\n",
    "mypoints = ax41.scatter(data['Wages'], converted_wheat, c=data['Year'], s=(purchasing_power*3)**2)\n",
    "fig4.colorbar(mypoints)\n",
    "\n",
    "# ajout d'une colonne vertébrale aux poitn pour aider à la lecture\n",
    "ax41.plot(data['Wages'], converted_wheat, '--', color='lightgrey')\n",
    "\n",
    "# todo dans un autre graphique\n",
    "# change size of subplots\n",
    "# remove spine on the second one\n",
    "# add in the second one the legend with the size of each dot for the puchasing power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49    3.132099\n",
       "50    2.606061\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchasing_power[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "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>Year</th>\n",
       "      <th>Wheat</th>\n",
       "      <th>Wages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1565</td>\n",
       "      <td>41.0</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1570</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1575</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1580</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1585</td>\n",
       "      <td>41.5</td>\n",
       "      <td>5.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1590</td>\n",
       "      <td>47.0</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1595</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1600</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1605</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1610</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1615</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1620</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1625</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1630</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1635</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1640</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1645</td>\n",
       "      <td>53.0</td>\n",
       "      <td>6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1650</td>\n",
       "      <td>42.0</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1655</td>\n",
       "      <td>40.5</td>\n",
       "      <td>6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1660</td>\n",
       "      <td>46.5</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1665</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1670</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1675</td>\n",
       "      <td>43.0</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1680</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1685</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1690</td>\n",
       "      <td>40.0</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1695</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1700</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1705</td>\n",
       "      <td>32.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1710</td>\n",
       "      <td>44.0</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1715</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1720</td>\n",
       "      <td>29.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1725</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1730</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1735</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1740</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1745</td>\n",
       "      <td>27.5</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1750</td>\n",
       "      <td>31.0</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1755</td>\n",
       "      <td>35.5</td>\n",
       "      <td>15.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1760</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1765</td>\n",
       "      <td>43.0</td>\n",
       "      <td>17.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1770</td>\n",
       "      <td>47.0</td>\n",
       "      <td>18.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1775</td>\n",
       "      <td>44.0</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1780</td>\n",
       "      <td>46.0</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1785</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1790</td>\n",
       "      <td>47.5</td>\n",
       "      <td>25.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1795</td>\n",
       "      <td>76.0</td>\n",
       "      <td>27.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1800</td>\n",
       "      <td>79.0</td>\n",
       "      <td>28.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1805</td>\n",
       "      <td>81.0</td>\n",
       "      <td>29.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1810</td>\n",
       "      <td>99.0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1815</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1820</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1821</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Wheat  Wages\n",
       "1   1565   41.0   5.00\n",
       "2   1570   45.0   5.05\n",
       "3   1575   42.0   5.08\n",
       "4   1580   49.0   5.12\n",
       "5   1585   41.5   5.15\n",
       "6   1590   47.0   5.25\n",
       "7   1595   64.0   5.54\n",
       "8   1600   27.0   5.61\n",
       "9   1605   33.0   5.69\n",
       "10  1610   32.0   5.78\n",
       "11  1615   33.0   5.94\n",
       "12  1620   35.0   6.01\n",
       "13  1625   33.0   6.12\n",
       "14  1630   45.0   6.22\n",
       "15  1635   33.0   6.30\n",
       "16  1640   39.0   6.37\n",
       "17  1645   53.0   6.45\n",
       "18  1650   42.0   6.50\n",
       "19  1655   40.5   6.60\n",
       "20  1660   46.5   6.75\n",
       "21  1665   32.0   6.80\n",
       "22  1670   37.0   6.90\n",
       "23  1675   43.0   7.00\n",
       "24  1680   35.0   7.30\n",
       "25  1685   27.0   7.60\n",
       "26  1690   40.0   8.00\n",
       "27  1695   50.0   8.50\n",
       "28  1700   30.0   9.00\n",
       "29  1705   32.0  10.00\n",
       "30  1710   44.0  11.00\n",
       "31  1715   33.0  11.75\n",
       "32  1720   29.0  12.50\n",
       "33  1725   39.0  13.00\n",
       "34  1730   26.0  13.30\n",
       "35  1735   32.0  13.60\n",
       "36  1740   27.0  14.00\n",
       "37  1745   27.5  14.50\n",
       "38  1750   31.0  15.00\n",
       "39  1755   35.5  15.70\n",
       "40  1760   31.0  16.50\n",
       "41  1765   43.0  17.60\n",
       "42  1770   47.0  18.50\n",
       "43  1775   44.0  19.50\n",
       "44  1780   46.0  21.00\n",
       "45  1785   42.0  23.00\n",
       "46  1790   47.5  25.50\n",
       "47  1795   76.0  27.50\n",
       "48  1800   79.0  28.50\n",
       "49  1805   81.0  29.50\n",
       "50  1810   99.0  30.00\n",
       "51  1815   78.0    NaN\n",
       "52  1820   54.0    NaN\n",
       "53  1821   54.0    NaN"
      ]
     },
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