{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Playfair analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importation de la librairie et chargement des données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "playfair = pd.read_csv(\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On regarde le début et la fin du dataframe pour avoir un premier sentiment sur les données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  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"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playfair.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>Wheat</th>\n",
       "      <th>Wages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>1805</td>\n",
       "      <td>81.0</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>1810</td>\n",
       "      <td>99.0</td>\n",
       "      <td>30.0</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": [
       "    Unnamed: 0  Year  Wheat  Wages\n",
       "48          49  1805   81.0   29.5\n",
       "49          50  1810   99.0   30.0\n",
       "50          51  1815   78.0    NaN\n",
       "51          52  1820   54.0    NaN\n",
       "52          53  1821   54.0    NaN"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playfair.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comme il n'y a pas beaucoup de données on peut vérifier la qualité par de simples graphiques.\n",
    "\n",
    "On importe matplotlib et on visualise les deux variables principales; on ne constate pas de valeurs anormales. La variabilité du prix du blé est plus grande que celle des salaires. Cela parait normal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 9) # afin que les graphiques apparaissent en grand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fec3fd470f0>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(playfair['Wheat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fec3fd27128>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(playfair['Wages'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On prend l'année comme index. Cela permettra que l'année figure comme abscice dans les graphiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Unnamed: 0</th>\n",
       "      <th>Wheat</th>\n",
       "      <th>Wages</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>2</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1575</th>\n",
       "      <td>3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1580</th>\n",
       "      <td>4</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1585</th>\n",
       "      <td>5</td>\n",
       "      <td>41.5</td>\n",
       "      <td>5.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1590</th>\n",
       "      <td>6</td>\n",
       "      <td>47.0</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>7</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600</th>\n",
       "      <td>8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1605</th>\n",
       "      <td>9</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1610</th>\n",
       "      <td>10</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1615</th>\n",
       "      <td>11</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1620</th>\n",
       "      <td>12</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>13</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>14</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>15</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640</th>\n",
       "      <td>16</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>17</td>\n",
       "      <td>53.0</td>\n",
       "      <td>6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>18</td>\n",
       "      <td>42.0</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>19</td>\n",
       "      <td>40.5</td>\n",
       "      <td>6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1660</th>\n",
       "      <td>20</td>\n",
       "      <td>46.5</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665</th>\n",
       "      <td>21</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>22</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>23</td>\n",
       "      <td>43.0</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>24</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>25</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>26</td>\n",
       "      <td>40.0</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1695</th>\n",
       "      <td>27</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>29</td>\n",
       "      <td>32.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710</th>\n",
       "      <td>30</td>\n",
       "      <td>44.0</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1715</th>\n",
       "      <td>31</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1720</th>\n",
       "      <td>32</td>\n",
       "      <td>29.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>33</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>34</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1735</th>\n",
       "      <td>35</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740</th>\n",
       "      <td>36</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745</th>\n",
       "      <td>37</td>\n",
       "      <td>27.5</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>38</td>\n",
       "      <td>31.0</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>39</td>\n",
       "      <td>35.5</td>\n",
       "      <td>15.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1760</th>\n",
       "      <td>40</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1765</th>\n",
       "      <td>41</td>\n",
       "      <td>43.0</td>\n",
       "      <td>17.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1770</th>\n",
       "      <td>42</td>\n",
       "      <td>47.0</td>\n",
       "      <td>18.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1775</th>\n",
       "      <td>43</td>\n",
       "      <td>44.0</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1780</th>\n",
       "      <td>44</td>\n",
       "      <td>46.0</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1785</th>\n",
       "      <td>45</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790</th>\n",
       "      <td>46</td>\n",
       "      <td>47.5</td>\n",
       "      <td>25.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>47</td>\n",
       "      <td>76.0</td>\n",
       "      <td>27.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1800</th>\n",
       "      <td>48</td>\n",
       "      <td>79.0</td>\n",
       "      <td>28.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1805</th>\n",
       "      <td>49</td>\n",
       "      <td>81.0</td>\n",
       "      <td>29.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1810</th>\n",
       "      <td>50</td>\n",
       "      <td>99.0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1815</th>\n",
       "      <td>51</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1820</th>\n",
       "      <td>52</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>53</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  Wheat  Wages\n",
       "Year                          \n",
       "1565           1   41.0   5.00\n",
       "1570           2   45.0   5.05\n",
       "1575           3   42.0   5.08\n",
       "1580           4   49.0   5.12\n",
       "1585           5   41.5   5.15\n",
       "1590           6   47.0   5.25\n",
       "1595           7   64.0   5.54\n",
       "1600           8   27.0   5.61\n",
       "1605           9   33.0   5.69\n",
       "1610          10   32.0   5.78\n",
       "1615          11   33.0   5.94\n",
       "1620          12   35.0   6.01\n",
       "1625          13   33.0   6.12\n",
       "1630          14   45.0   6.22\n",
       "1635          15   33.0   6.30\n",
       "1640          16   39.0   6.37\n",
       "1645          17   53.0   6.45\n",
       "1650          18   42.0   6.50\n",
       "1655          19   40.5   6.60\n",
       "1660          20   46.5   6.75\n",
       "1665          21   32.0   6.80\n",
       "1670          22   37.0   6.90\n",
       "1675          23   43.0   7.00\n",
       "1680          24   35.0   7.30\n",
       "1685          25   27.0   7.60\n",
       "1690          26   40.0   8.00\n",
       "1695          27   50.0   8.50\n",
       "1700          28   30.0   9.00\n",
       "1705          29   32.0  10.00\n",
       "1710          30   44.0  11.00\n",
       "1715          31   33.0  11.75\n",
       "1720          32   29.0  12.50\n",
       "1725          33   39.0  13.00\n",
       "1730          34   26.0  13.30\n",
       "1735          35   32.0  13.60\n",
       "1740          36   27.0  14.00\n",
       "1745          37   27.5  14.50\n",
       "1750          38   31.0  15.00\n",
       "1755          39   35.5  15.70\n",
       "1760          40   31.0  16.50\n",
       "1765          41   43.0  17.60\n",
       "1770          42   47.0  18.50\n",
       "1775          43   44.0  19.50\n",
       "1780          44   46.0  21.00\n",
       "1785          45   42.0  23.00\n",
       "1790          46   47.5  25.50\n",
       "1795          47   76.0  27.50\n",
       "1800          48   79.0  28.50\n",
       "1805          49   81.0  29.50\n",
       "1810          50   99.0  30.00\n",
       "1815          51   78.0    NaN\n",
       "1820          52   54.0    NaN\n",
       "1821          53   54.0    NaN"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playfair.set_index('Year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On n'a plus besoin de la colonne qui numérote les observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "playfair=playfair.drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On vérifie le type des variables et le nombre de variables non nulles. Les 3 NaN du salaire correspondent aux trois dernières observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53 entries, 0 to 52\n",
      "Data columns (total 3 columns):\n",
      "Year     53 non-null int64\n",
      "Wheat    53 non-null float64\n",
      "Wages    50 non-null float64\n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 1.3 KB\n"
     ]
    }
   ],
   "source": [
    "playfair.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les années vont de 5 en 5 sauf la dernière qui a les mêmes valeurs que l'avant dernière. On élimine donc la dernière observation qui n'apporte rien. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "playfair=playfair[:-1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " On vérifie que les années vont bien de de 5 en 5 et qu'il y a pas d'anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playfair['Year'].tolist()==list(range(1565,1825,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici le graphique, il est moins beau que celui crée par William Playfair car je ne maitrise pas matplotlib. Tout d'abord, je ne comprends pas pourquoi le 'plot' et 'fill_between' exécutés après le 'bar' ne recouvrent pas le bas des barres. D'autre part, lorsque l'on spécifie 'blue' comme couleur, les barres grises apparaissent décalées. En outre, il n'y a pas dans librairie de matplotlib la possibilité de faire apparaitre un gradient dans les barres comme c'est sur le graphique de William Playfair. Enfin, je ne connais pas bien pandas et je ne sais pas s'il est possible de simplifier le code en créant le graphique directement depuis pandas.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(playfair['Year'],playfair['Wheat'],width=2.5,label='Quarter of Wheat',color='grey')\n",
    "plt.plot(playfair['Year'],playfair['Wages'],label='Wages by Week',color='red', linewidth=6)\n",
    "plt.fill_between(playfair['Year'],playfair['Wages'],color='yellow') # color='blue' donne une bug sur le graphe\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Shilling')\n",
    "plt.legend()\n",
    "plt.title('English workers\\'purchasing power from the XVI to the XIX centuries')\n",
    "plt.show()"
   ]
  },
  {
   "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": 2
}