{
 "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": 1,
   "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": 20,
   "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>Year</th>\n",
       "      <th>Wheat</th>\n",
       "      <th>Wages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1565</td>\n",
       "      <td>41.0</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1570</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1575</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1580</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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",
       "0  1565   41.0   5.00\n",
       "1  1570   45.0   5.05\n",
       "2  1575   42.0   5.08\n",
       "3  1580   49.0   5.12\n",
       "4  1585   41.5   5.15"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playfair.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f55415b21d0>]"
      ]
     },
     "execution_count": 9,
     "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": [
    "plt.plot(playfair['Wheat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5541543ba8>]"
      ]
     },
     "execution_count": 10,
     "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": [
    "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": 11,
   "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": 11,
     "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": 14,
   "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": 16,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 52 entries, 0 to 51\n",
      "Data columns (total 3 columns):\n",
      "Year     52 non-null int64\n",
      "Wheat    52 non-null float64\n",
      "Wages    50 non-null float64\n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 1.3 KB\n"
     ]
    }
   ],
   "source": [
    "playfair=playfair[:-1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TODO vérifier que les années vont de 5 en 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55414ac048>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SlDkDvSRlzkAvSZkz0EtS5gz0kpQ5A70kZc5AL0mZM9BLUuYM9JKUOQO9JGXOQC9JmTPQS1LmDPSSlDkDvSRlzkAvSZkz0EtS5gz0kpQ5A70kZc5AL0mZM9BLUuYM9JKUOQO9JGXOQC9JmRuyQB8Rp0bEryLi4YhYNlTbkST1bUgCfUSMAb4G/CfgcOBjEXH4UGxLktS3oWrRHwM8nFJ6NKX0e+D7wOlDtC1JUh+GKtAfADxRMd1epkmShlmklBq/0oizgIUppXPK6U8Cx6SUllYsswRYUk4eBvyqYhWTgWeqrHqw6SM5T7O3P1x5mr394crT7O3XkqfZ2x+uPM3efi15Brqud6SUWnpZ7g0ppYYPwPHATyqmLwEuGUT+1Y1IH8l5mr1962k9m71969nYdfU1DNWlm/uAQyPi4IjYHTgbuGWItiVJ6sPYoVhpSunViPgfwE+AMcC1KaUNQ7EtSVLfhiTQA6SUfgj8sMbsVzcofSTnafb2hytPs7c/XHmavf1a8jR7+8OVp9nbryVPLevq1ZDcjJUkjRw+AkGSMmegl6TMGeglKXNDdjN2oCJiBsXjEQ4AEvAkcEtKqa2fPAcA96aUXqxIPxX4LZBSSveVz9c5FXiovDlcuY5vp5T+S5V1v5fiEQ47gRUppRci4m3AMuDdwNuA81NKD1bJ29mV9MmU0k8j4o+AeUAb8FPgQ8CBwKvARuB7KaXnB7Kf1HgRsW9Kaesg80xKKW0bqjINJ+u/C9V/sB3vGzkAFwP3UwTRT5TDss60XvKcD2wBVgKPA6dXzHsS+HdgNfC/gTuAPwe2UQTbW8rhX4AXy/FnK/KfW277S8DLwJ+V6VcDVwHvBXYAvwN+Bvx3oKUi/3XA9eX6vwP8M/BJ4N6ybJcCrcDfAV8FHgROauL+37eGPJMaXIYJwHLgofJ96nyvlgN795JnL+CRch//UUX6/sB6igfqTQIuA9YBNwAzgYkVw6Ty+DkTmFhRlmuAB4B/BP4GmFzOmws8CjwMvAJ8AzikStnmAncC36U4qd8OPA+sKY+jDeV0R3ms/rdG1b+c903g76vsg5U99kFn/fcBzuzxfnTugw3AzCr1/w3w6/J4PqTO+n9qsMfArl7/im39aMCfs2YFmbKgvwZ2q5K+O7CxlzzrgCfK8ekUQf1z5fTvKPrt7wG8AOxVpq8FngVOAk4sX7eU4xsr1n0fZeAud/q6cvwXFcuspTgZfKA8IDqAHwOLgfXlMmOBp4ExFWV+oBzfA7irHD8I+OVg32TgNooTWQ6Bbh3FCX//HvW4uJz/7irDKooT9RkUJ+t/At5avg9PUDQWHijXcRCwlOLb4mM9hp1lXR4tt/sN4CvAO4ALgOcrynQn8J5y/AngKWAT8PNy2anlvJ9TPLX1Y+VyZ5bpd5f7bhpwIfA/gUOBzRQNkrrrX+Z9vqxvz33wOvBSlfo/BrxSse3KffAksLJK/d9Z7re/6rkPaqj/Corjqtox8Ne97INdpf59HQNHA1tGS6B/iOJZDT3T30HRcn6gyrCjxxuzJ8UH/Erg5cqAXDH+FooP1O3AnDKt88P9S4qz+iQqfloM3Aj8phz/JjC3HN8A3Fex3G7AacD3KC7J7F6ubztvBND1FJePKOetqci/vZc3ubeD/GiKE9py8gh0zwP/q5fjI1EEwTt7DNuB31Us98Vy/Q9QnpSBTT3WtbncP7Mq0h6j+0n8/h55dgBjy/F/r0j/BW80Ak6g+Ib2VFm2TRXLVY7/ku7H5H3l6686j40G1H8S3T8Dldv/E4rGT7f6d9an2j6g+Hze37P+5XTl9iv3wXZgySDq/xYqPs89tvEaRXDeJevfzzFwZ2UZ+huaHehPpfjw/4iitXc1xYfxYYoW+ByKoFM5tAJbe6xnLPDtcqfs0bkDK+ZPoPhwTqMI4H/b+SZQtGofpfjQP0oZcCnOzr+l+Ip4L0VQfLR8I2f3Up8/LZf5DcUlplXA1ylaBk+V9XsI+HS5fEvlgTnAg/xO4PUey47mQHcbsBXYr2LefhQnqReBQ6vsmzbKb3UVaYvL8naenL/SY/66ivf/SmB8+V61U5x4Liqno8c+uw04meLb0VXAH1B8G/xOj/WPoTiet1J82zurPA7OKOdXnuw/RPksqAbXfwPw+4q0nvugrWf9y/Sq+4CigbC9Sv3/AthWpVxjym38ZKD1L6dfovjs9NwHTwF378L17/UYKJd5olp61WUHuuBQDRRntOOAj1BcRjiu3GHXAO+tsvw04Ae9rOukXtIn0z3AfZBeWpEVy+wBHFweELMpWtL7Ae/sJ99U3mjd7l3W6RjgiHJ8Ro/lbxvMQV7O30nFiaziQB+NgW4fistVD1Gc3H9bflj+kuL65WFV6v9/gD+vkn4dxf8g9Ez/j8BNFdMfovi29BTF/ZjKofPS3f4UjYeTKO67rC334Q8pLvG96ZJjmW82xQf9R8AMim9mz1E0Xh4sx/+ts15l2X7aoPqfWu7LPfvaB5X1L6f72gc/qVL/JcD1g6j/s1Xq/85y+RaKgPaXFfvg2XIfrKR46m2j639anfX/r33Uf04/9X++x/tfrf79HgNl3jP6ikXdlh3ogg5DM1AEus43+bcVb/JKykslVfL8AHh/lfThDHRjeynbQANd5Qf9K8D7e35Ayw/uDOCUKvPO6SX93F7Su62LovfUkf2sq5btn0pxP6S3slWr5/m8cVnsCIqT7h+W08dUzDuc4qT8h72lDyLPLIqbiYPJM9CyHVstT4/0bmWuchx9p5f0bw8mvbd55ft/42Dy1LKdGutyQrnPPlBl3nvL/fameX0NPgJhBIuIT6eUvjnQ9MHkKbuMHpJSWj+U2+lvXkScT/EY63spWkOfSyndXM57gqL3U1vlvIhYClxOcSmq3/Ra1lVnnpcoTtyVec6naIn2LPOXKL7RtVHcQzoG+L8UJ4TfU5yUxpbzjgXuAj5D8U14a4/0weSpZTv15OmrzC0U3Y0rnUxx2QKK+z4AAbyvj/Q7yu11pveVp3Mb1fL0tv2+8vS3nb7WdUJKaR+AiDgHOI+iofcB4KCU0oHlvHPLef9czvuXlNJyBmIwZwWH4R3ocZ29v/TRmIf+e1Ht2XNemeeXA02vZV3DladMX8ube4q9jd57ka2nuB8zmvL0t67v8uZecRspLh0ONP3Ecl61df16mPLUVOaKz0Nlz7+30/2mb8956wYcS5odzHb1geo9ix4oD/7Xq6Sv6yV9tObpqxfVjh77qnPeNrr3jugvvZZ1DVeerbzRs2Ntj7y99SJbO9ry9LOu+yl6bnXrFUfxDWDA6eXriMzTz7qq9vwr5/2uj3nd9mOfcabZgW5XHyj621frXdRBEQR6pk+n6JGTS57+elHNqTJvC/DaINJrWddw5enoTOfNPcVeonovstWdH/JRlKevdXX2FHtTr7ha0kdynmrp9N7zb0+KS2S9zevWS67PONPsQLerD/Teu+ga4PZe8jySSx767kV1BhW/L+iR50MDTa9lXcOVh+L3D/OrpE8G3t3LNqZS0YtslOTpa12zeqRV7RU32PSRnKevdVUsswdw8GDnVRu8GStJmfPplZKUOQO9JGXOQC9JmTPQS1LmDPSSlLn/Dw7Enm5GOro4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "playfair.plot.bar(y='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": 2
}