{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercice sujet 2 :  le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On importe les fonctions nécessaires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On importe les données via un lien : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données sont lues et formatées en table :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=0)\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On détecte les années avec une donnée manquante :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    .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>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",
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      ],
      "text/plain": [
       "    rownames  Year  Wheat  Wages\n",
       "50        51  1815   78.0    NaN\n",
       "51        52  1820   54.0    NaN\n",
       "52        53  1821   54.0    NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On retire les années avec une donnée manquante du tableau général :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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",
       "  </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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data.dropna().copy()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On classe les données par ordre chronologique par année pour pouvoir ensuite les visualiser :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Wheat</th>\n",
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       "  <tbody>\n",
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       "      <th>1565</th>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
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       "      <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",
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       "    </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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rownames  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"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data = data.set_index('Year').sort_index()\n",
    "sorted_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On trace les résultats semblables à ceux de Playfair :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "ax1.bar(sorted_data.index, sorted_data['Wheat'], width=5, color='black', label='Wheat')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(sorted_data.index, sorted_data['Wages'], color='red', label='Wages Line')\n",
    "ax2.fill_between(sorted_data.index, sorted_data['Wages'], color='skyblue', alpha=0.5, label='Wages Area')\n",
    "ax1.set_ylim(0,100)\n",
    "ax2.set_ylim(0,100)\n",
    "ax1.set_xlim(1565,1810)\n",
    "ax2.set_xlim(1565,1810)\n",
    "fig.legend(loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 2\n",
    "Améliorez la présentation de ces données. Pour commencer, Playfair a combiné les deux quantités dans un même graphique en simplifiant les unités \"shillings par quart de boisseau de blé\" et \"shillings par semaine\" à un simple \"shillings\", ce qui aujourd'hui n'est plus admissible. Utilisez deux ordonnées différentes, une à gauche et une à droite, et indiquez les unités correctes. À cette occasion, n'hésitez pas à proposer d'autres représentations que des barres et des surface/courbes pour les deux jeux de données si ceci vous paraît judicieux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "ax1.bar(sorted_data.index, sorted_data['Wheat'], width=5, color='black', label='Wheat price')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(sorted_data.index, sorted_data['Wages'], color='red', label='Wages')\n",
    "ax2.fill_between(sorted_data.index, sorted_data['Wages'], color='skyblue', alpha=0.5)\n",
    "ax1.set_ylim(0,100)\n",
    "ax2.set_ylim(0,100)\n",
    "ax1.set_xlim(1565,1810)\n",
    "ax2.set_xlim(1565,1810)\n",
    "ax1.set_ylabel('Weat price (Shillings par quart de boisseau de blé)')\n",
    "ax2.set_ylabel('Wages (Shillings par semaine)')\n",
    "fig.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 3\n",
    "\n",
    "3. L'objectif de Playfair était de montrer que le pouvoir d'achat des ouvriers avait augmenté au cours du temps. Essayez de mieux faire ressortir cette information. Pour cela, faites une représentation graphique du pouvoir d'achat au cours du temps, définie comme la quantité de blé qu'un ouvrier peut acheter avec son salaire hebdomadaire. Dans un autre graphique, montrez les deux quantités (prix du blé, salaire) sur deux axes différents, sans l'axe du temps. Trouvez une autre façon d'indiquer la progression du temps dans ce graphique. Quelle représentation des données vous paraît la plus claire ? N'hésitez pas à en proposer d'autres. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calcul du pouvoir d'achat, puis tracé : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pa = sorted_data['Wages']/sorted_data['Wheat']\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.bar(sorted_data.index, pa, color='blue', width=4)\n",
    "plt.title('Evolution du pouvoir d\\'achat = quantité de blé qu\\'un ouvrier peut acheter avec son salaire hebdomadaire')\n",
    "plt.xlabel('Année')\n",
    "plt.ylabel('quantité de blé qu\\'un ouvrier peut acheter\\n avec son salaire hebdomadaire \\n(quart de boisseau de blé)')\n",
    "plt.xlim(1565,1810)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evolution du prix du blé en fonction du salaire : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "plt.plot(sorted_data['Wages'], sorted_data['Wheat'], color='blue')\n",
    "plt.xlabel('Salaires (Shillings par semaine)')\n",
    "plt.ylabel('Prix du blé (Shillings par quart de boisseau de blé)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "plt.plot(sorted_data['Wages'], sorted_data['Wheat'], color='blue')\n",
    "plt.xlabel('Salaires (Shillings par semaine)')\n",
    "plt.ylabel('Prix du blé (Shillings par quart de boisseau de blé)')\n",
    "\n",
    "# Annoter chaque point avec l'année\n",
    "for year, x, y in zip(sorted_data.index, sorted_data['Wages'], sorted_data['Wheat']):\n",
    "    plt.annotate(str(year), (x, y), fontsize=8, alpha=0.6)\n",
    "\n",
    "plt.title(\"Prix du blé en fonction des salaires\\n(avec année annotée)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La représentation la plus claire pour démontrer le propos de Playfair est clairement celui du pouvoir d'achat en fonction du temps."
   ]
  }
 ],
 "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
}