{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    " %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Des valeurs obtenues par numérisation du graphe sont aujourd'hui téléchargeables, [la version en format CSV](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv) étant la plus pratique, qui commence en 1565 et se termine 1821."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <td>5.15</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
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       "      <td>5.25</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1595</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.54</td>\n",
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       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1600</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.61</td>\n",
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       "      <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",
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       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1615</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.94</td>\n",
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       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1620</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.01</td>\n",
       "    </tr>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1680</td>\n",
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       "      <td>7.30</td>\n",
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       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1685</td>\n",
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       "      <td>7.60</td>\n",
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       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1690</td>\n",
       "      <td>40.0</td>\n",
       "      <td>8.00</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>1695</td>\n",
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       "      <th>27</th>\n",
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       "      <td>1700</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.00</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1705</td>\n",
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       "      <td>10.00</td>\n",
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       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>1710</td>\n",
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       "      <td>11.00</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>1740</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.00</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>1745</td>\n",
       "      <td>27.5</td>\n",
       "      <td>14.50</td>\n",
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       "    <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",
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      ],
      "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\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": 3,
     "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 cherche les données manquantes : lines 51,52,53"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Year</th>\n",
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       "      <th>50</th>\n",
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       "      <th>51</th>\n",
       "      <td>52</td>\n",
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      "text/plain": [
       "    Unnamed: 0  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": 4,
     "metadata": {},
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    }
   ],
   "source": [
    " raw_data[raw_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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",
       "    <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": [
       "    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\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data.dropna().copy()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Première partie*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On extrait les colonnes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)\n",
    "\n",
    "X = list(df.iloc[:, 1])\n",
    "Y = list(df.iloc[:, 2])\n",
    "Z = list(df.iloc[:, 3])\n",
    "T = list(df.iloc[:, 0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Graphe du prix du blé en barre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Wheat')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SryZ5Rte+s8uQHNzd37F9udlZvwEOTXJF1/7srm2l9Pt+zwZuq6rru+WVvr3hF/sMK3tbvwn46yS3AH8DvK1rn9ptPXWHuYpVwL7A0cAzgPOTPJHBEHNH9QDty83O+r0VeHxV/TDJkcCnkxzOyun3/V7Bz7+TXunbG36xzyt9W78OeHNVfTLJy4FzgecxxdvagJg+W4BPdUPKS5Pcx+A6LTu7DMmW7v6O7ctNs99VtQDcP+10WZIbGYw2Vkq/SbIKeBlw5FDzit7erT5304sreVuvA87o7n8COKe7P7Xb2imm6fNp4FiAJE8GHsrgIl4XAqck2TPJocAa4NKq2grckeTobk7394GN/ZT+oDT7nWQug88JoRtRrAFuWkH9hsG7yG9X1fB0wkrf3r/Q5xnY1rcCz+3uHwvcP7U2vdu67739s/zFYHi9FbiHwbuFUxn8Y/wQsBm4HDh2aP0/ZXCEw3UMHc0ArO3WvxH4B7oTIKf1ayn9Bn4XuJrBUR6XAy9eSf3u2t8PvLax/rLf3kvp80rf1sBvApd1/bsEOHLat7VnUkuSmpxikiQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhLSIDX0/ygqG2lyf5XJ91SePmYa7SCJIcweDs16cx+DjcK4ETqurGB/Gaq6rq3t1UorTbGRDSiDL4jIo7gUcCd1TVXyRZB5zO4ES/bwKvr6r7kqxncLnnhwMfr6o/715jC/AvwAnAexlcPuE1DE6ouqqqXjnhbkk75bWYpNGdzeAM37uBtd2o4qXAMVV1bxcKpwAfAc6sqtu7aw59OckFVXVN9zp3VtWzAJJsBZ5QVXcnefTEeyQ9AANCGlFV3Znk48D/VtVdSZ7H4Mqz890HfT2c7ZdtfkWSUxn8jT2WwYfC3B8QHx962auBDyXZyOB6VNLUMCCkpbmv+4LB5ZjPq6p3DK+QZA2Dq3YeVVU/SvIh4GFDq9w5dP94BhdwOxH4syRHVNVPx1a9tAQexSTtui8CL0+yP0CSxyR5PINPDbsD+En3qWDHt57cXbn0kKr6EvDHwBzwiIlULo3AEYS0i6rqqiRnA19M8hAGO5pfC8wzmE7aDNwEfGMnL7EK+EiSRzF4s/aXVXXH+CuXRuNRTJKkJqeYJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWr6fxdbOzp2b+E1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(X, Y, color='g')\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Wheat\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Graphe des salaires par une surface bleue délimitée par une courbe rouge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'wages')"
      ]
     },
     "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.fill_between(X, Z, color='b')\n",
    "plt.plot(X,Z,'r')\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"wages\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Superposition  deux graphes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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.bar(X, Y, color='g')\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Wheat\")\n",
    "plt.fill_between(X, Z, color='b')\n",
    "plt.plot(X,Z,'r')\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"wages\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Deuxième partie*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5383c5b160>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "F = list(df.iloc[:, 1])\n",
    "D = list(df.iloc[:, 2]*6.8)\n",
    "K = list(df.iloc[:, 3]*(52/6.8))\n",
    "plt.plot(F, D, 'g')\n",
    "plt.plot(F, K, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/+BsH+WXpf4ddSUYooERECsGWLfidd3JvyddZ1vDxsKvJCAWUiEghuOEG4nH475JfhF1JxiigRETy3YoV+NSp3GWjeaXx+LCryRgFlIhIPksk4Morea20K//DT8OuJqN0FZ+ISD774x/hH/9gbKfJ1DV1DbuajFIPKofshvydckREImjfPvzHP2ZR+Rnc3fD1sKvJOPWgRETy1U9+gu/ew3c7P4k3Fl5/o/COSESkGCxYgE+cyF0lV/Ni/cCwq8kKBZSISL5Zvx6/5BI2l/fjZ/6rsKvJGgWUiEg+2bcPv/BCDux3LrS/sS/x/rAryhp9BiUiki8aG+FLX6Jp3Qa+VP4U1bGPhl1RVimgRETygTuMHg1PP81VnaYyN/aZsCvKOg3xiYjkg1tugXvu4aaysdzTMLL1/QuAelAiIlE3cyZ+3XXMLPsS18f/J+xqckY9KBGRKFuyBP/yl1lWXsFXmqbhRfS/7eI5UhGRfLNwIT5kCDvj3Rjms3jD3x12RTmlgBIRiaJnnsGHDGHbwa4MZj5bG48Nu6KcU0CJiETNo4/i55/P+ngfzubvvNzQJ+yKQqGAEhGJijffhGuugYsvZpmfzNlN89nU8KGwqwqNAkokR8xsipnVmtnKlLauZjbXzNYFt0enPDbWzNab2VozOy+cqiVnli7FKypg/HjuKP0eZzQsYFf8g2FXFSoFlOSFAlmqZCow9LC2McA8d+8HzAvuY2b9gRHAScFzJphZae5KlZxJJODmm/HTT2fP+n0M6/wE3226lXq6hF1Z6BRQIjni7guAvYc1DwemBdvTgItS2qe7e8zdNwLrgUE5KVRyp6YGzj0XrruOvyYu4F8Sy3ks9oWwq4oMBZTktQLoWfVw9x0Awe0xQftxwNaU/WqCtiOY2SgzqzKzqrq6uqwWKxn08sv4KacQW/AiV3W6h2Hxh9gZ7xZ2VZHSakCZWRczqzSzZWZWbWY3BO0aOxfJnuaS15vb0d0nuXuFu1d07949y2VJRuzZg19wAa/tNwaVLOauhm/R/H/y4pZODyoGnOPuA4CBwFAz+xQhjZ0XwF/MIql2mVlPgOC2NmivAXqn7NcL2J7j2iQbYjG45BLiG7dyiT3K8oaPhV1RZLUaUJ70enC3PPhxNHYukgmzgEMzf44EZqa0jzCzzmbWF+gHVIZQn2SSO3z727BgAaNKp/BM7NNhVxRpaX0GZWalZraU5F93c919IR0cO9e4uRQbM3sAeAE40cxqzOwK4EbgXDNbB5wb3Mfdq4EZwCpgDjDa3ZvCqVwyZsYMmDaNX5f9nKkNXw67mshLK6DcvcndB5IcZhhkZp94h93TGjvXuHk05cMQaj7U2Bx3v9zde7p7ubv3cvfJ7r7H3Ye4e7/gdm/K/uPc/SPufqK7zw6zdsmA+nr8+uup7jSQn8V/HnY1eaFNV/G5+6vAsyQ/W9LYuYhIum69Fdu8metKbiGBvtKWjnSu4utuZh8Itt8FfB5Yg8bOQ5WvvQiRorRzJ/7rX/PXsuHMrv9c2NXkjXQWLOwJTAuuxCsBZrj7X83sBWBGMI6+BbgUkmPnZnZo7DyOxs5FpNj9/OckDtYztuw3YVeSV1oNKHdfDpzSTPseYEgLzxkHjOtwda2wGwz/RbNfDRERiYbly/HJk5lg32VlwwlhV5NXNJOEiEi2uMMPfsDrpe/nV/6zsKvJO+kM8YmISHv87W8wbx43lN/O7kTXsKvJO3nRg9IFASKSdxob8R/9iI2dTuD2xivDriYvqQclIpINEydia9fy486ziFMedjV5SQElIpJp+/bhv/wlC8qH8GDswrCryVt5McQnIpJXbroJ37uP60puQbOUt58CSkQkk958E797Eo+VXsyi2MlhV5PXFFBZpgs8RIrM9OnY3j3cXX512JXkPX0GJSKSKe74HXfwcvlJzK4fHHY1eU89KBGRTHnhBeyll5hgV6PPnjpOASUikinjx/N62fuZ3PCVsCspCAqoPKLPs0QibMcO/C9/4Q/+Dd7gqLCrKQgKKBGRTJg0CYvHmVhyVdiVFAxdJCEi0lENDfjEiTxVOpRVjf3CrqZgqAclItJRDz+M7dzJxPJrwq6koCigREQ66o472FL+ER6pHxp2JQVFAVUAdPGESIiWLIHnn+dORuP6X2pG6bcpItIR48dTX/puJjV+I+xKCo4CSkSkvXbvxu+/n3v5Kq/ygbCrKTgKKBGR9po8GYvFmFiqefeyQZeZi4i0x6ZN+M0381zZYJY0fCLsagqSelCBTF5oUCgXLRTKcYhk3P79+IUX8sZrcb5bdlfY1RQs9aBERNoiHofLLiOxei2Xlc9haf3Hwq6oYKkH1Ywo9RxyUYt6jyJt8P3vw5w5fLdsAo/HhoRdTUFTQImIpGv8eBg/nt+V/YAJDf8ZdjUFTwElOadeluSl2bPxa6/l8fJhfD/+m7CrKQqtBpSZ9TazZ8xstZlVm9m1QXtXM5trZuuC26NTnjPWzNab2VozOy+bByAiknUrV+KXXcbqspO5vOk+EpSGXVFRSKcHFQd+6O4fBz4FjDaz/sAYYJ679wPmBfcJHhsBnAQMBSaYmf5rikh+2rIFv/BC9sSO4l95jP0JrfWUK60GlLvvcPclwfYBYDVwHDAcmBbsNg24KNgeDkx395i7bwTWA4MyXbh0jIbZRNKwbh1+1lkc3P4qw0seY0NDr7ArKipt+gzKzPoApwALgR7uvgOSIQYcE+x2HLA15Wk1QdvhrzXKzKrMrKqurq7tlYuIZNPy5fjZZ7N/15ucW/oMz9efGnZFRSftgDKzo4CHgO+5+/532rWZNj+iwX2Su1e4e0X37t3TLUNEJPuefBIfPJi6fWV81v7OC/WnhF1RUUoroMysnGQ43efuDwfNu8ysZ/B4T6A2aK8Beqc8vRewPTPliohkUX09XHstnHce6974EGfzHMti+iJuWNK5is+AycBqd78l5aFZwMhgeyQwM6V9hJl1NrO+QD+gMnMli4hkwYoV+Gmnwe9+x4SyaxjYsIiXG/qEXVVRS2eqozOBrwIrzGxp0PZfwI3ADDO7AtgCXArg7tVmNgNYRfIKwNHu3pTxykVEMiGRgNtvx8eMYR9Hc0Xnx3k0dn7YVQlpBJS7P0fznysBNDvPh7uPA8Z1oC4pMHaD4b844qNIkfAkElBdDT/8Icydy5yyf+Vb/J7t8WNaf67kRFHPJKFLrbNLv1+JlIYGeOEFuOkmGDaMRLfucPLJxJ5+jmvKJ/LF+EyFU8RoNnORCDCzTcABoAmIu3uFmXUF/gz0ATYB/+7u+8KqMa/U1sJjj8G6dbB5M75pE/7SUkpi9QBsKOvH/KaLWNTlLB6LfYGapiO+CSMRoIASiY7PufvulPuHZmu50czGBPevD6e0PLBjBzz1FNx/Pz53LtbURIN1YltJbzYljmdlyXdY2OUs5tafRW28R/I59eGWLO9MASUSXcOBwcH2NOBZFFCwezesWQNbtiR/1qzB//4ctuEVALaVf5j7+DF/Lr+clxpPwpuCTzKagh/JGwookWhw4Ekzc+Bud5/EYbO1mFmzH5CY2ShgFMDxxx+fq3qz7803Ye3a5IUMq1bB8uUkXlpKybaat+1WV9qD5xNn8GLnq5jfdDYvNp6KU6IwKgAKKJFoONPdtwchNNfM1qT7xCDMJgFUVFTk56WSjY2wZAn84x+wZAm+dCmsWYM1JVMmbmWsLzmRxYnBrOo8kJV+Emsb+rCV3hxsek/yNWIh1i9ZoYASiQB33x7c1prZIyQnWN5lZj2D3lPqbC35r7ERFi2Cp5+GZ54h8fwLlNS/CcCOsl4sbjqFVeUXs6L8ZF6q78/L3o/Gpk7J5yqIioYCSiRkZvYeoMTdDwTbXwB+xVuztdzI22dryT/V1fDEE7BqFV5djS9fQcnBN0hgVJcNYL6P+ucFDLvixyaf0xBuyRI+BZRI+HoAjyRnFaMMuN/d55jZIpqZrSVv7NwJjzyC/+EP2KJFAOwuPYZq70+1fZPnu3yW2fWD2Rv/YHJ/fWYkh1FAiYTM3TcAA5pp30MLs7VETm0tLF0KL70EVVUkXlxISU1y1Z3V5ScztdOt3Nd4Gduber71HAWStEIBJSJt09gI998PixfDqlUkVlZTsmvnPx/eUtaX55vOZFnnQTzV9DmqGgfQ8mxpIi1TQIlI+latwr/2NWzxYt4oOYrV1p9VifNY02UAi5sGsqhxAPviXZP76mIG6SAFlIi07rXX4J578J/+lP2J93Jlpwd5oOES/tkz0owMkgUKKBFJfil2587kdEF1dbB3b/Jn40b8uedg+XLMncfLhvNt7mZbY4+wK5YioIASKQYHDsBDD701PdCWLSRq6/Dde2HfXkoPvt7s0w6WvIcXOYPny3/JfAbzVMPZ6PMkyRUFlEgxqK+Hb3wDgNrSY9nsx7MjcTyvlQ7k1ZKu7O3cjZ3Wk5qmY9ne2J09fJC9dOVA4r2A6TtJEgoFlEgx6NaNv922jv8Y05vX6ju/1a4JVCXCinrBQpGiYcbrx36URHnn1vcViQgFlIiIRJICSkREIkkBJSIikaSAEhGRSFJAiYhIJCmgREQkkhRQIiISSQooERGJpFYDysymmFmtma1MaetqZnPNbF1we3TKY2PNbL2ZrTWz87JVuIiIFLZ0elBTgaGHtY0B5rl7P2BecB8z6w+MAE4KnjPBzEozVq2IiBSNVgPK3RcAew9rHg5MC7anAReltE9395i7bwTWA4MyVKuIiBSR9n4G1cPddwAEt8cE7ccBW1P2qwnajmBmo8ysysyq6urq2lmGiIgUqkxfJNHcQjHe3I7uPsndK9y9onv37hkuQ0RE8l17A2qXmfUECG5rg/YaoHfKfr2A7e0vT0REilV7A2oWMDLYHgnMTGkfYWadzawv0A+o7FiJIiJSjFpdsNDMHgAGA93MrAb4BXAjMMPMrgC2AJcCuHu1mc0AVgFxYLS7azk0ERFps1YDyt0vb+GhIS3sPw4Y15GiRERENJOEiIhEkgJKREQiSQElIiKRpIASEZFIUkCJiEgkKaBERCSSFFAiIhJJCigREYkkBZSIiESSAkpERCJJASUiIpGkgBIRkUhSQImISCQpoEREJJIUUCIRZmZDzWytma03szFh1yOSSwookYgys1LgTuB8oD9wuZn1D7cqkdxRQIlE1yBgvbtvcPcGYDowPOSaRHJGASUSXccBW1Pu1wRt7dbY2KF6RNKSqX9nrS75LiKhsWba/IidzEYBowCOP/74Fl/sk5+E4cOhqSlj9Yk0q7QUKio6/joKKJHoqgF6p9zvBWw/fCd3nwRMAqioqDgiwA7p1w+mT890iSLZoyE+kehaBPQzs75m1gkYAcwKuSaRnFEPSiSi3D1uZlcDTwClwBR3rw65LJGcUUCJRJi7Pw48HnYdImHQEJ+IiESSAkpERCJJASUiIpGUtYDSHGIiItIRWQkozSEmIiIdla0elOYQExGRDjH3Fr943v4XNfsSMNTdvxXc/ypwurtfnbLPP6dnAU4E1ma8kNZ1A3aH8L5RoGNvnw+7e/dMFpNJZlYHbA7hrfXvqTi199jTOo+y9T2oVucQS52eJSxmVuXuGZgxKv/o2Avz2MMKz0L+nbZGx569Y8/WEF9ac4iJiIi0JFsBpTnERESkQ7IyxJdHc4iFOsQYMh27ZFIx/0517FmSlYskREREOkozSYiISCQpoEREJJIKLqDMbIqZ1ZrZysParwmmXqo2s9+ktI8NpmNaa2bnpbSfamYrgsd+Z2bNXTofKc0du5n92cyWBj+bzGxpymOFfuwDzezF4NirzGxQymMFc+zZoPNI51FKW3jnkbsX1A/wGeCTwMqUts8BTwGdg/vHBLf9gWVAZ6Av8ApQGjxWCZxB8jtds4Hzwz629hz7YY//Fvh5sRw78OSh2oEvAs8W4rHn8Pep88h1HuXyPCq4HpS7LwD2HtZ8JXCju8eCfWqD9uHAdHePuftGYD0wyMx6Au9z9xc8+dv+I3BRbo6g/Vo4dgCCv2D+HXggaCqGY3fgfcH2+3nru3gFdezZoPNI51FqMyGdRwUXUC04ATjbzBaa2XwzOy1oPw7YmrJfTdB2XLB9eHs+OxvY5e7rgvvFwS5jAAABZ0lEQVTFcOzfA24ys63AzcDYoL0Yjj0bdB7pPMrpeVQsAVUGHA18CrgOmBH8JdTSlEytTtWUhy7nrb/6oDiO/Urg++7eG/g+MDloL4ZjzwadRzqPcnoeFUtA1QAPe1IlkCA5yWFLUzLVBNuHt+clMysDLgH+nNJcDMc+Eng42P4LyVn2oTiOPRt0Huk8yul5VCwB9ShwDoCZnQB0IjkD7yxghJl1NrO+QD+g0t13AAfM7FPBX4hfA2aGU3pGfB5Y4+6p3e5iOPbtwGeD7XOAQ8MyxXDs2aDzSOdRbs+jsK8ayfQPye73DqCRZJJfQfJE+hOwElgCnJOy/09IXn2ylpQrTYCKYP9XgPEEs25E+ae5Yw/apwLfaWb/gj524CxgMckrjRYCpxbisefw96nzSOdRTs8jTXUkIiKRVCxDfCIikmcUUCIiEkkKKBERiSQFlIiIRJICSkREIkkBJSIikaSAEhGRSPo/GbIl5ndVMH4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121) # Graphique côte à côte\n",
    "plt.bar(F, D, color='g')\n",
    "\n",
    "\n",
    "\n",
    "plt.subplot(122) \n",
    "\n",
    "plt.fill_between(F, K, color='b')\n",
    "plt.plot(F,K,'r')\n",
    "\n",
    "plt.tight_layout() # Adaptation de l'affichage \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Troisième partie*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = list(df.iloc[:, 1])\n",
    "Y = list(df.iloc[:, 2])\n",
    "Z = list(df.iloc[:, 3])\n",
    "C =  list(df.iloc[:, 3]/df.iloc[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f537fabef60>]"
      ]
     },
     "execution_count": 15,
     "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(X, C, 'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121) # Graphique côte à côte\n",
    "plt.bar(T, D, color='g')\n",
    "\n",
    "\n",
    "\n",
    "plt.subplot(122) \n",
    "\n",
    "plt.fill_between(T, K, color='b')\n",
    "plt.plot(T,K,'r')\n",
    "\n",
    "plt.tight_layout() # Adaptation de l'affichage \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}