{
 "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 numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import isoweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1570</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.05</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1575</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.08</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1580</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.12</td>\n",
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       "    <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": [
       "    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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\")\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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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>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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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous supprimons ces lignes qui ne contiennent pas des données valables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "  <tbody>\n",
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       "      <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_1 = data.dropna().copy()\n",
    "\n",
    "data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5\n",
       "1      5\n",
       "2      5\n",
       "3      5\n",
       "4      5\n",
       "5      5\n",
       "6      5\n",
       "7      5\n",
       "8      5\n",
       "9      5\n",
       "10     5\n",
       "11     6\n",
       "12     6\n",
       "13     6\n",
       "14     6\n",
       "15     6\n",
       "16     6\n",
       "17     6\n",
       "18     6\n",
       "19     6\n",
       "20     6\n",
       "21     6\n",
       "22     7\n",
       "23     7\n",
       "24     7\n",
       "25     8\n",
       "26     8\n",
       "27     9\n",
       "28    10\n",
       "29    11\n",
       "30    11\n",
       "31    12\n",
       "32    13\n",
       "33    13\n",
       "34    13\n",
       "35    14\n",
       "36    14\n",
       "37    15\n",
       "38    15\n",
       "39    16\n",
       "40    17\n",
       "41    18\n",
       "42    19\n",
       "43    21\n",
       "44    23\n",
       "45    25\n",
       "46    27\n",
       "47    28\n",
       "48    29\n",
       "49    30\n",
       "Name: Wages, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y1 = data_1['Wages'].astype(int)\n",
    "Y2 = data_1['Wheat'].astype(int)\n",
    "X1 = data_1['Year'].astype(int)\n",
    "Y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7f9539e8c908>"
      ]
     },
     "execution_count": 7,
     "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(X1,Y1,'r')\n",
    "plt.fill_between(X1, Y1,color='#539ecd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 50 artists>"
      ]
     },
     "execution_count": 8,
     "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.bar(X1,Y2,color='green')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 50 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax=plt.subplots()\n",
    "ax.plot(X1,Y1,'r')\n",
    "plt.fill_between(X1, Y1,color='#539ecd')\n",
    "ax.bar(X1,Y2,color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WrCyNNDSGa1BBTWIKrSXFhV9/CRwIvGpmAyQdCYxoXIzOOZf9xk8tYZuiQk7qv1PDK7uGvCjpJWBsOH0m8ELUxlGS0xYzWyupQFKBmb0u6bZkInXOuWy1cXMlz8xexon9urJtq8h3PFwdzOynYemiQwi6kd9vZk9FbR/lG1gvaVvgTeBRSauAyqSidc65LPXc7GWUV1Rx5v494w4ln7xNUJO1GpjamIZRrv8NA74ErgReBBbxdZ0k55zLC+OmltCn87YM7Nk+7lDygqQfE/TWO5VgDMD/SPpR1PZRHsItC3e0HfBsknE651zW+mDFBmaVrOfGE/dG8tFuU+SnwAAzWwsQjqj+NvBglMZRhsy4EPgFwdlTNWEJCmDXJAN2zrmsMn5qCUWFBZw6oFvcoeSTJUBpwnQpUBK1cZR7TtcAfc1sTSMDc865rLdpSxVPzVzKsX0706G4KO5wcp6kq8KPSwkevJ1EcEIzjOAyXyRRktMioLzRETrnXA54af4K1pdvYcQQ7wiRIm3D90Xhq8akxmwkSnK6Hnhb0rt8c8iMy+pu4pxzuWH81BJ6dGjDQbvuEHcoecHMbknFdqIkp/uAfwJzCe45uSaShA8s7Fz8PltbxtuL1nLNsX0oKPCOENkkSnKqNLOrGl7NOedyy4RpJRQITh/k5YqyTZTnnF6XdIGkrpI61LwaaiSph6TXJb0vab6ky8P5HSS9Iumj8H37Jh+Fc841UmVVNROnLeGoPXekS7vWDTdwGRUlOZ1NeN8JmB6+pkVoVwlcbWZ7EdTmu0TS3sB1wGtm1ht4LZx2zrmMen3BalaVbvaKEGkiqY+k1yTNC6f7SbohavsGk5OZ9arl1eAzTma23MxmhJ9LgfeBbgTdCR8KV3sIOCVqsM45lyrjpy5mx7atOHKPTnGHkq/+QnBiswXAzOYAZ0VtHGXIjG0k3SDp/nC6t6RGDb8raRdgAPAu0DlhpMTlwI51tLlA0jRJ0yorvZSfcy51VnyxiX9+sIrTB3WnRWHkURzykqT2kh6X9EF4G+agFN1+2cbMtn6uKfIv8yjfyt+ACuDgcHoJ8L9RdxAWjX0CuMLMIg/vbmb3m9lgMxvcooVXCHbOpc7j00uoNny028BdwItmtifQn+AqVypuv6yRtBvBA7iEo+Auj9o4SnLazcxu5+tTsy8JShg1SFJLgsT0qJk9Gc5eKalruLwrsKqu9s45l2rV1cb4aSUcvNsO7LxDcdzhxCqsmXoY8ACAmVWY2XpSc/vlEoJHkfaUtBS4ArgoauMoyalCUhu+zn67kfAwbl0UVE98AHjfzO5MWPQMcG74+Vwa+dSwc841xTsfr6Vk3ZfN5aypRc3tkfB1wVbLdwVWA3+TNFPSXyUVE/H2S33M7GMzOxroBOxpZoeY2aeRA4+wzk0EQ2X0kPQoMBT4rwjthgI/BOZKmhXO+zlwKzBB0ihgMXBG1GCdc66pxk0toV2blhzXt0vcoWRCpZkNrmd5C2AgcKmZvSvpLprYgzqhtt7W8wHY6mSl3sDqZWavSJpB0B1cwOVRisCa2WTqvvz3nSjBOedcKq0rq+CleSs4+4CetG5ZGHc42WAJsMTM3g2nHydITisldTWz5UncfqmprbcHsD/B1TIIxgF8M+pGovTWGwpsMrPngfbAzyXt3IhAnXMuKzw1cykVVdWcNaRZXNJrkJmtAEok7RHO+g7wHk24/WJmt4T19ToCA83sajO7GhgEdI+6nSiX9e4B+kvqTzB41IPAw8DhUXfinHNxq642xk9dzH492rNnl+3iDiebXAo8KqkI+Bg4j+DEpam3X3oS9PSuUQHsErVx1Np6JmkY8Acze0DSuQ22cs65LHLPG4v4cOVGfndm/7hDySpmNguo7b5UU2+/PAJMkfQUQYe6U/m6B2CDoiSnUknXAyOBwyQVAi2TidQ55+Lw5oeruePlBQzbbydO2c9Hu80EM/uVpH8Ah4azzjOzmVHbq6GhGyR1IaivN9XM/i2pJ3CEmT2cbNCNVVxcbGVlZZnaXdr5kBnOZU7JunJO+uNkOrdtzVOXHMw2Rel9qL+u/9+Nnd/QsoixlJtZTj7M1WByygaenJxzydi0pYoz7n2HT9eU8cylh9CrY/p/T3tySg2vC+Scy1s3TZrP3KVf8JdzBmckMbnUad4VD51zeWvclMWMn1bCT47cnWP27hx3OM2OpNuizKtLvclJUqGkMckE5pxzcZldsp7/mTSfQ3t35Mpj+sQdTnN1TC3zvhu1cb2X9cysSlInSUVmVlHfus45lw3WlVVw0ZjpdGrbij+cNYDCgkh1ql2KSLoIuBjYTdKchEVtgbeibifKPadPgbckPQN81Sshan0k55zLlKpq47KxM1lTVsETow9m++KiuENqjh4D/gH8H9+s01dqZuuibiRKcloWvgr4umaSc85lnd++vIDJC9dw+/f7sW/3dnGH0yyZ2ReSSoF9zeyzZLcTpfDrLclu3LlE3oXepdNL81fw538tYsSQngxvHsNhZC0zq5Y0W1JPM1uczDYaTE6SOgHXAn2B1gk7PyqZHTrnXKp9vHojV0+YTf/u7bj55L3jDscFugLzJU3hm7eETo7SOMplvUeB8cCJwGiCCrWrGx+nc86lXtnmSi58ZDpFLQr488hBtGrhQ2FkiSZddYuSnHYIi71ebmZvAG9IeqMpO3XOuVQwM372xBwWrd7II6MOoFv7NnGH5EJhvkhalIdwt4TvyyWdIGkAjRiTwznn0uXBtz7luTnL+elxezJ0945xh+MSSDpQ0lRJGyVVSKqStCFq+yhnTv8rqR1wNXA3sB1wZZLxOudcSrz78Vp+/cL7HNe3M6MP3zXucNy3/RE4C5hIMCTHOUDvqI2j9NZ7Lvz4BXBkEgE616x5L8XUW7lhE5c8NpOdO2zDHWf0R/IHbbORmS2UVGhmVcDfJL0dtW2UYdp3lfSspDWSVkmaJMn/THHOxaKispqLH51BeUUl9/1wEG1b+/ByWao8HF13lqTbJV0JRK6+G+We02PABKALsBPBKdrYZCLNdf7XmXPx+9Xz7zH9s8+5/fR+9O7sdQGy2A+BQuAnBF3JewDfj9o4yj0nmdkjCdNjJP2kUSE651wKPDVzCQ+98xk/PqQXJ/bbKe5wXD0SqkN8SRLdyqMkp9clXQeMIxgH/kzgeUkdwgAi10pyzrlkvbdsA9c/OZchvTrws+/uGXc4rgGSPiHIGd9gZpFuC0VJTmeG7xduNf9H4Y79/pNzLq2+KN/C6DHTademJX86eyAtC30ouhwwOOFza+AMoEPUxlF66/VKIiiH99JyLhWqq42rJsxi2fovGX/hgXRq26rBNpn4v+f/v+tnZmu3mvV7SZOB/4nS3odpd85ltT++vpDXPljFLSf3ZdDOkf/wdjGTNDBhsoDgTCpyDxZPTs65rPX6glX87tUPOWW/nTjnoJ3jDsc1zm8TPlcSjA04PGpjT04uJ/kllfxXsq6cK8bNYo/Obfm/0/ql9VEO//eUembWpKINUYbMGArMMrMySSOBgcBdTRlEyjnn6rNpSxWjx0yn2oz7fjiINkVeaTzXSLqqvuUNjaYepcvLPQRP+vYnGNfpM+DhyBFmKX+gNjr/WblMMjNueHoe85dt4Pdn7sfOO0QuKuCyy2DgIqBb+BoN7E1w36nBe09RklOlBee7wwjOmO6KsmFJD4bljuYlzLtZ0lJJs8LX9yLs3znXjDw2ZTGPT1/CZd/pzXf26hx3OC55HYGBZna1mV0NDAK6m9ktUUZYj5KcSiVdD4wkePi2EIhSzOrvwPG1zP+dme0Xvl6IsB3nXCjfz2JnLv6cm5+Zz+F9OnH5dyIXsI4s339+WaYnUJEwXQHsErVx1IdwzwZGmdkKST2B3zTUyMzelBQ5EOdc87Zm42YufnQGnbdrzV1n7UdhgSeSHPcIMEXSUwQFG04FHoraWOnsoRImp+fMbJ9w+mbgv4ANwDTgajP7vKHtFBcXW1lZWUOrNTa2RvfOaWybutbPtZ5BqYo3lcedjTFlYh+59m9na+vLKzj/4Wl8urb8W8s2VVRRUVXNExcdzD7d2kXaXmP/j6Xy/2Qm9t3U71tSuZnFdtMufNbp0HDyTTObGbVtlN56pXy7PtIXfJ1cPo66M4LOFb8Mt/dLgn7wP6pjvxcAFwAUFRU1YhfOuWxUVW1cNm4Ws0u+4LSB3Wq9xHbCvl0jJyaX/cxsBjAjmbZRLuvdCSwjGDpDBCMbdgEWAA8CR0TdmZmtrPks6S/Ac/Wsez9wPwRnTlH3kQq5/tdprvGfd/Nw16sf8uaHq/n1qfty9gE94w4nsnz/9xn2I5gGLDWzE8Oi3uMJ7g99CgyPcoUr1aJ0iDjezO4zs1Iz2xAmje+Z2Xhg+8bsTFLXhMlTgXl1reucyx+vvb+SP/xzIcMHd2fEkB5xh+O+6XLg/YTp64DXzKw38Fo4nXFRklO1pOGSCsJXYvmJOv+ckDQWeAfYQ9ISSaOA2yXNlTSHYMj3K5sUfQSZ6J3jPYCcq9una8q4Yvws9um2Hb8Yto//f8kikroDJwB/TZg9jK87LjwEnJLpuCDaZb0fAHcBfyZIRv8BRkpqQzDCYa3MbEQtsx9IJkjnXG4qr6hk9JjpFBaIe34wiNYtvdJDlvk9QXGFxGdXO5vZcgAzWy5px2Q2LOk04DZgR4JbQgo2adtFaR9lyIyPgZPqWDw5YpzOJSWXrvfnUqyZYGZc/+RcFqws5e/nDaFHh23iDqk5aiFpWsL0/eGtGSSdCKwys+mSjkjDvm8HTjKz9xtcsxZReut1As4nuDn21fpmVmsvO+ecA3jo7U+ZNGsZVx/Th8P7dIo7nOaq0swG17FsKHByWKmnNbCdpDHASkldw7OmrsCqJPe9MtnEBNEu600C/g28ClQluyPXfPgZRPZL1Xc0cVoJNzw9j82V1bUuP3qvHbnkyN2bvB+XemZ2PXA9QHjmdI2ZjZT0G+Bc4NbwfVKSu5gmaTzwNLA5Yb9PRmkcJTltY2Y/SzI451yemlWynv9+ah79urfj4N07fmt5cVEhZx/QkwKv9JBrbgUmhJ3YFhMMr56M7YBy4NiEeQZESk4NVoiQ9L/A23HWwWtKhYhUPpUd59PocUp3ZYxUfheNlYnqDdlYIaKp21m7cTMn3T2ZggLx3KWH0H6beB6Uj7NKg1eISK8oZ06XAz+XtBnYQiN7XDjn8ktlVTWXjp3J2rIKnrjo4NgSk8tukv5GLY8bRe2vEKW3XuQx353LVrl2tprN7nj5Q95etJbfnN7PSw25+iRWAGpNUHhhWdTGdSYnSXua2Qdh4b5vCWsmOeeakRfnLefeNxZx9gE9OWOwV3pwdTOzJxKnw8IMr0ZtX9+Z09UEXch/W9t+gaOi7sQ5l/sWrtrINRPn0L9He246ae+4w3G5pzfBGE+R1JmczOz88P3IFATlnMsyq0s3N7xSqKKqmtFjplPUooB7fjCQVi280oOrXy0jWqwAIvf8ru+y3mn1NYzaV905l12WfB6MpbT/ryJfYQGgQPDIqAPYqX2bdITl8kxT+yvUd1mvrpJF0Ii+6i67eMeA6PLxZ7VpSxUXjQluF9980t4UFkap/RzYs0tb9t+lQ7pCy8uft0tefZf1zstkIM659Ltp0nzmLv0CgP8a2ivmaJyrW32X9a6qr6GZ3Zn6cJxz6TJ2ymLGTyvh0qN255rb4o7GufrVd07ftoGXcy5HzC5Zz02T5nNYn05ccXSfuMNxeS4c+69Jg8nWd1nvlqZs2KVWqkoIpWr9ZNu49Kjvu1i7cTMXjZlOp7atuOvM/Sj0WncuzcysWtJsST3NbHEy26jvst61Zna7pLupvQTFZcns0DmXOVXVxmXjZrKmrIInRh/M9sVeashlTFdgvqQpwFfFUc3s5CiN6+utVzMOx7R61nEx87OXb8qXn0eqjuOOlxfw1sK13P79fuzbvWmlhvLlZ+sypklX3+q7rPds+P5QXes457LXi/NWcM+/FjFiSE+G7++lhlxmmdkbTWkfZSTcPsA1fHskXC9f5FyWWrR6I9dMnE2/7u2yrtSQ399sHraqEFEEtATKoo5oEWXIjInAvcBf8ZFwnct6ZZsrGf1IWGqbE5+6AAAQdElEQVRo5CBat/RSQy7ztq4QIekUYEjU9lGSU6WZ3dPYwJxz9UvH2YCZ8bMn5rBo9UYeGXUA3bzUkMsSZva0pOuirl9fb72aOiXPSroYeIpvjgO/LukonXNp8cDkT3huznKuPX4PhtYydLpzmbJVfdYCYDC19PyuS31nTtPDDdU8FPHThGUG7Bp1J8659Hv347X83z8+4Li+nbno8N0Av1fjYpVYn7US+BQYFrVxfb31vPCWcznkksdmsnOHbbjjjP5I/qCti1dT67NG6a13BvCimZVKugEYCPzSzGY2ZcfOucbZtCXoj/Tmh6trXV5eUcnY8w+gbeuWmQzLuVpJ6g7cDQwluNo2GbjczJZEaR+lQ8SNZjZR0iHAccAdBL33DkguZOdcY1VWVXPug1MAOCd839rtp/ejd2cve+myxt+Ax4AzwumR4bxjojSOkpxquo+fANxjZpMk3dzIIJ3LG3Hcx7ntxQ9495OgD9ITFx30reWDb4MT++2U0Zhq1PXz8PtdzV4nM/tbwvTfJV0RtXGU5LRU0n3A0cBtklpRfzVz51wKPTdnGX/59yece9DO/AIYtHP6BvxzLoXWSBoJjA2nRwBrozaOkmSGAy8Bx5vZeqAD3+y555xLk49WlnLt43MY2LM9/31C+is9eEcKl0I/IsgfK8LX6eG8SBo8czKzchKGZDez5cDyRofpnGuU0k1buPCR6WxTVMiffzCIohZ+wcLljnCojEgVyGuTtn/tkh6UtCpxwClJHSS9Iumj8H37dO3fuVx3zcTZfLaunD+ePZAu7VrHHY5zjSJpV0nPSlod5oJJkiI/H5vOP8X+Dhy/1bzrgNfMrDfwWjjtnKvFS/NXcv139+TAXXeIOxTnkvEYMIFgXKedCOq0jq23RYK0JSczexPYusTRMKBmCI6HgFPStX/nctVbC9cAcEK/row6xJ+FdzlLZvaImVWGrzGkqHxROnQO71lhZssl7VjXipIuAC4AKCry0Ttd87B0/ZdcOjZ4vv327/fzDgoul70eFnodR5CUzgSer6nb2lB91kwnp8jM7H7gfoDi4mJ/WMLlvc2VVVw8ZjoVldUAFLfK2v+ezkVxZvh+4Vbzf0SE+qyZ/te/UlLX8KypK7Aqw/t3Lmvd8ux7zF7yBfeOHMR3fxF3NM41TVPrs2a6b+ozwLnh53OBSRnev3NZacK0Eh57dzGjD9+N4/fpEnc4zsUunV3JxwLvAHtIWiJpFHArcIykjwjqK92arv07lyvmLf2CG56ex9Ddd+CaY/vEHY5rRiT1kPS6pPclzZd0eTg/9sd+0nZZz8xG1LHoO+nap3O55vOyCkaPmU7H4iL+cNYAWhT6g7YuoyqBq81shqS2wHRJrwD/RfDYz61hp4brgJ9lMjD/n+BcjC4fP4tVGzbz55GD2GHbVnGH45oZM1tuZjPCz6XA+0A3UvDYj6ShkorDzyMl3Slp56jtPTk5F6M3P1zNzSf3Zb8e7eMOxTVzknYBBgDvstVjP0Cdj/3U4x6gXFJ/4FrgM+DhqI09OTkXg1ffWwnA8MHdGTGkR8zRuDzWQtK0hNcFta0kaVvgCeAKM9uQon1XWjBmyjDgLjO7C4g84Jg/SOFchn26powrJ8wC4BfD9vEHbV06VZrZ4PpWkNSSIDE9amY1Rb5T8dhPqaTrCQYZPExSIRB5mGY/c3Iug8orKhk9ZjqFBUFCat2yMOaIXHOm4C+jB4D3zezOhEWpeOznTGAzMMrMVhDcy/pN1MaenJzLEDPj+ifnsmBlKX84a0Dc4TgHMBT4IXCUpFnh63uk4LEfM1thZnea2b/D6cVmFvmek1/Wcy5DHn7nMybNWsY1x/bhsD6d4g7HOcxsMlDXdeUmPfYjqZRvF3r9AphG0H394/rae3JyLgOmfbqOXz73HkfvtSMXH7F73OE4lwl3AssIhs4QcBbQBVgAPAgcUV9jv6znXAZc/OgMum/fht8O34+CAu8A4ZqF483sPjMrNbMNYTHv75nZeKDBihN+5uRcCnxeVgHAL597r9blpZsqeXjUENq1idxZyblcVy1pOPB4OH16wrIGR5rw5ORcE1VWVXPJYzMAGD+1pNZ17jijP3t22S6TYTkXtx8AdwF/JkhG/wFGSmoD/KShxp6cnGuiO17+kLcXrQVg3i3HfWu5fhGMautccxJ2eDipjsWTG2rvycm5Jnhx3nLufWMRZx/Qk/+LOxjnsoikTsD5wC4k5Boz+1GU9p6cnEvSwlUbuWbiHPr3aM9NJ+3tycm5b5oE/Bt4FahqbGNPTs4loWxzUOmhqEUB9/xgIK1aeKUH57ayjZklPcyGdyV3LgnXPj6Hj1dv5I8jBrBT+zZxh+NcNnourDaRFE9OziXh+bnLufb4PTl4945xh+JctrqcIEF9KWmDpFJJkSue+2U912wF1fzhsrEz61xn62XVYZvj+3bhwsN2TV9wzuU4M4s8PEZtPDm5Zmtc+EzS9M8+p6hF7RcR5i79otb5vzmjnw914VwtJO1pZh9IGljb8pqRdxvcTs1fj9msuLjYysrKkmoridqOsa75ybRJ1fz6xBlTPu57dsl6zrj3HT769feorKr+agiLTOy7oflx7jsbY/J9J/87WlK5mRUnvYHk9vkXMztf0uu1LDYzOyrKdvzMyTU7azdu5qIx0+nUthUfQa2JyTmXHDM7P3w/sinb8eTkmpWqauOycTNZU1bBkxcdzL7Xxx2Rc/lF0mn1LU8Ybbdenpxcs3LHywt4a+Fabj+9H/t0axd3OM7lo7pKFkFQY8+Tk3OJXpy3gnv+tYgRQ3oyfHCPuMNxLi+Z2Xmp2I4nJ9dsXDNxNv27t+Pmk/eOOxTn8pakq+pbbmZ3RtmOJ6dmpKKyGoAR9/+n1uV1zU+mTarmp3JbRS0KuGfkIC815Fx6Nen5phqenJqRXz0fDIRXUVVNYS3P6FRV191lta5l6Z6fym3d98NBXmrIuTQzs1tSsR1/zikFbXLhOaenZi7hyvGz+ey2E3Pq5+H7zo75vu/s2ndUMT3ndK2Z3S7pbmoZ8dbMLouyHT9zagbeX76B65+cy5BeHfgs7mCcc/nu/fB9WlM24skpz33x5RZGj5lOuzYt+dPZA5k4Ou6InHP5zMyeDd8fasp2YklOkj4FSgkGoKo0s8FxxJHvqquNq8bPYunnXzL+wgPp1LZV3CE555oJSX2Aa/j2SLhZX77oSDNbE+P+896fXl/Iax+s4paT+zJo5w5xh+Oca14mAvcCf8VHwnWJ7nz1Q04d0I1zDto57lCcc81PpZndk2zjuJKTAS9LMuA+M7s/HTv5vKwCgGPufKPW5XXNT6ZNqubXp7Hb2qNzW3596r4+tINzLmMk1VymeVbSxcBTwOaa5Wa2Lsp24kpOQ81smaQdgVckfWBmbyauIOkC4AKAoqKipHZSWBj8Uu7dedtvLXu1jvn1LUv3/Poks4+/nDOYNkX+wKlzLqOmE5yA1PxV/NOEZQZEGqUz9uecJN0MbDSzO+pax59zys5nLXzf+bfvbIzJ951bzzmlSu3Df6aRpGJJbWs+A8cC8zIdh3POufSRdEbC7/obJD0paUDU9hlPTkBnYLKk2cAU4HkzezGGOJxzzqXPjWZWKukQ4DjgIYLee5Fk/J6TmX0M9M/0fp1zzmVUTffxE4B7zGxSeBsnkjjOnJxzzuW/pZLuA4YDL0hqRSNyjicn55xrpiQdL2mBpIWSrkvx5ocDLwHHm9l6oAPf7LlXL38I1znnmiFJhcCfgGOAJcBUSc+Y2Xup2L6ZlZMwJLuZLQeWR23vZ07OOdc8DQEWmtnHZlYBjAOGxRzTVzw5Oedc89QNKEmYXhLOywo5cVmvvLzcJH2ZbPu6yvfUV9ansW1SNZ/gO6nMspgysW8/7szvu1HzU7yPWo87S/9PZuVxR9RGUuK4SvcnlIurbcNZM/psTiQnM2s2Z3iSpjXHIUT8uJsXP+6ssATokTDdHVgWUyzf0mx+6TvnnPuGqUBvSb0kFQFnAc/EHNNXcuLMyTnnXGqZWaWknxB09y4EHjSz+TGH9RVPTtknLcOH5AA/7ubFjzsLmNkLwAtxx1Gb2KuSO+ecc1vze07OOeeyjienNJP0oKRVkuZtNf/SsGzIfEm3J8y/PiwlskDScQnzB0maGy77g5rYvzTdGnPcknaR9KWkWeHr3oT1c/64JY1POLZPJc1KWJa333ddx90Mvu/9JP0nPLZpkoYkLMuL7zsjzMxfaXwBhwEDgXkJ844kGKy2VTi9Y/i+NzAbaAX0AhYBheGyKcBBBM8m/AP4btzHlsLj3iVxva22k/PHvdXy3wL/0xy+73qOO6+/b+DlmriB7wH/yrfvOxMvP3NKMwuGn1+31eyLgFvNbHO4zqpw/jBgnJltNrNPgIXAEEldge3M7B0L/iU/DJySmSNITiOPu1Z5dNwAhH8NDwfGhrPy/fsGaj3uWuXRcRuwXfi5HV8/O5Q333cmeHKKRx/gUEnvSnpD0v7h/LrKiXQLP289P9fUddwAvSTNDOcfGs7Ll+OucSiw0sw+Cqfz/fuusfVxQ35/31cAv5FUAtwBXB/Oby7fd0p4V/J4tAC2Bw4E9gcmSNqVusuJZHWZkUao67iXAz3NbK2kQcDTkvqSP8ddYwTfPHvI9++7xtbHne/f90XAlWb2hKThwAPA0TSf7zslPDnFYwnwZHgKP0VSNdCRusuJLAk/bz0/19R63Ga2Gqi51Ddd0iKCs6x8Oe6ammqnAYMSZuf7913rcYeXdfP5+z4XuDz8PBH4a/g577/vVPLLevF4GjgKQFIfoAhYQ1A65CxJrST1AnoDUywYB6VU0oHh9ftzgEnxhN4ktR63pE4KxpYhPJPqDXycR8cNwV/OH5hZ4uWbfP++oZbjbgbf9zLg8PDzUUDN5czm8H2nTtw9MvL9RXA5YzmwheAvpFEEv5THAPOAGcBRCev/N0EvngUk9NgBBofrLwL+SPgAdba+GnPcwPeB+QQ9mWYAJ+XTcYfz/w6MrmX9vP2+6zrufP++gUOA6eHxvQsMyrfvOxMvrxDhnHMu6/hlPeecc1nHk5Nzzrms48nJOedc1vHk5JxzLut4cnLOOZd1PDk5txUFJkv6bsK84ZJejDMu55oT70ruXC0k7UPwdP8AgiGsZwHHm9miJmyzhZlVpihE5/KaJyfn6qBgvKkyoBgoNbNfSjoXuITggeK3gZ+YWbWk+wmGTmgDjDezX4TbWALcBxwP/N7MJsZwKM7lHK+t51zdbiGoYFABDA7Ppk4FDjazyjAhnQU8BlxnZuvCWnKvS3rczN4Lt1NmZkPjOADncpUnJ+fqYGZlksYDG81ss6SjCaqpTwsHKm3D10MgjJA0iuD/1E4EA8vVJKfxmY3cudznycm5+lWHLwiGNnjQzG5MXEFSb4Iq1EPMbL2kMUDrhFXKMhKpc3nEe+s5F92rwHBJHQEk7SCpJ8Gop6XAhnBU0+NijNG5vOBnTs5FZGZzJd0CvCqpgKAS9WhgGsElvHnAx8Bb8UXpXH7w3nrOOeeyjl/Wc845l3U8OTnnnMs6npycc85lHU9Ozjnnso4nJ+ecc1nHk5Nzzrms48nJOedc1vHk5JxzLuv8P0Rpfx8LMMJMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax=plt.subplots()\n",
    "ax.plot(X1,Y1)\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('shillings par semaine')\n",
    "\n",
    "ax1 = ax.twinx()\n",
    "ax1.bar(X1,Y2,color = 'black')\n",
    "ax1.set_ylabel('shillings pour un quart de boisseau de blé')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.121951\n",
       "1     0.111111\n",
       "2     0.119048\n",
       "3     0.102041\n",
       "4     0.121951\n",
       "5     0.106383\n",
       "6     0.078125\n",
       "7     0.185185\n",
       "8     0.151515\n",
       "9     0.156250\n",
       "10    0.151515\n",
       "11    0.171429\n",
       "12    0.181818\n",
       "13    0.133333\n",
       "14    0.181818\n",
       "15    0.153846\n",
       "16    0.113208\n",
       "17    0.142857\n",
       "18    0.150000\n",
       "19    0.130435\n",
       "20    0.187500\n",
       "21    0.162162\n",
       "22    0.162791\n",
       "23    0.200000\n",
       "24    0.259259\n",
       "25    0.200000\n",
       "26    0.160000\n",
       "27    0.300000\n",
       "28    0.312500\n",
       "29    0.250000\n",
       "30    0.333333\n",
       "31    0.413793\n",
       "32    0.333333\n",
       "33    0.500000\n",
       "34    0.406250\n",
       "35    0.518519\n",
       "36    0.518519\n",
       "37    0.483871\n",
       "38    0.428571\n",
       "39    0.516129\n",
       "40    0.395349\n",
       "41    0.382979\n",
       "42    0.431818\n",
       "43    0.456522\n",
       "44    0.547619\n",
       "45    0.531915\n",
       "46    0.355263\n",
       "47    0.354430\n",
       "48    0.358025\n",
       "49    0.303030\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y3=Y1/Y2\n",
    "Y3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Year')"
      ]
     },
     "execution_count": 40,
     "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(X1,Y3)\n",
    "plt.ylabel(\"Pouvoir d'achat\")\n",
    "plt.xlabel('Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": [
    "fig , ax=plt.subplots()\n",
    "ax1 = ax.twinx()\n",
    "ax1.plot(Y1,'r--', label = \"salaire\")\n",
    "ax1.set_ylabel('salaire')\n",
    "plt.legend()\n",
    "\n",
    "ax.plot(Y2,'b', label = \"prix de blé\")\n",
    "ax.set_ylabel('prix de blé')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "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
}