diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 2f5760b107cc936e913c37821ff33022c962942b..007a527847a8ea42a7b3b5af2176c05d06e27dd3 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,9 +29,474 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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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",
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+       "      <td>1660</td>\n",
+       "      <td>46.5</td>\n",
+       "      <td>6.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
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+       "      <td>32.0</td>\n",
+       "      <td>6.80</td>\n",
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+       "    <tr>\n",
+       "      <th>21</th>\n",
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+       "      <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",
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+       "      <td>1680</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>7.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
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+       "      <td>1685</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>7.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
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+       "      <td>1690</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>8.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
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+       "    <tr>\n",
+       "      <th>27</th>\n",
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+       "      <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",
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+       "      <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",
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+       "    <tr>\n",
+       "      <th>31</th>\n",
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+       "      <td>12.50</td>\n",
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+       "      <th>32</th>\n",
+       "      <td>33</td>\n",
+       "      <td>1725</td>\n",
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+       "      <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",
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+       "      <th>36</th>\n",
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+       "      <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",
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+       "      <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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "raw_data = pd.read_csv(data_url)\n",
     "raw_data"
@@ -46,9 +511,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "    .dataframe tbody tr th:only-of-type {\n",
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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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+       "  </thead>\n",
+       "  <tbody>\n",
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+       "      <th>50</th>\n",
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+       "      <td>1820</td>\n",
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+       "    <tr>\n",
+       "      <th>52</th>\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": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "raw_data[raw_data.isnull().any(axis=1)]"
    ]
@@ -62,9 +592,399 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Wheat</th>\n",
+       "      <th>Wages</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1565</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1570</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>5.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>5.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1580</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>5.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1585</td>\n",
+       "      <td>41.5</td>\n",
+       "      <td>5.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1590</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>5.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1595</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>5.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1600</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>5.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1605</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>5.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1610</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>5.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1615</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>5.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1620</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>6.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1625</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>6.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1630</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>6.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1635</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>6.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1640</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>6.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1645</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>6.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1650</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>6.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1655</td>\n",
+       "      <td>40.5</td>\n",
+       "      <td>6.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1660</td>\n",
+       "      <td>46.5</td>\n",
+       "      <td>6.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1665</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>6.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1670</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>6.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1675</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>7.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1680</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>7.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1685</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>7.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1690</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>8.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1695</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1700</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1705</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>10.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1710</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>1715</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>11.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>1720</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>12.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>1725</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>13.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>1730</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>13.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>1735</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>13.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>1740</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>14.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>1745</td>\n",
+       "      <td>27.5</td>\n",
+       "      <td>14.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>15.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>1755</td>\n",
+       "      <td>35.5</td>\n",
+       "      <td>15.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>1760</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>16.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>1765</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>17.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>1770</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>18.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>1775</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>19.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>1780</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>21.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>1785</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>23.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>1790</td>\n",
+       "      <td>47.5</td>\n",
+       "      <td>25.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>1795</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>27.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>1800</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>28.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>1805</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>29.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>1810</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>30.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Year  Wheat  Wages\n",
+       "0   1565   41.0   5.00\n",
+       "1   1570   45.0   5.05\n",
+       "2   1575   42.0   5.08\n",
+       "3   1580   49.0   5.12\n",
+       "4   1585   41.5   5.15\n",
+       "5   1590   47.0   5.25\n",
+       "6   1595   64.0   5.54\n",
+       "7   1600   27.0   5.61\n",
+       "8   1605   33.0   5.69\n",
+       "9   1610   32.0   5.78\n",
+       "10  1615   33.0   5.94\n",
+       "11  1620   35.0   6.01\n",
+       "12  1625   33.0   6.12\n",
+       "13  1630   45.0   6.22\n",
+       "14  1635   33.0   6.30\n",
+       "15  1640   39.0   6.37\n",
+       "16  1645   53.0   6.45\n",
+       "17  1650   42.0   6.50\n",
+       "18  1655   40.5   6.60\n",
+       "19  1660   46.5   6.75\n",
+       "20  1665   32.0   6.80\n",
+       "21  1670   37.0   6.90\n",
+       "22  1675   43.0   7.00\n",
+       "23  1680   35.0   7.30\n",
+       "24  1685   27.0   7.60\n",
+       "25  1690   40.0   8.00\n",
+       "26  1695   50.0   8.50\n",
+       "27  1700   30.0   9.00\n",
+       "28  1705   32.0  10.00\n",
+       "29  1710   44.0  11.00\n",
+       "30  1715   33.0  11.75\n",
+       "31  1720   29.0  12.50\n",
+       "32  1725   39.0  13.00\n",
+       "33  1730   26.0  13.30\n",
+       "34  1735   32.0  13.60\n",
+       "35  1740   27.0  14.00\n",
+       "36  1745   27.5  14.50\n",
+       "37  1750   31.0  15.00\n",
+       "38  1755   35.5  15.70\n",
+       "39  1760   31.0  16.50\n",
+       "40  1765   43.0  17.60\n",
+       "41  1770   47.0  18.50\n",
+       "42  1775   44.0  19.50\n",
+       "43  1780   46.0  21.00\n",
+       "44  1785   42.0  23.00\n",
+       "45  1790   47.5  25.50\n",
+       "46  1795   76.0  27.50\n",
+       "47  1800   79.0  28.50\n",
+       "48  1805   81.0  29.50\n",
+       "49  1810   99.0  30.00"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data = raw_data.dropna().copy()\n",
     "data = data.drop(columns=\"Unnamed: 0\")\n",
@@ -80,9 +1000,354 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\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>Wheat</th>\n",
+       "      <th>Wages</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1565</th>\n",
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+       "      <th>1570</th>\n",
+       "      <td>45.0</td>\n",
+       "      <td>5.05</td>\n",
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+       "    <tr>\n",
+       "      <th>1575</th>\n",
+       "      <td>42.0</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1580</th>\n",
+       "      <td>49.0</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1585</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1590</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>5.25</td>\n",
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+       "    <tr>\n",
+       "      <th>1595</th>\n",
+       "      <td>64.0</td>\n",
+       "      <td>5.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1600</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>5.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1605</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>5.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1610</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>5.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1615</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>5.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1620</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>6.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1625</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>6.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1630</th>\n",
+       "      <td>45.0</td>\n",
+       "      <td>6.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1635</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>6.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1640</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>6.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1645</th>\n",
+       "      <td>53.0</td>\n",
+       "      <td>6.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1650</th>\n",
+       "      <td>42.0</td>\n",
+       "      <td>6.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1655</th>\n",
+       "      <td>40.5</td>\n",
+       "      <td>6.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1660</th>\n",
+       "      <td>46.5</td>\n",
+       "      <td>6.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1665</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>6.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1670</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>6.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1675</th>\n",
+       "      <td>43.0</td>\n",
+       "      <td>7.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1680</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>7.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1685</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>7.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1690</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>8.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1695</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1700</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1705</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>10.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1710</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1715</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>11.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1720</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>12.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1725</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>13.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1730</th>\n",
+       "      <td>26.0</td>\n",
+       "      <td>13.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1735</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>13.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1740</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>14.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1745</th>\n",
+       "      <td>27.5</td>\n",
+       "      <td>14.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1750</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>15.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1755</th>\n",
+       "      <td>35.5</td>\n",
+       "      <td>15.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1760</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>16.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1765</th>\n",
+       "      <td>43.0</td>\n",
+       "      <td>17.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1770</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>18.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1775</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>19.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>46.0</td>\n",
+       "      <td>21.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1785</th>\n",
+       "      <td>42.0</td>\n",
+       "      <td>23.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1790</th>\n",
+       "      <td>47.5</td>\n",
+       "      <td>25.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1795</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>27.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1800</th>\n",
+       "      <td>79.0</td>\n",
+       "      <td>28.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1805</th>\n",
+       "      <td>81.0</td>\n",
+       "      <td>29.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1810</th>\n",
+       "      <td>99.0</td>\n",
+       "      <td>30.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Wheat  Wages\n",
+       "Year              \n",
+       "1565   41.0   5.00\n",
+       "1570   45.0   5.05\n",
+       "1575   42.0   5.08\n",
+       "1580   49.0   5.12\n",
+       "1585   41.5   5.15\n",
+       "1590   47.0   5.25\n",
+       "1595   64.0   5.54\n",
+       "1600   27.0   5.61\n",
+       "1605   33.0   5.69\n",
+       "1610   32.0   5.78\n",
+       "1615   33.0   5.94\n",
+       "1620   35.0   6.01\n",
+       "1625   33.0   6.12\n",
+       "1630   45.0   6.22\n",
+       "1635   33.0   6.30\n",
+       "1640   39.0   6.37\n",
+       "1645   53.0   6.45\n",
+       "1650   42.0   6.50\n",
+       "1655   40.5   6.60\n",
+       "1660   46.5   6.75\n",
+       "1665   32.0   6.80\n",
+       "1670   37.0   6.90\n",
+       "1675   43.0   7.00\n",
+       "1680   35.0   7.30\n",
+       "1685   27.0   7.60\n",
+       "1690   40.0   8.00\n",
+       "1695   50.0   8.50\n",
+       "1700   30.0   9.00\n",
+       "1705   32.0  10.00\n",
+       "1710   44.0  11.00\n",
+       "1715   33.0  11.75\n",
+       "1720   29.0  12.50\n",
+       "1725   39.0  13.00\n",
+       "1730   26.0  13.30\n",
+       "1735   32.0  13.60\n",
+       "1740   27.0  14.00\n",
+       "1745   27.5  14.50\n",
+       "1750   31.0  15.00\n",
+       "1755   35.5  15.70\n",
+       "1760   31.0  16.50\n",
+       "1765   43.0  17.60\n",
+       "1770   47.0  18.50\n",
+       "1775   44.0  19.50\n",
+       "1780   46.0  21.00\n",
+       "1785   42.0  23.00\n",
+       "1790   47.5  25.50\n",
+       "1795   76.0  27.50\n",
+       "1800   79.0  28.50\n",
+       "1805   81.0  29.50\n",
+       "1810   99.0  30.00"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sorted_data = data.set_index('Year').sort_index()\n",
     "sorted_data"
@@ -97,9 +1362,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "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.plot(sorted_data.Wheat)\n",
     "plt.plot(sorted_data[\"Wages\"], color=\"red\")\n",
@@ -115,9 +1393,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "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.plot(data[\"Year\"], data[\"Wheat\"])\n",
     "plt.ylabel(\"Shillings par semaine\")\n",
@@ -136,9 +1427,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fa5f2bcf048>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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UJDJSkkKumLH0D/Jek5XLlkd+1g5n0zLhrAHEkrn/uBZChMw+5OJ0p53e9OFJr/PPNnMnyXf7FzmjmW8Ph/fQjtA3Mr1yvAOAy6KQbwdINyWRmmyQtIwQIvqOtvahtTd49zomDjr+4J4/SVpmebF35h4L+Xa/kpw0WqyhBncLxVmprJpm75hQ5MfxRiYJ7kLEkUPN1pGvAw/DGOtsX5mJywPXlGTz9etXsWN9aeQGOEOhnsg07Paw+0Qnl60oDLoDN1Ly4ri/jAR3IeLI4Za+kaPqpgruBgU5aROfrGQwKD6xtZLsSa6ZbaU5aXTbnQw4Jz+0Y8+ZbvqHXFy2PDopGb/8jPjtLyPBXYg4cqilj/Mq8zAa1KTBvcvuJDfdFPF+MdFW6q+YmSLv/vIxCyajgQurCqI6nnhu+yvBXYg4MeRyc7K9n3WLc1icm8apyWbuNmdUygOjLdS+7i8ds3DekjzMKdGtCfG3/Y3H/jIS3IWIEyfabLg8mjUl2VQWmDndMXlaZqLdqbHMf9zeZMG9octBXYc96ikZ8K5ZDLk8OKZIE8UiCe5CxInDLd7F1NUlWVQWZHC60z7hjLLb4Zy0xj1WFWV5D+2YLLi/fNwCEJWWA2PF80YmCe5CxInDLX1kpiRRlpdOZaGZgWE37X3B88HxOnNPNhpYlJvOrsPt9A0Gr+V/6ZiFJQVmKgqiX8IZz/1lQgruSqmrlVLHlVK1SqmvTHLdZqWUWyl1Y+SGKIQAONRiZWVJFgaDYokvsJ3qtI27zu3R9MTpzB3gmztWU9dh4x8fq2FweHQ6xOF08eaprqhtXBrL/wEZj4uqUwZ3pZQRuB+4BlgF3KyUWjXBdd8FdkV6kELMd26P5mhrH2tKsgGo9AX3YBUzvQ4nWo8/OzVeXLZ8Af/x9+t450w3d/1mHy63Z+S5N2q7cLo8s5JvB+8mJkjcmfsWoFZrfUpr7QSeBHYEue6zwO8BSwTHJ4TAexTe4LCH1SXe3ZjFWamkJhuCLqqOtB6I0+AOsGN9KfduX82LR9v58u8Pjhzg8dJxC2aTkS2VebMyDn/jtXjMuYdSR1QKNAZ83wScF3iBUqoU+CBwObA5YqMTQgCMtOZdXeoN7gaDoiLfHHTmPtJ6YJLdqfHgYxdU0G0f5ocvniA3PZmvXreSV45Z2FpdgClpdpYLzSYjpiRDwgb3YLsgxi7R/wj4stbaPdlWYKXUHcAdAGVlZaGOUYh571CzlZQkA1UBTb6WFJo51to/7tqzrQfid+bud/cVVfQ4nDz8+ml6B4ZpsQ7yuW3Vs/b+SikKzCa64rB5WCjBvQlYHPD9IqBlzDWbgCd9gb0AuFYp5dJa/ynwIq31Q8BDAJs2bYq/XQFCzJHDLX2sKM4kyXh2xlpZYOb5w+0Muz0kBzzeFULTsHihlOLr16/COjDM7/Y2Acxavt0vL8M00kI5noQS3PcA1UqpSqAZuAm4JfACrXWl/2ul1KPAM2MDuxBierTWHG6xcv26klGPVxZk4PJomnoGRhZYIbR2v/HEYFB878a1uDyaAaebBVmps/r+eXHaGXLK4K61diml7sJbBWMEfqG1PqyUutP3/INRHqMQ81pTzwB9g66RxVS/sxUztnHBPTMladby0rMh2WjgpzdvmJP3zjebqLOMLzmNdSE1ZtBa7wR2jnksaFDXWv/DzIclhPDzt/n1l0H6jdS6d9i5fMXZx7vtzkmP1xPhyTPHZ2fIxPloFyJB+dv8+g/X8Ms1m8hJTx5XMROvu1NjVZ7ZxMCwG4fTNddDCYsEdyFi3KEWK9ULMkhNNo57rrJgfDlklz1+d6fGogJ/C4I4q5iR4C5EjDvc0seqkuBHyQUL7t32IZm5R5D/NKt4S81IcBcihln6BunoHxqXb/dbUmCm1To4cnKR1tqXlonvDUyxJC9OO0NKcBciho3sTJ1w5u7d1HSmyzt7tw25GHZr8syxc3RevPOnuOKtv4wEdyFimL+H+2RpGTjbQCyUg7FFeM72l4mvjUwS3IWIYYea+6jITyczNfhMvKIgHTgb3Ed2p0rOPWIyU5IwGQ2yoCqEiJzDrVZWlwbPtwOkm5JYmJ3KKV93yG5b4vSViRVKKe9B2ZKWEUJEgtUxTGP3wIT5dj9vxYx3B2UiNQ2LJfG4kSm6R4cLIabtcGvwnaljVRSYefZgK3A2LSPBPbLyM6aeuWutqbXYeOdMNzVnejhp6edHH1lP1YLMSX8uWiS4CxGjDjdPXinjt6TATI9jmB67kx6Hk5QkA+mm8RuexPTlm028fbqbDz/4BjnpJnLTk8k1m8hNN6E1vNvQQ82Zbnoc3nNfCzJS6LIPsfNgG3dfIcFdCBHgcIuVhdmp5GdMXvkyUjHTZafL5t2dOtm5CiJ8N28pY8jlocfhpLHbwXtNTnocwzhd3iMAK/LT2bayiM2VeWyuyKMiP51rfrybPWe652zMEtyFiFGHWvqmnLVDQHDvsHt3p0rTsIg7b0k+5y3JH/WY1hqH043LrclOH1/NtKkilz++24zL7RnVh3+2yIKqEDFoyOXmVIeNlQunDu6L89IxGhSnO+2yO3UWKaUwpyQFDewAmyvysDvdHA1yWtZskOAuRAxq6hnAo6Ei3zzltclGA2V56ZzutNNld5I3QbARs8t/iPdcpWYkuAsRgxq6HACU56eHdH1lgZlTnXZ6ZOYeMxZmp1GakybBXQhxVr2vV0xZGMG9rsOG3elOiLNTE8WWyjz2nOlB69k/MlqCuxAxqL7bQbrJSOEUlTJ+lQXmkcoNqXGPHZsr8ui0DXHG95vYbJLgLkQMauhyUJaXHnJJ45KAM1QluMeOzRW5AOw5PfupGQnuQsSg+m5vcA9VZeHZ4C5Nw2JH1YIMctOT5yTvLsFdiBjj8Wgauh0hL6YCFGWmkuY7hi9XgnvMUEqxqSJPgrsQAtr7B3G6PJSFUAbpZzAoKnypGZm5x5bNFbmc6XJg6R+c1feV4C5EjKn3l0GGkZYBb97daFBkTdD7XcyNzRXeeveaMz2z+r4S3IWIMeHWuPtdt3YhHzq3FINB+srEktUl2aQmG3hnlhdVpbeMEDGmvtuO0aAoyUkL6+euPWch156zMEqjEtNlSjKwYXEuNfWzG9xl5i5EjKnvclCak0byHDSbEtGxuTKPIy199A8Oz9p7yv89QsSYcCtlROzbXJGLR8O7Db2z9p4S3IWIMQ1h1riL2HduWS5Gg6JmFksiJbgLEUOsA8P0OoZl5p5gzClJrC7JmtVFVQnuQsQQf6VMWV7oNe4iPmwqz2N/Y+9ID6Bok+AuRAyp7/Z2g5SZe+LZUpnLkMvDwWbrrLyfBHchZsE7p7v5v88exeWefNZWPzJzl+CeaDZVzO7hHRLchYiyP+5r4qMPv8XPXz015aytoctBQUYK5hTZgpJoCjJSWFJgnrVFVQnuQkSJ1pofvXiCL/z3Ac4pzQZgb/3kW9Dru+2Skklgmyu8h3d4PNE/vCOk4K6UulopdVwpVauU+kqQ53copd5TSu1XStUopbZGfqhCxA+ny8M9vz3Aj148yY0bF/HkHRdQlpc+ZX+Rhi5H2D1lRPzYXJmHdWCYkxZb1N9ryt/9lFJG4H7gSqAJ2KOUelprfSTgsr8CT2uttVJqLfAUsCIaAxYi1lkdw/zT4zW8daqbe65cxl2XV3lbv5bn8trJDrTWQQ/hGHK5ae0bDPloPRF/Rg7vONPN8uLMqL5XKDP3LUCt1vqU1toJPAnsCLxAa23TZw8JNAOzf2CgEDGgqcfB3z3wN96t7+XHN63ns1dUjwTyTRV5dNqcI4umYzV2D6C1VMoksrK8dKoXZGAfckX9vUJZtSkFGgO+bwLOG3uRUuqDwP8FFgDXBXshpdQdwB0AZWVl4Y5ViJj34xdP0mod5PFPnceWyrxRz23yzdpq6ntGeq8HavCVQUqNe+JSSvH8Fy4O+fjEmQhl5h5sFONm5lrrP2qtVwAfAO4L9kJa64e01pu01psKCwvDG6kQcaCh28GakuxxgR2gqjCDrNSkCasl6qfZ6lfEl9kI7BBacG8CFgd8vwhomehirfVrwFKlVMEMxyZE3Gm1DlKcnRr0OYNBsbE8l5oJKmbquxyYTUY5SUlERCjBfQ9QrZSqVEqZgJuApwMvUEpVKd/HkVLqXMAEdEV6sELEMq01bdZBFuYED+7gzbvXWmz02J3jnmvodlCWb561mZ1IbFMGd621C7gL2AUcBZ7SWh9WSt2plLrTd9mHgENKqf14K2s+ErDAKsS80GV34nR7WJg1SXAv9+bdg9W713fZpQxSRExI2+C01juBnWMeezDg6+8C343s0ISIL21W7wHICyc5QWnd4hySjYqa+h62rSoaedzj0TT2DLBtZdGEPytEOGSHqhAR0tI7AMDCCXLuAKnJRtaUZrN3zJFrbX2DOF0eqXEXESPBXcx7Ho/m5WMWZppJbOvzzdyzJz/7dFN5LgearAy53COPjVTKSBmkiBAJ7mLee+WEhdsf3cPuk50zep2W3kGSjWrKapeN5Xk4XR4OBTQRa5BWvyLCJLiLeW9/ozfIHmntm9HrtFkHKM5OxWCYvNplZDNTQJ+Z+i4HSQY1aUpHiHBIcBfznn8GfWyGwb3FOsjCrMlTMuBt/VpZYGZPYHDvdrAoN40ko/yVFJEh/yeJec/fY/1YW/+MXqfVOjBpjXugjeW5vNvQM5Lnb+jy1rgLESkS3MW81t43SEf/EFmpSdR12Bie4qSkiXg8mnbr0IS7U8faXJFLt93JqU5vrl1q3EWkSXAX89rBJu+sffv6EobdmlMd9mm9jn8DU8kUlTJ+G8u9vWdqznTT63DSN+iSxVQRURLcxbx2qMWKUvDBDYsAONY2vby7fwNTqDP3pYVmctOTqTnTI+emiqiQ4C7mtUPNVpYWZnBOaTZJBjXtvHuL1buBKdSZu1KKjeV57K3vob7b3w1Scu4iciS4i3ntYLOVNSVZmJIMVC3ImHbFTLgzd/CWRJ7qtLOvwVs1IzN3EUkS3MW8ZekfpL1viDW+w6uXF2dyfAYzd5PREFa7Xn8TsT8faGFBZgppJuO03luIYCS4i3nLX99+ji+4ryjOosU6iNUxHPZrtVkHKcpOmXIDU6BzFmVjSjLQaXPKrF1EnAR3MW8dbOpDKVg9Ety9BxYfbw9/9t7aOzhlT5mxUpKMrPW9tzQME5EmwV3MWwebrVQWmMlI8Xa+XrHQG9ynUzHT2jdAyTRaB2z0tSKQhmEi0iS4i3nrULN1JCUDUJyVSlZqUtgVMx6P9wSm4jBn7gCbffXuUuMuIk2Cu5iXOvqHaOsbHBXclVKsWJgVdsVMl93JsFtTEmLrgUAXLyvkf79/+aiDO4SIBAnuYl461OJdTF1dkj3q8ZXFmZxot+HxhN7bvdVX4148yfF6EzElGfjMZVUjqSEhIkWCu5iXDvnaDqwuzRr1+PLiLGxDLpp9pyqFotVX414yyfF6Qsw2Ce5iXvIvpmalJo96/Oyiauh591bfB0E4G5iEiDYJ7mJeOtRsHdm8FGhZkS+4h5F3b+0bDHsDkxDRJsFdzDtdtiFarIOcMyYlA5CRkkRZXjrHwqh1b+0dpDg7FaVC38AkRLRJcBfzjv9wjmAzd/C2IQhn5t5mHZTj8UTMkeAu5p3DLd7APbZSxm9lcSanO+0MDrtDer0W64AEdxFzJLiLeedgk5Xy/HSy05KDPr+8OAuPhlqLbcrX8ng07X2DLJRKGRFjJLiLeefgBIupfuFUzHTahxh2a5m5i5gjwV3MKz12J829A6N2po5VkW8mJckQUt69tddb4x5u0zAhok2Cu5hXDo5p8xuM0aBYVpQZUndI/wYmmbmLWCPBXcwrI5UyEyym+i0vzuRoayjB3buBSYK7iDUS3MW8cqjZSlleOtnpwRdT/VYUZ9JpG6LTNjTpdW3WQUxJBvJkA5OIMRLcxbxycEyb34msKPZucJrq2L0WX427bGASsUaCu4hpg8NuPvHoHvbWd8/4tXodTpp6BsY1Cwsm1IqZNuvAtLpBChFtEtxFTNvf2MtLxyx89Y+HcIfRhjeYQ83e6pdQZu4FGSkUZKRMWTHT0jso3SBFTJLgHmU/eP44T9U0zvUw4tbUKQEEAAAXHElEQVT+xl7AO4P+3d6Z/Xvc19ADTL2Y6reiePKKGf8GJukGKWJRSMFdKXW1Uuq4UqpWKfWVIM9/VCn1nu/PG0qpdZEfanz61Vv1fPfZYwy5QtvKLkbb19BDWV46G8py+I/nT2Afck37tV442s66xTnkhrj4uaI4k+Nt/RP+xtBpG8Ll0dM6O1WIaJsyuCuljMD9wDXAKuBmpdSqMZedBi7RWq8F7gMeivRA45F9yEWvY5guu5PnDrXN9XDi0v7GXjaU5fBv163E0j/EQ6+dmtbrtPQO8F6TlfevDv04u+XFmQy5PNR32YM+769xn87ZqUJEWygz9y1Ardb6lNbaCTwJ7Ai8QGv9hta6x/ftW8CiyA4zPgWe5vPrtxvmcCTxqdU6QHvfEOsX57CxPI/rzlnIQ6+dor1vMOzXev6w98P1/auLQ/6ZlQu9C68TLapKjbuIZaEE91IgMNnZ5HtsIp8Eng32hFLqDqVUjVKqpqOjI/RRxil/cL9yVRHvnO7mRBg9wgXsb/Dm29cvzgHgn69ejsvj4T+ePx72a+063E7VggyWFmaE/DNVCzJINxnZdTj4b12yO1XEslCCe7AC3qBJSKXUZXiD+5eDPa+1fkhrvUlrvamwsDD0Ucap5h5vcP/8tmpMRgO/fqt+jkcUX/Y39mIyGlhV4p1Bl+ebue2CCn67t4kjLaH3W++xO3nnTHdYKRmA1GQjt55fzp8PtHCmc3xqplU2MIkYFkpwbwIWB3y/CGgZe5FSai3wMLBDa90VmeHFt5beAZIMihXFWVx7TjF/eLd5RguC882+xl5WlmSRkmQceeyzl1eTlZrMt3ceRevQSiNfPNqO26PDSsn4feqiSpKMBh54pW7cc62ygUnEsFCC+x6gWilVqZQyATcBTwdeoJQqA/4AfExrfSLyw4xPzb0DFGenYjQobj2/nP4hF08fGPe5GDO6bEM89FodLrdnroeCy+3hYJOVDb6UjF92ejJ3X1HN67WdvHIitNTersPtlGSnhlTfPtaCzFRu3ryY37/bNGoNBbwHY0tKRsSqKYO71toF3AXsAo4CT2mtDyul7lRK3em77OtAPvCfSqn9SqmaqI04jrT0DlDq2+CysTyXFcWZPP5Wfcgzztn267cb+PbOY/x2b9NcD4UT7TYGht0j+fZAHzu/nPL8dL79l6NTfhA5nC52n+zgqtXF055h33HJUgAeenX07N07c5dKGRGbQqpz11rv1Fov01ov1Vp/y/fYg1rrB31ff0prnau1Xu/7symag44XzT1ng7tSio+eX87hlr6RjTnR1jc4jC2MNNBrvpnwj188GfIRc9Hi/3cULLibkgx85eoVnLTY+O8pNoi9eryDIZeHq8LMtwcqzUnjQ+cu4ok9jVj6vYuobv8JTDJzFzFKdqhGybDbQ1vfIKW5Z2d2H9xQitlk5PG3ol8WebDJyiXfe5m7n9gX0vV9g8Psa+xlS2UebX2DPD7Hi7/7G3vITU+mPD896PNXrylmS2Ue3991nG67c8LX2XW4jdz0ZLZU5M1oPJ++dCkut4eHd58GvCksl0dOYBKxS4J7lLT3DeLRjMzcATJSkvjAhlKeea+FXkfwgOTxaM502mnpHaDX4WRw2B12GmfPmW5u+X9v0eMY5rUTHVgHhqf8mTfrunB7NPdcuYyLqgu4/+Va+gen/rlo2d/Yy/rFOROmUpRS3LdjDbZBF9999ljQa5wuD389ZuGKlUUkGWf2v3pFgZkb1pXw+Fv19NidtFjlBCYR2yS4R4m/DHJsU6lbzy9nyOXhd0Hy2m/WdXH9T1/n0n9/hfd95yXW3/sCK772HFVffZY139jFhd95iQdfrZu0lcHukx187JG3KcxK4f5bzsXl0bxy3DLleF870YHZZGRDWS5fumo5PY5hHnn9dJh3HRn9g8OctNhYvzh30uuWF2fyya2V/HdNIzVnxneNfOtUF/2DrmlVyQTzmcuqcDjd/PJvp2nzbWCSvjIiViXN9QASVYvvL39gWga8ux43lufym7cb+OTWSpRSnOm08+2dR3n+iLeq4/+7YRWpyUYcTjcDw24cThcOp5uT7Ta+8+wxnningX+9diVXrSoaNbPddbiNz/5mH0sXZPBfn9xCbrqJggwTLx61sGP9ZPvOYPfJTi5YWoApycC6xTlcvbqYh3ef5uMXVMx6HffBJitaw/qy8fn2se6+opo/H2jh3/50iGc+u3XUDH3X4TbSTUYuqi6IyLiWFWVy9epiHn3jDJ+6aAkw/sNbiFghwT1KRmbuQX5tv/X8Mr7w3wd47lAbe+t7eOzNMyQbDXzpqmV86qIlpCYbx/2M32snOrjvmSP803/t5YIl+Xz9hlWsXJjFn/Y1c89vD3BOaTaP3b5l5KShK1YUsfNgK06XB1NS8F/UznTaaeh28KmLKkceu+eqZTx/pI0HXqnlq9eNbSUUXfv8i6mLpg7u5pQkvn7Dau58fO+ooOvxaF440s4lywon/fcZrs9cVsVzh9t4ePcpUpIM5E5xopMQc0XSMlHS3DtAvtlEmml8YLlmzUJy05P59K/f5ZG/neaDG0p55UuXctfl1VMGoouXFfLs5y7i3h2rOdrWx3U/2c0nH93DF57az+aKXB7/1HmjjpDbtqqI/iEX75ye+LCL3Se9VTIXV5/dNVxdlMkHNyzisTfrR3qozJZ9Db0sKTBPeRSe3/tXF3HZ8kJ++MKJkbHua+zF0j8UsZSM3zmLsrl0eSF9gy7ZwCRimgT3KGme5BCH1GQjX7xqOdtWFvHnu7byvRvXsSCM03ySjAY+fkEFr37pMm57XwWvnujg0mWFPHr7FjJSRv8ytrWqgNRkAy8cmbgr5WsnO1mclzauMuXz26rRWvOTv9aGPLaZ0lqPLKaGSinFN7evweXR3PfMEcDbKCzJoLhsxYKIj/Gzl1cBspgqYpsE9yhp7nGMqpQZ62Pnl/PwbZtYM41dk37Z6cl844bV7PnqNh65bXPQWX+aycjWqkJeONIetOpm2O3hzbouLqouHDcLXZyXzi1byniqpjFob5VoaO4doNM2FFK+PVBZfjp3XVbFzoNtvHLcwq7DbVywNJ/stMinTTaW5/GRTYu5ctX0a+eFiDYJ7lGgtaald3DcYmq05JpNGAwTpweuWlVEi3WQI0GOjNvX0IttyDUqJRPoM5dXYTIa+MELs9NVYrLNS1O545IlLCkwc89TBzjT5Yh4SibQd29cyye2Vk59oRBzRIJ7FPQ4hhkYdsdMJcVlKxagFLxwpH3cc6+d6MBoULyvKj/ozy7ITOX2Cyt4+kALx9pC78QYjNaal49ZeOT10xPW7u9v6MWUZGBF8dSHWI+VkmTkvg+socvuRCnvh5oQ85UE9yjwV8pMlpaZTYWZKZxblsuLR8cH990nO9iwOIes1InTF3dcvAST0cBTe6bfc+ZYWx8fe+Qdbn90D/c9c4QHXh3fZRG8M/c1JVkTVvZM5cKqAm45r4yrVxeHtY4hRKKR4B4F/u6BsRLcAbatLOJQcx8tAZ0Ne+xO3mu2ctEEKRm/nHQTly4v5Jn3WiY8T3QiHf1D/MsfDnLtj3dzsNnK169fxQ3rSvj+ruO8dGz0h82w28PBZuuUm5em8u0PnsMDt26c0WsIEe8kuEfBSHCfpZx7KPyLf38NmL2/XtuJ1nDRsqk3+WxfX4Klf4i3T4XWqn9w2M0Dr9Rx2b+/wm9rGr1VPf/7Uj6xtZLvfWgtqxZm8bkn9lNrsY38zPG2foZcnrAXU4UQ40lwj4KW3gHSko0xtcFlaaGZygIzzwfk3Xef7CArNYl1IWwWumJFEWaTMeR+9Hf95l2++9wxzl+Sx64vXMw3blhNTrp3p2uaychDH99ESrKBO35VM9L7xr95aWwPdyFE+CS4R0FzzwAlObG1wUUpxZWrinz9VobRWvPaiU62VhdgnKTSxi/NZOTKVUU8e6gNp2vyHurH2/p58aiFu6+o5uHbNgc9t7Q0J40Hbt1IY4+Du5/Yh9uj2d/QS77ZxKIY+o1HiHglwT0KWqwDlOYGb1U7l7atLGLYrXn1RAe1FhttfYMTlkAGs319CdaB4ZG+7xP55d9Ok5ps4Pb3VUx63eaKPL65fQ2vnujge7uOsb+xZ9JOkEKI0ElvmSho7hlgdUn4pXzRtrE8l9z0ZF440k573xAAW8NoqrW1qpCc9GSePtDCtgnKDLtsQ/xhXzM3blxEbggNx245r4wjrVZ+/uopAD4wRYMzIURoZOYeYQNON112Z0xVyvgZDYrLVxTx8jELLx+zsKTQzKIwfsMwJRm4Zs1CXjjSjsMZ/ISnJ95pwOnyTDlrD/SNG1azpdJ7mIYspgoRGRLcI2yiVr+x4spVRfQNuni9tjOslIzf9nUlDAy7efHo+B7xTpeHX71Zz8XLCqkuygz5NZONBn5+60b+7bqVXLAk+GYqIUR4JLhH2GStfmPBRdUFIxuELg6hBHKsLZV5FGWl8PT+8VUzfznYgqV/iE9cWBH26+aaTXzqoiUzPjFJCOElf5MirCUGa9wDmVOS2FpVQLJRcf40ZslGg+L6tSW8esKC1XH2GD6tNY+8fpqqBRlcsiz83wiEEJElwT3CmnsHMCgoiuGt7/967Ur+86MbSTdNbz19+7oSht2a5w63jjxWU9/DoeY+br+wQqpdhIgBEtwjrLlngOKsVJJjOL1QtSBjRu1q1y7Kpjw/fdSGpl+8fprstGT+bsOiSAxRCDFDsRuB4lRz70DMpmQiRSnFjnUlvFnXhaV/kMZuB7sOt3HLeWVBT54SQsw+Ce4R1tw7EDOtfqNp+/oSPBr+8l4rj71xBoNSfPyC8rkelhDCRzYxRZDbo2mzDsZkjXukVS3IZOXCLJ6qaaKp28G15yyUY+eEiCFxOXO39A1G9fWffKeB7T97nRPt/WH9nKV/EJdHz4uZO3gXVo+29tE/5JJTiYSIMXEX3J871MrF33+ZX7x+Gk8IvcUHh9088vpp9tb3hPT6B5usfO1/DvFek5UP/ecb7D45eR+VQCOHdCR4zt3vhnULATi3LGdax+IJIaIn7oL7hrJc3re0gHufOcJND71FfdfEBzf/9Wg7V/7wVe575gi3/eIdjrRMfkxc/+Awdz3xLgUZKfzl7q2U5qbxD7/cwxPvNIQ0tlg8pCOaFuWm838+sIZ7d6yZ66EIIcaIu+BelJXKI7dt4vs3ruVoax9X/2g3v3rzzKhZfGO3g089VsMnH6shJcnIz27ZQGZqErc/+s6ok4gCaa356h8P0djt4Mc3bWB1STa/vfMCtlYV8C9/OMi3dx6d8jeF+RbcAW49v5w1pdlzPQwhxBhxF9zBW4r34U2Lef6LF7O5Mo+v/89hPvrw29RabPzkryfZ9oNXeaOuk3+9dgU7776I69eW8MvbN+MYcvMPv3xn5HCIQE/VNPL0gRa+eOWykSZWmanJPHLbJj52fjkPvXaKT/96LwNO94TjaukdICc9GXOKrFMLIeZWXAZ3v4XZaTx2+2a+83fncLDZyrYfvMoPXjjBtlVF/PWeS7jj4qUjfVRWFGfx849t5HSnnTv/ay9DrrNB+kR7P994+jAXVuXz6UurRr1HktHAvTtW87XrV/H8kXY+8tCb9A2O/3AA3yEdUjEihIgBcR3cwTuLv2lLGbu+cDEfv6Cc//rkFu6/5dygZXnvqyrgezeu5c1TXXz5d+/h8WgGnG4+8+t3yUhJ4ocfWR/0VCKlFJ/cWsnPb93IwWYrP3nxZNCxtPQOzpvFVCFEbAspf6CUuhr4MWAEHtZaf2fM8yuAXwLnAl/VWv97pAc6ldKctJAW9j64YREtvYN8f9dxFuak0WN3ctJi41ef2MKCzMn7wVy1upi/37iYR984w83nlY06Pk5rTXPvABcslZa1Qoi5N+XMXSllBO4HrgFWATcrpVaNuawbuBuY9aA+Hf/r0qXccl4ZD7xSx5N7Gvlfly7l4hA7GX7p/ctJTTbyrb8cHfV434AL25BrXi2mCiFiVyhpmS1Ardb6lNbaCTwJ7Ai8QGtt0VrvAYIno2OMUop7t69m+7oSLlteyBeuXBbyzxZmpnD3FVW8dMzCK8fPHljRHOOtfoUQ80soaZlSoDHg+ybgvOgMZ/YkGQ385OYN0/rZf3hfJb95u4H7njnChVUFJBsNI8F9vuxOFULEtlBm7sGac0+9NTTYCyl1h1KqRilV09ER+s7PWGNKMvBv162irsPO42/VAwGHdEhwF0LEgFCCexOwOOD7RcD4M9ZCoLV+SGu9SWu9qbAwvk/ruWLlAi6qLuCHL5yg2+6kuXcAU5KBfLNprocmhBAhBfc9QLVSqlIpZQJuAp6O7rBin1KKr12/CrvTzQ9fOEFzzwClOWkYgpRSCiHEbJsy5661diml7gJ24S2F/IXW+rBS6k7f8w8qpYqBGiAL8CilPg+s0lpP3swlzi0ryuRj55fzqzfPUJCRwrKizLkekhBCACHWuWutdwI7xzz2YMDXbXjTNfPO57dV86f9zVj6h7h0eXynmoQQiSPud6jOtZx0E1/0lVKW5qTP8WiEEMJLOlxFwC1bymizDo70NxdCiLkmwT0CkowG/vnqFXM9DCGEGCFpGSGESEAS3IUQIgFJcBdCiAQkwV0IIRKQBHchhEhAEtyFECIBSXAXQogEJMFdCCESkNJ6Wq3ZZ/7GSnUA9dP88QKgM4LDiSfz9d7lvucXue+JlWutp2xkNWfBfSaUUjVa601zPY65MF/vXe57fpH7njlJywghRAKS4C6EEAkoXoP7Q3M9gDk0X+9d7nt+kfueobjMuQshhJhcvM7chRBCTCLugrtS6mql1HGlVK1S6itzPZ5oUUr9QillUUodCngsTyn1glLqpO+fuXM5xmhQSi1WSr2slDqqlDqslPqc7/GEvnelVKpS6h2l1AHffX/T93hC37efUsqolNqnlHrG933C37dS6oxS6qBSar9Sqsb3WMTuO66Cu1LKCNwPXAOsAm5WSq2a21FFzaPA1WMe+wrwV611NfBX3/eJxgXco7VeCZwPfMb33zjR730IuFxrvQ5YD1ytlDqfxL9vv88BRwO+ny/3fZnWen1A+WPE7juugjuwBajVWp/SWjuBJ4EdczymqNBavwZ0j3l4B/CY7+vHgA/M6qBmgda6VWv9ru/rfrx/4UtJ8HvXXjbft8m+P5oEv28ApdQi4Drg4YCHE/6+JxCx+4634F4KNAZ83+R7bL4o0lq3gjcIAgvmeDxRpZSqADYAbzMP7t2XmtgPWIAXtNbz4r6BHwH/DHgCHpsP962B55VSe5VSd/gei9h9x9sZqirIY1Luk4CUUhnA74HPa637lAr2nz6xaK3dwHqlVA7wR6XUmrkeU7Qppa4HLFrrvUqpS+d6PLPsQq11i1JqAfCCUupYJF883mbuTcDigO8XAS1zNJa50K6UWgjg+6dljscTFUqpZLyB/dda6z/4Hp4X9w6gte4FXsG75pLo930hsF0pdQZvmvVypdTjJP59o7Vu8f3TAvwRb9o5Yvcdb8F9D1CtlKpUSpmAm4Cn53hMs+lp4Dbf17cB/zOHY4kK5Z2iPwIc1Vr/IOCphL53pVShb8aOUioN2AYcI8HvW2v9L1rrRVrrCrx/n1/SWt9Kgt+3UsqslMr0fw1cBRwigvcdd5uYlFLX4s3RGYFfaK2/NcdDigql1BPApXi7xLUD3wD+BDwFlAENwIe11mMXXeOaUmorsBs4yNkc7L/izbsn7L0rpdbiXUAz4p10PaW1vlcplU8C33cgX1rmS1rr6xP9vpVSS/DO1sGbHv+N1vpbkbzvuAvuQgghphZvaRkhhBAhkOAuhBAJSIK7EEIkIAnuQgiRgCS4CyFEApLgLoQQCUiCuxBCJCAJ7kIIkYD+f+tf3n2RDSPzAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "pouvoir_achat = []\n",
     "\n",
diff --git a/module3/exo3/module3_fin.ipynb b/module3/exo3/module3_fin.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f1136fdff8fa12586b586e1964db8867b9ce3338
--- /dev/null
+++ b/module3/exo3/module3_fin.ipynb
@@ -0,0 +1,340 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Import des données puis affichage pour en vérifier la bonne présence"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "outputs": [],
+   "source": [
+    "data_url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\""
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "    Unnamed: 0  Year  Wheat  Wages\n0            1  1565   41.0   5.00\n1            2  1570   45.0   5.05\n2            3  1575   42.0   5.08\n3            4  1580   49.0   5.12\n4            5  1585   41.5   5.15\n5            6  1590   47.0   5.25\n6            7  1595   64.0   5.54\n7            8  1600   27.0   5.61\n8            9  1605   33.0   5.69\n9           10  1610   32.0   5.78\n10          11  1615   33.0   5.94\n11          12  1620   35.0   6.01\n12          13  1625   33.0   6.12\n13          14  1630   45.0   6.22\n14          15  1635   33.0   6.30\n15          16  1640   39.0   6.37\n16          17  1645   53.0   6.45\n17          18  1650   42.0   6.50\n18          19  1655   40.5   6.60\n19          20  1660   46.5   6.75\n20          21  1665   32.0   6.80\n21          22  1670   37.0   6.90\n22          23  1675   43.0   7.00\n23          24  1680   35.0   7.30\n24          25  1685   27.0   7.60\n25          26  1690   40.0   8.00\n26          27  1695   50.0   8.50\n27          28  1700   30.0   9.00\n28          29  1705   32.0  10.00\n29          30  1710   44.0  11.00\n30          31  1715   33.0  11.75\n31          32  1720   29.0  12.50\n32          33  1725   39.0  13.00\n33          34  1730   26.0  13.30\n34          35  1735   32.0  13.60\n35          36  1740   27.0  14.00\n36          37  1745   27.5  14.50\n37          38  1750   31.0  15.00\n38          39  1755   35.5  15.70\n39          40  1760   31.0  16.50\n40          41  1765   43.0  17.60\n41          42  1770   47.0  18.50\n42          43  1775   44.0  19.50\n43          44  1780   46.0  21.00\n44          45  1785   42.0  23.00\n45          46  1790   47.5  25.50\n46          47  1795   76.0  27.50\n47          48  1800   79.0  28.50\n48          49  1805   81.0  29.50\n49          50  1810   99.0  30.00\n50          51  1815   78.0    NaN\n51          52  1820   54.0    NaN\n52          53  1821   54.0    NaN",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Year</th>\n      <th>Wheat</th>\n      <th>Wages</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1565</td>\n      <td>41.0</td>\n      <td>5.00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1570</td>\n      <td>45.0</td>\n      <td>5.05</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1575</td>\n      <td>42.0</td>\n      <td>5.08</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1580</td>\n      <td>49.0</td>\n      <td>5.12</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>1585</td>\n      <td>41.5</td>\n      <td>5.15</td>\n    </tr>\n    <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>"
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_url)\n",
+    "raw_data"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "On se débarasse des années qui manquent des données, afin de ne pas les traiter"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "    Unnamed: 0  Year  Wheat  Wages\n50          51  1815   78.0    NaN\n51          52  1820   54.0    NaN\n52          53  1821   54.0    NaN",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Year</th>\n      <th>Wheat</th>\n      <th>Wages</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>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>"
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "On enlève ces données, puis on se débarasse de la colonne 'Unnamed 0' qui ne sert qu'à donner l'indice de la donnée, mais qui nous est sans utilitée"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "    Year  Wheat  Wages\n0   1565   41.0   5.00\n1   1570   45.0   5.05\n2   1575   42.0   5.08\n3   1580   49.0   5.12\n4   1585   41.5   5.15\n5   1590   47.0   5.25\n6   1595   64.0   5.54\n7   1600   27.0   5.61\n8   1605   33.0   5.69\n9   1610   32.0   5.78\n10  1615   33.0   5.94\n11  1620   35.0   6.01\n12  1625   33.0   6.12\n13  1630   45.0   6.22\n14  1635   33.0   6.30\n15  1640   39.0   6.37\n16  1645   53.0   6.45\n17  1650   42.0   6.50\n18  1655   40.5   6.60\n19  1660   46.5   6.75\n20  1665   32.0   6.80\n21  1670   37.0   6.90\n22  1675   43.0   7.00\n23  1680   35.0   7.30\n24  1685   27.0   7.60\n25  1690   40.0   8.00\n26  1695   50.0   8.50\n27  1700   30.0   9.00\n28  1705   32.0  10.00\n29  1710   44.0  11.00\n30  1715   33.0  11.75\n31  1720   29.0  12.50\n32  1725   39.0  13.00\n33  1730   26.0  13.30\n34  1735   32.0  13.60\n35  1740   27.0  14.00\n36  1745   27.5  14.50\n37  1750   31.0  15.00\n38  1755   35.5  15.70\n39  1760   31.0  16.50\n40  1765   43.0  17.60\n41  1770   47.0  18.50\n42  1775   44.0  19.50\n43  1780   46.0  21.00\n44  1785   42.0  23.00\n45  1790   47.5  25.50\n46  1795   76.0  27.50\n47  1800   79.0  28.50\n48  1805   81.0  29.50\n49  1810   99.0  30.00",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Year</th>\n      <th>Wheat</th>\n      <th>Wages</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1565</td>\n      <td>41.0</td>\n      <td>5.00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1570</td>\n      <td>45.0</td>\n      <td>5.05</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1575</td>\n      <td>42.0</td>\n      <td>5.08</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1580</td>\n      <td>49.0</td>\n      <td>5.12</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1585</td>\n      <td>41.5</td>\n      <td>5.15</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1590</td>\n      <td>47.0</td>\n      <td>5.25</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1595</td>\n      <td>64.0</td>\n      <td>5.54</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1600</td>\n      <td>27.0</td>\n      <td>5.61</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1605</td>\n      <td>33.0</td>\n      <td>5.69</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1610</td>\n      <td>32.0</td>\n      <td>5.78</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>1615</td>\n      <td>33.0</td>\n      <td>5.94</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>1620</td>\n      <td>35.0</td>\n      <td>6.01</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1625</td>\n      <td>33.0</td>\n      <td>6.12</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>1630</td>\n      <td>45.0</td>\n      <td>6.22</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>1635</td>\n      <td>33.0</td>\n      <td>6.30</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>1640</td>\n      <td>39.0</td>\n      <td>6.37</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>1645</td>\n      <td>53.0</td>\n      <td>6.45</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>1650</td>\n      <td>42.0</td>\n      <td>6.50</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>1655</td>\n      <td>40.5</td>\n      <td>6.60</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>1660</td>\n      <td>46.5</td>\n      <td>6.75</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>1665</td>\n      <td>32.0</td>\n      <td>6.80</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>1670</td>\n      <td>37.0</td>\n      <td>6.90</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>1675</td>\n      <td>43.0</td>\n      <td>7.00</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>1680</td>\n      <td>35.0</td>\n      <td>7.30</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>1685</td>\n      <td>27.0</td>\n      <td>7.60</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>1690</td>\n      <td>40.0</td>\n      <td>8.00</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>1695</td>\n      <td>50.0</td>\n      <td>8.50</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>1700</td>\n      <td>30.0</td>\n      <td>9.00</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>1705</td>\n      <td>32.0</td>\n      <td>10.00</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>1710</td>\n      <td>44.0</td>\n      <td>11.00</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>1715</td>\n      <td>33.0</td>\n      <td>11.75</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>1720</td>\n      <td>29.0</td>\n      <td>12.50</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>1725</td>\n      <td>39.0</td>\n      <td>13.00</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>1730</td>\n      <td>26.0</td>\n      <td>13.30</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>1735</td>\n      <td>32.0</td>\n      <td>13.60</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>1740</td>\n      <td>27.0</td>\n      <td>14.00</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>1745</td>\n      <td>27.5</td>\n      <td>14.50</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>1750</td>\n      <td>31.0</td>\n      <td>15.00</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>1755</td>\n      <td>35.5</td>\n      <td>15.70</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>1760</td>\n      <td>31.0</td>\n      <td>16.50</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>1765</td>\n      <td>43.0</td>\n      <td>17.60</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>1770</td>\n      <td>47.0</td>\n      <td>18.50</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>1775</td>\n      <td>44.0</td>\n      <td>19.50</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>1780</td>\n      <td>46.0</td>\n      <td>21.00</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>1785</td>\n      <td>42.0</td>\n      <td>23.00</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>1790</td>\n      <td>47.5</td>\n      <td>25.50</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>1795</td>\n      <td>76.0</td>\n      <td>27.50</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>1800</td>\n      <td>79.0</td>\n      <td>28.50</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>1805</td>\n      <td>81.0</td>\n      <td>29.50</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>1810</td>\n      <td>99.0</td>\n      <td>30.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.dropna().copy()\n",
+    "data = data.drop(columns=\"Unnamed: 0\")\n",
+    "data"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "A priori les données sont déjà triés, mais par précaution on s'assure que les années sont bien dans l'ordre croissant"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "      Wheat  Wages\nYear              \n1565   41.0   5.00\n1570   45.0   5.05\n1575   42.0   5.08\n1580   49.0   5.12\n1585   41.5   5.15\n1590   47.0   5.25\n1595   64.0   5.54\n1600   27.0   5.61\n1605   33.0   5.69\n1610   32.0   5.78\n1615   33.0   5.94\n1620   35.0   6.01\n1625   33.0   6.12\n1630   45.0   6.22\n1635   33.0   6.30\n1640   39.0   6.37\n1645   53.0   6.45\n1650   42.0   6.50\n1655   40.5   6.60\n1660   46.5   6.75\n1665   32.0   6.80\n1670   37.0   6.90\n1675   43.0   7.00\n1680   35.0   7.30\n1685   27.0   7.60\n1690   40.0   8.00\n1695   50.0   8.50\n1700   30.0   9.00\n1705   32.0  10.00\n1710   44.0  11.00\n1715   33.0  11.75\n1720   29.0  12.50\n1725   39.0  13.00\n1730   26.0  13.30\n1735   32.0  13.60\n1740   27.0  14.00\n1745   27.5  14.50\n1750   31.0  15.00\n1755   35.5  15.70\n1760   31.0  16.50\n1765   43.0  17.60\n1770   47.0  18.50\n1775   44.0  19.50\n1780   46.0  21.00\n1785   42.0  23.00\n1790   47.5  25.50\n1795   76.0  27.50\n1800   79.0  28.50\n1805   81.0  29.50\n1810   99.0  30.00",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Wheat</th>\n      <th>Wages</th>\n    </tr>\n    <tr>\n      <th>Year</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1565</th>\n      <td>41.0</td>\n      <td>5.00</td>\n    </tr>\n    <tr>\n      <th>1570</th>\n      <td>45.0</td>\n      <td>5.05</td>\n    </tr>\n    <tr>\n      <th>1575</th>\n      <td>42.0</td>\n      <td>5.08</td>\n    </tr>\n    <tr>\n      <th>1580</th>\n      <td>49.0</td>\n      <td>5.12</td>\n    </tr>\n    <tr>\n      <th>1585</th>\n      <td>41.5</td>\n      <td>5.15</td>\n    </tr>\n    <tr>\n      <th>1590</th>\n      <td>47.0</td>\n      <td>5.25</td>\n    </tr>\n    <tr>\n      <th>1595</th>\n      <td>64.0</td>\n      <td>5.54</td>\n    </tr>\n    <tr>\n      <th>1600</th>\n      <td>27.0</td>\n      <td>5.61</td>\n    </tr>\n    <tr>\n      <th>1605</th>\n      <td>33.0</td>\n      <td>5.69</td>\n    </tr>\n    <tr>\n      <th>1610</th>\n      <td>32.0</td>\n      <td>5.78</td>\n    </tr>\n    <tr>\n      <th>1615</th>\n      <td>33.0</td>\n      <td>5.94</td>\n    </tr>\n    <tr>\n      <th>1620</th>\n      <td>35.0</td>\n      <td>6.01</td>\n    </tr>\n    <tr>\n      <th>1625</th>\n      <td>33.0</td>\n      <td>6.12</td>\n    </tr>\n    <tr>\n      <th>1630</th>\n      <td>45.0</td>\n      <td>6.22</td>\n    </tr>\n    <tr>\n      <th>1635</th>\n      <td>33.0</td>\n      <td>6.30</td>\n    </tr>\n    <tr>\n      <th>1640</th>\n      <td>39.0</td>\n      <td>6.37</td>\n    </tr>\n    <tr>\n      <th>1645</th>\n      <td>53.0</td>\n      <td>6.45</td>\n    </tr>\n    <tr>\n      <th>1650</th>\n      <td>42.0</td>\n      <td>6.50</td>\n    </tr>\n    <tr>\n      <th>1655</th>\n      <td>40.5</td>\n      <td>6.60</td>\n    </tr>\n    <tr>\n      <th>1660</th>\n      <td>46.5</td>\n      <td>6.75</td>\n    </tr>\n    <tr>\n      <th>1665</th>\n      <td>32.0</td>\n      <td>6.80</td>\n    </tr>\n    <tr>\n      <th>1670</th>\n      <td>37.0</td>\n      <td>6.90</td>\n    </tr>\n    <tr>\n      <th>1675</th>\n      <td>43.0</td>\n      <td>7.00</td>\n    </tr>\n    <tr>\n      <th>1680</th>\n      <td>35.0</td>\n      <td>7.30</td>\n    </tr>\n    <tr>\n      <th>1685</th>\n      <td>27.0</td>\n      <td>7.60</td>\n    </tr>\n    <tr>\n      <th>1690</th>\n      <td>40.0</td>\n      <td>8.00</td>\n    </tr>\n    <tr>\n      <th>1695</th>\n      <td>50.0</td>\n      <td>8.50</td>\n    </tr>\n    <tr>\n      <th>1700</th>\n      <td>30.0</td>\n      <td>9.00</td>\n    </tr>\n    <tr>\n      <th>1705</th>\n      <td>32.0</td>\n      <td>10.00</td>\n    </tr>\n    <tr>\n      <th>1710</th>\n      <td>44.0</td>\n      <td>11.00</td>\n    </tr>\n    <tr>\n      <th>1715</th>\n      <td>33.0</td>\n      <td>11.75</td>\n    </tr>\n    <tr>\n      <th>1720</th>\n      <td>29.0</td>\n      <td>12.50</td>\n    </tr>\n    <tr>\n      <th>1725</th>\n      <td>39.0</td>\n      <td>13.00</td>\n    </tr>\n    <tr>\n      <th>1730</th>\n      <td>26.0</td>\n      <td>13.30</td>\n    </tr>\n    <tr>\n      <th>1735</th>\n      <td>32.0</td>\n      <td>13.60</td>\n    </tr>\n    <tr>\n      <th>1740</th>\n      <td>27.0</td>\n      <td>14.00</td>\n    </tr>\n    <tr>\n      <th>1745</th>\n      <td>27.5</td>\n      <td>14.50</td>\n    </tr>\n    <tr>\n      <th>1750</th>\n      <td>31.0</td>\n      <td>15.00</td>\n    </tr>\n    <tr>\n      <th>1755</th>\n      <td>35.5</td>\n      <td>15.70</td>\n    </tr>\n    <tr>\n      <th>1760</th>\n      <td>31.0</td>\n      <td>16.50</td>\n    </tr>\n    <tr>\n      <th>1765</th>\n      <td>43.0</td>\n      <td>17.60</td>\n    </tr>\n    <tr>\n      <th>1770</th>\n      <td>47.0</td>\n      <td>18.50</td>\n    </tr>\n    <tr>\n      <th>1775</th>\n      <td>44.0</td>\n      <td>19.50</td>\n    </tr>\n    <tr>\n      <th>1780</th>\n      <td>46.0</td>\n      <td>21.00</td>\n    </tr>\n    <tr>\n      <th>1785</th>\n      <td>42.0</td>\n      <td>23.00</td>\n    </tr>\n    <tr>\n      <th>1790</th>\n      <td>47.5</td>\n      <td>25.50</td>\n    </tr>\n    <tr>\n      <th>1795</th>\n      <td>76.0</td>\n      <td>27.50</td>\n    </tr>\n    <tr>\n      <th>1800</th>\n      <td>79.0</td>\n      <td>28.50</td>\n    </tr>\n    <tr>\n      <th>1805</th>\n      <td>81.0</td>\n      <td>29.50</td>\n    </tr>\n    <tr>\n      <th>1810</th>\n      <td>99.0</td>\n      <td>30.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_data = data.set_index('Year').sort_index()\n",
+    "sorted_data"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Affichage des données dans une format similaire à PlayFair"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(sorted_data.Wheat)\n",
+    "plt.plot(sorted_data[\"Wages\"], color=\"red\")\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Affichage des données d'une manière plus correcte, en séparant les axes des ordonnées des deux données"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data[\"Year\"], data[\"Wheat\"])\n",
+    "plt.ylabel(\"Shillings par semaine\")\n",
+    "plt.twinx()\n",
+    "plt.plot(data[\"Year\"], data[\"Wages\"], color=\"red\")\n",
+    "plt.ylabel(\"Shillings par quart de boisseau de blé\")\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Graphe montrant pour chaque année la quantité de blé pouvant être acheté, exprimé sous forme de fraction représentant la portion que pouvait se permettre un ouvrier, une augmentation graduelle est constasté (Suivi d'une chute mais le manque d'une suite de données ne permet pas de conclure)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[<matplotlib.lines.Line2D at 0x22347aef940>]"
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pouvoir_achat = []\n",
+    "\n",
+    "for value in sorted_data.values:\n",
+    "    pouvoir_achat.append(value[1] / value[0])\n",
+    "\n",
+    "plt.plot(pouvoir_achat)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file