diff --git a/module2/exo1/Work.ipynb b/module2/exo1/Work.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7fec51502cbc3200b3d0ffc6bbba1fe85e197f3d
--- /dev/null
+++ b/module2/exo1/Work.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module3/exo3/projekat.ipynb b/module3/exo3/projekat.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dff70097657fc92e950e50d0e89a4a878ff8c35e
--- /dev/null
+++ b/module3/exo3/projekat.ipynb
@@ -0,0 +1,1706 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## About\n",
+    "This dataset shows the evolution of the wheat price and average salaries from 1565 to 1821. \n",
+    "The numbers obtained from a scan of the graph, which was made by William Playfair\n",
+    "The data can be downloaded from the following [link](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports\n",
+    "We will use the following python libraries:\n",
+    "1. matplotlib (for information visualisation)\n",
+    "2. pandas (for data manipulation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will load the dataset using the pandas library. The data is in a CSV format so we will use pandas.read_cv() function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Wheat</th>\n",
+       "      <th>Wages</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1565</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1570</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>5.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1575</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>5.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1580</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>5.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1585</td>\n",
+       "      <td>41.5</td>\n",
+       "      <td>5.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1590</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>5.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1595</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>5.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>5.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1605</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>5.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1610</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>5.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>1615</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>5.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>1620</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>6.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>1625</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>6.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>1630</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>6.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>1635</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>6.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>1640</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>6.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>1645</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>6.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1650</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>6.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>1655</td>\n",
+       "      <td>40.5</td>\n",
+       "      <td>6.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>1660</td>\n",
+       "      <td>46.5</td>\n",
+       "      <td>6.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>21</td>\n",
+       "      <td>1665</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>6.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>22</td>\n",
+       "      <td>1670</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>6.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>1675</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>7.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>24</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>7.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>25</td>\n",
+       "      <td>1685</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>7.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>26</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>8.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>27</td>\n",
+       "      <td>1695</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>28</td>\n",
+       "      <td>1700</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>1705</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>10.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>30</td>\n",
+       "      <td>1710</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>31</td>\n",
+       "      <td>1715</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>11.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>32</td>\n",
+       "      <td>1720</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>12.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>33</td>\n",
+       "      <td>1725</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>13.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>34</td>\n",
+       "      <td>1730</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>13.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>35</td>\n",
+       "      <td>1735</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>13.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>36</td>\n",
+       "      <td>1740</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>14.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>37</td>\n",
+       "      <td>1745</td>\n",
+       "      <td>27.5</td>\n",
+       "      <td>14.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>38</td>\n",
+       "      <td>1750</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>15.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>39</td>\n",
+       "      <td>1755</td>\n",
+       "      <td>35.5</td>\n",
+       "      <td>15.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>40</td>\n",
+       "      <td>1760</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>16.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>41</td>\n",
+       "      <td>1765</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>17.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>42</td>\n",
+       "      <td>1770</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>18.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>43</td>\n",
+       "      <td>1775</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>19.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>44</td>\n",
+       "      <td>1780</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>21.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>45</td>\n",
+       "      <td>1785</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>23.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>46</td>\n",
+       "      <td>1790</td>\n",
+       "      <td>47.5</td>\n",
+       "      <td>25.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>47</td>\n",
+       "      <td>1795</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>27.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>48</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>28.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>49</td>\n",
+       "      <td>1805</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>29.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>50</td>\n",
+       "      <td>1810</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>30.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>51</td>\n",
+       "      <td>1815</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>52</td>\n",
+       "      <td>1820</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>53</td>\n",
+       "      <td>1821</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    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": [
+    "raw_data = pd.read_csv(\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\")\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that there is an unused column, we can delete it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Wheat</th>\n",
+       "      <th>Wages</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1565</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1570</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>5.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>5.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1580</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>5.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1585</td>\n",
+       "      <td>41.5</td>\n",
+       "      <td>5.15</td>\n",
+       "    </tr>\n",
+       "    <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",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>1815</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>1820</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>1821</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>NaN</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\n",
+       "50  1815   78.0    NaN\n",
+       "51  1820   54.0    NaN\n",
+       "52  1821   54.0    NaN"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.drop(['Unnamed: 0'], axis = 1).copy()\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will check if there are any rows with incomplete data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Wheat</th>\n",
+       "      <th>Wages</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>1815</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>1820</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>1821</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Year  Wheat  Wages\n",
+       "50  1815   78.0    NaN\n",
+       "51  1820   54.0    NaN\n",
+       "52  1821   54.0    NaN"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data.isnull().any(axis = 1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, rows 50, 51, 52 have incomplete data. We will delete them from our dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Wheat</th>\n",
+       "      <th>Wages</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1565</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1570</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>5.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>5.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1580</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>5.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1585</td>\n",
+       "      <td>41.5</td>\n",
+       "      <td>5.15</td>\n",
+       "    </tr>\n",
+       "    <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": [
+    "filtered_data = data.drop([50, 51, 52]).copy()\n",
+    "filtered_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will now check if there is any missing data. We will do that by going through the years of the dataset and checking if the amount of time between them is 5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "years = list(filtered_data['Year'])\n",
+    "prev = years[0]\n",
+    "i = 1\n",
+    "while i < len(years):\n",
+    "    if years[i] - prev != 5:\n",
+    "        print('Data is missing!')\n",
+    "    prev = years[i]\n",
+    "    i += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We got no output, so no data is missing.\n",
+    "We can now continue with graph creation.\n",
+    "\n",
+    "## Playfair's  graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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": [
+    "figure = plt.figure()\n",
+    "subplot1 = figure.add_subplot(211)\n",
+    "subplot2 = figure.add_subplot(212)\n",
+    "subplot1.bar(filtered_data['Year'], filtered_data['Wheat'], edgecolor='black', width = 2.5)\n",
+    "subplot2.plot(filtered_data['Year'], filtered_data['Wages'], color='r')\n",
+    "subplot2.fill_between(filtered_data['Year'], filtered_data['Wages'], color='b')\n",
+    "subplot1.grid(True)\n",
+    "subplot2.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The wheat prices are represented by bars and the salaries are represented by a red curve and blue surface area."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Improved representation of the data\n",
+    "\n",
+    "Playfair simplified two quantities \"shillings per quarter\" and \"shillings per week\" to a plain \"shillings\". We will now try to represent the data better by seperating these two into two different y-axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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, ax1 = plt.subplots()\n",
+    "\n",
+    "ax1.set_xlabel('Years')\n",
+    "ax1.set_ylabel('Price of wheat for 15 pounds')\n",
+    "ax1.plot(filtered_data['Year'], filtered_data['Wheat'])\n",
+    "ax1.tick_params(axis='y', labelcolor='b')\n",
+    "\n",
+    "ax2 = ax1.twinx()\n",
+    "\n",
+    "ax2.set_ylabel('Weekly wages', color='r')\n",
+    "ax2.plot(filtered_data['Year'], filtered_data['Wages'], color='r')\n",
+    "ax2.tick_params(axis='y', labelcolor='r')\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This graph better represents Playfairs data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display the purchasing power increase\n",
+    "\n",
+    "This was Playfairs original goal. We will now try to better represent this.\n",
+    "We will display the amount of kilograms of wheat that can be bought with a weekly salary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filtered_data['Wheat per salary'] = round(filtered_data['Wages'] / (filtered_data['Wheat'] / 6.8), 2)\n",
+    "plt.plot(filtered_data['Year'], filtered_data['Wheat per salary'])\n",
+    "plt.xlabel('Years')\n",
+    "plt.ylabel('Wheat per salary')\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This graph clearly shows us that the purchasing power has increased over time, as Playfair thought."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will now try to represent the data in a different way, using a scatter plot and without a time axis.\n",
+    "For this we will use the seaborn library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn\n",
+    "\n",
+    "seaborn.set()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'seaborn' has no attribute 'scatterplot'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-40-9789a43fbdf0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mseaborn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Wages'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Wheat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Year'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'scatterplot'"
+     ]
+    }
+   ],
+   "source": [
+    "seaborn.scatterplot(data = filtered_data, x = 'Wages', y = 'Wheat', hue = 'Year')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We try using the seaborn.scatterplot function, but the seaborn version isn't up to date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3b9304cf8>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "seaborn.regplot(x = filtered_data['Wages'], y = filtered_data['Wheat'], fit_reg = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function doesn't have a hue parameter, so we can't display time the way we intended."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conclusion\n",
+    "\n",
+    "The wheat per salary graph displays the data in the most intuitive way and confirms the point that William Playfair tried to prove."
+   ]
+  }
+ ],
+ "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
+}