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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import io\n",
+    "import requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url='https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv'\n",
+    "s=requests.get(url).content\n",
+    "df=pd.read_csv(io.StringIO(s.decode('utf-8')))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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",
+       "  </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"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>count</th>\n",
+       "      <td>53.000000</td>\n",
+       "      <td>53.000000</td>\n",
+       "      <td>53.000000</td>\n",
+       "      <td>50.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>1694.924528</td>\n",
+       "      <td>43.264151</td>\n",
+       "      <td>11.581600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>15.443445</td>\n",
+       "      <td>77.089571</td>\n",
+       "      <td>15.410287</td>\n",
+       "      <td>7.336287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1565.000000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>1630.000000</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>6.145000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>1695.000000</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>7.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>1760.000000</td>\n",
+       "      <td>47.000000</td>\n",
+       "      <td>14.875000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>53.000000</td>\n",
+       "      <td>1821.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0         Year      Wheat      Wages\n",
+       "count   53.000000    53.000000  53.000000  50.000000\n",
+       "mean    27.000000  1694.924528  43.264151  11.581600\n",
+       "std     15.443445    77.089571  15.410287   7.336287\n",
+       "min      1.000000  1565.000000  26.000000   5.000000\n",
+       "25%     14.000000  1630.000000  33.000000   6.145000\n",
+       "50%     27.000000  1695.000000  41.000000   7.800000\n",
+       "75%     40.000000  1760.000000  47.000000  14.875000\n",
+       "max     53.000000  1821.000000  99.000000  30.000000"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0   Year  Wheat  Wages\n",
+       "0        False  False  False  False\n",
+       "1        False  False  False  False\n",
+       "2        False  False  False  False\n",
+       "3        False  False  False  False\n",
+       "4        False  False  False  False\n",
+       "5        False  False  False  False\n",
+       "6        False  False  False  False\n",
+       "7        False  False  False  False\n",
+       "8        False  False  False  False\n",
+       "9        False  False  False  False\n",
+       "10       False  False  False  False\n",
+       "11       False  False  False  False\n",
+       "12       False  False  False  False\n",
+       "13       False  False  False  False\n",
+       "14       False  False  False  False\n",
+       "15       False  False  False  False\n",
+       "16       False  False  False  False\n",
+       "17       False  False  False  False\n",
+       "18       False  False  False  False\n",
+       "19       False  False  False  False\n",
+       "20       False  False  False  False\n",
+       "21       False  False  False  False\n",
+       "22       False  False  False  False\n",
+       "23       False  False  False  False\n",
+       "24       False  False  False  False\n",
+       "25       False  False  False  False\n",
+       "26       False  False  False  False\n",
+       "27       False  False  False  False\n",
+       "28       False  False  False  False\n",
+       "29       False  False  False  False\n",
+       "30       False  False  False  False\n",
+       "31       False  False  False  False\n",
+       "32       False  False  False  False\n",
+       "33       False  False  False  False\n",
+       "34       False  False  False  False\n",
+       "35       False  False  False  False\n",
+       "36       False  False  False  False\n",
+       "37       False  False  False  False\n",
+       "38       False  False  False  False\n",
+       "39       False  False  False  False\n",
+       "40       False  False  False  False\n",
+       "41       False  False  False  False\n",
+       "42       False  False  False  False\n",
+       "43       False  False  False  False\n",
+       "44       False  False  False  False\n",
+       "45       False  False  False  False\n",
+       "46       False  False  False  False\n",
+       "47       False  False  False  False\n",
+       "48       False  False  False  False\n",
+       "49       False  False  False  False\n",
+       "50       False  False  False   True\n",
+       "51       False  False  False   True\n",
+       "52       False  False  False   True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull() #check nan, wages at last 3 years are Nan\n",
+    "#df['Year']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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": [
+    "#reproduce the Playfair's graph.\n",
+    "plt.bar(df['Year'],df['Wheat'],width=1.8,label='Wheat price per quater')\n",
+    "plt.plot(df['Year'],df['Wages'],c='red',label='Weekly wages')\n",
+    "plt.legend()\n",
+    "plt.ylabel('shillings')\n",
+    "plt.xlabel('year')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#improve, change quarter to kg and shirling to pound\n",
+    "df['Wheat']=df['Wheat']/6.8 #change quarter to kg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Wheat']=df['Wheat']/20 #change shillings to pound\n",
+    "df['Wages']=df['Wages']/20 #change shillings to pound"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "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>0.301471</td>\n",
+       "      <td>0.2500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1570</td>\n",
+       "      <td>0.330882</td>\n",
+       "      <td>0.2525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1575</td>\n",
+       "      <td>0.308824</td>\n",
+       "      <td>0.2540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1580</td>\n",
+       "      <td>0.360294</td>\n",
+       "      <td>0.2560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1585</td>\n",
+       "      <td>0.305147</td>\n",
+       "      <td>0.2575</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0  Year     Wheat   Wages\n",
+       "0           1  1565  0.301471  0.2500\n",
+       "1           2  1570  0.330882  0.2525\n",
+       "2           3  1575  0.308824  0.2540\n",
+       "3           4  1580  0.360294  0.2560\n",
+       "4           5  1585  0.305147  0.2575"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "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": [
+    "#plot graph with two indipendent y-axis\n",
+    "fig,ax1=plt.subplots()\n",
+    "\n",
+    "color='blue'\n",
+    "ax1.set_xlabel('year')\n",
+    "ax1.set_ylabel('Wheat Price (Pound/Kg)',color=color)\n",
+    "ax1.plot(df['Year'],df['Wheat'],c=color)\n",
+    "ax1.tick_params(axis='y',labelcolor=color)\n",
+    "\n",
+    "ax2=ax1.twinx() #share the same x-axis\n",
+    "\n",
+    "color='red'\n",
+    "ax2.set_ylabel('Wages (Pound/Week)',color=color)\n",
+    "ax2.plot(df['Year'],df['Wages'],c=color)\n",
+    "ax2.tick_params(axis='y',labelcolor=color)\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plot purchasing power\n",
+    "#purchasing power define as how much wheat a work can buy with a weekly salary:\n",
+    "#in our dataframe is df['Wages']/df['Wheat']\n",
+    "#define a new column PW short for purchasing power\n",
+    "df['PW']=df['Wages']/df['Wheat']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "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",
+       "      <th>PW</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>49</td>\n",
+       "      <td>1805</td>\n",
+       "      <td>0.595588</td>\n",
+       "      <td>1.475</td>\n",
+       "      <td>2.476543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>50</td>\n",
+       "      <td>1810</td>\n",
+       "      <td>0.727941</td>\n",
+       "      <td>1.500</td>\n",
+       "      <td>2.060606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>51</td>\n",
+       "      <td>1815</td>\n",
+       "      <td>0.573529</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>52</td>\n",
+       "      <td>1820</td>\n",
+       "      <td>0.397059</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>53</td>\n",
+       "      <td>1821</td>\n",
+       "      <td>0.397059</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0  Year     Wheat  Wages        PW\n",
+       "48          49  1805  0.595588  1.475  2.476543\n",
+       "49          50  1810  0.727941  1.500  2.060606\n",
+       "50          51  1815  0.573529    NaN       NaN\n",
+       "51          52  1820  0.397059    NaN       NaN\n",
+       "52          53  1821  0.397059    NaN       NaN"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "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": [
+    "#pw vs year\n",
+    "plt.plot(df['Year'][:-3],df['PW'][:-3])\n",
+    "plt.xlabel('year')\n",
+    "plt.ylabel('Wheat bought with weekly salary (Kg)')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "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": [
+    "#wheat price vs wages\n",
+    "#because wages increase as years, so the advancement of time is the same as wages\n",
+    "plt.plot(df['Wages'][:-3],df['Wheat'][:-3])\n",
+    "plt.ylabel('Wheat price (Pound/kg)')\n",
+    "plt.xlabel('Wages (Pound/Week)')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}