{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subject 2: Purchasing power of English workers from the 16th to the 19th century"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import isoweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "William Playfair was one of the pioneers of the graphical presentation of data, being credited in particular with the invention of the histogram. One of his famous graphs, taken from his book \"A Letter on Our Agricultural Distresses, Their Causes and Remedies\", shows the evolution of the wheat price and average salaries from 1565 to 1821. First, we will replicate his famous graph and then present alternative versions of the graph to improve the readability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the original graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data used by Playfair are available on [github](https://vincentarelbundock.github.io/Rdatasets/doc/HistData/Wheat.html) in a csv format using the url:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the data are remove the first column that is unecessary. The array is made of three columns : the year, the wheat price (in Shilling/quarter) and the wages (in Shilling/week)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(data_url)\n",
    "data = raw_data.copy()\n",
    "data.pop('Unnamed: 0')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can replace the index by the column year and sort by increasing years. We verify that the gap between two points is not more than 5 years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidePrompt": true
   },
   "outputs": [],
   "source": [
    "sorted_data =  data.set_index('Year').sort_index()\n",
    "periods = sorted_data.index\n",
    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
    "    assert (p2-p1)<=5 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Eventually plotting Playfair's graph:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Price of a quarter of wheat (Shillings)')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.figure()\n",
    "ax1 = plt.gca()\n",
    "ax1.set_xlabel('Time (year)')\n",
    "ax1.plot(sorted_data['Wages'], linewidth=3)\n",
    "ax1.fill_between(sorted_data.index, 0, sorted_data['Wages'],\n",
    "                 color='red')\n",
    "ax1.bar(sorted_data.index, sorted_data['Wheat'], width=5,\n",
    "        color='grey', zorder=0)\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax1.set_ylim([0, 100])\n",
    "ax2.set_ylim([0, 100])\n",
    "ax2.set_ylabel('Price of a quarter of wheat (Shillings)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Re-labeling the axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The left and right axis can be labeled with the proper units 'Shillings/week' and 'Shillings/quarter', resp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f257a846c50>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.figure()\n",
    "ax1 = plt.gca()\n",
    "ax1.set_xlabel('Time (year)')\n",
    "ax1.set_ylabel('Wages (Shillings/week)', color='blue')\n",
    "ax1.plot(sorted_data['Wages'], linewidth=3)\n",
    "ax2 = ax1.twinx()\n",
    "ax1.set_ylim([0, 100])\n",
    "ax2.set_ylim([0, 100])\n",
    "ax2.set_ylabel('Price of wheat (Shillings/quarter)', \n",
    "               color='red')\n",
    "ax2.plot(sorted_data.index, sorted_data['Wheat'],\n",
    "         color='red', zorder=0, linewidth=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
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