{
 "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": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 53 artists>"
      ]
     },
     "execution_count": 63,
     "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)')\n",
    "ax1.plot(sorted_data['Wages'], linewidth=3)\n",
    "ax1.fill_between(sorted_data.index, 0, sorted_data['Wages'],\n",
    "                 color='red')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('Price of wheat (Shillings/quarter)')\n",
    "ax1.bar(sorted_data.index, sorted_data['Wheat'], width=5,\n",
    "        color='grey', zorder=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}