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module3/exo3/exercice.ipynb
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{ {
"cells": [], "cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sujet 2 : le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle\n",
"\n",
"## Importation des données\n",
"\n",
"Les données utilisées dans le cadre de cette étude proviennent des travaux de [William Playfair](https://fr.wikipedia.org/wiki/William_Playfair). Plus précisément, elles sont tirées de son livre \"[A Letter on Our Agricultural Distresses, Their Causes and Remedies](https://books.google.fr/books/about/A_Letter_on_Our_Agricultural_Distresses.html?id=aQZGAQAAMAAJ)\" dans lequel peut être trouvé un de ses [graphes](https://fr.wikipedia.org/wiki/William_Playfair#/media/File:Chart_Showing_at_One_View_the_Price_of_the_Quarter_of_Wheat,_and_Wages_of_Labour_by_the_Week,_from_1565_to_1821.png) célèbres, présentant l'évolution du prix du blé et du salaire moyen entre 1565 et 1821.\n",
"\n",
"Par la [numérisation](https://vincentarelbundock.github.io/Rdatasets/doc/HistData/Wheat.html) de ce graphe, des valeurs ont pu être obtenues au sein d'un [fichier au format CSV](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv). C'est à partir de ce fichier que l'ensemble des calculs présentés ici ont été réalisés."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dans un premier temps, on introduit l'ensemble des bibliothèques qui nous serviront pour le code lié aux calculs :"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les données présentes dans le fichier au format CSV sont les suivantes :"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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"\n",
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"<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",
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" <td>2</td>\n",
" <td>1570</td>\n",
" <td>45.0</td>\n",
" <td>5.05</td>\n",
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" <td>1575</td>\n",
" <td>42.0</td>\n",
" <td>5.08</td>\n",
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" <th>3</th>\n",
" <td>4</td>\n",
" <td>1580</td>\n",
" <td>49.0</td>\n",
" <td>5.12</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>1585</td>\n",
" <td>41.5</td>\n",
" <td>5.15</td>\n",
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" <th>5</th>\n",
" <td>6</td>\n",
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" <td>47.0</td>\n",
" <td>5.25</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
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" <th>7</th>\n",
" <td>8</td>\n",
" <td>1600</td>\n",
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" <td>5.61</td>\n",
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" <th>8</th>\n",
" <td>9</td>\n",
" <td>1605</td>\n",
" <td>33.0</td>\n",
" <td>5.69</td>\n",
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" <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",
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" <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",
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" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>1705</td>\n",
" <td>32.0</td>\n",
" <td>10.00</td>\n",
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" <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",
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" <tr>\n",
" <th>45</th>\n",
" <td>46</td>\n",
" <td>1790</td>\n",
" <td>47.5</td>\n",
" <td>25.50</td>\n",
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" <tr>\n",
" <th>46</th>\n",
" <td>47</td>\n",
" <td>1795</td>\n",
" <td>76.0</td>\n",
" <td>27.50</td>\n",
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" <tr>\n",
" <th>47</th>\n",
" <td>48</td>\n",
" <td>1800</td>\n",
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" <tr>\n",
" <th>48</th>\n",
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" <tr>\n",
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" <td>50</td>\n",
" <td>1810</td>\n",
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" <td>51</td>\n",
" <td>1815</td>\n",
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" <td>NaN</td>\n",
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" <th>51</th>\n",
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" <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": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_url)\n",
"raw_data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
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...@@ -16,10 +546,9 @@ ...@@ -16,10 +546,9 @@
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