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module3/exo3/Wheat.csv 0 → 100644
View file @ 65ea7df1
"","Year","Wheat","Wages"
"1",1565,41,5
"2",1570,45,5.05
"3",1575,42,5.08
"4",1580,49,5.12
"5",1585,41.5,5.15
"6",1590,47,5.25
"7",1595,64,5.54
"8",1600,27,5.61
"9",1605,33,5.69
"10",1610,32,5.78
"11",1615,33,5.94
"12",1620,35,6.01
"13",1625,33,6.12
"14",1630,45,6.22
"15",1635,33,6.3
"16",1640,39,6.37
"17",1645,53,6.45
"18",1650,42,6.5
"19",1655,40.5,6.6
"20",1660,46.5,6.75
"21",1665,32,6.8
"22",1670,37,6.9
"23",1675,43,7
"24",1680,35,7.3
"25",1685,27,7.6
"26",1690,40,8
"27",1695,50,8.5
"28",1700,30,9
"29",1705,32,10
"30",1710,44,11
"31",1715,33,11.75
"32",1720,29,12.5
"33",1725,39,13
"34",1730,26,13.3
"35",1735,32,13.6
"36",1740,27,14
"37",1745,27.5,14.5
"38",1750,31,15
"39",1755,35.5,15.7
"40",1760,31,16.5
"41",1765,43,17.6
"42",1770,47,18.5
"43",1775,44,19.5
"44",1780,46,21
"45",1785,42,23
"46",1790,47.5,25.5
"47",1795,76,27.5
"48",1800,79,28.5
"49",1805,81,29.5
"50",1810,99,30
"51",1815,78,NA
"52",1820,54,NA
"53",1821,54,NA
module3/exo3/exercice.ipynb
View file @ 65ea7df1
...@@ -9,11 +9,504 @@ ...@@ -9,11 +9,504 @@
"Dans le cadre de cette exercice évalué par les pairs (Mooc RR, mod3) j'ai choisi le sujet n°2 intitulé :\n", "Dans le cadre de cette exercice évalué par les pairs (Mooc RR, mod3) j'ai choisi le sujet n°2 intitulé :\n",
"**Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle**\n", "**Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle**\n",
"\n", "\n",
"## Contexte de l'étude\n",
"\n",
"William Playfair un des pionnier de la représentation graphique des données, a réalisé un graphique montrant l'évolution du prix du blé et du salaire moyen entre 1565 et 1821. Ce graphique a été publié en 1822 dans son livre *A Letter on our Agricultural Distresses, Their Causes and Remedies*. Ci-dessous une reproduction hébergée sur [Wikipédia][graph original].\n", "William Playfair un des pionnier de la représentation graphique des données, a réalisé un graphique montrant l'évolution du prix du blé et du salaire moyen entre 1565 et 1821. Ce graphique a été publié en 1822 dans son livre *A Letter on our Agricultural Distresses, Their Causes and Remedies*. Ci-dessous une reproduction hébergée sur [Wikipédia][graph original].\n",
"\n", "\n",
"![Chart Showing at One View the Price of the Quarter of Wheat, and Wages of Labour by the Week, from 1565 to 1821](playfair_ori_prixble_salaire.png)\n", "![Chart Showing at One View the Price of the Quarter of Wheat, and Wages of Labour by the Week, from 1565 to 1821](playfair_ori_prixble_salaire.png)\n",
"\n", "\n",
"[graph original]: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" "[graph original]: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\n",
"\n",
"Le premier objectif de l'étude est de reproduire ce graphe, puis dans un second temps de corriger le graphique.Een effet W. Playfair a utilisé la même unité pour représenter deux quantités différentes sur l'axe des ordonnées. Enfin dans un troisième temps le but sera d'améliorer la représentation du pouvoir d'achat des agriculteurs anglais sur cette période.\n",
"\n",
"## Les données\n",
"\n",
"W. Playfair n'a pas publié les données numériques brutes de son étude. Néanmoins une version numérisée est diponible [ici][data_url], réalisé par [Vincent Arel-Bundock] et publié sur son site [R datasets][vab r datasets].\n",
"\n",
"[data_url]: https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\n",
"[Vincent Arel-Bundock]: https://github.com/vincentarelbundock\n",
"[vab r datasets]: https://vincentarelbundock.github.io/Rdatasets/\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fichier déjà téléchargé\n"
]
},
{
"data": {
"text/plain": [
"['exercice_python_en.org',\n",
" 'exercice_fr.ipynb',\n",
" 'Wheat.csv',\n",
" 'exercice.ipynb',\n",
" 'exercice_fr.Rmd',\n",
" 'playfair_ori_prixble_salaire.png',\n",
" 'exercice_python_fr.org',\n",
" 'exercice_R_en.org',\n",
" 'exercice_R_fr.org',\n",
" 'exercice_en.Rmd',\n",
" 'exercice_en.ipynb',\n",
" '.ipynb_checkpoints']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import des bibliothèques\n",
"import urllib\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from os import listdir\n",
"\n",
"# téléchargement du fichier\n",
"data_url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv'\n",
"filename = 'Wheat.csv'\n",
"\n",
"curFiles = set(listdir())\n",
"\n",
"# téléchargement automatique du fichier\n",
"# si non présent dans le répertoire\n",
"if not(filename in curFiles):\n",
" print('Téléchargement du fichier')\n",
" urllib.request.urlretrieve(data_url, filename)\n",
"else:\n",
" print('Fichier déjà téléchargé')\n",
"\n",
"listdir()"
]
},
{
"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>1</th>\n",
" <td>1565</td>\n",
" <td>41.0</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1570</td>\n",
" <td>45.0</td>\n",
" <td>5.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1575</td>\n",
" <td>42.0</td>\n",
" <td>5.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1580</td>\n",
" <td>49.0</td>\n",
" <td>5.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1585</td>\n",
" <td>41.5</td>\n",
" <td>5.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1590</td>\n",
" <td>47.0</td>\n",
" <td>5.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1595</td>\n",
" <td>64.0</td>\n",
" <td>5.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1600</td>\n",
" <td>27.0</td>\n",
" <td>5.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1605</td>\n",
" <td>33.0</td>\n",
" <td>5.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1610</td>\n",
" <td>32.0</td>\n",
" <td>5.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1615</td>\n",
" <td>33.0</td>\n",
" <td>5.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1620</td>\n",
" <td>35.0</td>\n",
" <td>6.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1625</td>\n",
" <td>33.0</td>\n",
" <td>6.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1630</td>\n",
" <td>45.0</td>\n",
" <td>6.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1635</td>\n",
" <td>33.0</td>\n",
" <td>6.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1640</td>\n",
" <td>39.0</td>\n",
" <td>6.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1645</td>\n",
" <td>53.0</td>\n",
" <td>6.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>1650</td>\n",
" <td>42.0</td>\n",
" <td>6.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1655</td>\n",
" <td>40.5</td>\n",
" <td>6.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1660</td>\n",
" <td>46.5</td>\n",
" <td>6.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1665</td>\n",
" <td>32.0</td>\n",
" <td>6.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1670</td>\n",
" <td>37.0</td>\n",
" <td>6.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1675</td>\n",
" <td>43.0</td>\n",
" <td>7.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>1680</td>\n",
" <td>35.0</td>\n",
" <td>7.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>1685</td>\n",
" <td>27.0</td>\n",
" <td>7.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1690</td>\n",
" <td>40.0</td>\n",
" <td>8.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>1695</td>\n",
" <td>50.0</td>\n",
" <td>8.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1700</td>\n",
" <td>30.0</td>\n",
" <td>9.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>1705</td>\n",
" <td>32.0</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>1710</td>\n",
" <td>44.0</td>\n",
" <td>11.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>1715</td>\n",
" <td>33.0</td>\n",
" <td>11.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>1720</td>\n",
" <td>29.0</td>\n",
" <td>12.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>1725</td>\n",
" <td>39.0</td>\n",
" <td>13.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>1730</td>\n",
" <td>26.0</td>\n",
" <td>13.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>1735</td>\n",
" <td>32.0</td>\n",
" <td>13.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>1740</td>\n",
" <td>27.0</td>\n",
" <td>14.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>1745</td>\n",
" <td>27.5</td>\n",
" <td>14.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>1750</td>\n",
" <td>31.0</td>\n",
" <td>15.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>1755</td>\n",
" <td>35.5</td>\n",
" <td>15.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>1760</td>\n",
" <td>31.0</td>\n",
" <td>16.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>1765</td>\n",
" <td>43.0</td>\n",
" <td>17.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>1770</td>\n",
" <td>47.0</td>\n",
" <td>18.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>1775</td>\n",
" <td>44.0</td>\n",
" <td>19.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>1780</td>\n",
" <td>46.0</td>\n",
" <td>21.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>1785</td>\n",
" <td>42.0</td>\n",
" <td>23.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>1790</td>\n",
" <td>47.5</td>\n",
" <td>25.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>1795</td>\n",
" <td>76.0</td>\n",
" <td>27.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>1800</td>\n",
" <td>79.0</td>\n",
" <td>28.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>1805</td>\n",
" <td>81.0</td>\n",
" <td>29.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>1810</td>\n",
" <td>99.0</td>\n",
" <td>30.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>1815</td>\n",
" <td>78.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>1820</td>\n",
" <td>54.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</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",
"1 1565 41.0 5.00\n",
"2 1570 45.0 5.05\n",
"3 1575 42.0 5.08\n",
"4 1580 49.0 5.12\n",
"5 1585 41.5 5.15\n",
"6 1590 47.0 5.25\n",
"7 1595 64.0 5.54\n",
"8 1600 27.0 5.61\n",
"9 1605 33.0 5.69\n",
"10 1610 32.0 5.78\n",
"11 1615 33.0 5.94\n",
"12 1620 35.0 6.01\n",
"13 1625 33.0 6.12\n",
"14 1630 45.0 6.22\n",
"15 1635 33.0 6.30\n",
"16 1640 39.0 6.37\n",
"17 1645 53.0 6.45\n",
"18 1650 42.0 6.50\n",
"19 1655 40.5 6.60\n",
"20 1660 46.5 6.75\n",
"21 1665 32.0 6.80\n",
"22 1670 37.0 6.90\n",
"23 1675 43.0 7.00\n",
"24 1680 35.0 7.30\n",
"25 1685 27.0 7.60\n",
"26 1690 40.0 8.00\n",
"27 1695 50.0 8.50\n",
"28 1700 30.0 9.00\n",
"29 1705 32.0 10.00\n",
"30 1710 44.0 11.00\n",
"31 1715 33.0 11.75\n",
"32 1720 29.0 12.50\n",
"33 1725 39.0 13.00\n",
"34 1730 26.0 13.30\n",
"35 1735 32.0 13.60\n",
"36 1740 27.0 14.00\n",
"37 1745 27.5 14.50\n",
"38 1750 31.0 15.00\n",
"39 1755 35.5 15.70\n",
"40 1760 31.0 16.50\n",
"41 1765 43.0 17.60\n",
"42 1770 47.0 18.50\n",
"43 1775 44.0 19.50\n",
"44 1780 46.0 21.00\n",
"45 1785 42.0 23.00\n",
"46 1790 47.5 25.50\n",
"47 1795 76.0 27.50\n",
"48 1800 79.0 28.50\n",
"49 1805 81.0 29.50\n",
"50 1810 99.0 30.00\n",
"51 1815 78.0 NaN\n",
"52 1820 54.0 NaN\n",
"53 1821 54.0 NaN"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# lecture du fichier\n",
"\n",
"rawdata = pd.read_csv(filename, index_col=0)\n",
"rawdata"
] ]
}, },
{ {
......
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