diff --git a/module3/exo3/data_william.csv b/module3/exo3/data_william.csv new file mode 100644 index 0000000000000000000000000000000000000000..1a201c33a10d88009da4e15b06b46c6698bdcdf8 --- /dev/null +++ b/module3/exo3/data_william.csv @@ -0,0 +1,54 @@ +"","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 diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..d550766ef4b34fb959ffd60c85860e1165fcfe65 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -1,5 +1,594 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sujet 2 : le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le but de ce travail est de reproduire le [graphique](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) de William Playfair qui montre l'évolution du prix du blé et du salaire moyen entre 1565 et 1821. Ce graphique est publié dans son [livre](https://books.google.fr/books?id=aQZGAQAAMAAJ&printsec=frontcover&hl=fr&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false) : \"A Letter on Our Agricultural Distresses, Their Causes and Remedies\".\n", + "\n", + "Les données numériques brutes que William Playfair a utilisées ne sont malheureusement pas disponible. Des valeurs obtenues par numérisation du graphe sont toutefois disponible [ici](https://vincentarelbundock.github.io/Rdatasets/doc/HistData/Wheat.html). Nous utiliserons ici la [version en format CSV](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Téléchargement des données\n", + "\n", + "Nous vérifions dans un premier temps que les données ne sont pas déjà présente dans un fichier local. Si ce n'est pas le cas, nous effectuons une copie des données dans un fichier local qui servira dans la suite des analyses\n", + "Les données disponible et réalisons une copie locale de ces données\n", + "\n", + "Le but de la manoeuvre est de permettre d'accèder aux données ultérieurement même si le lien initiale des données est modifié (suppression ou modification)ou que les données utilisée venaient à être modifiée (avec une nouvelle numérisation du graphique par exemple)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url='https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aucun fichier local avec les données étudiées n'est disponible. Un nouveau fichier est fabriqué à partir du lien donné\n" + ] + } + ], + "source": [ + "import os\n", + "import urllib.request\n", + "fileName = 'data_william.csv'\n", + "if not os.path.exists(fileName):\n", + " print('Aucun fichier local avec les données étudiées n\\'est disponible. Un nouveau fichier est fabriqué à partir du lien donné')\n", + " urllib.request.urlretrieve(data_url, fileName) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous pouvons à présent ouvrir le fichier local et travailler avec celui-ci tout au long de l'étude.\n", + "La première colonne correspond à l'ID. Nous avons dès lors décidé de passer cette colonne comme index (au moins dans un premier temps)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "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": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "raw_data = pd.read_csv(fileName,index_col=0)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Informations relatives aux données:\n", + "Il s'agit d'une base de données avec 53 observations sur les 3 variables suivantes:\n", + "* Year = années espacée de 5 ans entre 1565 et 1821\n", + "* Wheat = prix du blé (shillings pour un quart de boisseau de blé)\n", + "* Wages = salaires hebdomadaire (shillings par semaine)\n", + "\n", + "**Remarques**\n", + "* Jusqu'en 1971, la livre sterling était divisée en 20 shillings, et un shilling en 12 pences.\n", + "* Le prix du blé est donné en shillings pour un quart de boisseau de blé. Un quart de boisseau équivaut 15 livres britanniques ou 6,8 kg.\n", + "* Les salaires sont donnés en shillings par semaine." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(raw_data['Year'][1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Afin de pouvoir réaliser des graphique numérique des données, nous convertissons les date en format datetime compatible avec les graphique matplotlib\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "year=[]\n", + "year=[datetime.date(raw_data['Year'][i+1],1,1)for i in range(0,len(raw_data['Year'])-1)]\n", + "width=[(year[j+1]-year[j]).days for j in range(0,len(year)-1)]\n", + "width.append(365)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(21.0, 100)" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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LINf/676mvwK8e5nkeivw58BDS5BnoFzL4LyfV1U9sozO+1cBt1fVw0lO1Hm/aM1f0S/g9cCbqups4E3AB7rx+e6nFbPfENcBn6mqC4GbgXeOOleSJ3bbXACcCXweuPYE5lrIQv+Ow7bYXAAkWQFcB7ynqr486lxJNgA/W1XXL0GWgXMx+vN+XsvlvE/ybOAdwG8fGZpnH8vi/euP16KfAD7WzX+EH96eOQCcPWe9dcz+uPYgs1da18/Z5sJlkGsDQFXd1/2ouINFXNUOIddCDvCjt0SO5B11riO2A/uq6l1LkAkWn+t5wEVJ9gOfBn4uyU3LINeoz/uFjPy8T7KO2X+X11bVfd3wiTrvF+3xWvQPAL/UzV/G7I9+ALuAq7r7bc8E1gO3dSfTPwKXduttBL446lzA14Dzkhz57XWXA3edwFzzqqqDwLeTPLe7h/paYOeocwEkeRuz91uvWYI8A+WqqvdV1ZlVdQ6zLz5+qaouXQa5Rn3eL2Sk5313S+sG4Nqq+syRlU/geb94o341eKkfzP6IfhB4hNnvuFcz+59pD7PvZLkVuGjO+n/E7L3Ie5jzijnwDOBTzP6YOAU8fZnkeh2zJ/nnmf1PefoJzrWf2RexvtOtf143Pg7s7TK/l+5T2KPMxewVVnX/Xnd0j98cda6j9ncOw3nXzbC+jqM+7xfKNbLzHvhjZl8buGPOY/VSnPfDevgrECSpcY/XWzeS9Lhh0UtS4yx6SWqcRS9JjbPoJalxFr0kNc6il6TG/R+Zqhe22pKtZAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "ax = plt.subplot(111)\n", + "plt.bar(year[48:52],raw_data['Wheat'][48:52],align='edge',width=width[48:52])\n", + "ax.xaxis_date()\n", + "plt.ylim(raw_data['Wheat'].min()-5,100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +605,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -