{ "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": 2, "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": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0YearWheatWages
01156541.05.00
12157045.05.05
23157542.05.08
34158049.05.12
45158541.55.15
56159047.05.25
67159564.05.54
78160027.05.61
89160533.05.69
910161032.05.78
1011161533.05.94
1112162035.06.01
1213162533.06.12
1314163045.06.22
1415163533.06.30
1516164039.06.37
1617164553.06.45
1718165042.06.50
1819165540.56.60
1920166046.56.75
2021166532.06.80
2122167037.06.90
2223167543.07.00
2324168035.07.30
2425168527.07.60
2526169040.08.00
2627169550.08.50
2728170030.09.00
2829170532.010.00
2930171044.011.00
3031171533.011.75
3132172029.012.50
3233172539.013.00
3334173026.013.30
3435173532.013.60
3536174027.014.00
3637174527.514.50
3738175031.015.00
3839175535.515.70
3940176031.016.50
4041176543.017.60
4142177047.018.50
4243177544.019.50
4344178046.021.00
4445178542.023.00
4546179047.525.50
4647179576.027.50
4748180079.028.50
4849180581.029.50
4950181099.030.00
5051181578.0NaN
5152182054.0NaN
5253182154.0NaN
\n", "
" ], "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": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reproduction du graphe de Playfair\n", "\n", "Au sein de cette partie, on tente de reproduire le graphique initial de Playfair. Il s'agit dans un premier temps d'extraire les données du tableau ci-dessus." ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X=raw_data['Year']\n", "Y1=raw_data['Wages']\n", "Y2=raw_data['Wheat']\n", "\n", "plt.grid(True)\n", "plt.plot(X, Y1,\"r\",label='Salaire (Shillings/Semaine)',linewidth=2)\n", "plt.bar(X,Y2,label='Prix du blé (Shillings/Quart de boisseau)',color='black',width=2.5)\n", "plt.fill_between(X,Y1,color='blue',alpha=0.25)\n", "\n", "plt.xlabel('Années (/5 ans)')\n", "plt.ylabel('Shillings')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }