diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..09f475c0b3a8552066d7784ce9e208dea763d371 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -1,5 +1,535 @@ { - "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": { + "text/html": [ + "
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" + ], + "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": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +546,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -