{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sujet 2 : le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Unnamed: 0 Year Wheat Wages\n", "50 51 1815 78.0 NaN\n", "51 52 1820 54.0 NaN\n", "52 53 1821 54.0 NaN" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Voir les lignes avec des données manquantes\n", "\n", "data[data.isnull().any(axis = 1)]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Supprimer la ligne qui ne contient pas de données valables\n", "# Copier les données\n", "my_data = data.dropna().copy()\n", "my_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Représentation graphique du prix du blé " ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([18., 8., 16., 3., 0., 1., 1., 2., 0., 1.]),\n", " array([26. , 33.3, 40.6, 47.9, 55.2, 62.5, 69.8, 77.1, 84.4, 91.7, 99. ]),\n", " )" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(my_data[\"Wheat\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Représentation grahique des salaires" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(my_data[\"Wages\"], my_data[\"Unnamed: 0\"], \"r--\")\n", "\n", " \n", "x = my_data[\"Wages\"]\n", "y1 = my_data[\"Unnamed: 0\"]\n", " \n", "plt.fill_between(x, y1, color='#539ecd')" ] }, { "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 }