{ "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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" ], "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" ] }, "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": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(my_data[\"Unnamed: 0\"], my_data[\"Wheat\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Représentation grahique des salaires" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 89, "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.plot(my_data[\"Unnamed: 0\"], my_data[\"Wages\"], \"r--\")\n", "\n", " \n", "y1 = my_data[\"Wages\"]\n", "x = my_data[\"Unnamed: 0\"]\n", " \n", "plt.fill_between(x, y1, color='#539ecd')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 87, "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": [ "# Superposition des deux graphiques\n", "\n", "p = plt.bar(my_data[\"Unnamed: 0\"], my_data[\"Wheat\"]), plt.plot(my_data[\"Unnamed: 0\"], my_data[\"Wages\"], \"r--\")\n", "\n", "\n", "x = my_data[\"Unnamed: 0\"]\n", "y2 = my_data[\"Wages\"]\n", "\n", "\n", "plt.fill_between(x, y2, color='#539ecd')\n", "#plt.legend([p1, p2])\n", "#plt.show()\n" ] }, { "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 }