{ "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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv')\n", "data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Unnamed: 0YearWheatWages
5051181578.0NaN
5152182054.0NaN
5253182154.0NaN
\n", "
" ], "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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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
\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" ] }, "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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\n", "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": "markdown", "metadata": {}, "source": [ "## Superposition des deux graphiques" ] }, { "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\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 }