diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..eb4b1d8e0c9e69934442e424006fd81b25adb181 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -1,5 +1,604 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle# " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "url=\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\"\n", + "c=pd.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rownamesYearWheatWages
01156541.05.00
12157045.05.05
23157542.05.08
34158049.05.12
45158541.55.15
56159047.05.25
67159564.05.54
78160027.05.61
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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
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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
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5051181578.0NaN
5152182054.0NaN
5253182154.0NaN
\n", + "
" + ], + "text/plain": [ + " rownames 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": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c1 = pd.DataFrame(c,\n", + " columns=['rownames','Year','Wheat','Wages'])\n", + "c1" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([18., 8., 16., 5., 0., 1., 1., 3., 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": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax1 = plt.subplots(figsize=(9, 6))\n", + "\n", + "# Instantiate a second axes that shares the same x-axis\n", + "ax2 = ax1.twinx() \n", + "plt.plot(x,y2, color='r')\n", + "plt.fill_between(x, y2) \n", + "plt.bar(x,y1,width = 5, color='black')\n", + "plt.xlabel('year')\n", + "ax1.set_ylabel('Wheat')\n", + "ax2.set_ylabel('Wages')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +615,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -