{ "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": 2, "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": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reproduction du graphe de Playfair\n", "\n", "Au sein de cette partie, on tente de reproduire le graphique initial de Playfair. Il s'agit dans un premier temps d'extraire les données du tableau ci-dessus. Les données sont stockées dans les variables X,Y1 et Y2 qui vont par la suite nous permettre de tracer les graphiques souhaités." ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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qZQ4/8eijj/LAAw8wfPjwHOP3xMfHM2vWLNauXcsnn3zCqlWrCh3PypUrefbZZ9m4cSM///wzn3zySZb5W7ZsYfbs2Sxbtox169YREhLi85962bJlREdHAxAVFUV4eDhNmzZl5MiRfPHFFznWwcqVK3n++eeZMmVKoWPOVLt2bdasWcOdd97p6V6ZMmUKvXr1Ys2aNQwZMoRdu3YBzlFCgwYNWL9+PZs2baJfv36A03UWFRWFiLB582ZPGzJ16tSJH3/8Mc846taty7fffsuaNWuYPXu2p6sMnPU7bdo0fvzxR55++mn+8pe/sG7dOmbMmJFlGZnbcunSpTm25dixY3nppZeIj48nJiaGcePG+Yxjw4YNzJ8/n+XLl/Pkk0+SmJjIJ598wrp161i/fj2LFi3i/vvv9+zUM82cOZO7776bdevWsXr1aiIiInyur9TUVMaPH8/cuXOJj49n1KhRPPLIIwBcc801rFq1ivXr19O6dWv+85//5LnOwDkqtXNapY/dKFcMkpOTPcNCX3bZZYwePZrExEQaN26ca7/+5MmT6dy5M5UqVfLZ/bJkyRKGDBniGfb6TG5469KlC82aNQNg6NChLF26NEvf9OLFi4mPj/eMAZWcnEzdunVzLMd76PCQkBAWLlzIqlWrWLx4Mffccw/x8fE88cQTgLMzgdPDY58p7+VkJralS5d6huju168f5557LuAM6z1x4kQmTZrEwIEDueyyywCyDPyn7phU3rQAo/+mpqZy1113eRLo9u3bPfO6dOmS5egpN97bslq1ap5tWdCh2wHPkOlhYWH07NmTlStXsnTpUoYOHUpISAjh4eF0797dc3Sa6eKLL2batGkkJCR4Hpzka31t2rQp1yHUN23axKOPPuoZUNF7tN3c1KlTh8TExHzrmcBiCaIYZJ6DyC5ziG5fDh48yPHjx0lNTSUlJcVn3dzOgZQvX97TXZTb8OC+ft/XDnLEiBH84x//yHUZ4LTP+3NEhC5dutClSxf69u3LyJEjPQkic4jssxk2PLfl5LZDb9GiBfHx8SxYsICHHnqIyy+/nMmTJ/PNN9/w8ccfA9C2bVtWr16dZee5Zs0azyNTc1un//znPwkPD2f9+vVkZGR4BjuEvLdvdr62ZUGHbvf1+yJSoAR38803c+GFFzJ//nyuuOIKXn/9dXr16pVjfQ0ZMiTXIdRvvfVWPvvsM6KionjrrbeIi4sDsq4zVc3yOFgb9rx0si6mADF27FimTp3KsGHDmDRpUo753bp149NPPyU5OZljx45l6cpp0qSJ5+RqXicjV65cya+//kpGRgazZ8/OMc5R7969mTt3Lnv37gWcpPXbb7/lWI730OGJiYmsWbPGMy9zCO6S0LVrVz766CMAvvnmGw4dOuSJqXLlygwfPpyJEyeyZs0azyivmQ8BmjhxIv/4xz88RzU7d+7k+eef95wvyG2dHjlyhPr161OuXDneffddn+doAKpVq8axY8d8zsttWxZ06HaAefPmkZKSwoEDB4iLi6Nz585069aN2bNnk56ezr59+/juu+/o0qVLlt/75ZdfaNasGRMmTODqq69mw4YNPtdXXkOoHzt2jPr165OampqlC9J7nc2bN88znDzATz/9ZMOel0J2BBEA3nnnHcqXL8/NN99Meno6l1xyCf/973/p1auXp07Hjh258cYb6dChA40bN/Z0m4Czs7vhhht49913s/xOdhdffDEPPvggGzdupFu3bjkuPWzTpg1PPfUUl19+ORkZGYSGhvLyyy/n2OEPGDCAuLg4+vTpQ2pqKhMnTiQxMZFKlSpRp06dHFdtFZfHH3+coUOHMnv2bLp37079+vWpVq0acXFx3H///ZQrV47Q0FD+/e9/8+2332a5KqpDhw4888wzXHXVVZw8eZKdO3cSGxtLy5YtgdzX6bhx47j22muZM2cOPXv2zPWooVatWlx66aW0a9eO/v37ZzkPkbktL730Upo2bZplWxZk6HZwurMGDBjArl27eOyxx2jQoAFDhgxh+fLlnvMs06dPp169elm69mbPns17771HaGgo9erVY/LkyaxatSrH+sprCPWpU6dy4YUX0rhxYyIjIz2JcMyYMQwaNIguXbrQu3fvLOtmyZIlDBgwoJBb2PhdQYZ8DdSXDfftH0lJSXrhhRdqWlpakS+7MG1NSUnR1NRUVVX9/vvvNSoqKte6o0eP1uXLl+c6f9KkSdqjRw89efJkwYM9S4G2XYvTJZdcogcPHvQ5L9j+Z224b1OmhYWFMWXKFH7//XfOO+88v8Wxa9cubrjhBjIyMqhQoYLnck9fXn/99TyX9fTTTxd1eMa1b98+7rrrLs9FBKb0sARhzkhBrlwpbs2bN2ft2rX+DsPko06dOvne92ICk52kNsYY41NQJggtwOV+xhj/s//VwBZ0CaJSpUocOHDA/vCMCXCqyoEDB7LcS2ICS9Cdg4iIiCAhIYF9+/b5O5Qik5KSUmb+iaytwSm3tlaqVImIiAg/RGQKIugSRGhoaIGGOyhN4uLiuOCCC/wdRomwtganstTWYBJ0XUzGGGOKhiUIY4wxPlmCMMYY45MlCGOMMT5ZgjDGGOOTJQhjjDE++SVBiMg9IrJZRDaJyIciUklEaorItyKyw/1pI3sZY4wflXiCEJGGwASgk6q2A0KAm4AHgcWq2hxY7L43xphSTUQ8r9LGX11M5YEwESkPVAYSgUHA2+78t4HBforNGGMMIP4Ys0hE7gamAcnAN6o6TEQOq+o5XnUOqWqObiYRGQuMBQgPD4+eNWtWSYXtN8ePH6dq1ar+DqNEWFuDU1lua+ZjWAGio6PzLS8JPXv2jFfVTvnVK/EE4Z5b+Bi4ETgMzAHmAv8qSILw1qlTJ129enVxhhsQ4uLi6NGjh7/DKBHW1uBUltvq3bXkvb/NrbwkiEiBEoQ/upj6AL+q6j5VTQU+AS4B/hSR+gDuz71+iM0YY4zLHwliF3CRiFQWJ4X2BrYAnwMj3DojgHl+iM0YY85I5olo766j0q7ER3NV1R9EZC6wBkgD1gKvAlWBj0RkNE4Sub6kYzPGGHOaX4b7VtXHgcezFZ/EOZowxpgyy5/nJrKzO6mNMcb4ZAnCGGOMT5YgjDHG+BR0jxw1xpjiFEjnCIqbHUEYY4zxyRKEMcYYnyxBGGOM8ckShDHGGJ8sQRhjjPHJEoQxxhifLEEYY4zxyRKEMcYYnyxBGGOM8ckShDHGGJ8sQRhjjPHJEoQxxhifLEEYY4zxyRKEMcYYnyxBGGOM8ckShDHGGJ8sQRhjjPHJEoQxxhifLEEYY4zxyRKEMcYYnyxBGGOM8ckShDHGGJ8sQRhjjPHJEoQxxhifyvs7AOMQEc+0qvoxEmOMcdgRhDHGGJ8sQRhjjPHJEoQxxhifLEEYY4zxyS8JQkTOEZG5IrJVRLaIyMUiUlNEvhWRHe7Pc/0RmzHGGEeBEoSIXCoiVdzp4SLynIg0PovPfQFYqKqtgChgC/AgsFhVmwOL3ffGGGP8pKBHEP8GkkQkCngA+A1450w+UESqA92A/wCo6ilVPQwMAt52q70NDD6T5RtjjCkaUpBr7kVkjap2FJHJwO+q+p/MskJ/oEgH4FXgR5yjh3jgbne553jVO6SqObqZRGQsMBYgPDw8etasWYUNISDFx8d7pqOjo7PMO378OFWrVi3pkPzC2hqcgqmtuf2vZpZHREQQHh5e4PoFLS9KPXv2jFfVTvlWVNV8X8D/gIeA7UA9IATYWJDf9bGsTkAacKH7/gVgKnA4W71D+S0rOjpagwXgeWUXGxtb8gH5ibU1OAVTW3P7X80si4mJKVT9gpYXJWC1FmB/XdAuphuBk8BoVf0DaAjMKODvZpcAJKjqD+77uUBH4E8RqQ/g/tx7hss3xhhTBAqUIFT1D1V9TlWXuO93qeoZnYNwE8xuEWnpFvXG6W76HBjhlo0A5p3J8o0xxhSNAo3FJCLHcA55vB0BVgP3qeovhfzc8cD7IlIB+AUYiZOsPhKR0cAu4PpCLtMYY0wRKuhgfc8BicAHgAA34ZyL2Aa8AfQozIeq6jqccxHZ9S7McowxxhSfgp6D6Keqr6jqMVU9qqqvAleq6mzAbmgzxpggVNAEkSEiN4hIOfd1g9c8G5vaGGOCUEETxDDgrzhXFv3pTg8XkTDgrmKKzRhjjB8V6ByEexL6qlxmLy26cIwxxgSKgl7FVAcYAzTx/h1VHVU8YRljjPG3gl7FNA9YAiwC0osvHGOMMYGioAmisqpOKtZIjDHGBJSCnqT+UkSuLNZIjDHG5EpEsrxKQkETxN04SSJZRI6KyDEROVqcgRljjPGvgl7FVK24AzFlh/e3Hy3AcPPGGP/IM0GISCtV3SoiPp/7oKpriicsY4wx/pbfEcR9OJe3PutjngK9ijwiY4wxASHPBKGqY9yfPUsmHGOMMYEivy6ma/Kar6qfFG04xhhjAkV+XUy5Da8BTheTJQhjjAlS+XUxjSypQIwJRnbFlinN8utiujev+ar6XNGGY4wxJlDk18Vk9z/kw74hGmOCVX5dTFNKKhBjjDGBJb8upgdUdbqIvISPJ8ep6oRii8wYY4xf5dfFtMX9ubq4AzHGmEBi3cf5dzF94f58u2TCCX72R2dM0bH/p+JV0CfKtQAmkvOJcjbUhjHGBKmCPjBoDjATeB17opwJAPbN0ZjiV9AEkaaq/y7WSIwxppjZF4vCye8qppru5BciMg74FDiZOV9VDxZjbMYYY/wovyOIeJzLWzPT7sRs85sVeUR+Zt8wcmfrxpiyJb8EcSOwW1X3AIjICOBaYCfwRLFGZowxxq/yeyb1TNwuJRHpBvwDeBs4ArxavKEZY4ASf1B9MLB1VjTyO4II8TrPcCPwqqp+DHwsIuuKNzRjjDH+lN8RRIiIZCaR3sB/veYV9AooYwKWfdMsHWw7+Ud+O/kPgf+JyH4gGVgCICLn43QzGWOMCVJ5HkGo6jTgPuAtoKuevnSlHDD+bD5YREJEZK2IfOm+ryki34rIDvfnuWez/KJm32CKn4gQHx9v69iUOPv/9i2/LiZUdYWqfqqqJ7zKtqvqmrP87Ls5PRggwIPAYlVtDix235da9gdnjCnt8k0QxUFEIoABOEN3ZBqEc4UU7s/BJRCH7cCNMSYXfkkQwPPAA0CGV1l45v0W7s+6/gjMBB5L5Mb4h5T0HbEiMhC4UlXHiUgPYKKqDhSRw6p6jle9Q6qa4zyEiIwFxgKEh4dHz5o164xjiY+PByA6OjpHWaCUAxw/fpyqVavm15xil1eMRbGc+Ph4IiIiSEhI8LluCrq+iiKWolpOUWzXoorRn872b9gf/39nuoyIiAjCw8OLNcbs8wqrZ8+e8araKd+KqlqiL5yb7RJw7sb+A0gC3gO2AfXdOvWBbfktKzo6Ws8GzjAiPssCpVxVNTY2trBNKxZ5xVgUywE0JiYm13VTkrEU1XKKYrsWVYz+dLZ/w/74/zvTZcTExBR7jEXw97paC7C/LvEuJlV9SFUjVLUJcBPwX1UdDnwOjHCrjQDmlXRspmjZifrAYtvDFJa/zkH48jTQV0R2AH3d98YYY/zEr3dDq2ocEOdOH8C5W9sYY0wACKQjCGOMMQHExlMyZYI9y8KYwrMjCGOMMT5ZgjDGGOOTJQhjjDE+2TkIY0yJs3NCpYMliDLM/klzZ+vGGOtiMsYYkwtLEMYYY3yyBGGMMcYnOwdRShWmj7yw/enFXd8UDVvvprjZEYQxxhif7AgiyGR+qyyr3yhL87fqQIs90OIxJc+OIIwxxvhkCcIYU2wyH1CU/XGZ+dUvKw81KgdU8HcQebAuJmOMKQF1ABYvhg0bYMMGVgFtgb/7N6w82RGEMSWssN+qTSmlCsuW8TLwO7AXoE8fuPdeeOstOgFhQGN/xpgPSxDGGFOEav7xBzzyCDRrBl27Mg5oABwF9rW4hB197mD1yJfpCpwDPOLXaPNmXUzGBBm7+sgPfv+d+4BhwAUxMZ7ipJoNefng73wArANiX1nmmbfszb+VdJSFZgnCGGPOQA2A//wH3n8fjYsjMy2kVKrE7xcNI6H7MFIv7sYD/Urvbrb0Rm6MMX5QD3gWuAbgttsAyAirPlstAAAVTUlEQVStyKepJ3kf6P7EE3S4cBJQ+newpT1+Y0wAKDPdWvPmsRGoDWQAf7bpyW+XDedI72u4/oZzAehaPnh2q3aSOsBlXu1SVq4LD3Rl7Tp9X8rkOjhxAu64AwYPpjbwDdAU+PFf/yXpplGE1jnHzwEWj+BJdcYYUwwuAIiOhm3byAitwH2pp3gBUCDYc6QdQRhTxpXJI4ICug9YAbBtG0catSX26ZU8j5McygI7gjDGmOzS0ngNuM19u/Xy8ewa9wwVaoT5M6oSZ0cQxhjj7cQJGDSI24AkYAiQOOnFMpccwBKEMcZ41AHo2RMWLGA/0Bv4DChXRveUZbTZxhiT1V+A7wFWreJEeFMuwT3/UIZZgjDGlHmdcZLD+cDBph1ZNv17dvg5pkBgJ6mNMWVXRga88AJLgIrAQqDcjDgq1Krm58ACgx1BGGPKpsRE6NcP7r2XisD/AVeBJQcvdgRhjClzBgG0bw8HDnCyem1uOLqfz/0dVAAq8SMIEWkkIrEiskVENovI3W55TRH5VkR2uD/PLenYjDHBrTIwE+fKJA4cYE/k5fzvxQ2WHHLhjy6mNOA+VW0NXAT8TUTaAA8Ci1W1ObDYfW+MMWcvLQ3efJMtwO1ACrB6+PNsjvmKCo3r+zm4wFXiCUJV96jqGnf6GLAFaIhz1Pe2W+1tYHBJx2aMCUKffeZ0J40axXnAWpyrlo6PvpvyFew0bF78unZEpAnOWFg/AOGqugecJALU9V9kxpjSrhvufQ1DhsCWLZyo25RhQDSwya+RlR7ir7HbRaQq8D9gmqp+IiKHVfUcr/mHVDXHeQgRGQuMBQgPD4+eNWvWGceQ+dD46OjoHGWBVB4REUFCQkKhlhMosRe2PK+2BkqMZ1ueWRYREUF4eHhAxnimbcqtbkm1NfTQIerGxlJl3jwa7NoFQEqNc9l63V/Z3X8g23du8NRv2fL0crZti89R7qusIOURERFUqRJe4Ppn0tbs8wqrZ8+e8araKd+KqlriLyAU+Bq416tsG1Dfna4PbMtvOdHR0Xo2cAZl9FkWSOUxMTGFXk6gxF6UbQ2UGM+2PPN9TEyM32Mp6jblVrc421oFdBio9uunGhKiCqqgR0AfBf3642MaG6saG5t1OZlluZUXpq53eUxMTKHqn8k6yD6vsIDVWoB9tT+uYhLgP8AWVX3Oa9bnwAh3egQwr6RjM8aUEkePwqxZzAb+BN4DWLiQDITfLxjAUJxvmU8BFWpW9WOgpZs/7oO4FPgrsFFE1rllDwNPAx+JyGhgF3C9H2IzxgSo2sDVAAMGwKJFcOoUN7jzlgEVR77Mob43EFq/NrN62rMtikKJJwhVXQrktvV6l2QsxpgAl5oKCxbwOXAlEAKwYAEqwr5W3Zi29Ts+w/lGGXvLOEL9GWsQsjupjTEB53yAhx6Ct96CP/7gKuAUzonL2re9ytEeV1O+YTgv2pFCsbKLgI0x/qcKW7fydyAOnJFUn34a/viDow1acS/OzVIDgKRhYyjfMDz3ZZkiY0cQxhi/qAL0ArjzTli4EHbu5J/uvCRgT7db2X35baRfeAn/7GvfZf3BEoQxpmQcPw7ffw//+x9LgS4417szcyYAJ6vVZs6x/XwFfAnMm/Im4J53MH5hCcIYU/ROnYKtW7kZiAKGvvQSPPigMyYSzqWM6cByoMp1UzjYuR/pF3Tir5dbOggkliCMMWdOFRISuBKIBNoDREbC1q2Qlsb7mfV++w0tV46DzTqzt3V3Js6PYSlwFIj922TAjhQCkSUIY0z+0tJg1y7YsYNxQBuchMC558KRI8z3rrtpEyrCsXrN+faPHWwE2o8eTbX+zxFaqzoAC+bHlHQLzBmwBGGMoSoQAc4NaL//DgkJvAw0xr3ktHJl554E4GXvXzxyhJPVarHs2AE2ABuBW2asJK1FG0KqV+E69zLUmNatiXaTgyk9LEEYUxYcOEB3oDVuIrj1Vk8iOAJ4dt19+3p+ZZz376emklQzgmP1zufzH+PYBmwAHnwvEerVo3ef01cZ/bVTZ+suChKWIIwJMo1whrpuD84zlzduhMRE4rwrvf22Z7I6zmWlvwPV2/Qg+dyGJNVsyMwvprMb556EFz4/QUi1ygCM9bo57eGG9rCdYGYJwphSrgbAp5863UOLFrHLe+bXXwOQVrEyq08msRlnWIqBd7xOap2GpNZtyODx7TnsVo99Odbzqy99Md0znZkcTNliCcKY0ubkSVi+nKlAH5yno3HNNZ7ZR3AetLIa6P/Ap6Q0jySjcVP6eF1C2vPG0Z7pwxjjmyUIY0qBtsAVAP37w3ffQVISj7rzTgEHWnXjj3Z9OBTdh94PXUK6O69X/8EIdgmpOTOWIIwJVDt3wgcfsAknQQDOkBTA4UaRvLF7I4txjha+/Pf/PL+WjjFFwxKEMQGkFu6DULp2hWXLACc57APmA63ueo/jF/YmpGE97utlI5ma4mUjYBnjb0lJ8OGHfAHsAf4NsGwZaRUrs/OSoQwAGgAjgZRrh1E+oh5iucGUADuCMMYPQoAmW7fCX//qXIF04gQDgTTgK+Dcv73Lsd6DCT23Kgt6fujfYE2ZZQnCmGJWE3dYihdfhI0bWYHTbVT19dc9dfaffxFP/LSCj3C6k2KvG25PRzN+ZwnCmCJSAWgF8O67zs1pGzaQgPOgGwDuvhuAC923B+vUIaH7XezrczPlWpzPy3ZOwQQYSxDGFEIFnKEqmgP861/w00+wYwdbgWa4zze45RZP/YbAcWATUKfXGA43iuTetyewEXhk0iSio++zS1BNwLIEYUymw4ed8Yl+/52RODv3hgADB3rKT3rXHz/eM9kSyAC2AZW7XMuh89pzomkkw2dcw6+AArGPvQrAd29PKJHmGHO2LEGYUqESwJ9/wtGjcPQo3XHGEKoE8MEHznDUqamMxfkWXwlg6lRIToakJGYCYe6LgQOd8uRk1uCMZFofnKGrXW94f/j804NZpwGJwE/Aeb3Hcjz8fFIizmfM9Gv4GUgGYp+Z66n/y4yiXAvGlCxLECZXlQAOHvTsTCPdskoA334LKSlw8iQ3AxXdFy+84DxN7NQppuB0yVQC57nDJ09CSgpz3fJQgD59nGGkU1P5AWjy7LNcBdCokedz03Gvx65XzxNbnHegw4Z5Jl/xLp882TN5u3e51w7/Aq/i1IpVSK7ZkOSaEXy9+b/8DiQAwx/4lNTwhqTXi6DPsAZkuPVjHz39aZumY0zQsQRRmqg6O9mTJ6mLs0OuBLBhg7MzTUnhcpxvxLz5Jhw5AkeOEIMzoFsYwPXXOzvwkydZjLOjDgNo3dq5Hj8pieM4D5QHoFYtz8dv8I7l8ss9k+97l//9757Jyd7l7nOHAa71Ll+82DPZBWDPHuoCJCR4yssBKQDV65AaVp3UsOps3LmWYzjf2LtcMpSMkPJoSCjfxr1BKnAS6Df4EdIrhJFeMYxX372PZJxRS+99+HMyKoahlcIYP6krSTj3H3y24BhSzjlRPMJrxNLr+w8GQMCTHIwpCyxBFLNy4Dys3f023AKvLo1XXoHERPj9d+YDdXC/hbds6fkWvheo/sgj3AVQ7vR9jX96f0hUlGfy68yJUaM8Zfd51517uvujl3f51q2eyczkkAKUq3IOaaFhZFQIY+feX0h2y5u3601GaCXSQyuyZOUnnMTZKfe6Yjzp5SuQEVqRjz77f5xy6/919MtkhFZEK1bimReGcwpIBR6b8i2UL4+WD+XeSV256557eOqf/+TVd35DK4VBWBi9r6pGBhA7b68nxp5eO/DYaR94pu+IO905FH33U57pme+eXgvj+l7lmV7rtQoyk4MxxlF2E4QqH+B2cwwe7HRzpKWxGGellAe46CJIT4f0dNbi3NwUAtCihdPnnZZGgls3BJw+7PR0yMggyS2rAFCtmudjt3nHcMcdnskrvcu3b/dM1gHnqMGVufM9nHyMFJxv0HUbRZJeoRLpoWGs3fodSUCHbiM4VbkGqZWq89ZnT3EU59vzhPtmOzvq0Ao8MvVKTrnL+OfMH9GwymhYZa68oS4puCdWvzzk+ewsO+WXFnmmh3mXP/iiZ/rJz/6fZ3rQ8NOPn/n4heGe6Und+nimVwJ7GzZkO6CNzvOU27d2Y/yj7CYIEW7A3bHPm+cpzvKt+ocfPJMdvMt37PBMNvQuP3x64OQwr+K0CmGkV6hMeoUwEg4mcAL4A2jRczTJ5zTgZK0GPPH6nezF+bb90itboEIFNLQC1w1vxOSpU7n/scf4alE6EuIcRWTZWb9zuvOnn1seO+UtT9mzn53+Jn3bwBtO/97U0zFmtGztmU7GGGPKcoIAhuGMfHnfA59AaCgaUp5Hn+xPGs7VKs/MWA4hIVCuHHf9vRPpbvkrr22D8uUhJIShtzQh3V3OnI8PeOpfeXV10nGGYo79OsnzmVl27JNP30n75et3eqYzWrTyTO8HToWFcRI8ycEYY0pCmU4Qs92f4/oN8Qx+9t8nT89P63SRZ3q91+9lnN/CM53ovcCaNT2T9i3cGFPa2VdSY4wxPlmCMMYY45MlCGOMMT5ZgjDGGONTwCUIEeknIttE5CcRedDf8RhjTFkVUAlCREKAl4H+QBtgqIi08W9UxhhTNgVUgsAZjucnVf1FVU8Bs4BBfo7JGGPKpEC7D6IhsNvrfQKnH8BVbJKS8PkQ+KSknGWlpTyQYimq8kCKpajKAymWoioPpFiKqry4PzNQiar6OwYPEbkeuEJVb3Pf/xXooqrjveqMBca6b1uSbXijwqtWGcoF+EO9TtaAikf8HUXJsLYGJ2tr7k4kO4O7lajGqlonv0qBdgSRADTyeh9BtpuVVfVV4NWSDMrfRGS1anInf8dREqytwcnaWjoF2jmIVUBzEWkqIhWAm4DP/RyTMcaUSQF1BKGqaSJyF85jDUKAN1R1s5/DMsaYMimgEgSAqi4AFvg7jgBTlrrUrK3BydpaCgXUSWpjjDGBI9DOQRhjjAkQliD8RETeEJG9IrIpW/l4d6iRzSIy3av8IXf4kW0icoVXebSIbHTnvSji644O/ylMO0WkiYgki8g69zXTq35AtxN8t1VEZnu1Z6eIrPOaVyq3KRSurUG6XTuIyAq3PatFpIvXvFK7XXNQVXv54QV0AzoCm7zKegKLgIru+7ruzzY4zyyqCDQFfgZC3HkrgYsBAb4C+vu7bWfRzibe9bItJ6DbmVtbs81/Fphc2rfpGbQ16LYr8E1mrDiPlI8Lhu2a/WVHEH6iqt8BB7MV3wk8raon3Tp73fJBwCxVPamqvwI/AV1EpD5QXVWXq/MX+A4wuGRaUDCFbKdPpaGdkGtbAXC/Ld4AfOgWldptCoVuq0+lvK0KVHena3D6fq1SvV2zswQRWFoAl4nIDyLyPxHp7Jb7GoKkoftK8FEe6HJrJ0BTEVnrll/mlpXWdnq7DPhTVXe474Ntm3rL3lYIvu36d2CGiOwGYoCH3PKg2q4Bd5lrGVceOBe4COgMfCQizXAOSbPTPMoDXW7t3AOcp6oHRCQa+ExE2lJ62+ltKFm/UQfbNvWWva3BuF3vBO5R1Y9F5AbgP0Afgmy7WoIILAnAJ+4h6EoRyQBqk/sQJAnudPbyQOeznaq6D8jsdooXkZ9xjjZKazsBEJHywDVAtFdxsG1TwHdb3a7EYNuuI4C73ek5wOvudFBtV+tiCiyfAb0ARKQFUAHYjzPcyE0iUlFEmgLNgZWqugc4JiIXuf2+twDz/BN6ofhsp4jUEeeZILhHFM2BX0pxOzP1AbaqqncXQ7Bt00w52hqk2zUR6O5O9wIyu9OCa7v6+yx5WX3hHILvAVJxvl2MxtlRvgdsAtYAvbzqP4JzRcQ2vK5+ADq59X8G/oV782OgvArTTuBaYDPOVSBrgKtKSztza6tb/hZwh4/6pXKbFratwbhdga5AvNumH4DoYNiu2V92J7UxxhifrIvJGGOMT5YgjDHG+GQJwhhjjE+WIIwxxvhkCcIYH0TkdhE5199xGONPliBMqSciQ0RERaRVES1vMnBQVQ8V0fLmutf/Z75/SESGicitIrLPa5TT24ri83KJIVJE3iqu5ZvgZAnCBIOhwFKcZ5ifNVV9UlXnFMWy3CElQlT1F6/iy3FGAwWYraod3NfrOZdQNFR1IxAhIucV12eY4GMJwpRqIlIVuBTn5qWbvMp7iEic++19q4i8nzn+vjjPKpgiImvc8flbueVV3LH/V7kDyw1yy0NEZIZbvkFEbnfL64vId+63/01eg9B5G4bXHbMiUh2ooM6wIgVt42ciEi/OszPGepUfF5FpIrJenGcThLvl17vxrBeR77wW9QVFlERN2WAJwpR2g4GFqrodOCgiHb3mXYAz6mYboBlOIsm0X1U7Av8GJrpljwD/VdXOOM+smCEiVXCSzxG3vDMwxh1G4Wbga1XtAEQB68jpUpw7bjP1ARZ7vb/WTTpzRaQRvo1S1WicO3EniEgtt7wKsEJVo4DvgDFu+WTgCrf8aq/lrMYZadWYArEEYUq7ocAsd3qW+z7TSlVNUNUMnJ13E695n7g/473KLwceFOdJaHFAJeA8t/wWt/wHoBbOGDurgJEi8gQQqarHfMRXH/A+WuiH87AYcL7RN1HV9jgPUHo7lzZOEJH1wAqcgeCau+WngC99tGMZ8JaIjAFCvJazF2iQy2cYk4ON5mpKLfebdC+gnYgozs5QReQBt8pJr+rpZP17P+mjXIBrVXVbts8RYLyqfu0jhm7AAOBdEZmhqu9kq5KMk2gydcEZKhpVPeBV/hrwjI/l98A56rhYVZNEJM5real6eqwcTztU9Q4RudCNa52IdHA/q5IbjzEFYkcQpjS7DnhHVRurahNVbQT8ijOQ2pn4Ghjvda7iAq/yO0Uk1C1v4Z6vaAzsVdXXcJ4H0NHHMrcA57u/1xZnpNN09319r3pXu3WzqwEccpNDK5xnaORJRP6iqj+o6mSc0YAzu65a4AwWZ0yB2BGEKc2GAk9nK/sY59zA7DNY3lTgeWCDmyR2AgNxxvpvAqxxy/fhnPvoAdwvIqnAcZwhnLOb79ZbBPQHFnrNmyAiVwNpOI+0vNXH7y8E7hCRDTijg64oQDtmiEhznCOixTgjjoJzXmV+AX7fGAAbzdWY4iQiYUAszsnqhcAt6jwboKTjqAj8D+iqqmkl/fmmdLIEYUwxE5ErgC2qusuPMTQHGqpqnL9iMKWPJQhjjDE+2UlqY4wxPlmCMMYY45MlCGOMMT5ZgjDGGOOTJQhjjDE+WYIwxhjj0/8HRPXP2zz0mxAAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X=raw_data['Year']\n", "Y1=raw_data['Wages']\n", "Y2=raw_data['Wheat']\n", "\n", "plt.grid(True)\n", "plt.plot(X, Y1,\"r\",label='Salaire (Shillings/Semaine)',linewidth=2)\n", "plt.bar(X,Y2,label='Prix du blé (Shillings/Quart de boisseau)',color='black',width=2.5)\n", "plt.fill_between(X,Y1,color='blue',alpha=0.25)\n", "\n", "plt.xlabel('Années (/5 ans)')\n", "plt.ylabel('Shillings')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Amélioration de la présentation des données\n", "\n", "Il s'agit désormais de modifier le graphique précédent afin d'obtenir une représentation plus pertinente des données.\n", "\n", "On va procéder différemment du paragraphe précédent en créant axes et courbes étape par étape. À l'inverse du paragraphe précédent où l'on a créé la courbe avant de définir les axes des abscisses et ordonnées, on créé et on défini ici d'abord les axes puis l'on trace les courbes correspondant à ces axes : " ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax1 = plt.subplots()\n", "\n", "ax1.set_xlabel('Années (/5 ans)')\n", "ax1.set_ylabel('Salaire (Shillings/Semaine)',color='red')\n", "ax1.plot(X, Y1, \"r\")\n", "ax1.fill_between(X, Y1, color='blue',alpha=0.15)\n", "\n", "ax2=ax1.twinx()\n", "ax2.set_ylabel('Prix du blé (Shillings/Quart de boisseau)',color='black')\n", "ax2.bar(X, Y2, color='black',width=2.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Utiliser deux courbes serait cependant peut être plus lisible, il suffit alors de remplacer simplement le type de graphique pour ax2 (\"bar\" devient \"plot\") :" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 109, "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": [ "fig, ax1 = plt.subplots()\n", "\n", "ax1.set_xlabel('Années (/5 ans)')\n", "ax1.set_ylabel('Salaire (Shillings/Semaine)',color='red')\n", "ax1.plot(X, Y1, \"r\")\n", "\n", "ax2=ax1.twinx()\n", "ax2.set_ylabel('Prix du blé (Shillings/Quart de boisseau)',color='blue')\n", "ax2.plot(X, Y2, \"b\")" ] }, { "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 }